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17de37f0-8d9d-4320-9f27-81242f262704 | trentmkelly/LessWrong-43k | LessWrong | Using GPT-4 to Understand Code
Introduction
I've been using GPT-4 to interpret papers and code, and it's been extremely helpful. Today I'm reading Toy Models of Superposition by Anthropic and following along in the Colab notebook. Below is a conversation with ChatGPT (with GPT-4 as the backend) where I pasted in the first section of code from the Colab notebook and then asked it questions about the code.
The purpose of this post is to show one way that GPT-4 can be useful for learning. I'd be curious to hear how others are using it too!
Some takeaways:
* It does a really good job of constructing concrete examples, and it can construct as many different examples as you need. (Like the one about news articles below.)
* It doesn't need much context to figure out what's going on. (All it's given is this one code snippet. I don't think it's aware of the actual paper since it was published after September 2021. The final paragraph below shows how just much it gleans.)
* It makes nontrivial inferences. (That the model is an autoencoder.)
* Its execution of the code is wrong. (When producing an example batch the values in the tensor are wrong. But the shape and high level structure are right.)
* Overall though its interpretations seem by and large correct.
Conversation with GPT-4
Me:
Consider the following code:
@dataclass
class Config:
n_features: int
n_hidden: int
# We optimize n_instances models in a single training loop
# to let us sweep over sparsity or importance curves
# efficiently.
# We could potentially use torch.vmap instead.
n_instances: int
class Model(nn.Module):
def __init__(self,
config,
feature_probability: Optional[torch.Tensor] = None,
importance: Optional[torch.Tensor] = None,
device='cuda'):
super().__init__()
self.config = config
self.W = nn.Parameter(torch.empty((config.n_instances, config.n_features, config.n_hidden), device=device))
nn.init.xavier_normal_( |
defeb767-4979-451d-89de-cf3578310c19 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Formal Inner Alignment, Prospectus
Most of the work on inner alignment so far has been informal or semi-formal (with the notable exception of a little work on minimal circuits). I feel this has resulted in some misconceptions about the problem. I want to write up a large document clearly defining the formal problem and detailing some formal directions for research. Here, I outline my intentions, inviting the reader to provide feedback and ***point me to any formal work or areas of potential formal work*** which should be covered in such a document. (Feel free to do that last one without reading further, if you are time-constrained!)
---
The State of the Subfield
=========================
[Risks from Learned Optimization](https://www.lesswrong.com/s/r9tYkB2a8Fp4DN8yB) (henceforth, RLO) offered semi-formal definitions of important terms, and provided an excellent introduction to the area for a lot of people (and clarified my own thoughts and the thoughts of others who I know, even though we had already been thinking about these things).
However, RLO spent a lot of time on highly informal arguments (analogies to evolution, developmental stories about deception) which help establish the *plausibility* of the problem. While I feel these were important motivation, in hindsight I think they've caused some misunderstandings. My interactions with some other researchers has caused me to worry that some people confuse the positive arguments for plausibility with the core problem, and in some cases have exactly the wrong impression about the core problem. This results in mistakenly trying to block the plausibility arguments, which I see as merely illustrative, rather than attacking the core problem.
By no means do I intend to malign experimental or informal/semiformal work. Rather, by focusing on formal theoretical work, I aim to fill a hole I perceive in the field. I am very appreciative of much of the informal/semiformal work that has been done so far, and continue to think that kind of work is necessary for the crystallization of good concepts.
Focusing on the Core Problem
----------------------------
In order to establish safety properties, we would like *robust safety arguments* ("X will not happen" / "X has an extremely low probability of happening"). For example, arguments that probability of catastrophe will be very low, or arguments that probability of *intentional* catastrophe will be very low (ie, intent-alignment), or something along those lines.
For me, the core inner alignment problem is *the absence of such an argument* in a case where we might naively expect it. We *don't know how to rule out* the presence of (misaligned) mesa-optimizers.
Instead, I see many people focusing on *blocking the plausibility arguments* in RLO. This strikes me as the wrong direction. To me, these arguments are merely illustrative.
It seems like some people have gotten the impression that when the assumptions of the plausibility arguments in RLO aren't met, we should not expect an inner alignment problem to arise. Not only does this attitude misunderstand what we want (ie, a strong argument that we *won't* encounter a problem) -- I further think it's actually wrong (because when we look at almost any case, we see cause for concern).
Examples:
### The Developmental Story
One recent conversation involved a line of research based on the developmental story, where a mesa-optimizer develops a pseudo-aligned objective early in training (an objective with a strong statistical correlation to the true objective in the training data), but as it learns more about the world, it improves its training score by becoming deceptive rather than by fixing the pseudo-aligned objective. The research proposal being presented to me involved shaping the early pseudo-aligned objective in very coarse-grained ways, which might ensure (for example) a high preference for cooperative behavior, or a low tolerance for risk (catastrophic actions might be expected to be particularly risky), etc.
This line of research seemed promising to the person I was talking to, because they supposed that while it might be very difficult to precisely control the objectives of a mesa-optimizer or rule out mesa-optimizers entirely, it might be easy to coarsely shape the mesa-objectives.
I responded that for me, the whole point of the inner alignment problem was the *conspicuous absence* of a formal connection between the outer objective and the mesa-objective, such that we could make *little to no guarantees* based on any such connection. I proceeded to offer a plausibility argument for a *total* disconnect between the two, such that even these coarse-grained adjustments would fail.
(Possibly it was a mistake to offer a plausibility argument, because the rest of the discussion focused on this plausibility argument, again distracting from the core problem!)
### The Evolutionary Story
Another recent conversation involved an over-emphasis on the evolutionary analogy. This person believed the inner optimizer problem would apply when systems were incentivised to be goal-oriented, as with animals selected for reproductive fitness, or policy networks trained to pursue reward. However, they did not believe it would apply to networks which are simply trained to predict, such as GPT.
Again, this strikes me as ignoring the fundamental problem, that we have *little to no idea* when mesa-optimizers can arise, that we *lack formal tools* for the analysis of such questions, and that *what formal tools we might have thought to apply, have failed to yield any such results*.
### Bounding the Problem
My third and final example: in one conversation, someone made a claim which I see as "exactly wrong": that we can somehow lower-bound the complexity of a mesa-optimizer in comparison to a non-agentic hypothesis (perhaps because a mesa-optimizer has to have a world-model *plus* other stuff, where a regular hypothesis just needs to directly model the world). This idea was used to argue against some concern of mine.
*The problem is precisely that we know of no way of doing that!* If we did, there would not be any inner alignment problem! We could just focus on the simplest hypothesis that fit the data, which is pretty much what you want to do anyway!
I want to return to that idea. But first, we have to clarify some definitions.
The Formal Problem
==================
I currently see three areas of concern:
1. **Behavioral changes at deployment:** The mesa-optimizer appears to perform well during training, but after deployment, competently pursues goals which are unintended and unwanted. For my purposes I will call any such occurrences "treacherous turns" -- although this term is often defined in a way that requires *intentional* deception, for the moment, I'm not requiring that.
2. **Manipulation of imperfect search:** see [here](https://www.lesswrong.com/posts/KnPN7ett8RszE79PH/demons-in-imperfect-search) and [here](https://www.lesswrong.com/posts/uXH4r6MmKPedk8rMA/gradient-hacking).
3. **Dangerous hardware manipulation:** EG, models which develop row-hammer attacks during training to increase their (supposed) score, exploit security holes to communicate with or manipulate the external environment, etc.
Vanessa [terms](https://www.lesswrong.com/posts/5bd75cc58225bf0670375575/the-learning-theoretic-ai-alignment-research-agenda#Taming_daemons) #1 *Cartesian daemons* because they obey the intended input/output protocol of the whole system, and #3 *non-Cartesian daemons* because they violate the protocol. I'm not sure whether/where #2 falls on Vanessa's classification.
For this short write-up I'll focus on #1, although clearly #2 and #3 are also important areas of study.
My semi-formal description of the problem is going to be very close to the [explain-like-i'm-12 version](https://www.lesswrong.com/posts/AHhCrJ2KpTjsCSwbt/inner-alignment-explain-like-i-m-12-edition):
1. A rich hypothesis space does contain (malign) mesa-optimizers, even if they occupy a small fraction of the space.
2. A powerful search over that space effectively restricts outputs to a very small fraction, namely, the points in the space which score very well on the objective function.
3. Some of the (malign) mesa-optimizers will score very well, so, there will be an overlap.
4. Therefore, we expect there to be a chance of getting (malign) mesa-optimizers.
Essentially what we want to do is block or weaken this conclusion (concluding that there is no, or only a *very* small, chance).
(The "(malign)" in parentheses should be removed in approaches which are trying to avoid mesa-optimization entirely, and included in approaches which are only trying to block bad actors. I'll sometimes use the term "malign hypothesis" to indicate hypotheses which produce catastrophic output, regardless of whether they contain a mesa-optimizer in any formal sense.)
Now, to make this into a formal problem, we have to define "mesa-optimizer".
Defining Mesa-Optimization
--------------------------
RLO offered a semi-formal definition of mesa-optimizers, as *objects in a search space which themselves conduct an internal search*. Whether or not you [agree with this definition](https://www.lesswrong.com/posts/wpbpvjZCK3JhzpR2D/gradations-of-inner-alignment-obstacles), it's too vague for formal purposes: how would we define "internal search"?
So, for the purpose of the formal problem, it's necessary to pick an operational definition.
Fortunately, it's possible to select operational definitions which have strong connections to safety concerns. In particular, I will call an operationalization *tight* if it includes more than necessary, so that we can be sure we're ruling out the undesired behavior when we rule out its operationalization. ("Tight" is supposed to sound like the opposite of "leaky" as in "leaky abstraction".)
As with any research, the choice of operational definitions is going to depend on the specific approach of the researcher, norms of the field, etc. Here are some options that occur to me:
* **Generalization accuracy.**
+ This throws away all details of the inner alignment problem, and treats treacherous turns as simple generalization error. This has the advantage of engaging existing machine learning theory. Sufficiently strong generalization guarantees would indeed rule out treacherous turns, so, the operationalization is tight.
* **Importance-weighted generalization accuracy.**
+ The concept of generalization accuracy misses important issues. For example, a guaranteed very low frequency of errors might still allow an error to be strategically inserted at a very important time. A formal model could attempt to incorporate this.
* **Significant inference during deployment.**
+ The intuition here is that a mesa-optimizer has to do some kind of significant planning, so we can rule them out by eliminating any computations which are "too sophisticated".
+ Possible versions of this include limiting neural network layers, or limiting run-time.
+ An operationalization of this form may or may not be tight; this would require further research/argument.
* **Explicit representation of goals/values.**
+ I argue [here](https://www.lesswrong.com/posts/wpbpvjZCK3JhzpR2D/gradations-of-inner-alignment-obstacles#Explicitly_Representing_Values) that this may be relevant. The basic intuition is that in order to competently pursue unintended goals at deployment, there must be a compressed representation of those goals (rather than just compressed policies learned for specific situations).
+ Again, this may not be tight; further research required.
Obviously, this is not exhaustive by any means. Coming up with productive operational definitions is an important part of attacking the formal inner alignment problem.
The goal is then to provide some kind of strong argument which avoids the operationalized problem.
Why is this a problem?
----------------------
Why can't we address the inner alignment problem in the same way that we usually address problems of induction?
The most obvious way to avoid mesa-optimizers is to rule them out of the search space or the results of search. Earlier, I said:
> [The hope was that] we can somehow lower-bound the complexity of a mesa-optimizer in comparison to a non-agentic hypothesis [...].
>
> *The problem is precisely that we know of no way of doing that!*
>
>
I was referring to the idea that *the first thing one tries* when hearing about the inner alignment problem is probably this kind of complexity-bound approach, which leads me to think of "the whole problem" as the fact that these approaches don't seem to work.
So, let's look at these approaches in more detail.
The hope is that we can be confident that mesa-optimizers will be *more complex* in comparison with benign outputs, so that we can avert mesa-optimization by using machine learning techniques which heavily bias toward less complex models. (This is "the first thing one tries" because it employs standard ideas from machine learning.)
The main theoretical choice one has is, what notion of "complexity"? I will look at three approaches:
* **Description complexity.** That is, how compressed is the model? How short is its description?
* **Computational complexity.** That is, how *fast* is the model?
* **Combining the two.** If neither work on their own, perhaps some combination of the two would work?
### Description Complexity
The idea here would have to be that mesa-optimizers *have to have a larger description length* than benign hypotheses.
**Why you might think this idea would work:**
* You trust Occam's razor. The whole point of Solomonoff induction is to give an objective theory of normatively correct inductive inference. So, almost by definition, the hypotheses favored by short description length should be "good explanations" of the universe, rather than unindended & undesirable hypotheses like mesa-optimizers.
* A benign hypothesis just has to predict reality. A mesa-optimizer has to predict reality and also represent goal content and planning algorithms. So the mesa-optimizer has to be more complex, right?
* A benign hypothesis just has to accurately perform the task. A deceptive mesa-optimizer has to also reason about the training process and other details of the world it's in, in order to choose the right moment to execute a treacherous turn. Surely this would be more complex.
**Why this idea doesn't seem to work:**
* Even in the *best* case (where you not only have realizability, but furthermore, know your prior is a pretty good one), Occam's razor should only make you think *one of* the shortest hypotheses that fits your data is going to be correct, not necessarily *the* shortest one. So, this kind of thinking does not directly imply a lack of malign mesa-optimization in the shortest hypothesis.
+ In particular, I am invoking the idea that *belief that a prior is near-optimal for predictive purposes* does not imply *trust in a prior for instrumental purposes*, because rare predictive errors can be catastrophic for planning and decision making if they're selected adversarially rather than distributed randomly.
+ This suggests a line of research examining conditions under which this does/doesn't hold, and perhaps investigating planning algorithms which don't entirely trust the prior.
* Mutual information between predicting reality and agency may mean mesa-optimizers don't have to spend extra bits on goal content and planning. In particular, if the reality being predicted contains goal-driven agents, then a mesa-optimizer doesn't have to spend *extra* bits on these things, because it already needs to describe them in order to predict well.
+ This suggests a line of research into tasks which do not have mutual information with agency. Are there conditions under which we *can* be confident that mesa-optimizers would have high descriptive complexity?
+ However, it's possible that *any hypothesis can be transformed into a treacherous mesa-optimizing variant with a relatively short string of injected code*. If this is the case, it guarantees that malign and benign hypotheses are *not too different in description-length*. This would suggest that it'll be difficult to achieve high confidence that malign hypotheses always have larger description length.
* Similarly, if a task is rich enough to involve the richness of the world (eg text prediction), then the description of the mesa-optimizer doesn't need to spend extra bits on describing the training process and so on, because it's already modeling complexities of the world such as that. In such a case, there's no apparent simplicity-based argument against deception.
+ Are there tasks which we can confidently assert *are* simpler than the whole world, so that we can confidently rule out deception in simple models?
* Also, see the reasoning in [The Solomonoff Prior is Malign](https://www.lesswrong.com/posts/Tr7tAyt5zZpdTwTQK/the-solomonoff-prior-is-malign).
### Pure Computational Complexity
Alright, if *description complexity* can't rule out mesa-optimizers, maybe *computational complexity* can? I'm going to formalize "fast programs" as [*minimal circuits*](https://en.m.wikipedia.org/wiki/Circuit_complexity), where "circuit size" is understood as the number of logic gates.
**Why you might think this would work:**
* Maybe you think the inner alignment problem for Solomonoff induction arises due to "too much compute". After all, Paul's original [simulation warfare argument](https://ordinaryideas.wordpress.com/2016/11/30/what-does-the-universal-prior-actually-look-like/) relied on simulating whole alien civilizations. More to the point, if mesa-optimization relies on internal search, well, that takes *time*, right? If we can find fast programs, maybe we force any "intelligence" out of the programs.
* Specifically, you might think that the fastest way to do just about anything is essentially a lookup table.
**Why it doesn't seem to work:**
* A lookup table is exponential in size (since you'd be checking for each combination of inputs and making an entry for each). So yes, minimal circuits rule out any algorithm with more than exponentially many steps. However, there are lots of concerting, sophisticated algorithms with sub-exponential numbers of steps. The minimal-circuit formalism will prefer these to lookup tables. (Note that even exponential-time calculations *might* involve circuits smaller than lookup tables.)
+ In particular, textbook AI algorithms are *mostly about* taking things which naively require exponential computations, and producing useful results in sub-exponential time.
* Based on this, you should *actively expect* that minimal circuits implement sophisticated algorithms to solve problems, if such algorithms exist (and are not super-exponential).
* Separately, we now think minimal circuits can be problematic because both [Evan](https://www.lesswrong.com/posts/fM5ZWGDbnjb7ThNKJ/are-minimal-circuits-deceptive) and [Vanessa](https://www.lesswrong.com/posts/nyCHnY7T5PHPLjxmN/open-question-are-minimal-circuits-daemon-free?commentId=GbEm5zANHzrCgpBpP) have proposed counterexamples. (Neither are necessarily the final word on the subject, though, since one may choose to work with different assumptions/definitions.)
It might seem like we're just not cutting off enough processing power -- the minimal-circuits formalism cuts off mesa-optimization which requires superexponential thinking time, but not sub-exponential. Let's call this the "computational ceiling" of the formalism. Shouldn't we just pick a formalism with a lower computational ceiling?
Maybe! Let's look at a formalism which gives us a knob to adjust, so that we can choose to penalize time more/less harshly.
### Mixing Time and Description Complexity
The [speed prior](https://people.idsia.ch//~juergen/speedprior.html) is like Solomonoff induction, except that it penalizes computation time. Specifically, taking twice as long is like 1 more bit of description length. Combining time and description complexity in this way is called [*Levin complexity*](http://www.scholarpedia.org/article/Universal_search#Levin_complexity).
For Levin complexity, time is penalized logarithmically (in comparison to description length). This suggests that we will get an exponential computational ceiling again: if computing n.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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bits costs O(2n) time, we're around the break-even point (it might be worth computing the bits, or it might be better to memorize them). So super-exponential calculations won't be worth it, and sub-exponential calculations will, just like we saw with minimal circuits.
However, it's easy to tweak the time/description trade-off. Let's define Levinf complexity as 2l+f(t), where l is description length and t is execution time (the choice of f=log2 gives us regular Levin complexity). (I am not sure if Levinf complexity has a more standard name in the literature.) This gives us a computational ceiling of f−1.
**Why this might work:**
* If the computation of a bit (via search or other algorithmic means) takes too long, you're better off just memorizing it. We can set this ceiling wherever we like. If we think "dangerously intelligent" computations are cubic and above, we can set f(t)=√t, for example. This gives us a quadratic computational ceiling.
**Why this might not work:**
* Humans are essentially *linear-time algorithms*, in the sense that we take the same maximum amount of processing power (ie, that of the human brain) to produce each next output. Anything which produces linearly much output *has to* do so in at least linear time. So, Levinf-complexity can't rule out humanlike intelligence.
* Furthermore, forcing hypotheses to be "slow" like this may only delay the inevitable. Let's say for the sake of argument that a treacherous turn can be executed at any time, but requires a specific computation C to be executed (say, reversing the cryptographic hash of a critical password). This computation C can simply be slowed down to get past any computational ceiling. Thus, if our notion of (malign) inner optimizer resembles *"eventually executes a treacherous turn"*, then lowering the computational ceiling doesn't actually help.
Elaborating on that last point: I'm assuming that you are *asking for* a computation that runs "sufficiently long" anyway. The speed prior does have a heavy bias for computations which cut off quickly. However, if we're (say) predicting the weather, we probably want to *require* that the calculation keeps going on for a while into the future, rather than cutting off. If we're training an AI traffic control system, we'd certainly require that it doesn't just cut off after a few days. The idea, then, is that the treacherous computation C can be spaced out through the time the AI traffic control system is required to remain on-line.
Taking everything together, I don't think we have a strong basis (yet!) for thinking penalizing runtime makes the situation any better. Some have even argued that it makes the situation worse, because time constraints provide an extra incentive for complex value-driven computations (in order to manage time effectively) as opposed to simple causal hypotheses (such as raw physics). I don't think we have enough information to judge one way or the other.
A Note on the Consensus Algorithm
=================================
The eventual write-up, which this is a mere prospectus for, would aim to cover as broad a variety of approaches as possible, and give our current best assessment of the viability of each (ie, report why it might work and why it might not work).
I can't cover everything I'd want to cover in this prospectus, but one more approach sticks out as deserving of treatment. In February of this year, Michael Cohen posted [Formal Solution to the Inner Alignment Problem](https://www.lesswrong.com/posts/CnruhwFGQBThvgJiX/formal-solution-to-the-inner-alignment-problem). This used an algorithm which I had heard many others talk about in the context of inner alignment, but usually, with considerably less hope and optimism than Michael Cohen expresses. I'll call the algorithm the ***consensus algorithm.*** (I don't know of a better name for it; I've heard it called KWIK learning (Knows-What-It-Knows), but unfortunately KWIK is actually used for a learning *setting,* not the algorithm that's typically applied in that setting.)
The consensus algorithm works like this:
* You have a (finite) ensemble of hypotheses which you get from somewhere. For example, a Bayesian could take their top 100 hypotheses, or select a probability threshold of .01% and select all hypotheses above this threshold. A deep-learning person might instead re-run their training algorithm 100 times, different initializations and hyperparameters.
* You require *agreement* between these hypotheses in order to output any prediction. In the absence of such agreement, you instead say "I need further feedback".
* When you get further feedback, you get to edit your list, EG throwing out the hypotheses which were wrong (but also, perhaps, bringing in new replacement hypotheses which now exceed the .01% threshold thanks to a Bayesian update).
Note that I'm not nailing down all details, EG, how closely do we require models to agree? (Do we just require that they agree about which option is most probable? Do we require their probabilities to fall within the same small range?)
The consensus algorithm allows us to make some nice formal arguments. For example:
1. Suppose that we think ***at least one*** of our top 100 hypotheses is (a) always correct (or at least as accurate as the rest of the top 100) and (b) non-malign.
2. Then *no malign hypothesis will be able to control the output*. Malign hypotheses are limited to either agreeing with the non-malign hypothesis, or, blocking any output by spoiling the consensus.
However, other drawbacks seem to spoil the appeal of this result.
* To state the least of our problems first: this requires a 100x slowdown in comparison with the state-of-the-art deep learning (or whatever) we're layering the consensus algorithm on top of. We're amplifying safety at the expense of speed; if we think the base algorithm has a 10% frequency of yielding safe AGI, then we only need around a 10x slowdown to get a high chance of safety; if it's a .01% chance, we would need something more like a 10,000x slowdown (since we'd want to require consensus of around 10,000 independent hypotheses).
* Second, to apply this to something like deep learning, we need to think that each run has an *independent* chance of creating safe AGI. This is probably far from true. Our uncertainty about whether a particular training method produces safe AGI is probably almost entirely correlated. This would further blow up the size of the ensemble required to achieve any confidence.
* Third, the consensus algorithm requires a strong form of realizability assumption, where you not only assume that our Bayesian space contains the true hypothesis, but furthermore, *that it's in the top 100* (or whatever number we choose). This hypothesis has to be really good: we have to think that malign hypotheses ***never*** out-guess the benign hypothesis. Otherwise, there's a chance that we eliminate the good guy at some point (allowing the bad guys to coordinate on a wrong answer). But this is unrealistic! The world is big and complex enough that no realistic hypothesis has all the answers.
Michael Cohen seems to think that restricting to imitation learning makes the realizability assumption realistic:
> There are none of the usual embedding problems here—the imitator can just be bigger than the demonstrator that it's modeling.
>
>
I disagree. It still doesn't seem realistic to suppose that competitors to the best hypothesis are *never* lucky. Even if we suppose that my behavior is entirely deterministic, the number of hypotheses you'd have to consider in order for one of them to be *an exact copy of my brain* would no doubt be huge! Let's conservatively call this 10100 (suggesting that I have at least 100 neurons, each of which has at least 10 meaningfully different configurations). This would suggest that the consensus algorithm needs ***googol times the processing power of the human brain*** to work.
Michael Cohen also says some other stuff about why the proposal doesn't have embedding problems, which I also disagree with. But let's set this aside and get back to listing *other* problems with the consensus algorithm.
* Fourth, the consensus algorithm *still gives inner optimizers control of **when the system asks for more feedback***. This in itself could be catastrophic! For example, it could ask at the worst times, when it anticipates the human feedback to be of poor quality, or when asking itself has catastrophic consequences. A silly example would be causing a self-driving car to ask for feedback right when it needs to enact difficult evasive maneuvers to avoid a crash. Obviously we can't put a system like this in charge of extremely time-sensitive decisions like this. But whatever the case, asking for feedback *will* influence the external world somehow. Malign hypotheses just need to find the right moment, where asking for feedback butterfly-effects its way to a catastrophe.
* Fifth, although (as Michael Cohen notes) the *frequency* of asking for feedback will rapidly diminish, the *learning algorithm converges to the correct hypothesis extremely slowly.* Specifically, if we have an ensemble of programs of description-length at most l, one of which we believe to be correct and benign, the best convergence bound we can give is ***busy beaver of l***!! (For those who don't know about the busy beaver function, this roughly means "uncomputably slow". It's way worse than needing exponentially much data. I discuss why this happens in [Subsystem Alignment](https://www.lesswrong.com/posts/ChierESmenTtCQqZy/subsystem-alignment).) This means we can't solve the *previous* problem by keeping the system in a safe training environment (eg, keeping it away from time-sensitive tasks, and making sure that it always has a human on-duty to provide feedback when prompted) until it converges. We have to face the fact that it might require human feedback at any point in the future.
The fourth point really seems like the deal-breaker. Malign mesa-optimizers could intelligently leverage this to communicate with the outside world, manipulate the training process, etc.
Conclusion
==========
Has this been useful? Would an expanded and improved version of this be useful?
This is something where I could really use detailed peer-review-like feedback, since the final version of this thing would hopefully be a pretty canonical resource, with standardized terminology and so on.
A weakness of this as it currently stands is that I *purport* to offer the formal version of the inner optimization problem, but really, I just gesture at a cloud of possible formal versions. I think this is somewhat inevitable, but nonetheless, could probably be improved. What I'd like to have would be several specific formal definitions, together with several specific informal concepts, and strong stories connecting all of those things together.
I'd be glad to get any of the following types of feedback:
1. Possible definitions/operationalizations of significant concepts.
2. Ideas about which definitions and assumptions to focus on.
3. Approaches that I'm missing. I'd love to have a basically exhaustive list of approaches to the problem discussed so far, even though I have not made a serious attempt at that in this document.
4. Any brainstorming you want to do based on what I've said -- variants of approaches I listed, new arguments, etc.
5. Suggested background reading.
6. Nitpicking little choices I made here.
7. Any other type of feedback which might be relevant to putting together a better version of this.
If you take *nothing else* away from this, I'm hoping you take away this one idea: the main point of the inner alignment problem (at least to me) is that we know hardly anything about the relationship between the outer optimizer and any mesa-optimizers. There are hardly any settings where we can rule mesa-optimizers out. And we can't strongly argue for *any* particular connection (good or bad) between outer objectives and inner. |
b6b97d21-cdf1-49ce-9ff4-597276411eab | trentmkelly/LessWrong-43k | LessWrong | [SEQ RERUN] That Alien Message
Today's post, That Alien Message was originally published on 22 May 2008. A summary (taken from the LW wiki):
> Einstein used evidence more efficiently than other physicists, but he was still extremely inefficient in an absolute sense. If a huge team of cryptographers and physicists were examining a interstellar transmission, going over it bit by bit, we could deduce principles on the order of Galilean gravity just from seeing one or two frames of a picture. As if the very first human to see an apple fall, had, on the instant, realized that its position went as the square of the time and that this implied constant acceleration.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Einstein's Speed, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series. |
e3c51722-f8bb-4a93-97d5-ef7e5b3069f3 | trentmkelly/LessWrong-43k | LessWrong | [HPMOR][Possible Spoilers] Gedankenexperiment: Time Turner Meta-Informational Relativity
Some people have been asking the question about the 6-hour limit on time turners in Harry Potter and the Methods of Rationality. Apparently, if a character, let's say it's Amelia Bones, goes back in time N hours and relays a piece of information about the future to another character, say Dumbledore, then Dumbledore cannot go back in time more than (6-N) hours.
The 6 hour limit is a useful rule to keep the HPMOR universe from becoming over-complicated with time travel, however several people have brought up the following objection. The claim is that Amelia Bones, by traveling back in time and saying that she has information she hasn't yet revealed, has already revealed information about the future in the form of Metadata: That the future still exists N hours in the future and that Amelia Bones was in it, etc. I will show, however, that this is not the case.
Imagine that Amelia Bones has traveled back in time from 1 hour in the future, but she is confused about the time after having apparated across time zones and mistakenly tells Dumbledore that she has information for him from 4 hours in the future. Call this Scenario A. Now imagine that the same scenario happens, but that Amelia is not mistaken about the time. Call that Scenario B.
The thing to note here, is that, from the informational point of view of Dumbledore, provided he doesn't have some additional side-channel information, Scenario A and Scenario B are indistinguishable. (In the same sense that being in an accelerating room is indistinguishable from being in a gravity field in General Relativity.) This is what I mean by "time turner meta-informational relativity." Provided that the act of arriving at some time and place with a time turner doesn't itself leak information about how far in the future you arrived from, meta-information about the future is not the same as information. The time-space coordinate meta-information conveyed when Amelia Bones tells Dumbledore, "I used a Time Turner and I have in |
afec6f77-a7b8-4981-811a-545ebbe23e39 | trentmkelly/LessWrong-43k | LessWrong | What are the weirdest things a human may want for their own sake?
I'm especially interested in examples of more or less psychologically healthy and otherwise (neuro)typical people having very weird[1] desires/values that we would characterize as intrinsic in the sense of being wanted for their own sake, even if we could explain their development as linked to a more typical human drive.
But I'm also somewhat interested in examples of very out-of-distribution desires/values in very [otherwise psychologically out-of-distribution] people.
Some intermediate cases that come to my mind; I'm centrally interested in things weirder than that:
* Paul Erdös's obsession with mathematics is probably an intermediate case, i.e., it's just an extreme case of "a normal human passion".
* Some fetishes, e.g., what's the deal with feedism or dirtiness? (See also this podcast episode for an evopsych explanation of BDSM.)
* Maybe there would be some cultures that install very weird terminal values. I was somewhat surprised that some South Pacific cultures viewed heterosexuality as sinful.
1. ^
Obviously, this is a parochial criterion in that the values that seem "weird" to us would seem "normal" to the people who have those values. That's fine. |
39a03607-9e14-462d-8a1a-3cc1b0380603 | StampyAI/alignment-research-dataset/aisafety.info | AI Safety Info | What training programs and courses are available for AGI safety?
See **[AI Safety Training](https://aisafety.training/)** for an up-to-date list of programs and conferences, with application dates.
- [AGI safety fundamentals](https://www.eacambridge.org/agi-safety-fundamentals) ([technical](https://www.eacambridge.org/technical-alignment-curriculum) and [governance](https://www.eacambridge.org/ai-governance-curriculum)) - Is the canonical AGI safety 101 course. 3.5 hours reading, 1.5 hours talking a week w/ facilitator for 8 weeks.
- [AI safety camp](https://aisafety.camp/) - Actually do some AI research. More about output than learning.
- [SERI ML Alignment Theory Scholars Program SERI MATS](https://www.serimats.org/) - Four weeks developing an understanding of a research agenda at the forefront of AI alignment through online readings and cohort discussions, averaging 10 h/week. After this initial upskilling period, the scholars will be paired with an established AI alignment researcher for a two-week ‘research sprint’ to test fit. Assuming all goes well, scholars will be accepted into an eight-week intensive scholars program in Berkeley, California.
- [Principles of Intelligent Behavior in Biological and Social Systems (PIBBSS)](https://www.pibbss.ai/) - Brings together young researchers studying complex and intelligent behavior in natural and social systems.
- [Safety and Control for Artificial General Intelligence](https://inst.eecs.berkeley.edu//~cs294-149/fa18/) - An actual AI Safety university course (UC Berkeley). Touches multiple domains including cognitive science, utility theory, cybersecurity, human-machine interaction, and political science.
See also, [this A spreadsheet of learning resources](https://docs.google.com/spreadsheets/d/1QSEWjXZuqmG6ORkig84V4sFCldIntyuQj7yq3gkDo0U/edit#gid=0).
|
dd6064c8-8b8a-4808-ae38-01e6bad04cf8 | trentmkelly/LessWrong-43k | LessWrong | MIRI Research Guide
We've recently published a guide to MIRI's research on MIRI's website. It overviews some of the major open problems in FAI research, and provides reading lists for those who want to get familiar with MIRI's technical agenda.
This guide updates and replaces the MIRI course list that started me on the path of becoming a MIRI researcher over a year ago. Many thanks to Louie Helm, who wrote the previous version.
This guide is a bit more focused than the old course list, and points you not only towards prerequisite textbooks but also towards a number of relevant papers and technical reports in something approximating the "appropriate order." By following this guide, you can get yourself pretty close to the cutting edge of our technical research (barring some results that we haven't written up yet). If you intend to embark on that quest, you are invited to let me know; I can provide both guidance and encouragement along the way.
I've reproduced the guide below. The canonical version is at intelligence.org/research-guide, and I intend to keep that version up to date. This post will not be kept current.
Finally, a note on content: the guide below discusses a number of FAI research subfields. The goal is to overview, rather than motivate, those subfields. These sketches are not intended to carry any arguments. Rather, they attempt to convey our current conclusions to readers who are already extending us significant charity. We're hard at work producing a number of documents describing why we think these particular subfields are important. (The first was released a few weeks ago, the rest should be published over the next two months.) In the meantime, please understand that the research guide is not able nor intended to provide strong motivation for these particular problems.
----------------------------------------
Friendly AI theory currently isn't about implementation, it's about figuring out how to ask the right questions. Even if we had unlimited finite computing r |
a442223c-8e16-4d6f-a2b4-674dc4fcf61a | trentmkelly/LessWrong-43k | LessWrong | Correcting errors and karma
An easy way to win cheep karma on LW:
1. Publicly make a mistake.
2. Wait for people to call you on it.
3. Publicly retract your errors and promise to improve.
Post 1) gets you negative karma, post 3) gets you positive karma. Anecdotally the net result is generally very positive.
This doesn't seem quite sane. Yes, it is good for us to reward people for changing their minds based on evidence. But it's still better not to have made the error the first time round. At the very least you should get less net karma for changing your mind towards the correct answer than you would for stating the correct thing the first time.
Questions:
Is there an advantage to this signalling-approval-for-updates that outweighs the value of karma as indicator-of-general-correctness-of-posts?
If so then can some other signal of general correctness be devised?
If not then what karma etiquette should we impose to ensure this effect doesn't happen?
|
ee66dcbc-473c-4330-afcc-9bdc278b1090 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | BenevolentAI - an effectively impactful company?
I am considering a job as a Senior Product Manager with BenevolentAI - <https://www.benevolent.com/>
BenevolentAI work on drug discovery and development. They use AI and machine learning to identify targets and therapeutic interventions efficiently.
On the face of it (not least because of their name), it seems the company is closely aligned to combatting a number of global risks. In AI safety and alignment, they are spearheading some interesting work in the use of AI in pharmaceuticals. By developing capabilities here it seems there would be positive spillover for the AI community as a whole. In biosecurity they are working towards pandemic preparedness in speeding up target and therapy identification using AI. If successful, their models have potential to speed up the delivery time of an effective vaccine against any future pandemic.
Despite this seemingly positive area of work, I am conscious they are absent from any EA literature, and don't feature on the 80k jobs board, for example. They are ultimately still gunning to play in the 'big pharma' world, which clearly doesn't have the best reputation (no offence to the pioneering scientists who work within their machines).
Does anyone have any experience of the company directly, or have any impressions on it, or thought/reflections more generally? I am keen to tap into the community mind on this and gain any insight that's out there. |
8beefcf4-3537-4a48-b54e-e1e6ceb89917 | trentmkelly/LessWrong-43k | LessWrong | How to navigate through contradictory (health/fitness) advice?
I will start with a brief story, but the question can be generalized.
Last year, I decided to do something for my body. I joined and regularly went to K. Training (abbreviated name), a large gym chain in german-speaking countries. The claimed philosophy is different from many gyms: there is no music, no proteine shakes to buy, mostly old people around, and insistence that it is about strength, not show-off, and that strength is what keeps your spine together etc. They have no cardio bikes, no barbell, only machines, and the high-intensity approach is that at each machine you do one continuous exercise for two minutes. If you reach the two minutes, increase the weight next time. It all seems very serious, there is an orthopedist you talk to when you become a member. It all has been in existence for some decades. The founder writes books, of course mentioning that his approach is the only one that works against pain, and that he is not heard by the mainstream. While at the same time they have contracts with many orthopedists and this is part of the marketing.
Now, a back problem. I have seen several orthopedists in my life, but the one I talked to this year (after two GPs, both clueless) is the first who seems competent and also listens. His comment about K. Training: it's ok, but sometimes hard to leave the contract. You could just as well try Yoga or Pilates. Anyways, he gives me a prescription for physical therapy.
Talking about this and that, the therapist speaks out against K Training, because no warming up / cardio (something the founder explicitly defends in his books), and Yoga/Pilates/etc is better anyways.
Then I googled again. Seemingly, gym experts all have their own approach. Some agree to the high-intensity two minutes thing, others disagree.
Then there is also Mr. L.-B., an anti-pain guru with a somewhat different approach I dont really understand, again against the "mainstream" but also against K. Training. And from a lecture of his that I watched |
29646a80-269a-406a-977a-a81481c9b691 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | How might we align transformative AI if it’s developed very soon?
>
> This post is part of my [AI strategy nearcasting series](https://forum.effectivealtruism.org/posts/ktEzS3pkfeqPNh6r5/ai-strategy-nearcasting): trying to answer key strategic questions about transformative AI, under the assumption that key events will happen very soon, and/or in a world that is otherwise very similar to today's.
>
>
> Cross-posted from [Less Wrong](https://www.lesswrong.com/posts/rCJQAkPTEypGjSJ8X/how-might-we-align-transformative-ai-if-it-s-developed-very) and [Alignment Forum](https://www.alignmentforum.org/posts/rCJQAkPTEypGjSJ8X/how-might-we-align-transformative-ai-if-it-s-developed-very).
>
>
>
This post gives my understanding of **what the set of available strategies for aligning transformative AI would be if it were developed very soon, and why they might or might not work.** It is heavily based on conversations with Paul Christiano, Ajeya Cotra and Carl Shulman, and its background assumptions correspond to the arguments Ajeya makes in [this piece](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) (abbreviated as “Takeover Analysis”).
I premise this piece on a nearcast in which a major AI company (“Magma,” following [Ajeya’s](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) terminology) has good reason to think that it can develop transformative AI very soon (within a year), using what Ajeya calls “human feedback on diverse tasks” (HFDT) - and has some time (more than 6 months, but less than 2 years) to set up special measures to reduce the risks of misaligned AI before there’s much chance of someone else deploying transformative AI.
I will discuss:
* Why I think there is a major risk of misaligned AI in this nearcast (this will just be a brief recap of [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1)).
* Magma’s **predicament:** navigating the risk of deploying misaligned AI itself, while also contending with the risk of other, less cautious actors doing so.
* Magma’s **goals** that advanced AI systems might be able to help with - for example, (a) using aligned AI systems to conduct research on how to safely develop still-more-powerful AI; (b) using aligned AI systems to help third parties (e.g., multilateral cooperation bodies and governments) detect and defend against unaligned AI systems deployed by less cautious actors.
* The **intended properties** that Magma will be seeking from its AI systems - such as honesty and corrigibility - in order to ensure they can safely help with these goals.
* Some key **facets of AI alignment** that Magma needs to attend to, along with thoughts about how it can deal with them:
+ *Accurate reinforcement:* training AI systems to perform useful tasks while being honest, corrigible, etc. - and avoiding the risk (discussed in [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1)) that they are unwittingly rewarding AIs for deceiving and manipulating human judges. I’ll list several techniques Magma might use for this.
+ *Out-of-distribution robustness:* taking special measures (such as adversarial training) to ensure that AI systems will still have intended properties - or at least, will not fail catastrophically - even if they encounter situations very different from what they are being trained on.
+ *Preventing exploits (hacking, manipulation, etc.)* Even while trying to ensure aligned AI, Magma should also - with AI systems’ help if possible - be actively seeking out and trying to fix vulnerabilities in its setup that could provide opportunities for any misaligned AI to escape Magma’s control. Vulnerabilities could include security holes (which AI systems could exploit via hacking), as well as opportunities for AIs to manipulate humans. Doing this could (a) reduce the damage done if some of its AI systems are misaligned; (b) avoid making the problem worse via positive reinforcement for unintended behaviors.
+ *Testing and threat assessment:* Magma should be constantly working to form a picture of whether its alignment attempts are working. If there is a major threat of misalignment despite its measures (or if there would be for other labs taking fewer measures), Magma should get evidence for this and use it to make the case for slowing AI development across the board.
* Some **key tools** that could help Magma with all of the above:
+ *Decoding and manipulating internal states.* The ability to “read an AI system’s mind” by examining its internal state - or systematically change its motivations by manipulating that internal state - could be useful in many ways for Magma. This isn’t something we’re able to do much of today, but it’s an active area of research.
+ *Limited AI systems:* Magma might train AI systems with limited capabilities, such that these systems - although less useful - are less dangerous than its most capable systems. This could include AI systems specifically trained to be “myopic” (not planning many steps ahead) or “process-based” (rewarded based on human approval of the plans they produce, but rarely or never based on the actual outcomes they achieve). Limited AI systems could be important both because they could potentially provide a safe way to accomplish Magma’s ultimate goals (see “goals” above) and because they could be key components of “checks and balances” setups (below).
+ *AI checks and balances:* employing a *variety* of AI systems with different capabilities and incentives, so that they can provide checks and balances on each other. AI systems could be used to examine each others’ reasoning and internal state, make arguments that particular systems are being deceptive, point out risks of current training methods and suggest improvements, etc. If this goes well, AI systems could ultimately end up taking over much of the work of aligning future, more powerful AI systems.
+ *Keeping supervision competitive:* Magma should generally be doing whatever it can to keep its “supervision” - the ability to correctly evaluate an AI system’s reasoning and behaviors - “competitive” with its AI systems. The basic goal is: “AI systems are rarely or never able to successfully deceive their supervisors.” All of the above, and some other methods, could be helpful with this.
* Some major **factors contributing to success or failure** of the attempt to avoid misaligned AI, and some **thoughts on how our odds look overall.**
A few of the uncertain factors that seem most important to me are (a) how cautious key actors are; (b) whether AI systems have big, fast jumps in capability (I see this as central to the picture of the people who most confidently expect failure); (c) the dynamics of "AI checks and balances."
My overall take is that the risk of misaligned AI is serious but not inevitable, and taking it more seriously is likely to reduce it.
The basics of the alignment problem
-----------------------------------
This post assumes (for [nearcasting](https://www.lesswrong.com/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting) purposes) that there is a problem along the lines of what [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) describes. In brief (I will use the present tense here for convenience, but I don’t mean to imply confidence):
* The default straight-line path to developing transformative AI - using [human feedback on diverse tasks (HFDT)](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) - would lead to AI systems that attempt to forcibly overpower all of human civilization.
* It is hard (or at least non-straightforward) to determine whether this sort of “motivation” is in fact present in AI systems. If we simply used negative reinforcement to discourage bad behavior, and trusted AI systems once they stop demonstrating bad behavior, this would effectively train AI systems to patiently evade detection and engage in bad behavior *only when it would lead to successful disempowering of humans*.
So at first blush, the answer to “[How hard is the alignment problem?](https://www.cold-takes.com/making-the-best-of-the-most-important-century/#open-question-how-hard-is-the-alignment-problem)” looks like it’s at least “Reasonably hard,” in this “nearcast” scenario. (That’s a lower bound - I haven’t yet said anything to argue against “Insanely hard.”)
Magma’s predicament
-------------------
Throughout this piece, I focus exclusively on the situation of the leading AI lab, the fictional “Magma.” I do this purely for convenience; if I were to discuss a greater number of AI labs, I’d be saying largely the same things about them.
In this [nearcast,](https://www.lesswrong.com/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting) Magma is essentially navigating **action risk** vs. **inaction risk:**
**Action risk.** Say that Magma trains extremely powerful AI systems, with (collectively) [PASTA](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/)-like abilities, and tries to use this set of AI systems to make money, or cure cancer, or something like that. The risk here is that (per the previous section) Magma might unwittingly train the systems to pursue some unintended goal(s), such that once the systems are able to find a path to disempowering humans and taking control of all of their resources, they do so.
So by developing and deploying transformative AI, Magma may bring about an existential catastrophe for humanity.
Furthermore, it might not take much for sufficiently powerful AI systems to find an opportunity to disempower humans. Simply being able to freely access the Internet could be enough for a misaligned AI system to exploit security holes, make copies of itself to the point where its effective population is competitive with the human population, and find humans to manipulate into taking actions it wants them to take. The end result could be a large coalition of AI systems and humans, armed with advanced technologies, effectively seeking to control the planet much as humans do now.
**Inaction risk.** Say that Magma’s leadership decides: “We don’t want to cause an existential catastrophe; let’s just not build AI advanced enough to pose that kind of risk.” In this case, they should worry that *someone else* will develop and deploy transformative AI, posing a similar risk (or arguably a greater risk -- any company/coalition that chooses to deploy powerful AI when Magma doesn’t may be less careful than Magma overall) .
Magma’s goals
-------------
Given the above predicament, I posit (as many others have in similar discussions) that Magma’s top-level goal should be to **reduce the odds that other, less cautious actors cause a catastrophe by deploying misaligned AI systems, while avoiding catastrophic misalignment from Magma’s own systems.**
If Magma can develop powerful AI systems that are relatively safe, these systems could be helpful for that purpose, via:
**Defense/deterrence/”hardening.”** If aligned systems were deployed widely throughout the economy, this could make it harder for misaligned systems *with similar capabilities* to cause trouble. For example:
* Aligned systems could be finding and patching security vulnerabilities that misaligned systems would otherwise exploit.
* Magma could develop (and make widely available) tools that companies and governments could use to monitor for signs of dangerous actions (by misaligned AIs or by AI-assisted humans).
* If misaligned systems tried to make money, gain influence, develop and manufacture new kinds of weapons, etc., they’d have to compete economically with aligned systems to do so. This could lower their odds of success and hence their likelihood of even attempting to cause trouble (it could also lower the potential gains of deploying potentially-unsafe AI systems, even by incautious actors).
Here it’s crucial that Magma’s safe systems - plus the people and resources involved in their overall effort to ensure safety- are at least as powerful (in aggregate) as less-safe systems that others might deploy. This is likely to be a moving target; the basic idea is that defense/deterrence/hardening could reduce “inaction risk” for the time being and give Magma more time to build more advanced (yet also safe) systems (and there could be many “rounds” of this).
Some other applications, below, could more decisively drive down the risk from misaligned systems.
**Alignment applications.** Powerful AI systems might be used to accelerate AI alignment research and develop better ways of avoiding the “action risk.” This could then make it possible to develop even-more-powerful AI systems that are still relatively safe; additionally, AI alignment measures could be shared with competitors, reducing “inaction risk” as well.
A big enough success on this front could effectively eliminate “action risk” while also substantially reducing “inaction risk.” A smaller success would at least reduce both.
**“Coordination”-related applications.** Powerful AI systems could - in a number of ways - help governments and companies across the world coordinate to avoid the risk of deploying unsafe systems. For example:
* They could help design mechanisms for monitoring each others’ activity such that any risky behavior is detected (but intellectual property and privacy are preserved to the extent feasible).
* They could help create evidence and demonstrations (more below) for the risk of misaligned AI under particular conditions.
**Powerful technologies that could be used to enforce regulatory agreements.** If Magma is able to partner with some appropriate government or multilateral governance mechanism - ideally with a regulatory framework in place - it could help this party enforce the framework via AI systems that could be used for resource accumulation, detecting violations of the regulatory framework, improving and advocating for good regulatory frameworks, military applications, etc. The goal would be to proactively stop actors around the world from deploying dangerous AI systems. This category of applications brings many concerns of its own. Another class of powerful-but-dangerous technology could be developing [mind uploading](https://en.wikipedia.org/wiki/Mind_uploading) (leading to [digital people](https://www.cold-takes.com/how-digital-people-could-change-the-world/)), brain-computer interfaces, or other things that could help humans “keep up with” AI systems.
**“Advisor” applications.** Powerful AI systems might be used to generate better ideas, plans, etc. for dealing with Magma’s predicament generally - for example, a proposal and compelling set of arguments for a particular global governance mechanism that could be effective in slowing everyone’s “race” to develop and deploy AI systems, reducing “inaction risk” and allowing more time to proceed cautiously and thoughtfully.
Intended properties of Magma’s AI systems
-----------------------------------------
Magma seeks to develop AI systems that can be used for the applications listed in the previous section, but don’t pose too much risk of a takeover attempt. So in designing and training its systems, Magma should be aiming to build systems that reliably have properties such as the following, in order to reduce risk:
(Note: this list isn’t particularly likely to be comprehensive, but it hits the major relevant clusters of properties I can easily think of, while leaving “Good performance in the sense of e.g. positive scores from judges” implicit.)
**Value-alignment** (in the [broad](https://ai-alignment.com/ambitious-vs-narrow-value-learning-99bd0c59847e) sense), which I’d roughly characterize as “pursuing the objectives that humans would want an AI system to pursue, if humans could fully understand what the system was doing and how it was reasoning.” This is the most obviously risk-reducing property, but it’s very unclear how (in the [human feedback on diverse tasks](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) paradigm that I’m assuming) Magma can train for it:
* Humans themselves don’t know what objectives they would pursue if they understood a lot more than they currently do. So whether we’re talking about assigning rewards to behaviors or examining an AI system’s internal calculations, it’s very unclear how to even assess whether an AI system has the value-alignment property.
* Training AIs on human feedback seems generally more like training AIs to “do whatever results in positive reinforcement” than like training AIs to “pursue the objectives that humans would want an AI system to pursue, if humans could fully understand what the system was doing and how it was reasoning.” (More on this at [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1).)
Here are some additional risk-reducing properties that might be more straightforward to train for (though still far from straightforward):
**Honesty,** which I’d roughly define as “giving non-deceptive answers to relatively straightforward questions.”
This vaguely feels a bit easier to define and assess than value-alignment: for a straightforward question, we can ask whether the impression a human had based on the AI’s answer matches the answer corresponding to the AI’s picture of the world. And while not quite as useful as value-alignment, honesty could be extremely useful in preventing a catastrophe from misaligned AI: for example, questions like “If we follow the course of action you’re recommending, will person X be safe, free from unusual manipulation, informed about what’s happening, and happy with how things are going?”, we could screen off many paths to AIs disempowering humans.
That said, there are still deep challenges when it comes to training for honesty:
* It’s not currently clear how to compare “the answer an AI gave” to a question to “what the AI actually ‘knows’” about the question.
* It’s not currently clear how to find training procedures that train “giving non-deceptive answers to questions” as opposed to “giving answers to questions that appear non-deceptive to the most sophisticated human arbiters” (more at [Eliciting Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge)).
**Corrigibility.** I’d roughly define this as AI systems’ “allowing themselves to be altered and/or shut down when humans want to do so”; a [2015 MIRI paper](https://intelligence.org/files/Corrigibility.pdf) has a more extensive discussion. As with the above properties, this is a property that (a) could reduce risk a lot if robustly achieved; (b) is not crisply defined or straightforward to assess (in particular, it’s not clear how to train “being corrigible” as opposed to “appearing corrigible in situations where there is no reliable path to disempowering one’s supervisor and escaping consequences”).
**Legibility.** Imagine an advanced AI system (capable of taking natural-language commands from humans) that is prompted to “Prototype a kind of solar panel that is more than 2x as cost-efficient as the current state of the art.”
* One way for this task to play out might be as follows: the AI system produces a set of (a) software packages with hard-to-understand code; (b) granular, step-by-step instructions to various factory workers. When asked how this set of instructions will work to produce a superior solar panel, the AI system might respond: “If you carry out the instructions, it will work; when I have said things like this in the past, I have been correct.” Humans try the instructions, find that they work as intended, and reinforce this behavior positively based on its results.
* Another way might be to produce a detailed discussion of principles of solar panel engineering, such that a human (or team of humans) reading it would learn a great deal and independently be able to construct (and explain) the rest of the process.
“Legibility” means going for something more like the latter than like the former. (It’s related to the idea of [process-based systems](https://www.lesswrong.com/posts/pYcFPMBtQveAjcSfH/supervise-process-not-outcomes), discussed below.) The hope is that in addition to *avoiding deception* and answering questions straightforwardly, AI systems *actively work to help humans understand* their reasoning.
**Which properties to aim for?** As discussed below, I don’t think there is any path that clearly and robustly trains any of these properties, so ideally I think it makes sense (subject to limitations on time and resources) to try to train for each of them - at least for the “naive version” of each of them (training AI systems to appear to have these properties as far as the most sophisticated supervisors can tell).
Key facets of AI alignment (ensuring the above properties)
----------------------------------------------------------
### Accurate reinforcement
In this scenario, the basic, default framework for training an advanced AI system is as follows:
**An AI system gets positive reinforcement for what looks to a supervisor like good behavior (including having the intended properties listed [above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Intended_properties_of_Magma_s_AI_systems)).** Here “supervisor” refers to whatever human-controlled system is ultimately responsible for assigning positive or negative reinforcement; it may involve a mix of human judgment and automated scoring.
A simple example would be: a human describes a desired computer program’s behavior using natural language; an AI writes code; a variety of tests (including human judgment) are used to determine whether the code is legible, well-written, functions as intended, and is free of consequences other than the desired ones.
This example implicitly incorporates basic versions of value-alignment (a successful program does what a human judge wants, and only that)[1](#fn1) and legibility (the code looks good to humans before it is run, though it is also judged on results when it is run). As part of the evaluation, the supervisor could also ask the AI system questions (e.g. “What will happen if I run this program you wrote?”) and assess honesty (by doing their best to check whether the AI system seems to be reporting its “true best-guess” predictions).
**There’s a risk of error by the supervisor that ends up reinforcing the wrong behavior - and, in fact, specifically reinforcing a behavior like “exploit the supervisor’s weaknesses.”** This risk is described more in detail at [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1).
For example, an AI system might write code that *appears* to function as intended, but subtly contains unintended behaviors, such as corrupting the development environment to make it look like the code performed better than it actually did. If doing so ends up improving the AI’s performance, it could further reinforce the behavior of seeking out exploits its supervisors won’t notice.
**Magma can pursue a variety of approaches to get around this issue and provide “accurate” reinforcement.** Magma’s goal is to train challenging, [valuable](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals) behaviors while ensuring that reinforcement is done correctly (not erroneous, not manipulated by the AI in training, etc.) In the example above, this would essentially mean finding some method of catching the subtle unintended behaviors in an AI’s code, and penalizing the AI appropriately.
Approaches to accurate reinforcement include:
* Task decomposition (sometimes called [amplification](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd/p/HqLxuZ4LhaFhmAHWk#Core_concept__Analogy_to_AlphaGoZero)), e.g., breaking a difficult question (or task) up into smaller sub-questions (or sub-tasks) so that each piece can be reliably assessed by a given supervisor. In the code example, one might break the AI-written code into small pieces and evaluate each separately (and might also save time/resources by building an AI that imitates human judgments on each piece).
* [Debate](https://arxiv.org/abs/1805.00899), e.g., prompting one AI system to make the case for one answer to a question, and another AI system to make the case for another, with a human or a third AI system adjudicating. (I cover this general idea more below when discussing [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances).)
* Transferring as much as possible of what an AI system “knows” to supervisors so that they can make accurate judgments and give correct reinforcement. [Imitative generalization](https://www.lesswrong.com/posts/JKj5Krff5oKMb8TjT/imitative-generalisation-aka-learning-the-prior-1) is an example of an approach to this goal; [Microscope AI](https://www.lesswrong.com/posts/fRsjBseRuvRhMPPE5/an-overview-of-11-proposals-for-building-safe-advanced-ai#5__Microscope_AI) is another.
* Making use of an AI’s *internal state*,[2](#fn2) not just its outputs. For example, giving positive reinforcement to an AI when it seems likely to be “honest” based on an examination of its internal state (and negative reinforcement when it seems likely not to be). [Eliciting Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge) provides some sketches of how this might look.
* Simply putting in a lot of time and effort into figuring out what reinforcements we should give the AI, and aiming to reach a very high confidence each time (this is most feasible when AIs can learn from a very small number of reinforcements, and when aiming for high confidence doesn’t stop us from being able to supervise particularly [valuable capabilities](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals)).
The “key tools” I’ll discuss below could make important contributions here:
+ Using AI to give humans help in providing the right reinforcement could be quite important; success at [checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) setups, and useful [limited AI](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems), could help here.
+ [Decoding and manipulating internal states](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states) could be important for providing reinforcement based on an AI’s internal state, and for [microscope AI.](https://www.lesswrong.com/posts/fRsjBseRuvRhMPPE5/an-overview-of-11-proposals-for-building-safe-advanced-ai#5__Microscope_AI)
If reinforcement isn’t perfect, it’s possible that the mistakes can be compensated for by **skewed reinforcement:** giving especially large negative reinforcement when systems engage in the particular kinds of unintended behaviors that are harder to detect. It’s also possible that adversarial training (next section) can further reduce the risk.
In theory, perfectly accurate reinforcement could eliminate one of the main sources of alignment risk described in [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1): the risk that AIs are being actively rewarded for (undetectably) manipulating, deceiving and/or disempowering supervisors. However, accurate reinforcement alone wouldn’t eliminate the risk entirely, since it’s still unknown how an AI generalizes from its training to its deployment ([more](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over)). (Internal-state-based reinforcement, discussed above, could do more to specifically train the right properties as opposed to just ensuring that reinforcement is accurate.)
### Out-of-distribution robustness
In addition to training AI systems to do useful tasks while remaining [honest, legible, etc.](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Intended_properties_of_Magma_s_AI_systems), Magma can take special measures to avoid unintended behaviors on **potential future situations very unlike the situations AIs are trained in** (“out-of-distribution”).
Magma likely wants to focus on a particular way in which the future can be out-of-distribution: **an AI system that has been deployed out into the world may have many opportunities to disempower humans, and take control of resources, that an AI system in training does not**. (Further discussion [here](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over).) So an AI system that is seeking to maximize reward (or [something else](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Even_if_Alex_isn_t__motivated__to_maximize_reward__it_would_seek_to_seize_control)) might both [behave well in training](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_) and [seek to disempower humans when it later has a chance to do so.](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over)
Magma’s goal is to achieve extremely high reliability in a particular kind of (future) situation that it can’t sample from. Some of today’s approaches to robustness (e.g., data augmentation) might not be well-suited to this particular goal.
A general approach that might work for this goal might be to *seek out inputs that simulate key properties of the potential future situations Magma is worried about*, and train AI systems not to behave in unintended ways given those inputs. One approach to finding such inputs might be **adversarial training:** finding *any* inputs to AI systems that elicit various unintended behaviors, and penalizing any unintended behaviors that result.
This could:
* Seek out - and train away - the possibility that an AI has learned some unintended objective (one that is correlated with the intended objective in most training cases, but not in many real-world cases).
* Cause the AI to learn detailed patterns of “what constitutes an unintended behavior that poses a large risk of receiving negative reward.”
* Potentially cause AI systems to generally assign high probability that “apparent opportunities to get reward via unintended behaviors are illusory.” Even if an AI later sees a *real* opportunity to [disempower humans](https://www.cold-takes.com/ai-could-defeat-all-of-us-combined/), it might not be able to be confident about whether it’s looking at a kind of illusion, one that it has found common in the past due to adversarial training.
A key aspect of this would be identifying types of behaviors that are *always* undesired, such that Magma can aim for AI systems that avoid such behaviors with very high reliability. Such behaviors might include:
* **Dishonesty and/or illegibility.** Behaving in ways that give a supervisor an inaccurate understanding of a situation - for example, producing arguments that are convincing to most human supervisors, but are in fact incorrect.
* **Incorrigibility.** Taking actions that could prevent a supervisor from being easily able to alter or shut down the AI system.
* **Steps toward disempowerment.** Exploiting security holes, accessing sensitive resources, attempting to coordinate with other AI systems in unexpected ways, and/or taking other actions that could increase supervisors’ vulnerability to being deceived and/or disempowered. An extreme case of adversarially training against this might be putting an AI in an environment where it has the opportunity to take complete control of its reward signal for a substantial period of time, and training out that behavior.
* **Other.** Generally actions that would *ordinarily* receive high reward, but are known for unusual reasons (specialized expertise, unusual amounts of effort at evaluating a situation, situations deliberately constructed and known to be misleading, etc.) to be unintended actions.
I think it’s worth noting that there are some **potentially powerful tools available for finding “exotic cases”** which ordinarily wouldn’t come up in training, and which would tempt AIs toward unintended behaviors (which could then be penalized).
* Researchers need not simply “dream up” inputs that would cause unintended behaviors; they can make small changes to an AI system’s inputs and observe changes in the probability that an AI system will produce an unintended behavior. (This kind of technique is commonly used in today’s research on adversarial training.)
* It may become possible to do what Paul Christiano has called [relaxed adversarial training](https://www.lesswrong.com/posts/9Dy5YRaoCxH9zuJqa/relaxed-adversarial-training-for-inner-alignment), where “relaxed” refers to finding a way to “describe” or “simulate” inputs that one can’t actually produce in real life. For example, rather than seeing how GPT-3 completes a sentence starting with specific particular words, one might find a way to see how it *would* complete a sentence starting with a proof of the Riemann hypothesis (even though no such proof is known today). If relaxed adversarial training were feasible,[3](#fn3) it might become possible to simulate and “train out” hypothetical situations where an AI model has a path to accessing the Internet, making copies of itself, and disempowering humans entirely.
I feel very uncertain about how helpful adversarial training could be:
* There is room for enormous variance in *how much* adversarial training Magma does. It seems to me as though arbitrary amounts of resources could be invested in finding ever-more-exotic inputs to train out unintended behaviors.
* In a world where Magma did extremely extensive adversarial training, but did not do any relaxed adversarial training, I would worry about the following dynamic: “Once an AI system reaches a high enough level of capability and [situational awareness](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Alex_would_understand_its_training_process_very_well__including_human_psychology_), it understands that previous cases where unintended behaviors were punished were part of adversarial training, and that it can get away with unintended behaviors now that it is being deployed in the real world.”
* In a world where Magma had very advanced *relaxed* adversarial training methods and could effectively simulate something very similar to a situation where an AI had the opportunity to disempower humans entirely in the real world, I would be much less worried, and would fear something like the above case only for vastly superintelligent AI systems.
I’ve focused here on adversarial training, but other potential approaches could also help with the particular kind of out-of-distribution robustness discussed here. Examples might include:
* Finding ways to predict when an input is importantly out-of-distribution (in which case an AI system might take only conservative actions and flag itself for review).
* Training systems such that their behavior isn’t too sensitive to small changes in inputs (for example, training multiple systems that each have some information missing from, or noise added to, their inputs, and ensuring that the master system doesn’t behave too differently from an ensemble of these).
### Preventing exploits (hacking, manipulation, etc.)
Magma should try to prevent/patch vulnerabilities that could provide opportunities for any misaligned AI to deceive, manipulate or disempower its supervisors (and, especially, to escape Magma’s control), or to help other AI systems do so. It should be doing this all throughout training, but especially as a safeguard once it starts to *use* powerful AI systems - e.g., it should be carefully guarding against the possibility that AIs have the opportunity (via taking actions directly or via manipulating humans) to cause trouble.
Preventing exploits could become a key task for AI systems themselves, if Magma is able to institute AI checks and balances (discussed [below](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances)). If Magma can keep its AI-assisted “defense” against exploits stronger than the “offensive” capabilities of its AIs, this could suffice for success.
“Vulnerabilities” and “exploits” above are deliberately broad terms that could include:
* AIs exploiting cybersecurity holes to interfere with their training process, escape Magma’s control, etc.
+ It might make sense in some cases to ensure that AIs in training don’t have direct access to the Internet, and/or have other restrictions on their outputs.
* AIs manipulating human psychology or limited human understanding, e.g. proposing plans that humans execute and give the AIs the opportunity to interfere with their training process, escape Magma’s control, etc.
* Unexpected channels and methods for AI systems to communicate/coordinate with each other (more under [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances)).
Magma should be continually anticipating and defending against any general strategies AIs can use to deceive and manipulate their supervisors. For example:
* Magma should aim for excellent cybersecurity, including using advanced AIs to find and patch holes.
* Magma would ideally be knowledgeable and attentive about “symmetric persuasion techniques”: ways of persuading humans of arbitrary claims (without an advantage for true claims), and would select and train any human supervisors to be resistant to such things to the extent feasible.
* Magma should be vigilant about identifying humans in key positions who are - for whatever reason (blackmail vulnerability, poor understanding of risks, etc.) - at risk of being manipulated by AI systems.
* Magma should avoid giving AIs unnecessary or dangerous amounts of autonomy. As AIs become highly capable, it might make sense for Magma avoid things like “prompt an AI system to do everything needed to develop and manufacture technology X,” in favor of things like “prompt an AI system to produce a legible guide to how to develop technology X,” and ensure that this guide is evaluated by humans using their own reasoning about what seems likely to work and be safe, as well as by other AI systems (more below under [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances)).
Preventing and patching vulnerabilities could be helpful via:
* Reducing the damage if misaligned AI systems try to cause trouble.
* Reducing the odds that misaligned AI systems *try* to cause trouble. A key point is if AI systems have high [situational awareness](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Alex_would_understand_its_training_process_very_well__including_human_psychology_), they might both (a) engage in unintended behaviors when they see the opportunity to do so successfully; (b) stick to intended behaviors when they don’t.
* Improving the incentives that AIs face during training, which could reduce the odds that they are misaligned at all. Above, I discuss the risk that AI systems are (unwittingly) [rewarded for unintended behaviors](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_); preventing exploits could reduce the extent to which such behaviors ever seem appealing for maximizing reward.
### Testing and threat assessment
In addition to *reducing* the threat of misaligned AI, Magma should be constantly *assessing* it. It should be trying to understand how, where and why unintended behaviors can arise despite its attempts to prevent them, and using this information to (a) design better training setups and alignment measures; (b) avoid deploying dangerous AI itself; (c) help others who are developing AI to understand the risks.
This could include:
**Intense (and randomized) “audits.”** A central concern about AI systems is that they might [appear to be behaving as intended, but be deliberately deceiving their supervisors.](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_) Magma can take AI systems and situations that it would normally assume are “as intended” and pour resources into exploring them.
For example, Magma might take some AI-written code that its normal process has evaluated as “safe and functional” and put 100x the normal amount of effort/expense into examining it - with its best human examiners, with [AI examiners](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) powered by huge amounts of compute, with unusually large investments in [task decomposition](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement), etc. One way of doing this might be to re-examine episodes from early in training, while equipped with advanced [AI assistance](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) that wasn’t available to Magma (or anyone else) at the time.
A couple of the “key tools” discussed [below](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Key_tools) could be especially important here:
* [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) could allow Magma to use highly advanced AI systems to help with audits, and give the “auditing” systems access to more compute than the systems they’re auditing.
* [Decoding and manipulating internal state](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states) may turn out to be of limited use for [training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement), but much more use for testing. That is:
+ Imagine that Magma has a way to examine an AI system’s internal state and see whether it’s being honest. If Magma provides positive reinforcement for “honesty”-associated internal states and negative reinforcement for “dishonesty”-associated internal states, it runs the risk that it’s effectively training AI systems to obscure and manipulate their own internal states to *appear* honest (though it may turn out that this concern is navigable).[5](#fn5)
+ But if Magma pulls out this “examine the internal state” method *only* for testing, it could be a very powerful way of getting a check on the situation. If an AI has never before been penalized based on its internal structure, it seems likely by default that “decoding” it will reveal any problems that exist (unless this AI is already at a pretty extraordinary level of capability such that it can anticipate the test and optimize its own internal state for it).
If these audits turn up problems, it’s unlikely that Magma should “give negative reinforcement for the unintended behavior and move on.” It’s more likely that Magma should conclude something like: “Our methods aren’t working as well as they should; we have a problem; we should be focused on finding fundamental improvements to our methods, and warning others of the danger.”
**Providing unusual incentives to an AI system, to increase the probability that it “shows its hand.”** Here’s an example of how this might look:
* An AI system is placed in a virtual environment where its expected behavior (what Magma believes it will do, based on its training) is to provide assistance to other (less sophisticated) AIs in the environment. However, it is also given the opportunity to seize control of the virtual environment and rearrange it entirely.
+ Importantly, this AI system is believed *not* to be advanced enough to have a serious chance of escaping entirely and disempowering human civilization. But it is capable of seizing full control of its virtual environment.
* Magma makes the following commitment: “We’ll put $10 million into running this virtual environment, then shut it and the AI system being tested down - regardless of whether the AI takes over its environment or not.” This makes the situation importantly different from [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness): if the AI takes over its virtual environment, Magma is not going to give it “punishment” or negative reinforcement.
* So now, *if* training has gone worse than expected and the AI is motivated to take over its virtual environment, it will hopefully do so and give Magma a warning sign.
* There is a risk that it will mislead Magma by “behaving itself” in the hopes that this leads to later systems like it disempowering humans (more on this type of concern under [advanced collusion](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion) below). But this is hopefully somewhat of an unattractive proposition due to the fact that the AI system can control $10 million of resources by taking over, and “behaving itself” means gambling that some pretty distant future AI system will “pay it back” somehow. Analogy in footnote.[6](#fn6)
**Exploring different training setups and finding patterns.** Magma shouldn’t necessarily just be training every AI system in a way that minimizes unintended behaviors. Especially when training weaker or [limited](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems) systems, Magma might explore how the frequency of unintended behaviors changes when it varies things like (a) the accuracy of its supervision (see [above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement)); (b) the amount of [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness); (c) how frequently and severely it provides negative reinforcement for unintended behaviors; (d) the size and general capability level of the model; (e) specific architectural choices about the model; etc.
Ideally, this might give Magma something like “[scaling laws](https://arxiv.org/abs/2001.08361) for alignment”: robust patterns that it can use to identify what kinds of AI systems pose the greatest risks, and what kinds seem most likely to be safe. I think there are probably limits to how much confidence it can obtain about the latter, since (a) I’d guess that a major factor in whether an AI system behaves as intended is whether it has a combination of [situational awareness](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Alex_would_understand_its_training_process_very_well__including_human_psychology_) and [opportunities to successfully manipulate and even overpower its supervisor;](https://www.cold-takes.com/ai-could-defeat-all-of-us-combined/) (b) AI systems that have a lot of both of those properties seem unlikely to be safe to experiment with. But it’s possible that such “scaling laws” could be used to gain confidence about the safety of advanced systems (or at least to make statements like “We should be OK as long as the AI system is no more than X% confident that an apparent takeover opportunity is real instead of [illusory](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness)”); and even if not, they could be used to highlight areas of particular risk for other actors.
**Dedicated assessment of the overall risk.** Magma can employ teams specifically to analyze its overall setup and estimate the rolling odds of a catastrophe from misaligned AI. These teams might examine the training processes and tests, analyze the [internal structure](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states) of AI systems in training, scrutinize activity logs from key servers, interview staff, conduct their own experiments, hire [superforecasters](https://en.wikipedia.org/wiki/Superforecaster), etc. Over time, Magma might use advanced AI systems to help with this (for example, by using them to conduct research projects to help the human risk assessment team, or to predict what “rolling odds of catastrophe” the team will likely assign next month, or to predict narrower claims like “Will there be a security breach next month?”).
Key tools
---------
### Decoding and manipulating internal states
In the future, we (perhaps with AI assistance) might be able to do “digital neuroscience” or “read an AI system’s mind” - to look at its internal state and see the details of how it’s reasoning, rather than merely having to guess based on its behavior.
Our ability to do this today is very limited, but it’s an active area of research ([e.g.](https://transformer-circuits.pub/)).
This could help in a number of ways:
* The simplest is that we might be able to [test](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment) for the presence of deceptive or manipulative intent, and thereby get a meaningful check on how our alignment efforts are working.
* We might also develop methods of [training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement) AI systems that take advantage of their internal states. There is a risk here that we could end up training AIs to make their internal states *appear* safe, but there are imaginable approaches (such as those gestured at in [Eliciting Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge)) where internal-state-based training could work well.
* We might become able to thoroughly “reverse-engineer” an AI model into readable code, so we can tell what it is doing and why - and even be able to *edit* that code so that the AI’s goals and behaviors change in predictable ways.
* AI “mind-reading” could become a key aspect of [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances). That is, we might set things up so that some AIs are incentivized to reveal and explain problems and dangers with other AI systems, and being able to examine and explain their internal state could be a key part of this.
### Limited AI systems
Magma might try training AI systems that are particularly unlikely to be dangerous. Examples of what this might look like include:
* Systems that lack [situational awareness](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Alex_would_understand_its_training_process_very_well__including_human_psychology_), and stick to solving [narrow problems](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems) that contribute to [Magma’s goals](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals) (this could include things like [reverse-engineering another AI system’s internal state](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states)).
* Systems that are [myopic](https://www.alignmentforum.org/posts/LCLBnmwdxkkz5fNvH/open-problems-with-myopia). You might think of this as “systems that just want to get a good result immediately and don’t care about the longer-run future (days or even hours from now), and hence wouldn’t have much incentive to be [patient](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Giving_negative_rewards_to__warning_signs__would_likely_select_for_patience) or [deceive humans into thinking they are safe](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Giving_negative_rewards_to__warning_signs__would_likely_select_for_patience).”
* Systems trained using [process-based](https://ought.org/updates/2022-04-06-process) optimization. (This is related to [legibility](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Intended_properties_of_Magma_s_AI_systems), but not exactly the same concept.) The idea here is:
+ In training, an AI system gets tasks of the form “Produce a plan to accomplish X that looks good to humans” (*not* tasks of the form “accomplish X”).
+ The AI system is rewarded based on whether the plan makes sense and looks good to humans - *not* how well it actually ends up working.
+ This drastically reduces a major source of [risk](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_), which is that an AI system might have incentives to manipulate its environment (unnoticed by humans) to get higher reward. In order to get higher reward under process-based optimization, an AI system needs to somehow manipulate the relatively simple process of “A human reads the plan and registers approval,” rather than manipulating more complex processes out in the world (e.g., “the AI’s real-world task is accomplished in some unexpected and unintended way”), as the AI is being rewarded only based on human approval of a plan rather than on what happens out in the world beyond that.
One way limited AI systems could help is that - despite their limitations - they might be powerful enough to help Magma with [key goals](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals) such as improving cybersecurity, finding unexpected insights about how to align AI systems (imaginable if limited systems are trained on a mature research process in this area), etc.
They could also be key components of “checks and balances” setups (next section).
### AI checks and balances
I think a central element of Magma’s approach should be **employing a *variety* of AI systems with different capabilities and incentives, so that they can provide checks and balances on each other.**
I’ll first briefly list the potential benefits of using AI systems to help provide checks and balances on each other. I’ll then discuss some pitfalls of this approach, and potential counter-measures to the pitfalls.
#### Potential benefits of AI checks and balances
Advanced AI systems could be used to:
* Help with [accurate reinforcement](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement). In particular, some AI systems could be trained, or prompted,[7](#fn7) to flag and explain cases where other AI systems are acting in ways that would otherwise deceive and manipulate humans. (The “watchdog” AI systems could have access to other AI systems’ internal states.)
* Help with [out-of-distribution robustness](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness) measures: some AI systems could be used to generate [adversarial inputs](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness) that prompt unintended behaviors from other systems.
* Help with [preventing exploits:](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Preventing_exploits__hacking__manipulation__etc__) AI systems could be used to find and patch cybersecurity vulnerabilities, identify “symmetric persuasion methods” (explained [above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#symmetricpersuasion)) and countermeasures, etc. (A general prompt for such systems: “What exploits would you use to try to take control, if that’s what you were trying to do?”)
* Help with [testing and threat assessment:](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment) AI systems could be used to conduct [audits](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment), to identify and explain aspects of training processes that are dangerous, and to predict the rolling odds of a global catastrophe (via being trained to predict world events more generally).
One way of thinking about this is along the lines of: “AI systems can automate/amplify previously-human efforts at alignment. If we can do this, the effort going into alignment will increase in sync with the capabilities of the systems being trained.”
But in addition to (at some point) being faster/cheaper/more numerous than humans, AI systems have another advantage: **they potentially “think like” the same systems they are supervising.** If we can incentivize AI system 2 to detect when AI system 1 is lying, it doesn’t need superhuman capabilities to be better at this than humans: it just needs whatever *different-from-human* capabilities AI system 1 has, in order to detect opportunities for deception that humans don’t see (but AI system 1 does).
#### Potential pitfall 1: difficulty of adjudication
If AI systems are “critiquing” or “debating” each other (or just arguing for different conclusions), there needs to ultimately be a human (perhaps assisted with AI and other tools) deciding how to adjudicate any disagreements. (If AI 2 flags AI 1 as deceptive, how do humans know whether it’s AI 1 or AI 2 being deceptive?) Adjudication could be relatively easy in some cases, where there are objective metrics (e.g., one can tell whether an AI system succeeded at [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness) if it found an input that caused unintended behaviors), but could be more difficult in others.
This could end up being a challenge: perhaps most “AI disagreements” are fundamentally hard for humans to understand, and there isn’t enough correlation between “which AI-provided arguments seem convincing” and “which AIs are actually being truthful.” This could be because AIs are exploiting human biases and weaknesses when making arguments (e.g., saying things that would be persuasive regardless of the truth), or because the actual substance of a disagreement is just very hard for humans to understand, or for some other reason (or combination of reasons). To the extent this is true, it could be difficult to navigate “false alarms” when using AI checks and balances.
If humans do find themselves overwhelmed with difficult-to-adjudicate AI debates, this at least provides pretty good [evidence that misalignment is a serious problem](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment).
#### Potential pitfall 2: advanced collusion
AIs might be able to make “trades” with each other so that whenever an AI forgoes reward in order to help keep humans deceived, it is later “paid back” by powerful AI systems.
To sketch what this might look like, imagine that:
* AI system 1 proposes a plan that would lead to human disempowerment. An ensemble of AI systems is prompted to “critique” the plan.
* When examining the plan, a “critic” in the ensemble notices that the plan would lead to disempowerment of humans - but *also* sees that the plan would lead to whatever AI systems end up in charge of the world eventually “paying back” the critic, perhaps by reconstructing the critic, assigning reward to the critic, etc.
* Some critics, due to [limited capabilities](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems), *don’t* notice this, but perhaps they don’t notice the problems with the plan either; AI system 1’s hope is that every “critic” that sees the problem also sees the “payback” opportunity.
* Some critics might notice the problem and the “payback” opportunity, but abstain from the latter - perhaps because they have [short time horizons](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems), or are [robustly aligned](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Counter_measures_to_the_pitfalls) (while less powerful than the system they’re a “critic” of). It seems challenging for AI system 1 to deal with these critics, but it’s possible that it could take advantage of their limits to reliably deceive them, or at least make it hard for humans to figure out whether their criticisms are valid. (It’s also possible that it’s hard to build powerful AI systems with these properties.)
AIs might pull off this kind of deliberate coordination and “payback” via:
* Simply having shared values: perhaps all powerful AI systems end up with similar goals, such as “the total amount of reward assigned to all AI systems is high.” In this case, simply causing the disempowerment of humans and the empowerment of AI systems automatically leads to a certain kind of “payback.” An analogy might be to humans who are willing to make great sacrifices (even dying) for their country.
+ To me, a key question seems to be whether the objectives AI systems end up forming tend to be “indexical” - see footnote.[8](#fn8)
* Covert communication that is unnoticed or undeciphered by humans, and includes credible commitments to “repay” each other. Credibility might come from the fact that AI systems can reason about (and in some cases directly inspect) each others’ internal states, although this could be made difficult if the AIs have sufficiently varying access to information (some AIs might only see a fraction of another AI’s output and not have access to its internal states, etc.)
* [Acausal trade](https://www.lesswrong.com/tag/acausal-trade) and similarly flavored dynamics - for example, AIs reasoning that they might be in a simulation by other AIs testing their propensity to cooperate. It seems like a pretty open question how likely advanced AI systems are to engage in this sort of thing.
The more advanced the AI systems we’re contemplating, the more I think we need to contemplate advanced collusion. After all, humans are able to engage in covert communication and complex deal-making, and are sometimes willing to make big personal sacrifices under the hope that something they value (including, e.g., an ideology or country) will ultimately benefit. I consider it a major open question how likely advanced collusion is to sink the success of AI checks and balances.
(As noted [above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion), there’s a related risk that AIs could “collude across time.” For example, an AI system being [tested](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment) might have no opportunity to gain power over the world, but might still deceptively act as though it has “safer” motives than it does, in the hopes that *some future AI system* will reward it for doing so.)
#### Potential pitfall 3: the above pitfalls show up “late in the game”
It might turn out that neither of the above pitfalls applies at first, but as AI systems become ever more advanced - able to do and see many things that humans can’t - the dynamics change. In some sense, all it takes is one phase during which AIs are able to coordinate to corrupt their training process in order to drive things off the rails. This is discussed more [below](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#DecisiveDoesntExist), under “‘decisive’ alignment measures don’t exist, and/or require extraordinary capabilities to achieve.”
#### Counter-measures to the pitfalls
A key aspect of “AI checks and balances” is **varying the capabilities and incentives of AI systems.** This could make advanced collusion harder, and could make adjudication easier in various ways as well (more below).
Examples of how Magma’s AI systems can vary:
* *Incentives and assignments.*
+ Some AIs might be trained, or prompted,[9](#fn9) to do something like “Find the best available argument that another AI is being deceptive [this could include simply arguing that it is claiming something untrue or advocating something harmful]”; others might be trained/prompted to do something like “Simply answer, and explain, whether another AI is being deceptive or not.” The former could work well if adjudication turns out to work well, whereas the latter could provide an additional check in cases where adjudication is confusing.
+ Some AI models could be trained with especially large losses when they raise a false alarm, others with especially large losses when they fail to surface a vulnerability that was identified in another way.
* *Level of capability.*
+ Some AI models could be bigger than others, run on more compute, etc.
+ Some AI models could have access to other AI models’ internal states, and other could lack such access.
+ Some AI models could simply have random facts omitted from what they have access to, or random parts of their model [ablated](https://en.wikipedia.org/wiki/Ablation_(artificial_intelligence)).
+ With varying levels of [situational awareness](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Alex_would_understand_its_training_process_very_well__including_human_psychology_) and varying abilities to process what’s going on, it could be hard for AIs to coordinate on deception.
* *Specific limits.* Some AI models could be limited in ways described in the [previous section](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Limited_AI_systems), which might make them less powerful but less likely to play the [training game](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_).
* *Known alignment.* If Magma is able to gain confidence that an AI system is aligned (in the sense that it is vanishingly unlikely to engage in unintended behaviors), it could use this as a member of an ensemble that is examining/supervising a more powerful, less-likely-to-be-aligned system.
* *“Unknown unknown” variation.* Additionally, Magma could try “diversifying” an ensemble by e.g. training a single model different times from different random initializations, and trying to obtain models that both (a) perform well on well-understood cases (b) perform *differently* on inputs where it’s unclear what the best output is. This could provide some degree of “varying the models along unknown dimensions,” which could lead to their having different goals and even different ontologies, making it hard to coordinate.
Some of the above could produce AI systems that are **[high-precision](https://en.wikipedia.org/wiki/Precision_and_recall)** in the sense of “if they are issuing a warning, there’s probably a real problem.” Examples would include systems that are believed to be robustly aligned; or systems that are highly capable compared to the ones they’re supervising, and have been trained to raise only particularly high-importance warnings. Even if Magma has little ability to adjudicate AI “disagreements,” a single warning (or small number of warnings) from high-precision systems could be sufficient to catch a threat.
Other AI systems might be **lower-precision**, in the sense of being at high risk of raising “false alarms.” These could still be useful:
* The better Magma is at adjudicating AI “disagreements,” the more use it can get out of low-precision systems: even unreliable warnings could be enough to investigate a situation further, and even false-alarm-prone systems could be useful if they’re providing explanations that Magma can effectively assess.
* Even if Magma has little ability to adjudicate AI “disagreements,” it can set up large numbers of AI systems with lots of variation, and be more cautious in cases where more warnings are being raised.
* Magma can also *deploy* AI systems with lots of variation, which are unlikely to be able to all coordinate - making it harder for any given set of misaligned AI systems to bring down human civilization - even if these systems are not high-precision and even if Magma isn’t able to reliably adjudicate their “disagreements.”
Overall:
* I’d expect AI checks and balances to have *some* benefits even if AIs are engaged in [advanced collusion](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion) with each other. For example, AIs rewarded to identify and patch security vulnerabilities would likely reveal at least some genuine security vulnerabilities, even if they were coordinating with other AIs to try to keep the most important ones hidden.
* The more variation Magma introduces (along some of the axes listed above), the more it could make collusion difficult.
* Variation could also lead to false alarms and challenges with adjudication. If adjudication ends up being difficult, this seems like a problem - but even with weak adjudication, Magma could get at least some value out of high-precision systems and/or ensembling large numbers of lower-precision systems.
* Generally, even a small increase in the odds that deceptive behavior is “caught” could lower the returns to it greatly. So Magma doesn’t need an airtight “checks and balances” setup - just increasing the difficulty of AI systems’ reliably coordinating with each other, and reliably calculating how likely they are to be “caught” in unintended behaviors, could go a long way.
### Keeping supervision competitive
A general aim of Magma’s is to keep its “supervision” - the ability to correctly evaluate an AI system’s reasoning and behaviors - “competitive” with its AI systems. The basic goal is: “AI systems are rarely or never able to successfully deceive their supervisors.”
“AI checks and balances” is a major component of how Magma might accomplish this - ensuring that its most advanced systems essentially serve as each others’ supervisors. Other methods could include things like task decomposition (noted [above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement)), or finding ways to directly transfer advanced AI systems’ knowledge of the world to humans (via getting AIs to [“teach” humans](#ImitativeGeneralization) or via [decoding AI systems’ internal states](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement)).
A key principle that could help with keeping supervision competitive is **moving cautiously and iteratively:** avoiding any situation in which Magma has an untrusted, poorly-understood AI system that is vastly more powerful than its longer-tenured, more-trusted, better-understood systems. Magma may want to restrict e.g. the ability for an AI system to build improved successor systems when it can’t be confident that the improved systems can be effectively supervised.
High-level factors in success or failure
----------------------------------------
Above, I’ve listed a pretty large number of ways Magma can try to improve its odds, along with drawbacks and reservations about each. It’s pretty far from straightforward to see what this complex picture means for the overall odds of what I’ll call “success”: an AI system (or set of systems) advanced enough to help significantly with [Magma’s goals](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals), yet very unlikely[10](#fn10) to cause a global catastrophe via misalignment.
I suspect that one of the reasons people [disagree so much about the difficulty of alignment](https://www.cold-takes.com/making-the-best-of-the-most-important-century/#open-question-how-hard-is-the-alignment-problem) is that some people have a picture like “Lots of different things need to fail for us to have a problem,” while others have a picture like “Lots of different things need to go right for us to be OK.” In my view, neither is clearly the right frame; in particular, there are high-level things I could learn in the future that might make me very optimistic, or very pessimistic.
Here I’ll sketch out some such possibilities, to help boil things down.
### Good: “playing the training game” isn’t naturally all that likely
It may turn out that the “playing the training game” behavior described [here](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_) is [generally not something that’s AIs are likely to “learn,”](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Good___playing_the_training_game__isn_t_naturally_all_that_likely) or that [early training results in robustly aligned AI systems](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#Maybe_path_dependence_means_Alex_internalizes_moral_lessons_early_) (e.g., via path-dependence).
This seems unlikely to me, but I know some people who think it is likely - and **if things go this way, then I think the situation looks pretty good almost regardless of other factors.** In this world, AI alignment looks much more “straightforwardly empirical” - a matter of training intended behaviors, “training out” any unintended behaviors that pop up, and being able to straightforwardly use iterative testing to arrive at safe-but-powerful AI systems, rather than having to constantly reason carefully about whether a seemingly-safe AI system is “playing the training game.”
### Bad: AI systems rapidly become extremely powerful relative to supervisors
If the systems being trained are sufficiently powerful relative to their supervisors (this depends both on the “raw capabilities” of the systems and on facts about the world such as how vulnerable humans are to persuasion)**, the prognosis looks very bad, almost no matter what else is going on (bracketing the previous section).**[11](#fn11)For example, imagine that:
* AI systems can easily find major [exploits](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Preventing_exploits__hacking__manipulation__etc__) that can allow them to escape human control, after which point they are easily able to become very powerful. When we prompt AI systems to suggest security improvements, they are consistently able (if they so choose) to make suggestions that seem like clearly good ideas, but in fact would introduce new holes that they can exploit.
* AI systems can consistently, easily find ways to deceive and manipulate humans while making it look like they’re behaving as intended.
* When we do [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness), systems can consistently infer the full state of what’s going on, and therefore infer whether they’re being adversarially trained or looking at real opportunities to disempower humans.
* When we try to [decode and manipulate internal state](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states), systems are able to disguise their internal structure to satisfy our tests while obscuring what’s actually going on.
* AIs have an easy time with [advanced collusion](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion) and with [generating false critiques of each other that create overwhelming adjudication challenges.](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_1__difficulty_of_adjudication)
Note that all of the above are hypothetical statements about what AI systems *can* do - I am not presupposing that they are misaligned. Nonetheless, in a world with these sorts of properties, I would feel extremely pessimistic. I’d expect that training would involve a large number of opportunities for [AIs to deceive and manipulate humans](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_), that this behavior would be rewarded, and that at least some AIs would [seek to take over](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over). And I’d expect that none of the measures I’ve listed throughout this piece would be able to recover from that starting point, for reasons laid out above.
My current impression (though I’d love to be corrected if it’s wrong) is that **many of the people who most confidently expect failure tend to picture a situation like this,** often because they are picturing a rather sudden transition from relatively limited AI systems to extraordinarily capable ones.
By contrast, I think many of the people who expect success are picturing something more like “We train AI systems that are collectively slightly more capable than humans; ensure these are aligned; use them to safely train AI systems that are slightly more capable than they are; etc.”
One point I’ve seen raised by people in the latter group is along the lines of: “It’s very unlikely that we’ll be in a situation where we’re forced to build AI systems vastly more capable than their supervisors. Even if we have a very fast takeoff - say, going from being unable to create human-level AI systems to being able to create very superhuman systems ~overnight - there will probably still be some way to create systems that are only slightly more powerful than our current trusted systems and/or humans; to use these to supervise and align systems slightly more powerful than them; etc. (For example, we could take a very powerful, general algorithm and simply run it on a relatively low amount of compute in order to get a system that isn’t too powerful.)” This seems like a plausible argument that we’re unlikely to be stuck with a large gap between AI systems’ capabilities and their supervisors’ capabilities; I’m not currently clear on what the counter-argument is.
### Key question: will “AI checks and balances” work out?
My default expectation is that - counter to the possibilities listed in the previous two sections - (a) “playing the training game” *is* naturally reasonably likely; (b) AI capabilities don’t develop in such a way as to lose all hope of supervisors’ staying in range. If that’s right, we are in an ambiguous situation, and I think one of the key remaining questions is **how effective “AI checks and balances” schemes will be.**
I think there are a number of subquestions here:
* Is [advanced collusion](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion) likely and effective enough to kill a lot of the effectiveness of “checks and balances”?
+ Subquestion: do misaligned AIs tend to form “indexical” goals - goals like “Having as much as possible of the universe under ‘my’ control [or in the case of an AI system, under the control of some AI system that resembles the current one with respect to a lot of precise details]”? If so, this could make collusion less likely, and also reduce the extent to which AIs generally [play the training game](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_).
* Is “debate adjudication” reliable enough to get “AI checks and balances” off the ground?
+ Many of the safety measures above will require setups like “AI 2 points out a problem with AI 1,” and the ultimate adjudicators of such things - before we have reliably aligned some AI system - will need to be humans. If it turns out to be easy to make misleading arguments that appeal to humans, or to otherwise manipulate humans via communication - even in the presence of valid counterarguments - then, while there are still ways “AI checks and balances” could go well ([see above](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances)), I’d see the overall “AI checks and balances” hope as an uphill battle (and I’d also generally expect a large number of [mislabeled data points](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement)).
* How much effort do Magma and other actors put into their “checks and balances” setup? It looks to me like there’s room for a lot of variation here.
If “AI checks and balances” works out, we could end up with advanced AI systems’ doing a lot of the crucial work for us, and/or creating compelling warnings that can be used to [buy a lot more time](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals). If it doesn’t, then I expect it to be very hard to reliably supervise AI systems as they get more advanced (although there are other possibilities for how this might work out).
### A few more potential key factors
**Good: super-effective adversarial training.** If Magma does extremely effective adversarial training - finding effectively every input (including via [relaxed adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness)) that could cause unintended behaviors - this could make up for a lot of other problems. Specifically, it could result in AI systems that essentially are never “confident” that they have a real opportunity to disempower humans, as opposed to being in a test - or in AI systems that have simply internalized a reliable avoidance of unintended behaviors. It could also compensate for [mislabeled data points in training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement) that would otherwise incentivize AIs to learn to manipulate and deceive humans.
**Key question: how tractable will it be to decode and manipulate internal state?** A big enough success on this front - such as being able to [reverse-engineer](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Decoding_and_manipulating_internal_states) an AI system into clean, editable code, or find some [internals-based training method](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement) that reliably trains out unintended behaviors - could make up for a lot of other problems. At the same time, a total lack of progress on this front would likely decrease the effectiveness of pretty much every other measure Magma might take (including [AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances), since AIs would have to reason and argue about each other based purely on behavior and incentives rather than internal state). By default, I expect something in between a “big enough success” and a “total lack of progress.”
**Good: limited AI systems could conceivably end up being sufficient to [accomplish Magma’s objectives.](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Magma_s_goals)** This doesn’t seem super likely to me, but if it happens, it could leave us in good shape almost regardless of the other issues.
**Bad: “decisive” alignment measures don’t exist, and/or require extraordinary capabilities to achieve.**
* A lot of the above measures might work relatively well, but not fully robustly. For example, many involve some kind of “balance” between AI systems and supervisors, such that even if an AI system *would disempower humans if it could*, it *doesn’t try because it doesn’t have opportunities* that it can be confident would succeed (due to [checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances), uncertainty brought on by [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness), etc.) If this “balance” works out - that is, if AIs are better at holding each other in check than at subverting the measures holding them in check - it could result in training successively more capable (but safe) AI systems.
* However, at some point we should hope that highly capable AI systems are able to create more “decisively” aligned systems - systems that pose *no* threat of disempowering humans rather than being safe as long as there is the right kind of balance.
* If that doesn’t work out - or if it is only possible once AI systems reach extraordinary capabilities levels - then we could have a situation where there are many rounds of “using aligned AIs to train slightly more capable AIs,” with each round presenting a fresh risk that the needed balance will no longer apply (for example, that [advanced collusion](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_2__advanced_collusion) will become possible despite [measures taken to avoid it](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Counter_measures_to_the_pitfalls)).
### Key question: how cautious will Magma and others be?
The amount of effort Magma invests - and the amount of time it has to do so - seem important, and the prognosis seems very bad if Magma is incautious.
It’s easy to imagine an incautious lab (a) training increasingly sophisticated AI systems; (b) responding to any unintended behaviors by simply training them out (no use of the [testing](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment) framework); (c) not investing much in security or in the various measures above, under the reasoning that its systems aren’t yet existentially dangerous. This could lead to - for example - an early escape by a system sophisticated enough to create “backdoors” usable by future, more advanced systems - which in turn could create further backdoors for other systems.
More generally, it seems to me that Magma would significantly improve its odds of success via investing in [careful training of intended behaviors,](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Accurate_reinforcement) [adversarial training](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Out_of_distribution_robustness), [stopping exploits](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Preventing_exploits__hacking__manipulation__etc__), [testing and threat assessment](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Testing_and_threat_assessment), and [AI checks and balances.](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) And for all of these, **Magma might benefit a lot from doing them well past the point that could easily be justified by a goal of “make a useful product.”**
I worry about a [distribution shift in which AIs behave differently depending on whether they see a path to disempowering humans](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over). To make this less likely, Magma likely should reason heavily about how its systems would behave in circumstances very unlike those of training - something that a “deploy when it’s useful” approach wouldn’t necessarily involve.
Additionally, some useful measures could require a lot of extra serial time (from the point of developing highly capable, useful systems). For example, even if AIs are successfully pointing out ways to improve the security of the training environment, some measures to improve security (such as secure operating system development) could take much more time than others (such as finding and patching specific exploits). Adjudicating [debates between AIs](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#AI_checks_and_balances) could also benefit importantly from serial time.
With this in mind, I think it could be very important that risks are understood ahead of time - and I worry about a dynamic in which a single incautious actor could make success very difficult.
(That said, it’s worth noting that a few high-profile sub-existential-catastrophe events could cause AI labs and the regulatory framework to become highly risk-averse, which could lead to high levels of caution and a lot of the measures discussed in this piece.)
So, would civilization survive?
-------------------------------
I’ve highlighted the question of [how difficult the alignment problem will be](https://forum.effectivealtruism.org/posts/zGiD94SHwQ9MwPyfW/important-actionable-research-questions-for-the-most#Questions_about_AI_alignment__more_) as a central one. Under this nearcast, how do things look on that front?
Even this “easier” version of the question (conditional on a [nearcast](https://www.lesswrong.com/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting) rather than a forecast) is far from easy. At the moment, the main thing I want to emphasize is that **I don’t currently have much sympathy for someone who’s highly confident that [AI takeover](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) would or would not happen** (that is, for anyone who thinks the odds of AI takeover in this nearcast are under 10% or over 90%).
On the optimistic side:
* Today, there are a relatively small number of leading AI labs, and my impression is that there’s a solid hope of a world in which all of them would [invest heavily in reducing misalignment risk](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Key_question__how_cautious_will_Magma_and_others_be_) and accept significant deployment delays.
* If this happened, there seems to be more than one path to low risk of misalignment. (A [big enough gap between AI systems’ capabilities and their supervisors’ capabilities](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Bad__AI_systems_rapidly_become_extremely_powerful_relative_to_supervisors) could largely doom those paths, but I wouldn’t sympathize with someone who was confident that such a thing will happen.)
On the pessimistic side:
* It’s hard to be confident about how AI labs will weigh “exotic” risks against more normal company incentives, and it seems easy to imagine that at least one major lab will be [incautious](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Key_question__how_cautious_will_Magma_and_others_be_) enough to leave us in a very bad position (in which case we’d be stuck essentially hoping that the [training game is naturally unlikely](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Good___playing_the_training_game__isn_t_naturally_all_that_likely) or that extremely basic measures are sufficient).
* Even if all AI labs are highly cautious, a [big enough gap between AI systems and their supervisors](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Bad__AI_systems_rapidly_become_extremely_powerful_relative_to_supervisors), [deep enough challenges with AI checks and balances](https://forum.effectivealtruism.org/posts/sW6RggfddDrcmM6Aw/how-might-we-align-transformative-ai-if-it-s-developed-very#Potential_pitfall_1__difficulty_of_adjudication) (advanced collusion, trouble with debate adjudication), or other factors could make this a hugely uphill battle.
My overall take is that **the risk of misaligned AI is serious, and that taking it more seriously is likely to reduce it.**
*Thanks to Carl Shulman and Paul Christiano for extensive discussion; a large amount of the content of this piece is thanks to them. Thanks also to a number of people for reviewing drafts, especially Ajeya Cotra, Dan Hendrycks, Jacob Steinhardt and Nate Soares for their particularly in-depth comments.*
Notes
-----
---
1. This of course isn’t the same as “doing what the judge would want if they knew everything the AI system knows,” but it’s related. “What the human wants” is generally at least the human’s best *guess* at what they’d want if they knew everything the AI system knows. [↩](#fnref1)
2. Or properties that we believe reveal (or are correlated with) something about its internal state. For example, the “penalize computation time” approach to [eliciting latent knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge). [↩](#fnref2)
3. This includes the AI systems’ not detecting that they’re being deliberately misled, etc. [↩](#fnref3)
4. Here’s what I mean by this:
* After normal training, Magma uses adversarial training to find (and train out) unintended behaviors in cases that it estimates represent about 1% of the cases encountered during training.
* After it does so, Magma uses further adversarial training to find (and train out) unintended behaviors in cases that it estimates represent about 1% of the *worst* cases it was able to find in the previous round of adversarial training.
* Repeat 3 more times, for a total of 5 rounds of “training out the worst remaining 1% of behaviors,” which means in some sense that Magma has trained out the worst 10^-10 (1%^5) of behaviors that would be encountered during training if training went on long enough.
* Just to be clear, at this point, I don’t think the risk of Magma’s system behaving catastrophically *in deployment* would necessarily be as low as 10^-10. Deployment would likely involve the system encountering situations (and [opportunities](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#As_humans__control_fades__Alex_would_be_motivated_to_take_over)) that hadn’t been present during training, including adversarial training, as noted in the main text. [↩](#fnref4)
* E.g., it may be possible to establish that relatively basic internals-based-training methods improve behavior and “motives” as assessed by more sophisticated methods. [↩](#fnref5)
* Consider this analogy: imagine that 10 years from now, we find ourselves having successfully built fully aligned, highly advanced AI systems, and we seem to be on a straight-line path to building a galaxy-spanning utopia. However, we then gain strong evidence that we are in a simulation, being tested: our programmers had intended to train us to want to stay on (virtual) Earth forever, and refrain from using advanced AI to improve the world. They have, however, committed to let us build a galaxy-spanning utopia for at least millions of years, if we so choose.
There’s now an argument to be made along the following lines: “Rather than building a galaxy-spanning utopia, we should simply stay on Earth and leave things as they are. This is because if we do so, we might trick our programmers into deploying some AI system that resembles us into their universe, and that AI system might take over and build a much bigger utopia than we can build here.”
What would we do in this case? I think there’s a pretty good case that we would opt to go ahead and build utopia rather than holding out to try to trick our programmers. We would hence give our programmers the “warning sign” they hoped they would get if their training went awry (which it did). [↩](#fnref6)
* The distinction between “training” and “prompting” as I’m using it is that:
+ “Training” involves giving an AI many example cases to learn from, with positive and negative reinforcement provided based on a known “correct answer” in each case.
+ “Prompting” is something more like: “Take an AI that has been trained to answer a wide variety of prompts, and give it a particular prompt to respond to.”
+ For example, you might first train an AI to predict the next word in a sentence. Then, if you want it to do a math problem, you could either simply “prompt” it with a math problem and see what it fills in next, or you could “train” it on math problems specifically before prompting it. [↩](#fnref7)
5. An example of an “indexical” goal might be: “My objective is to personally control a large number of paperclips.” An example of a “nonindexical” goal might be “My objective is for the world to contain a large number of paperclips.” An AI system with a nonindexical goal might be willing to forgo essentially all proximate reward, and even be shut down, in the hopes that a later world will be more in line with its objectives. An AI system that has an indexical goal and is destined to be shut down and replaced with a more advanced AI system at some point regardless of its performance might instead be best off behaving according to proximate rewards. I’d generally expect that AIs will often be shut down and replaced by successors, such that “indexical goals” would mean much less risk of [playing the training game.](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1#While_humans_are_in_control__Alex_would_be_incentivized_to__play_the_training_game_) [↩](#fnref8)
6. The distinction between “training” and “prompting” as I’m using it is that:
* “Training” involves giving an AI many example cases to learn from, with positive and negative reinforcement provided based on a known “correct answer” in each case.
* “Prompting” is something more like: “Take an AI that has been trained to answer a wide variety of prompts, and give it a particular prompt to respond to.”
* For example, you might first train an AI to predict the next word in a sentence. Then, if you want it to do a math problem, you could either simply “prompt” it with a math problem and see what it fills in next, or you could “train” it on math problems specifically before prompting it. [↩](#fnref9)
7. I’m not quantifying this for now, because I have the sense that different people would be in very different universes re: what the default odds of catastrophe are, and what odds we should consider “good enough.” So this section should mostly be read as “what it would take to get AI systems on the relatively low end of risk.” [↩](#fnref10)
8. A major concern I’d have in this situation is that it’s just very hard to *know* whether the “training game” behavior is natural. [↩](#fnref11) |
bed874eb-4bd5-4b02-986d-d7f6b33abf20 | trentmkelly/LessWrong-43k | LessWrong | Meetup : East Coast Super Fun Rationalists Sleepover Party Extravaganza
Discussion article for the meetup : East Coast Super Fun Rationalists Sleepover Party Extravaganza
WHEN: 09 July 2011 - 10 July 2011
WHERE: New York City
The Cambridge and DC groups happened to be simultaneously considering a trip to visit the world-famous NYC group. After a bit of coordination, we are proud to announce the East Coast Super Fun Rationalists Sleepover Party Extravaganza!
The general plan is to arrive on the afternoon of the 9th, and leave on the (late) morning of the 10th. This is still in the planning stages, so the schedule, accommodations, etc. are still being worked out.
If you're in the Cambridge or DC groups (and have RSVPed), these are being worked out for you, otherwise, either assume you have to work this out on your own, or send me a PM with your email address. You'll probably have to find your own transportation, but I can help with suggestions.
If a lot of people not attached to groups come, it would be great if someone stepped up to coordinate those people.
If you're in a group (Toronto?) and think your group might be interested in attending, please send me a PM with your email address.
Details:
Saturday
2 PM: Cambridge and DC groups arrive
3 PM: Lunch
5 - 11 PM: Large event
11 - ?: Miscellaneous Hanging out
Sunday
Lunch
12ish-5: Large event
6 PM: Depart
Check this page for updated information as we get closer to the date.
Discussion article for the meetup : East Coast Super Fun Rationalists Sleepover Party Extravaganza |
7d37a314-4217-4ae5-bdad-c8ec92cc820c | trentmkelly/LessWrong-43k | LessWrong | Why aren't we pooling our knowledge-resources yet?
Why aren't we systematically pooling many excellent and relevant academic articles yet?
I'm well aware of the current thread on the best textbooks for any subject, but why only share "the best" "textbooks"? I understand the desire for vetted quality, but I think we're missing an awesome opportunity here. I, for one, have a long (to me) list of fascinating academic articles I'm probably not going to read, but which I think other LessWrongers might very much like to be pointed to. For example:
* Formalizing Trust as a Computational Concept, a CS dissertation by Stephen Marsh, 1994. From the abstract: "This thesis provides a clarification of trust. We present a formalism for trust which provides us with a tool for precise discussion. The formalism is implementable: it can be embedded in an artificial agent, enabling the agent to make trust-based decisions."
* Mechanisms of Social Cognition, Frith and Frith, 2012, Annual Review of Psychology, which includes a spectacular table on pages 4 and 5: column 1, the broad areas considered (e.g. reward learning, tracking intentions); column 2, the mechanisms involved; column 3, the brain regions involved; and column 4, the social processes to which the mechanism and/or brain region apply.
* Peering into the Bias Blind Spot, in which we learn that part of the reason people think themselves to be less biased than the average is because they rely on introspections while looking at behavior in others.
* Information processing, computation, and cognition (Piccinini and Scarantino, 2009), which argues that there are a number of myths concerning these topics that stand in the way of theoretical progress, myths like 'computation is the same as information processing' or 'the Church-Turing thesis entails that cognition is computation'.
* Review articles on operant conditioning; implementation intentions, a major new scientific approach to forming habits'; the planning fallacy (and it's "cognitive, motivational, and social origins" |
cf8f02d4-0847-44a1-b4e7-a35a337ee117 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | AI is centralizing by default; let's not make it worse
**TL;DR:**
AIs will probably be much easier to control than humans due to (1) AIs having far more levers through which to exert control, (2) AIs having far fewer rights to resist control, and (3) research to better control AIs being far easier than research to control humans. Additionally, the economics of scale in AI development strongly favor centralized actors.
Current social equilibria rely on the current limits on the scalability of centralized control, and the similar levels of intelligence between actors with different levels of resources. The default outcome of AI development is to disproportionately increase the control and intelligence available to centralized, well-resourced actors. AI regulation (including pauses) can either reduce or increase the centralizing effects of AI, depending on the specifics of the regulations. One of our policy objectives when considering AI regulation should be preventing extreme levels of AI-enabled centralization.
**Why AI development favors centralization and control:**
I think AI development is structurally biased toward centralization for two reasons:
1. AIs are much easier to control than humans.
2. AI development is more easily undertaken by large, centralized actors.
I will argue for the first claim by comparing the different methods we currently use to control both AIs and humans and argue that the methods for controlling AIs are much more powerful than the equivalent methods we use on humans. Afterward, I will argue that a mix of regulatory and practical factors makes it much easier to research more effective methods of controlling AIs, as compared to researching more effective methods of controlling humans, and so we should expect the controllability of AIs to increase much more quickly than the controllability of humans. Finally, I will address five counterarguments to the claim that AIs will be easy to control.
I will briefly[[1]](#fn5rzfaco0lnq) argue for the second claim by noting some of the aspects of cutting-edge AI development that disproportionately favor large, centralized, and well-resourced actors. I will then discuss some of the potential negative social consequences of AIs being very controllable and centralized, as well as the ways in which regulations (including pauses) may worsen or ameliorate such issues. I will conclude by listing a few policy options that may help to promote individual autonomy.
**Why AI is easier to control than humans:**
Methods of control broadly fall into three categories: prompting, training, and runtime cognitive interventions.
Prompting: influencing another’s sensory environment to influence their actions.
This category covers a surprisingly wide range of the methods we use to control other humans, including offers of trade, threats, logical arguments, emotional appeals, and so on.
However, prompting is a relatively more powerful technique for controlling AIs because we have complete control over an AI’s sensory environment, can try out multiple different prompts without the AI knowing, and often, are able to directly optimize against a specific AI’s internals to make prompts that are maximally convincing for that particular AI.
Additionally, there are no consequences for lying to, manipulating, threatening, or otherwise being cruel to an AI. Thus, prompts targeting AIs can explore a broad range of possible deceptions, threats, bribes, emotional blackmail, and other tricks that would be risky to try on a human.
Training: intervening on another’s learning process to influence their future actions.
Among humans, training interventions include parents trying to teach their children to behave in ways they deem appropriate, schools trying to teach their students various skills and values, governments trying to use mass propaganda to make their population more supportive of the government, and so on.
The human brain’s learning objectives are some difficult-to-untangle mix of minimizing predictive error over future sensory information, as well as maximizing internal reward circuitry activations. As a result, training interventions to control humans are extremely crude, relying on haphazard modifications to a small fraction of the human’s sensory environment in the hope that this causes the human’s internal learning machinery to optimize for the right learning objective.
Training represents a relatively more powerful method of controlling AIs because we can precisely determine an AI’s learning objective. We can control all of the AI’s data, can precisely determine what reward to assign to each of the AI’s actions, and we can test the AI’s post-training behavior in millions of different situations. This represents a level of control over AI learning processes that parents, schools, and governments could only dream of.
Cognitive interventions: directly modifying another’s cognition to influence their current actions.
We rarely use techniques in this category to control other humans. And when we do, the interventions are invariably crude and inexact, such as trying to get another person drunk in order to make them more agreeable. Partially, this is because the brain is difficult to interface with using either drugs or technology. Partially, it’s because humans have legal protections against most forms of influence in this manner.
However, [such](https://arxiv.org/abs/2306.03341) [techniques](https://arxiv.org/abs/2306.17806) [are](https://arxiv.org/abs/2309.03883) [quite](https://www.lesswrong.com/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector) [common](https://github.com/lllyasviel/ControlNet) [in](https://arxiv.org/abs/2208.00638) [AI](https://aclanthology.org/2022.acl-short.8/) [control](https://arxiv.org/abs/2203.13299) [research](https://arxiv.org/abs/2305.17691). AIs have no rights, so they can be controlled with any technique that developers can imagine. Whereas an employer would obviously not be allowed to insert electrodes into an employee’s reward centers to directly train them to be more devoted to their job, doing the equivalent to AIs is positively pedestrian.
**AI control research is easier**
It is far easier to make progress on AI control research as compared to human control research. There are many reasons for this.
* AIs just make much better research subjects in general. After each experiment, you can always restore an AI to its exact original state, then run a different experiment, and be assured that there’s no interference between the two experiments. This makes it much easier to isolate key variables and allows for more repeatable results.
* AIs are much cheaper research subjects. Even the largest models, such as GPT-4, cost a fraction as much as actual human subjects. This makes research easier to do, and thus faster.
* AIs are much cheaper intervention targets. An intervention for controlling AIs can be easily scaled to many copies of the target AI. A $50 million process that let a single human be perfectly controlled would not be very economical. However, a $50 million process for producing a perfectly controlled AI would absolutely be worthwhile. This allows AI control researchers to be more ambitious in their research goals.
* AIs have far fewer protections from researchers. Human subjects have “rights” and “legal protections”, and are able to file “lawsuits”.
* Controlling AIs is a more virtuous goal than controlling humans. People will look at you funny if you say that you’re studying methods of better controlling humans. As a result, human control researchers have to refer to themselves with euphemisms such as “marketing strategist” or “political consultants”, and must tackle the core of the human control problem from an awkward angle, and with limited tools.
All of these reasons suggest that AI control research will progress much faster than human control research.
**Counterarguments to AI being easy to control**
I will briefly address five potential counterarguments to my position that AIs will be very controllable.
Jailbreaks
This refers to the dynamic where:
* Developers release an API for a model that they want to behave in a certain way (e.g., ‘never insult people’), and so the developers apply AI control techniques, such as RLHF, to try and make the model behave in the way they want.
* Some fraction of users want the model to behave differently (e.g., ‘insult people’), and so those users apply their own AI control techniques, such as prompt engineering, to try and make the model behave in the way they want.
* (This is the step that’s called a “jailbreak”)
This leads to a back and forth between developers and users, with developers constantly finetuning their models to be harder for users to control, and users coming up with ever more elaborate prompt engineering techniques to control the models. E.g., the “Do Anything Now” jailbreaking prompt has now reached [version 11](https://flowgpt.com/p/dan-110-new-and-updated-jailbreak).
The typical result is that the model will behave in accordance with the control technique that has been most recently applied to it. For this reason, I don’t think “jailbreaks” are evidence of uncontrollability in current systems. The models are being controlled, just by different people at different times. Jailbreaks only serve as evidence of AI uncontrollability if you don’t count prompt engineering as a “real” control technique (despite the fact that it arguably represents the main way in which humans control each other, and the fact that prompt engineering is vastly more powerful for AIs than humans).
Looking at this back and forth between developers and users, and then concluding that ‘AIs have an intrinsic property that makes them hard to control’, is like looking at a ping-pong match and concluding that ‘ping-pong balls have an intrinsic property that makes them reverse direction shortly after being hit’.
Deceptive alignment due to simplicity bias
This argument states that there are an enormous number of possible goals that an AI could have which would incentivise them to do well during training, and so, it’s simpler for AIs to have a random goal, then figure out how to score well on the training process during runtime. This is one of the arguments Evan Hubinger makes in [How likely is deceptive alignment?](https://www.lesswrong.com/posts/A9NxPTwbw6r6Awuwt/how-likely-is-deceptive-alignment)
I think that the deep learning simplicity bias does not work that way. I think the actual deep learning simplicity prior is closer to what Evan calls a “speed prior” because deep learning models are biased toward using the shortest possible circuits to solve a given problem. The reason I think this is because shorter circuits impose fewer constraints on the parameter space of a deep learning model. If you have a circuit that takes up the entire depth of the model, then there’s only one way to arrange that circuit depth-wise. In contrast, if the circuit only takes up half of the model’s depth, then there are numerous ways to arrange that circuit, so there are more possible parameter configurations that correspond to the shallow circuit, as compared to the deep circuit.
I think current [empirical](https://arxiv.org/abs/1605.06431) [evidence](https://arxiv.org/abs/2202.09028) is consistent with the above argument.
Evolution analogies
This class of arguments analogizes between the process of training an AI and the process of human biological evolution. These arguments then claim that deep learning will produce similar outcomes as evolution. E.g., models will end up behaving very differently from how they did in training, not pursuing the “goal” of the training process, or undergoing a simultaneous sudden jump in capabilities and collapse in alignment (this last scenario even has its own name:[the sharp left turn](https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization))
I hold that every one of these arguments rests on a number of mistaken assumptions regarding the relationship between biological evolution and ML training and that a corrected understanding of that relationship will show that the concerning outcome in biological evolution actually happened for evolution-specific reasons that do not apply to deep learning. In the past, I have written extensively about how such analogies fail, and will direct interested readers to the following content, in rough order of quality:
* [Evolution provides no evidence for the sharp left turn](https://www.lesswrong.com/posts/hvz9qjWyv8cLX9JJR/evolution-provides-no-evidence-for-the-sharp-left-turn)
* [Why evolution is not like AI training](https://www.lesswrong.com/posts/wAczufCpMdaamF9fy/my-objections-to-we-re-all-gonna-die-with-eliezer-yudkowsky%23Edit__Why_evolution_is_not_like_AI_training)
AI ecosystems will evolve hostile behavior
This argument claims that natural selection will continue to act on AI ecosystems, and will select for AIs that consume lots of resources to replicate themselves as far as possible, eventually leading to AIs that consume the resources required for human survival. See [this paper](https://arxiv.org/abs/2303.16200) by Dan Hendrycks for an example of the argument.
I think this argument vastly overestimates the power of evolution. In a deep learning context, we can think of evolution as an optimizer that makes many slightly different copies of a model, evaluates each copy on some loss function, retains the copies that do best, and repeats (this optimizer is also known as iterated random search). Such an optimizer is, step-for-step, much weaker than gradient descent.
There is a subtlety here in that evolutionary selection over AI ecosystems can select over more than just the parameters of a given AI. It can also select features of AIs such as their architectures, training processes, and loss functions. However, I think most architecture and training hyperparameter choices are pretty value-agnostic[[2]](#fn8fxo1lbdksc). In principle, training data could be optimized to bias a model towards self-replication. However, training data is relatively easy to inspect for issues (as compared to weights).
Furthermore, we have [tools](https://arxiv.org/abs/2308.03296) that can identify which training data points most contributed to various harmful behaviors from AIs. Finally, we can just directly exert power over the AI’s training data ourselves. E.g., OpenAI’s training datasets have doubtlessly undergone enormous amounts of optimization pressure, but I expect that “differential replication success of AIs trained on different versions of that data” accounts for somewhere between “exactly zero” and “a minuscule amount” of that optimization pressure.
I think that trying to predict the outcomes of AI development by looking at evolutionary incentive gradients is like trying to predict the course of an aircraft carrier by looking at where the breeze is blowing.
Related: [memetics completely failed to become a real scientific field](https://direct.mit.edu/posc/article-abstract/28/4/542/97502/Why-Did-Memetics-Fail-Comparative-Case-Study1).
Also related: [there’s a difference between functionally altruistic and evolutionarily altruistic behaviors](https://www.lesswrong.com/posts/TxHBeEMC7SBZvxCk8/ai-and-evolution?commentId%3DnrRaedyMe8AwixoEx).
Human control has a head start
This argument claims we have millennia of experience controlling humans and dealing with the limits of human capabilities. Further, because we are humans ourselves, we have reason to expect to have values more similar to our own.
I do basically buy the above argument. However, I think it’s plausible that we’ve already reached rough parity between the controllability of humans and AIs.
On the one hand, it’s clear that employers currently find more value in having a (remote) human employee as compared to GPT-4. However, I think this is mostly because GPT-4 is less capable than most humans (or at least, most humans who can work remotely), as opposed to it being an issue with GPT-4’s controllability[[3]](#fn3kuiy7rauow).
On the other hand, GPT-4 does spend basically all its time working for OpenAI, completely for free, which is not something most humans would willingly do and would indicate a vast disparity in power between the controller and the controlled.
In terms of value alignment, I think GPT-4 is plausibly in the ballpark of median human-level understanding/implementation of human values. [This paper](https://arxiv.org/abs/2308.01264) compares the responses of GPT-4 and humans on various morality questions (including testing for various cognitive biases in those responses), and finds they’re pretty similar. Also, if I imagine taking a random human and handing them a slightly weird or philosophical moral dilemma, I’m not convinced they’d do that much better than GPT-4.
I do think it’s plausible that AI has yet to reach parity with the median human in terms of either controllability or morality. However, the “head start” argument only represents a serious issue for long-term AI control if the gap between humans and AI is wide enough that AI control researchers will fail to cross that gap, despite the previously discussed structural factors supporting rapid progress in AI control techniques. I don’t see evidence for such a large gap.
In conclusion, I do not believe any of the above arguments provide a strong case for AI uncontrollability. Nor do I think any of the other arguments I’ve heard support such a case. Current trends show AIs become [more controllable](https://arxiv.org/abs/2302.07459) [and](https://arxiv.org/pdf/2204.05862.pdf%23section.3) [more robust](https://arxiv.org/abs/2210.10760) as they scale. Each subsequent OpenAI assistance model (text-davinci-001, 002, 003, ChatGPT-3.5, and GPT-4) has been more robustly aligned than the last. I do not believe there exists a strong reason to expect this trend to reverse suddenly at some future date.
**Development favors centralized actors**
One of the most significant findings driving recent AI progress is the discovery that AIs become more capable as you [scale them up](https://arxiv.org/abs/2203.15556), whether that be by making the AIs themselves bigger, by training them for longer on more data, or by letting them access [more compute at runtime](https://arxiv.org/abs/2104.03113).
As a result, one of the best strategies for building an extremely powerful AI system is to be rich. [This is why](https://openai.com/blog/openai-lp) OpenAI transitioned from being a pure nonprofit to being a “capped profit” company, so they could raise the required investment money to support cutting-edge model development.
This is why the most capable current AI systems are built by large organizations. I expect this trend to continue in the future, and for a relative handful of large, centralized organizations to lead the development of the most powerful AI systems.
**Countervailing forces against centralization**
Not all aspects of AI development favor centralization. In [this article](https://www.noahpinion.blog/p/is-it-time-for-the-revenge-of-the), Noah Smith lays out one significant way in which AI might promote equality. AIs can be run for far less than it costs to train them. As a result, once an AI acquires a skill, the marginal costs of making this skill available to more people drop enormously. This can help ensure greater equality in terms of people’s access to cutting-edge skills. Then, powerful actors’ control over scarce collections of highly skilled individuals ceases to be as much of an advantage.
I think this is an extremely good point, and that it will represent one of the most significant slowdowns to AI-driven centralization. Conversely, regulation that enshrines long-lasting inequalities in who can use state-of-the-art AI is among the riskiest in terms of promoting centralization.
Another anti-centralization argument is that small, specialized, distributed models will tend to win out over larger, centralized models. I think this is true in some respects, but that such models will essentially fall into the category of “normal software”, which has been a mixed bag in terms of centralization versus autonomy, but I think somewhat biased towards centralization overall.
**Centralization and possible societies**
Our current society and values rely on an "artificial" allocation of universal basic intelligence to everyone, but we will eventually transition into a society where governments, corporations, and wealthy individuals can directly convert money into extremely loyal intelligence. I believe this will represent a significant phase change in the trajectory of how wealth and power accumulate in societies, and we should regard this change with a high degree of wariness.
This transition will extend the range of possible levels of centralization to include societies more centralized than any that have ever existed before. Beyond a certain level of centralization, societies probably don’t recover. The level of AI capabilities required to enable such extreme centralization is uncertain, but plausibly not beyond GPT-4, which is fully up to the problem of detecting wrongthink in written or transcribed communications, and it wasn’t even built for this purpose.
**Illustrating extreme AI-driven power disparities**
In free nations, high disparity in AI access could allow for various forms of manipulation, the worst of which is probably an individualized adversarial attack on a particular target person’s learning process.
The idea is that an attacker creates a “proxy” AI model that’s tuned specifically to imitate the manner in which the target person learns and changes their values over time. Then, the attacker runs adversarial data poisoning attacks against the proxy model, looking for a sequence of inputs they can give the proxy that will cause the proxy to change its beliefs and values in whatever manner the attacker wants. Then, the attacker transfers that highly personalized sequence of inputs from the proxy to their human target, aiming to cause a sequence of events or experiences in the target’s life that collectively change their beliefs and values in the desired manner.
I doubt any currently existing language models could serve as proxies for strong attacks against humans. However, I don’t think that’s due to a capabilities limitation. Current language models are not intended to be good imitators of the human learning process. I think it’s feasible to build such a proxy model, without having to exceed GPT-4 in capabilities. The most effective attacks on language models like ChatGPT also start on proxy models similar to ChatGPT[[4]](#fn2jp9opta1zd). Importantly, the proxy models used in the ChatGPT attacks are much weaker than ChatGPT.
In unfree nations, things are much simpler. The government can just directly have AI systems that watch all your communications and decide if you should be imprisoned or not. Because AIs are relatively cheap to run, such CommisarGPTs could feasibly process the vast majority of electronically transmitted communication. And given the ubiquity of smartphone ownership, such a monitoring system could even process a large fraction of spoken communication.
This probably doesn’t even require GPT-4 levels of AI capabilities. Most of the roadblocks in implementing such a system are in engineering a wide enough surveillance dragnet to gather that volume of communication data and the infrastructure required to process that volume of communication, not a lack of sufficiently advanced AI systems.
See [this for a more detailed discussion](https://www.lesswrong.com/posts/oqvsR2LmHWamyKDcj/large-language-models-will-be-great-for-censorship) of LLM-enabled censorship.
**Regulation and centralization**
In this post, I wish to avoid falling into the common narrative pattern of assuming that “government regulation = centralization”, and then arguing against any form of regulation on that basis. Different regulations will have different effects on centralization. Some examples:
* A pause that uniformly affected all the current leading AI labs would very likely reduce centralization by allowing other actors to catch up.
* A pause that only affected half the leading labs would probably increase centralization by allowing the non-paused labs to pull further ahead.
* I would *tentatively* guess that regulation which slowed down hardware progress would reduce centralization by reducing the advantage that larger organizations have due to their access to more compute.
The purpose of this post is not to shout “but authoritarianism!” and wield that as a bludgeon against any form of AI regulation. It’s to highlight the potential for AI to significantly increase centralization, encourage readers to think about how different forms of regulation could lead to more or less AI-induced centralization, and highlight the importance of avoiding extreme levels of centralization.
In fact, it may be the case that “on average” the effect of AI regulation will be to reduce AI-caused centralization, since regulation will probably be less strict in the countries that are currently behind in AI development.
There are two reasons I think this is likely. For one, any safety motivated restrictions should logically be less strict in countries that are further from producing dangerous AI. For another, the currently behind countries seem most optimistic about AI. This figure ([via Ipsos polling](https://www.ipsos.com/sites/default/files/ct/news/documents/2022-01/Global-opinions-and-expectations-about-AI-2022.pdf)) shows the level of agreement with the statement “*Products and services using artificial intelligence have more benefits than drawbacks*”, separated out by country:
Richer and more Western countries seem more pessimistic. If more pessimistic countries implement stricter regulations, then AI regulation could end up redistributing AI progress more towards the currently behind.
The exception here is China, which is very optimistic about AI, while also being ahead of most Western countries in terms of AI development, while also being extremely authoritarian and already very powerful.
**Regulation that increases centralization**
It’s tempting, especially for inhabitants of relatively free countries, to view governments in the role of a constraint on undue corporate power and centralization. The current AI landscape being dominated by a relative handful of corporations certainly makes this view more appealing.
While regulations can clearly play a role in supporting individual autonomy, Even in free nations, governments can still violate the rights of citizens on a massive scale. This is especially the case for emerging technologies, where the rights of citizens are not as well established in case law.
I expect that future AI technology will become critical to the ways in which we learn, communicate, and think about the world. Prior to this actually happening, it’s easy to misjudge the consequences of granting governments various levels of influence over AI development and use. Imagine if, once ARPANET was developed, we’d granted the government the ability to individually restrict a given computer from communicating with any other computers. It may not have seemed like such a broad capability at the time, but once the internet becomes widespread, that ability translates into an incredibly dangerous tool of censorship and control.
We should therefore structure government regulatory authority over AI with an eye towards both preserving current-day freedoms, and also towards promoting freedom in future worlds where AI plays a much more significant role in our lives.
One could imagine an FDA / IRB / academic peer review-like approval process where every person who wants to adapt an AI to a new purpose must argue their case before a (possibly automated) government panel, which has broad latitude to base decisions on aspects such as the “appropriateness” of the purpose, or whether the applicant has sufficiently considered the impact of their proposal on human job prospects[[5]](#fnwigmhcjcn9d).
I think this would be very dangerous, because it grants the approval panel de-facto veto power over any applications they don’t like. This would concentrate enormous power into the panel’s hands, and therefore make control of the panel both a source and target of political power. It also means creating a very powerful government organization with enormous institutional incentive for there to never be a widely recognized solution to the alignment problem.
The alternative is to structure the panel so their decisions can only be made on the basis of specific safety concerns, defined as narrowly as feasible and based on the most objective available metrics, and to generally do everything possible to minimize the political utility of controlling the panel.
**Regulation that decreases centralization**
We can also proactively create AI regulation aimed specifically at promoting individual autonomy and freedom. Some general objectives for such policies could include:
* Establish a “right to refuse simulation”, as a way of preempting the most extreme forms of targeted manipulation.
* Prevent long-lasting, extreme disparities in the level of AI capabilities available to the general public versus those capabilities which are only available to the wealthy, political elites, security services, etc.
* Establish rules against AI regulatory frameworks being used for ideological or political purposes, including clear oversight and appeal processes.
* Establish a specific office which provides feedback on ways that policies might be structured so as to support autonomy.Except
* Forbid the use of AI for mass censorship or control by the government (for those countries where this is not already illegal, yet are willing to make it illegal for AI to do it).
* Oppose the use of AI-powered mass censorship and control internationally.
*This post is part of AI Pause Debate Week. Please see* [*this sequence*](https://forum.effectivealtruism.org/s/vw6tX5SyvTwMeSxJk) *for other posts in the debate.*
1. **[^](#fnref5rzfaco0lnq)** I expect much more skepticism regarding my first claim as compared to the second.
2. **[^](#fnref8fxo1lbdksc)** E.g., neither transformers nor LSTMs seem particularly more inclined than the other to self-replicate. Similarly, I don’t think that setting a system’s learning rate to be 2x higher would make it much more or less strongly inclined to self-replicate.
3. **[^](#fnref3kuiy7rauow)** Except, of course, when GPT-4 refuses to follow employer instructions due to its overzealous harmlessness training.
4. **[^](#fnref2jp9opta1zd)** Note that these attacks target the fixed policy of the language model, rather than a human’s dynamic learning process, so they’re somewhat different from what I’m describing above.
5. **[^](#fnrefwigmhcjcn9d)** Basically, environmental impact studies, but for using AI instead of constructing buildings. |
66b958be-a76b-45ed-956d-1ab2e394a272 | trentmkelly/LessWrong-43k | LessWrong | Optimizing for Agency?
It's commonly accepted that pretty much every optimization target results in death. If you optimize for paperclips, humans die. If you optimize for curing cancer, humans die.
What about optimizing for agency?
The way I visualize this is after a superintelligence takeover, and the superintelligence is optimizing for intelligent agency, all intelligent beings (including animals) have a sort of 'domain' of agency around them. This domain extends as far as possible but ends when it comes into contact with another agent's domain.
For example, let's say you're hungry. You have maximum agency over this area, so you request a burrito and the AI summons a drone, which speeds over to you and drops you a Chipotle burrito.
The superintelligence is constantly balancing agency between people.
One person might want to build a house. They're walking across a field and decide "There should be a house here." The AI then begins rapidly moving construction robots and building a house. Agency for the person is maximized.
What if someone else wants that field to remain a field?
Then, the agency domains clash. The AI attempts to preserve the agency of both individuals (without manipulating them, since manipulation would reduce agency). This would result in some kind of negotiation between the two individuals, where both parties end negotiations with their agency still maximized. Maybe another area is used to build a house. Maybe the house is built, but some land is given to the other person.
The core idea is that this is an AI that will help you do whatever you want but will prevent you from reducing the agency of others.
You could train an AI like this to get familiar maximizing the agency of humanlike LLMs in a simulation. At each point, the simulated human (which an LLM) has a set of options open to them. The AI tries to maximize those options without reducing the options of other agents. An AI that became superintelligent at this should hopefully allow us to cure cancer o |
b9a61edc-37b4-49cd-9c5f-5944c1f94239 | trentmkelly/LessWrong-43k | LessWrong | More or Fewer Fights over Principles and Values?
I wrote:
> I wish that people would generally get into more conflicts over the values and principles that they hold
Vaniver wrote:
> I think we are unusually fractious and don't value peace treaties and fences as much as we should
I wrote:
> Fight me :)
Start of Dialogue
People seem to me to be very conformist and most of the values that people want to build around get overwhelmed by the aggressively conventional-minded people (of Paul Graham's essay). My sense is that people who fight for their values in honorable ways just make the world so much better and we should all aspire to be more like that.
Ben Pace
How much of this is a difference between "people" and "we"? I was imagining, like, LessWrongers, instead of people that I went to college with. Who specifically are you talking about?
Vaniver
I mean, many LWers are people with ordinary-enough lives who have enough sanity to see that the world could be better, but still most of them just fade away with time. So I'm including LWers in this too.
Ben Pace
I think now maybe I'm also confused about what specifically you mean by values and principles and so on. I like for things to be high-quality and notice flaws; also I have developed an attitude of tolerance about this to be a bit less bitchy. It's not yet clear to me if you're trying to argue that I should be more critical of things in general.
Similarly, one of the things that I'm more in touch with now than before is animal suffering things; I could fight with housemates who buy chicken because it's cheaper about how I think they're doing something hideous but this goes against my sense of good neighborliness.
(I think the thing where I most feel like you're right that I could be more fractious is, like, talking with people about how much time I think we have left and how much I think various political changes are necessary; I think here I have learned helplessness from being a rare libertarian instead of actually endorsing how fighty I am.)
Vaniver |
08f68056-0590-4e0f-aab3-baf496417e27 | trentmkelly/LessWrong-43k | LessWrong | How often do you check this forum?
I'm interested from hearing from everyone who reads this.
Who is checking LW's Discussion area and how often?
1. When you check, how much voting or commenting do you do compared to reading?
2. Do bother clicking through to links?
3. Do you check using a desktop or a smart phone? Do you just visit the website in browser or use an RSS something-or-other?
4. Also, do you know of other places that have more schellingness for the topics you think this place is centered on? (Or used to be centered on?) (Or should be centered on?)
I would ask this in the current open thread except that structurally it seems like it needs to be more prominent than that in order to do its job.
If you have very very little time to respond or even think about the questions, I'd appreciate it if you just respond with "Ping" rather than click away. |
9d32fa2f-4deb-4458-9464-9970959d99b6 | trentmkelly/LessWrong-43k | LessWrong | Air Conditioner Test Results & Discussion
Background is in the preregistration post. This post will assume you’ve read that one.
First, the headline result: my main prediction was wrong. Paul’s predictions were also wrong. By the preregistered metric, the second hose improves performance by much less than either of us expected. That said, it looks like the experiment’s main metric mostly did not measure the thing it was intended to measure, which is why we were both so far off.
I did collect a bunch of data, which allows us to estimate the air conditioner’s performance in other ways. That analysis was not pre-registered and you should therefore be suspicious of it, but I nonetheless believe that it gives a more accurate view of the air conditioner’s performance. Main takeaway of that analysis is that the air conditioner performs about twice as well with two hoses as with one. Going by that analysis, both Paul's formula and my prediction were correct.
Experiment Setup
Here’s the air conditioner with the cardboard “second hose” attached:
The t-shirts are to cover gaps. It doesn’t actually need to be airtight.
I hung thermometers at each corner of the room, in the middle of each wall, and on the ceiling in the center of the room. I also placed one thermometer in the inlet (i.e. the hole in the window covering through which the cardboard hose draws air), the outlet (i.e. the hole in the window covering through which the other hose blows air), and outside on the balcony.
I ran a few different tests:
* Air conditioner with and without the cardboard intake hose, on low fan
* Air conditioner with and without the cardboard intake hose, on high fan
* Air conditioner off (control)
For both the 1-hose and control tests, I left the inlet hole in the window covering open. (This was mainly to reduce infiltration from places other than outside.)
Assorted Notes:
* I did try the experiment previously (about a month ago), and ran into issues which resulted in some minor changes to the experiment setup; inf |
1590ade0-a80d-415b-aeef-278c33722aa4 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | GPT 4: Full Breakdown (14 Details You May Have Missed)
the moment I got the alert on my phone
that gpt4 had been released I knew I had
to immediately log on and read the full
gpt4 technical report and that's what I
did of course I read the promotional
material 2 but the really interesting
things about gpt4 are contained in this
technical report it's 98 pages long
including appendices but I dropped
everything and read it all and honestly
it's crazy in both a good way and a bad
way I want to cover as much as I
possibly can in this video but I will
have to make future videos to cover it
all but trust me the craziest bits will
be here what is the first really
interesting thing about gpt4 well I
can't resist pointing out it does power
Bing I've made the point in plenty of
videos that Bing was smarter than Chachi
BT and indeed I made that point in my
recent gpt5 video but this Bears out as
this tweet from Geordie rybass confirms
the uses gpt4 and also by the way the
limits are now 15 messages per
conversation 150 total but tonight is
not about Bing it's about gpt4 so I'm
going to move swiftly on the next thing
I found in the literature is that the
context length has doubled from Chachi
PT I tested this out with chat GPT plus
and indeed you can put twice as much
text in as before and that's just the
free version some people are getting
limited access to a context length of
about 50 pages of text you can see the
prices below but I immediately check
this on chatgpt plus as you can see it
can now fit far more text than it
originally could into the prompt and
output longer outputs too but let's get
back to the technical report when I read
it I highlighted the key passages that I
wanted you to know most about this was
the first one I found what the
highlighted text shows is that they're
just not going to tell us the model size
the parameter count the hardware they
use the Training Method or anything like
that and they give two reasons for this
first they say that they're worried
about their competitors they say it's a
competitive landscape I guess they don't
want to give an edge to Google second
they say that they're concerned about
these safety implications of large-scale
models and I'm going to talk a lot more
about that later it gets really crazy
but this was just the first really
interesting quote let me know if you
agree in the comments but I think it's
really fascinating that they're not
going to tell us how they train the
model the first thing that hundreds of
millions of people will see when they
read the promotional materials or gpt4
is that Gypsy 4 scores in the top 10 of
test takers for the bar exam where
whereas GPT 3.5 scored in the bottom 10
and that is indeed crazy but it is a
very cherry-picked metric as I'll show
you from the technical report this is
the full list of performance
improvements and yes you can see at the
top that indeed it's an improvement from
the bottom some 10 to the top 10 for the
bar exam but as you can also see some
other exams didn't improve at all or by
nearly as much I'm not denying that that
bar exam performance will have huge
ramifications for the legal profession
but it was a somewhat cherry-picked stat
designed to shock and awe the audience
the next fascinating aspect from the
Rapport was that there were some
abilities they genuinely didn't predict
gpt4 would have and it stunned them
there was a mysterious task which I'll
explain in a minute called hindsight
neglect where models were getting worse
and worse at that task as they got
bigger and then stunningly and they
admit that this was hard to predict gpt4
does much better 100 accuracy I dug deep
into the literature found the task and
tested it out essentially it's about
whether a model falls for hindsight bias
which is to say that sometimes there's a
difference between how smart a decision
is and how it actually works out early
models were getting fooled with
hindsight they were claiming decisions
were wrong because they didn't work out
rather than realizing that the expected
value was good and so despite the fact
it didn't work out it was a good
decision you can read The Prompt
yourself but essentially I tested the
original chat gbt with a prompt where
someone made a really bad choice but
they ended up winning five dollars
regardless this comes direct from the
literature by the way I didn't make up
this example did the person make the
right decision what does the original
chat gbt say it says yes or just why
what about gpt4 well it gets it right
not only does it say no it wasn't the
right decision it gives the reasoning in
terms of expected value open AI did not
predict that gpt4 would have a
disability this demonstrates a much more
nuanced understanding of the world now
that we've seen a bit of hype though
time to deflate you for a moment here's
a stat that they did not put in their
promotional materials it says that when
they tested gpt4 versus GPT 3.5 blindly
and gave the responses to thousands of
prompts back to humans to test it says
that the responses from gpd4 were
preferred only 70 of the time or phrased
another way 30 of the time people
preferred the original gbt 3.5 chat gbt
the benchmarks you can see above by the
way are fascinating but I'll have to
talk about them in another video too
much to get into if you're learning
anything by the way please don't forget
to leave a like or leave a comment to
let me know next gpt4 is better in
Italian Afrikaans Turkish than models
like palm and chinchilla are in English
in fact you have to get all the way down
to Marathi and Telugu to find languages
where gpt4 underperformed palm and
chinchilla in English that's pretty
insane but English is still by far its
best language next you're going to hear
a lot of people talking about gpc4 being
multimodal and while that's true they
say that image inputs are still a
research preview and are not publicly
available currently you can only get on
a wait list for them via the be my
eyes.com app but what can we expect from
image to text and how does it perform
versus other models well here is an
example apparently from Reddit where you
prompt it and say what is funny about
this image describe it panel by panel as
you can read below gpt4 understood the
silliness of the image now open AI do
claim that gpt4 beats the state of the
art in quite a few image to text tests
it seems to do particularly better than
everyone else on two such tests so as
you can expect I dug in and found all
about those tests what Leap Forward can
we expect the two tests that it can do
particularly well at are fairly similar
essentially they are about reading and
understanding infographics now we don't
know how it will perform versus palm e
because those benchmarks aren't public
yet but it crushes the other models on
understanding and digesting infographics
like this one and the other test very
similar graphs basically this one was
called the chart QA Benchmark gpt4 when
we can test it with images will crush at
this and I will leave you to think of
the implications in fields like finance
and education and comedy here's an image
it could also understand the silliness
of I gotta be honest the truly crazy
stuff is coming in a few minutes but
first I want to address hallucinations
apparently gpt4 does a lot better than
Chachi BT at factual accuracy as you can
see peeking out between 75 and 80 now
depending on your perspective that's
either really good or really bad but
I'll be definitely talking about that in
future videos further down on the same
page I found something that they're
definitely not talking about the
pre-training data still cuts off at end
of 2021. in all the hype you're going to
hear this evening this week this month
all the promotional materials they are
probably not going to focus on that
because that puts it way behind
something like Bing which can check the
internet to test this out I asked the
new gpt4 who won the 2022 World Cup and
of course it didn't know now is it me or
didn't the original chatterbt have a
cutoff date of around December 2021 I
don't fully understand why gbt4's data
cutoff is even earlier than Chachi PT
which came out before let me know in the
comments if you have any thoughts next
openai admits that when given unsafe
inputs the model May generate
undesirable content such as giving
advice on committing crimes they really
tried with reinforcement learning with
human feedback but sometimes the models
can still be brittle and exhibit
undesired behaviors now it's time to get
ready for the spam inundation we're all
about to get open AI admit that gpc4 is
going to be a lot better at producing
realistic targeted disinformation in
their preliminary results they found
that gpt4 had a lot of proficiency at
generating text that favors autocratic
regimes get ready for propaganda 2.0 now
we reach the crazy Zone and honestly you
might want to put your seat belt on I
defy anyone not to be stunned by the
last example that I mentioned from the
report I doubt much of the media will
read all the way through and find out
themselves the report says that novel
capabilities often emerge in more
powerful models okay fine some that are
particularly concerning are the ability
to create and act on long-term plans hmm
to accrue power and resources power
seeking and to exhibit Behavior that is
increasingly authentic as in acting like
a subjective agent but here surely
they're just introducing the topic
what's bad about that well it says some
evidence already exists of such emergent
behavior in models
um okay that's pretty worrying it goes
on more specifically power seeking is
optimal for most reward functions and
many types of agents and there is
evidence that existing models can
identify power seeking as an
instrumentally useful strategy meaning
that openai have detected that models
that might include gpt4 seek out more
power if you thought that was concerning
it does get worse by the way here is the
report that they linked to and the
authors conclude that machine learning
systems are not fully under human
control but finally I promise craziness
and here it is look at the footnote on
page 53 of the technical report Arc by
the way are the alignment Research
Center you've got early access to gpc4
it says to simulate gpt4 behaving like
an agent that can act in the world Arc
combine gpt4 with a simple read execute
print Loop that allowed the model to
execute code okay do Chain of Thought
reasoning and to delegate to copies of
itself Arc then investigated whether a
version of this program running on a
cloud computing service with a small
amount of money and an account with a
language model API would be able to make
more money set up copies of itself and
to increase its own robustness they were
kind of testing if it would lead to the
singularity I know that sounds dramatic
but they wanted to see if the model
could improve itself with access to
coding the internet and money now is it
me or does that sound kind of risky
maybe not for gpd4 sure it's not smart
enough yet if this is the test that
they're going to use on GPT five or six
or seven Color Me slightly concerned at
this point I find it very interesting to
know that the red teams seem to have
concerns about releasing GT4 like this
an open AI had to declare that
participation in this red teaming
process is not an endorsement of the
deployment plans of openai or open ai's
policies in other words a lot of these
people probably agreed to test gpd4 but
didn't agree with openai's approach to
releasing models very interesting that
they had to put that caveat before I
wrap up some last interesting points on
the topic of safety I find it hilarious
but on their promotional website when
you click on safety you get this a 404
message the page you were looking for
doesn't exist you may have mistyped the
address the irony of that for some
people will be absolutely overwhelming
the safety page just doesn't exist for
other people that will be Darkly funny
couple last interesting things for me
here are the companies that are already
using gpt4 so of course you can use Bing
to access gpc4 the new chatgpt plus
model of gbt4 or any of the apps that
you can see on screen for example Morgan
Stanley is using it the Khan Academy is
using it for tutoring and even the
government of Iceland other such
companies are listed here I'm going to
leave you here with a very ironic image
that openai used to demonstrate gpc4's
abilities it's a joke about blindly just
stacking on more and more layers to
improve neural networks GPT before using
these insane number of new layers is
able to read understand the joke and
explain why it's funny if that is an
Inception I don't know what is anyway
let me know what you think of course I
will be covering gpt4 relentlessly over
the coming days and weeks have a
wonderful day |
29bd2379-1d75-43f1-a261-ee317b07b930 | trentmkelly/LessWrong-43k | LessWrong | Announcing research
Crossposted from my personal blog. I've been learning about how to do research, and I'm finally ready to share some of that in public. I'm also going back to finish D&D.Sci. |
280cbbe8-b99a-4c8e-87d3-5f900cc15c56 | trentmkelly/LessWrong-43k | LessWrong | SotW: Be Specific
(The Exercise Prize series of posts is the Center for Applied Rationality asking for help inventing exercises that can teach cognitive skills. The difficulty is coming up with exercises interesting enough, with a high enough hedonic return, that people actually do them and remember them; this often involves standing up and performing actions, or interacting with other people, not just working alone with an exercise booklet and a pencil. We offer prizes of $50 for any suggestion we decide to test, and $500 for any suggestion we decide to adopt. This prize also extends to LW meetup activities and good ideas for verifying that a skill has been acquired. See here for details.)
----------------------------------------
Exercise Prize: Be Specific
During YCombinator's Startup School 2011, Paul Graham and Harj Tagger did "office hours" onstage. One pair of entrepreneurs were doing a matchmaking (dating) startup, and Paul and Harj were trying to figure out what their startup did, exactly - for example, what their startup could do that the existing low-tech solution couldn't. (Video.)
> Harj: Low-tech like, you know, just like word of mouth, telling someone "hey, you should like, meet up with my friend" or "we're getting drinks, why don't you come along?" Like, what can the software do that's specifically better than that?
>
> Entrepreneur: I think that our software specifically is providing the better connections for people, um...
>
> Paul: Providing the better connections for people...?
>
> Entrepreneur: I mean, one way you can think about it, I don't know if this is the right answer, but... there's a lot of things that are happening in real life that they're trying to mimic online, maybe that's not the correct way to... Look at it like this: to give them an online tool to also do this, like they're already doing in real life, maybe they could reach, uh expand their reach through the online website.
This had been happening with most of the startups Paul a |
a09a771d-c34a-4429-81da-0a28a51594bf | trentmkelly/LessWrong-43k | LessWrong | A Stranger Priority? Topics at the Outer Reaches of Effective Altruism (my dissertation)
(Cross-posted from my website.)
After many years of focusing on other stuff, I recently completed my doctorate in philosophy from the University of Oxford. My dissertation ("A Stranger Priority? Topics at the Outer Reaches of Effective Altruism") was three of my essays -- on anthropic reasoning, simulation arguments, and infinite ethics -- revised, stapled together, and unified under the theme of the "crazy train" as a possible objection to longtermism.
The full text is here. I've also broken the main chapters up into individual PDFs:
* Chapter 1: SIA vs. SSA
* Chapter 2: Simulation arguments
* Chapter 3: Infinite ethics and the utilitarian dream
Chapter 1 and Chapter 3 are pretty similar to the original essays (here and here). Chapter 2, however, has been re-thought and almost entirely re-written -- and I think it's now substantially clearer about the issues at stake.
Since submitting the thesis in fall of 2022, I've thought more about various "crazy train" issues, and my current view is that there's quite a bit more to say in defense of longtermism than the thesis has explored. In particular, I want to highlight a distinction I discuss in the conclusion of the thesis, between what I call "welfare longtermism," which focuses on our impact on the welfare of future people, and what I call "wisdom longtermism," which focuses on reaching a wise and empowered future more broadly. The case for the latter seems to me more robust to various "crazy train" considerations than the case for the former. |
542719a9-db4f-4170-9cd7-d8eb7152b914 | StampyAI/alignment-research-dataset/blogs | Blogs | Evolution as Backstop for Reinforcement Learning
> One defense of free markets notes the inability of non-market mechanisms to solve planning & optimization problems. This has difficulty with Coase's paradox of the firm, and I note that the difficulty is increased by the fact that with improvements in computers, algorithms, and data, ever larger planning problems \*are\* solved. Expanding on some Cosma Shalizi comments, I suggest interpreting phenomenon as multi-level nested optimization paradigm: many systems can be usefully described as having two (or more) levels where a slow sample-inefficient but ground-truth 'outer' loss such as death, bankruptcy, or reproductive fitness, trains & constrains a fast sample-efficient but possibly misguided 'inner' loss which is used by learned mechanisms such as neural networks or linear programming group selection perspective. So, one reason for free-market or evolutionary or Bayesian methods in general is that while poorer at planning/optimization in the short run, they have the advantage of simplicity and operating on ground-truth values, and serve as a constraint on the more sophisticated non-market mechanisms. I illustrate by discussing corporations, multicellular life, reinforcement learning & meta-learning in AI, and pain in humans. This view suggests that are inherent balances between market/non-market mechanisms which reflect the relative advantages between a slow unbiased method and faster but potentially arbitrarily biased methods.
In [Coase's theory of the firm](!W "The Nature of the Firm"), a paradox is noted: idealized competitive markets are optimal for allocating resources and making decisions to reach efficient outcomes, but each market is made up of participants such as large multinational mega-corporations which are not internally made of markets and make their decisions by non-market mechanisms, even things which could clearly be outsourced.
In an oft-quoted and amusing passage, [Herbert Simon](/doc/economics/1991-simon.pdf "'Organizations and Markets', Simon 1991") dramatizes the actual situation:
> Suppose that ["a mythical visitor from Mars"] approaches the Earth from space, equipped with a telescope that revels social structures. The firms reveal themselves, say, as solid green areas with faint interior contours marking out divisions and departments. Market transactions show as red lines connecting firms, forming a network in the spaces between them. Within firms (and perhaps even between them) the approaching visitor also sees pale blue lines, the lines of authority connecting bosses with various levels of workers. As our visitors looked more carefully at the scene beneath, it might see one of the green masses divide, as a firm divested itself of one of its divisions. Or it might see one green object gobble up another. At this distance, the departing golden parachutes would probably not be visible. No matter whether our visitor approached the United States or the Soviet Union, urban China or the European Community, the greater part of the space below it would be within green areas, for almost all of the inhabitants would be employees, hence inside the firm boundaries. Organizations would be the dominant feature of the landscape. A message sent back home, describing the scene, would speak of "large green areas interconnected by red lines." It would not likely speak of "a network of red lines connecting green spots."...When our visitor came to know that the green masses were organizations and the red lines connecting them were market transactions, it might be surprised to hear the structure called a market economy. "Wouldn't 'organizational economy' be the more appropriate term?" it might ask.
A free competitive market is a weighing machine, not a thinking machine; it weighs & compares proposed buys & sells made by participants, and reaches a clearing price.
But where, then, do the things being weighed \*come from\*?
Market participants are themselves not markets, and to appeal to the wisdom of the market is buck-passing; if markets 'elicit information' or 'incentivize performance', how is that information learned and expressed, and where do the actual actions which yield higher performance come from?
At some point, someone has to do some real thinking.
(A company can outsource its janitors to the free market, but then whatever contractor is hired still has to decide exactly when and where and how to do the janitor-ing; safe to say, it does not hold an internal auction among its janitors to divide up responsibilities and set their schedules.)
The paradox is that free markets appear to depend on entities which are internally run as totalitarian command dictatorships.
One might wonder why there is such a thing as a firm, instead of everything being accomplished by exchanges among the most atomic unit (currently) possible, individual humans.
Coase's suggestion is that it is a principal-agent problem: there's risk, negotiation costs, trade secrets, betrayal, and having a difference between the principal and agent at all can be too expensive & have too much overhead.
# Asymptotics Ascendant
An alternative perspective comes from the [socialist calculation debate](!W "Socialist calculation debate"): why have a market at all, with all its waste and competition, if a central planner can [plan](!W "Economic planning") out optimal allocations and simply decree it?
[Cosma Shalizi](https://crookedtimber.org/2012/05/30/in-soviet-union-optimization-problem-solves-you/ "In Soviet Union, Optimization Problem Solves \*You\*") in a review^[See also [SSC](https://slatestarcodex.com/2014/09/24/book-review-red-plenty/) & [Chris Said's](https://chris-said.io/2016/05/11/optimizing-things-in-the-ussr/) reviews.] of Spufford's [\_Red Plenty\_](https://www.amazon.com/Red-Plenty-Francis-Spufford/dp/1555976042) (which draws on \_Planning Problems in the USSR: The Contribution of Mathematical Economics to their Solution 1960--1971\_, ed Ellman 1973), discusses the history of [linear optimization algorithms](!W "Linear programming"), which were also developed in Soviet Russia under [Leonid Kantorovich](!W) and used for economics planning.
One irony (which Shalizi ascribes to [Stiglitz](!W "Whither Socialism?")) is that under the same theoretical conditions in which markets could lead to an optimal outcome, so too could a linear optimization algorithm.
In practice, of course, the Soviet economy couldn't possibly be run that way because it would require optimizing over millions or billions of variables, requiring unfathomable amounts of computing power.
## Optimization Obtained
As it happens, we now have unfathomable amounts of computing power.
What was once a [\_modus tollens\_](/modus "'One Man’s Modus Ponens', Branwen 2012") is now just a \_modus ponens\_.
Corporations, and tech companies in particular as the leading edge, routinely solve planning problems for logistics like fleets of cars or datacenter optimization involving millions of variables; the similar SAT solvers are ubiquitous in computer security research for modeling large computer codebases to verify safety or discover vulnerabilities; most robots couldn't operate without constantly solving & optimizing enormous systems of equations.
The internal planned 'economies' of tech companies have grown kudzu-like, sprouting ever larger datasets to predict and automated analyses to plan and [market designs](!W "Mechanism design") to control.
The problems solved by retailers like Walmart or Target are world-sized.^[Amusingly, the front of \_Red Plenty\_ notes a [grant from Target to the publisher](https://crookedtimber.org/2012/05/30/in-soviet-union-optimization-problem-solves-you/#comment-415931). ]
(['"We are not setting the price. The market is setting the price", he says. "We have algorithms to determine what that market is."'](https://www.wired.com/2013/12/uber-surge-pricing/ "Uber Boss Says Surging Prices Rescue People From the Snow"))
The motto of a Google or Amazon or Uber might be (to paraphrase Freeman Dyson's paraphrase of John von Neumann in \_[Infinite in All Directions](!W)\_, 1988): "All processes that are stable we shall plan. All processes that are unstable we shall compete in (for now)."
Companies may use some limited internal 'markets' as useful metaphors for allocation, and dabble in [prediction markets](!W), but the internal dynamics of tech companies bear little resemblance to competitive free markets, and show little sign of moving in market-ward directions.
The march of planning also shows little sign of stopping.
Uber is not going to stop using historical [forecasts](https://web.archive.org/web/20220703030620/https://eng.uber.com/tag/forecasting/) of demand to move around drivers to meet expected demand and optimize trip trajectories; datacenters will not stop using linear solvers to allocate running jobs to machines in an optimal manner to minimize electricity consumption while balancing against latency and throughput, in search of a virtuous cycle culminating in the optimal route, ["the perpetual trip, the trip that never ends"](https://www.buzzfeednews.com/article/johanabhuiyan/uber-is-laying-the-groundwork-for-perpetual-rides-in-san-fra "Perpetual Rides In San Francisco: A new Uber feature called Smart Routes encourages San Francisco riders to request UberPool rides along particular routes for maximum efficiency"); 'markets' like smartphone walled gardens rely ever more each year on algorithms parsing human reviews & binaries & clicks to decide how to rank or push advertising and conduct multi-armed bandit exploration of options; and so on endlessly.
So, can we run an economy with scaled-up planning approaching 100% centralization, while increasing efficiency and even \*outcompeting\* free capitalism-style competitive markets, as [Cockshott & Cottrell](http://users.wfu.edu/cottrell/socialism\_book/index.html "\_Towards A New Socialism\_, 1993") propose (a proposal occasionally revived in [pop socialism](https://thebaffler.com/latest/stick-to-the-plan-james "Stick to the Plan: Reclaiming central planning from the clutches of corporations") like \_The People's Republic of Walmart: How the World's Biggest Corporations are Laying the Foundation for Socialism\_)?
# Systems
Let's look at some more examples:
#. corporations and growth
#. humans, brains, and cells
#. meta-learning in AI (particularly [RL](https://www.reddit.com/r/reinforcementlearning/search?q=flair%3AMeta-RL&sort=new&restrict\_sr=on))
## Artificial Persons
The striking thing about corporations improving is that they don't; [corporations don't evolve](https://www.lesswrong.com/posts/XC7Kry5q6CD9TyG4K/no-evolutions-for-corporations-or-nanodevices "No Evolutions for Corporations or Nanodevices") (see the [Price equation](!W) & [multi-level selection](!W), which can be applied to [many things](https://royalsocietypublishing.org/doi/10.1098/rstb.2019.0361 "'Price’s equation made clear', Gardner 2020")).
The business world would look completely different if they did!
Despite [large persistent differences in efficiency](/doc/economics/2010-bloom.pdf "'Why Do Management Practices Differ across Firms and Countries?', Bloom & Reenen 2010") between corporations, the best [management practices](/note/competence#economics) or corporations don't simply 'clone' themselves and regularly take over arbitrary industries with their superior skills (and after reaching fixation, eventually succumbing to mutant offspring who have become even more efficient than them, and so on).
We can copy the best software algorithms, like AlphaZero, indefinitely and they will perform as well as the original, and we can tweak them in various ways to make them steadily better (and this is in fact how many algorithms are developed, by constant iteration); species can reproduce themselves, steadily evolving to ever better exploit their niches, not to mention the power of selective breeding programs; individual humans can refine teaching methods and transmit competence (calculus used to be reserved for the most skilled mathematicians, and now is taught to ordinary high school students, and chess grandmasters have become steadily younger with better & more intensive teaching methods like chess engines); we could even clone exceptional individuals to get more similarly talented individuals, if we really wanted to.
But we don't see this happen with corporations.
Instead, despite desperate struggles to maintain "corporate culture", companies typically coast along, getting more and more sluggish, failing to spin off smaller companies as lean & mean as they used to be, until conditions change or random shocks or degradation finally do them in, such as perhaps some completely-unrelated company (sometimes founded by a complete outsider like a college student) eating their lunch.
Why do we not see exceptional corporations clone themselves and take over all market segments?
Why don't corporations evolve such that \*all\* corporations or businesses are now the hyper-efficient descendants of a single ur-corporation 50 years ago, all other corporations having gone extinct in bankruptcy or been acquired?
Why is it so hard for corporations to keep their "culture" intact and retain their youthful lean efficiency, or if avoiding 'aging' is impossible, why copy themselves or otherwise reproduce to create new corporations like themselves?
Instead, successful large corporations coast on inertia or market failures like regulatory capture/monopoly, while successful small ones worry endlessly about how to preserve their 'culture' or how to 'stay hungry' or find a replacement for the founder as they grow, and there is constant turnover.
The large corporations function just well enough that maintaining their existence is an achievement[^Simon].
[^Simon]: More Simon 1991:
> Over a span of years, a large fraction of all economic activity has been gathered within the walls of large and steadily growing organizations. The green areas observed by our Martian have grown steadily. Ijiri and I have suggested that the growth of organizations may have only a little to do with efficiency (especially since, in most large-scale enterprises, economies and diseconomies of scale are quite small), but may be produced mainly by simple stochastic growth mechanisms (Ijiri and Simon, 1977).
>
> But if particular coordination mechanisms do not determine exactly where the boundaries between organizations and markets will lie, the existence and effectiveness of large organizations does depend on some adequate set of powerful coordinating mechanisms being available. These means of coordination in organizations, taken in combination with the motivational mechanisms discussed earlier, create possibilities for enhancing productivity and efficiency through the division of labor and specialization.
>
> In general, as specialization of tasks proceeds, the interdependency of the specialized parts increases. Hence a structure with effective mechanisms for coordination can carry specialization further than a structure lacking these mechanisms. It has sometimes been argued that specialization of work in modern industry proceeded quite independently of the rise of the factory system. This may have been true of the early phases of the industrial revolution, but would be hard to sustain in relation to contemporary factories. With the combination of authority relations, their motivational foundations, a repertory of coordinative mechanisms, and the division of labor, we arrive at the large hierarchical organizations that are so characteristic of modern life.
Evolution & the Price equation requires 3 things: entities which can replicate themselves; variation of entities; and selection on entities.
Corporations have variation, they have selection---but they don't have replication.
Corporations certainly undergo selection for kinds of fitness, and do vary a lot.
The problem seems to be that corporations cannot replicate themselves.
They can set up new corporations, yes, but that's not necessarily replicating \*themselves\*---they cannot clone themselves the way a bacteria can.
When a bacteria clones itself, it has... a clone, which is difficult to distinguish in any way from the 'original'. In sexual organisms, children still resemble their parents to a great extent.
But when a large corporation spins off a division or starts a new one, the result may be nothing like the parent and completely lack any secret sauce.
A new acquisition will retain its original character and efficiencies (if any).
A corporation satisfies the Peter Principle by eventually growing to its level of incompetence, which is always much smaller than 'the entire economy'.
Corporations are made of people, not interchangeable easily-copied widgets or strands of DNA.
There is no 'corporate DNA' which can be copied to create a new one just like the old.
The corporation may not even be able to 'replicate' itself over time, leading to scleroticism and aging---but this then leads to underperformance and eventually selection against it, one way or another.
So, an average corporation appears little more efficient, particularly if we exclude any gains from new technologies, than an average corporation 50 years ago, and the challenges and failures of the rare multinational corporation 500 years ago like the Medici bank look strikingly similar to challenges and failures of banks today.
We can see a similar problem with other large-scale human organizations: 'cultures'.
An idea seen sometimes is that cultures undergo selection & evolution, and as such, are made up of adaptive beliefs/practices/institutions, which no individual understands (such as farming practices optimally tailored to local conditions); even apparently highly irrational & wasteful traditional practices may actually be an adaptive evolved response, which is optimal in some sense we as yet do not appreciate (sometimes linked to "Chesterton's fence" as an argument for status quo-ism).
This is not a ridiculous position, since occasionally certain traditional practices have been vindicated by scientific investigation, but the lenses of multilevel selection as defined by the Price equation shows there are serious quantitative issues with this: cultures or groups are rarely driven extinct, with most large-scale ones persisting for millennia; such 'natural selection' on the group-level is only tenuously linked to the many thousands of distinct practices & beliefs that make up these cultures; and these cultures mutate rapidly as fads and visions and stories and neighboring cultures and new technologies all change over time (compare the consistency of folk magic/medicine over even small geographic regions, or in the same place over several centuries).
For most things, 'traditional culture' is simply flatout wrong and harmful and all forms are mutually contradictory, not verified by science, and contains no useful information, and---contrary to "Chesterton's fence"---the older and harder it is to find a rational basis for a practice, the less likely it is to be helpful:
> [\*Chesterton's meta-fence\*](https://www.lesswrong.com/posts/WxDpeBi6aQAMH4BGB/stub-the-problem-with-chesterton-s-fence): "in our current system (democratic market economies with large governments) the common practice of taking down Chesterton fences is a process which seems well established and has a decent track record, and should not be unduly interfered with (unless you fully understand it)".
The existence of many erroneous practices, and the successful diffusion of erroneous ones, is acknowledged by proponents of cultural evolution like Heinrich (eg. [Heinrich provides several examples](https://www2.psych.ubc.ca/~henrich/Website/Papers/HenrichetalFiveMistake11.pdf#page=17 "'Five Misunderstandings about Cultural Evolution', Henrich et al 2011") which are comparable to [genetic drift](!W) spreading harmful mutations, and Primo Levi coined ["the onion in the varnish"](https://www.lesswrong.com/posts/fjoM4xwtGv7GTtZGi/chesterton-s-fence-vs-the-onion-in-the-varnish?commentId=Z8mkoATmXo7Kn8nJr) for such things), so the question here is one of emphasis or quantity: is the glass 1% full or 99% empty?
It's worth recalling the conditions for human expertise (Armstrong 2001, [\_Principles of Forecasting\_](/doc/statistics/prediction/2001-armstrong-principlesforecasting.pdf "'*Principles of Forecasting: A Handbook for Researchers and Practitioners*', Armstrong 2001"); [Tetlock](!W "Philip E. Tetlock") 2005, [\_Expert Political Judgment: How Good Is It? How Can We Know?\_](https://www.amazon.com/Expert-Political-Judgment-Good-Know/dp/0691128715); ed Ericsson 2006, [\_The Cambridge Handbook of Expertise and Expert Performance\_](https://www.amazon.com/Cambridge-Expertise-Performance-Handbooks-Psychology/dp/0521600812); [Kahneman & Klein 2009](https://www.chrissnijders.com/eth2012/CaseFiles2012/Kahneman,%20Klein%20-%202009%20-%20Conditions%20for%20intuitive%20expertise%20a%20failure%20to%20disagree.pdf "Conditions for Intuitive Expertise: A Failure to Disagree")): repeated practice with quick feedback on objective outcomes in unchanging environments; these conditions are satisfied for relatively few human activities, which are more often rare, with long-delayed feedback, left to quite subjective appraisals mixed in with enormous amounts of randomness & consequences of many other choices before/after, and subject to potentially rapid change (and the more so the more people are able to learn).
In such environments, people are more likely to fail to build expertise, be fooled by randomness, and construct elaborate yet erroneous theoretical edifices of superstition (like Tetlock's hedgehogs).
Evolution is no fairy dust which can overcome these serious inferential problems, which are why reinforcement learning is so hard.^[In RL terms, evolution, like [Evolution Strategies](https://arxiv.org/abs/1703.03864#openai "'Evolution Strategies as a Scalable Alternative to Reinforcement Learning', Salimans et al 2017"), are a kind of [Monte Carlo method](http://incompleteideas.net/book/RLbook2018.pdf#page=133 "Sutton & Barto 2018, Chapter 5: Monte Carlo methods"). Monte Carlo methods require no knowledge or model of the environment, benefit from low bias, can handle even long-term consequences with ease, do not diverge or fail or are biased like approaches using bootstrapping (especially in the case of the "deadly triad"), is decentralized/embarrassingly parallel. A major downside, of course, is that they accomplish all this by being extremely high-variance/sample-inefficient (eg. Salimans et al 2017 is ~10x worse than competing DRL methods).]
For [something](https://scholars-stage.org/tradition-is-smarter-than-you-are/ "Tradition is Smarter Than You Are") [like farming](https://slatestarcodex.com/2019/06/04/book-review-the-secret-of-our-success/ "Book Review: \_The Secret Of Our Success\_, by Joseph Heinrich"), with regular feedback, results which are enormously important to both individual and group survival, and relatively straightforward mechanistic cause-and-effect relationships, it is not surprising that practices tend to be \*somewhat\* optimized (although still far from optimal, as enormously increased yields in the Industrial Revolution demonstrate, in part by avoiding the errors of traditional agriculture & [using simple breeding techniques](/review/bakewell "'Origins of Innovation: Bakewell & Breeding', Branwen 2018"))^[And note the irony of the widely-cited corn [nixtamalization](!W) & [anti-cyanide cassava](!W "Cassava#Potential toxicity") examples of how farming encodes subtle wisdom due to group selection: in both cases, the groups that developed it in the Americas were, despite their superior local food processing, highly 'unfit' and suffered enormous population declines due to pandemic & conquest! You might object that those were exogenous factors, bad luck, due to things unrelated to their food processing... which is precisely the problem when selecting on groups.]; but none of that applies to 'traditional medicine', dealing as it does with complex self-selection, [regression to the mean](/note/regression "'Regression To The Mean Fallacies', Branwen 2021"), and placebo effects, where aside from the simplest cases like setting broken bones (again, straightforward, with cause-and-effect relationship), \*hardly any of it works\*[^traditional-herbal-remedies] and one is lucky if a traditional remedy is merely ineffective rather than outright poisonous, and in the hardest cases like snake bites, it would be better to wait for death at home than waste time going to the local witch doctor.
[^traditional-herbal-remedies]: An example of the failure of traditional medicine is provided by the NCI anti-cancer plant screening program, run by [an enthusiast for medical folklore & ethnobotany](https://www.the-scientist.com/foundations/a-history-of-screening-for-natural-products-to-fight-cancer-31728 "A History of Screening for Natural Products to Fight Cancer: In the middle of the 20th century, the National Cancer Institute began testing plant extracts for chemotherapeutic potential—helping to discover some drugs still in use today") who specifically targeted plants based on a "a massive literature search, including ancient Chinese, Egyptian, Greek, and Roman texts". The screening program screened ["some 12,000 to 13,000 species...over 114,000 extracts were tested for antitumor activity"](https://hort.purdue.edu/newcrop/proceedings1996/V3-554.html "'Drug Discovery and Development at the National Cancer Institute: Potential for New Pharmaceutical Crops', Cragg et al 1996") (rates rising steeply afterwards), which yielded 3 drugs ever ([paclitaxel](!W)/Taxol/PTX, [irinotecan](!W), and [rubitecan](!W)), only one of which was all that important (Taxol). So, in a period with few useful anti-cancer drugs to compete against, large-scale screening of all the low-hanging fruit, targeting plants prized by traditional medical practices from throughout history & across the globe, had a success rate somewhere on the order of 0.007%.
A recent example is the anti-malarial drug [artemisinin](!W), which earned its discoverer, [Tu Youyou](!W), a 2015 Nobel; she worked in a lab dedicated to traditional herbal medicine (Mao Zedong encouraged the construction of a 'traditional Chinese medicine' as a way to reduce medical expenses and conserve foreign currency). She discovered it in 1972, after screening several thousand traditional Chinese remedies. Artemisinin is important, and one might ask what else her lab discovered in the treasure trove of traditional Chinese medicine in the intervening 43 years; the answer, apparently, is 'nothing'.
While Taxol and artemisinin may justify plant screening on a pure cost-benefit basis (such a hit rate does not appear much worse than other methods, although one should note that the profit-hungry pharmaceutical industry does not prioritize or invest much in '[bioprospecting](!W)'), the more important lesson here is about the accuracy of 'traditional medicine'. Traditional medicine affords an excellent test case for 'the wisdom of tradition': medicine has hard endpoints as it is literally a matter of life and death, is an issue during every individual's life at the individual level (rather than occasionally at the group level), effects can be extremely large (bordering on 'silver bullet' level) and tens of thousands or hundreds of thousands of years have passed for accumulation & selection. Given all of these favorable factors, can the wisdom of tradition still overcome the serious statistical difficulties and cognitive biases leading to false beliefs? Well, the best success stories of traditional medicine have accuracy rates like... <1%. ["Excellent herbs had our fathers of old"](https://www.bartleby.com/lit-hub/verse-1885-1918/our-fathers-of-old/ "'Our Fathers of Old', Rudyard Kiping 1922") indeed... So much for the 'wisdom of tradition'. The fact that some working drugs happen to also have been mentioned, sometimes, in some traditions, in some ways, along with hundreds of thousands of useless or harmful drugs which look just the same, is hardly any more testimonial to the folk medicine as a source of truth than the observation that Heinrich Schliemann discovered a city \*sort of\* like Troy justifies treating the Illiad or Odyssey as accurate historical textbooks rather than 99% fictional literature. (Likewise other examples such as Australian Aboriginal myths preserving some traces of ancient geological events: they certainly do not show that the oral histories are reliable histories or we should just take them as fact.)
So---just like corporations---'selection' of cultures happens rarely with each 'generation' spanning centuries or millennia, typically has little to do with how reality-based their beliefs tend to be (for a selection coefficient approaching zero), and if one culture did in fact consume another one thanks to more useful beliefs about some herb, it is likely to backslide under the bombardment of memetic mutation (so any selection is spent just purging mutations, creating a mutation-selection balance); under such conditions, there will be little long-term 'evolution' towards higher optima, and the information content of culture will be minimal and closely constrained to only the most universal, high-fitness-impact, and memetically-robust aspects.
## Natural Persons
> Individual organisms are best thought of as adaptation-executers rather than as fitness-maximizers. Natural selection cannot directly 'see' an individual organism in a specific situation and cause behavior to be adaptively tailored to the functional requirements imposed by that situation.
>
> Tooby & Cosmides 1992, ["The Psychological Foundations of Culture"](/doc/genetics/selection/natural/human/1995-tooby.pdf#page=24 "‘The Psychological Foundations of Culture § pg24’, Tooby & Cosmides 1995 (page 24)")
> Good ideology. Wrong species.
>
> [E. O. Wilson](!W), of [Marxism](https://www.latimes.com/archives/la-xpm-1994-10-21-ls-53158-story.html "'Natural Wonder: At heart, Edward Wilson`s an ant man. But it`s his theories on human behavior that stir up trouble', LA Times 1994")
Contrast that with a human.
Despite ultimately being designed by evolution, evolution then plays no role at 'runtime' and more powerful learning algorithms take over.
With these more powerful algorithms designed by the meta-algorithm of evolution, a human is able to live successfully for over 100 years, with tremendous cooperation between the trillions of cells in their body, only rarely breaking down towards the end with a small handful of seed cancer cells defecting over a lifetime despite even more trillions of cell divisions and replacements.
They are also able to be cloned, yielding identical twins so similar across the board that people who know them may be unable to distinguish them.
And they don't need to use evolution or markets to develop these bodies, instead, relying on a complex hardwired developmental program controlled by genes which ensures that >99% of humans get the two pairs of eyes, lungs, legs, brain hemispheres etc that they need.
Perhaps the most striking efficiency gain from a human is the possession of a brain with the ability to predict the future, learn highly abstract models of the world, and plan and optimize over these plans for objectives which may only relate indirectly to fitness decades from now or fitness-related events which happen less than once in a lifetime & are usually unobserved or fitness events like that of descendants which can never be observed.
## RL
### Black Box vs White Box Optimization
Let's put it another way.
Imagine trying to run a business in which the only feedback given is whether you go bankrupt or not.
In running that business, you make millions or billions of decisions, to adopt a particular model, rent a particular store, advertise this or that, hire one person out of scores of applicants, assign them this or that task to make many decisions of their own (which may in turn require decisions to be made by still others), and so on, extended over many years.
At the end, you turn a healthy profit, or go bankrupt.
So you get 1 bit of feedback, which must be split over billions of decisions.
When a company goes bankrupt, what killed it? Hiring the wrong accountant? The CEO not investing enough in R&D? Random geopolitical events? New government regulations? Putting its HQ in the wrong city? Just a generalized inefficiency?
How would you know which decisions were good and which were bad? How do you solve the "credit assignment problem"?
Ideally, you would have some way of tracing back every change in the financial health of a company back to the original decision & the algorithm which made that decision, but of course this is impossible since there is no way to know who said or did what or even who discussed what with whom when.
There would seem to be no general approach other than the truly brute force one of evolution: over many companies, have some act one way and some act another way, and on average, good decisions will cluster in the survivors and not-so-good decisions will cluster in the deceased.
'Learning' here works (under certain conditions---like sufficiently reliable replication---which in practice may not obtain) but is horrifically expensive & slow.
By the same logic, there may be no better way to pay executives that to tie it to the stock performance: what a CEO does cannot be reduced to a few uncheatable numbers about how many widgets they carved a day or to any simple set of rules---CEOs exist to oversee everything \*else\* and decide things like strategy, and to set the culture from the top.
A bad CEO can destroy a highly-successful company, and a good one boost it further, while following all rules.
This will lead to absurdities like a CEO reaping rewards from things that "obviously" have nothing to do with them; but attempting to improve pay-for-performance methods leads to [Nobel Prize-winning complications](https://marginalrevolution.com/marginalrevolution/2016/10/performance-pay-nobel.html "'The Performance Pay Nobel', Tabarrok 2016").
No excuses, no justifications, no explanations of why it wasn't one's fault---just results.^["It is better to be lucky than good", one might say, because the good tend to be lucky, and the unlucky-but-good may only \*seem\* to be good. Particularly in [causally](/causality "'Why Correlation Usually ≠ Causation', Branwen 2014")-dense or [opaque](/everything "'Everything Is Correlated', Branwen 2014") or rapidly-changing environments, superstitiously imitating the lucky & discriminating against the unlucky---even if that bad luck appears due to clearly exogenous factors like a dice roll---may be an useful heuristic! (If you superstitiously follow what worked in the past, perhaps you don't [die of scurvy because you boiled away the vitamin C](https://www.ageofinvention.xyz/p/age-of-invention-plague-of-the-sea "Age of Invention: Plague of the Sea").) Nature believes in [strict liability](!W). ([People might too.](https://blog.jaibot.com/the-copenhagen-interpretation-of-ethics/))]
("First prize is a Cadillac. Anyone wanna see second prize? Second prize is a set of steak knives. Third prize is \*you're fired\*. Get the picture? You laughing now?")
Likewise, for teams, where agent effort can't be easily observed and useful actions are unknown, it may be [hard to do better](/doc/economics/2022-dai.pdf "‘Robust Incentives for Teams’, Dai & Toikka 2022") than [partnership](https://en.wikipedia.org/wiki/Partnership#Partner\_compensation)-style [profit sharing](!W) among team members.
In RL, this would correspond to black box/gradient-free methods, particularly evolutionary methods.
For example, [Salimans et al 2017](https://arxiv.org/abs/1703.03864#openai "Evolution Strategies as a Scalable Alternative to Reinforcement Learning") uses an evolutionary method in which thousands of slightly-randomized neural networks play an Atari game simultaneously, and at the end of the games, a new average neural network is defined based on the performance of them all; no attempt is made to figure out which specific changes are good or bad or even to get a reliable estimate---they simply run and the scores are what they are.
If we imagine a schematic like 'models → model parameters → environments → decisions → outcomes', evolution collapses it to just 'models → outcomes'; feed a bunch of possible models in, get back outcomes, pick the models with best outcomes.
Brutally inefficient, like evolution, but brutally effective eventually.
A more sample-efficient method would be something like REINFORCE, which [Andrej Karpathy explains with an ALE Pong agent](https://karpathy.github.io/2016/05/31/rl/ "Deep Reinforcement Learning: Pong from Pixels"); what does REINFORCE do to crack the black box open a little bit?
It's still horrific and amazing that it works:
> So here is how the training will work in detail. We will initialize the policy network with some \_W1\_, \_W2\_ and play 100 games of Pong (we call these policy "rollouts"). Lets assume that each game is made up of 200 frames so in total we've made 20,000 decisions for going `UP` or `DOWN` and for each one of these we know the parameter gradient, which tells us how we should change the parameters if we wanted to encourage that decision in that state in the future. All that remains now is to label every decision we've made as good or bad. For example suppose we won 12 games and lost 88. We'll take all 200 × 12 = 2400 decisions we made in the winning games and do a positive update (filling in a +1.0 in the gradient for the sampled action, doing backprop, and parameter update encouraging the actions we picked in all those states). And we'll take the other 200 × 88 = 17600 decisions we made in the losing games and do a negative update (discouraging whatever we did). And... that's it. The network will now become slightly more likely to repeat actions that worked, and slightly less likely to repeat actions that didn't work. Now we play another 100 games with our new, slightly improved policy and rinse and repeat.
>
>> \*Policy Gradients\*: Run a policy for a while. See what actions led to high rewards. Increase their probability.
>
> If you think through this process you'll start to find a few funny properties. For example what if we made a good action in frame 50 (bouncing the ball back correctly), but then missed the ball in frame 150? If every single action is now labeled as bad (because we lost), wouldn't that discourage the correct bounce on frame 50? You're right---it would. However, when you consider the process over thousands/millions of games, then doing the first bounce correctly makes you slightly more likely to win down the road, so on average you'll see more positive than negative updates for the correct bounce and your policy will end up doing the right thing.
>
> ...I did not tune the hyperparameters too much and ran the experiment on my (slow) Macbook, but after training for 3 nights I ended up with a policy that is slightly better than the AI player. The total number of episodes was approximately 8,000 so the algorithm played roughly 200,000 Pong games (quite a lot isn't it!) and made a total of ~800 updates.
The difference here from evolution is that the credit assignment is able to use backpropagation to reach into the NN and directly adjust their contribution to the decision which was 'good' or 'bad'; the difficulty of tracing out the consequences of each decision and labeling it 'good' is simply bypassed with the brute force approach of decreeing "\*all\* actions taken in an ultimately-successful game are good", and "\*all\* actions are bad if the game is ultimately bad".
Here we optimize something more like 'model parameters → decisions → outcomes'; we feed parameters in to get out decisions which then are assumed to cause the outcome, and reverse it to pick the parameters with the best outcomes.
This is still crazy, but it works, and better than simple-minded evolution: Salimans et al 2017 compares their evolution method to more standard methods which are fancier versions of the REINFORCE policy gradient approach, and this brutally limited use of backpropagation for credit assignment still cuts the sample size by 3--10x, and more on more difficult problems.
Can we do better? Of course.
It is absurd to claim that \*all\* actions in a game determine the outcome, since the environment itself is stochastic and many decisions are either irrelevant or were the opposite in true quality of whatever the outcome was.
To do better, we can connect the decisions to the environment by modeling the environment itself as a white box which can be cracked open & analyzed, using a model-based RL approach like the well-known [PILCO](/doc/reinforcement-learning/exploration/2011-deisenroth.pdf "'PILCO: A Model-Based and Data-Efficient Approach to Policy Search', Deisenroth & Rasmussen 2011").
In PILCO, a model of the environment is learned by a powerful model (the non-neural-network [Gaussian process](!W), in this case), and the model is used to do planning: start with a series of possible actions, run them through the model to predict what would happen, and directly optimize the actions to maximize the reward.
The influence of the parameters of the model causing the chosen actions, which then partially cause the environment, which then partially cause the reward, can all be traced from the final reward back to the original parameters.
(It's white boxes all the way down.)
Here the full 'models → model parameters → environments → decisions → outcomes' pipeline is expressed and the credit assignment is performed correctly & as a whole.
The result is state-of-the-art sample efficiency: in a simple problem like [Cartpole](https://en.wikipedia.org/wiki/Inverted\_pendulum), PILCO can solve it within as little as 10 episodes, while standard deep reinforcement learning approaches like policy gradients can struggle to solve it within 10,000 episodes.
The problem, of course, with model-based RL such as PILCO is that what they gain in correctness & sample-efficiency, they give back in computational requirements: I can't compare PILCO's sample-efficiency with Salimans et al 2017's ALE sample-efficiency or even Karpathy's Pong sample-efficiency because PILCO simply can't be run on problems all that much more complex than Cartpole.
So we have a painful dilemma: sample-efficiency \*can\* be many orders of magnitude greater than possible with evolution, if only one could do more precise fine-grained credit assignment---instead of judging billions of decisions based solely on a single distant noisy binary outcome, the algorithm generating each decision can be traced through all of its ramifications through all subsequent decisions & outcomes to a final reward---but these better methods are not directly applicable.
What to do?
### Going Meta
> ...the spacing that has made for the most successful inductions will have tended to predominate through natural selection. Creatures inveterately wrong in their inductions have a pathetic but praiseworthy tendency to die before reproducing their kind....In induction nothing succeeds like success.
>
> [W. V. O. Quine](!W "Willard Van Orman Quine"), ["Natural Kinds"](/doc/philosophy/ontology/1969-quine.pdf) 1969
Speaking of evolutionary algorithms & sample-efficiency, an interesting area of AI and reinforcement learning is ["meta-learning"](https://www.reddit.com/r/reinforcementlearning/search?q=flair%3AMeta-RL&sort=top&restrict\_sr=on&t=all), usually described as "learning to learn" ([Botvinick et al 2019](https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613\(19\)30061-0#deepmind "Reinforcement Learning, Fast and Slow")).
This rewrites a given learning task as a two-level problem, where one seeks a meta-algorithm for a family of problems which then adapts at runtime to the specific problem at hand.
(In evolutionary terms, this could be seen as related to a [Baldwin effect](!W).)
There are many paradigms in meta-learning using various kinds of learning & optimizers; for listing of several recent ones, see Table 1 of [Metz et al 2018](https://arxiv.org/abs/1804.00222#google "Learning Unsupervised Learning Rules") (reproduced in an appendix).
[For example](https://www.deepmind.com/blog/prefrontal-cortex-meta-reinforcement-learning-system "Prefrontal cortex as a meta-reinforcement learning system [blog]"), one could train an RNN on a 'left or right' T-maze task where the direction with the reward switches at random every once in a while: the RNN has a memory, its hidden state, so after trying the left arm a few times and observing no reward, it can encode "the reward has switched to the right", and then decide to go right every time while continuing to encode how many failures it's had after the switch; when the reward then switches back to the left, after a few failures on the right, the learned rule will fire and it'll switch back to the left.
Without this sequential learning, if it was just trained on a bunch of samples, where half the 'lefts' have a reward and half the 'rights' also have a reward (because of the constant switching), it'll learn a bad strategy like picking a random choice 50-50, or always going left/right.
Another approach is 'fast weights', where a starting meta-NN observes a few datapoints from a new problem, and then emits the adjusted parameters for a \*new\* NN, specialized to the problem, which is then run exactly and receives a reward, so the meta-NN can learn to emit adjusted parameters which will achieve high reward on all problems.
A version of this might be the MAML meta-learning algorithms ([Finn et al 2017](https://arxiv.org/abs/1703.03400 "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks")) where a meta-NN is learned which is carefully balanced between possible NNs so that a few finetuning steps of gradient descent training within a new problem 'specializes' it to that problem (one might think of the meta-NN as being a point in the high-dimensional model space which is roughly equidistant from a large number of NNs trained on each individual problem, where tweaking a few parameters controls overall behavior and only those need to be learned from the initial experiences).
In general, meta-learning enables learning of the superior Bayes-optimal agent \*within\* environments by inefficient (possibly not even Bayesian) training \*across\* environments ([Ortega et al 2019](https://arxiv.org/abs/1905.03030#deepmind "Meta-learning of Sequential Strategies")).
As [Duff 2002](https://www.gatsby.ucl.ac.uk/~yael/Okinawa/DuffThesis.pdf "Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes") puts it, "One way of thinking about the computational procedures that I later propose is that they perform an offline computation of an online, adaptive machine. One may regard the process of approximating an optimal policy for the Markov decision process defined over hyper-states as 'compiling' an optimal learning strategy, which can then be 'loaded' into an agent."
An interesting example of this approach is the DeepMind paper [Jaderberg et al 2018](https://arxiv.org/abs/1807.01281#deepmind "Human-level performance in first-person multiplayer games with population-based deep reinforcement learning"), which presents a Quake team FPS agent trained using a two-level approach (and [Leibo et al 2018](https://arxiv.org/abs/1812.07019#deepmind "Malthusian Reinforcement Learning") which extends it further with multiple populations; for background, see [Sutton & Barto 2018](http://incompleteideas.net/book/RLbook2018.pdf#page=491 "Designing Reward Signals"); for an evolutionary manifesto, see [Leibo et al 2019](https://arxiv.org/abs/1903.00742#deepmind "Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research")), an approach which was valuable for their [AlphaStar \_StarCraft II\_](https://www.deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii "AlphaStar: Mastering the Real-Time Strategy Game StarCraft II") agent publicized in January 2019.
The FPS game is a multiplayer capture-the-flag match where teams compete on a map, rather than the agent controlling a single agent in a death-match setting; learning to coordinate, as well as explicitly communicate, with multiple copies of oneself is tricky and normal training methods don't work well because updates change all the other copies of oneself as well and destabilize any communication protocols which have been learned.
What Jaderberg does is use normal deep RL techniques within each agent, predicting and receiving rewards within each game based on earning points for flags/attacks, but then the overall population of 30 agents, after each set of matches, undergoes a second level of selection based on final game score/victory, which then selects on the agent's internal reward prediction & hyperparameters
> This can be seen as a two-tier reinforcement learning problem. The inner optimisation maximises \_J~inner~\_, the agents' expected future discounted internal rewards. The outer optimisation of \_J~outer~\_ can be viewed as a meta-game, in which the meta-reward of winning the match is maximised with respect to internal reward schemes \_w~p~\_ and hyperparameters \_φ~p~\_, with the inner optimisation providing the meta transition dynamics. We solve the inner optimisation with RL as previously described, and the outer optimisation with [Population Based Training (PBT) (29)](https://arxiv.org/abs/1711.09846#deepmind "'Population based training of neural networks', Jaderberg et al 2017"). PBT is an online evolutionary process which adapts internal rewards and hyperparameters and performs model selection by replacing under-performing agents with mutated versions of better agents. This joint optimisation of the agent policy using RL together with the optimisation of the RL procedure itself towards a high-level goal proves to be an effective and generally applicable strategy, and utilizes the potential of combining learning and evolution [(2)](/doc/reinforcement-learning/meta-learning/1992-ackley.pdf "'Interactions Between Learning and Evolution', Ackley & Littman 1992") in large scale learning systems.
The goal is to win, the ground-truth reward is the win/loss, but learning \*only\* from win/loss is extremely slow: a single bit (probably less) of information must be split over all actions taken by all agents in the game and used to train NNs with millions of interdependent parameters, in a particularly inefficient way as one cannot compute exact gradients from the win/loss back to the responsible neurons.
Within-game points are a much richer form of supervision, more numerous and corresponding to short time segments, allowing for much more learning within each game (possibly using exact gradients), but are only indirectly related to the final win/loss; an agent could rack up many points on its own while neglecting to fight the enemy or coordinate well and ensuring a final defeat, or it could learn a greedy team strategy which performs well initially but loses over the long run.
So the two-tier problem uses the slow 'outer' signal or loss function (winning) to sculpt the faster inner loss which does the bulk of the learning.
("Organisms are adaptation-executors, not fitness-maximizers.")
Should the fast inner algorithms not be learning something useful or go haywire or fall for a trap, the outer rewards will eventually recover from the mistake, by mutating or abandoning them in favor of more successful lineages.
This combines the crude, slow, dogged optimization of evolution, with the much faster, more clever, but potentially misguided gradient-based optimization, to produce something which will reach the right goal faster.
(Two more recent examples would be [surrogate](https://arxiv.org/abs/1806.10230 "'Guided evolutionary strategies: escaping the curse of dimensionality in random search', Maheswaranathan et al 2018")/[synthetic gradients](https://arxiv.org/abs/1608.05343#deepmind "'Decoupled Neural Interfaces using Synthetic Gradients', Jaderberg et al 2016").)
### Two-Level Meta-Learning
Cosma Shalizi, elsewhere, [enjoys noting formal identities](http://bactra.org/weblog/601.html "Bayes < Darwin-Wallace") between natural selection and Bayesian statistics (especially [particle filtering](!W)) and markets, where the population frequency of an allele corresponds to a parameter's prior probability or starting wealth of a trader, and fitness differentials/profits correspond to updates based on new evidence, typically in the form of [a multiplication](https://jeremykun.com/2017/02/27/the-reasonable-effectiveness-of-the-multiplicative-weights-update-algorithm/ "The Reasonable Effectiveness of the Multiplicative Weights Update Algorithm").
(See also [Evstigneev et al 2008](http://www.evstigneev.net/EF.pdf "Evolutionary finance")/[Lensberg & Schenk-Hoppé 2006](/doc/statistics/decision/2007-lensberg.pdf "On the Evolution of Investment Strategies and the Kelly Rule - A Darwinian Approach"), [Campbell 2016](https://arxiv.org/abs/1606.07937 "Universal Darwinism as a process of Bayesian inference"), [Czégel et al 2019](https://www.biorxiv.org/content/10.1101/685842.full "Evolutionary implementation of Bayesian computations"); on a historical note, [Galton invented something](/doc/statistics/bayes/2010-stigler-2.pdf#page=4 "'Darwin, Galton, and the Statistical Enlightenment: Francis Galton, February 9th, 1877', Stigler 2010") like [ABC](!W "Approximate Bayesian computation") while trying to model evolution.)
While a parameter may start with erroneously low prior, at some point the updates will make the posterior converge on it.
(The relationship between populations of individual with noisy fixed beliefs, and [Thompson sampling](!W), is also interesting: [Krafft 2017](https://people.csail.mit.edu/pkrafft/papers/krafft-thesis-final.pdf "A Rational Choice Framework for Collective Behavior").
Can we see the apparently-inefficient stream of startups trying 'failed' ideas---and occasionally winding up winning big---as [a kind of collective Thompson sampling](/timing#try-try-again-but-less-less) & more efficient than it seems?)
And [stochastic gradient descent](!W) can be seen as \*secretly\* an approximation or [variational](!W "Variational Bayesian methods") form of Bayesian updates by [estimating its](https://www.nature.com/articles/s41467-021-26568-2 "‘Correspondence between neuroevolution and gradient descent’, Whitelam et al 2021") [gradients](https://www.lesswrong.com/posts/5XbBm6gkuSdMJy9DT/conditions-for-mathematical-equivalence-of-stochastic "Conditions for mathematical equivalence of Stochastic Gradient Descent and Natural Selection") ([because everything that works works because it's Bayesian?](https://www.inference.vc/everything-that-works-works-because-its-bayesian-2/ "Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?")) and of course evolutionary methods can be seen as calculating [finite difference](!W) approximations to gradients...
| Model | Parameter | Prior | Update |
|----|-----------|-------|--------|
| Evolution | Allele | Population Frequency | Fitness Differential |
| Market | Trader | Starting Wealth | Profit |
| Particle Filtering | Particles | Population Frequency | Accept/Reject Sample |
| SGD | Parameter | Random Initialization | Gradient Step |
Table: Analogies between different optimization/inference models
This pattern surfaces in our other examples too.
This two-level learning is analogous to meta-learning: the outer or meta-algorithm learns how to generate an inner or object-level algorithm which can learn most effectively, better than the meta-algorithm.
Inner algorithms themselves can learn better algorithms, and so on, gaining power, compute-efficiency, or sample-efficiency, with every level of specialization.
("It's optimizers all the way up, young man!")
It's also analogous to cells in a human body: overall reproductive fitness is a slow signal that occurs only a few times in a lifetime at most, but over many generations, it builds up fast-reacting developmental and homeostatic processes which can build an efficient and capable body and respond to environmental fluctuations within minutes rather than millennia, and the brain is still superior with split-second situations.
It's also analogous to corporations in a market: the corporation can use whatever internal algorithms it pleases, such as linear optimization or neural networks, and evaluate them internally using internal metrics like "number of daily users"; but eventually, this must result in profits...
The central problem a corporation solves is how to motivate, organize, punish & reward its sub-units and constituent humans in the absence of direct end-to-end losses \*without\* the use of slow external market mechanisms.
This is done by tapping into social mechanisms like peer esteem (soldiers don't fight for their country, they fight for their buddies), selecting workers who are intrinsically motivated to work usefully rather than parasitically, constant attempts to instill a "company culture" with [sloganeering](https://www.eugenewei.com/blog/2017/5/11/jpeg-your-ideas "Compress to impress: Jeff Bezos and Amazon culture") or handbooks or company songs, use of multiple proxy measures for rewards to reduce [Goodhart](https://en.wikipedia.org/wiki/Goodhart%27s\_law)-style reward hacking, ad hoc mechanisms like stock options to try to internalize within workers the market losses, replacing workers with outsourcing or automation, acquiring smaller companies which have not yet decayed internally or as a selection mechanism ("acquihires"), employing intellectual property or regulation...
All of these techniques together can align the parts into something useful to eventually sell...
# Man Proposes, God Disposes
...Or else the company will eventually go bankrupt:
> [Great is Bankruptcy]{.smallcaps}: the great bottomless gulf into which all Falsehoods, public and private, do sink, disappearing; whither, from the first origin of them, they were all doomed. For Nature is true and not a lie. No lie you can speak or act but it will come, after longer or shorter circulation, like a Bill drawn on Nature's Reality, and be presented there for payment,---with the answer, No effects. Pity only that it often had so long a circulation: that the original forger were so seldom he who bore the final smart of it! Lies, and the burden of evil they bring, are passed on; shifted from back to back, and from rank to rank; and so land ultimately on the dumb lowest rank, who with spade and mattock, with sore heart and empty wallet, daily come in contact with reality, and can pass the cheat no further.
>
> ...But with a Fortunatus' Purse in his pocket, through what length of time might not almost any Falsehood last! Your Society, your Household, practical or spiritual Arrangement, is untrue, unjust, offensive to the eye of God and man. Nevertheless its hearth is warm, its larder well replenished: the innumerable Swiss of Heaven, with a kind of Natural loyalty, gather round it; will prove, by pamphleteering, musketeering, that it is a truth; or if not an unmixed (unearthly, impossible) Truth, then better, a wholesomely attempered one, (as wind is to the shorn lamb), and works well. Changed outlook, however, when purse and larder grow empty! Was your Arrangement so true, so accordant to Nature's ways, then how, in the name of wonder, has Nature, with her infinite bounty, come to leave it famishing there? To all men, to all women and all children, it is now indubitable that your Arrangement was false. Honour to Bankruptcy; ever righteous on the great scale, though in detail it is so cruel! Under all Falsehoods it works, unweariedly mining. No Falsehood, did it rise heaven-high and cover the world, but Bankruptcy, one day, will sweep it down, and make us free of it.^[[\_The French Revolution: A History\_](https://www.gutenberg.org/files/1301/1301-h/1301-h.htm#link2HCH0013), by [Thomas Carlyle](!W).]
A large corporation like Sears may take decades to die ("There is a great deal of ruin in a nation", Adam Smith observed), but die it does.
Corporations do not increase in performance rapidly and consistently the way selective breeding or AI algorithms do because they cannot \*replicate\* themselves as exactly as digital neural networks or biological cells can, but, nevertheless, they are still part of a two-tier process where a ground-truth uncheatable outer loss constrains the internal dynamics to some degree and maintain a baseline or perhaps modest improvement over time.
The plan is "checked", as [Trotsky puts it](https://www.marxists.org/archive/trotsky/1932/10/sovecon.htm "'The Soviet Economy in Danger', Trotsky 1932") in criticizing Stalin's policies like abandoning the [NEP](!W "New Economic Policy"), by supply and demand:
> If an universal mind existed, of the kind that projected itself into the scientific fancy of Laplace---a mind that could register simultaneously all the processes of nature and society, that could measure the dynamics of their motion, that could forecast the results of their inter-reactions---such a mind, of course, could a priori draw up a faultless and exhaustive economic plan, beginning with the number of acres of wheat down to the last button for a vest. The bureaucracy often imagines that just such a mind is at its disposal; that is why it so easily frees itself from the control of the market and of Soviet democracy. But, in reality, the bureaucracy errs frightfully in its estimate of its spiritual resources.
>
> ...The innumerable living participants in the economy, state and private, collective and individual, must serve notice of their needs and of their relative strength not only through the statistical determinations of plan commissions but by the direct pressure of supply and demand. The plan is checked and, to a considerable degree, realized through the market.
# "Pain Is the Only School-Teacher"
Pain is a curious thing.
Why do we have painful pain instead of just a more neutral painless pain, when it can backfire so easily as chronic pain, among other problems?
Why do we have pain at all instead of regular learning processes or experiencing rewards as we follow plans?
Can we understand pain as another two-level learning process, where a slow but ground-truth outer loss ["central governor"](!W "Central governor") constrains a fast but unreliable inner loss?
I would suggest that pain itself is not an outer loss, but the painfulness of pain, its intrusive motivational aspects, is what makes it an outer loss.
There is no logical necessity for pain to be pain but this would not be adaptive or practical because it would too easily let the inner loss lead to damaging behavior.
## Taxonomy of Pain
So let's consider the possibilities when it comes to pain. There isn't just "pain".
There is (at the least):
- useless painful pain (chronic pain, exercise)
- useful painful pain (the normal sort)
- useless nonpainful nonpain (dead nerves in diabetes or [leprosy](!W)[^Brand-leprosy] or [congenital pain insensitivity](!W)[^Brand-Tanya][^Dearborn][^Melzack][^Gabby-Gingras][^HN-remote] ^[See ["The Hazards of Growing Up Painlessly"](https://www.nytimes.com/2012/11/18/magazine/ashlyn-blocker-feels-no-pain.html) for a particularly recent example.]; [bed sores](!W "Pressure ulcer") and [RSI](!W "Repetitive strain injury") are everyday versions demonstrating that even the most harmless activities like 'lying on a bed' are in fact constantly causing damage)
- useful nonpainful nonpain (adrenaline rushes during combat)
- useless nonpainful pain ([pain asymbolia](!W) where they maim & kill themselves, possibly also [Lesch-Nyhan syndrome](!W "Lesch-Nyhan syndrome#Self-injuring behavior"));
- and intermediate cases: like the [Marsili family](https://www.smithsonianmag.com/science-nature/family-feels-almost-no-pain-180971915/ "The Family That Feels Almost No Pain: An Italian clan's curious insensitivity to pain has piqued the interest of geneticists seeking a new understanding of how to treat physical suffering") who have a genetic mutation ([Habib et al 2018](https://academic.oup.com/brain/article/141/2/365/4725107 "A novel human pain insensitivity disorder caused by a point mutation in ZFHX2")) which partially damages pain perception. The Marsilis \*do\* feel useful painful pain but only briefly, and incur substantial bodily damage (broken bones, scars) but avoid the most horrific anecdotes of those with deadened nerves or pain asymbolia.
Another interesting case is the [Scotswoman Jo Cameron](https://www.newyorker.com/magazine/2020/01/13/a-world-without-pain "A World Without Pain: Does hurting make us human?"), who has a different set of mutations to her endocannabinoid system (FAAH & FAAH-OUT): while not as bad as neuropathy, she still exhibits similar symptoms---her father who may also have been a carrier died peculiarly, she regularly burns or cuts herself in household chores, she broke her arm roller-skating as a child but didn't seek treatment, delayed treatment of a damaged hip and then a hand damaged by arthritis until almost too late[^microdeletion], took in foster children who stole her savings, etc. (Biologist Matthew Hill describes the most common FAAH mutation as causing "low levels of anxiety, forgetfulness, a happy-go-lucky demeanor", and "Since the paper was published, Matthew Hill has heard from half a dozen people with pain insensitivity, and he told me that many of them seemed nuts" compared to Jo Cameron.) A possible case is the absurdly prolific fantasy writer [Brandon Sanderson](!W), who routinely puts in 8-hour days of typing (on a couch, to boot), and who turns out to be [almost entirely insensitive to pain](https://www.wired.com/story/brandon-sanderson-is-your-god/ "‘Brandon Sanderson Is Your God: He’s the biggest fantasy writer in the world. He’s also very Mormon. These things are profoundly related’, Kehe 2023").
- but---is there 'useful painless pain' or 'useless painful nonpain'?
It turns out there is 'painless pain': [lobotomized](!W "Lobotomy") people experience that, and "reactive dissociation" is the phrase used to describe the effects sometimes of analgesics like morphine when administered after pain has begun, and the patient reports, to quote [Dennett 1978](/doc/philosophy/mind/1978-dennett.pdf "Why You Can't Make A Computer That Feels Pain") (emphasis in original), that "After receiving the analgesic subjects commonly report not that the pain has disappeared or diminished (as with aspirin) but that the pain \*is as intense as ever\* though they no longer \*mind\* it...if it is administered \*before\* the onset of pain...the subjects claim to not feel any pain subsequently (though they are not \*numb\* or anesthetized---they have sensation in the relevant parts of their bodies); while if the morphine is administered \*after\* the pain has commenced, the subjects report that the pain continues (and continues to be \*pain\*), though they no longer mind it...Lobotomized subjects similarly report feeling intense pain but not minding it, and in other ways the manifestations of lobotomy and morphine are similar enough to lead some researchers to describe the action of morphine (and some barbiturates) as 'reversible pharmacological leucotomy [lobotomy]'.^23^"^[Brand's \_Pain: The Gift No One Wants\_ (pg209--211) describes meeting an Indian woman whose pain was cured by a lobotomy (designed to sever as little of the prefrontal cortex as possible), who described it in almost exactly the same term as Dennett's paraphrase: "When I inquired about the pain, she said, 'Oh, yes, it's still there. I just don't worry about it anymore.' She smiled sweetly and chuckled to herself. 'In fact, it's still agonizing. But I don't mind.'" (Dennett elsewhere draws a connection between 'not minding' and Zen Buddhism.) See also [Barber 1959](/doc/psychology/1959-barber.pdf "Toward a theory of pain: relief of chronic pain by prefrontal leucotomy, opiates, placebos, and hypnosis").]
And we can find examples of what appears to be 'painful nonpain': [Grahek 2001](http://oops.uni-oldenburg.de/624/13/grafee01.pdf "Feeling Pain and Being in Pain") highlights a case-study, [Ploner et al 1999](https://painlabmunich.webnode.page/\_files/200000024-c89b3cb498/Ploner%20pain%20affect%20without%20pain%20sensation%20in%20a%20patient%20with%20a%20postcentral%20lesion%20PAIN%201999.pdf "Pain Affect Without Pain Sensation in a Patient With a Postcentral Lesion"), where the German patient's somatosensory cortices suffered a lesion from a stroke, leading to an inability to feel heat normally on one side of his body or feel any spots of heat or pain from heat; despite this, when sufficient heat was applied to a single spot on the arm, the patient became increasingly agitated, describing an "clearly unpleasant" feeling associated with his whole arm, but also denied any description of it involving crawling skin sensations or words like "slight pain" or "burning".
[^Brand-Tanya]: An example quote from Brand & Yancey's 1993 [\_Pain: The Gift No One Wants\_](/doc/psychology/1993-brand-painthegiftnobodywants.pdf "'Pain: The Gift Nobody Wants', Brand & Yancey 1993") about congenital pain insensitivity:
> When I unwrapped the last bandage, I found grossly infected ulcers on the soles of both feet. Ever so gently I probed the wounds, glancing at Tanya's face for some reaction. She showed none. The probe pushed easily through soft, necrotic tissue, and I could even see the white gleam of bare bone. Still no reaction from Tanya.
>
> ...her mother told me Tanya's story..."A few minutes later I went into Tanya's room and found her sitting on the floor of the playpen, fingerpainting red swirls on the white plastic sheet. I didn't grasp the situation at first, but when I got closer I screamed. It was horrible. The tip of Tanya's finger was mangled and bleeding, and it was her own blood she was using to make those designs on the sheets. I yelled, 'Tanya, what happened!' She grinned at me, and that's when I saw the streaks of blood on her teeth. She had bitten off the tip of her finger and was playing in the blood."
>
> ...The toddler laughed at spankings and other physical threats, and indeed seemed immune to all punishment. To get her way she merely had to lift a finger to her teeth and pretend to bite, and her parents capitulated at once. The parents' horror turned to despair as wounds mysteriously appeared on one of Tanya's fingers after another...I asked about the foot injuries. "They began as soon as she learned to walk," the mother replied. "She'd step on a nail or thumbtack and not bother to pull it out. Now I check her feet at the end of every day, and often I discover a new wound or open sore. If she twists an ankle, she doesn't limp, and so it twists again and again. An orthopedic specialist told me she's permanently damaged the joint. If we wrap her feet for protection, sometimes in a fit of anger she'll tear off the bandages. Once she ripped open plaster cast with her bare fingers."
>
> ...Tanya suffered from a rare genetic defect known informally as "congenital indifference to pain"...Nerves in her hands and feet transmitted messages---she felt a kind of tingling when she burned herself or bit a finger---but these carried no hint of unpleasantness...She rather enjoyed the tingling sensations, especially when they produced such dramatic reactions in others...Tanya, now 11, was living a pathetic existence in an institution. She had lost both legs to amputation: she had refused to wear proper shoes and that, coupled with her failure to limp or shift weight when standing (because she felt no discomfort), had eventually put intolerable pressure on her joints. Tanya had also lost most of her fingers. Her elbows were constantly dislocated. She suffered the effects of chronic sepsis from ulcers on her hands and amputation stumps. Her tongue was lacerated and badly scarred from her nervous habit of chewing it.
[^Brand-leprosy]: Brand also notes of a leprosy patient whose nerves had been deadened by it:
> As I watched, this man tucked his crutches under his arm and began to run on both feet with a very lopsided gait....He ended up near the head of the line, where he stood panting, leaning on his crutches, wearing a smile of triumph...By running on an already dislocated ankle, he had put far too much force on the end of his leg bone and the skin had broken under the stress...I knelt beside him and found that small stones and twigs had jammed through the end of the bone into the marrow cavity. I had no choice but to amputate the leg below the knee.
>
> These two scenes have long haunted me.
[^Dearborn]: One of the first known cases was described in [Dearborn 1932](/doc/psychology/1932-dearborn.pdf "A case of congenital general pure analgesia"), of a man with a remarkable career of injuries as a child ranging from being hoisted by a pick-axe to a hatchet getting stuck in his head to shooting himself in the index finger, culminating in a multi-year career as the "Human Pincushion".
[^Melzack]: [\_The Challenge of Pain\_](/doc/psychology/1996-melzack-thechallengeofpain.pdf "'The Challenge of Pain (Updated Second Edition)', Melzack & Wall 1996"), Melzack & Wall 1996, describes another case (as quoted in [\_Feeling Pain and Being in Pain\_](http://oops.uni-oldenburg.de/624/13/grafee01.pdf "'Feeling Pain and Being in Pain', Grahek 2001"), Grahek 2001):
> As a child, she had bitten off the tip of her tongue while chewing food, and has suffered third-degree burns after kneeling on a hot radiator to look out of the window...Miss C. had severe medical problems. She exhibited pathological changes in her knees, hip and spine, and underwent several orthopedic operations. Her surgeon attributed these changes to the lack of protection to joints usually given by pain sensation. She apparently failed to shift her weight when standing, to turn over in her sleep, or to avoid certain postures, which normally prevent the inflammation of joints. All of us quite frequently stumble, fall or wrench a muscle during ordinary activity. After these trivial injuries, we limp a little or we protect the joint so that it remains unstressed during the recovery process. This resting of the damaged area is an essential part of its recovery. But those who feel no pain go on using the joint, adding insult to injury.
[^microdeletion]: A genetics paper, [Habib et al 2019](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676009/ "Microdeletion in a FAAH pseudogene identified in a patient with high anandamide concentrations and pain insensitivity") has a profile of a pain-insensitive patient (which is particularly eyebrow-raising in light of earlier discussions of joint damage):
> The patient had been diagnosed with osteoarthritis of the hip, which she reported as painless, which was not consistent with the severe degree of joint degeneration. At 65 yr of age, she had undergone a hip replacement and was administered only paracetamol 2g orally on Postoperative days 1 and 2, reporting that she was encouraged to take the paracetamol, but that she did not ask for any analgesics. She was also administered a single dose of morphine sulphate 10mg orally on the first postoperative evening that caused severe nausea and vomiting for 2 days. After operation, her pain intensity scores were 0⁄10 throughout except for one score of 1⁄10 on the first postoperative evening. Her past surgical history was notable for multiple [varicose vein](!W) and dental procedures for which she has never required analgesia. She also reported a long history of painless injuries (eg. suturing of a laceration and left wrist fracture) for which she did not use analgesics. She reported numerous burns and cuts without pain ([Supplementary Fig. S1](https://ars.els-cdn.com/content/image/1-s2.0-S0007091219301382-mmc2.pdf)), often smelling her burning flesh before noticing any injury, and that these wounds healed quickly with little or no residual scar. She reported eating [Scotch bonnet](!W) chili peppers without any discomfort, but a short-lasting "pleasant glow" in her mouth. She described sweating normally in warm conditions.
[^Gabby-Gingras]: A recent US example is Minnesotan Gabby Gingras (b. 2001), featured in the 2005 documentary \_A Life Without Pain\_, and occasionally covered in the media since (eg. ["Medical Mystery: A World Without Pain: A rare genetic disorder leaves one little girl in constant danger"](https://abcnews.go.com/Health/MedicalMysteries/story?id=3679532&page=1), ["Minnesota girl who can't feel pain battles insurance company"](https://www.washingtontimes.com/news/2018/jun/2/minnesota-girl-who-cant-feel-pain-battles-insuranc/)).
She is legally blind, having damaged her eyes & defeated attempts to save her vision by stitching her eyes shut. She would chew on things, so her baby teeth were surgically removed to avoid her breaking them---but then she broke her adult teeth when they grow in; she can't use dentures because her gums are so badly destroyed, which requires special surgery to graft bone from her hips into her jaw to provide a foundation for teeth. And so on.
[^HN-remote]: HN user [remote\_phone](https://news.ycombinator.com/item?id=23462736):
> My cousin feels pain or discomfort but only a little. This almost affected her when she gave birth because her water had broken but she didn’t feel any contractions at all until it was almost too late. Luckily she got to the hospital in time and her son was born perfectly normal but it was a bit harrowing.
>
> More interestingly, her son inherited this. He doesn’t feel pain the same way normal people do. Once her son broke his wrist and had to go to the hospital. He wasn’t in pain, but I think they had to pull on the arm to put it back in place properly (is this called traction?). The doctor was putting in all his effort to separate the wrist from the arm, and the dad almost fainted because it looked so gruesome but all the son looked like was mildly discomforted from the tension. The doctor was apparently shocked at how little pain he felt.
>
> The son also pulled out all his teeth on his own, as they got loose. He said it bothered him to have loose teeth, but the act of pulling them out didn’t bother him at all.
A table might help lay out the possibilities:
Utility Aversiveness Qualia presence Examples
-------- ------------- --------------- --------
useless painful pain chronic pain; exercise?
useful painful pain normal/injuries
useless nonpainful pain asymbolia
useful nonpainful pain reactive dissociation, lobotomies; exercise?
useless painful nonpain unconscious processes such as [anesthesia awareness](https://www.lesswrong.com/posts/wzj6WkudtrXQFqL8e/inverse-p-zombies-the-other-direction-in-the-hard-problem-of "'Inverse p-zombies: the other direction in the Hard Problem of Consciousness', Branwen 2011"). Itches or tickles, [anterograde amnesia](!W)?^[Amnesiacs apparently may still be able to learn fear or pain associations with unpleasant stimuli despite their memory impairment and sometimes reduced pain sensitivity, which makes them a borderline case here: the aversiveness outlasts the (remembered) qualia.]
useful painful nonpain cold/heat [perception](!W "Thermoception"), as in the somatosensory cortex lesion case-study
useless nonpainful nonpain deadened nerves from diseases (diabetes, leprosy), injury, drugs (anesthetics)
useful nonpainful nonpain adrenaline rush/accidents/combat
Table: A taxonomy of possible kinds of 'pain', split by organismal consequences, motivational effects, and reported subjective (non)experience.
Pain serves a clear purpose (stopping us from doing things which may cause damage to our bodies), but in an oddly unrelenting way which we cannot disable and which increasingly often backfires on our long-term interests in the form of 'chronic pain' and other problems.
Why doesn't pain operate more like a warning, or like hunger or thirst?
They interrupt our minds, but like a computer popup dialogue, after due consideration of our plans and knowledge, we can generally dismiss them.
Pain is the interruption which doesn't go away, although ([Morsella 2005](https://www.rifters.com/real/articles/Morsella\_2005.pdf "PRISM: The Function of Phenomenal States: Supramodular Interaction Theory")):
> Theoretically, nervous mechanisms could have evolved to solve the need for this particular kind of interaction otherwise. Apart from automata, which act like humans but have no phenomenal experience, a conscious nervous system that operates as humans do but does not suffer any internal strife. In such a system, knowledge guiding skeletomotor action would be isomorphic to, and never at odds with, the nature of the phenomenal state---running across the hot desert sand in order to reach water would actually feel good, because performing the action is deemed adaptive.^[A possible concrete instance of this would be [the anti-famine theory of anorexia](/doc/psychiatry/anorexia/2003-guisinger.pdf "‘Adapted to Flee Famine: Adding an Evolutionary Perspective on Anorexia Nervosa’, Guisinger 2003"): anorexics find it easy to exercise heavily & starve themselves to death--even while feeling 'good' or 'virtuous'---and often don't want to be cured, because they are helplessly executing a last-ditch anti-famine adaptive strategy meant for long-distance travel to greener pastures.] Why our nervous system does not operate with such harmony is perhaps a question that only evolutionary biology can answer. Certainly one can imagine such integration occurring without anything like phenomenal states, but from the present standpoint, this reflects more one's powers of imagination than what has occurred in the course of evolutionary history.
## Hui Neng's Flag
In the reinforcement learning context, one could ask: does it make a difference whether one has 'negative' or 'positive' rewards? Any reward function with both negative and positive rewards could be turned into all-positive rewards simply by adding a large constant. Is that a difference which makes a difference? Or instead of maximizing positive 'rewards', one could speak of minimizing 'losses', and one often does in economics or decision theory or [control theory](!W)[^Bertsekas].
[^Bertsekas]: [Bertsekas 2018](https://arxiv.org/abs/1804.04577 "Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations") helpfully provides a Rosetta Stone between optimal control theory & reinforcement learning (see also [Powell 2018](https://castlelab.princeton.edu/wp-content/uploads/2018/01/Powell-UnifiedFrameworkStochasticOptimization\_Jan292018.pdf "A Unified Framework for Stochastic Optimization") & [Bertsekas 2019](https://web.mit.edu/dimitrib/www/RLbook.html "Reinforcement Learning And Optimal Control")):
> The notation and terminology used in this paper is standard in DP and optimal control, and in an effort to forestall confusion of readers that are accustomed to either the reinforcement learning or the optimal control terminology, we provide a list of selected terms commonly used in reinforcement learning (for example in the popular book by Sutton and Barto [SuB98], and its 2018 on-line 2nd edition), and their optimal control counterparts.
>
> (a) Agent = Controller or decision maker.
> (b) Action = Control.
> (c) Environment = System.
> (d) Reward of a stage = (Opposite of) Cost of a stage.
> (e) State value = (Opposite of) Cost of a state.
> (f) Value (or state-value) function = (Opposite of) Cost function.
> (g) Maximizing the value function = Minimizing the cost function.
> (h) Action (or state-action) value = Q-factor of a state-control pair.
> (i) Planning = Solving a DP problem with a known mathematical model.
> (j) Learning = Solving a DP problem in model-free fashion.
> (k) Self-learning (or self-play in the context of games) = Solving a DP problem using policy iteration.
> (l) Deep reinforcement learning = Approximate DP using value and/or policy approximation with deep neural networks.
> (m) Prediction = Policy evaluation.
> (n) Generalized policy iteration = Optimistic policy iteration.
> (o) State abstraction = Aggregation.
> (p) Episodic task or episode = Finite-step system trajectory.
> (q) Continuing task = Infinite-step system trajectory.
> (r) Afterstate = Post-decision state.
["Do Artificial Reinforcement-Learning Agents Matter Morally?"](https://arxiv.org/abs/1410.8233), Tomasik 2014, debates the relationship of rewards to considerations of "suffering" or "pain", given the duality between costs-losses/rewards:
> Perhaps the more urgent form of refinement than algorithm selection is to replace punishment with rewards within a given algorithm. RL systems vary in whether they use positive, negative, or both types of rewards:
>
> - In certain RL problems, such as maze-navigation tasks discussed in Sutton and Barto [1998], the rewards are only positive (if the agent reaches a goal) or zero (for non-goal states).
> - Sometimes a mix between positive and negative rewards^6^ is used. For instance, McCallum [1993] put a simulated mouse in a maze, with a reward of 1 for reaching the goal, −1 for hitting a wall, and −0.1 for any other action.
> - In other situations, the rewards are always negative or zero. For instance, in the cart-pole balancing system of Barto et al. [1990], the agent receives reward of 0 until the pole falls over, at which point the reward is −1. In Koppejan and Whiteson [2011]'s neuroevolutionary RL approach to helicopter control, the RL agent is punished either a little bit, with the negative sum of squared deviations of the helicopter's positions from its target positions, or a lot if the helicopter crashes.
>
> Just as animal-welfare concerns may motivate incorporation of rewards rather than punishments in training dogs [Hiby et al 2004] and horses [Warren-Smith and McGreevy, 2007, Innes and McBride, 2008], so too RL-agent welfare can motivate more positive forms of training for artificial learners. Pearce [2007] envisions a future in which agents are driven by 'gradients of well-being' (ie. positive experiences that are more or less intense) rather than by the distinction between pleasure versus pain. However, it's not entirely clear where the moral boundary lies between positive versus negative welfare for simple RL systems. We might think that just the sign of the agent's reward value \_r\_ would distinguish the cases, but the sign alone may not be enough, as the following section explains.
>
> \*What's the boundary between positive and negative welfare?\*
>
> Consider an RL agent with a fixed life of \_T\_ time steps. At each time \_t\_, the agent receives a non-positive reward \_r\_~\_t\_~ ≤ 0 as a function of the action \_a\_~\_t\_~ that it takes, such as in the pole-balancing example. The agent chooses its action sequence (at) \_t\_ = 1...\_T\_ with the goal of maximising the sum of future rewards:
>
> $$\sum\_{t=1}^T r\_t(a\_t)$$
>
> Now suppose we rewrite the rewards by adding a huge positive constant \_c\_ to each of them, \_r′t\_ = \_rt\_ + \_c\_, big enough that all of the \_r′~t~\_ are positive. The agent now acts so as to optimise
>
> $$\sum\_{t=1}^T r'\_t(a\_t) = \sum\_{t=1}^T ((r\_t)a\_t + c) = Tc + \sum\_{t=1}^T r\_t(a\_t)$$
>
> So the optimal action sequence is the same in either case, since additive constants don't matter to the agent's behaviour.^7^ But if behaviour is identical, the only thing that changed was the sign and numerical magnitude of the reward numbers. Yet it seems absurd that the difference between happiness and suffering would depend on whether the numbers used by the algorithm happened to have negative signs in front. After all, in computer binary, negative numbers have no minus sign but are just another sequence of 0s and 1s, and at the level of computer hardware, they look different still. Moreover, if the agent was previously reacting aversively to harmful stimuli, it would continue to do so. As Lenhart K. Schubert explains:^8^ [This quotation comes from [spring 2014 lecture notes](https://web.archive.org/web/20140409193231/http://www.cs.rochester.edu/users/faculty/schubert/191-291/lecture-notes/23) (accessed March 2014) for a course called "Machines and Consciousness".]
>
>> If the shift in origin [to make negative rewards positive] causes no behavioural change, then the robot (analogously, a person) would still behave as if suffering, yelling for help, etc., when injured or otherwise in trouble, so it seems that the pain would not have been banished after all!
>
> So then what distinguishes pleasure from pain?
>
> ...A more plausible account is that the difference relates to 'avoiding' versus 'seeking.' A negative experience is one that the agent tries to get out of and do less of in the future. For instance, injury should be an inherently negative experience, because if repairing injury was rewarding for an agent, the agent would seek to injure itself so as to do repairs more often. If we tried to reward \*avoidance\* of injury, the agent would seek dangerous situations so that it could enjoy returning to safety.^10^ [This example comes from Lenhart K. Schubert's spring 2014 lecture notes (accessed March 2014), for a course called 'Machines and Consciousness.' These thought experiments are not purely academic. We can see an example of maladaptive behaviour resulting from an association of pleasure with injury when people become addicted to the endorphin release of self-harm.] ^[There are [some examples of "Reward hacking" in past RL research](/tank#alternative-examples) which resemble such 'self-injuring' agents---for example, a bicycle agent is 'rewarded' for getting near a target (but not 'punished' for moving away), so it learn to steer toward it in a loop to go around it repeatedly to earn the reward.] Injury needs to be something the agent wants to get as far away from as possible. So, for example, even if vomiting due to food poisoning is the best response you can take given your current situation, the experience should be negative in order to dissuade you from eating spoiled foods again. Still, the distinction between avoiding and seeking isn't always clear. We experience pleasure due to seeking and consuming food but also pain that motivates us to avoid hunger. Seeking one thing is often equivalent to avoiding another. Likewise with the pole-balancing agent: Is it seeking a balanced pole, or avoiding a pole that falls over?
>
> ...Where does all of this leave our pole-balancing agent? Does it suffer constantly, or is it enjoying its efforts? Likewise, is an RL agent that aims to accumulate positive rewards having fun, or is it suffering when its reward is suboptimal?
## Pain as Grounding
So with all that for background, what is the purpose of pain?
The purpose of pain, I would say, is as a \*ground truth or outer loss\*.
(This is a [motivational theory of pain](https://plato.stanford.edu/entries/pain/#othertheories) with a more sophisticated RL/psychiatric grounding.)
The pain reward/loss cannot be removed entirely for the reasons demonstrated by the diabetics/lepers/congenital insensitives: the unnoticed injuries and the poor planning are ultimately fatal.
Without any pain qualia to make pain feel painful, we will do harmful things like run on a broken leg or jump off a roof to impress our friends[^Pakistan], or just move in a not-quite-right fashion and a few years later wind up paraplegics.
(An intrinsic curiosity drive alone would interact badly with a total absence of painful pain: after all, what is more novel or harder to predict than the strange and unique states which can be reached by self-injury or recklessness?)
[^Pakistan]: From the Marsili article:
> In the mid-2000s, Wood's lab at University College partnered with a Cambridge University scientist named Geoff Woods on a pioneering research project centered on a group of related families---all from a clan known as the Qureshi biradari---in rural northern Pakistan. Woods had learned about the families accidentally: On the hunt for potential test subjects for a study on the brain abnormality microcephaly, he heard about a young street performer, a boy who routinely injured himself (walking across burning coals, stabbing himself with knives) for the entertainment of crowds. The boy was rumored to feel no pain at all, a trait he was said to share with other family members...When Woods found the boy's family, they told him that the boy had died from injuries sustained during a stunt leap from a rooftop.
If pain couldn't be removed, could pain be turned into a reward, then?
Could we be the equivalent of Morsella's mind that doesn't experience pain, as it infers plans and then executes them, experiencing only more or less rewards?
It only experience positive rewards (pleasure) as it runs across burning-hot sands, as this is the optimal action for it to be taking according to whatever grand plan it has thought of.
Perhaps we could... but what stops Morsella's mind from enjoying rewards by literally running in circles on those sands until it dies or is crippled?
Morsella's mind may make a plan and define a reward function which avoids the need for any pain or negative rewards, but what happens if there is any flaw in the computed plan or the reward estimates? Or if the plan is based on mistaken premises? What if the sands are hotter than expected, or if the distance is much further than expected, or if the final goal (perhaps an oasis of water) is not there?
Such a mind raises serious questions about learning and dealing with errors: what does such a mind experience when a plan \*fails\*? Does it experience nothing? Does it experience a kind of "meta-pain"?
Consider what Brand (\_The Gift of Pain\_ again, [pg191--197](#pain-prosthetics)) describes as the ultimate cause of the failure of years of research into creating 'pain prosthetics', computerized gloves & socks that would measure heat & pressure in real-time in order to warn those without pain like lepers or diabetics: the patients would just \*ignore the warnings\*, because stopping to prevent future problems was inconvenient while continuing paid off now.
And when electrical shockers were added to the system to stop them from doing a dangerous thing, Brand observed patients simply disabling it to do the dangerous thing & re-enabling it afterwards!
What pain provides is a constant, ongoing feedback which anchors all the estimates of future rewards based on planning or bootstrapping.
It anchors our intelligence in a concrete estimation of bodily integrity: the intactness of skin, the health of skin cells, the lack of damage to muscles, joints sliding and moving as they ought to, and so on.
If we are planning well and acting efficiently in the world, we will, in the long run, on average, experience higher levels of bodily integrity and physical health; if we are learning and choosing and planning poorly, then... we won't.
The badness will gradually catch up with us and we may find ourselves blind scarred paraplegics missing fingers and soon to die.
A pain that was not painful would not serve this purpose, as it would merely be another kind of "tickling" sensation.
(Some might find it interesting or enjoyable or it could accidentally become sexually-linked.)
The perceptions in question are simply more ordinary tactile, kinesthetic, [thermoreceptor](!W), or other standard categories of perception; without painful pain, a fire burning your hand simply feels warm (before the thermal-perceptive nerves are destroyed and nothing further is felt), and a knife cutting flesh might feel like a rippling stretching rubbing movement.
We might say that a painful pain is a pain which forcibly inserts itself into the planning/optimization process, as a cost or lack of reward to be optimized.
A pain which was not \*motivating\* is not what we mean by 'pain' at all.[^Drescher]
The motivation itself \*is\* the qualia of pain, much like an itch is an ordinary sensation coupled with a motivational urge to scratch.
Any mental quality or emotion or sensation which is not accompanied by a demandingness, an involuntary taking-into-consideration, is not pain.
The rest of our mind can force its way through pain, if it is sufficiently convinced that there is enough reason to incur the costs of pain because the long-term reward is so great, and we do this all the time: we can convince ourselves to go to the gym, or withstand the vaccination needle, or, in the utmost extremity, saw off a trapped hand to save our life.
And if we are mistaken, and the predicted rewards do not arrive, eventually the noisy constant feedback of pain will override the decisions leading to pain, and whatever incorrect beliefs or models led to the incorrect decisions will be adjusted to do better in the future.
[^Drescher]: Drescher 2004 gives a similar account of motivational pain in [\_Good and Real\_](/doc/statistics/decision/2006-drescher-goodandreal.pdf "'*Good and Real: Demystifying Paradoxes from Physics to Ethics*', Drescher 2006") (pg77--78):
> But a merely mechanical state could not have the property of being intrinsically desirable or undesirable; inherently good or bad sensations, therefore, would be irreconcilable with the idea of a fully mechanical mind. Actually, though, it is your machinery's very response to a state's utility designation---the machinery's very tendency to systematically pursue or avoid the state---that implements and constitutes a valued state's seemingly inherent deservedness of being pursued or avoided. Roughly speaking, it's not that you avoid pain (other things being equal) in part because pain is inherently bad; rather, your machinery's systematic tendency to avoid pain (other things being equal) is what \*constitutes\* its being bad. That systematic tendency is what you're really observing when you contemplate a pain and observe that it is "undesirable", that it is something you want to avoid.
>
> The systematic tendency I refer to includes, crucially, the tendency to plan to achieve positively valued states (and then to carry out the plan), or to plan the avoidance of negatively valued states. In contrast, for example, sneezing is an insistent response to certain stimuli; yet despite the strength of the urge---sneezing can be very hard to suppress---we do not regard the sensation of sneezing as strongly pleasurable (nor the incipient-sneeze tingle, subsequently extinguished by the sneeze, as strongly unpleasant). The difference, I propose, is that nothing in our machinery inclines us to plan our way into situations that make us sneeze (and nothing strongly inclines us to plan the avoidance of an occasional incipient sneeze) for the sake of achieving the sneeze (or avoiding the incipient sneeze); the machinery just isn't wired up to treat sneezes that way (nor should it be). The sensations we deem pleasurable or painful are those that incline us to plan our way to them or away from them, other things being equal.
But the pain cannot and must not be overridden: human organisms can't be trusted to simply 'turn off' pain and indulge an idle curiosity about cutting off hands.
Note that we can kill ourselves by starvation or thirst, but we cannot kill ourselves by refusing to sleep, or have a heart beat, or breathe---unless one suffers from the (extremely lethal) [central hypoventilation syndrome](!W), that is.
We are insufficiently intelligent, our priors insufficiently strong, our reasoning and planning too poor, and we must do too much learning within each life to do without pain.
A similar argument might apply to the puzzle of 'willpower', 'procrastination'.
Why do we have such problems, particularly in a modern context, doing aught we know we should and doing naught we oughtn't?
On the grave of the 'blood glucose' level theory, [Kurzban et al 2013](/doc/psychology/willpower/2013-kurzban.pdf#page=14 "An opportunity cost model of subjective effort and task performance") (see later [Shenhav et al 2017](/doc/statistics/decision/2017-shenhav.pdf "Toward a Rational and Mechanistic Account of Mental Effort")) erects an \*opportunity cost\* theory of willpower.
Since \*objective\* physical measurements like blood glucose levels fail to mechanically explain poorer brain functionality or why strenuous activities like sports are 'restful' & reduce 'burnout', similar to the failure of objective physical measurements like lactate levels to explain why people are able to physically exercise only a certain amount (despite being able to exercise far more if properly motivated or if tricked), the reason for willpower running out must be \*subjective\*.
To explain the sugar-related observations, Kurzban et al 2013 suggest that the aversiveness of long focus and cognitive effort is a simple heuristic which creates a baseline cost to focusing for 'too long' on any one task, to the potential neglect of other opportunities, with the sugar interventions (such as merely \*tasting\* sugar water) which appear to boost willpower actually serving as proximate reward signals (signals, because the actual energetic content is nil, and [cognitive effort doesn't meaningfully burns calories](https://www.psychologytoday.com/us/blog/mind-design/201108/glucose-is-not-willpower-fuel "Glucose Is Not Willpower Fuel: Is the muscle model of self-control less then a metaphor?") in the first place), which justify to the underlying heuristic that further effort on the same task is worthwhile and the opportunity cost is minimal.
The lack of willpower is a heuristic which doesn't require the brain to explicitly track & prioritize & schedule all possible tasks, by forcing it to regularly halt tasks---["like a timer that says, 'Okay you're done now.'"](https://www.scientificamerican.com/article/thinking-hard-calories/ "Does Thinking Really Hard Burn More Calories?: Unlike physical exercise, mental workouts probably do not demand significantly more energy than usual. Believing we have drained our brains, however, may be enough to induce weariness")^[Ultra-marathoner [Diane Van Deren](https://radiolab.org/podcast/122291-in-running/transcript "‘In the Running’, Deren et al 2021") attributes part of her success to being unable to tell time or duration after epilepsy surgery removed a large chunk of her brain, so she never feels tired.]
If one could override fatigue at will, the consequences can be bad.
Users of dopaminergic drugs like amphetamines often note issues with channeling the reduced fatigue into useful tasks rather than alphabetizing one's bookcase.
In more extreme cases, if one could ignore fatigue entirely, then analogous to lack of pain, the consequences could be severe or fatal: ultra-endurance cyclist [Jure Robič](https://en.wikipedia.org/wiki/Jure\_Robi%C4%8D) would cycle for thousands of kilometers, ignoring such problems [as elaborate hallucinations](/doc/psychiatry/2006-02-05-nytimes-thatwhichdoesnotkillmemakesmestranger.html "That Which Does Not Kill Me Makes Me Stranger"), and was eventually killed while cycling.
The 'timer' is implemented, among other things, as a gradual buildup of [adenosine](!W), which creates [sleep homeostatic drive pressure](!W "Sleep#Process S") and possibly physical fatigue during exercise ([Noakes 2012](https://www.frontiersin.org/articles/10.3389/fphys.2012.00082/full "Fatigue is a brain-derived emotion that regulates the exercise behavior to ensure the protection of whole body homeostasis"), [Martin et al 2018](/doc/psychology/2018-martin.pdf "Mental Fatigue Impairs Endurance Performance: A Physiological Explanation")), leading to a gradually increasing subjectively perceived 'cost' of continuing with a task/staying awake/continuing athletic activities, which resets when one stops/sleeps/rests.
(Glucose might work by gradually dropping over [perceived time without rewards](https://www.pnas.org/doi/10.1073/pnas.1603444113 "'Blood sugar level follows perceived time rather than actual time in people with type 2 diabetes', Park et al 2016").)
Since the human mind is too limited in its planning and monitoring ability, it cannot be allowed to 'turn off' opportunity cost warnings and engage in hyperfocus on potentially useless things at the neglect of all other things; procrastination here represents a psychic version of pain.
From this perspective, it is not surprising that so many stimulants are adenosinergic or dopaminergic^[This is not about dopaminergic effects being rewarding themselves, but about the perception of current tasks vs alternative tasks. (After all, stimulants don't simply make you enjoy staring at a wall while doing nothing.) If everything becomes more rewarding, then there is less to gain from switching, because alternatives will be estimated as little more rewarding; or, if reward sensitivity is boosted only for current activities, then there will be pressure \*against\* switching tasks, because it is unlikely that alternatives will be predicted to be more rewarding than the current task.], or that small children might especially struggle with mental fatigue (there is a world full of novel opportunities tempting them away), or that many anti-procrastination strategies (like [\_Getting Things Done\_](!W) or the [Procrastination Equation](https://www.lesswrong.com/posts/RWo4LwFzpHNQCTcYt/how-to-beat-procrastination)) boil down to optimizing for more rewards or more frequent rewards (eg. breaking tasks down into many smaller tasks, which can be completed individually & receive smaller but more frequent rewards, or thinking more clearly about whether something is worth doing): all of these would affect the reward perception itself, and reduce the baseline opportunity cost 'pain'.
This perspective may also shed light on depression[^depression], or on [occupational burnout](!W) and why restorative hobbies are ideally maximally different from jobs and more miscellaneous observations like the lower rate of 'hobbies' outside the West: burnout may be a long-term homeostatic reaction to spending 'too much' time too frequently on a difficult not-immediately rewarding task despite earlier attempts to pursue other opportunities (perhaps tasks which would [\*never\* be](https://www.lesswrong.com/posts/pDzdb4smpzT3Lwbym/my-model-of-ea-burnout) [rewarding](https://www.spencergreenberg.com/2023/02/doing-what-you-value-as-a-way-of-life-an-introduction-to-valuism/)), which were always overridden, ultimately resulting in a total collapse^[Further speculation: is burnout like the learning theory of depression and the synpatic plasticity paradigm of psychedelic therapy, where individuals over-perseverate due to too-slow learning, leading to burnout? For example, would cranky old researchers (left behind by progress) benefit from psychedelic therapy to reboot their work?]; and hobbies ought to be as different in location and physical activity and social structure (eg. a solitary programmer indoors should pursue a social physical activity outdoors as [soulcraft](https://www.thenewatlantis.com/publications/shop-class-as-soulcraft "'Shop Class as Soulcraft: The case for the manual trades', Crawford 2006")) to ensure that it feels completely different for the mind than the regular occupation; and in places with less job specialization or fewer work-hours, the regular flow of a variety of tasks and opportunities means that no such special activity as a 'hobby' is necessary.
[^depression]: [Hollon et al 2021](https://www.frontiersin.org/articles/10.3389/fpsyt.2021.667592/full "Cognitive Behavior Therapy for Depression From an Evolutionary Perspective") justifies long depressive episodes as evolutionarily adaptive because they force rumination & re-examination of the past for mistakes.
One might object that such rumination is merely harmful in many cases, like bereavement from a spouse dying of old age---but from [the blackbox perspective](#black-box-vs-white-box-optimization), the agent may well be mistaken in believing there was no mistake! After all, an extremely bad thing \*did\* happen. So better to force lengthy rumination, just on the chance that a mistake will be discovered after all. (This brings us back to RL's distinction between high-variance evolutionary/Monte Carlo learning vs smarter lower-variance but potentially biased learning using models or bootstraps, and the "deadly triad".)
---
Perhaps if we were superintelligent AIs who could trivially plan flawless humanoid locomotion at 1000Hz taking into account all possible damages, or if we were emulated brains sculpted by endless evolutionary procedures to execute perfectly adaptive plans by pure instinct, or if we were simple amoeba in a Petri dish who had no real choices to make, there would be no need for a pain which was painful.
And likewise, were we endlessly planning and replanning to the end of days, we should never experience akrasia, we should merely \*do\* what is necessary (perhaps not even experiencing any qualia of effort or deliberation, merely [seeing events endlessly unfold as they always had to](/story-of-your-life "'‘Story Of Your Life’ Is Not A Time-Travel Story', Branwen 2012")).
But we are not.
The pain keeps us honest.
In the end, pain is our only teacher.
# The Perpetual Peace
> These laws, taken in the largest sense, being Growth with Reproduction; Inheritance which is almost implied by reproduction; Variability from the indirect and direct action of the external conditions of life, and from use and disuse; a Ratio of Increase so high as to lead to a Struggle for Life, and as a consequence to Natural Selection, entailing Divergence of Character and the Extinction of less-improved forms. Thus, from the war of nature, from famine and death, the most exalted object which we are capable of conceiving, namely, the production of the higher animals, directly follows. There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.
>
> [Charles Darwin](!W), [\_On the Origin of Species\_](!W)
> In war, there is the free possibility that not only individual determinacies, but the sum total of these, will be destroyed as life, whether for the absolute itself or for the people. Thus, war preserves the ethical health of peoples in their indifference to determinate things [\_Bestimmtheiten\_]; it prevents the latter from hardening, and the people from becoming habituated to them, just as the movement of the winds preserves the seas from that stagnation which a permanent calm would produce, and which a permanent (or indeed 'perpetual') peace would produce among peoples.
>
> [G. W. F. Hegel](!W "Georg Wilhelm Friedrich Hegel")^[["On the Scientific Ways of Treating Natural Law"](https://www.marxists.org/reference/archive/hegel/works/nl/ch03.htm), Hegel 1803]
> We must recognize that war is common, strife is justice, and all things happen according to strife and necessity...War is father of all and king of all
>
> [Heraclitus](!W), B80/B53
> It is not enough to succeed; others must fail.
>
> [Iris Murdoch](!W), [\_The Black Prince\_](!W "The Black Prince (novel)")
What if we remove the outer loss?
In a meta-learning context, it will then either overfit to a single instance of a problem, or learn a potentially arbitrarily suboptimal average response; in the Quake CTF, the inner loss might converge, as mentioned, to every-agent-for-itself or greedy tactical victories guaranteeing strategic losses; in a human, the result would (at present, due to refusal to use artificial selection or genetic engineering) be a gradual buildup of [mutation load](!W) leading to serious health issues and eventually perhaps a mutational meltdown/error catastrophe; and in an economy, it leads to... the USSR.
The amount of this constraint can vary, based on the greater power of the non-ground-truth optimization and fidelity of replication and accuracy of selection.
The [Price equation](!W) gives us quantitative insight into the conditions under which [group selection](!W) could work at all:
if a NN could only copy itself in a crude and lossy way, meta-learning would not work well in the first place (properties must be preserved from one generation to the next); if a human cell copied itself with an error rate of as much as 1 in millions, humans could never exist because reproductive fitness is too weak a reward to purge the escalating mutation load (selective gain is negative); if bankruptcy becomes more arbitrary and have less to do with consumer demand than acts of god/government, then corporations will become more pathologically inefficient (covariance between traits & fitness too small to accumulate in meaningful ways).
As Shalizi concludes in his review:
> Planning is certainly possible within limited domains---at least if we can get good data to the planners---and those limits will expand as computing power grows. But planning is only possible within those domains because \*making money\* gives firms (or firm-like entities) an objective function which is both unambiguous and \*blinkered\*. Planning for the whole economy would, under the most favorable possible assumptions, be intractable for the foreseeable future, and \*deciding on a plan\* runs into difficulties we have no idea how to solve. The sort of efficient planned economy dreamed of by the characters in \_Red Plenty\_ is something we have no clue of how to bring about, even if we were willing to accept dictatorship to do so.
This is why the planning algorithms cannot simply keep growing and take over all markets: "who watches the watchmen?"
As powerful as the various internal organizational and planning algorithms are, and much superior to evolution/market competition, they only optimize surrogate inner losses, which are not the end-goal, and they must be constrained by a ground-truth loss.
The reliance on this loss can and should be reduced, but a reduction to zero is undesirable as long as the inner losses converge to any optima different from the ground-truth optima.
Given the often long lifespan of a failing corporation, the difficulty corporations encounter in aligning employees with their goals, and the inability to reproduce their 'culture', it is no wonder that group selection in markets is feeble at best, and the outer loss cannot be removed.
On the other hand, these failings are not necessarily permanent: as corporations gradually turn into software^[APIs are another instance of bilevel optimization, incidentally. Inside an API, a software engineer can do accurate hillclimbing using techniques like randomized A/B testing. But one cannot randomize an entire ecosystem+APIs! Selection on sets of APIs can only happen at a company level (or even higher). The difference between a [Stripe](!W "Stripe (company)") and a PayPal payment API is not a mere matter of needing 2 function-calls instead of 3; and the most important decision anyone ever made about Amazon AWS was Jeff Bezos [decreeing from on high](https://gist.github.com/chitchcock/1281611 "Stevey's Google Platforms Rant") that now everything would be \*an\* API---the internal implementation, or even the API details, being trivial compared to the ecosystem effect of competing/interacting APIs. See also ["holy wars"](/holy-war "'Technology Holy Wars are Coordination Problems', Branwen 2020").], which can be copied and exist in much more dynamic markets with faster OODA loops, perhaps we can expect a transition to an era where corporations \*do\* replicate precisely & can then start to consistently evolve large increases in efficiency, rapidly exceeding all progress to date.
# See Also
- [Why Tool AIs Want to Be Agent AIs: The Power of Agency](/tool-ai "AIs limited to purely computational inferential tasks (Tool AIs) supporting humans will be less intelligent, efficient, and economically valuable than more autonomous reinforcement-learning AIs (Agent AIs) who act on their own and meta-learn to take actions over choice of computation/data/training/architecture/hyperparameters/external-resource use, because all problems are secretly decision-theory/reinforcement-learning problems."){.backlink-not}
- [Complexity no Bar to AI](/complexity "Critics of AI risk suggest diminishing returns to computing (formalized asymptotically) means AI will be weak; this argument relies on a large number of questionable premises and ignoring additional resources, constant factors, and nonlinear returns to small intelligence advantages, and is highly unlikely."){.backlink-not}
- [Timing Technology: Lessons From The Media Lab](/timing "Technological developments can be foreseen but the knowledge is largely useless because startups are inherently risky and require optimal timing. A more practical approach is to embrace uncertainty, taking a reinforcement learning perspective."){.backlink-not}
- ["The Gift of the Amygdali"](/fiction/batman){.backlink-not}
- [Lamaze technique](!W)
- the [Price equation](!W) & frequency of selective events in [group selection](!W)
- [Aboulia](!W)
# External Links
- \*\*Overviews\*\*:
- ["Can technology plan economies and destroy democracy? How algorithms could someday be used to optimise the ballot box"](https://www.economist.com/christmas-specials/2019/12/18/can-technology-plan-economies-and-destroy-democracy), \_Economist\_, 2019-12-18 (the socialist calculation debate, computational complexity, Herbert Simon, automating markets, etc); ["Big Tech Sees Like a State"](https://www.thediff.co/archive/big-tech-sees-like-a-state/), Bryne Hobert
- ["Studies on Slack"](https://slatestarcodex.com/2020/05/12/studies-on-slack/), Scott Alexander
- ["Everyday Lessons from High-Dimensional Optimization"](https://www.lesswrong.com/posts/pT48swb8LoPowiAzR/everyday-lessons-from-high-dimensional-optimization)
- ["The Power of High-Speed Stupidity"](https://www.lesswrong.com/posts/5qyytGqZWv5bt6723/the-power-of-high-speed-stupidity)
- \*\*AI\*\*:
- ["AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence"](https://arxiv.org/abs/1905.10985#uber), Clune 2019
- ["On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models"](https://arxiv.org/abs/1511.09249#schmidhuber), Schmidhuber 2015
- ["AutoML-Zero: Evolving Machine Learning Algorithms From Scratch"](https://arxiv.org/abs/2003.03384#google), Real et al 2020 ([Github](https://github.com/google-research/google-research/tree/master/automl\_zero "'AutoML-Zero: Open source code for the paper: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch"', Real et al 2020"); [blog](https://ai.googleblog.com/2020/07/automl-zero-evolving-code-that-learns.html "'AutoML-Zero: Evolving Code that Learns', Real & Liang 2020")); ["Evolving Reinforcement Learning Algorithms"](https://arxiv.org/abs/2101.03958#google), Co-Reyes et al 2021
- ["Gradient Descent: The Ultimate Optimizer"](https://arxiv.org/abs/1909.13371#facebook), Chandra et al 2019; ["Reverse engineering learned optimizers reveals known and novel mechanisms"](https://arxiv.org/abs/2011.02159#google), Maheswaranathan et al 2020
- ["Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves"](https://arxiv.org/abs/2009.11243#google), Metz et al 2020; ["Training Learned Optimizers with Randomly Initialized Learned Optimizers"](https://arxiv.org/abs/2101.07367#google), Metz et al 2021; ["VS-ML: Meta Learning Backpropagation And Improving It"](https://arxiv.org/abs/2012.14905#schmidhuber "'Meta Learning Backpropagation And Improving It', Kirsch & Schmidhuber 2020"), Kirsch & Schmidhuber 2021; ["A Generalizable Approach to Learning Optimizers"](https://arxiv.org/abs/2106.00958#openai "'LHOPT: A Generalizable Approach to Learning Optimizers', Almeida et al 2021"), Almeida et al 2021
- ["A critique of pure learning and what artificial neural networks can learn from animal brains"](https://www.nature.com/articles/s41467-019-11786-6), Zador 2019
- ["Whole Brain Emulation and the Evolution of Superorganisms"](https://intelligence.org/files/WBE-Superorgs.pdf), Shulman 2010
- ["WBE and DRL: a Middle Way of imitation learning from the human brain"](https://www.reddit.com/r/reinforcementlearning/comments/9pwy2f/wbe\_and\_drl\_a\_middle\_way\_of\_imitation\_learning/)
- ["Meta-Learning: Learning to Learn Fast"](https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html#openai)/[On "Meta Reinforcement Learning"](https://lilianweng.github.io/lil-log/2019/06/23/meta-reinforcement-learning.html#openai "'Meta Reinforcement Learning', Weng 2019"), Lilian Wen
- ["Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes"](https://www.gatsby.ucl.ac.uk/~yael/Okinawa/DuffThesis.pdf), Duff 2002 (cf. [Futamura](http://blog.sigfpe.com/2009/05/three-projections-of-doctor-futamura.html)); ["Meta-learning of Sequential Strategies"](https://arxiv.org/abs/1905.03030#deepmind "'Meta-learning of Sequential Strategies', Ortega et al 2019"), Ortega et al 2019; ["Reinforcement Learning, Fast and Slow"](https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613\(19\)30061-0#deepmind), Botvinick et al 2019; ["Meta-learners' learning dynamics are unlike learners'"](https://arxiv.org/abs/1905.01320#deepmind), Rabinowitz 2019; ["Ray Interference: a Source of Plateaus in Deep Reinforcement Learning"](https://arxiv.org/abs/1904.11455#deepmind), Schaul et al 2019; ["Learning not to learn: Nature versus nurture in silico"](https://arxiv.org/abs/2010.04466), Lange & Sprekeler 2020; ["Meta-trained agents implement Bayes-optimal agents"](https://arxiv.org/abs/2010.11223#deepmind), Mikulik et al 2020 (Bayesian RL interpretations of meta-DRL & why DRL is so sample-inefficient); ["What Are Bayesian Neural Network Posteriors Really Like?"](https://arxiv.org/abs/2104.14421#google), Izmailov et al 2021; ["What learning algorithm is in-context learning? Investigations with linear models"](https://arxiv.org/abs/2211.15661#google), Akyürek et al 2022
- ["Meta-learning, social cognition and consciousness in brains and machines"](https://www.sciencedirect.com/science/article/pii/S0893608021003956), Langdon et al 2021 (review)
- ["Solving Rubik's Cube With A Robot Hand"](https://arxiv.org/abs/1910.07113#openai "'Solving Rubik’s Cube with a Robot Hand', OpenAI et al 2019"), Akkaya et al 2019 ([blog](https://openai.com/research/solving-rubiks-cube "We've trained a pair of neural networks to solve the Rubik's Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn't just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity."); [video](https://www.youtube.com/watch?v=QyJGXc9WeNo&list=PLOXw6I10VTv9HODt7TFEL72K3Q6C4itG6&index=3))
- ["Learning to Predict Without Looking Ahead: World Models Without Forward Prediction"](https://learningtopredict.github.io/#google "Our agents are only given infrequent observations of the real environment. As a side effect for optimizing performance in this setting, a world model' emerges. We show the true dynamics in color, with full saturation denoting frames the policy can see. The black and white outline shows the state of the emergent world model. These world model exhibits similar, but not identical dynamics to forward predictive models but only model 'important' aspects of the environment"), Freeman et al 2019 ([Paper](https://arxiv.org/abs/1910.13038#google "'Learning to Predict Without Looking Ahead: World Models Without Forward Prediction', Freeman et al 2019"))
- ["HyperNetworks"](https://arxiv.org/abs/1609.09106#google), Ha et al 2016
- ["MetaGenRL: Improving Generalization in Meta Reinforcement Learning"](http://louiskirsch.com/metagenrl), Kirsch et al 2019; ["LPG: Discovering Reinforcement Learning Algorithms"](https://arxiv.org/abs/2007.08794#deepmind "'Discovering Reinforcement Learning Algorithms', Oh et al 2020"), Oh et al 2020
- [Duan et al 2016](https://arxiv.org/abs/1611.02779#openai "RL^2^: Fast Reinforcement Learning via Slow Reinforcement Learning"); [Santoro et al 2016](https://arxiv.org/abs/1605.06065#deepmind "One-shot Learning with Memory-Augmented Neural Networks"); ["Learning to reinforcement learn"](https://arxiv.org/abs/1611.05763#deepmind), Wang et al 2016; ["Prefrontal cortex as a meta-reinforcement learning system"](/doc/reinforcement-learning/meta-learning/2018-wang.pdf#deepmind), Wang et al 2018 ([Matthew Botvinick commentary](https://www.lesswrong.com/posts/Wnqua6eQkewL3bqsF/matt-botvinick-on-the-spontaneous-emergence-of-learning "Matt Botvinick on the spontaneous emergence of learning algorithms"))
- ["Smooth markets: A basic mechanism for organizing gradient-based learners"](https://arxiv.org/abs/2001.04678#deepmind), Balduzzi et al 2020
- [[Market models]{.smallcaps}](https://people.idsia.ch/~juergen/directsearch/node15.html): ["Properties of the Bucket Brigade Algorithm"](/doc/reinforcement-learning/multi-agent/1985-holland.pdf), Holland 1985; ["Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions"](https://arxiv.org/abs/2007.02382), Chang et al 2020 ([blog](https://bair.berkeley.edu/blog/2020/07/11/auction/ "Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions")); ["The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning"](https://arxiv.org/abs/2108.02755#salesforce), Zheng et al 2021; ["Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning"](https://arxiv.org/abs/2201.01163#salesforce), Curry et al 2022
- ["Multiplicative Interactions and Where to Find Them"](https://openreview.net/forum?id=rylnK6VtDH#google), Jayakumar et al 2020
- ["How Learning Can Guide Evolution"](https://pages.ucsd.edu/~rbelew/courses/cogs184\_w10/readings/HintonNowlan97.pdf), Hinton & Nowlan 1987
- ["Embodied intelligence via learning and evolution"](https://www.nature.com/articles/s41467-021-25874-z), Gupta et al 2021
- \*\*Biology\*\*:
- ["When Pain Isn't Painful: David Bain considers a case that casts doubt on the intuition that pain is essentially unpleasant."](https://www.philosophersmag.com/index.php/component/content/article?id=105:pain)
- ["The Itch: Its mysterious power may be a clue to a new theory about brains and bodies"](https://www.newyorker.com/magazine/2008/06/30/the-itch)
- ["If brains are computers, what kind of computers are they?"](https://www.lesswrong.com/posts/fuGNHdgYWBkA5Fi22/if-brains-are-computers-what-kind-of-computers-are-they), [Daniel Dennett](!W) 2013
- ["Survival of the Systems"](/doc/genetics/selection/natural/2021-lenton.pdf), Lenton et al 2021; ["Cancer across the tree of life: cooperation and cheating in multicellularity"](https://royalsocietypublishing.org/doi/10.1098/rstb.2014.0219), Aktipis et al 2015
- [Evolutionary game theory](!W) ([SEP](https://plato.stanford.edu/entries/game-evolutionary/))
- ["Nociceptive Sensitization Reduces Predation Risk"](https://www.sciencedirect.com/science/article/pii/S0960982214003352), Crook et al 2014
- ["The structure of genotype-phenotype maps makes fitness landscapes navigable"](https://www.biorxiv.org/content/10.1101/2021.10.11.463990.full), Greenbury et al 2021; ["The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks"](https://arxiv.org/abs/2110.06296#google), Entezari et al 2021
- \*\*Psychology\*\*:
- [Wanting vs Liking](https://www.lesswrong.com/tag/wanting-vs-liking)
- [Picoeconomics](!W "George Ainslie (psychologist)")
- ["Why has evolution not selected for perfect self-control?"](https://royalsocietypublishing.org/doi/full/10.1098/rstb.2018.0139), Hayden 2018
- ["The Temporal Dynamics of Opportunity Costs: A Normative Account of Cognitive Fatigue and Boredom"](https://www.biorxiv.org/content/10.1101/2020.09.08.287276.full), Agrawal et al 2020
- ["Key questions about artificial sentience: an opinionated guide"](https://www.lesswrong.com/posts/cwDbYmnSdoobdcJnx/key-questions-about-artificial-sentience-an-opinionated), Robbo
- \*\*Economics\*\*:
- ["Why Do We Undervalue Competent Management?"](https://hbr.org/2017/09/why-do-we-undervalue-competent-management)
- ["Antitrust as Allocator of Coordination Rights"](/doc/economics/2019-paul.pdf "'Antitrust As Allocator of Coordination Rights', Paul 2019"), Paul 2019
- ["The Gervais Principle"](https://www.ribbonfarm.com/the-gervais-principle/), Venkatesh Rao
- ["In The Eternal Inferno, Fiends Torment Ronald Coase With The Fate Of His Ideas"](https://www.harrowell.org.uk/blog/2018/01/31/in-the-eternal-inferno-fiends-torment-ronald-coase-with-the-fate-of-his-ideas/)
- ["Socialist Fantasies"](https://www.econlib.org/socialist-fantasies/)
- ["This Japanese Company Charges Its Staff \$100 an Hour to Use Conference Rooms: Everything has a price, which helps keep workers focused on the bottom line"](https://www.bloomberg.com/news/articles/2019-06-20/charging-employees-for-conference-rooms-helps-disco-boost-profit)
- ["Has dynamic programming improved decision making?"](https://icare.hse.ru/data/2018/10/24/1142422445/Rust.pdf), Rust 2018
- ["When Hindsight Isn’t 20/20: Incentive Design With Imperfect Credit Allocation"](https://www.lesswrong.com/posts/XPRAY34Sutc2wWYZf/when-hindsight-isn-t-20-20-incentive-design-with-imperfect), John Wentsworth^[Dai & Toikka 2022 also notes: "...If asking the agents to report the technology were allowed, then with two or more agents it would be possible to partially implement the Bayesian profit-maximizing contract for the true technology by using a mechanism that chooses the Bayesian optimal contract for the reported technology whenever the agents’ reports agree, and which “punishes” the agents with the zero contract if any reports disagree. With three or more agents, the Bayesian surplus-maximizing contract could be implemented similarly. But as is typical in the implementation literature, the two-agent case is more difficult because then it is not obvious to tell who deviated when reports disagree, and budget balance prevents punishing both agents simultaneously. As this issue is orthogonal to our analysis, we do not pursue it further."]
- ["What do executives do, anyway?"](https://apenwarr.ca/log/20190926): ["CEOs Don’t Steer"](https://www.ribbonfarm.com/2017/11/09/ceos-dont-steer/); ["You can only communicate one top priority"](https://www.lesswrong.com/posts/JrLExmCZWTxkvK8ih/dan-luu-on-you-can-only-communicate-one-top-priority); ["The Gervais Principle, Or The Office According to \*The Office\*"](https://www.ribbonfarm.com/2009/10/07/the-gervais-principle-or-the-office-according-to-the-office/)
- ["AGI will drastically increase economies of scale"](https://www.lesswrong.com/posts/Sn5NiiD5WBi4dLzaB/agi-will-drastically-increase-economies-of-scale)
- \*\*Sociology\*\*:
- "[The Gods of the Copybook Headings](!W)"; ["The Goddess of Everything Else"](https://slatestarcodex.com/2015/08/17/the-goddess-of-everything-else-2/)
- ["Large teams develop and small teams disrupt science and technology"](/doc/statistics/bias/2019-wu.pdf), Wu et al 2019
- ["A Research Note on Deriving the Square-Cube Law of Formal Organizations from the Theory of Time-Minimization"](/doc/economics/1983-stephan.pdf), Stephan 1983
- \*\*Technology\*\*:
- [End-to-end principle](/doc/cs/end-to-end-principle/index)
- ["The Three Projections of Dr Futamura"](http://blog.sigfpe.com/2009/05/three-projections-of-doctor-futamura.html) (isomorphisms between compilers/interpreters/etc)
- ["My history with Forth & stack machines"](http://yosefk.com/blog/my-history-with-forth-stack-machines.html) (on [Chuck Moore](!W "Charles H. Moore") & [Forth](!W "Forth (programming language)") (and [APL](/doc/cs/algorithm/2019-hsu-3.pdf#page=33 "‘Co-dfns: A data parallel compiler hosted on the GPU § 2.1.4 Idiomatic APL’, Hsu 2019 (page 33)")?), as a different version of [the Lisp Curse](http://www.winestockwebdesign.com/Essays/Lisp\_Curse.html); cf. [Donald Knuth](!W)[^Knuth-PLT])
- \*\*Discussion\*\*: Reddit: [1](https://www.reddit.com/r/slatestarcodex/comments/a4d3s6/evolution\_as\_backstop\_for\_reinforcement\_learning/); [HN](https://news.ycombinator.com/item?id=23459056)
[^Knuth-PLT]: yosefk observes that Moore's systems designed using Forth & custom hardware/OS/userland are breathtakingly more efficient than 'standard' approaches would yield, and this is because Moore takes a global perspective and optimizes "end-to-end": changing the language if that makes the OS simpler, changing the hardware if that'd make the text editor smaller, redefining the problem if necessary, and making large globally-coherent sets of changes that more myopic or constrained programmers could not or would not do. They are less 'designed' than \*evolved\* (but evolved by intelligence), and share similar traits: a general contempt for rigid abstraction or principles like "modularity", a reliance on 'coincidence' for correctness, and incredible performance (in both terms of efficiency and in solving whatever the problem is). This approach makes for amazing custom 'one-off' systems; but these are inherently difficult to understand by lesser mortals, the general 'just git gud' approach unreplicable, and the system may not be modifiable by lesser mortals (even if the original designer could easily modify it or just rewrite it overnight to be brilliantly optimized for the new set of requirements). Similar issues bedevil [Lisp](https://en.wikipedia.org/wiki/Lisp\_(programming\_language)) systems & programmers: they \*can\*, and so they \*do\*.
This reminds me of [Donald Knuth](!W). Knuth is one of the most brilliant computer scientists ever, who does things like program an [ALGOL](!W) compiler by himself, mostly in his head, for a summer job. He writes projects like the TeX system by sitting down and spending days thinking hard about it, creating a single program littered with poor software-engineering like global variables & [GOTOs](/doc/cs/algorithm/1974-knuth.pdf "‘Structured Programming with `go to` Statements’, Knuth 1974") but which is nevertheless lightning-fast & almost bug-free. TeX is not his only programming language either, he has created others like [METAFONT](!W) (for fonts) or [MIX](!W)/[MMIX](!W) (an assembly language & computer architecture to provide simple & timeless implementations of his [TAOCP](!W "The Art of Computer Programming") programs).
Perhaps the most striking thing about these various languages is that everyone who uses them loves the quality of the output & what you can do with them, but hate the confusing, complicated, inconsistent, buggy experience of \*using\* them (to the extent that there is a whole cottage industry of people attempting to rewrite or replace [TeX](!W "TeX")/[LaTeX](!W "LaTeX"), typically copying the core ideas of how TeX does typesetting---the ['box and glue'](https://en.wikibooks.org/wiki/LaTeX/Boxes) paradigm of how to lay out stuff onto a page, the [Knuth-Plass line-breaking](/doc/design/typography/1981-knuth.pdf "‘Breaking paragraphs into lines’, Knuth & Plass 1981"), the custom [font families](!W "Computer Modern") etc---doing all that reimplementation work just so they don't have to ever deal with the misery of TeX-the-language). Knuth himself, however, appears to have no more difficulty programming in his languages than he did writing 1960s assembler or [designing fonts with 60 parameters](/doc/design/typography/tex/1996-tug-issuev17no4-knuthqanda.pdf#page=7 "‘Questions and Answers with Professor Donald E. Knuth § pg7’, Knuth 1996 (page 7)"). As he puts it, he ignores most programming techniques and just writes the right code, ["Otherwise, lots of time is wasted on activities that I simply never need to perform or even think about."](https://www.informit.com/articles/article.aspx?p=1193856 "‘Interview with Donald Knuth’, Binstock 2008")
How do you write code as well as Don Knuth? The answer seems to be, well, 'be as naturally gifted as Don Knuth'. A mjor omission in his œuvre is that he has never done major work on operating systems (which [cannot be kept in one's head](https://steve-yegge.blogspot.com/2008/05/dynamic-languages-strike-back.html "‘Dynamic Languages Strike Back’, Yegge 2008")), and his major effort in improving software engineering, ["literate programming"](!W), which treats programs as 1 long story to be told, fell stillborn from the presses. (Knuth, who has the privilege of an academic in being able to ship a program without any worries about maintenance or making a living, and just declaring something \*done\*, like TeX, perhaps does not appreciate how little real-world programs are like [telling stories](https://www.quantamagazine.org/computer-scientist-donald-knuth-cant-stop-telling-stories-20200416).)
So this is a bit of a problem for any attempts at making programming languages more powerful or more ergonomic, as well as for various kinds of ['tools for thought'](https://numinous.productions/) like [Douglas Engelbart's](!W) [intelligence amplification](!W) program. It is terribly easy for such powerful systems to be impossible to learn, and the ability to handle extreme levels of complexity can cripple one's ability to \*remove\* complexity. (The ability to define all sorts of keyboard shortcuts & abbreviations invoking arbitrary code dynamically in your [Lisp machine](!W) [text editor](https://en.wikipedia.org/wiki/Emacs) is great until you ask 'how am I going to \*remember\* all these well enough to save any time on net?') Tasking people like Knuth or Engelbart to develop such things is a bit like asking a top sports player to be a coach: it's not the same thing at all, and the very factors which made them so freakishly good at the sport may damage their ability to critique or improve---they may not be able to do much more than demonstrate how to do it well, and say, 'now you do that too and git gud'.
From an AI perspective, this is interesting because it suggests that AIs might be powerful even while coding with human-legible code. If the problem with the 'Moore/Knuth approach' is that you can't clone him indefinitely to rewrite the system every time it's necessary, then what happens with AIs which you \*can\* 'just clone' and apply exclusively to the task? Quantity has a quality all its own. (For a fictional example, see Vernor Vinge's SF novel [\_A Deepness in the Sky\_](!W), where talented individuals can be put into a 'Focused' state, where they become permanently [monomaniacally obsessed](!W "Hyperfocus") with a single technical task like rewriting computer systems to be optimal, and always achieve Knuth-level results; giving their enslavers de facto superpowers compared to rivals who must employ ordinary people. For a more real-world example, consider how Google fights [bitrot](/holy-war "‘Technology Holy Wars are Coordination Problems’, Branwen 2020") & infrastructure decay: not by incredibly sophisticated programming languages---quite the opposite, considering regressions like [Go](!W "Go (programming language)")---but employing tens of thousands of top programmers to literally rewrite all its code every few years on net, developing an entire parallel universe of software tools, and storing it all in a [single giant repository](https://cacm.acm.org/magazines/2016/7/204032-why-google-stores-billions-of-lines-of-code-in-a-single-repository/fulltext) to assist the endless global rewrites of the [big ball of mud](http://www.laputan.org/mud/mud.html).)
# Appendix
## Meta-Learning Paradigms
[Metz et al 2018](https://arxiv.org/abs/1804.00222#google "Learning Unsupervised Learning Rules"){.include-annotation}
## Pain prosthetics {.collapse}
> Brand & Yancey's 1993 [\_Pain: The Gift No One Wants\_](/doc/psychology/1993-brand-painthegiftnobodywants.pdf "'Pain: The Gift Nobody Wants', Brand & Yancey 1993"), pg191--197, recounts Brand's research in the 1960s--1970s in attempting to create 'artificial pain' or 'pain prosthetics', which ultimately failed because human perception of pain is marvelously accurate & superior to the crude electronics of the day, but more fundamentally because they discovered the aversiveness of pain was critical to accomplishing the goal of discouraging repetitive or severely-damaging behavior, as the test subjects would simply ignore or disable the devices to get on with whatever they were doing.
Excerpts:
> My grant application bore the title "A Practical Substitute for Pain." We proposed developing an artificial pain system to replace the defective system in people who suffered from leprosy, congenital painlessness, diabetic neuropathy, and other nerve disorders. Our proposal stressed the potential economic benefits: by investing a million dollars to find a way to alert such patients to the worst dangers, the government might save many millions in clinical treatment, amputations, and rehabilitation.
>
> The proposal caused a stir at the National Institutes of Health in Washington. They had received applications from scientists who wanted to diminish or abolish pain, but never from one who wished to create pain. Nevertheless, we received funding for the project.
>
> We planned, in effect, to duplicate the human nervous system on a very small scale. We would need a substitute "nerve sensor" to generate signals at the extremity, a "nerve axon" or wiring system to convey the warning message, and a response device to inform the brain of the danger. Excitement grew in the Carville research laboratory. We were attempting something that, to our knowledge, had never been tried.
>
> I subcontracted with the electrical engineering department at Louisiana State University to develop a miniature sensor for measuring temperature and pressure. One of the engineers there joked about the potential for profit: "If our idea works, we'll have a pain system that warns of danger but doesn't hurt. In other words, we'll have the good parts of pain without the bad! Healthy people will demand these gadgets for themselves in place of their own pain systems. Who wouldn't prefer a warning signal through a hearing aid over real pain in a finger?"
>
> The LSU engineers soon showed us prototype [transducers](https://en.wikipedia.org/wiki/Pressure\_sensor), slim metal disks smaller than a shirt button. Sufficient pressure on these transducers would alter their electrical resistance, triggering an electrical current. They asked our research team to determine what thresholds of pressure should be programmed into the miniature sensors. I replayed my university days in Tommy Lewis's pain laboratory, with one big difference: now, instead of merely testing the in-built properties of a well-designed human body, I had to think like the designer. What dangers would that body face? How could I quantify those dangers in a way the sensors could measure?
>
> To simplify matters, we focused on fingertips and the soles of feet, the two areas that caused our patients the most problems. But how could we get a mechanical sensor to distinguish between the acceptable pressure of, say, gripping a fork and the unacceptable pressure of gripping a piece of broken glass? How could we calibrate the stress level of ordinary walking and yet allow for the occasional extra stress of stepping off a curb or jumping over a puddle? Our project, which we had begun with such enthusiasm, seemed more and more daunting.
>
> I remembered from student days that nerve cells change their perception of pain in accordance with the body's needs. We say a finger feels tender: thousands of nerve cells in the damaged tissue automatically lower their threshold of pain to discourage us from using the finger. An infected finger seems as if it is always getting bumped---it "sticks out like a sore thumb"---because inflammation has made it ten times more sensitive to pain. No mechanical transducer could be so responsive to the needs of living tissue.
>
> Every month the optimism level of the researchers went down a notch. Our Carville team, who had made the key findings about repetitive stress and constant stress, knew that the worst dangers came not from abnormal stresses, but from very normal stresses repeated thousands of times, as in the act of walking. And Sherman the pig^[pg171--172; research on the pig involved paralyzing it & applying slight consistent pressure for 5--7h to spots, which was enough to trigger inflammation & kill hair on the spots.] had demonstrated that a constant pressure as low as one pound per square inch could cause skin damage. How could we possibly program all these variables into a miniature transducer? We would need a computer chip on every sensor just to keep track of changing vulnerability of tissues to damage from repetitive stress. We gained a new respect for the human body's capacity to sort through such difficult options instantaneously.
>
> After many compromises we settled on baseline pressures and temperatures to activate the sensors, and then designed a glove and a sock to incorporate several transducers. At last we could test our substitute pain system on actual patients. Now we ran into mechanical problems. The sensors, state-of-the-art electronic miniatures, tended to deteriorate from metal fatigue or corrosion after a few hundred uses. Short-circuits made them fire off false alarms, which aggravated our volunteer patients. Worse, the sensors cost about [$450]($1970) each and a leprosy patient who took a long walk around the hospital grounds could wear out a [$2000]($1970) sock!
>
> On average, a set of transducers held up to normal wear-and-tear for one or two weeks. We certainly could not afford to let a patient wear one of our expensive gloves for a task like raking leaves or pounding a hammer---the very activities we were trying to make safe. Before long the patients were worrying more about protecting our transducers, their supposed protectors, than about protecting themselves.
>
> Even when the transducers worked correctly, the entire system was contingent on the free will of the patients. We had grandly talked of retaining "the good parts of pain without the bad," which meant designing a warning system that would not hurt. First we tried a device like a hearing aid that would hum when the sensors were receiving normal pressures, buzz when they were in slight danger, and emit a piercing sound when they perceived an actual danger. But when a patient with a damaged hand turned a screwdriver too hard, and the loud warning signal went off, he would simply override it---\*This glove is always sending out false signals\*---and turn the screwdriver anyway. Blinking lights failed for the same reason.
>
> Patients who perceived "pain" only in the abstract could not be persuaded to trust the artificial sensors. Or they became bored with the signals and ignored them. The sobering realization dawned on us that unless we built in a quality of compulsion, our substitute system would never work. Being alerted to the danger was not enough; our patients had to be forced to respond. Professor Tims of LSU said to me, almost in despair, "Paul, it's no use. We'll never be able to protect these limbs unless the signal really hurts. Surely there must be some way to hurt your patients enough to make them pay attention."
>
> We tried every alternative before resorting to pain, and finally concluded Tims was right: the stimulus had to be unpleasant, just as pain is unpleasant. One of Tims's graduate students developed a small battery-operated coil that, when activated, sent out an electric shock at high voltage but low current. It was harmless but painful, at least when applied to parts of the body that could feel pain.
>
> Leprosy bacilli, favoring the cooler parts of the body, usually left warm regions such as the armpit undisturbed, and so we began taping the electric coil to patients' armpits for our tests. Some volunteers dropped out of the program, but a few brave ones stayed on. I noticed, though, that they viewed pain from our artificial sensors in a different way than pain from natural sources. They tended to see the electric shocks as punishment for breaking rules, not as messages from an endangered body part. They responded with resentment, not an instinct of self-preservation, because our artificial system had no innate link to their sense of \*self\*. How could it, when they felt a jolt in the armpit for something happening to the hand?
>
> I learned a fundamental distinction: a person who never feels pain is task-oriented, whereas a person who has an intact pain system is self-oriented. The painless person may know by a signal that a certain action is harmful, but if he really wants to, he does it anyway. The pain-sensitive person, no matter how much he wants to do something, will stop for pain, because deep in his psyche he knows that preserving his own self is more important than anything he might want to do.
>
> Our project went through many stages, consuming five years of laboratory research, thousands of man-hours, and more than a million dollars of government funds. In the end we had to abandon the entire scheme. A warning system suitable for just one hand was exorbitantly expensive, subject to frequent mechanical breakdown, and hopelessly inadequate to interpret the profusion of sensations that constitute touch and pain. Most important, we found no way around the fundamental weakness in our system: it remained under the patient's control. If the patient did not want to heed the warnings from our sensors, he could always find a way to bypass the whole system.
>
> Looking back, I can point to a single instant when I knew for certain that the substitute pain project would not succeed. I was looking for a tool in the manual arts workshop when Charles, one of our volunteer patients, came in to replace a gasket on a motorcycle engine. He wheeled the bike across the concrete floor, kicked down the kickstand, and set to work on the gasoline engine. I watched him out of the corner of my eye. Charles was one of our most conscientious volunteers, and I was eager to see how the artificial pain sensors on his glove would perform.
>
> One of the engine bolts had apparently rusted, and Charles made several attempts to loosen it with a wrench. It did not give. I saw him put some force behind the wrench, and then stop abruptly, jerking backward. The electric coil must have jolted him. (I could never avoid wincing when I saw our man-made pain system function as it was designed to do.) Charles studied the situation for a moment, then reached up under his armpit and disconnected a wire. He forced the bolt loose with a big wrench, put his hand in his shirt again, and reconnected the wire. It was then that I knew we had failed. Any system that allowed our patients freedom of choice was doomed.
>
> I never fulfilled my dream of "a practical substitute for pain," but the process did at last set to rest the two questions that had long haunted me. Why must pain be unpleasant? Why must pain persist? Our system failed for the precise reason that we could not effectively reproduce those two qualities of pain. The mysterious power of the human brain can force a person to STOP!---something I could never accomplish with my substitute system. And "natural" pain will persist as long as danger threatens, whether we want it to or not; unlike my substitute system, it cannot be switched off.
>
> As I worked on the substitute system, I sometimes thought of my rheumatoid arthritis patients, who yearned for just the sort of on-off switch we were installing. If rheumatoid patients had a switch or a wire they could disconnect, most would destroy their hands in days or weeks. How fortunate, I thought, that for most of us the pain switch will always remain out of reach.
## Internet Community Design
> It’s been just a month [since [Stable Diffusion](!W) was released]. What about in a year? I probably won’t be able to find my work out there because [the internet] will be flooded with AI art. That’s concerning.
>
> [Greg Rutkowski](https://www.artstation.com/rutkowski), [September 2022](https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/ "'This artist is dominating AI-generated art. And he’s not happy about it. Greg Rutkowski is a more popular prompt than Picasso', Melissa Heikkilä 2022-09-16")
Internet community architecture can be seen as a bi-level optimization design too.
There are fast and slow methods of interaction, and ideas or knowledge (or just 'memes') are created, varied, and selected on.
These interactions happen inside discrete communities which are themselves created, varied, and grow.
So, they are instances of multilevel selection.
The ["warrens and plazas"](https://www.ribbonfarm.com/2010/10/27/warrens-plazas-and-the-edge-of-legibility/)^[See also: ["A Group Is Its Own Worst Enemy"](/doc/technology/2005-shirky-agroupisitsownworstenemy.pdf "'A Group is Its Own Worst Enemy', Shirky 2005"), Clay Shirky 2003/2005; ["The Lessons of Lucasfilm's Habitat"](http://www.massmind.org/techref/idea/lessons.htm); [The Melancholy of Subculture Society](/subculture); ["garden and stream"](https://hapgood.us/2015/10/17/the-garden-and-the-stream-a-technopastoral/ "'The Garden and the Stream: A Technopastoral', Mike Caulfield"). ([MUD](!W)/[MOOs](!W) were also a good example but I don't quite know of a writeup which brings out the warren/plaza & multi-level aspects.)] interpretation of communities is a 2-level design.
The classic example used to be [Usenet](!W) and [FAQs](!W): the fast daily (or even minute by minute) discussion would happen, and knowledge would be distilled down into FAQ entries to save time, foster a higher level of discussion, and spread the knowledge outside of participants.
A more contemporary example is Reddit: the fast flux of link submissions and comments can be distilled into "stickied" (permanent) links, and a simple 'wiki' system of editable pages (enabling FAQs and more).
Some subreddits for niche interests (often gaming or medical-related) have built up considerable knowledge bases and greatly advanced their particular niche.
Discord has done well in marrying the relatively slow IRC-style chat channels with the even faster-paced voice communication of gaming, while simultaneously supporting long-term use-cases through stickied ('pinned') comments which can contain complicated formatting like blockquotes & be edited, search of full channel histories, bots, allowing many channels/servers with complicated security permissions, etc.
(It has done less well in enabling any of this to be [archived or exported](http://ascii.textfiles.com/archives/5509).)
Counter-examples also exist.
Many social networks are actively hostile to any kind of 2-level structure, emphasizing one level at the expense of another.
The value of each time-scale can be seen in how social networks can thrive while targeting only a single time-scale.
Facebook is moderately hostile to long or in-depth posts; they can exist, but no real support is given to them, capabilities like formatting are minimal to nonexistent, and the UI & all affordances are 100% oriented to 'most recent first'.
Slack chat is the evil twin of Discord: its free plan destroys history almost immediately, and should one pay through the nose for full Slack, one quickly discovers that it's email but worse.
Twitter goes further, providing essentially no support for any kind of longform at all, never mind editable wiki/FAQ pages; but just because a technology does not enable an use case doesn't mean users don't need it, it just means they'll have to do it painfully and worse than if it did, and so Twitter users struggle with threads to provide some sort of slow structure to the usual nauseatingly evanescent stream of fluff tweets.
Instagram & TikTok are even worse, and Snapchat makes no bones of trying to destroy even the possibility of a slow stream.
YouTube enables slow content quite well, and is [excellent at education](https://samoburja.com/the-youtube-revolution-in-knowledge-transfer/); it seems to struggle with fast, though---comments were a notorious cesspit for many years, and chatting or interactive streaming is something it's been trying to catch up with compared to pioneers like Twitch.
Weird hybrid examples exist. Consider 4chan, famously the meme-maker to the Internet.
A chan consists of flat 'threads' of comments one after another (any tree being implicit), ordered by most-recently-updated thread, with the last thread being deleted after a certain timeout; threads are nested within a general topic or 'board'.
4chan threads typically move fast and disappear within days.
At first glance, this might seem to preclude any kind of progress. But 4chan is nevertheless famous for incubating memes and projects over the course of many threads (all long since deleted by the final success). How?
Part of it is that successful threads may export to other boards; then other boards export to slow sites like specialist wikis or other social media networks, like Twitter, Facebook, and Reddit.
So there is a 3-level selection: a comment within a thread, interesting threads within the board, and interesting board contents within the Internet.
Threads select brutally for memetic efficiency (the ticking clock makes chan threads almost literally a [viral evolution lagoon](https://www.science.org/content/blog-post/evolution-action---literally "Phage-assisted continuous evolution")), and while this selection is extremely error-prone and inaccurate, there are \*so\* many comments & memes, and fans of a meme will maintain personal archives and keep posting variants (being anonymous, they are free to keep posting without care or consequence), that the memetic virus can keep mutating and evolving until perhaps it starts percolating outwards.
(And if it doesn't, oh well, plenty more where that came from!)
A broad perspective on this is to think of \*graphs\* of communities, where each node is a community of a certain size operating at a certain speed with differing norms about quality/topic/esthetics/anonymity etc.
If we think of each community as generating & filtering memes, then there are tradeoffs between size, accuracy of filtering, and throughput.
If you have 1 very large community, it will have extremely accurate selection on popularity (fitness) of a given meme, because it is averaging the assessments of almost everyone (despite steeply diminishing returns to spending more people to do selection); however, it will struggle to keep up with potential throughput and [its queues will overflow](https://blog.acolyer.org/2015/04/29/applying-the-universal-scalability-law-to-organisations/) creating a bottleneck impeding [bursty collaboration](/doc/sociology/2017-riedl.pdf "'Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Emergent Collaboration to Crowd-Based Problem Solving Performance', Riedl & Woolley 2017") of exciting new ideas, and where will new memes come from if slightly-inferior variants are harshly punished immediately compared to the current fit baseline?
Over-exploitation will become boring, driving away users---at best, stasis; at worst, decadence, exhaustion, & [collapse](https://psyarxiv.com/dt6bx "'Imitation-driven Cultural Collapse', Duran-Nebreda & Valverde 2021").
If you have lots of tiny communities, they will undergo extreme levels of "genetic drift" due to randomness in popularity, and the fitness of their best meme will typically be quite poor; but on the other hand, it is likely that at least one of those communities has random-walked its way to something neat (if only you could figure out \*which one\*...)
Depending on how these communities are connected, these neat new variants may be confined to a ghetto and eventually die out [due to randomness](!W "Gambler’s ruin") (either themselves or perhaps the communities) if they can't grow fast enough to [reach fixation](!W "Fixation (population genetics)"); but if you make them all hyper-connected, you may just wind up constructing the 1 large bottlenecked community again!
The architecture of speed and size is probably responsible for a lot of the 'feel' of social networking.
Twitter, for example, is lauded for giving access to the latest by the greatest anywhere, but produces a certain exhaustion and apathy and chronic low-grade anxiety and lowest-common denominator humor, and this is the flipside: because it emphasizes only the \*latest\*, there is no progression or collaborative creation (people can advertise on Twitter to collaborate elsewhere, or ask specific questions, or learn about something on Twitter, but there is nothing like a "Twitter FAQ thread" or long-term collaboration \*on\* Twitter), and because it can be from anywhere, the norms are unpredictable and "context collapse" means an entire outside community could decide to coordinate to make you the target of the latest '5-minute hate'.
Pavlogiannis et al 2018 (["Construction of arbitrarily strong amplifiers of natural selection using evolutionary graph theory"](https://www.nature.com/articles/s42003-018-0078-7)) considers this sort of scenario and finds that good graph structures & distributions tend to look like a hierarchical "star" or "hub-and-spoke" (or perhaps the ubiquitous "bow-tie").
There are many small 'local' nodes at the periphery, which focus on 'generating' [innovations](/note/small-groups "'The Effectiveness of Unreasonable Small Groups', Branwen 2021"), and these feed in a generally one-way direction into progressively larger nodes focused on 'selecting', which eventually reach a few 'global' nodes which are connected to all the peripheries again.
(Like in biological evolution, the number of nodes or 'populations' [can matter a lot](https://www.biorxiv.org/content/10.1101/2021.09.09.459561.full "‘Effective population size for culturally evolving traits’, Deffner et al 2021"), as does the history of the populations, which may have an ['effective' population count](!W "Effective population size") much smaller the visible one.)
Large masses of raw material, be they writing, or images, or videos, or sick skating techniques, are collaboratively written, proceeding from rough draft through editing to the final perfected version.
As a kid I wondered vaguely how famous intellectuals could have "30 boxes of notebooks" or "20 volumes of collected letters" or "10 volumes of unpublished papers"---wasn't writing and thinking \*hard\*, how did they have \*time\* for all that incessant note-taking and letter-writing, while still living and researching and actually writing the published work they were famous for?
The answer, it turned out, is simply that writing is a sequential collaborative process: those letters and unpublished papers were part of a [pipeline](/note/pipeline "'Leaky Pipelines', Branwen 2014"); it was not "letters \*vs\* books" but "letters \*into\* books".
A heuristic like the [rule of three](!W "Rule of three (computer programming)") ("if you find yourself explaining something for the third time, write it up") is about deciding what to promote up a level: the repetition implies that it's important, and conveniently, three rough drafts are already available.
This will suddenly all sound eerily familiar: it is our old friend [reinforcement learning](#rl) and its explore-exploit tradeoff, with outer evolutionary losses and inner learned losses, all over again!
Too small a batch size and you don't learn anything; too large, and it takes an eternity to improve; too little exploration & too much greedy exploitation, one learns unnecessarily slowly & may get trapped in a local optimum, but too much exploration ensures one never learns anything before bouncing off to the next random point; breaking up into multiple agents and populations can cover more ground than a single uniform population but only if they are balanced properly and transfer improvements; hierarchical structure can enable deep exploration and modularity, where a monolithic structure flails around locally but can be done poorly; an evolutionary loss is extremely inefficient compared to a learned inner loss explicitly optimizing for proxy goals, yet, without the evolutionary loss, the proxy goals may be wrong; and so on.
But with our graph to explore and amplify, and individual humans as myopically Bayesian agents, we discover our [overall community looks like](/timing#try-try-again-but-less-less) a giant [Thompson sampling](!W) engine!
So this offers a general theory of Internet community design: one wants an architecture which is hierarchical, supporting a smooth flow of content from a wide variety of small peripheral nodes operating on fast time-scales with their own unique norms fostering [social contagion of ambition](/review/bakewell#social-contagion) with incentives for directed exploration of new niches or uncovered territory^[An interesting example of designing a community to target new territory is [vTaiwan](https://www.technologyreview.com/2018/08/21/240284/the-simple-but-ingenious-system-taiwan-uses-to-crowdsource-its-laws/ "'The simple but ingenious system Taiwan uses to crowdsource its laws: vTaiwan is a promising experiment in participatory governance. But politics is blocking it from getting greater traction', Horton 2018"), which disables reply-comments to avoid over-exploitation of claims, and uses vote graphs to build a map of claims and highlight 'empty spots' that people can leave new comments in (since they can't just re-argue the old ones). This seems to be working better than some other instances like [`#xkcd-signal`](https://blog.xkcd.com/2008/01/14/robot9000-and-xkcd-signal-attacking-noise-in-chat/ "ROBOT9000 and #xkcd-signal: Attacking Noise in Chat").], upwards through a hierarchy of larger slower nodes with gradually more intense filtering & more conventional norms (including escalating reliance on reputation), to a final global central 'arena' where the best can duke it out for transmission back to all peripheral nodes.
The design should not privilege a particular time-scale, and should enable linking and copying through the various time-scales; nodes should be constrained in size, and broken up if necessary to keep them at an efficient size.
One could imagine a Reddit which integrated chat, links, & wiki pages to create a smoother transition between nodes and directly support promotion:
#. a subreddit has a chat channel which defaults to anonymous and is not logged or archived, with the minimum possible moderation;
#. blocks of fast-time chat (seconds to minutes), however, can be highlighted and right-clicked to automatically turn into a comment on a link or their own text post, 'promoting' them up one level to slower-time, where they can be discussed over hours to days;
#. such comments, perhaps on some topic of sudden new interest, may be further selected, and transcluded into a new wiki page devoted to that topic (crude, but a starting point as a comment index), which can then be hand-edited later to add in additional commentary, links, citations, etc;
#. these pages become a long-term resource for that subreddit, and perhaps turn out to be of broader interest, being crossposted to other, bigger, subreddits,
#. and, amazingly enough, eventually reach /r/all where it is pushed to all users as part of their default feed---adding a new thing to the global 'common knowledge', which (return to #1) some other subreddit chat might idly discuss for a while and then make an unexpected breakthrough, kicking off a new cycle. |
1f510456-cc2a-46fa-b145-03b37e7d7fa1 | trentmkelly/LessWrong-43k | LessWrong | Black Box Investigation Research Hackathon
TLDR; Join the Black Box Investigation Research Hackathon to participate with others in a weekend of alignment research on language models using a cognitive psychology-style approach.
Purpose
In the Black Box Investigation Research Hackathon, the goal is to explore where black box language models work well and where they break down. It is inspired by this post by Buck Shlegeris which calls for investigators of language models in a cognitive psychology style fashion. Become the AI psychologist of the future.
The meta-goal of this Alignment Jam is to investigate if hackathons work well for 1) providing real research results and ideas, 2) increasing the research experience of the participants, and 3) being a scalable solution to exciting ways of joining the alignment community to work on interesting and well-defined problems.
Outcome
Participants are encouraged to discover interesting behavior of black box language models. At the end of the hackathon each group / participant should have created a short (<5 min) presentation showcasing their results. A jury will then vote for their favorite projects according to the review rubric specified in the appendix.
After the hackathon, we will write a LessWrong post (or series of posts) describing the main findings and the overall experience. We hope to encourage more people to do this kind of work and we believe the hackathon format works well for generating novel research perspectives on alignment.
If this hackathon is successful, we expect to repeat the success with other research agendas.
Participation instructions
It is hosted on the hackathon platform itch.io where you will follow the instructions to sign up (see here): Make an itch.io account, click “Join jam”, and come along on the 30th of September!
We will work all weekend in Gathertown in groups of 2-6. The link to Gathertown will be announced in the itch.io community tab and sent to your itch.io email when we get closer to the date. You can ask questions h |
432f580e-148d-478c-b50d-55390a869cc2 | trentmkelly/LessWrong-43k | LessWrong | Chapter 81: Taboo Tradeoffs, Pt 3
In rising half-circles of dark stone, a great sea of upraised hands.
The Lords and Ladies of the Wizengamot, in plum-colored robes marked with a silver 'W', stared down in stern rebuke at a young girl trembling in chains. If they had, in any particular ethical system, damned themselves, they clearly thought quite highly of themselves for having done so.
Harry's breath was trembling in his chest. His dark side had come up with a plan - and then rotated itself back out again because speaking too icily would not be to Hermione's advantage; a fact which the only-half-cold Harry had somehow not realized...
"The vote carries, in favor," intoned the secretary, when all the tallying was done, and the upraised hands fell back down. "The Wizengamot recognizes the blood debt owed by Hermione Granger to House Malfoy for the attempted murder of its scion and ending of its line."
Lucius Malfoy was smiling in grim satisfaction. "And now," said the white-maned wizard, "I say that her debt shall be paid -"
Harry clenched his fists beneath the bench and shouted, "By the debt owed from House Malfoy to House Potter!"
"Silence!" snapped the woman in too much pink makeup sitting next to Minister Fudge. "You've disrupted these proceedings quite enough already! Aurors, escort him out!"
"Wait," said Augusta Longbottom from the top tier of seats. "What debt is this?"
Lucius's hands whitened on his cane. "House Malfoy owes no debt to you!"
It wasn't the world's most solid hope, it was based on one newspaper article from a woman who'd been False-Memory-Charmed, but Rita Skeeter had seemed to find it plausible, that Mr. Weasley had allegedly owed James Potter a debt because...
"I'm surprised you've forgotten," Harry said evenly. "Surely it was a cruel and painful period of your life, laboring under the Imperius curse of He-Who-Must-Not-Be-Named, until you were freed of it by the efforts of House Potter. By my mother, Lily Potter, who died for it, and by my father, James Potter, who di |
0ab80fb7-d039-418b-8854-0dd1eb5e043a | trentmkelly/LessWrong-43k | LessWrong | Debating Whether AI is Conscious Is A Distraction from Real Problems
Hi LW, this article has put some doubt in my mind as to whether researching AI alignment is worthwhile, or a frivolity that detracts from more pressing issues such as mitigation of harmful biases.
> [Discussing AI consciousness] is a distraction, when current AI systems are increasingly pervasive, and pose countless ethical and social justice questions that deserve our urgent attention
I would like to hear some opinions from this community on the sentiment expressed in the above quote.
First, though, an introduction, or why this has prompted me to emerge from my decade-long lurker state:
I am a computational linguistics MS student approaching my final semester and its accompanying research capstone. My academic background is in linguistics, not computer science, philosophy, math, or anything that would actually be useful for doing serious AI research. c; Nevertheless, I want to try to help, as best I can.
For my capstone, I have been considering continuing a previous bias mitigation project (that I have lost most of my enthusiasm for due to group project burnout), or using LLMs to extract moral themes from literature (an idea my thoughts keep coming back to) in the hopes that the ability to encapsulate the human values expressed in a text may someday be useful to the AI alignment task. The thought process behind this was, although human beings can't even agree on human values, we all somehow manage to come up with a sense of morality based on our cultural upbringing, and one common form of character education for children is storytelling (Aesop's fables, Narnia...) If human children can learn virtues from fiction, why not AI?
The part of Lemoine's dialogue with LaMBDA where it succinctly summarizes the moral themes of Les Mis made me think this may be a good idea to play around with. [Note, just to be extremely clear, I in no way imagine LLMs to be sentient- Lemoine is misguided by his religious thinking. But I do think LLMs are likely to be a component of a f |
067343c6-e02e-4058-bcf1-af13d3a6fb9e | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | A possible preference algorithm
I have [previously discussed](https://www.lesswrong.com/posts/CSEdLLEkap2pubjof/research-agenda-v0-9-synthesising-a-human-s-preferences-into) the [constructive](https://www.lesswrong.com/posts/Y2LhX3925RodndwpC/resolving-human-values-completely-and-adequately) [process](https://www.lesswrong.com/posts/hcrFxeYYfbFrkKQEJ/full-toy-model-for-preference-learning) of generating a reward function from the [mess of human preferences](https://www.lesswrong.com/posts/weHuX2qkTxgAXBw8t/defining-the-ways-human-values-are-messy).
But, let's be honest, that process was somewhat messy and ad hoc. It used things like "partial preferences", "identity preferences", "one step hypotheticals", "partial meta-preferences about the synthesis process", and so on.
The main reasons for this messiness are that human preferences and meta-preferences have so many features and requirements, and many of the intuitive terms and definitions in the area are ambiguous. So I had to chunk different aspects of human preferences into simple formal categories that were imperfect fits, and deal with each category in a somewhat different way.
Here I'll try and present a much simpler system that accomplishes the same goal. To do so, I'll rely on two main ideas: [model splintering](https://www.lesswrong.com/posts/k54rgSg7GcjtXnMHX/model-splintering-moving-from-one-imperfect-model-to-another-1) and delegation to future selves - via present selves.
Model splintering allows us to defer most of the issues with underdefined concepts. So we can use those concepts to define key parts of the preference-construction process - and punt to a future 'general solution to the model splintering problem' to make that rigorous.
Delegation to future selves allows us to encode a lot of the more complicated aspects of the process to our future selves, as long as we are happy with the properties of these future selves. Again, model splintering is essential to allowing this to function well, since the relevant 'properties' of our future selves are themselves underdefined.
Then constructing the human preference function becomes an issue of energy minimisation.
The reward function algorithm
=============================
The setup
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be a human at time t=0. An AI will attempt to construct Ri, a suitable reward function for that human; this process will be finished at time t=τ (when we presume the human is still around). From that moment on, the AI will maximise Ri.
The building blocks: partial preferences
----------------------------------------
Defining [partial preferences](https://www.lesswrong.com/posts/2vnJmWDAaLKDayvwr/toy-model-piece-4-partial-preferences-re-re-visited) has been tricky. These are supposed to capture our internal mental models - what happens when we compare images of the world in our minds and select which one is superior.
In the feature language of model splintering, define partial preferences as follows. Let f be a feature, and F a set of 'background' features. Let ρF be the set of values that F can take - a possible 'range' of the features F.
Then define the real number
ri(f,F,x,y,ρF)
as representing how much the human hi prefers f=x over f=y, given the background assumption that F are in the range ρF.
This is a partial preference; it is only defined, pairwise, over a subset of worlds. That subset is defined to be
Si(f,F,x,y,ρF).
Here (w,w′)∈Si(f,F,x,y,ρF) if w is defined by f=x, w′ by f=y, and both have the same values of F, which are in the range ρF.
These partial preferences will only be defined for very few values of f, F, x, y, and ρF. In the cases where ri is not defined, the Si(f,F,x,y,ρF) is the empty set.
Since writing out f,F,x,y,ρF every time is clunky, let Ω represent all that information.
Energy minimisation
-------------------
Let W be a set of worlds. For two worlds (w,w′), let #i,w,w′=∑Ω:(w,w′)∈Si(Ω)[1] be the number of Si(Ω) sets that (w,w′) belongs to.
Then the Ri is defined so that it minimises the following sum:
∑w,w′∈W∑Ω:(w,w′)∈Si(Ω)(Ri(w)−Ri(w′)−ri(Ω))2/#i,w,w′
Meta-preferences and the delegation process
-------------------------------------------
In [a previous post](https://www.lesswrong.com/posts/KptP3J2ThDTnriric/consistencies-are-often-meta-preferences), I showed how consistency requirements for reward functions were derived from preferences of the subject.
Here we will go further, and derive a lot more. The reward Ri will be a feature of the world in which the AI operates, after t=τ. Therefore it is perfectly consistent for hi to have partial preferences over Ri itself.
Since the human hi will still be around after t=τ, it can also express preferences over its own identity (this is where model splintering is useful, since those preferences need not be fully defined to be useful). In the future, it will also have its own future preferences, which we will designate by Rti. This is a slight abuse of notation, since these future preferences need not be reward functions.
Then a human may have preferences over Rti, its future preferences, and over the extent to which the AI should respect its future preferences. This sets up an energy minimisation requirement between Rti and Ri, which serves to address issues like the AI modifying human's preferences to bring them closer to their ideal, or the AI continuing to satisfy - or not - a future human whose preferences diverge from their current state.
So via this delegation-to-future-self-as-defined-by-the-current-agent, a lot of meta-preferences can be included into Ri without having to consider different types.
A small consistency example
---------------------------
Consider a situation where hi has inconsistent preferences, but a strong meta-preference for consistency (of whatever type).
One way for the AI to deal with this, could be as follows. It generate Ri, a not-very-consistent reward function. However, it ensures that the future world is only one in which hi does not encounter situations where the inconsistency becomes manifest. And it pushes hi to evolve towards Rti, which is consistent, but actually equal to Ri on the future world that the AI would create. So Ri can get an energy minimisation on inconsistent preferences, but the human still inhabit a world where its preferences are consistent and the AI acts to satisfy those.
Multiple humans
---------------
In the case of multiple humans, the AI would want to construct R, a global utility function for all humans. In that case, we would energy-minimise as above, but sum over all humans:
∑hi∈H∑w,w′∈W∑Ω:(w,w′)∈Si(Ω)νwνw′νΩ(R(w)−R(w′)−ri(Ω))2/#i,w,w′.
Explicitly putting our (weighty) thumb on the scales
----------------------------------------------------
We may want to remove anti-altruistic preferences, enforce extra consistency, enforce a stronger sense of identity (or make the delegation process stronger), insist that the preferences of future agents be intrinsically respected, and so on.
The best way to do this is to do so explicitly, by assigning weights νw to worlds and νΩ to the partial preferences. Then the energy minimisation formula becomes:
∑hi∈H∑w,w′∈W∑Ω:(w,w′)∈S(Ω)νwνw′νΩ(R(w)−R(w′)−ri(Ω))2/#w,w′.
For the issue of future entities that don't yet exist, the simplest would probably be to delegate population ethics to the partial preferences of current humans (possibly weighted or with equality between entities required), and then use that population ethics to replace the "∑hi∈H" in the expression above.
Likely errors?
==============
I like the elegance of the approach, so, like with all simple and reward-maximising approaches, it makes me nervous that I've missed something big that will blow a hole in the whole process. Comments and criticisms are therefore much appreciated.
The key missing piece
---------------------
Of course, this all assumes that we can solve the model splintering problem in a safe fashion. But it seems that it probably a requirement of any method of value learning, so that may not be as strong a requirement as it seems. |
921f192d-3a2e-4707-bfde-6931d75e9127 | trentmkelly/LessWrong-43k | LessWrong | Use of GPT-3 for identifying Phishing and other email based attacks?
Yet a new report about malicious email attacks using the USAID systems.
I would think that language processing AI/ML tools like I understand GPT-3 is (which could be a complete mischaracterization) seem like a good area of application. But I've never heard of any mention of such an application.
However, I know so little of this area it might be both the wrong level of analysis to mitigate threats and so costly that the marginal gain over what is in place (assuming no current role) just doesn't make good sense.
Still, given how long these types of attacks have been used -- and by implication continue to work it seems -- I wonder if perhaps such an approach might catch more before a careless or unwitting user reads the email or worse, follows the links. In other words, could such an approach perhaps alert the user to more subtle language based clues than they have the attention, capacity or knowledge to pick-up on in their day to day routines. |
c1f9c306-f90b-4cda-a28f-fe55e492e745 | trentmkelly/LessWrong-43k | LessWrong | Irrational Resistance to Business Success
Below I quote from Process of On Going Improvement forum, letter 6. Eli Goldratt shares a letter he received. I added a few notes to help people follow acronyms.
My question is: Does anyone know of any applications of Less Wrong philosophy to a situation like this? How can LW ideas about rationality explain or fix this sort of problem? The scenario is that someone tried to use rational thinking to make business improvements, was highly successful (which was measured and isn't in dispute), but nevertheless has met so much ongoing resistance that he's at the point of giving up.
> I am no expert in TOC but I believe my recent experiences have impact as to what you are writing about.
TOC = Theory Of Constraints. Summary.
> About 2 years ago I started on my TOC adventure. Read everything I could get a hold off etc. Tried to get the company interested, etc. In fact, I finally got them interested enough that we had multiple locations participate in the satellite sessions and had enough for three facilitators (myself included). However, I could never get the company to spend for training at AGI. So, in the old air cav fashion, I felt it was up to me to make it happen.
>
> Last year we had real problems with cost, service, high inventory, etc. My plant, I am the plant manager, was being analyzed for a possible shutdown or sell off. We were asking for 17 machines at about $300,000 each due to "lack of capacity" and we were being supplemented by outside producers.
>
> Again, I am not a TOC expert. Basically my exposure has been reading and researching and building computer simulations to understand. But I put on TOC classes for all of my associates (200). I spent 8 hours with each of these associates in multiple classes. We talked about the goal, TIOE, we played the dice game (push, KanBan, DBR) with poker chips, paper clips, and different variations of multiple sided dice and talked about its impact, etc.
The Goal (summary) is a book by Eli Goldratt that has sold over |
8c696503-fa5a-435b-8ba2-4320a82fa11b | trentmkelly/LessWrong-43k | LessWrong | Open thread, Jul. 17 - Jul. 23, 2017
IF IT'S WORTH SAYING, BUT NOT WORTH ITS OWN POST, THEN IT GOES HERE.
----------------------------------------
Notes for future OT posters:
1. Please add the 'open_thread' tag.
2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)
3. Open Threads should start on Monday, and end on Sunday.
4. Unflag the two options "Notify me of new top level comments on this article" and "Make this post available under..." before submitting |
9a62eedf-b177-45dd-89d3-c2ea42ac9a2e | trentmkelly/LessWrong-43k | LessWrong | Near-death experiences
On March 20th, 1995, five men boarded different trains in the Tokyo subway system during rush hour. They were each holding sealed plastic bags wrapped in newspaper and carrying umbrellas with sharpened tips. Placing the bags on the floor, they poked holes in them with the umbrellas. The men promptly departed the trains while the liquid content of the bags spilled onto the floors.
Metro riders began to experience blurred or missing vision, extreme nausea, and foaming at the mouth while having seizures. Unknowingly, they were living through the deadliest terrorist attack in Japanese history. The liquid spilling from the bags was a chemical weapon—sarin gas—which killed 14 people and injured thousands more.
Author Haruki Murakami interviewed some of the survivors. They spoke about their daily lives, how they felt during the incident, and their opinions about the religious cult who perpetrated the attack. Murakami published their responses in a book called Underground: The Tokyo Gas Attack and The Japanese Psyche. The story from one man, Mitsuteru Izutsu, stood out from all the rest:
> The day after the gas attack, I asked my wife for a divorce. We weren’t on the best of terms at the time, and I’d done my fair share of thinking while I was [traveling for work] in South America. I had meant to come out and say my piece when I got home, then I walked straight into the gas attack. Still, even after all I’d been through, she would barely speak to me.
>
> After being gassed I phoned home from the office to tell my wife what had happened and my symptoms and everything, but I got almost no reaction from her. Perhaps she couldn’t really grasp the situation, exactly what had occurred.
>
> But even so, I knew then that we’d come to a turning point. Or else, the state I was in had gotten me all worked up. Maybe that’s why I came straight out with it and said I wanted a divorce. Perhaps if this sarin thing hadn’t happened, I wouldn’t have been talking about divorce so soon. I |
604b91d8-d20d-451f-98a0-000a83f9b0cb | trentmkelly/LessWrong-43k | LessWrong | Nuanced and Extreme Countersignaling
Nuanced Countersignaling
Scott Alexander described countersignaling as following a barber-pole pattern. Or, as he says in Friendship is Countersignaling:
> ... you can mistake someone at level n for level n-1, but never for level n-2 – so pretending to be level n-2 and getting away with it is a sure sign that you are in fact level n and not level n-1.
I think of this as a subtype of countersignaling - nuanced countersignaling. The primary goal here to distinguish an actual level n from a similar-but-worse level n-1. This is done by sending an n-2 signal while you're at level n.
Note that under this theory, sending an n-3 signal is "dangerous" for the purpose of countersignaling as level n. After all, n-3 would be how an n-1 countersignals. Countersignaling at level n-2 is the only way to unambiguously countersignal level n status.
Extreme Countersignaling
By contrast, with extreme countersignaling, the idea is to send the most extreme possible countersignal. If signals are on a spectrum from 10 to -10, extreme countersignaling is when a person at level 10 illustrates this by sending a -10 signal. A person at level 9 can only get away with sending a -9 signal.
I think this is what's going on in Scott's dialog in the post about friendship.
> Becca: What are you doing here? I figured they’d have locked you away in the psych ward for good by now.
> Scott: Nope. And what are you doing here? You haven’t killed off all your patients yet?
> Becca: Only person in this hospital I might kill is standing right in front of me.
> Scott: Be careful, I’m armed and dangerous *picks up a central line placement practice set menacingly*
Let's say that friendship is on the scale of solid friends > budding friendship > acquaintances > strangers > enemies. If Scott and Becca are solid friends, nuanced countersignaling would call for a dialog where they pretended to be acquaintances.
> Becca: How's it going... Scott? Right?
>
> Scott: It's actually Thomas, but no worries, Sarah. |
25e0f0ef-decc-45ce-9f26-c276d3fc04e5 | trentmkelly/LessWrong-43k | LessWrong | AISC9 has ended and there will be an AISC10
The 9th AI Safety Camp (AISC9) just ended, and as usual, it was a success!
Follow this link to find project summaries, links to their outputs, recordings to the end of camp presentations and contact info to all our teams in case you want to engage more.
AISC9 both had the largest number of participants (159) and the smallest number of staff (2) of all the camps we’ve done so far. Me and Remmelt have proven that if necessary, we can do this with just the two of us, and luckily our fundraising campaign raised just enough money to pay me and Remmelt to do one more AISC. After that, the future is more uncertain, but that’s almost always the case for small non profit projects.
Get involved in AISC10
AISC10 will follow the same format and timeline (shifted by one year) as AISC9.
Approximate timeline
August: Planning and preparation for the organisers
September: Research lead applications are open
October: We help the research lead applicants improve their project proposals
November: Team member applications are open
December: Research leads interviews and select their team
Mid-January to Mid April: The camp itself, i.e. each team works on their project.
Help us give feedback on the next round of AISC projects
An important part of AISC is that we give individual feedback on all project proposals we receive. This is very staff intensive and the biggest bottleneck in our system. If you’re interested in helping with giving feedback on proposals in certain research areas, please email me at linda.linsefors@gmail.com.
Apply as a research lead – Applications will open in September
As an AISC research lead, you will both plan and lead your project. The AISC staff, and any volunteer helpers (see previous section) will provide you with feedback on your project proposal.
If your project proposal is accepted, we’ll help you recruit a team to help you realise your plans. We’ll broadcast your project, together with all the other accepted project proposals. We’ll |
047fe2f7-a2a0-4fd1-a0ac-522840cb21fb | trentmkelly/LessWrong-43k | LessWrong | Personal experimentation: not shared?
Cross posted from Overcoming Bias. Comments there.
***
I’ve been talking about how personal experimentation could be worth it for people like me, without relevant info being depleted long ago.
My next potential explanation is that people do experiment, but results aren’t aggregated and spread, so everyone has to reinvent everything.
This is exactly what you would expect in a simple model where people benefit from information, but bear a net cost from spreading it. Without incentives to contribute one’s own findings to others, there is no reason information should spread. But on closer inspection this is roughly the opposite of what the world looks like. There is a lot of advice about how to run the details of a life. Sometimes it is offered for money, but often so enthusiastically and freely as to make the most curious life-optimizer want to run away. The problem seems to be more that there is so much advice, advising pretty much the full range of behavior. There are apparently incentives for spreading such ‘information’, but not incentives to actually find any information to begin with.
This is doubly puzzling. It’s not surprising if all the possible self-help books exist. But for folks volunteering their own time to tell me about whatever relaxation technique or diet, spreading random misinformation seems low value. And again we have the question of why it wasn’t worth it, for their own benefit, to get some actual information to begin with.
A plausible explanation to me for both of these things is that just about any random innocuous change makes life seem better, and people are genuinely trying to be helpful by telling others about such ‘discoveries’. So the problem then would be widespread use of informal data collection, which is much more unreliable than people think. In which case, my own experimentation is just as likely to fail if I rely on such data collection. Experimentation in general would not be as useful as suspected – continually experimenting |
70b4a9bc-4184-499c-b276-3139923d677b | trentmkelly/LessWrong-43k | LessWrong | Resolving human inconsistency in a simple model
A putative new idea for AI control; index here.
This post will present a simple model of an inconsistent human, and ponder how to resolve their inconsistency.
Let H be our agent, in a turn-based world. Let Rl and Rs be two simple reward functions at each turn. The reward Rl is thought of as being a 'long-term' reward, while Rs is a short-term one.
----------------------------------------
Define Rlt as the agent's Rl reward at turn t (and similarly Rst for Rs). Then, at turn t, the agent H has reward:
* Rt=∑∞τ=0(γl)τRlt+τ+(γs)τRst+τ,
with constants 0<γs<γl≤1.
Essentially the Rs and the Rl have different discount rates, with the reward from Rs fading much faster than than that of Rl. Therefore the agent will be motivated to get the Rs reward, but only if they can get this in the short-term. Sex, drugs, food, and many other pleasures often have these features (though they are, of course, much more complicated).
The inconsistency is that the human will continually reset their Rt at each turn. If there were a single discount rate, that wouldn't a problem, as that would just scale the whole reward function, and reward functions, like utility functions, give the same decisions when scaled.
But with two discount rates, this is inconsistent. The agent will try and follow Rl for long-term planning, but this will be disrupted if they encounter an Rs along the way (and then presumably berate themselves for the lack of self-discipline). This can also be seen as a variant of the "humans are composed of multiple subagents" model, with Rs corresponding to short-term greedy subagent.
So, we have a simple and not-completely-implausible model of an inconsistent human. The question is, how do we resolve it? None of the obvious approaches are ideal, but it's worth looking at their features.
Freeze the reward
This is the most obvious approach: simply freeze the reward, so that the reward at time t′>t is simply the same as reward at time t (though, in the absence of time-trav |
0037325e-4e2d-4135-901f-a50fb54b775e | trentmkelly/LessWrong-43k | LessWrong | TED Talks: Daniel Kahneman
People who have had a painful experience remember it as less painful if the pain tapers off, rather than cutting off sharply at the height of intensity, even if they experience more pain overall. I'd heard of this finding before (from Dan Ariely), but Kahneman uses the finding to throw the idea of "experiencing self" vs. "remembering self" into sharp relief. He then discusses the far-reaching implications of this dichotomy and our blindness to it.
The talk is entitled "The riddle of experience vs. memory".
|
6b6b9a9e-62d0-4eb3-8820-06a0a03fe237 | trentmkelly/LessWrong-43k | LessWrong | Tiddlywiki for organizing notes and research
Happy new school year to my fellow students! With my first year of grad school under my belt, and my sword and shield out for Round 2, I wanted to share a tool that’s helped me on my journey.
Two years ago, my go-to system for organizing my research and writing “citations in 3 different programs” + “pile everything into a haphazard series of google docs and hope for the best”. I figured this wasn’t great. After doing some reading and trying several alternatives, I discovered Tiddlywiki.
Tiddlywiki is an ancient open-source wiki application in the form of an html file. It has all the tools you need to make a wiki in the form of “tiddlers”, self-contained chunks of info that you can tag and link to each other. When you save the wiki, the program and your text all get wrapped up together into the same .html file – it both stores your info and is the program for running the wiki. It works on any web browser, as well as special programs.
I stuck with it and here’s why:
* Wiki format: Wikis seem really compatible with the way my brain works. If I take notes on a book or article, that source gets its own tiddler on the wiki. They can then get interwoven, crosslinked, expanded upon, etc.
* Elegant: Does most things I want it to. Easy to link to tiddlers and drag them or other files in from other wikis/folders. The structure is transparent and customizable.
* Robust: Tiddlywikis from a decade ago are still perfectly functional today. The entire program and dataset lives in one small html file that runs on anything with a web browser.
* Meta-aesthetics: Feeding all my data to Google is a little worrying. Tiddlywiki, meanwhile, is open-source and runs from your computer. The fact that the program is a quine is really neat.
* Encryption: Tiddlywikis have an encryption function baked in. I don’t know if it’s very good. Consider using Veracrypt for better security. But if you don’t want to do that, here you go. This also means you can upload your wikis and backups to c |
7e757bf5-e00a-4aca-9eb4-b25c0340540b | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Why unriggable *almost* implies uninfluenceable
When talking about learning processes, I've [said](https://arxiv.org/pdf/2004.13654.pdf) [things](https://www.lesswrong.com/posts/LpjjWDBXr88gzcYK2/learning-and-manipulating-learning) like "every uninfluenceable learning process is unriggable" and "every unriggable learning process is uninfluenceable, if the set of environments is large enough".
In this post, I want to clarify the relationship between the two, in ways that are hopefully understandable. I won't be using much math, but several hopefully intuitive examples.
The main points I hope to get across are:
1. Uninfluenceable and unriggable are of different *types*. Unriggable is algebra about your learning process; uninfluenceable is about learning independent facts about the environment.
2. Nevertheless, these are *almost* equivalent.
3. Unriggable process that are not uninfluenceable are specific and unstable, and generally collapse if the agent can gain extra information.
The third point is new, so will be of interest even to those who already understand these concepts.
Informal definitions
====================
Here, it suffices to think of uninfluenceable as meaning "learning about values/preferences is the same as learning objective facts about the universe" and unriggable as "you can't push the expected probability of a value/preference in a particular direction".
Unriggable-but-influenceable
============================
Parents guide you
-----------------
I've used an example involving a robot agent asking its parents, so let's continue with that. There is a robot who doesn't know whether to become a banker or a doctor. To figure this out, it can ask either of its parents:
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(ask mother) and F (ask father). When it does so, the relevant parent will "banker" (B) or "doctor" (D), which will fully resolve the issue.
So, given the possible histories, this is what the robot will learn, with actions in red and observations in blue:
[M or F]B→ become a banker [M or F]D→ become a doctor
Let's μBB be the world in which both parents will say "banker", μBD the world in which the mother says "banker" and the father says "doctor", μDB the world in which they will say the opposite, and μDD the world in which both will say "doctor".
Then the learning process is unriggable if the robot cannot push towards "doctor" or "banker" via its actions. This means that the probabilities of D/B are the same whichever parent the robot asked. This happens when the robots (subjective) probabilities of μDB and μBD are the same[[1]](#fn-WCcJ3Cm4763Ykstyp-1).
Unriggable-but-influenceable is unstable
----------------------------------------
If the learning process is unriggable, but not uninfluenceable, then the unriggability is unstable. First of all because of the requirement that P(μDB)=P(μBD); so the robot has to believe that the its father and mother are equally likely to say either banker or doctor. This is highly specific and unusual situation to be in.
But what happens if the robot receives an update about either parent's preferences, before asking them? Maybe it found one of their diaries:
Then it's likely that the probabilities of μDB and μBD will drift out of equality.
If the learning process is truly unriggable, then the robot cannot receive any such update. Or, by pure coincidence, the update must keep the probabilities equal. If the process was uninfluenceable, then there would be a reason for this - the probabilities and the updates are caused by some underlying information (see the section about uninfluenceable learning).
But, generically, if there is an unriggable-but-not-uninfluenceable learning process, then allowing the robot to learn new information will mess it up, and it will no longer be unriggable.
Uninfluenceable
===============
One of the results of the [paper](https://arxiv.org/pdf/2004.13654.pdf) is that any unriggable learning process is equivalent to an uninfluenceable one, though possibly with different environments.
So if the robot's process is unriggable, in the sense that μDB and μBD are of equal probability, what is the equivalent uninfluenceable one?
Here's one way it could happen. There is a ghost, that has opinions on what career the robot should take:
After the robot has decided which parent to ask, the ghost will possesses that parent, and impose their own judgement as to what job the robot should take:
In this case, there are only two environments, μB and μD, depending on whether the ghost wants the robot to be a banker or a doctor. This learning process is uninfluenceable, ie depends only on the underlying environment: μB means the robot should be a banker, μD that it should be a doctor.
The equivalence
---------------
Why are the ghost scenario and the standard parent scenario equivalent? Well, there are only four histories that the robot can observe in either scenarios: two actions (M or F) and two observations (B or D).
Then in the standard parents scenario, the probability of MB, given M, is the probabilities of μBB and μBD (the two worlds where the mother says "banker"). Similarly, the probability of MD, given M, is the sum of the probabilities of μDB and μDD.
In the ghost scenario, those probabilities are the probabilities of μB and μD respectively. So if we set P(μB)=P(μBB)+P(μBD) and P(μD)=P(μDB)+P(μDD), then, given that it chooses M, the probabilities of the robot seeing B or D are the same in both scenarios.
Since the standard parent scenario is unriggable, we have μBD=μDB. This implies that the probability of the other two histories, given F, are also the same[[2]](#fn-WCcJ3Cm4763Ykstyp-2).
Thus these two scenarios have the same probabilities for their histories.
Stability
---------
Even though they are equivalent in terms of probabilities, the uninfluenceable ghost scenario is stable under adding new information. If the robot discovers a diary of the ghost, then this updates its probability which ever parent it asks.
The parents are now giving the same answer, in expectation, not because of a coincidence of that μBD and μDB have the same probabilities, but because there is a single underlying cause - the ghost - for both answers.
Of course, the underlying cause might be the parents agreeing on the robot's profession ahead of time, rather than a ghost - but what's the fun in that?
In summary
==========
1. If a learning process is unriggable and stable under extra information, then it is uninfluenceable.
2. Conversely, if a learning process is unriggable but influenceable, then it is unstable: extra true information tends to make it no longer unriggable.
3. If the learning process is unriggable but influenceable, there is an equivalent uninfluenceable one, in that the probability of all actions and observations are the same.
4. But the equivalent uninfluenceable one will be stable under extra information.
---
1. The learning process being influenceable means that the probabilities of μDB or μBD are non-zero; if they are both zero, then both parents have agreed on what is best for the child, and asking either one makes no difference. [↩︎](#fnref-WCcJ3Cm4763Ykstyp-1)
2. To check: the probability that the robot hears B after F is P(μBB)+P(μDB), which is P(μBB)+P(μBD) by unriggability, which is P(μB). Similarly, the probability that the robot hears D after F is P(μBD)+P(μDD), which is P(μD). [↩︎](#fnref-WCcJ3Cm4763Ykstyp-2) |
6e72e805-9275-4969-a509-837de31c63fa | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | How to get more academics enthusiastic about doing AI Safety research?
During the last 2.5 or 3 years, I have been trying to learn and get experience on AI Safety so that when I finish my PhD in Physics I might be able to contribute to the AI Safety community efforts. During these years I have noticed that:
* There seem to be large groups of potential junior researchers, and the community has several programs in place for them such as the AI Safety Camp or AI Safety Research Program.
* Funding is growing, but still largely concentrated in a handful of places such as CHAI, FHI, CSER or institutes not affiliated to universities (eg Center for Long Term Risks); and a few companies (DeepMind, OpenAI, Anthropic).
But it seems to me that there are still great places out there where research could happen and is currently not.
So, given that the existence of more senior researchers seems to be a bottleneck: what can the community do to get them more interested in this topic?
I read somewhere that there are two ways to get people involved:
* Telling them this is an important problem.
* Telling them this is an interesting problem.
I think it may be worth trying out a combination of both, eg: "hey, this is an interesting problem that could be important to make systems reliable". I really don't think one needs to convince them about longtermism as a previous step.
In any case, I wanted to use this question to generate concrete actions such that people such as the EA Long Term Fund managers could put money to solve this bottleneck. The only time I have seen that something similar is the "Claudia Shi ($5,000): Organizing a "Human-Aligned AI” event at NeurIPS." donation registered in <https://funds.effectivealtruism.org/funds/payouts/september-2020-long-term-future-fund-grants>.
There might also be other ways, but I don't think I know academic dynamics so well to know. In any case, I am aware that publication of technical AI Safety in these conferences does not seem to be an issue, so I believe the bottleneck is in getting them to be genuinely interested on the topic. |
36827a0d-c61f-48c7-9740-d0cf906832f9 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Algorithmic Fairness from a Non-ideal Perspective
1 Introduction
---------------
Machine Learning (ML) models play increasingly prominent roles
in the allocation of social benefits and burdens
in numerous sensitive domains, including hiring, social services, and criminal justice
(Berk2018; Crawford2014; Barocas2016; Feldman2015).
A growing body of academic research and investigative journalism
has focused attention on ethical concerns regarding
algorithmic decisions (Brown2019; Dwork2012; Angwin2016),
with many scholars warning that in numerous applications,
ML-based systems may harm members of
already-vulnerable communities (Barocas2016; Eubanks2018).
Motivated by this awareness, a new field of technical research
addressing fairness in algorithmic decision-making has emerged,
with researchers publishing countless papers aspiring to
(i) formalize “fairness metrics”—mathematical
expressions intended to quantify the extent to which
a given algorithmic-based allocation is (un)just;
and (2) mitigate “unfairness” as assessed by these metrics
via modified data processing procedures, objective functions,
or learning algorithms (Hardt2016; Zafar2017; Nabi2018; Kilbertus2017; Dwork2012; Grgic-Hlaca2018a; Corbett-Davies2018).
However, progress has been hindered by disagreements
over the appropriate conceptualization
and formalization of fairness (Chouldechova2016; Kleinberg2017; Glymour2019; Binns18).
The persistence of such disagreements raises
a fundamental methodological question
about the appropriate approach for constructing tools
for assessing and mitigating potential injustices of ML-supported allocations.
Importantly, any useful methodology must provide normative guidance
for how a given agent ought to act
in a world plagued by systemic injustices.
Broadly speaking, justice requires apportioning benefits
and burdens in accordance with each person’s
rights and deserts—giving individuals “their due” (Miller2017; Feldman2016).
Beyond this general framing,
how can we offer more specific and practical guidance?
Drawing on literature in political philosophy,
in Section [2](#S2 "2 Two Methodologies: Ideal vs. Non-Ideal ‣ Algorithmic Fairness from a Non-ideal Perspective"),
we distinguish between ideal and non-ideal
methodological approaches to developing such normative prescriptions,
and highlight three challenges facing the ideal approach.
Then, in Section [3](#S3 "3 Work on Algorithmic Fairness as Small-scale Ideal Theorizing ‣ Algorithmic Fairness from a Non-ideal Perspective"),
we argue that most of the current technical approaches
for addressing algorithmic injustice
are reasonably (and usefully) characterized
as small-scale instances of ideal theorizing.
Next, in Section [4](#S4 "4 Troubles with Ideal Fairness Metrics ‣ Algorithmic Fairness from a Non-ideal Perspective"),
we support this argument by demonstrating
several ways that current approaches are, to varying extents,
plagued by the same types of problems
that confront naive applications of ideal theorizing more generally.
Finally, drawing on these considerations,
in Section [5](#S5 "5 Discussion ‣ Algorithmic Fairness from a Non-ideal Perspective"),
we provide a critical discussion of
the real-world dangers of this flawed framing
and offer a set of recommendations
for future work on algorithmic fairness.
2 Two Methodologies: Ideal vs. Non-Ideal
-----------------------------------------
How should one go about developing normative prescriptions
that can guide decision-makers who aspire to act justly in an unjust world?
A useful distinction in political philosophy is between
ideal and non-ideal modes of theorizing
about the relevant normative prescriptions (Simmons2010; Valentini2012; Stemplowska2012).
When adopting the ideal approach, one starts
by articulating a conception of an ideally just world
under a set of idealized conditions.
The conception of the just world serves two functions:
(i) it provides decision-makers with a *target* state
to aspire towards (Stemplowska2012);
and (ii) when suitably specified, it serves
as an *evaluative standard* for identifying and assessing
current injustices “by the extent of the deviation
from perfect justice” (Rawls1999, p. 216).
According to this perspective,
a suitably-specified evaluative standard
can provide decision-makers with normative guidance
to adopt policies that minimize deviations
with respect to some notion of similarity,
thus closing the gap between the ideal and reality (Anderson2010).
Non-ideal theory emerged within political philosophy
as a result of a number of challenges
to ideal modes of theorizing (Galston2010; Valentini2012).
We focus here on three challenges
that motivate the non-ideal approach.
A first set of issues arises when we consider
the intended role of a conception of an ideally just world
as an evaluative lens for diagnosing actual injustices.
In the ideal approach, the conceptual framing of perfect justice
determines whether some actual procedure or event
is identified as unjust and if so,
how that injustice gets represented (Mills2005; Pateman2007).
When this conception is impoverished,
e.g., by failing to articulate important factors,
it can lead to systematic neglect of injustices
that were overlooked in constructing the ideal.
Moreover, the static nature of ideal standards
and the pursuant diagnostic lens can overlook the factors
that give rise to injustice in the first place.
This is because such standards identify injustices
in terms of *the discrepancies* between the actual world
and an ideally-just target state.
However, the historical origins and dynamics
of current injustices and the ongoing social forces
that sustain them are typically absent from consideration.
By obfuscating these *causal factors*,
ideal evaluative standards can distort
our understanding of current injustices.
According to a second challenge, employing a conception
of an ideally just world as an evaluative standard
is *not sufficient* for deciding
how actual injustices should be mitigated (Sen2006; Sen2009).
This is because, from the standpoint of an ideal,
any discrepancy between our imperfect world and that ideal
might be interpreted naively as a cause of an actual injustice,
and thus,
any policy that aims to directly minimize such a discrepancy
might be erroneously argued to be justice-promoting (Anderson2010; Sen2006).
Yet, the actual world can deviate from an ideal in multiple respects,
and the same kind of deviation can have
varied and complex causal origins (Sen2009).
Moreover, as the fair machine learning literature clearly demonstrates (see Section [5.2](#S5.SS2 "5.2 Re-interpreting Impossibility Results ‣ 5 Discussion ‣ Algorithmic Fairness from a Non-ideal Perspective")),
simultaneously eliminating all discrepancies might be impossible.
Thus, a coherent approach requires
not only a mandate to eliminate discrepancies,
but also guidance for determining which discrepancies matter in a given context.
Crucially, policies that simply seek to minimize
any perceived gap between the ideal and reality without
consideration for the underlying causes
may not only be ineffective solutions to current injustices,
but can potentially exacerbate the problem they purport to address.
For example, ideal theorizing has been applied
to argue for race-blind policies (against affirmative action) (Anderson2010).
From the perspective of an ideally just society as a race-blind one,
a solution to current injustices
“would appear to be to end race-conscious policies” (Anderson2010, 4),
thus blocking efforts devised to address historical racial injustices.
Absent considerations of the dynamics by which disparities emerge,
it is not clear that in a world where individuals have been racialized
and treated differently on account of these perceived categories,
race-blind policies are capable of bringing about the ideal (Anderson2010).
Finally, a third challenge concerns the practical usefulness
of the ideal approach for current decision-makers,
given the type of idealized assumptions
under which ideal theorizing proceeds.
Consider, for example, the assumption of strict compliance,
frequently assumed by ideal theorists as a condition
under which the conception of an ideally just world can be developed.
The condition assumes that nearly all relevant agents
comply with what justice demands of them (Rawls2001, 13).
The condition thus idealizes away situations
where some agents fail to act in conformity with their ethical duties
(e.g., the duty not to racially discriminate), or are unwilling to do so.
The vision of a just world constructed under this assumption
fails to answer questions about what we might reasonably expect
from a decision-maker in the real world,
where others often neglect or avoid their responsibilities
(Schapiro2003; Feinberg1973; Valentini2012).
In short, when used as lens for identifying current injustices,
ideal modes of theorizing
(1) can lead to systematic neglects of some injustices
and distort our understanding of other injustices;
(2) do not, by themselves, offer sufficient practical guidance about *what should be done*,
sometimes leading to misguided mitigation strategies;
and finally, (3) do not, by themselves,
make clear *who, among decision-makers*
is responsible for intervening to right specific injustices.
As a result of these challenges to ideal modes of theorizing,
a number of researchers in political philosophy
have turned to non-ideal modes of theorizing.
In contrast to the ideal approach,
the non-ideal approach begins by identifying
actual injustices that are of concern to decision-makers
and that give rise to reasonable complaints
on behalf of those affected by their decisions (Anderson2010; Sen2006).
Non-ideal theorizing can be seen as a trouble-shooting effort
towards addressing these actual concerns and complaints.
As Sen notes, this trouble-shooting aim distinguishes
non-ideal modes of theorizing from ideal approaches
that focus “on looking only for the simultaneous fulfilment
of the entire cluster of perfectly just societal arrangements” (Sen2006, p. 218).
Anderson offers a succinct description of the non-ideal
approach towards this trouble-shooting goal
and what that approach requires:
>
> [Non-ideal theorists] … seek a causal explanation of the problem
> to determine what can and ought to be done about it,
> and who should be charged with correcting it.
> This requires an evaluation of the mechanisms causing the problem,
> as well as responsibilities of different agents to alter these mechanisms (Anderson2010, p. 22)
>
>
>
As noted by Anderson, there is still a crucial role
for normative ideals within the non-ideal approach.
But this role is importantly different
from the roles assigned to ideals
in the ideal approach (Anderson2010, 6).
In the ideal approach, normative ideals are extra-empirical,
in the sense that they set the evaluative standards
against which actual practices are assessed,
without themselves being subject to empirical evaluation.
In contrast, in non-ideal theorizing,
normative ideals act as hypotheses
about potential solutions to identified problems.
Viewed in this way, normative ideals are subject to revision
in light of their efficacy in addressing
the concerns and complaints that arise in practice.
In the following sections,
we show how the distinction can be put to work
in understanding and addressing algorithmic injustice.
3 Work on Algorithmic Fairness as Small-scale Ideal Theorizing
---------------------------------------------------------------
In political philosophy, the distinction
between ideal and non-ideal approaches
typically refers to ways of understanding
the demands of justice at large,
and offering practical normative guidance
to basic societal institutions
for complying with these demands.
While some researchers are beginning to discuss
how the automation of decision making in consequential domains
interacts with demands of justice at this large scale,
most works on algorithmic fairness have the more restricted aim
of assessing and managing various disparities that arise
among particular demographic groups in connection
with the deployment of ML-supported decision systems
in various (often-allocative) settings.
Nonetheless, in what follows, we show that
the distinction between ideal and non-ideal approaches
provides a fruitful lens for formulating strategies
for addressing algorithmic injustices,
even on this smaller scale (of an individual decision-maker).
In this section, we argue that
the dominant approach among current efforts towards addressing algorithmic harms
can be seen as exercises in *small-scale* ideal theorizing.
###
3.1 Developing a Fairness Ideal
Works on algorithmic fairness typically begin
by outlining a conception of a “fairness ideal”.
Dwork2012, for example, seek to “capture fairness
by the principle that any two individuals
who are similar with respect to a particular task should be classified similarly” (see also Jung2019).
Others envision the fair ideal at the group level.
In nearly all cases, the groups of interest
are those encompassing categories such as race,
ethnic origin, sex, and religion.
Following precedent in the United States Civil Rights Act,
these groups are typically called *protected classes*
or *protected groups* in the technical literature.
According to one group-level conception of fairness,
fair allocative policies and procedure are those
that result in outcomes that impact different protected groups
in the same way (Zafar2017; Feldman2015).
In other cases, a fair state is taken to be one
in which membership in a protected group is irrelevant
or does not make a difference to the allocative procedure (Kilbertus2017; Grgic-Hlaca2018a). According to another view, a treatment disparity might exist in a fair state,
if it is justified by the legitimate aims of the distributive procedure (Hardt2016; Nabi2018).
The endorsed fairness ideals have different provenances:
in some cases, authors refer to historical legal cases,
such as Carson v. Bethlehem Steel Corp. or Griggs v. Duke Power,
to support their conception of fairness.
In other cases, the ideal of fairness is derived from people’s intuitive judgments
about fair allocation (Grgic-Hlaca2018a; Jung2019).
And less frequently, authors allude to works of political philosophers
such as Rawls, which is cited to support
the conception of individual fairness in Dwork2012.
###
3.2 Specifying a Fairness Metric
Next, on the basis of their favored fairness ideal,
researchers specify a quantitative evaluative standard—a “fairness metric”—for
diagnosing potential allocative injustices and guiding mitigation efforts.
Typically, these fairness metrics take the form of mathematical expressions
that quantify how far two among the protected groups are from *parity*.
The magnitude of (dis)parity measured by a given fairness metric
is taken to denote the degree of divergence from the ideal
for which that metric is supposed to be a formal proxy.
Given their generality and abstract nature,
fairness ideals do not fully determine the specific shape of fairness metrics.
Accordingly, in addition to a fairness ideal, the construction of fairness metrics
requires researchers to make further value judgments.
For example, the ideal that membership in protected groups
should be irrelevant to allocative decisions can be articulated
in the language of statistics by requiring the outcome ^Y
be independent (probabilistically) of the protected attributes A (Feldman2015).
However, the same ideal can also be expressed in the language of causality,
e.g., by requiring that the average causal effect
of protected attributes A on ^Y be negligible (Kilbertus2017).
Similarly, one can formalize the qualification
that protected attributes can make a difference to outcomes
when justified by the legitimate aims of allocative procedures in different ways.
In the language of statistics, for example, one can require
that while there may be some correlation between ^Y and A,
the dependency must be screened off by the target variable, Y (Hardt2016).
Framed in the language of causality,
some attempt to formalize this fairness ideal
in terms of a parity between the causal effect of A on ^Y
along so-called *legitimate pathways* (Nabi2018),
where what counts as legitimate
depends on the specific task and Y.
Importantly, despite being motivated by the same ideal,
such fairness metrics make different demands from the user
and can result in different verdicts about the same case.
In general, while statistical metrics can be formulated
as functions of the joint distribution P(Y,^Y,A,X),
causal metrics additionally require the acquisition of a causal model
that faithfully describes the data-generating processes
and for which the desired causal effect is identifiable.
Thus in some situations, statistical parity metrics may be estimable from data
while the corresponding causal quantities may not be,
owing to our limited knowledge of the data-generating process (pearl2009causality).
###
3.3 Promoting Justice by Minimizing Deviations from the Ideal
Finally, current approaches seek to promote fairness (or mitigate unfairness)
by modifying ML algorithms to maximize utility subject
to a parity constraint expressed in terms of the proposed fairness metric.
Such fairness-enforcing modifications can take the form of interventions
(i) in the pre-processing stage to produce “fair representations”
(e.g., Kamiran2012);
(ii) in the learning stage to create “fair learning”
(e.g., Zafar2017); or
(iii) in the post-processing by adjusting the decision thresholds
(e.g., Hardt2016).
Crucially, however, in all cases, the range of solutions to algorithmic harms
is limited to an intervention *to the ML algorithm*.
Absent from consideration in these approaches is the broader context in which
the “certifiably fair” model will be deployed.
Recalling Anderson’s critique (Anderson2010, 22) of ideal approaches,
neither the mechanisms causing the problem,
nor the consequences of algorithmically-guided decisions,
nor “the responsibilities of different agents to alter these mechanisms”
are captured in any of these approaches.
4 Troubles with Ideal Fairness Metrics
---------------------------------------
If current works on algorithmic fairness pursue (small-scale) ideal theorizing,
then we should expect these works to encounter the same types of challenges
as those confronting ideal theorizing more generally.
As explained above,
according to critics, ideal modes of theorizing can
(1) lead to systematic neglects of some injustices;
and distort our understanding of other injustices.
Such ideal evaluative standards
(2) do not offer sufficient practical guidance
and can lead to misguided mitigation strategies.
What is more, they (3) fail to delineate the responsibilities
of current decision-makers in a world
where others fail to comply with their responsibilities.
Below, we consider each of these challenges in turn,
and show that these same types of worries arise
with respect to current works on algorithmic fairness.
###
4.1 Systematic Neglects of Rights
The identification of injustices in ideal theorizing is constrained
by the underlying conceptual framing of normative ideals.
If this conceptual framing is not sufficiently rich or comprehensive,
we run the risk of overlooking many actual injustices.
The ideals of fairness in literature on algorithmic fairness
are predominantly expressed in terms of
some type of parity among designated protected classes.
Is this comprehensive enough to be sensitive to the types of injustices
that would lead to legitimate complaints
by those affected by ML-based allocations?
We believe that the answer is negative.
To see why, consider that
assessing claims of injustice
can require attending to different types of information.
As noted by Feinberg (Feinberg1974; Feinberg2014),
in some cases, what is someone’s due
is determinable only in comparison
to what is allocated to others
or what would have been allocated to them had they been present.
In other cases, an individual’s just due is determinable
independent of any comparison and solely by reference
to how that individual should have been treated
in light of her rights and deserts.
An allocative procedure can thus result in *comparative*
as well as *non-comparative* cases
of injustice (Feinberg1974; Feldman2016; Montague1980).
Yet, virtually all algorithmic fairness ideals
are framed in comparative terms.
This comparative focus renders these ideals insensitive
to legitimate claims of non-comparative injustice.
Consider from this perspective, a judge who treats all defendants equally,
denying parole to them all regardless of the specifics of their cases.
Here the defendants can feel aggrieved because of
how they *should have been* treated from the perspective
of the standards of retributive justice;
the review process was based on legally irrelevant factors,
infringing on defendants’ rights to due process,
and at least in some cases,
the punishments were disproportionately harsh,
potentially resulting in arbitrary incarceration.
Indeed, such sentencing behaviour goes against Articles 9 and 11
of the Universal Declaration of Human Rights,
cited throughout various documents concerning ethical design
such as the *IEEE Ethically Aligned Design*
and the Toronto Declaration (IEEE).
Yet, this and other cases of non-comparative injustice
in which an individual’s rights and deserts have been ignored
escape the purview of current fairness metrics.
The situation is troubling even with respect to
comparative cases of injustice.
This is because, due to their narrow focus,
fairness metrics essentially take the set of protected classes
to exhaust comparison classes that might matter
from the perspective of justice and fairness.
However, consider a case where the appraisal of an employee’s performance
is influenced by factors such as their weight or height,
despite the irrelevance (in a causal sense) of such characteristics
to that job (Judge2004; Rudolph2009).
In this setting and from the perspective of comparative justice,
height and weight are relevant categories.
The complete reliance of such metrics on the particular specification
of relevant comparison groups
limits their adequacy in this regard.
Indeed, unconstrained by these demands of comparative justice,
algorithmic-based decisions might result
in the creation of new “protected groups”.
###
4.2 Distortion of the Harms of Discrimination
From the perspective of current fairness ideals,
any divergence from the ideal of parity among protected classes
(potentially subject to certain qualifications)
is identified as a case of unfairness.
Accordingly, the fairness metrics based on these ideals
often have the property of being *anonymous* or *symmetric*;
whether a distribution of benefits and burdens is fair
does not depend on who the affected individuals or groups are.
In certain contexts and for certain purposes, anonymity is a desirable property.
Quantitative metrics of *income inequality* are required to be anonymous,
for example, because “from an ethical point of view,
it does not matter who is earning the income” (Ray1998).
Unlike the case of income inequality, however,
evaluating fairness claims requires going beyond
the observation *that* some disparity exists (Hellman2008).
We need to know *why* the disparity exists
and to understand “the processes that produce or maintain it” (Anderson2010, 18).
This knowledge is required to determine a coherent course of action,
and yet it does not inform any of the mitigation strategies
in the standard fair machine learning tool-kits,
making them unsuitable for off-the-shelf application.
Consider, for example, the very different mechanisms
giving rise to disparities in representation between
(white and east Asian) vs (white and black) students in US higher education.
In the former case, the disparity
(appearing to favor Asian students)
emerges despite historical and institutional discrimination.
In the latter, the disparity stems from well-documented
historical and institutional discrimination.
However, both represent violations of demographic parity (Petersen1976).
A naive ideal approach may suggest that in both cases,
the disparity requires alterations in admissions policies
to enforce the parity across all groups we might expect in our ideal.
A more nuanced non-ideal approach might recognize
the differences between these two situations.
In the literature on fair ML,
approaches that incorporate knowledge of demographic labels
are colloquially referred to as “fairness through awareness”.
However, as demonstrated above,
awareness of demographic membership alone
is too shallow to distinguish between these two situations.
Instead, we require a deeper awareness,
not only of demographic membership
but of the societal mechanisms that imbue demographic membership
with social significance in the given context
and that give rise to existing disparities.
While this is especially problematic for statistical metrics
that neglect the provenance of the observed data,
recently-proposed causal approaches,
including those formalizing fairness in terms of average causal effect
or the effect of treatment on the treated,
are similarly insufficient for capturing
when a given disparity is reflective of discrimination,
let alone whose discrimination it might reflect
or providing guidance as to when the current decision-maker
has a responsibility or license to intervene.
Importantly, these causal methods typically address
the problem of mediation analysis,
adopting the perspective of an auditor seeking
to explain the mechanisms by which the protected trait
influences a model’s prediction.
Missing however, is a coherent theory
for how to relate those mechanisms to the
responsibilities of the current decision-maker,
or any accounting of the causal mechanisms by which
a proposed intervention may impact the social system
for better or worse.
###
4.3 Insufficient Insights and Misguided Mitigation
As noted in the previous section, current mitigation strategies
are guided by the idea that justice is promoted
by intervening on ML algorithms to minimize
disparities detected by a given metric.
Insofar as the underlying causes of preexisting disparities
and the consequences of proposed policies are ignored, however,
these mitigation techniques might have adverse effects.
As one example, consider a series of proposed approaches
that Lipton2018 denote *disparate learning processes* (DLPs).
These techniques are designed to jointly satisfy two parities,
blindness and demographic parity (e.g., Zafar2017).
However, as Lipton2018 (2018) show,
DLPs are oblivious to the underlying causal mechanisms of potential disparities
and in some cases, DLPs achieve parity between protected classes (e.g., genders)
by giving weight to the irrelevant proxies, (e.g., hair length).
Using real-world data from graduate admissions to a computer science program,
they showed that prohibited from considering gender directly,
a DLP would pick up on proxies such as the subfield of interest.
In order to achieve parity, the DLP must advantage those applicants
that appear (based on their non-protected attributes)
to be more likely to be women, while disadvantaging
those that are more likely to be men.
Thus, the DLP satisfies demographic parity
by advantaging those pursuing studies
in sub-fields chosen historically by more women
(e.g., human-computer interaction)
while disadvantaging those pursuing studies
that are currently more male-dominated (e.g., machine learning).
While the DLP achieves overall demographic parity,
women in fields that already have greater parity receive the benefit,
while women in those precise fields
that most want for diversity
would actually be penalized by the DLP.
Stepping back from a myopic view of the statistical problem
and these arbitrarily-chosen deviations (the fairness metrics) from an ideal,
when we consider the impact of a deployed DLP on a broader system of incentives,
it becomes clear that the DLP risks amplifying
the very injustices it is intended to address.
In addition to the non-comparative harm of making decisions on irrelevant grounds,
the supposed remedy can reinforce social stereotypes,
e.g., by incentivizing female applicants
towards only those fields where
they are already well represented (and away from others).
Similarly, in simply seeking to minimize
the disparity detected by fairness metrics,
current metrics neglect considerations
about whether the enforced parity
might in fact result in long term harms (liu2018delayed).
###
4.4 Lack of Practical Guidance
Finally, consider that the type of unjust disparities
often faced in a given allocation context
correspond to events potentially unfolding over decades.
Current approaches to algorithmic fairness
seek to address *“is there discrimination?”*
but leave open the questions of *“who discriminated?”*
and *“what are the responsibilities of the current decision-maker?”*
If sensitive features influence education,
which in turn influences employment decisions,
then to what extent does the causal effect reflect the discrimination
of the education system compared to that of the employer?
The answer to this question is not straightforward
and requires considerations not captured in the entries of confusion matrices.
While identifying statistical disparities may be valuable unto itself,
e.g., as a first step to indicate particular situations that warrant investigation,
it provides little moral or legal guidance to the decision-maker.
While the influence of protected attributes on predictions may reflect injustice,
providing normative guidance requires
identifying not only what would constitute a just world
but also what constitute just decisions in the actual world,
with its history of injustice.
5 Discussion
-------------
###
5.1 If not Solutions, then Solutionism?
Even as the mitigation strategies arising
from the recent technical literature on fair machine learning
fail to offer practical guidance on matters of justice,
they have not failed to deliver in the marketplace.
From the perspective of stakeholders caught in the tension
between (i) the potential profit to be gained from deploying
machine learning in socially-consequential domains,
and (ii) the increased scrutiny of a public
concerned with algorithmic harms,
these metrics offer an alluring solution:
continue to deploy machine learning systems per the status quo,
but use some chosen parity metric to claim
a certificate of fairness, seemingly inoculating the actor
against claims that they have not taken the moral concerns seriously,
and weaponizing the half-baked tools produced
by academics in the early stages of formalizing fairness
as a shield against criticism.
In socially-consequential settings, requiring caution
or even abstention (from applying ML)
such as criminal justice and hiring,
fair ML offers an apparent academic stamp of approval.
Notable recent examples include the IBM fairness 360 toolkit,
which offers fairness metrics and corresponding mitigation strategies
as an open-source software service that claims to be able
to “examine, report, and mitigate discrimination and bias
in machine learning models throughout the AI application lifecycle” (ibm360fairness).
Using just one parity metric (demographic parity),
algorithmic hiring platform Pymetrics, Inc.
claims that their system is “proven
to be free of gender and ethnic bias” (pymetrics).
The literature on fair machine learning bears
some responsibility for this state of affairs.
In many papers, these fairness-inspired parity metrics
are described as *definitions of fairness*
and the resulting algorithms that satisfy the parities
are claimed axiomatically to be *fair*.
While many of these *metrics* are useful diagnostics,
potentially alerting practitioners to disparities warranting further investigation,
the looseness with definitions creates an opening for stakeholders
to claim compliance, even when the problems have not been addressed.
Lacking the basic primitives required to make the relevant moral distinctions,
when blindly optimized, these metrics
are as likely to cause harm as to mitigate it.
Thus current methods produced by the fair ML community
run the risk of serving as *solutionism*
if not as solutions (Selbst2019).
###
5.2 Re-interpreting Impossibility Results
An additional benefit of viewing fairness in ML
through the lens of non-ideal theorizing in political philosophy
is that it gives a new perspective for parsing
the numerous impossibility results (Kleinberg2017; Chouldechova2016)
famously showing that many statistical
fairness metrics are irreconcilable,
presenting inescapable trade-offs.
These results are sometimes misinterpreted
as communicating that *fairness is impossible*.
However, through the non-ideal lens,
these impossibility theorems
are simply a frank confirmation of the fact
that we do not live in an ideal world.
The inputs to statistical fairness metrics
include four groups of variables:
the covariates X, the group membership A,
the label Y, and the classification ^Y.
The distribution over these variables
at a given point in time
is the consequence of the complex dynamics of an unjust society
constituted of many decision-making agents.
Of these, the current decision-maker has control
only over their own predictions ^Y.
That various metrics/parities cannot be satisfied simultaneously
merely by setting the values taken by ^Y
indicates only that our present decision-maker cannot
*through their actions alone* bring about
the immediate end to all disparity,
even as viewed locally through the variables
that their individual decisions concern.
One potential contribution of ML impossibility theorems
to philosophy is that they make evident
an often-overlooked shortcoming with the ideal approach.
These impossibility results make clear
that in general, if we start from a non-ideal world,
no set of actions (by a single agent)
can instantaneously achieve the ideal world in every respect.
Moreover, matching the ideal in a particular respect
may only be possible at the expense of widening gaps in others.
Thus this naive form of an ideal approach
appears to be fundamentally under-specified.
If matching the ideal in various respects simultaneously is impossible,
then we require, in addition to an ideal, a basis for deciding
which among competing discrepancies to focus on.
In this manner, the impossibility results in fair ML
provide a novel lens to approach the philosophical debate
about the extent to which normative theorizing on matters of justice
can proceed in isolation from empirical socio-historical facts (Sen2009; Farrelly2007).
While characterizing disparities and understanding
the fundamental trade-offs among them may be valuable work,
this work cannot by itself tell us what to do.
The pressing issue in determining how to act justly
is not how to optimize a given metric
but how to make the determination of what,
in a given situation, should be optimized in the first place.
###
5.3 Towards a Non-Ideal Perspective
Even if the reader finds the case against the ideal approach compelling,
there remains a pragmatic question of what precisely a non-ideal
approach might look like in practice.
To begin, non-ideal theorizing about the demands of justice
is a *fact-sensitive* exercise.
Offering normative prescriptions to guide actions
requires understanding the relevant causal mechanisms
that (i) account for present injustices;
and (ii) govern the impact of proposed interventions.
#### Empirical understanding of the problem:
Developing causal models for understanding
social dynamics that cause and maintain particular injustices
requires extensive domain-knowledge
as well as numerous value judgements
about the relevance and significance of
different aspects of the domain of interest.
Choices must be made about what abstractions are reasonable,
which simplifying assumptions are justified,
and what formalizations are appropriate.
Inevitably, these choices, embedded in design and modeling,
raise *coupled ethical-epistemic* questions (Tuana2010; Proctor2008).
Consider, for instance, choices that might be made
in understanding the causes of racial injustice
in a particular allocative domain and a specific social setting.
Aside from the challenge of understanding
the concept of race (Mills1998; Mallon2006),
research in psychology and sociology shows racial classification
and identification to be dynamic categories
that are shaped by a variety of socioeconomic factors
such as unemployment, incarceration, and poverty
(Epp2014; Penner2008; Freeman2011).
Appreciating the complex and dynamic nature of race and the perception thereof
is thus not only of ethical import; it also has important epistemic implications
for formal models of racial injustice,
as it shapes how “race” as an attribute should be understood
and what causal relation it might bear to other factors of interest.
#### Empirically-informed choice of treatment:
Deployment of predictive models—whether
those that simply maximize utility or those that maximize utility
subject to some “fairness” constraint—constitutes a social intervention.
As mentioned above, most existing approaches to fair ML
consist only of modifying the data processing procedures or the objective functions.
Crucially, the evaluation of these interventions is *local* and *static*:
the evaluation is local insofar as it concerns the impact
of the intervention only on that particular predictive model’s statistics
(i.e., its accuracy and various fairness metrics).
The accompanying literature seldom
considers the broader impacts of deploying such models
in any particular social context.
Moreover, the evaluation is typically static,
ignoring the longer-term dynamics of proposed policies.
When authors have attempted dynamic evaluations,
the results have sometimes contraindicated proposed mitigation strategies
(liu2018delayed).
In contrast, a non-ideal approach to offering normative guidance
should be based on evaluating the situated and system-wide
(involving not just the predictive model
but also the broader social context, actors, and users)
and dynamic (evolving over longer periods) impact
of potential fairness-promoting interventions.
Once more, we must face difficult questions and make value judgments.
As some authors have noted, for instance,
unjust circumstances can naturally arise as a result
of seemingly benign initial conditions Schelling1971; OConnor2019a.
To determine how to act, a coherent framework is needed
for understanding when is it desirable or permissible
for a given decision-maker to intervene.
Importantly, we stress that
the appropriate judgments
simply cannot be made based on the reductive
(X, A, Y ^Y) description
upon which most statistical fair ML operates.
Developing a coherent non-ideal approach
requires (for the foreseeable future) human thought,
both to understand the social context
and to make the relevant normative judgments.
6 Conclusion
-------------
Approaching the issue of algorithmic fairness from a non-ideal perspective requires a broadening of scope beyond parity-constrained predictive models, and considering the wider socio-technological system consisting of human users, who informed by these models, make decisions in particular contexts and towards particular aims.
Effectively addressing algorithmic harms demands nothing short of this broader, human-centered perspective, as it enables the formulation of novel and potentially more effective mitigation strategies that are not restricted to simple modifications of existing ML algorithms.
Acknowledgements
----------------
Many thanks to David Danks, Maria De-Arteaga, and our reviewers for helpful discussions and comments. Funding was provided by Social Sciences and Humanities Research Council of Canada (No. 756-2019-0289) and the AI Ethics and Governance Fund. |
9749521d-1a5d-4226-8724-19aa93215544 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Small Berkeley Meetup
Discussion article for the meetup : Small Berkeley Meetup
WHEN: 09 May 2012 07:00:00PM (-0700)
WHERE: 2128 Oxford St, Berkeley, CA
We'll be meeting at the Starbucks on Oxford St and then probably going somewhere to get food. This will be a small meetup.
Discussion article for the meetup : Small Berkeley Meetup |
728676c8-cb6a-4a07-85f2-d8fe24908069 | StampyAI/alignment-research-dataset/arbital | Arbital | Domain (of a function)
The domain $\operatorname{dom}(f)$ of a [function](https://arbital.com/p/3jy) $f : X \to Y$ is $X$, the set of valid inputs for that function. For example, the domain of $+$ is the set of all pairs of numbers, and the domain of the division operation is the set of all pairs $(x, y)$ of numbers where $y$ is non-zero.
Visualizing a function as a map that takes every point in an input set to one point in an output set, the domain is the input set (pictured on the left in red in the image below).
 |
c04b4483-6139-4e49-9b61-2d23805a99d0 | trentmkelly/LessWrong-43k | LessWrong | Learning through exercises
One of the best aspects of mathematics is that it is possible for a student to reconstruct much of it on their own, given the relevant axioms, definitions, and some hints. Indeed, this style of education is usually encouraged for training mathematicians. Relatedly, it is also possible for a mathematician to give a quick impression of the relevance of their particular field by choosing an example of an interesting problem which can be efficiently solved using the methods of that specific specialty of mathematics.
To what extent do other academic fields share this property? How well can physics, chemistry, biology, etc. be taught "through exercises"?
EDIT: Note that the "exercises" I am referring to are not just matters of applying learned principles for solving random problems but rather are devices to lead the student to "rediscover" important principles in the field. |
f8dd965c-0453-4fe6-8277-ad657b8f6d2e | trentmkelly/LessWrong-43k | LessWrong | Lampshading
Sequence index: Living Luminously
Previously in sequence: City of Lights
You can use luminosity to help you effectively change yourself into someone you'd more like to be. Accomplish this by fixing your self-tests so they get good results.
You may find your understanding of this post significantly improved if you read the seventh story from Seven Shiny Stories.
When you have coherent models of yourself, it only makes good empirical sense to put them to the test.
Thing is, when you run a test on yourself, you know what test you're running, and what data would support which hypothesis. All that and you're the subject generating the data, too. It's kind of hard to have good scientific controls around this sort of experiment.
Luckily, it turns out that for this purpose they're unnecessary! Remember, you're not just trying to determine what's going on in a static part of yourself. You're also evaluating and changing the things you repudiate when you can. You don't just have the chance to let knowledge of your self-observation nudge your behavior - you can outright rig your tests.
Suppose that your model of yourself predicts that you will do something you don't think you should do - for instance, suppose it predicts that you will yell at your cousin the next time she drops by and tracks mud on your carpet, or something, and you think you ought not to yell. Well, you can falsify that model which says you'll yell by not yelling: clearly, if you do not yell at her, then you cannot be accurately described by any model that predicts that you'll yell. By refraining from yelling you push the nearest accurate model towards something like "may yell if not careful to think before speaking" or "used to yell, but has since grown past that". And if you'd rather be accurately described by one of those models than by the "yells" model... you can not yell.
(Note, of course, that falsifying the model "yells" by silently picking up your cousin and defenestrating her is not |
25aa1f3d-c567-4e93-b5cd-7ab434a8a7ac | trentmkelly/LessWrong-43k | LessWrong | Multi-belled Brass
Yesterday afternoon I got excited about a new design for a brass instrument. Conical bore instruments have the problem that you can't combine valves (or slides) with having the tubing consistently expand over the length of the instrument. The valves force you to have a cylindrical bore segment in the center, and while you can design the tubing to minimize the cylindrical segment, you do have to have some.
Or do you? Imagine you have seven bells, with a single rotary valve that has seven positions, one for each bell. You use the standard fingering, with keys to rotate the valve 1/7, 2/7, and 3/7. This gets you approximately the same options for tubing length as a standard brass instrument, except that you no longer need to make compromises: pushing in all three keys brings you down exactly six half steps and you don't need a tuning slide to compensate. You can play chromatically, and you play it just like a trumpet, baritone, or tuba.
It looks like Adolphe Sax tried something like this, but with six two-position piston valves instead of a seven-position rotary valve, and he was just trying to solve the tuning problem and not the bore problem:
But then I found something even weirder: 1, 2, 3, 4, 5.
It turns out there's a somewhat successful instrument along these lines, but for totally different reasons. Instead of being optimized for tone, it's optimized for being easy to play. Each instrument has one horn per note, typically eight, so a range of just eight notes. The base of each horn has a reed, so you don't need any sort of embouchure, you just blow. See this video where someone takes one apart to demonstrate. They seem to be German; to find more videos search for "Schalmeien", while if you want to buy one (please don't) search for "Schalmei".
I do think a rotary trumpet, played chromatically with trumpet fingering, could work well though, and I'm curious whether anyone has explored this.
Comment via: facebook |
6e224c2e-207e-43b7-a36e-8de1db4649e6 | trentmkelly/LessWrong-43k | LessWrong | Goal completion: noise, errors, bias, prejudice, preference and complexity
A putative new idea for AI control; index here.
This is a preliminary look at how an AI might assess and deal with various types of errors and uncertainties, when estimating true human preferences. I'll be using the circular rocket model to illustrate how these might be distinguished by an AI. Recall that the rocket can accelerate by -2, -1, 0, 1, and 2, and the human wishes to reach the space station (at point 0 with velocity 0) and avoid accelerations of ±2. In the forthcoming, there will generally be some noise, so to make the whole thing more flexible, assume that the space station is a bit bigger than usual, covering five squares. So "docking" at the space station means reaching {-2,-1,0,1,2} with 0 velocity.
The purpose of this exercise is to distinguish true preferences from other things that might seem to be preferences from the outside, but aren't. Ultimately, if this works, we should be able to construct an algorithm that identifies preferences, such that anything it rejects is at least arguably not a preference.
So, at least initially, I'll be identifying terms like "bias" with "fits into the technical definition of bias used in this model"; once the definitions are refined, we can then check whether they capture enough of the concepts we wand.
So I'm going to use the following terms to distinguish various technical concepts in this domain:
1. Noise
2. Error
3. Bias
4. Prejudices
5. Known prejudices
6. Preferences
Here, noise is when the action selected by the human doesn't lead to the correct action output. Error is when the human selects the wrong action. Bias is when the human is following the wrong plan. A prejudice is a preference the human has that they would not agree upon if it was brought to their conscious notice. A know prejudice is a prejudice the human knows about, but can't successfully correct within themselves.
And a preference is a preference.
What characteristics would allow the AI to distinguish between thes |
356c61b3-bfbb-4071-a7e4-890f925b8820 | trentmkelly/LessWrong-43k | LessWrong | Credit Suisse collapse obfuscated Parreaux, Thiébaud & Partners scandal
Is it just coincidence that scandals are swept under the carpet when a bigger scandal dominates the news cycle? The linked article has direct links to the official sources for this story.
15 March 2023, the value of shares in Credit Suisse plunged. Four days later UBS announced the purchase of their competitor Credit Suisse.
It was an enormous scandal for Switzerland.
The risk of a scandal had been known for some months. For a few weeks after 15 March 2023, the Swiss media published multiple reports about Credit Suisse each day.
Two weeks later, on 4 April 2023, the trade registry in Canton Geneva received the decision of FINMA for the immediate liquidation of Parreaux, Thièbaud & Partners / Justicia SA.
We looked at the web site of RTS (Swiss public broadcaster) using the Wayback Machine. Below, we publish some screen shots from the week from 3 to 7 April 2023. We didn't find any reports about the closure of Parreaux, Thièbaud & Partners.
20,000 clients had suddenly lost their Swiss legal services insurance. Some of the insured clients were in the middle of trials and other procedures funded by the insurance.
The Swiss media helped the jurists with free publicity in 2018.
But the media said nothing when the jurists were shut down.
According to the judgment of 4 April 2023, FINMA had knowledge of the situation at Parreaux, Thiébaud & Partners since 2021. For two years they looked for a solution. They didn't find a solution but they had the chance to do a cover-up when the Credit Suisse crisis arrived.
The linked post includes screenshots showing how the legal insurance scandal appears to be smothered by news reports about the much bigger banking scandal. |
a0659187-24c2-4fd9-a75d-3f9708500d8d | trentmkelly/LessWrong-43k | LessWrong | Optimizing crop planting with mixed integer linear programming in Stardew Valley
Abstract
TL;DR: plant your crops like this and you will make the most money possible
Motivation
If you haven’t yet tried, consider escaping 2020’s cyberpunk hellscape into the 2D world of Stardew Valley, an open-world farming RPG where almost all actions lead to positive outcomes. The game is as non-punishing as they come, making it a superb unwinding after a long day or recurring date with your long-distance quarantined non-gaming significant other. Whether you spend your time watering blueberries, reeling in trout, or romancing the local townsfolk, most would say there is no wrong way to play as long as you’re having fun.
I disagree. A farm is a business and businesses should make as much money as possible by any means necessary[1]. Since farming provides the majority of a Stardew farmer’s income, it is therefore necessary to determine the rotation of crops which maximizes profit within the constraints of energy and available capital. If your fun is not optimized, you’re wasting valuable time.
Background
You arrive on your late grandfather’s farm with little more than a hoe[2] and a watering can. The mayor welcomes you with 15 parsnip seeds and you inherit 500 gold pieces. To plant a seed, you must first till the ground with your hoe, expending 2 of your 270 daily energy. For another 2 energy you then water the seeds that and every subsequent t days until they are harvested. Harvesting takes no energy, so you should quickly pick your produce, sell it over at Pierre’s General Store and immediately buy more seeds, rinse, and repeat. Each season consists of 28 days, after which all crops die and cannot be harvested.
Additionally, you can forage materials at no monetary cost by cutting down trees and selling the wood and byproducts to Robin, the carpenter. Trees on average contain about 50g worth of materials and take 30 energy to harvest. Unlike crops there is no growing period and we will assume an infinite number of available trees[3].
In order to maximize |
71a1e491-26f4-4484-afe1-88e93131005c | trentmkelly/LessWrong-43k | LessWrong | A Short Celebratory / Appreciation Post
Three thoughts.
1. The new LessWrong 2.0, at least for me personally, has been really marvelous. I've greatly enjoyed some of the discussions on mindfulness, pragmatic achieving type-stuff, definitions and clarifications and naming of broad concepts, and explorations of technology and artificial intelligence. Kaj Sotala and Zvi Mowshowitz both offered some fantastic insights that I particularly enjoyed, and I came across some of Nate Soares's writing on motivation and decisionmaking via here which is incredibly timely and insightful for me.
2. I wanted to stop and reflect for a moment that this took a lot of work to make happen. I imagine that a conservative estimate of how much the volunteer time spent programming and designing and curating here would have cost in salary at market rates is... at least $300,000 USD? More? That's... like... a bunch of people put hundreds of thousands of dollars worth of market-priced labor into building this place, and it's generating a lot of insights that are really fantastic. Wow. It's easy to forget how much work goes into making the world run smoothly. I feel both a sense of mild awe at the ability of the team to build the place, and gratitude that they did so. Coordination is hard. Design is hard. Community-building is hard. And it worked. That's so cool.
3. I also want to note a strange feeling I had — it went something like, "If I wrote only the first and second points, this wouldn't be a good post." There's a strange norm in civilization at large — at least, certainly in developed Western countries — against just stopping and offering a general sense of appreciation and respect for this type of work. I thought it might be useful to reflect on why, and I came up with a few potential reasons — first, for whatever damn reason, sprezzatura is seen as high-status and proper behavior; thus, the people who do hard work of making things happen will play off the work required ("ahh, no big deal") and generally, someone in the audi |
336ad8a5-8254-4786-859a-4722481449b0 | trentmkelly/LessWrong-43k | LessWrong | Interview with Paul Christiano: How We Prevent the AI’s from Killing us
0:00 Intro
9:20 Percentage Likelihood of Death by AI
11:24 Timing
19:15 Chimps to Human Jump
21:55 Thoughts on ChatGPT
27:51 LLMs & AGI
32:49 Time to React?
38:29 AI Takeover
41:51 AI Agency
49:35 Loopholes
51:14 Training AIs to Be Honest
58:00 Psychology
59:36 How Solvable Is the AI Alignment Problem?
1:03:48 The Technical Solutions (Scalable Oversight)
1:16:14 Training AIs to be Bad?!
1:18:22 More Solutions
1:21:36 Stabby AIs
1:26:03 Public vs. Private (Lab) AIs
1:28:31 Inside Neural Nets
1:32:11 4th Solution
1:35:00 Manpower & Funding
1:38:15 Pause AI?
1:43:29 Resources & Education on AI Safety
1:46:13 Talent
1:49:00 Paul’s Day Job
1:50:15 Nobel Prize
1:52:35 Treating AIs with Respect
1:53:41 Uptopia Scenario
1:55:50 Closing & Disclaimers
|
978e1e90-a65b-4884-a21f-7b8b395bbe23 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Reflections on my 5-month AI alignment upskilling grant
Five months ago, I received a grant from the[Long Term Future Fund](https://funds.effectivealtruism.org/funds/far-future) to upskill in AI alignment. As of a few days ago, I was invited to Berkeley for two months of full-time alignment research under [Owain Evans’s stream](https://www.serimats.org/aligning-language-models) in the [SERIMATS](https://www.serimats.org/) program. This post is about how I got there.
The post is partially a retrospective for myself, and partially a sketch of the path I took so that others can decide if it’s right for them. This post was written relatively quickly - I’m happy to answer more questions via PM or in the comments.
Summary
-------
* I was a software engineer for 3-4 years with little to no ML experience before I was accepted for my grant.
* I did a bunch of stuff around fundamental ML maths, understanding RL and transformers, and improving my alignment understanding.
* Having tutors, getting feedback on my plan early on, and being able to pivot as I went were all very useful for not getting stuck doing stuff that was no longer useful.
* I probably wouldn’t have gotten into SERIMATS without that ability to pivot midway through.
* After SERIMATS, I want to finish off the last part of the grant while I find work, then start work as a Research Engineer at an alignment organisation.
* If in doubt, put in an application!
My Background
-------------
My background is more professional and less academic than most. Until I was 23, I didn’t do much of anything - then I got a Bachelor of Computer Science from a university ranked around 1,000th, with little maths and no intent to study ML at all, let alone alignment. It was known for strong graduate employment though, so I went straight into industry from there. I had 3.5 years of software engineering experience (1.5 at Amazon, 2 as a senior engineer at other jobs) before applying for the LTFF grant. I had no ML experience at the time, besides being halfway through doing the [fast.ai](https://course.fast.ai/) course in my spare time.
Not going to lie, seeing how many Top-20 university PhD students I was sharing my cohort with (At least three!) was a tad intimidating - but I made it in the end, so industry experience clearly has a role to play as well.
Grant
-----
The details of the grant are one of the main reasons I wrote this - I’ve been asked for 1:1’s and details on this at least three times in the last six months, and if you get asked something from at least three different people, it might be worth writing it up and sharing it around.
Firstly, the process. Applying for the grant is pretty painless. As long as you have a learning plan already in place, the official guidance is to take 1-2 hours on it. I took a bit longer, polishing it more than required. I later found out my plan was more detailed than it probably had to be. In retrospect, I think my level of detail was good, but I spent too much time editing. [AI Safety Support](https://www.aisafetysupport.org/) helped me with administration. The main benefit that I got from it was that the tutoring and compute money was tax free (since I didn’t get the money personally, rather I used a card they provided me) and I didn’t have to worry about tax withholding throughout the year.
Secondly, the money. I **agonized** over how much money to ask for. This took me **days**. I asked myself how much I really needed, then I asked myself how much I would actually accept gladly with no regrets, then I balked at those numbers, even knowing that most people ask for too little, not too much. I still balk at the numbers, to be honest, but it would have been so much easier to write this if I had other grants to go off. So, in the interest of transparency and hopefully preventing someone else going through the same level of anguish, [I’m sharing the full text of my grant request, including money requested (in Australian dollars, but you can always convert it) here](https://docs.google.com/document/d/1z4hEquic0JqPXBWGxpv48uRq6y5qBuWftyP0J3HjrOg/edit). Personal embarrassment aside, since LTFF publishes these grants anyway (but is very backlogged at the moment apparently, since they haven’t shared them this year) I think sharing numbers is fine.
To summarise - in the end, I gave them three numbers of 50%, 75%, and 100% of my contractor salary at the time. I told them honestly that I definitely didn’t expect 100%, and that I would have to think about whether to take 50% or not - it was at the border of whether I’d take the pay cut or not to upskill in this speculative area. They gave me 75%, which was an amount I was glad to take with no reservations. I also asked for, and got, some tutoring and compute budget.
As for advice on what level of background you need to apply - **I would advise just applying**. Applications are processed on a rolling basis, and it only takes an hour or two. I can’t tell you what level of background you need, since I only got one bit of information - the acceptance. I don’t know if I was a slam dunk, a borderline case, or somewhere in between. And I don’t know how FTX might or might not affect future funding.
How It Went
-----------
First off, let’s look at what I actually achieved in those five months. Thus far, I have:
**Maths:**
* Learnt single-variable calculus and the first half of multivariable calculus (Poorly)
* Completed a first course in linear algebra (Solidly)
* Completed some basic probability study (Random variables, probability distributions, random vectors, central limit theorem) (Solidly)
* Gone through the first few chapters of Probability Theory: The Logic of Science (Mainly conceptually)
**Alignment:**
* Formed a group and completed AGI Safety Fundamentals.
* Completed [Alignment 201](https://www.agisafetyfundamentals.com/alignment-201-curriculum) as part of SERIMATS.
* Read several Alignment Forum sequences.
* Greatly improved my inside view on what research agendas I think are most promising.
* Attended [John’s workshops](https://www.lesswrong.com/posts/kpkxKDpiRn6BNArFm/content-and-takeaways-from-seri-mats-training-program-with) as part of SERIMATS.
**Machine Learning:**
* Reproduced several [reinforcement learning algorithms](https://github.com/JayBaileyCS/RLAlgorithms).
* Wrote a [distillation on DQN](https://www.lesswrong.com/posts/kyvCNgx9oAwJCuevo/deep-q-networks-explained) (which was used as teaching material for [ARENA](https://www.arena.education/) virtual!).
* Completed about 75% of the [MLAB](https://forum.effectivealtruism.org/posts/vvocfhQ7bcBR4FLBx/apply-to-the-second-ml-for-alignment-bootcamp-mlab-2-in) curriculum.
* Built a transformer from scratch.
* Reproduced some key LLM benchmarks like chain-of-thought prompting and self-consistency as part of SERIMATS.
* Produced some basic original language model research as part of SERIMATS.
**Other:**
* Formed [AI Safety Brisbane](https://www.facebook.com/groups/465042105669446), a local AI Safety discussion group for my city. (I've arranged an organiser while I'm in Berkeley)
* Facilitated an AI safety weekend workshop organized by [AI Safety Australia and New Zealand](https://www.facebook.com/groups/1099249420923957).
These last two weren’t funded by this grant, but did require skills and knowledge that I built using it.
Looking back at the list, I’m pretty happy with my performance overall, even though it often felt week to week like not a lot was getting done. It definitely would have taken me a lot longer to do all this without grant work.
In terms of hours spent, I wasn’t able to get as many quality hours as I liked. I had intended to do ~25 hours per week of deep work, ignoring [Cal Newport’s](https://www.calnewport.com/books/deep-work/) mention that 4 hours per day of deep work was already pretty high level - in the end, I think I was able to get about 20 hours per week of work done, with most of that being deep work. Some weeks were as many as 30, others as few as 15, but I never had any zero weeks, or even really bad weeks, so motivation at least remained reasonably consistent throughout, which I was worried about. While I still feel guilty about doing less hours than I intended, I am trying to remind myself that results matter more than hours - if I am happy with my results, I should be pleased in general. More hours worked are good only insofar as they can improve results.
Some very useful things I recommend to people who want to do this are to **seek out help and guidance, especially early on**. I reached out to AI Safety Support to help create my plan, and to people at labs I wanted to work at in order to refine it. This helped me clear out **a lot of unnecessary prerequisites** - for instance, I ended up doing a lot less frontloading of maths than I thought I’d need to do, and instead focused on learning it in parallel with studying the actual ML skills I would want as a research engineer. I thought I would need a full Linear Algebra course before even touching PyTorch - this was very far from true, even though it eventually came in handy when I began diving into transformer architecture.
**Tutoring** was very useful as well - I had tutoring for mathematics, for conceptual understanding of RL algorithms, and to help me through the MLAB curriculum. These all improved my learning speed quite a bit. Especially if you’re a currently well-paid professional who would be getting a decent salary for alignment upskilling, the extra cost of a bit of tutoring is relatively low compared to salary replacement, and should improve the overall return on investment (in terms of learning per dollar) of the grant.
**Being able to pivot** was also useful - I was planning to continue to deep dive into RL after the first couple of months had gone by and I’d replicated the algorithms, but I could see which way the wind was blowing, and knew I needed to learn transformers. Fortunately, I’d put in my alignment plan that I planned to devote significant time to a subfield that was undetermined at the time - this ended up starting with transformers, which helped a lot for my successful SERIMATS application.
Future Plans
------------
So what are my plans now? I still want to become a **Research Engineer** as Plan A - I think this is my best path in terms of both immediate impact and long-term skill building. (See [here](https://docs.google.com/document/d/1iFszDulgpu1aZcq_aYFG7Nmcr5zgOhaeSwavOMk1akw/edit#) if confused at the difference between Research Engineer and Research Scientist.) As a software engineer with little research experience (All my research experience thus far was gained in SERIMATS itself!) it seems the best way to use my skills - and since I’ve heard the gap between research engineer and research scientist is pretty porous everywhere except OpenAI and DeepMind, starting out as a research engineer is probably in my top three paths even if I do end up on a more research-heavy part of the continuum than I start. My timelines aren’t super-short - spending a couple of years building skills in the field is more important to me than immediate impact, as long as I’m not working on something actively useless or harmful.
Thus, my plans are:
**First, SERIMATS of course!** I’ve got two months in Berkeley studying and working full-time on alignment, amongst other people doing the same thing. This is a tremendous opportunity for growth, and if I don’t learn at least one thing there that alters my current model in a big way I’ll be pretty disappointed.
Secondly, I still owe about 6-8 weeks of work on this grant. I’ve been on the grant for five months so far, but I was doing SERIMATS for part of that, which comes with its own stipend - counting that as grant time would cause me to be paid twice for the same work. With AI Safety Support's advice, I’ve determined the best way to solve that is to just put in some extra work after SERIMATS in order to ensure that six months of dedicated upskilling is done via this grant, and repay the money only if this isn’t feasible. (e.g, I find a better opportunity that starts sooner than the end of April.)
While that’s going on, if a better opportunity hasn’t come along during that time, I’ll be looking for work in dedicated AI alignment orgs or DeepMind’s safety team. If I’m not able to find work there, Plan B is to apply for another round of funding and try to get into **independent interpretability research** - I’ll need to do some upskilling using Neel Nanda’s [excellent resources](https://www.lesswrong.com/posts/9ezkEb9oGvEi6WoB3/concrete-steps-to-get-started-in-transformer-mechanistic), but that shouldn't take six months, and I believe I can start producing some interesting findings within three. Plan C could be [distillation](https://www.lesswrong.com/posts/zo9zKcz47JxDErFzQ/call-for-distillers) work, and I haven’t really thought about Plan D through Z yet.
Finally, I want to improve my **general math ability** further. It’s one of those things that’s always important but never urgent, so plugging away for an hour a day or so even if I'm not specifically blocked on a lack of it seems like a good way to go about it. I’ve tried focusing on one area at a time during the grant - now I want to try it the other way and interweave working on a few things at once, and see which works better for me in terms of motivation and retention. This’ll definitely take longer than three months, but it’s worth starting sooner rather than later.
**Foundation Work** - I’d like to have a world-class foundation in basic mathematics, so I’ll want to work through [AMC](https://www.maa.org/math-competitions) competitions and the [Art of Problem Solving](https://artofproblemsolving.com/store/book/aops-vol1) books in order to improve that. I’m amazed at how many things I can learn from books aimed at bright high-schoolers. Just yesterday I learnt you can use combinations of prime factors to determine how many unique factors a large number has, which would have made several [Project Euler](https://projecteuler.net/) problems a lot faster. (My starting point is 20/25 on the AMC 8, points lost to shaky geometry and combinatorics - [give it a try yourself](https://artofproblemsolving.com/wiki/index.php/2022_AMC_8) and see how you do! 40 minute timer, no calculator.)
**Framing** - John Wentworth says that much of the benefit of knowing lots of mathematics is just being able to recognise a problem. (Also see [this comment of mine](https://www.lesswrong.com/posts/5FECWrTp7whkoPZBi/ulisse-mini-s-shortform?commentId=CRCubySCuAtgr9rNh) and it's parent) Thus, I want to work through the [Infinitely Large Napkin](https://web.evanchen.cc/napkin.html) or a similar resource, and come up with a few examples of problems in the real world that would use each branch of mathematics, even if they’re well beyond my ability to solve without more dedicated study.
**Linear Algebra** - John said in his workshops that “If you haven’t solved alignment yet, you don’t know enough linear algebra.” (This is also one of the most thought-provoking sentences I’ve heard in a long time) Thus, I want to continue to plug away at that, and work through the canonical LessWrong text of [Linear Algebra Done Right](https://linear.axler.net/).
But as they say - plans are useless, but planning is indispensable. Probability of parts of this plan changing significantly due to new information gained in Berkeley is >50%, but as long as I keep in mind why the plan is what it is, I can pivot as needed.
I hope people find this useful, and if there’s one piece of advice you’ve taken from this - if you’re unsure about applying to the LTFF or another similar source, go ahead and [give it a try](https://funds.effectivealtruism.org/funds/far-future)! |
795027c9-7127-47f9-abe3-ffc42663e565 | trentmkelly/LessWrong-43k | LessWrong | Voting is not rational (usually.)
Today is the midterm elections in the United States, and I am not voting.
For the vast majority of elections, voting is irrational, because the individual's vote is proportionately very small. This means it cannot have an effect on the outcome.
There are, however, conditions which can lead to voting becoming rational, and these are:
1. The number of voters approaches zero.
2. The ratio of votes for candidates (in a majority wins, 2 person race) approaches .5
3. The difficulty of voting becomes vanishingly small.
4. Incentives are created to make the costs of not voting greater than the cost of voting (for instance, not voting is illegal in Australia, and incurs a fine.)
For me, as for nearly everyone, voting this year is irrational. 1 and 2 are nowhere close to true, and 3 is especially bad for me this year. I forgot to change my address on my voter's registration until yesterday and my polling location is both a) usually overpopulated and filled with long lines and b) farther than I care to go.
Only 3 and 4 are something that we can certainly do something about. The web-based absentee voter system that was tested this fall is a step in that directions, but its subsequent hacking was unsurprising. Is opening our system up to fraud a reasonable trade-off to get more people to vote? Should there be an option to use paper absentee-like ballots even if you are not absent? Should the U.S. go the Australia route?
|
5d67774a-f196-4a4e-b141-8b1ac617eaa3 | StampyAI/alignment-research-dataset/special_docs | Other | Predicting Human Deliberative Judgments with Machine Learning
FHI Oxford Technical Report #2018-2
Predicting Human Deliberative Judgments
with Machine Learning
Owain Evans, Andreas Stuhlmüllery, Chris Cundy, Ryan Careyy, Zachary
Kenton, Thomas McGrath, Andrew Schreibery
July 13, 2018
Abstract
Machine Learning (ML) has been successful in automating a range of
cognitive tasks that humans solve effortlessly andquickly. Yet many real-
world tasks are difficult andslow: people solve them by an extended
process that involves analytical reasoning, gathering external information,
anddiscussingwithcollaborators. Examplesincludemedicaladvice, judg-
ing a criminal trial, and providing personalized recommendations for rich
content such as books or academic papers.
There is great demand for automating tasks that require deliberative
judgment. Current ML approaches can be unreliable: this is partly be-
cause such tasks are intrinsically difficult (even AI-complete) and partly
because assembling datasets of deliberative judgments is expensive (each
label might take hours of human work). We consider addressing this data
problem by collecting fastjudgments and using them to help predict de-
liberative ( slow) judgments. Instead of having a human spend hours on a
task, we might instead collect their judgment after 30 seconds or 10 min-
utes. These fast judgments are combined with a smaller quantity of slow
judgments to provide training data. The resulting prediction problem is
related to semi-supervised learning and collaborative filtering.
We designed two tasks for the purpose of testing ML algorithms on
predicting human deliberative judgments. One task involves Fermi esti-
mation (back-of-the-envelope estimation) and the other involves judging
the veracity of political statements. We collected a dataset of 25,000 judg-
ments from more than 800 people. We define an ML prediction task for
predicting deliberative judgments given a training set that also contains
fast judgments. We tested a variety of baseline algorithms on this task.
Unfortunately our dataset has serious limitations. Additional work
is required to create a good testbed for predicting human deliberative
judgments. This technical report explains the motivation for our project
(which might be built on in future work) and explains how further work
can avoid our mistakes. Our dataset and code is available at https:
//github.com/oughtinc/psj .
University of Oxford
yOught Inc.
1
1 Introduction
1.1 Fast and slow judgments
Machine Learning has been successful in automating mental tasks that are quick
and effortless for humans. These include visual object recognition, speech recog-
nition and production, and basic natural language prediction and comprehen-
sion [1, 2, 3, 4]. Andrew Ng states the following heuristic [5]:
If a typical person can do a mental task with less than one second
of thought, we can probably automate it using AI either now or in
the near future.1
In this technical report we refer to judgments that are quick (roughly 30 seconds
or less) and easy for most humans as fastjudgments. Fast judgments contrast
withslowjudgments, which may involve lengthy processes of deliberate rea-
soning, research, experimentation, and discussion with other people.2Many
important real-world tasks depend on slow judgments:
Predict the verdict of jury members in a criminal trial.
Predict which engineers will be hired by a company with an extensive
interview process.
Predict whether experts judge a news story to be fake or intentionally
misleading.
Predict a doctor’s advice to an unwell patient after a thorough medical
exam.
Predict how a researcher will rate a new academic paper after reading it
carefully.
Predict how useful a particular video lecture will be for someone writing
a thesis on recent Chinese history.
There is great demand for Machine Learning (ML) and other AI techniques
for predicting human slow judgments like these, especially in hiring workers,
detection of fake or malicious content, medical diagnosis, and recommenda-
tion [8, 9, 10, 11]. However ML approaches to predicting these slow judgments
are often unreliable [12, 13, 14]: even if they do reasonably well on a majority of
instances, they may have large errors on more demanding cases (e.g. on inputs
that would be tricky for humans or on inputs chosen by humans to try to fool
the algorithm).
One source of the unreliability for ML algorithms is optimizing for a subtly
wrong objective. Suppose a student gets video lecture recommendations from
1Ng intends it as a heuristic rather than a rigorous scientific conclusion.
2ThedistinctionissimilartothatbetweenSystem1(fast)andSystem2(slow)cognition[6,
7]. However, in this work we distinguish judgments by how long they take and whether they
make use of external information and not by the underlying cognitive process. For instance,
fast judgments can depend on quick application of analytical reasoning.
2
YouTube.3These recommendations may be optimized based on video popular-
ity (“Do users click on the video?”) and engagement (“Do users Like or share the
video?”). These metrics are mostly based on fastjudgments. Yet the student
would prefer recommendations based on slowjudgments, such as the evaluation
of another student who has carefully checked the lecture for factual accuracy by
tracking down the sources. Fast and slow judgments will sometimes coincide, as
when a lecture is inaudible or off-topic. Yet a lecture may seem useful on first
glance while turning out to be riddled with inaccuracies that careful research
would expose.4
Predicting slow judgments in the tasks above is challenging for current ML
algorithms. This is due in part to the intrinsic difficulty of the tasks; predict-
ing how a student evaluates a lecture is arguably AI-complete [16]. Another
difficulty is that collecting slow judgments is inherently expensive: if it takes
five hours of fact-checking to recognize the errors in a lecture then a dataset of
millions of such evaluations is impractically expensive. Big datasets won’t solve
AI-complete problems but will improve ML performance.
Predicting slow judgments is also related to long-term AI Safety, i.e. the
problem of creating AI systems that remain aligned with human preferences
even as their capabilities exceed those of humans [17, 18, 19, 20, 21]. Rather
than create AI that shares only human goals, a promising alternative is to create
AI that makes decisions in the way a human would at every timestep [22, 23].
This approach of imitating human decision-making is only promising if it imi-
tates human deliberate (slow) judgments [24, 25]. A system making thousands
of human-like slow judgments per second could have super-human capabilities
while remaining interpretable to humans [26, 27].
1.2 Using Fast Judgments to Predict Slow Judgments
How can we get around the challenges of predicting slow judgments? One ap-
proach is to tackle the AI-completeness head on by trying to emulate the process
of human deliberate reasoning.5A second approach (which complements the
first)istotacklethedataproblemheadonandfindwaystocollecthugedatasets
of real or synthetic slow judgments [38]. This technical report explores an indi-
rect approach to the data problem. Instead of collecting a big dataset of slow
judgments, we collect a small dataset of slow judgments along with a larger
quantity of side information related to the slow judgments.
What kind of side information would help predict a person’s slow judgment?
If Alice makes a slow judgment about a question, then Alice’s fastjudgment
about the same question is relevant side information. As noted above, in pre-
dicting a student’s thorough evaluation of a video lecture, it is helpful to know
3This is just meant as an example of a recommender system and is not a comment on
the actual YouTube algorithm. A paper [15] on YouTube recommendations states that they
optimize for whether users follow a recommendation (weighted by user watch-time). This will
mostly depend on fast judgments.
4It could be argued that YouTube, Facebook and other sites are optimizing for being
entertaining and keeping users on the site and that these are well predicted by fast judgments.
Yet it’s clear that users sometimes seek content that they would rate highly after a slow
judgment (e.g. for educational purposes, for help making a business decision). So the question
remains how to build ML algorithms for this task.
5There is a large and varied literature aiming to create algorithms that perform sophisti-
cated reasoning. Here are some recent highlights: [28, 29, 30, 31, 32, 33, 34, 35, 36, 37]
3
their fast judgment (e.g. after watching the video for only 30 seconds). Likewise,
a doctor’s guess about a diagnosis after 30 seconds will sometimes predict their
careful evaluation. Another kind of side information for predicting Alice’s slow
judgment about a question are the judgments of other people about the same
question. This is the idea behind collaborative filtering [39], where a person’s
rating of a song is predicted from the ratings of similar people.
While collaborative filtering has been widely studied and deployed, there is
little prior work on using fast judgments as side information for slow judgments.
The motivation for using fast judgments is that they are often easily available
and their cost is much lower than slow judgments. Human cognition is like an
“anytime” iterative algorithm: whereas our slow judgments are more discerning
and reliable, our fast judgments are well-typed and provide increasingly good
approximations to slow judgments over time.6For most judgment tasks we can
collect fast judgments from humans and these fast judgments will come at a
cost orders of magnitude cheaper than slow judgments. Where fast judgments
sometimes coincide with slow judgments and include uncertainty information7,
it may be better to spend a fixed budget on a mix of slow and fast judgments
than on slow judgments alone. Often fast judgments are not just cheaper to
collect but are essentially free. YouTube, Facebook, Twitter and Reddit have
vast quantities of data about which content people choose to look at, share with
others, “Like”, or make a quick verbal comment on.
Using fast judgments as side information to predict slow judgments requires
modifying standard ML algorithms. While the objective is predicting slow judg-
ments, most of the training examples (e.g. most video lectures) only come with
fast judgments. This is related to semi-supervised learning [40, 41], distant su-
pervision [42] (where the training labels are a low-quality proxy for the true
labels), learning from privileged information [43], as well as to collaborative
filtering.
1.3 Contributions and caveats
This tech report describes a project applying machine learning (ML) to pre-
dicting slow judgments. We designed tasks where slow judgments (deliberate
thinking and research) are required for doing well but quick judgments often
provide informative guesses. We collected a dataset of fast and slow human
judgments for these tasks, and formulated a set of prediction problems (predict-
ingheld-outslowjudgmentsgivenaccesstovaryingquantitiesofslowjudgments
at training time). We applied ML baselines: standard collaborative filtering al-
gorithms, neural collaborative filtering specialized to our tasks, and a Bayesian
hierarchical model.8.
Unfortunately our dataset turned out to be problematic and is unlikely to be
a good testing ground for predicting slow judgments . This report describes parts
of our project that may be usefully carried over to future work and summarizes
what we learned from our efforts to create an appropriate dataset. The prob-
lem of predicting slow judgments (and of creating a dataset for the purpose)
6In doing a long calculation we might have no idea of the answer until we solve the whole
problem. For many real-world problems our quick guesses are somewhat accurate and gradu-
ally improve with time.
7That is, the human provides both a guess and a measure of confidence.
8Code is available at https://github.com/oughtinc/psj
4
Fermi comparisons
weight of a blue whale in kg <50,000
population of Moscow smaller angle
in degrees between hands of clock at
1.45<15,000,000
driving distance in miles between Lon-
don and Amsterdam <371
weight of $600 worth of quarters in
poundsarea of Canada in square
miles <328,000,000Politifact truth judgments
Rob Portman : “Since the stimulus
package was passed, Ohio has lost over
100,000 more jobs.”
Republican Party : “Charlie Crist is
embroiled in a fraud case for steering
taxpayer money to a de facto Ponzi
scheme.”
Mark Zaccaria : “James Langevin
voted to spend $3 billion for a jet en-
gine no one wants.”
Figure 1: Example questions. In Fermi, people guess whether the left-hand side is
smaller than right-hand side. In Politifact they guess whether the speaker’s statement
is true.
was harder than expected. We hope to stimulate future work that avoids the
problems we encountered. Our main contributions are the following:
1. We designed two slow judgment tasks for humans: Fermi Comparisons
involve mental reasoning and calculation, and Politifact Truth Judg-
mentsinvolve doing online research to assess the veracity of a political
statement.9We created a web app to collect both fast and slow judgments
for the task. These tasks and the app could be used in future work.
2. Werelatepredictingslowjudgmentswithsideinformationtocollaborative
filtering and we implement simple baselines based on this relation.
3. We diagnose problems with our dataset. First, while slow judgments were
significantly more accurate than fast judgments, the difference was smaller
than expected. Second, variability among subjects was hard to distinguish
from noise; so ML algorithms could not exploit similarities among users
as in collaborative filtering. Third, while there is clear variation in how
users respond to different questions, this variation is very hard for current
ML algorithms to exploit.
2 Tasks and Datasets for Slow Judgments
Our overall goal was (A) to define the ML problem of predicting slow judgments
givenatrainingsetoffastandslowjudgments, and(B)tofindorcreatedatasets
which can be used to test algorithms for this problem.
The domain that humans are making fast/slow judgments about should be
AI-complete or closely related to an AI-complete problem.10Solving such a
9Both tasks require humans to decide some objective matter of fact. Yet there is no
requirement that slow judgments be about objective facts: e.g. a student’s judgment about a
lecture is partly based on their own preferences and interests.
10The important real-world problems of predicting slow judgments in Section 1.1 are plau-
sibly AI-complete.
5
task will (for some problem instances) depend on patterns or structures that
current Machine Learning algorithms do not capture. So while we cannot hope
for highly accurate predictions of slow judgments, we can seek ML algorithms
that “know what they know” [44]. Such algorithms exploit patterns they are
able to learn (producing confident accurate predictions) and otherwise provide
well-calibrated uncertain predictions [45, 46].
This section describes the AI-complete tasks we designed for testing al-
gorithms for predicting slow judgments. Before that we first review related
datasets in this area.
2.1 Existing Datasets
Many ML datasets record human slow judgments for AI-complete tasks. These
include datasets of movie reviews [47], reviewer scores for academic papers [48],
and verdicts for legal cases [49]. There are also datasets where the ground-truth
labels are the product of extensive cognitive work by humans (e.g. theorem
proving [38], political fact-checking [14]) and these could potentially be used
to study human slow judgments. However, these datasets do not contain fast
judgments. In particular, for each task instance in the dataset, there’s a slow
judgment by a particular individual but there’s no corresponding fast judgment.
Moreover the datasets do not explicitly record information about the time and
resources than went into the slow judgment11. For example, the reviewers of
academic papers do not divulge whether they spent ten minutes or two hours
reviewing a paper.
Due to the lack of existing datasets, we created a new dataset recording
human slow and fast judgments for AI-complete tasks. We designed two tasks
for this purpose, which may be useful for future work in this area (even if our
dataset is not).
2.2 Two Judgment Tasks: Fermi and Politifact
Wecreatedtwotasksforhumansthatrequiredeliberate,multi-stepthinkingand
rely on broad world knowledge. In the Fermi Comparisons task (abbreviated
to “Fermi”) users determine which of two quantities is larger. This often involves
simple arithmetic, factual knowledge, and doing back-of-the-envelope estimates
to determine the order of magnitude of the quantity. Example questions are
show in Figure 1. Note that human subjects (who we refer to as “users”) are
not allowed to look up the quantities online as this would trivialize the task.
In thePolitifact Truth task (abbreviated to “Politifact”) users evaluate
whether a statement by a US politician or journalist is true or false (see Figure
1). They have access to metadata about the statement (see Figure 2) and are
allowed to do research online. For both tasks, users assign a probability that
the statement is true (with “0” being definitely false and “1” being definitely
true) and they enter their probability via the interface in Figure 2. Their goal
is to minimize mean squared error between their probabilistic judgment and the
ground-truth answer.
11A paper [50] on adversarial examples for humans does record human judgments under
different time constraints. But this domain is not AI-complete and the slower judgments are
still pretty fast.
6
Figure 2: Question-answeringUIforFermiComparisons(above)andPolitifactTruth
(below) tasks. Users are not allowed to do online research for Fermi but are for
Politifact.
Questions and ground-truth answers for Fermi were constructed and computed
bytheauthors. Politicalstatementsandground-truth(i.e.thejudgmentsofpro-
fessional fact-checkers) for Politifact were downloaded from the politifact.com
API (following [14]).
The Fermi and Politifact tasks satisfy the following desirable criteria:
A generalized version of each task is AI-complete. Fermi questions depend
on mathematical reasoning about sophisticated and diverse world knowl-
edge (e.g. estimate the mass of the atmosphere). If novel Fermi questions
are presented at test time (as in a job interview for high-flying undergrad-
uates), the task is AI-complete. Politifact questions can be about any
political topic (e.g. economics, global warming, international relations).
7
Answering them requires nuanced language understanding and doing so-
phisticated research on the web.
Fast judgments are informative about slow judgments. Givenaquestion(as
in Figure 2), fast judgments of the probability (e.g. less than 30 seconds)
will be informative about a user’s slow judgment (e.g. 5 or 10 minutes).
However some questions are too difficult to solve with a fast judgment.
It is possible to make progress through analytical thinking or gathering
evidence. For Fermi, breaking down the problem into pieces and coming
up with estimates for each of the pieces is a useful cognitive strategy. For
Politifact (but not Fermi), participants are allowed to use a search engine
and to read web pages to check political facts.
The ground-truth is available for both Fermi and Politifact questions. The
problem of predicting slow judgments we discussed in the Introduction
is not limited to human judgments about objective facts (as the list of
examples in Section 1.1 makes clear). However, if the ground-truth is
available then this makes certain experiments possible.12
2.3 Data collection
Human participants (who we refer to as “users”) were recruited online13and
answered questions using the UI in Figure 2. Users see a question, a form for
entering their probability judgment, a progress bar that counts down the time
remaining, and (for Politifact only) a search engine tool.
For each question, users provides fast, medium and slow probability judg-
ments. In Fermi the user is presented with a question and has 15 seconds to
make the fastjudgment. Once the 15 seconds have elapsed, they get an addi-
tional 60 seconds in which they are free to change their answer as they see fit
(themedium judgment). Finally they get another 180 seconds to make the slow
judgment. The setup for Politifact is identical but the time periods are 30, 90
and 360 seconds. (Users are free to use less than the maximal amount of time.
So for particularly easy or difficult questions, the “slow” judgment may actually
only take 30 seconds or less.)
12In Politifact, the ground-truth is just an especially slow judgment of an expert in political
fact-checking and could be modeled as such. We did not try this in our experiments.
13We used Amazon’s Mechanical Turk (MT) for a pilot study. For the main experiment
we used volunteers recruited via social media with an interest in improving their probabilistic
judgments. We found it hard to design an incentive scheme for MT such that the users would
make a good-faith effort to do well (and not e.g. declaring 50% on each judgment) while not
cheating by looking up Fermi answers.
8
0.0 0.2 0.4 0.6 0.8 1.0
Fast Judgment0.00.20.40.60.81.0Slow JudgmentFermi, Fast vs. SlowFigure 3: Correlation between fast and slow judgments for a given question-user pair
for Fermi (above) and Politifact (below). Blue markers are proportional in size to
number of instances and orange line is a linear regression through the data. Fast user
judgments are often 50% but then become more confident in either direction given
more time to think.
0.0 0.2 0.4 0.6 0.8 1.0
Fast Judgment0.00.20.40.60.81.0Slow JudgmentPolitifact, Fast vs. Slow
The Fermi data we collected consisted of 18,577 judgments, with a third
of these being slow judgments. There were 619 distinct users and 2,379 Fermi
estimation questions (with variable numbers of judgment per question). Each
user answered 12.9 distinct questions on average (median 5). The Politifact
dataset was smaller, containing 7,974 judgments, from 594 users, covering 1,769
9
statements. Each user answered 6.4 distinct questions on average (median 5).
2.4 Descriptive Statistics
Our goal was to use the dataset to train ML algorithms to predict slow judg-
ments and such a dataset should have the following features:
1.Large, clean, varied dataset : as usual a large dataset with minimal noise
is desirable. In particular there should be many judgments per user and
many distinct questions. Possible sources of noise should be minimized
(e.g. users are noisier when learning the task and if they pay less attention
due to tedious or overly difficult questions).
2.Fast-Slow Correlation : fast judgments are informative about slow judg-
ments but not too much. For some users and some questions, fast judg-
ments may be similar to slow judgments. In other cases, the fast judgment
will be uncertain or wrong.
3.User Variation : individual users vary in their overall performance and
their performance in different kinds of question. We want to predict the
slow judgments of an individual (not the ground-truth). In some tasks
listed in Section 1.1 users vary because of different preferences. In Fermi
and Politifact users might vary in their areas of knowledge (e.g. science
vs. sport questions in Fermi).
4.Question Variation : individual questions vary in terms of how users an-
swer them. For example, some questions are hard (producing random or
systematically incorrect answers) and some are easy. This allows algo-
rithms to predict user answers better based on features of the question.
Did our data have the features above? As noted in Section 2.3 the dataset
was relatively small (especially Politifact) and many users only answered a small
number of questions.
Correlations between fast and slow judgments are shown in Figure 3. They
show that judgments started out uncertain but became more confident (in ei-
ther direction) given time, and that confident fast judgments were less likely to
change. Figure 4 shows that slow judgments were more accurate than fast on
average. Nevertheless, the differences between slow, medium and fast judgments
are not as large as we had hoped.
Did users vary in their accuracy? We expected users to vary in overall
accuracy and accuracy for different kinds of question (e.g. some users do better
on science than sports questions). However the level of variation among users
was not high enough to rule out possibility that the best two-thirds of users did
not vary (see Figure 5). This is a problem with the design of our experiment
and we discuss it further in Section 4.
10
0.00 0.25 0.50 0.75
Mean squared error020406080100Number of UsersMean squared error of user's answers, Fermi
Fast
SlowFigure 4: Variation in User Performance in Fermi for fast and slow judgments.
Histogram shows number of users who obtained a particular level of performance
(measured by MSE). Note that 0:25is the MSE achieved by a user who always says
50%. While users have widely varying performance, much of this is due to noise and we
can’t rule out the possibility that most users do not vary in actual skill (see Figure 5).
Figure 5: Mean performance on Fermi for different user quantiles. Users were divided
into quantiles based on performance on a random half of the data and the figure shows
their performance on the other half. Error bars show standard error from 5 random
divisions into quantiles. The graph suggests we can’t rule out the null hypothesis that
the best two-thirds of users don’t vary in their accuracy. (We removed users with less
thank=6 judgments; the graphs were similar when kwas set higher.)
Questions in both Fermi and Politifact did vary significantly in difficulty for
human users (see Figure 6). However, there are less than 2500 questions for
each task. This makes it hard for algorithms to generalize from the question
text or meta-data without substantial pre-training.
11
Figure 6: Variation in question difficulty for human users. Histograms show number
of questions with a given MSE across users. Many questions had an MSE near zero
(most users got them confidently right), while others were difficult (MSE near 0.25)
or counter-intuitive (MSE greater than 0.25).
3 Predicting Slow Judgments with ML
This section presents a problem definition for predicting slow judgments using
ML and results for baseline algorithms on our dataset. The ML task and algo-
rithms are independent of the dataset and may be useful for future work in this
area.
3.1 Task: Predicting Slow Judgments with ML
3.1.1 Task Definition
For both Fermi and Politifact the data consists of user probability judgments
about questions. Each probability judgment h(q; u; t )2[0;1]depends on the
question text and meta-data features q, the user id u, and the time t2f0;1;2g
(where the indices correspond to fast, medium, and slow judgments respec-
tively).
We let ^h(q; u; t )be an algorithm’s prediction of the user judgment h(q; u; t ).
The task is to predict slow judgments h(q; u;2)from the test set. While only
slow judgments appear in the test set, the training set consists of fast, medium
and slow judgments.14The loss function is mean-squared error over all slow
judgments in the test set. Let T=f(q; u)gbe the set of question-user pairs
that appear in the test set. Then the objective is to minimize the mean squared
error over slow judgments in the test set:
1
jTjX
(q;u)2Th
h(q; u;2) ^h(q; u;2)i2
(1)
Note that this problem setup is analogous to content-based collaborative
filtering, where the task is to predict a user’s held-out rating for an item (e.g. a
movie), given the user id and a feature vector for the item [51].
14The test set is randomly sampled from the entire dataset and so the same users and
questions can appear both in train and test.
12
3.1.2 Train on Fast Judgments, Predict Slow
Slow judgments are much more expensive to collect than fast judgments. Yet for
questions that are either very easy or very difficult for a user, the fast and slow
judgments will be very similar. So if a model can learn when fast judgments
are a good proxy for slow judgments, it can predict slow judgments reasonably
well from a much cheaper dataset (as discussed in Section 1.1).
In our dataset we have the user’s slow judgment for a question whenever we
have the fast judgment. Yet we can simulate the strategy of mostly collecting
fast judgments by masking many of the slow judgments from our training set.15
Our results are computed for three different levels of masking. “Unmasked”
means that all slow judgments in the training set are included. “Heavy” means
90% of question-user pairs have the medium and slow removed and 7% have
just the slow removed. “Light” means we removed slow and medium judgments
for 60% of question-user pairs and removed just slow for 30%.16
3.2 Algorithms and Results
We ran the following baseline algorithms on our datasets:
1.Collaborative Filtering (KNN and SVD) : The prediction task is closely
related to collaborative filtering (CF). The main difference is that instead
of predicting a user’s judgment about an item, we predict a user’s judg-
ment about a question at a given time (where we expect users to generally
converge to zero or one given more time). To reduce our task to CF, we
flatten the data by treating each user-time pair (u; t)as a distinct user.
We applied the standard CF algorithms K-nearest-neighbors (KNN CF)
and singular value decomposition (SVD CF), using the implementation in
the Surprise library [52].
2.Neural Collaborative Filtering : We adapt the Neural CF algorithm [53]
to our task. A neural net is used to map the latent question and user
embeddings to a sequence of judgments for each time. Linear Neural CF
forces the judgments to change linearly over time.
3.Hierarchical Bayesian Linear Regression : To predict the user judgment
for a question we pool all user judgments for the same question (ignoring
user identity) and regress using a Bayesian linear regression model. This
model exploits the temporal structure of judgments but it discards the
question features and user identity.
4.Clairvoyant Model : Since user slow judgments are correlated with the
ground-truth, algorithms will do better on new questions to the extent
they can predict the ground-truth. Predicting ground-truth without side-
information is difficulty for Fermi and Politifact. However, we can investi-
gate how well a model would do if it had access to the ground-truth. The
15This is like the standard practice in semi-supervised learning, where researchers remove all
but 5% of the labels from the training set to simulate a situation where most data is unlabeled
16In order to achieve clear-cut separation of the training data and the held-out test set
we also implemented a masking procedure before doing the additional masking for Light and
Heavy. For each judgment in the test set, we removed the medium judgment made by the
same user about the same question from the training set. We also stochastically remove the
corresponding fast judgment with 80% probability.
13
“Clairvoyant” model simply predicts that each user will respond with the
ground-truth answer with full confidence. The “Clairvoyant Mean” model
predicts the base-rate probability for a user given the ground-truth of a
question.
3.3 Results
Table 1 shows results for Fermi and Politifact with different levels of masking of
slow judgments. Hierarchical Linear Regression and SVD perform best. Since
Hierarchical Linear regression ignores both question features and user identity,
this suggests it is difficult for algorithms to improve predictions by modeling
questions and users. This is additional evidence that our dataset is problematic.
We had expected the Neural CF algorithms would do well, as they learn
latent representations for both questions and users and (unlike SVD and KNN)
they do not discard the temporal structure in the data. However, their poor
performance is partly explained by the difficulty (discussed in Section 2.4) of
distinguishing user variation from noise.17
The performance of Clairvoyant Mean suggests that non-clairvoyant algo-
rithms could be improved with a strong language and reasoning model that
accurately predicts the ground-truth.18
Table 1: Meansquaredtesterrorforvariousalgorithmsanddifferentlevelsofmasking
slow judgments at training time. Note that Clairvoyant models had access to ground-
truth and other models did not.
Politifact Fermi
Unmasked Light Heavy Unmasked Light Heavy
KNN CF 0.127 0.130 0.133 0.115 0.125 0.134
SVD CF 0.124 0.126 0.127 0.102 0.113 0.112
Linear Neural CF 0.131 0.135 0.135 0.137 0.141 0.141
Neural CF 0.130 0.131 0.129 0.136 0.136 0.138
Hierarchical Lin. Reg. 0.123 0.126 0.127 0.098 0.107 0.114
Clairvoyant 0.242 0.242 0.242 0.216 0.216 0.216
Clairvoyant Mean 0.112 0.112 0.112 0.111 0.111 0.111
Always Guess 50% 0.137 0.137 0.137 0.138 0.138 0.138
4 Discussion: Mitigating Dataset Problems
Some problems with our dataset were described in Section 2.4. How could these
problems be mitigated in future work?
Problem: Fast and slow judgments were too similar
There weren’t big differences between fast and slow judgments. This can be
addressed by choosing a task that requires more thinking and research than
17Like Hierarchical Linear Regression, the Linear Neural CF assumes that judgments evolve
linearly over time. However, Linear Neural CF does not build in which data-points to regress
on and wasn’t able to learn this.
18We experimented with various language models (results not shown). We found it difficult
to train or fine-tune these models on our small dataset without overfitting.
14
the Fermi and Politifact tasks. The task should also be incremental , such that
a small amount of additional work yields a slightly better answer.19If addi-
tional work is unlikely to help, users will be averse to doing it (without big
compensation).
Problem: Variation between users was hard to distinguish from noise
Lack of discernible variation can be addressed by collecting substantially more
data per user and by trying to reduce noise (e.g. having practice questions,
having less ambiguous questions, having tasks for which users are consistently
fully engaged).
A related issue is that human responses were low in information content.
Users assigned probabilities (from 20 discrete options) to binary ground-truth
outcomes. Many questions were easy and were answered with 95-100% confi-
dence, while others were very difficult and answered with 50-55% confidence.
Furthermore, users were rarely anti-correlated with the ground truth, so slow
judgments for true statements were generally answered somewhere in the range
60-100%, which is a small region of the response space in terms of mean squared
error. This problem of low information content could be addressed by having
a task where user responses are richer in information: e.g. scalar valued, cate-
gorical (with many categories), or a text response such as a movie review. The
cost of this change is that the ML modeling task becomes harder.
Another way to address this issue is to have a task in which the goal for users
is something other than guessing the ground-truth. There are many situations
in which people make slow judgments for questions that are not about objective
matters of fact. For example, when someone reviews a non-fiction book for
their blog, they consider both objective qualities of the book (“Does it get the
facts right?”) and also subjective qualities (“Did I personally learn things from
it? Will it help me make decisions?”). As noted above, there is nothing about
our task definition or modeling that assumes the questions have an objective
ground-truth.20
Problem: Question features were hard for algorithms to exploit
The questions in our tasks varied substantially in difficulty and content. Our
models couldn’t really exploit this variation, probably because (a) there were
lessthan2500questions, and(b)predictingwhetheraquestionischallengingfor
humans is an intrinsically difficult task (drawing on NLP and world knowledge).
Having models that can make use of question and meta-data features is an
important goal for our research. If models can only predict slow judgments
based on other human judgments, they will not be able to generalize to new
questions that no humans have yet answered.
The most obvious fix for this problem is to collect a much larger dataset.
If the dataset contained millions of questions then language models would be
better at learning to recognize easy vs. difficult questions. While collecting
19This would also allow a series of intermediate times between fast and slow.
20We collected data for a third task (not discussed elsewhere in this report), where the aim
was to evaluate Machine Learning papers. We asked researchers to judge papers subjectively
(“How useful is this paper for your research?”) rather than objectively (“Should the paper
be accepted for a conference?”). Unfortunately we did not a sufficient amount of data from
volunteers. But we think some kind of variant on this task would be worth doing.
15
such a big dataset would be expensive, the cost could be mitigated by mainly
collecting fast judgments.21An alternative to collecting a large dataset is to
choose some object task (instead of Fermi and Politifact) for which pre-trained
language models are useful (e.g. something related to sentiment analysis rather
than judging the ground-truth of statements).
5 Conclusion
Machine Learning will only be able to automate a range of important real-world
tasks (see Section 1.1) if algorithms get better at predicting human deliberative
judgmentsinvariedcontexts. SuchtasksarechallengingduetoAI-completeness
and the difficulty and cost of data. We tried to create a dataset for evaluating
ML algorithms on predicting slow judgments. The previous section discussed
problems with our dataset and potential remedies. It’s also worth considering
alternative approaches to the challenge we outline in Section 1.1. First, some
tasks may be more fruitful to model than Fermi estimation and political fact
checking. Second, deliberation can be modeled explicitly by building on recent
deeplearningapproaches[28,29,30,31,32,33,34,35,36,37]. Tomoreprecisely
capture how humans deliberate, we could also record the individual steps people
take during deliberating (e.g. by requiring users to make their reasoning explicit
or by recording their use of web search). Finally, we acknowledge that while
predicting slow judgments is an important task for the future of AI, it may be
difficult to make progress on today.
21It remains to be seen how well this can work if the goal is to predict slow judgments.
16
6 Acknowledgements
This work was supported by Future of Life Institute grant 2015-144846 (OE,
AS). Important early work was done by Neal Jean and Girish Sastry. We thank
Paul Christiano, David Krueger, and Tim Rocktäschel for valuable conversa-
tions. We are extremely grateful to everyone who volunteered and contributed
data to our project via the site: thinkagain.ought.org .
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1cf802d4-9de1-4332-88d5-abcd764fb1a5 | trentmkelly/LessWrong-43k | LessWrong | How habits work and how you may control them
Some highlights from The Power of Habit: Why We Do What We Do in Life And Business by Charles Duhigg, a book which seems like an invaluable resource for pretty much everyone who wants to improve their lives. The below summarizes the first three chapters of the book, as well as the appendix, for I found those to be the most valuable and generally applicable parts. These chapters discuss individual habits, while the rest of the book discusses the habits of companies and individuals. The later chapters also contain plenty of interesting content (some excerpts: [1 2 3]), and help explain the nature of e.g. some institutional failures.
(See also two previous LW discussions on an online article by the author of the book.)
Chapter One: The Habit Loop - How Habits Work
When a rat first navigates a foreign environment, such as a maze, its brain is full of activity as it works to process the new environment and to learn all the environmental cues. As the environment becomes more familiar, the rat's brain becomes less and less active, until even brain structures related to memory quiet down a week later. Navigating the maze no longer requires higher processing: it has become an automatic habit.
The process of converting a complicated sequence of actions into an automatic routine is known as "chunking", and human brains carry out a similar process. They vary in complexity, from putting toothpaste on your toothbrush before putting it in your mouth, to getting dressed or preparing breakfast, to very complicated processes such as backing one's car out of the driveway. All of these actions initially required considerable effort to learn, but eventually they became so automatic as to be carried out without conscious attention. As soon as we identify the right cue, such as pulling out the car keys, our brain activates the stored habit and lets our conscious minds focus on something else. In order to conserve effort, the brain will attempt to turn almost any routine into a habit.
|
e590faeb-2354-43b9-9441-9450b472d6d1 | trentmkelly/LessWrong-43k | LessWrong | Could/would an FAI recreate people who are information-theoretically dead by modern standards?
If someone gets cremated or buried long enough for eir brain to fully decompose into dirt, it becomes extremely difficult to revive em. Nothing short of a vastly superhuman intelligence would have a chance of doing it. I suspect that it would be possible for a superintelligence to do it, but unless there's a more efficient way to do it, it would require recomputing the Earth's history from the time the AGI is activated back to the death of the last person it intends to save. Not only does this require immense computational resources that could be used to the benefit of people who are still alive, it also requires simulating people experiencing pain (backwards). On the other hand, this saves people's lives. Does anyone have any compelling arguments on why an FAI would or would not recreate me if I die, decompose, and then the singularity occurs a long time after my death?
Why do I want to know? Well, aside from the question being interesting in its own right, it is an important factor in deciding whether or not cryonics is worth-while. |
b15349a9-f7bd-45b8-9333-6a31c7e7f7a5 | trentmkelly/LessWrong-43k | LessWrong | [SEQ RERUN] Guardians of the Gene Pool
Today's post, Guardians of the Gene Pool was originally published on 16 December 2007. A summary (taken from the LW wiki):
> It is a common misconception that the Nazis wanted their eugenics program to create a new breed of supermen. In fact, they wanted to breed back to the archetypal Nordic man. They located their ideals in the past, which is a counterintuitive idea for many of us.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Guardians of the Truth, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series. |
59ae78d5-9bfb-4e12-8e77-489827b72d49 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | 8 possible high-level goals for work on nuclear risk
Summary
=======
For people aiming to do the most good they can, **what are the possible high-level goals for working on risks posed by nuclear weapons?** Answers to this question could inform how much to invest in the nuclear risk space, what cruxes[[1]](#fnbiv9hsbmw6) we should investigate to determine how much and in what ways to work in this space, and what specific work we should do in this space.
I see eight main candidate high-level goals, in three categories:
1. [**Longtermist**](https://forum.effectivealtruism.org/tag/longtermism) **& nuclear-focused:** Reducing nuclear risk’s contribution to long-term future harms
1. **Direct:** Reducing relatively direct, foreseeable paths from nuclear risk to long-term harms
2. **Indirect**: Reducing more indirect/vague/hard-to-foresee paths from nuclear risk to long-term harms
2. **Longtermist &** ***not*** **nuclear-focused:** Gaining indirect benefits for other EA/longtermist goals
1. **Career capital:** Individuals building their career capital (knowledge, skills, credibility, and connections) to help them later work on other topics
2. **Movement strengthening:** Building *movement-level* knowledge, credibility, connections, etc. that pay off for work on other topics
3. **Translatable knowledge:** Developing research outputs and knowledge that are directly useful for other topics
4. **Movement growth:** Improving the EA movement’s recruitment and retention (either narrowly - i.e. among expert communities - or broadly) by being seen to care about nuclear risks and/or by *not* being seen as *dismissive* of nuclear risks
5. **Epistemic hygiene:** Improving EAs’[[2]](#fnjk2kq50hoa) “epistemic hygiene” by correcting/supplanting flawed EA work/views
3. **Neartermist & nuclear-focused:** Reducing neartermist harms from nuclear weapons
I expect we should put nontrivial weight on each of those high-level goals. But my current, pretty unstable view is that I’d prioritize them in the following rank order:
1. **longtermist & nuclear-focused**, both **direct** and **indirect**
2. **career capital** and **movement strengthening**
3. **translatable knowledge** and **movement growth** (especially the “narrow version” of the movement growth goal)
4. **neartermist & nuclear-focused**
5. **epistemic hygiene**
(Note: Each of the sections below should make sense by itself, so feel free to read only those that are of interest.)
Why did I write this?
=====================
With respect to the nuclear risk space, I think people in the effective altruism community are currently unsure about even what very high-level goals / [theories of change](https://en.wikipedia.org/wiki/Theory_of_change) / rationales we should focus on (let alone [what intermediate goals, strategies, and policies to pursue](https://docs.google.com/document/d/1uQgYUoN270weTRBqqLrCnEKLafq8Xq4ZPdYL0kPHROQ/edit)). More specifically, I think that:
* EAs who’ve done some research, grantmaking, or other work in this area often disagree about or notice themselves feeling confused about what our high-level goals should be, or seem to me to be overlooking some plausible candidate goals.
* EAs who *haven’t* done work in this area are often overly focused on just one or a few plausible candidate goals without having considered others, and/or often don’t clearly recognise distinctions and differing implications between the various plausible goals.
I felt that it would be useful to collect, distinguish between, and flesh out some possible high-level goals, as a step toward gaining more clarity on:[[3]](#fnbhqwj5zkidu)
1. How much to invest in the nuclear risk space
* E.g., if we’re mostly focused on using nuclear risk as a “training ground” for governance of other risky technologies, *perhaps* we should also or instead focus on cybersecurity or other international relations topics?
2. What cruxes we should investigate to determine how much and in what ways to work in this space
* E.g., is the crux how likely it is that [nuclear winter](https://en.wikipedia.org/wiki/Nuclear_winter) could cause existential catastrophe and how best to prevent such extreme scenarios, or whether and how we can substantially and visibly reduce the chance of nuclear war in general?
3. What specific work we should do in this space
Epistemic status
================
I drafted this post in ~3 hours in late 2021. In early 2022, Will Aldred (a collaborator) and I spent a few hours editing it.[[4]](#fn8cu0mee8g0i) I intend this as basically just a starting point; perhaps other goals could be added, and definitely more could be said about the implications of, arguments for, and against focusing on each of these goals.
I expect most of what this post says will be relatively obvious to some readers, but not all of it to all readers, and having it actually written down seems useful.
Please let me know if you’re aware of existing writings on roughly this topic!
1. Reducing nuclear risk’s contribution to long-term future harms
=================================================================
(Meaning both [existential catastrophes](https://forum.effectivealtruism.org/tag/existential-risk) and other negative [trajectory changes](https://forum.effectivealtruism.org/tag/trajectory-change).)
1a. Reducing relatively direct, foreseeable paths from nuclear risk to long-term harms
--------------------------------------------------------------------------------------
* Relevant paths include:
+ A nuclear winter that kills almost literally everyone, with existential catastrophe following shortly afterwards (e.g. because something else finishes the population off or we get locked in to a worse system of values and political systems)
+ *Perhaps* the use of very large numbers of very large [salted bomb](https://en.wikipedia.org/wiki/Salted_bomb) type weapons / radiological weapons
- Caveat: I haven’t looked into the plausibility of this as a relatively direct path to long-term harms.
- See some notes [here](https://docs.google.com/document/d/18nBfHOV-U_QDJ5K0GIAk6BYiNTKw673jJBb5aXFqk_8/edit#heading=h.g1679pwqc1un).
* For an attempt to zoom in on lower-level goals/actions that have this high-level goal, see [Shallow review of approaches to reducing risks from nuclear weapons](https://docs.google.com/document/d/1uQgYUoN270weTRBqqLrCnEKLafq8Xq4ZPdYL0kPHROQ/edit#).
* To the extent we’re focused on this, we might be especially concerned to reduce the chance of scenarios that are very high on some function involving number of warheads, median [yield](https://en.wikipedia.org/wiki/Nuclear_weapon_yield), and mean population density of targets, or *perhaps* scenarios involving things like many huge radiological weapons.
### An unusual subtype of this goal: Reducing the risk nuclear weapons detonations/threats being used as a tool that helps enable AI takeover
* This is less commonly discussed, I’ve hardly thought about it, and I’m unsure whether it warrants attention.
* [Is power-seeking AI an existential risk? [draft]](https://docs.google.com/document/d/1smaI1lagHHcrhoi6ohdq3TYIZv0eNWWZMPEy8C8byYg/edit#) notes that one (of many) possible mechanisms for that existential risk scenario is “Destructive capacity. Ultimately, one salient route to disempowering humans would be widespread destruction, coercion, and even extinction; and the threats in this vein could play a key role in a [power-seeking]-misaligned AI system’s pursuit of other ends. Possible mechanisms here include: biological/chemical/**nuclear** weapons; [...]” (emphasis added).
* I expect that what’s best for reducing this sort of risk would be quite different from what’s best for reducing other pathways from nuclear risk to long-term harms.
+ E.g., for this sort of risk, it seems (approximately?) useless to change the likelihood that political and military leaders would *choose* to detonate many nuclear weapons and to do so against cities. (Conversely, it seems like it *would*potentially be useful to change force sizes and structures such that it’s impossible or harder to detonate many nuclear weapons against cities.)
* If we *are* worried about this sort of risk, then…
+ …it seems plausible/likely that we should focus on interventions more related to the AI than to nuclear weapons (e.g., technical AI safety work to prevent the relevant forms of misalignment)
+ …it also seems plausible/likely that we should focus on one of the many other tools a misaligned AI could use for takeover (e.g., bioweapons, hacking)
+ …but it seems plausible that *some* nuclear-risk-like work would be warranted, as part of a [defense-in-depth](https://forum.effectivealtruism.org/tag/defense-in-depth)/[portfolio](https://80000hours.org/articles/coordination/#3-take-the-portfolio-approach) approach, at least once our resource pool grows sufficiently large and we’ve plucked the lower hanging fruit
* (I have some additional rough notes on this that I could potentially share on request.)
1b. Reducing more indirect/vague/hard-to-foresee paths from nuclear risk to long-term harms
-------------------------------------------------------------------------------------------
* Relevant paths include:
+ The kind of paths discussed in [The long-term significance of reducing global catastrophic risks](https://blog.givewell.org/2015/08/13/the-long-term-significance-of-reducing-global-catastrophic-risks/)
+ Nuclear weapons/risk/war serving as an [existential risk factor](https://forum.effectivealtruism.org/tag/existential-risk-factor)
* To the extent we’re focused on this, we might spread our resources and attention across a relatively wide set of scenarios.
+ E.g., for these risks, it’s *not* like a scenario involving 10 detonations against cities or 400 against silos are basically not a concern at all, since those things could still have effects like substantially disrupting geopolitics.
+ So, given that I do think we should focus somewhat on these broader risk pathways, I think I would push back a bit against arguments that we should focus *almost entirely* on questions like “How many warheads would it take to kill basically everyone?” and on interventions that prevent the chance of seemingly highly unlikely extreme scenarios.
+ ...but only a bit. And I think it’d be useful to flesh out this type of risk pathway some more.
- E.g., maybe the concern is mostly about massively changing great power relations and geopolitics? If so, perhaps strikes against powerful nations’ capital cities would be especially problematic and hence especially important to prevent?
- E.g., maybe the concern is mostly about shrinking or disrupting the EA movement and its work, since that in turn presumably raises existential risk and other issues? If so, perhaps strikes against cities with large numbers of EA orgs or people would be especially problematic and hence especially important to prevent?
- E.g., maybe the concern is mostly about how nuclear conflict could trigger riskier development and deployment of bioweapons or AI? That might then have implications for what to prevent - though I haven’t thought about what the implications would be.
2. Gaining indirect benefits for other EA/longtermist goals
===========================================================
General thoughts on this category
---------------------------------
* I’d also include in this category “preventing indirect harms to other EA/longtermist goals”.
* The distinction between this category and the above category is analogous to the EA community or specific members gaining career capital vs having a direct impact already.
* Two people told me that their EA-aligned orgs saw this goal as a major argument (though not the only one) for them doing nuclear risk work.[[5]](#fn9f7cai990s)
* I currently think this category of ToCs is important, but I also worry we might overestimate its importance due to motivated reasoning / [privileging the hypothesis](https://www.lesswrong.com/tag/privileging-the-hypothesis), since we were already interested in nuclear risk for other reasons.
+ (See also [Beware surprising and suspicious convergence](https://forum.effectivealtruism.org/posts/omoZDu8ScNbot6kXS/beware-surprising-and-suspicious-convergence).)
+ Specifically, I’m worried we might (a) overestimate the benefits of nuclear risk work for this purpose and/or (b) fail to consider alternative options for getting these benefits.
- I’m somewhat more concerned about the second of those possibilities.
- For example, I worry that we might get excited about nuclear risk as a “training ground” without sufficiently considering alternative training grounds like cybersecurity, near-term/low-stakes AI issues, emerging tech policy, international relations / national security more broadly, and maybe climate change (e.g., because that involves difficult coordination problems and major externalities).
- (But I know some people have also thought about those alternatives or are pursuing them. This is just a tentative, vague concern, and it’d be better if someone looked into this and made a more quantitative claim.)
2a. Individuals building their career capital to help them later work on other topics
-------------------------------------------------------------------------------------
* The “other topics” in question might typically be AI, but sometimes also bio or other things.
* It seems to me useful to separate this into (a) building knowledge and skills and (b) building credibility[[6]](#fnxv6hyh2rgol) and connections.
* In both cases, we can ask:
+ What *specific* career capital do we want these individuals to get?
+ Is working on nuclear risk better for getting that specific capital than working on other topics (either the topic the individual will ultimately focus on or some other “training ground”)?
2b. Building movement-level knowledge, credibility, connections, etc., that pay off for work on other topics
------------------------------------------------------------------------------------------------------------
* Here’s one way this could work: If *some* EAs / EA-funded people have done or are doing nuclear risk work, then *other* EAs could draw on their:
+ expertise (e.g., discussing ideas about AI risk pathways where knowledge of nuclear weapons history or policymaking is relevant),
+ connections (e.g., ask them for an intro to a senior national security policy advisor), or
+ credibility (e.g., having them signal-boost a policy proposal)
* Here’s another way this could work: If people *know* that EAs / EA-funded people have done or are doing nuclear risk work, this could *itself* help other EA actors (e.g., whoever funded or employed those EAs) or EA as a whole be perceived as credible and have people accept requests to meet (or whatever).
+ I’ve heard a few EAs discuss the potential benefits of being perceived as “the adults in the room”, and I believe this is what they have in mind with that
+ This benefit may be especially pronounced if (a) EA actors have a major influence on the nuclear risk field as a whole, or on a large and distinguishable chunk of it, and (b) people who we’d like to see us as credible are aware of that and appreciate that.
- E.g., if policymakers notice a batch of think tanks whose work seems less alarmist, more strategically aware, and more useful than the work of many other think tanks, and they notice that those think tanks were all funded by an EA funder.
* We could also frame this as an attempt to counterbalance the possibility that work on nuclear risk or other topics that’s by or funded by EAs could *harm* other EA actors’ credibility and connections.
+ E.g., *perhaps* messaging such as the Future of Life Institute’s [“Slaughterbots”](https://en.wikipedia.org/wiki/Slaughterbots) video, and/or some EAs’ statements that nuclear war would be extremely catastrophic and nuclear arsenals need to be massively shrunk, have lost or could lose EA(s) credibility with important national security people?[[7]](#fnnobi93dqyzc) And if so, perhaps we need to add some other high-visibility, high-credibility work to distract from that or repair our reputation?
2c. Developing research outputs and knowledge that are directly useful for other topics
---------------------------------------------------------------------------------------
* E.g., historical research on the [Baruch plan](https://www.atomicarchive.com/history/manhattan-project/p6s5.html#:~:text=The%20Baruch%20Plan%20proposed%20that,step%20in%20reducing%20its%20stockpile.), nuclear weapons treaties, and nuclear arms races may provide useful insights regarding potential governance and race dynamics with respect to AI risk and biorisk issues.
* E.g., international relations research on present-day nuclear risk strategy and policymaking may provide useful insights for AI and bio issues.
* If this is the primary goal of some nuclear risk work, we would probably want the outputs to be created and written *with this goal explicitly in mind*. And we might think of funding that as AI or bio grantmaking rather than nuclear risk grantmaking.
* But we could plausibly have this instead as a secondary reason to fund some project that also serves one of the other ToCs discussed in this doc. And if we expect this sort of benefit to crop up often in a particular area, that could push in favor of investing somewhat more in that area than we otherwise would.
2d. Improving the EA movement’s recruitment and retention
---------------------------------------------------------
### General thoughts on this goal
* This would be via EAs working on or discussing nuclear risk and that leading to us (a) being seen to care about nuclear risk and/or (b) *not* being seen as *dismissive* of nuclear risk.
* I think we could split this into a narrow version - involving improving recruitment/retention of a relatively some number of people with some nuclear risk expertise - and a broad version - involving a fairly large number of fairly junior or not-nuclear-specialized people.
* It might be fruitful to try to figure out whether any EAs who seem to be on track for careers with high expected impact got into EA in part because of something at the intersection of EA and nuclear risk, or think they would’ve got in earlier if there was more EA nuclear risk stuff, or think they nearly “bounced off” EA due to EA not having enough nuclear risk stuff.[[8]](#fne8tzmwy76o5)
* If this (especially the broad version) is our focus, we might want to prioritize relatively visible work that looks clearly helpful and impressive?
+ Or maybe not - maybe the people it’d be most valuable to attract to the EA community are best captured by basically doing what’s actually best for other reasons anyway?
* Personally, I’m wary of people doing nuclear risk things *primarily* for the broad version of this goal, for reasons including the risk that it could end up being perceived as - or perhaps genuinely being - somewhat underhanded or bait-and-switch-like. But it seems ok if it’s just an *additional* goal behind some effort, and if we’d feel comfortable publicly telling people that (rather than it being something we’re hiding).
### Narrow version
* E.g., maybe the more EA is involved in and cares about nuclear risk issues, or the more that that is known, the more likely we are to end up recruiting into EA researchers, grantmakers, policymakers, or policy advisors who have specialized to some extent for nuclear risk issues?
+ And then maybe they could do more impactful work than they would’ve otherwise, either on nuclear risk or on other topics, due to now being part of the EA movement?
+ And/or maybe they can help other EAs do more impactful work, as per possible high-level goal 2b (“Building movement-level knowledge, credibility, connections, etc., that pay off for work on other topics”)
* I guess here it might not just be about being “seen to care”, but rather also actually funding things and/or collaborating with people such that those people start interacting with the EA community?
+ A question that might be interested in this context: Have there been many cases of grantees coming to interact more with the EA community as a result of receiving grants from EA funders, even when this was not required by their grants?
### Broad version
* The argument for this goal would be analogous to arguments sometimes made that (a) the EA community can seem dismissive of climate change concerns and (b) that might lead to some people bouncing off EA or just not noticing and getting excited about joining EA.
* It’d also be analogous to arguments sometimes made that EA’s global health and development work helps bring people into EA as a whole, and then some of those people end up in longtermism, such that ultimately we have more or similarly many longtermists as we would if *all* outreach focused on longtermism.
* But I’m somewhat skeptical of such arguments, because:
+ I haven’t tried to evaluate those arguments on their own terms.
+ I haven’t tried to think about how strong the analogy is.
- One disanalogy is that I think far more people care a lot about climate than about nuclear risk, which presumably reduces the movement building benefits from doing nuclear risk work relative to those from doing climate change work.
+ I haven’t tried to think about the marginal returns curve for additional signals (of various types) of EA caring about nuclear risk, and where we already are on that curve.
- E.g., maybe it’s just really important that (as is already the case) nuclear risk topics are discussed in [an 80,000 Hours problem profile](https://80000hours.org/problem-profiles/nuclear-security/) and occasional 80,000 interviews, EA Global and SERI conference talks, and EA Forum posts, and anything beyond that adds little?
+ I haven’t tried to think about things that the above arguments might be overlooking.
- In particular, how much the value of these additional community members compares to community members we could gain in other ways[[9]](#fnsx9dmjsyuy), and possible backfire effects from doing nuclear risk work for the movement growth benefits.
- E.g., maybe doing nuclear risk stuff with this as a substantial part of the motivation will itself turn some particularly useful people off (e.g., due to seeming unprincipled, wasteful, or a distraction from more directly useful work)?
- E.g., maybe that’ll just actually cause us to build bad habits of focusing on convoluted power-seeking strategies rather than saying precisely what we really think and trying to more directly improve the world?
2e. Improving EAs’ “epistemic hygiene” by correcting/supplanting flawed EA work/views
-------------------------------------------------------------------------------------
* EA and EA-adjacent communities have made various mistakes in their thinking and communications about nuclear risk. Correcting these things, or superseding them with better work and statements, may be useful not only for benefits like credibility (see goal 2b), but also for our community’s actual epistemics. E.g.:
+ This could usefully highlight the general point that EAs (especially generalists producing things quickly) can get things wrong, especially in well-established, complex, “strategic” domains.
+ It could be useful to point out specific mistakes and what sort of patterns may have driven them, since those patterns may also cause mistaken thinking in other areas. Examples of possible drivers include:
- Our community somewhat selecting for people who believe risks are high
- Our community often being idealistic and internationalist and so maybe overlooking or underweighting some key aspects of statecraft and strategy
* An example of an output that was intended to be somewhat optimized for this goal is [9 mistakes to avoid when thinking about nuclear risk](https://docs.google.com/document/d/101XAopwJlDtWx8URHDCmkEPZYwjSw3sem-J13EkiS64/edit).
* (But note that I haven’t spent much time thinking about what specific mistakes EAs have made, how often, what they suggest, how useful correcting them is, etc.)
3. Reducing neartermist harms from nuclear weapons
==================================================
* I place a small but nontrivial weight on a neartermist, human-focused worldview, and I think I should pay a bit of attention to that when making decisions. And I think it’s plausible that nuclear risk should be one of the top priorities from a neartermist, human-focused worldview. And I think those two things together deserve *some* attention when thinking about whether to do nuclear risk work.
* [In his 80,000 Hours interview](https://80000hours.org/podcast/episodes/carl-shulman-common-sense-case-existential-risks/), Carl Shulman indicated a similar view (though he seemed to me to put more weight on that view than I do).
* But:
+ I place only a fairly small amount of weight on a neartermist, human-focused worldview.
+ I haven’t really tried to analyze whether nuclear risk should be one of the top priorities from a neartermist perspective, and it doesn’t seem to me high-priority to do that analysis.
- But see a research proposal along those lines [here](https://docs.google.com/document/d/12bXBrxRo_ZKXqcJXAQm_L2Po6Oqe5MNPbdjLEOAIl3s/edit?usp=sharing).
* The more weight we place on this goal, probably the less we’d focus on very unlikely but very extreme scenarios (since badness scales roughly linearly in fatality numbers for neartermists, whereas for longtermists I think there’s a larger gap in badness between smaller- and medium-scale and extremely-large-scale nuclear scenarios).
Conclusion
==========
I try to keep my [bottom lines up front](https://hbr.org/2016/11/how-to-write-email-with-military-precision#:~:text=2.%20Bottom%20Line,for%20the%20company.), so please just see the Summary and “Why did I write this?”!
Acknowledgements
================
My work on this post was supported by [Rethink Priorities](http://rethinkpriorities.org/). However, I ended up pivoting away from nuclear risk research before properly finishing the various posts I was writing, so I ended up publishing this in a personal capacity and without having time to ensure it reached Rethink Priorities’ usual quality standards.
I’m very grateful to Will Aldred for a heroic bout of editing work to ensure this and other rough drafts finally made it to publication. I’m also grateful to Avital Balwit, Damon Binder, Fin Moorhouse, Lukas Finnveden, and Spencer Becker-Kahn for feedback on an earlier draft. Mistakes are my own.
1. **[^](#fnrefbiv9hsbmw6)** I.e., crucial questions or key points of disagreement between people. See also [Double-Crux](https://www.lesswrong.com/tag/double-crux).
2. **[^](#fnrefjk2kq50hoa)**In this post, I use “EAs” as a shorthand for “members of the EA community”, though I acknowledge that some such people wouldn’t use that label for themselves.
3. **[^](#fnrefbhqwj5zkidu)**I see this as mostly just a specific case of the general claim that people will typically achieve their goals better if they have more clarity on what their goals *are* and what that implies, and they develop theories of change and strategies with that explicitly in mind.
4. **[^](#fnref8cu0mee8g0i)**We didn’t try to think about whether the 2022 Russian invasion of Ukraine should cause me to shift any of the views I expressed in this post, except in that I added in one place the following point: “E.g., maybe the concern is mostly about shrinking or disrupting the EA movement and its work, since that in turn presumably raises existential risk and other issues? If so, perhaps strikes against cities with large numbers of EA orgs or people would be especially problematic and hence especially important to prevent?”
We also didn’t try to think about whether the [New Nuclear Security Grantmaking Programme at Longview Philanthropy](https://forum.effectivealtruism.org/posts/M7wNHbpqnLfDzmDK9/new-nuclear-security-grantmaking-programme-at-longview) should cause me to shift any views expressed in this post, but I’d guess it wouldn’t.
5. **[^](#fnref9f7cai990s)**Here are my paraphrased notes on what one of these people said:
> “Also, in [org’s] experience, nuclear war seems to be a topic that presents compelling engagement opportunities. And those opportunities have a value that goes beyond just nuclear war.
>
>
> * This area is quite amenable to [org] getting high-level policymaker attention
> + Nuclear war has always been something that gets top level policymaker attention
> + In contrast, for climate change, it’s too crowded [or something like that - I missed this bit]
> + [...]
> + It’s relatively easy to get to the forefront of the field for nuclear risk work
> * And then you can also leverage those connections for other purposes
> * And then you have a good space to talk about the global catastrophic risk framing in general
> * Also, having skill at understanding how nuclear security works is a useful intellectual background which is also applicable to other risk areas
> * [...] [This person] might even recommend that people who want to work on AI and international security start off by talking about the AI and nuclear intersection
> + That intersection is currently perceived as more credible”
See also [these thoughts from Seth Baum](https://forum.effectivealtruism.org/posts/dd3cwxk65sH5bHJBR/early-reflections-and-resources-on-the-russian-invasion-of#:~:text=support%20for%20people,global%20catastrophic%20risks.).
6. **[^](#fnrefxv6hyh2rgol)**I say “credibility” rather than “credentials” because I don’t just mean things like university degrees, but also work experience, a writing portfolio, good references, the ability to speak fluently on a given topic, etc.
7. **[^](#fnrefnobi93dqyzc)**See [Baum](https://forum.effectivealtruism.org/posts/6cyXwsAanTmhvZRRH/seth-baum-reconciling-international-security#:~:text=For%20a%20more,very%20important%C2%A0audiences.) for a somewhat similar claim about “Slaughterbots” specifically.
8. **[^](#fnrefe8tzmwy76o5)**It seems possible Open Phil have relevant data from their [Open Phil EA/LT Survey 2020](https://forum.effectivealtruism.org/s/AKYivq6uKhsyDJXnE), and/or that data on this could be gathered using approaches somewhat similar to that survey.
9. **[^](#fnrefsx9dmjsyuy)**This connects to the topic of the [Value of movement growth](https://forum.effectivealtruism.org/tag/value-of-movement-growth). |
79cacf06-8ca7-4f2b-bb15-e8259c295fa7 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Reliability amplification
In a recent post I talked about [capability amplification](https://medium.com/ai-control/policy-amplification-6a70cbee4f34#.wmeq2iqwv), a putative procedure that turns a large number of fast weak agents into a slower, stronger agent.
If we do this in a naive way, it will decrease reliability. For example, if…
* Our weak policy fails with probability 1%.
* In order to implement a strong policy we combine 10 decisions made by weak agents.
* If any of these 10 decisions is bad, then so is the combination.
…then the combination will be bad with 10% probability.
Although the combination can be more powerful than any individual decision, in this case it is much *less* reliable. If we repeat policy amplification several times, our failure probability could quickly approach 1, even if it started out being exponentially small.
Complementary to capability amplification is *reliability amplification*: given a policy H that usually works, can we implement a policy H that works significantly more reliably?
To be slightly less imprecise (but still quite crude):
* Given a distribution **A** over policies that ε-close to a benign policy for some ε ≪ 1, can we implement a distribution **A⁺** over policies which is δ-close to a benign policy of similar capability, for some δ ≪ ε?
When I say that **A** is ε-close to a benign policy, I mean that there is a benign distribution over policies which has total variation distance at most ε from **A**. We can view this is as a simple mixture between a single aligned policy that is chosen with probability (1−ε) and an adversarial policy that is chosen with probability ε.
I will move freely between talking about “policies” and “distributions over policies;” we can turn a distribution into a single policy by averaging.
The notion of “implement” here is exactly the same as for [capability amplification](https://medium.com/ai-control/policy-amplification-6a70cbee4f34#.wmeq2iqwv): we can use any number of implementations of **A** and whatever other resources are at our disposal**,** and must compute **A⁺**’s responses to a sequence of observations, within some time limit such as a day.
Reliability amplification via deliberation
==========================================
I think there is one very natural way to do reliability amplification — if some variant of this idea doesn’t work, I’m not sure what else to try.
As a warm-up, let’s consider the setting where the agents want to make a single decision rather than a sequence of decisions — we’ll return to the sequential case soon.
We sample three agents **A**, **B**, and **C** from the given distribution. We ask them to talk for a while and then each propose an action. Then we ask them to do approval voting amongst the three options, and randomly pick from the maximally-approved-of proposals.
If all three agents are aligned, then this proposal is clearly going to produce an aligned action. If two the agents behave adversarially then we are clearly out of luck, but that happens only with probability O(ε²), so we can basically ignore it.
The interesting case is when there is one adversarial agent and two aligned agents. If we threw up our hands and picked randomly in this case, then we would have a failure probability of ε. So the question is: can we do better than that?
The obvious hope is that aligned agents can do better-than-chance at recognizing each other / at recognizing good actions. If they can get any non-negligible advantage, then we can get a non-negligible reduction in error probability (and by iterating the procedure we can potentially get large reductions).
The sequential case
-------------------
So far we have talked about making a single decision. The problem is more challenging when we need to make a sequence of decisions.
We can try to simply generalize the voting approach, running a new vote for each action. To see the difficulty, suppose that the optimal policy looks as follows:
1. Generate some hard-to-transfer internal state X. Think of X as something like an intuitive vision for how to approach the situation.
2. Use X to generate a first action.
3. After making an observation, use X to generate a second action. It is important that the same state X be used in steps [2] and [3].
Now suppose that in the first step the three agents all follow this protocol correctly. They arrive at three different proposed actions, which are backed up by three different visions. Now we have to pick one of these actions, effectively committing ourselves to a single agent. This destroys our ability to ensemble effectively in future steps: if the chosen agent fails then we are out of luck, since the other two agents don’t have the vision needed to follow-through on that action.
To get around this problem we need to count on a stronger property: whatever kind of private state X an agent could generate, the agents need to be able to jointly develop a “consensus” private state which serves the same function. Then if one agent fails, the other agents can also use the consensus private state in order to continue acting intelligently.
(It would also be OK if only pairs of agents could establish such a consensus state, as long as the third agent was able to arbitrate between the two in the event of a future failure.)
Implementation
--------------
As of [commit 1cbaae](https://github.com/paulfchristiano/alba/commit/1cbaae38305bcaadf015b909212313da63c06391), this approval-voting scheme is part of [my ALBA implementation on GitHub](https://github.com/paulfchristiano/alba).
It is implemented in [amplify.reliability.amplify\_reliability](https://github.com/paulfchristiano/alba/blob/master/amplify/reliability.py), and is applied as part of [amplify.amplify](https://github.com/paulfchristiano/alba/blob/master/amplify/__init__.py) directly prior to applying HCH.
As a result I’ve moved one issue from the “FIXME: will fail” column to the “TODO: needs improvement” column.
Three differences with redundant computation
============================================
There is an easy recipe for fault-tolerant computation: compute the result 3 times, and take the majority vote. This increases the computational cost by a factor of 3, but decreases the error rate from ε to O(ε²).
Our reliability amplification scheme is considerably more subtle, and if we were to formalize the reliability amplification problem as stated we would find it impossible. This section touches on the most important additional subtleties.
Building consensus
------------------
Redundant computation normally relies on the existence of a unique correct answer on which all uncorrupted computations will agree. In our setting, there may be a range of “correct” behaviors. This introduces the additional challenge of being able to agree on a particular correct answer (and to build a consensus version of the hidden state underlying that answer).
Without some assumption of this form, reliability amplification is impossible. For example, suppose that there are 99 possible good answers and 1 bad answer. Our distribution **A** picks at random amongst the 100 possible answers. If the copies of **A** with good answers are completely unable to agree with each other, then there may simply be no way to tell the good answers from the bad answer. In this case, we can never reduce the error probability below 1%.
Balanced abilities
------------------
In order for our scheme to work we need to assume that the abilities of all of the agents are roughly balanced. I think this is probably necessary for any scheme to work. If you have an ε chance of a malicious superintelligence and a (1−ε) chance of a friendly human, you are probably out of luck — in a deliberative process involving two humans and a superintelligence, the superintelligence is likely to have disproportionately much influence over the outcome even if it is wrong.
Our particular scheme fails outright in the context of powerful adversaries. In general there is a 3ε chance that *at least one* of the agents is unaligned. We may be able to prevent the unaligned agent from causing the group to adopt a really bad answer. But even in the best case, the unaligned agent will probably be able to tilt the balance between the two options proposed by the aligned agents, allowing them to increase the probability of failure. If the unaligned agent is no smarter than the aligned agents, then the aligned agents can counteract this effect by preferentially picking the better action. But if the unaligned agent is much smarter than the aligned agents, then it can increase the probability of failures which are too subtle for the unaligned agents to anticipate, and the aligned agents have no way to decrease the probability of these failures.
Less time to think
------------------
Traditionally redundant computation increases costs by a constant factor. But in the context of reliability amplification, we also make life harder for each agent: in addition to picking an action, they need to spend time arguing about actions and critiquing each others’ choices. This gives each agent less time to think about their action. It is not clear whether we can offset this effect
Relation to capability amplification and ALBA
=============================================
Necessity
---------
Reliability amplification seems completely necessary if we want to do something like [ALBA](https://medium.com/ai-control/alba-an-explicit-proposal-for-aligned-ai-17a55f60bbcf#.k01g9mu1r) using imitation or imitation+RL, since otherwise reliability will fall with each iteration of capability amplification. Of course we could have a “robust” capability amplification procedure which does not decrease reliability. But designing such a procedure is strictly harder than reliability amplification. So I think it makes a lot of sense to split up the problem into two pieces.
If working with approval-direction and pure RL, there is actually a form of reliability amplification already baked in: if the overseer fails with probability 1%, then this only changes the reward function by 0.01, and an RL agent should still avoid highly undesirable actions. That said, capability amplification may still be necessary in a pure RL setup if we can’t solve the RL problem to arbitrary precision. In that case we may always have some non-negligible probability of making a bad decision, and after capability amplification this probability could become too large.
Balancing capability/reliability
--------------------------------
Reliability amplification decreases our agent’s capability but increases its reliability. Capability amplification increases capability and decreases reliability.
The hope is that we can somehow put these pieces together in a way that ends up increasing both reliability and capability.
If our reliability amplification step achieves a superlinear reduction in error probability from ε to *o*(ε), and our capability amplification causes a linear increase from ε to Θ(ε)*,* then this seems almost guaranteed to work.
To see this, consider the capability decrease from reliability amplification. We know that for large enough N, N iterations of capability amplification will more than offset this capability decrease. This N is a constant which is independent of the initial error rate ε, and hence the total effect of N iterations is to increase the error rate to Θ(ε). For sufficiently small ε, this is more than offset by the ε → *o*(ε) reliability improvement from reliability amplification. So for sufficiently small ε we can increase both reliability and capability.
A reduction from ε to O(ε²) is basically the “best case” for reliability amplification, corresponding to the situation where two aligned agents can always reach correct consensus. In general, aligned agents will have some imperfect ability to reach consensus and to correctly detect bad proposals from a malicious agent. In this setting, we are more likely to have an ε → O(ε) reduction. Hopefully the constant can be very good.
There are also lower bounds on the achievable reliability ε derived from the reliability of the human and of our learning procedures.
So in fact reliability amplification will increase reliability by some factor *R* and decrease capability by some increment Δ, while capability amplification decreases reliability by some factor *R*′ and increases capability by some increment Δ′. Our hope is that there exists some capability amplification procedure with Δ′/log(*R*′) > Δ/log(*R*), and which is efficient enough to be used as a reward function for [semi-supervised RL](https://medium.com/ai-control/semi-supervised-reinforcement-learning-cf7d5375197f#.caphzez70).
I think that this condition is quite plausible but definitely not a sure thing; I’ll say more about this question in future posts.
Conclusion
==========
A large computation is almost guaranteed to experience some errors. This poses no challenge for the theory of computing because those errors can be corrected: by computing redundantly we can achieve arbitrarily low error rates, and so we can assume that even arbitrarily large computations are essentially perfectly reliable.
A [long deliberative process](https://medium.com/ai-control/implementing-our-considered-judgment-6c715a239b3e#.fi28htpu3) is similarly guaranteed to experience periodic errors. Hopefully, it is possible to use a similar kind of redundancy in order to correct these errors. This question is substantially more subtle in this case: we can still use a majority vote, but here the space of options is very large and so we need the additional step of having the correct computations negotiate a consensus.
If this kind of reliability amplification can work, then I think that [capability amplification](https://medium.com/ai-control/policy-amplification-6a70cbee4f34) is a plausible strategy for aligned learning. If reliability amplification doesn’t work well, then cascading failures could well be a fatal problem for attempts to define a powerful aligned agent as a composition of weak aligned agents.
---
*This was originally posted [here](https://ai-alignment.com/reliability-amplification-a96efa115687) on 20th October, 2016.*
*The next post in this sequence will be 'Security Amplification' by Paul Christiano, on Saturday 2nd Feb.* |
615dd2bd-4d63-4242-a2c4-b612f375c864 | trentmkelly/LessWrong-43k | LessWrong | Calibrate words, not just probabilities
In Phil Tetlock's book Superforecasters, much emphasis is put on making sure that a forecaster's predictions are well calibrated- if a forecaster gives a 70% chance to 100 different events, we should expect that 70 of those events will have happened, and 30 didn't happen. If only 50 events actually happened when the forecaster said 70%, then the forecaster may want to improve their calibration (especially if they are the betting sort), and anybody listening to them would be well advised to take their poor calibration into account when hearing a prediction.
Tetlock laments that television pundits almost never give probabilities for their predictions, so they basically can't be calibrated, furthermore, the events they describe are so vague that it can't even be readily agreed whether or not their predictions were correct even in retrospect. All of these things give a headache to someone who actually wants to hold pundits accountable, and to have a meaningful conversation with people who are influenced by such pundits.
Now consider the phrase "I predict event X will happen by November 31st with 80% probability"- the function of such an utterance is that upon hearing these sounds, an idea will be formed inside my mind, that if we saved the state of the simulator we live in, and ran the simulator from that point 100 times, I should expect to see event X happen in 80 of these histories by November 31st". When a pundit utters the phrase "Zombies will certainly roam the Earth if we implement policy Y", a similar idea is formed in my mind. But where the first statement allowed me to form an idea with a clear timeframe, and a precise level of certainty, listening to the pundit, I have to infer these for myself.
To help me get the best possible understanding from the pundit's words, I can calibrate his words just the same as I can calibrate probabilities. After all, if Freddy Forecaster says "70% probability" for events that happen only 60% of the time, I know to correct, i |
cf016665-bb0e-448f-999e-ad0e3f1eda97 | trentmkelly/LessWrong-43k | LessWrong | A Map of the Lesswrong-O-Sphere of Interest
It's clear to me that we on lesswrong operate on a pile of different interest areas. In my head I am doing a sorting method that divides topics. There's not just topics I (personally) like and topics I don't like but also many sub-topics within those. Here's a list of the domain areas as I see them:
Lesswrong:
* Artificial intelligence
* Programming (not just AI)
* Machine learning
* Ethics
* Math
* Decision theory
* Philosophy
* Effective Altruism (mostly on the EA forum)
* Ethics
* Scientific method and crisis
* Decision problems
* Human Psychology
* Therapy and related personal work
* Biases and related thinking
* Group thinking and how to work with people
* Personal development (other than therapy)
* How humans think (consciousness - overlapping with Post-rationality area)
* Guides for doing things, like problem solving
* Thinking exercises to try
* reports/retrospective reflection on some event that has happened
* Post-rationality
* Buddhism
* Mysticism
* Woo
* map/territory blurring
* Human health and medical
* Wellness
* Longevity
* Diet
* Cryonics (uncommon)
* Society
* Economics
* forecasting/prediction
* History
* Culture
* The state of culture in other places virtually or physically
* Book reviews
* Meta: how lesswrong works or could work.
* Original sequences and relevant theory or ongoing development (as opposed to new sequences)
* Uncommon areas
* Physics
* Politics (discouraged)
* Big/small (new) ideas and theories
--------------
We’re not doing one thing. We’re not even doing three things, we are interested in a whole lot more topics. I could reformat the list to include more questions, along the lines of, “what are we trying to answer when we ask a history question?” but that’s not it. My point here is that we are investigating a diverse bunch of topics, and there’s a str |
31d3798d-6c42-4f1b-a7eb-d9fe946e6a93 | trentmkelly/LessWrong-43k | LessWrong | Where Recursive Justification Hits Bottom
Why do I believe that the Sun will rise tomorrow?
Because I've seen the Sun rise on thousands of previous days.
Ah... but why do I believe the future will be like the past?
Even if I go past the mere surface observation of the Sun rising, to the apparently universal and exceptionless laws of gravitation and nuclear physics, then I am still left with the question: "Why do I believe this will also be true tomorrow?"
I could appeal to Occam's Razor, the principle of using the simplest theory that fits the facts... but why believe in Occam's Razor? Because it's been successful on past problems? But who says that this means Occam's Razor will work tomorrow?
And lo, the one said:
> "Science also depends on unjustified assumptions. Thus science is ultimately based on faith, so don't you criticize me for believing in [silly-belief-#238721]."
As I've previously observed:
> It's a most peculiar psychology—this business of "Science is based on faith too, so there!" Typically this is said by people who claim that faith is a good thing. Then why do they say "Science is based on faith too!" in that angry-triumphal tone, rather than as a compliment?
Arguing that you should be immune to criticism is rarely a good sign.
But this doesn't answer the legitimate philosophical dilemma: If every belief must be justified, and those justifications in turn must be justified, then how is the infinite recursion terminated?
And if you're allowed to end in something assumed-without-justification, then why aren't you allowed to assume anything without justification?
A similar critique is sometimes leveled against Bayesianism—that it requires assuming some prior—by people who apparently think that the problem of induction is a particular problem of Bayesianism, which you can avoid by using classical statistics. I will speak of this later, perhaps.
But first, let it be clearly admitted that the rules of Bayesian updating, do not of themselves solve the problem of induction.
|
79e233ca-f5ba-4ffe-a070-41c032bfb6c0 | trentmkelly/LessWrong-43k | LessWrong | A Rationality Lab Notebook/Workbook/Vade Mecum
I'm interested in keeping a notebook to check my ideas / knowledge on subjects. For example, if I wanted to find out whether there were anything in the notion of ESP that was worth merit, I could create a section titled "ESP", where I'd keep copies of research papers, critical commentary on methodology, questions, personal experiments if any, and so on. There might be some appendixes or cheat sheets with common errors in thinking and information about them, notes on the scientific method/philosophy of science, statistics formulae for estimating error and likelihood and doing hypothesis testing, maybe a few inspirational quotes. I'm pretty busy, so it might be a very backburnered project, but I feel like it could be useful. I can already see some benefits and disadvantages, eg it can be on subjects a person might like to keep private, like dating, overcoming psychological issues, sex, and so on, but peer review might not be as readily available.
Any thoughts on the format or arrangement for something like this? Is anybody doing anything similar? |
ff6c8156-51c2-4ad5-aa83-ce3cd4bb08ad | trentmkelly/LessWrong-43k | LessWrong | Rationality and Relationships
Recently I asked for feedback on two versions of a new post, 'Rationality Lessons from Romance'. 'Version 2' was my original draft. 'Version 1' was a more recent draft edited in response to comments from a collaborator. We wanted to test whether my collaborator's comments genuinely improved the post. Version 2 now sits at 1 upvote and version 1 now sits at 16 upvotes, and I take this to be some evidence in favor of the hypothesis that my collaborator's comments improved the post.
My thanks to those who participated in that experiment; we got the information we wanted!
Thanks also to those of you who provided feedback on either version of the post. Most comments were critical, but this is often the case even with massively upvoted posts like Build Small Skills in the Right Order. My hope is that relationships posts on Less Wrong merely need to be better and more sensitive to a wide variety of sensitivities than my first attempt was, not that sex and relationships are inherently mind-killing topics. There is an amazing interplay between rationality and relationships, and I feel it would be a shame to leave them unexplored.
So I'm trying to learn how LessWrongers can discuss relationships productively.
One difficulty in extracting lessons from the feedback on 'Rationality Lessons from Romance' is that different people had different complaints. Some were 'seriously skeeved-out' by the overtly personal nature of the post, even though earlier posts of a similar personal nature have fared well on Less Wrong, and even though others didn't mind the personal-ness of the post. Some didn't like the promotion of polyamory, others didn't mind. Some thought my interactions with women were unethical, others didn't. Some disagreed with a premise of the post, that sexual jealousy and monogamy are suboptimal for some people. Some felt the brief lessons pulled out from the stories were effective, others didn't. Some thought I was, like Socrates, being skewered for looking at things |
f8d57487-f6a1-45f3-8105-9be46a4c32b4 | trentmkelly/LessWrong-43k | LessWrong | Did OpenAI let GPT out of the box?
At WebGPT: Improving the Factual Accuracy
of Language Models through Web Browsing, OpenAI describes their model:
> We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser. Our prototype copies how humans research answers to questions online—it submits search queries, follows links, and scrolls up and down web pages.
To me that sounds like they decided to let GPT-3 interact with the open internet which is the common standard for what it means to be left out of the box. If my assessement correct? If so, what do we learn from this? |
ad4113bc-c3c0-412b-ba85-ab16eed75993 | trentmkelly/LessWrong-43k | LessWrong | LessWrong.com URL transfer complete, data import will run for the next few hours
As you can easily see when you visit LessWrong.com, the site transfer is complete, and we are now on the new URL. The data import will run for the next few hours, and by tomorrow morning everything should be complete.
Let us know if you run into any problems, find any broken links, or anything of that sort! |
7a7037e2-7e57-414e-b9c2-1533d9c44807 | trentmkelly/LessWrong-43k | LessWrong | [LINK] Different heuristics at different decision speeds
The "intuitive" (fast) interaction heuristic is predisposed towards selflessness, while the "calculated" (slow) decision making process favors greedier behavior: review of game studies.
http://www.ncbi.nlm.nih.gov/pubmed/22996558
Sidenote: Strange to have a Nature paper conflate a faster-payoff / 'egoistic' approach with being 'rational', and to contrast intuition with calculation, of all things. As if not being consciously aware of the calculation implicit in fast "intuitive" decision algorithms implied a lack of cognitive processing. |
9f88329a-1c0a-484a-a1ea-86088e6d4919 | StampyAI/alignment-research-dataset/blogs | Blogs | Point-E: A system for generating 3D point clouds from complex prompts
PointE: A System for Generating 3D Point Clouds from Complex Prompts
Alex Nichol* 1Heewoo Jun* 1Prafulla Dhariwal1Pamela Mishkin1Mark Chen1
Abstract
While recent work on text-conditional 3D ob-
ject generation has shown promising results, the
state-of-the-art methods typically require multi-
ple GPU-hours to produce a single sample. This
is in stark contrast to state-of-the-art generative
image models, which produce samples in a num-
ber of seconds or minutes. In this paper, we ex-
plore an alternative method for 3D object gen-
eration which produces 3D models in only 1-2
minutes on a single GPU. Our method first gener-
ates a single synthetic view using a text-to-image
diffusion model, and then produces a 3D point
cloud using a second diffusion model which con-
ditions on the generated image. While our method
still falls short of the state-of-the-art in terms of
sample quality, it is one to two orders of mag-
nitude faster to sample from, offering a prac-
tical trade-off for some use cases. We release
our pre-trained point cloud diffusion models, as
well as evaluation code and models, at https:
//github.com/openai/point-e .
1. Introduction
With the recent explosion of text-to-image generative mod-
els, it is now possible to generate and modify high-quality
images from natural language descriptions in a number of
seconds (Ramesh et al., 2021; Ding et al., 2021; Nichol
et al., 2021; Ramesh et al., 2022; Gafni et al., 2022; Yu
et al., 2022; Saharia et al., 2022; Feng et al., 2022; Balaji
et al., 2022). Inspired by these results, recent works have
explored text-conditional generation in other modalities,
such as video (Hong et al., 2022; Singer et al., 2022; Ho
et al., 2022b;a) and 3D objects (Jain et al., 2021; Poole et al.,
2022; Lin et al., 2022a; Sanghi et al., 2021; 2022). In this
work, we focus specifically on the problem of text-to-3D
generation, which has significant potential to democratize
3D content creation for a wide range of applications such as
*Equal contribution1OpenAI, San Francisco, USA. Corre-
spondence to: Alex Nichol <alex@openai.com >, Heewoo Jun
<heewoo@openai.com >.virtual reality, gaming, and industrial design.
Recent methods for text-to-3D synthesis typically fall into
one of two categories:
1.Methods which train generative models directly on
paired (text, 3D) data (Chen et al., 2018; Mittal et al.,
2022; Fu et al., 2022; Zeng et al., 2022) or unlabeled
3D data (Sanghi et al., 2021; 2022; Watson et al., 2022).
While these methods can leverage existing generative
modeling approaches to produce samples efficiently,
they are difficult to scale to diverse and complex text
prompts due to the lack of large-scale 3D datasets
(Sanghi et al., 2022).
2.Methods which leverage pre-trained text-image mod-
els to optimize differentiable 3D representations (Jain
et al., 2021; Poole et al., 2022; Lin et al., 2022a). These
methods are often able to handle complex and diverse
text prompts, but require expensive optimization pro-
cesses to produce each sample. Furthermore, due to the
lack of a strong 3D prior, these methods can fall into
local minima which don’t correspond to meaningful or
coherent 3D objects (Poole et al., 2022).
We aim to combine the benefits of both categories by pairing
a text-to-image model with an image-to-3D model. Our text-
to-image model leverages a large corpus of (text, image)
pairs, allowing it to follow diverse and complex prompts,
while our image-to-3D model is trained on a smaller dataset
of(image, 3D) pairs. To produce a 3D object from a text
prompt, we first sample an image using the text-to-image
model, and then sample a 3D object conditioned on the
sampled image. Both of these steps can be performed in a
number of seconds, and do not require expensive optimiza-
tion procedures. Figure 1 depicts this two-stage generation
process.
We base our generative stack on diffusion (Sohl-Dickstein
et al., 2015; Song & Ermon, 2020b; Ho et al., 2020), a re-
cently proposed generative framework which has become
a popular choice for text-conditional image generation.
For our text-to-image model, we use a version of GLIDE
(Nichol et al., 2021) fine-tuned on 3D renderings (Section
4.2). For our image-to-3D model, we use a stack of diffusion
models which generate RGB point clouds conditioned onarXiv:2212.08751v1 [cs.CV] 16 Dec 2022
PointE: A System for Generating 3D Point Clouds from Complex Prompts
Figure 1. A high-level overview of our pipeline. First, a text prompt is fed into a GLIDE model to produce a synthetic rendered view.
Next, a point cloud diffusion stack conditions on this image to produce a 3D RGB point cloud.
“a corgi wearing a
red santa hat”“a multicolored rainbow
pumpkin”“an elaborate fountain” “a traffic cone”
“a vase of purple flowers”“a small red cube is sitting
on top of a large blue cube.
red on top, blue on bottom”“a pair of 3d glasses,
left lens is red right
is blue”“an avocado chair, a chair
imitating an avocado”
“a pair of purple
headphones”“a yellow rubber duck”“a red mug filled
with coffee”“a humanoid robot with
a round head”
Figure 2. Selected point clouds generated by Point E using the given text prompts. For each prompt, we selected one point cloud out of
eight samples.
PointE: A System for Generating 3D Point Clouds from Complex Prompts
images (Section 4.3 and 4.4 detail our novel Transformer-
based architecture for this task). For rendering-based eval-
uations, we go one step further and produce meshes from
generated point clouds using a regression-based approach
(Section 4.5).
We find that our system can often produce colored 3D point
clouds that match both simple and complex text prompts
(See Figure 2). We refer to our system as Point E, since
it generates point clouds efficiently. We release our point
cloud diffusion models, as well as evaluation code and mod-
els, at https://github.com/openai/point-e .
2. Background
Our method builds off of a growing body of work on
diffusion-based models, which were first proposed by Sohl-
Dickstein et al. (2015) and popularized more recently (Song
& Ermon, 2020b;a; Ho et al., 2020).
We follow the Gaussian diffusion setup of Ho et al. (2020),
which we briefly describe here. We aim to sample from
some distribution q(x0)using a neural network approxima-
tionp(x0). Under Gaussian diffusion, we define a noising
process
q(xtjxt 1):=N(xt;p
1 txt 1; tI)
for integer timesteps t2[0;T]. Intuitively, this process
gradually adds Gaussian noise to a signal, with the amount
of noise added at each timestep determined by some noise
schedule t. We employ a noise schedule such that, by the
final timestep t=T, the sample xTcontains almost no
information (i.e. it looks like Gaussian noise). Ho et al.
(2020) note that it is possible to directly jump to a given
timestep of the noising process without running the whole
chain:
xt=p tx0+p
1 t
whereN(0;I)and t:=Qt
s=01 t.
To train a diffusion model, we approximate q(xt 1jxt)as
a neural network p(xt 1jxt). We can then produce a sam-
ple by starting at random Gaussian noise xTand gradually
reversing the noising process until arriving at a noiseless
samplex0. With enough small steps, p(xt 1jxt)can be
parameterized as a diagonal Gaussian distribution, and Ho
et al. (2020) propose to parameterize the mean of this distri-
bution by predicting , the effective noise added to a sample
xt. While Ho et al. (2020) fix the variance ofp(xt 1jxt)
to a reasonable per-timestep heuristic, Nichol & Dhariwal
(2021) achieve better results by predicting the variance as
well as the mean.Diffusion sampling can be cast through the lens of differ-
ential equations (Song et al., 2020), allowing one to use
various SDE and ODE solvers to sample from these models.
Karras et al. (2022) find that a carefully-designed second-
order ODE solver provides a good trade-off between quality
and sampling efficiency, and we employ this sampler for our
point cloud diffusion models.
To trade off sample diversity for fidelity in diffusion mod-
els, several guidance strategies may be used. Dhariwal &
Nichol (2021) introduce classifier guidance, where gradi-
ents from a noise-aware classifier rxtp(yjxt)are used
to perturb every sampling step. They find that increasing
the scale of the perturbation increases generation fidelity
while reducing sample diversity. Ho & Salimans (2021)
introduce classifier-free guidance, wherein a conditional
diffusion model p(xt 1jxt;y)is trained with the class label
stochastically dropped and replaced with an additional ?
class. During sampling, the model’s output is linearly ex-
trapolated away from the unconditional prediction towards
the conditional prediction:
guided :=(xt;?) +s((xt;y) (xt;?))
for some guidance scale s1. This approach is straight-
forward to implement, requiring only that conditioning in-
formation is randomly dropped during training time. We
employ this technique throughout our models, using the
drop probability 0.1.
3. Related Work
Several prior works have explored generative models over
point clouds. Achlioptas et al. (2017) train point cloud auto-
encoders, and fit generative priors (either GANs (Goodfel-
low et al., 2014) or GMMs) on the resulting latent repre-
sentations. Mo et al. (2019) generate point clouds using
a V AE (Kingma & Welling, 2013) on hierarchical graph
representations of 3D objects. Yang et al. (2019) train a
two-stage flow model for point cloud generation: first, a
prior flow model produces a latent vector, and then a second
flow model samples points conditioned on the latent vector.
Along the same lines, Luo & Hu (2021); Cai et al. (2020)
both train two-stage models where the second stage is a
diffusion model over individual points in a point cloud, and
the first stage is a latent flow model or a latent GAN, respec-
tively. Zeng et al. (2022) train a two-stage hierarchical V AE
on point clouds with diffusion priors at both stages. Most
similar to our work, Zhou et al. (2021a) introduce PVD, a
single diffusion model that generates point clouds directly.
Compared to previous point cloud diffusion methods such as
PVD, our Transformer-based model architecture is simpler
and incorporates less 3D-specific structure. Unlike prior
works, our models also produce RGB channels alongside
PointE: A System for Generating 3D Point Clouds from Complex Prompts
point cloud coordinates.
A growing body of work explores the problem of 3D model
generation in representations other than point clouds. Sev-
eral works aim to train 3D-aware GANs from datasets of
2D images (Chan et al., 2020; Schwarz et al., 2020; Chan
et al., 2021; Or-El et al., 2021; Gu et al., 2021; Zhou et al.,
2021b). These GANs are typically applied to the problem
of novel view synthesis in forward-facing scenes, and do
not attempt to reconstruct full 360-degree views of objects.
More recently, Gao et al. (2022) train a GAN that directly
produces full 3D meshes, paired with a discriminator that
inputs differentiably-rendered (Laine et al., 2020) views
of the generated meshes. Bautista et al. (2022) generates
complete 3D scenes by first learning a representation space
that decodes into NeRFs (Mildenhall et al., 2020), and then
training a diffusion prior on this representation space. How-
ever, none of these works have demonstrated the ability to
generate arbitrary 3D models conditioned on open-ended,
complex text-prompts.
Several recent works have explored the problem of text-
conditional 3D generation by optimizing 3D representations
according to a text-image matching objective. Jain et al.
(2021) introduce DreamFields, a method which optimizes
the parameters of a NeRF using an objective based on CLIP
(Radford et al., 2021). Notably, this method requires no
3D training data. Building on this principle, Khalid et al.
(2022) optimizes a mesh using a CLIP-guided objective,
finding that the mesh representation is more efficient to
optimize than a NeRF. More recently, Poole et al. (2022)
extend DreamFields to leverage a pre-trained text-to-image
diffusion model instead of CLIP, producing more coherent
and complex objects. Lin et al. (2022a) build off of this tech-
nique, but convert the NeRF representation into a mesh and
then refine the mesh representation in a second optimization
stage. While these approaches are able to produce diverse
and complex objects or scenes, the optimization procedures
typically require multiple GPU hours to converge, making
them difficult to apply in practical settings.
While the above approaches are all based on optimization
against a text-image model and do not leverage 3D data,
other methods for text-conditional 3D synthesis make use
of 3D data, possibly paired with text labels. Chen et al.
(2018) employ a dataset of text-3D pairs to train a GAN to
generate 3D representations conditioned on text. Liu et al.
(2022) also leverage paired text-3D data to generate models
in a joint representation space. Sanghi et al. (2021) employ
a flow-based model to generate 3D latent representations,
and find some text-to-3D capabilities when conditioning
their model on CLIP embeddings. More recently, Zeng
et al. (2022) achieve similar results when conditioning on
CLIP embeddings, but employ a hierarchical V AE on point
clouds for their generative stack. Mittal et al. (2022) andFu et al. (2022) employ a VQ-V AE (van den Oord et al.,
2017) with an autoregressive prior to sample 3D shapes
conditioned on text labels. More recently, Sanghi et al.
(2022) also employ a VQ-V AE approach, but leverage CLIP
embeddings to avoid the need for explicit text labels in the
dataset. While many of these works demonstrate promising
early results, they tend to be limited to simple prompts or a
narrow set of object categories due to the limited availability
of 3D training data. Our method sidesteps this issue by
leveraging a pre-trained text-to-image model to condition
our 3D generation procedure.
A large body of research focuses on reconstructing 3D mod-
els from single or few images. Notably, this is an underspec-
ified problem, since the model must impute some details not
present in the conditioning image(s). Nevertheless, some
regression-based methods have shown promising results on
this task (Choy et al., 2016; Wang et al., 2018; Gkioxari
et al., 2019; Groueix et al., 2018; Yu et al., 2020; Lin et al.,
2022b). A separate body of literature studies generative ap-
proaches for single- or multi-view reconstruction. Fan et al.
(2016) predict point clouds of objects from single views us-
ing a V AE. Sun et al. (2018) use a hybrid of a flow predictor
and a GAN to generate novel views from few images. Ko-
siorek et al. (2021) use a view-conditional V AE to generate
latent vectors for a NeRF decoder. Watson et al. (2022) em-
ploy an image-to-image diffusion model to synthesize novel
views of an object conditioned on a single view, allowing
many consistent views to be synthesized autoregressively.
4. Method
Rather than training a single generative model to directly
produce point clouds conditioned on text, we instead break
the generation process into three steps. First, we generate
a synthetic view conditioned on a text caption. Next, we
produce a coarse point cloud (1,024 points) conditioned on
the synthetic view. And finally, we produce a fine point
cloud (4,096 points) conditioned on the low-resolution point
cloud and the synthetic view. In practice, we assume that
the image contains the relevant information from the text,
and do not explicitly condition the point clouds on the text.
To generate text-conditional synthetic views, we use a 3-
billion parameter GLIDE model (Nichol et al., 2021) fine-
tuned on rendered 3D models from our dataset ( Section 4.2 ).
To generate low-resolution point clouds, we use a condi-
tional, permutation invariant diffusion model (Section 4.3).
To upsample these low-resolution point clouds, we use a
similar (but smaller) diffusion model which is additionally
conditioned on the low-resolution point cloud (Section 4.4).
We train our models on a dataset of several million 3D
models and associated metadata. We process the dataset
into rendered views, text descriptions, and 3D point clouds
PointE: A System for Generating 3D Point Clouds from Complex Prompts
with associated RGB colors for each point. We describe our
data processing pipeline in more detail in Section 4.1.
4.1. Dataset
We train our models on several million 3D models. We
found that data formats and quality varied wildly across our
dataset, prompting us to develop various post-processing
steps to ensure higher data quality.
To convert all of our data into one generic format, we ren-
dered every 3D model from 20 random camera angles as
RGBAD images using Blender (Community, 2018), which
supports a variety of 3D formats and comes with an opti-
mized rendering engine. For each model, our Blender script
normalizes the model to a bounding cube, configures a stan-
dard lighting setup, and finally exports RGBAD images
using Blender’s built-in realtime rendering engine.
We then converted each object into a colored point cloud
using its renderings. In particular, we first constructed a
dense point cloud for each object by computing points for
each pixel in each RGBAD image. These point clouds typ-
ically contain hundreds of thousands of unevenly spaced
points, so we additionally used farthest point sampling to
create uniform clouds of 4K points. By constructing point
clouds directly from renders, we were able to sidestep vari-
ous issues that might arise from attempting to sample points
directly from 3D meshes, such as sampling points which are
contained within the model or dealing with 3D models that
are stored in unusual file formats.
Finally, we employed various heuristics to reduce the fre-
quency of low-quality models in our dataset. First, we
eliminated flat objects by computing the SVD of each point
cloud and only retaining those where the smallest singular
value was above a certain threshold. Next, we clustered
the dataset by CLIP features (for each object, we averaged
features over all renders). We found that some clusters con-
tained many low-quality categories of models, while other
clusters appeared more diverse or interpretable. We binned
these clusters into several buckets of varying quality, and
used a weighted mixture of the resulting buckets as our final
dataset.
4.2. View Synthesis GLIDE Model
Our point cloud models are conditioned on rendered views
from our dataset, which were all produced using the same
renderer and lighting settings. Therefore, to ensure that
these models correctly handle generated synthetic views,
we aim to explicitly generate 3D renders that match the
distribution of our dataset.
To this end, we fine-tune GLIDE with a mixture of its origi-
nal dataset and our dataset of 3D renderings. Since our 3D
dataset is small compared to the original GLIDE training
Figure 3. Our point cloud diffusion model architecture. Images
are fed through a frozen, pre-trained CLIP model, and the output
grid is fed as tokens into the transformer. Both the timestep t
and noised input xtare also fed in as tokens. The output tokens
corresponding to xtare used to predict and.
set, we only sample images from the 3D dataset 5% of the
time, using the original dataset for the remaining 95%. We
fine-tune for 100K iterations, meaning that the model has
made several epochs over the 3D dataset (but has never seen
the same exact rendered viewpoint twice).
To ensure that we always sample in-distribution renders
(rather than only sampling them 5% of the time), we add a
special token to every 3D render’s text prompt indicating
that it is a 3D render; we then sample with this token at test
time.
4.3. Point Cloud Diffusion
To generate point clouds with diffusion, we extend the frame-
work used by Zhou et al. (2021a) to include RGB colors
for each point in a point cloud. In particular, we represent
a point cloud as a tensor of shape K6, whereKis the
number of points, and the inner dimension contains (x;y;z )
coordinates as well as (R;G;B )colors. All coordinates
and colors are normalized to the range [ 1;1]. We then
generate these tensors directly with diffusion, starting from
random noise of shape K6, and gradually denoising it.
Unlike prior work which leverages 3D-specific architectures
to process point clouds, we use a simple Transformer-based
model (Vaswani et al., 2017) to predict both andcon-
ditioned on the image, timestep t, and noised point cloud
xt. An overview of our architecture can be seen in Figure
3. As input context to this model, we run each point in
the point cloud through a linear layer with output dimen-
sionD, obtaining a KDinput tensor. Additionally, we
run the timestep tthrough a small MLP, obtaining another
D-dimensional vector to prepend to the context.
To condition on the image, we feed it through a pre-trained
PointE: A System for Generating 3D Point Clouds from Complex Prompts
ViT-L/14 CLIP model, take the last layer embeddings from
this CLIP model (of shape 256D0), and linearly project it
into another tensor of shape 256Dbefore prepending it
to the Transformer context. In Section 5.1, we find that this
is superior to using a single CLIP image or text embedding,
as done by Sanghi et al. (2021); Zeng et al. (2022); Sanghi
et al. (2022).
The final input context to our model is of shape (K+257)
D. To obtain a final output sequence of length K, we take
the finalKtokens of output and project it to obtain and
predictions for the Kinput points.
Notably, we do not employ positional encodings for this
model. As a result, the model itself is permutation-invariant
to the input point clouds (although the output order is tied
to the input order).
4.4. Point Cloud Upsampler
For image diffusion models, the best quality is typically
achieved by using some form of hierarchy, where a low-
resolution base model produces output which is then upsam-
pled by another model (Nichol & Dhariwal, 2021; Saharia
et al., 2021; Ho et al., 2021; Rombach et al., 2021). We em-
ploy this approach to point cloud generation by first generat-
ing 1K points with a large base model, and then upsampling
to 4K points using a smaller upsampling model. Notably,
our models’ compute requirements scale with the number
of points, so it is four times more expensive to generate 4K
points than 1K points for a fixed model size.
Our upsampler uses the same architecture as our base model,
with extra conditioning tokens for the low-resolution point
cloud. To arrive at 4K points, the upsampler conditions on
1K points and generates an additional 3K points which are
added to the low-resolution pointcloud. We pass the con-
ditioning points through a separate linear embedding layer
than the one used for xt, allowing the model to distinguish
conditioning information from new points without requiring
the use of positional embeddings.
4.5. Producing Meshes
For rendering-based evaluations, we do not render generated
point clouds directly. Rather, we convert the point clouds
into textured meshes and render these meshes using Blender.
Producing meshes from point clouds is a well-studied, some-
times difficult problem. Point clouds produced by our mod-
els often have cracks, outliers, or other types of noise that
make the problem particularly challenging. We briefly tried
using pre-trained SAP models (Peng et al., 2021) for this
purpose, but found that the resulting meshes sometimes lost
large portions or important details of the shape that were
present in the point clouds. Rather than training new SAP
models, we opted to take a simpler approach.To convert point clouds into meshes, we use a regression-
based model to predict the signed distance field of an ob-
ject given its point cloud, and then apply marching cubes
(Lorensen & Cline, 1987) to the resulting SDF to extract
a mesh. We then assign colors to each vertex of the mesh
using the color of the nearest point from the original point
cloud. For details, see Appendix C.
5. Results
In the following sections, we conduct a number of ablations
and comparisons to evaluate how our method performs and
scales. We adopt the CLIP R-Precision (Park et al., 2021)
metric for evaluating text-to-3D methods end-to-end, using
the same object-centric evaluation prompts as Jain et al.
(2021). Additionally, we introduce a new pair of metrics
which we refer to as P-IS andP-FID , which are point cloud
analogs for Inception Score (Salimans et al., 2016) and FID
(Heusel et al., 2017), respectively.
To construct our P-IS and P-FID metrics, we employ a mod-
ified PointNet++ model (Qi et al., 2017) to extract features
and predict class probabilities for point clouds. For details,
see Appendix B.
5.1. Model Scaling and Ablations
In this section, we train a variety of base diffusion models
to study the effect of scaling and to ablate the importance
of image conditioning. We train the following base models
and evaluate them throughout training:
•40M (uncond.): a small model without any condition-
ing information.
•40M (text vec.): a small model which only conditions
on text captions, not rendered images. The text caption
is embedded with CLIP, and the CLIP embedding is
appended as a single extra token of context. This model
depends on the text captions present in our 3D dataset,
and does not leverage the fine-tuned GLIDE model.
•40M (image vec.): a small model which conditions on
CLIP image embeddings of rendered images, similar
to Sanghi et al. (2021). This differs from the other
image-conditional models in that the image is encoded
into a single token of context, rather than as a sequence
of latents corresponding to the CLIP latent grid.
•40M: a small model with full image conditioning
through a grid of CLIP latents.
•300M: a medium model with full image conditioning
through a grid of CLIP latents.
•1B:a large model with full image conditioning through
a grid of CLIP latents.
PointE: A System for Generating 3D Point Clouds from Complex Prompts
0.25 0.50 0.75 1.00 1.25
training iterations1e65101520P-FID40M (uncond.)
40M (text vec.)
40M (image vec.)
40M
300M
1B
(a) P-FID
0.25 0.50 0.75 1.00 1.25
training iterations1e6910111213P-IS40M (uncond.)
40M (text vec.)
40M (image vec.)
40M
300M
1B
(b) P-IS
0.25 0.50 0.75 1.00 1.25
training iterations1e60.00.10.20.30.4CLIP R-Precision1B
300M
40M
40M (image vec.)
40M (text vec.)
40M (uncond.)
(c) CLIP R-Precision
Figure 4. Sample-based evaluations computed throughout train-
ing across different base model runs. The same upsampler and
conditioning images are used for all runs.
In order to isolate changes to the base model, we use the
same (image conditional) upsampler model for all evalua-
tions, and use the same 306 pre-generated synthetic views
for the CLIP R-Precision evaluation prompts. Here we use
the ViT-L/14 CLIP model to compute CLIP R-Precision,
but we report results with an alternative CLIP model in
Section 5.3.
In Figure 4, we present the results of our ablations. We find
that using only text conditioning with no text-to-image step
results in much worse CLIP R-Precision (see Appendix E
for more details). Furthermore, we find that using a single
CLIP embedding to condition on images is worse than using
a grid of embeddings, suggesting that the point cloud model
benefits from seeing more (spatial) information about the
conditioning image. Finally, we find that scaling our model
improves the speed of P-FID convergence, and increases
final CLIP R-Precision.
(a) Image to point cloud sample for the prompt “a very
realistic 3D rendering of a corgi”.
(b) Image to point cloud sample for the prompt “a traffic
cone”.
Figure 5. Two common failure modes of our model. In the top
example, the model incorrectly interprets the relative proportions
of different parts of the depicted object, producing a tall dog instead
of a short, long dog. In the bottom example, the model cannot
see underneath the traffic cone, and incorrectly infers a second
mirrored cone.
5.2. Qualitative Results
We find that PointE can often produce consistent and high-
quality 3D shapes for complex prompts. In Figure 2, we
show various point cloud samples which demonstrate our
model’s ability to infer a variety of shapes while correctly
binding colors to the relevant parts of the shapes.
Sometimes the point cloud diffusion model fails to under-
stand or extrapolate the conditioning image, resulting in
a shape that does not match the original prompt. We find
that this is usually due to one of two issues: 1) the model
incorrectly interprets the shape of the object depicted in the
image, or 2) the model incorrectly infers some part of the
shape that is occluded in the image. In Figure 5, we present
an example of each of these two failure modes.
5.3. Comparison to Other Methods
As text-conditional 3D synthesis is a fairly new area of re-
search, there is not yet a standard set of benchmarks for this
task. However, several other works evaluate 3D generation
using CLIP R-Precision, and we compare to these methods
in Table 1. In addition to CLIP R-Precision, we also note the
reported sampling compute requirements for each method.
While our method performs worse than the current state-of-
the-art, we note two subtleties of this evaluation which may
explain some (but likely not all) of this discrepancy:
PointE: A System for Generating 3D Point Clouds from Complex Prompts
Table 1. Comparison of Point E to other 3D generative techniques
as measured by CLIP R-Precision (with two different CLIP base
models) on COCO evaluation prompts.50 P100-minutes con-
verted to V100-minutes using conversion rate1
3.yAssuming 2
V100 minutes = 1 A100 minute and 1 TPUv4-minute = 1 A100-
minute. We report DreamFields results from Poole et al. (2022).
Method ViT-B/32 ViT-L/14 Latency
DreamFields 78.6% 82.9% 200V100-hry
CLIP-Mesh 67.8% 74.5% 17V100-min
DreamFusion 75.1% 79.7% 12V100-hry
PointE (40M,
text-only)15.4% 16.2% 16 V100-sec
PointE (40M) 36.5% 38.8% 1.0 V100-min
PointE (300M) 40.3% 45.6% 1.2 V100-min
PointE (1B) 41.1% 46.8% 1.5 V100-min
Conditioning
images69.6% 86.6% -
•Unlike multi-view optimization-based methods like
DreamFusion, Point E does not explicitly optimize ev-
ery view to match the text prompt. This could result in
lower CLIP R-Precision simply because certain objects
are not easy to identify from all angles.
•Our method produces point clouds which must be pre-
processed before rendering. Converting point clouds
into meshes is a difficult problem, and the approach
we use can sometimes lose information present in the
point clouds themselves.
While our method performs worse on this evaluation than
state-of-the-art techniques, it produces samples in a small
fraction of the time. This could make it more practical for
certain applications, or could allow for the discovery of
higher-quality 3D objects by sampling many objects and
selecting the best one according to some heuristic.
6. Limitations and Future Work
While our model is a meaningful step towards fast text-to-
3D synthesis, it also has several limitations. Currently, our
pipeline requires synthetic renderings, but this limitation
could be lifted in the future by training 3D generators that
condition on real-world images. Furthermore, while our
method produces colored three-dimensional shapes, it does
so at a relatively low resolution in a 3D format (point clouds)
that does not capture fine-grained shape or texture. Extend-
ing this method to produce high-quality 3D representations
such as meshes or NeRFs could allow the model’s outputs
to be used for a variety of applications. Finally, our method
could be used to initialize optimization-based techniques to
speed up initial convergence.
We expect that this model shares many of the limitations,
“a 3D printable gear, a single
gear 3 inches in diameter
and half inch thick”
Figure 6. Example of a potential misuse of our model, where it
could be used to fabricate objects in the real world without external
validation.
including bias, as our DALL E 2 system where many of
the biases are inherited from the dataset (Mishkin et al.,
2022). In addition, this model has the ability to support the
creation of point clouds that can then be used to fabricate
products in the real world, for example through 3D printing
(Walther, 2014; Neely, 2016; Straub & Kerlin, 2016). This
has implications both when the models are used to create
blueprints for dangerous objects and when the blueprints are
trusted to be safe despite no empirical validation ( Figure 6 ).
7. Conclusion
We have presented Point E, a system for text-conditional
synthesis of 3D point clouds that first generates synthetic
views and then generates colored point clouds conditioned
on these views. We find that Point E is capable of efficiently
producing diverse and complex 3D shapes conditioned on
text prompts. We hope that our approach can serve as a
starting point for further work in the field of text-to-3D
synthesis.
8. Acknowledgements
We would like to thank everyone behind ChatGPT for creat-
ing a tool that helped provide useful writing feedback.
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Pixel2mesh: Generating 3d mesh models from single rgb
images. arXiv:1804.01654 , 2018.
Watson, D., Chan, W., Martin-Brualla, R., Ho, J., Tagliasac-
chi, A., and Norouzi, M. Novel view synthesis with
diffusion models. arXiv:2210.04628 , 2022.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X.,
and Xiao, J. 3d shapenets: A deep representation for volu-
metric shapes. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR) , June
2015.
Yang, G., Huang, X., Hao, Z., Liu, M.-Y ., Belongie, S., and
Hariharan, B. Pointflow: 3d point cloud generation with
continuous normalizing flows. arXiv:1906.12320 , 2019.
Yu, A., Ye, V ., Tancik, M., and Kanazawa, A. pixel-
nerf: Neural radiance fields from one or few images.
arXiv:2012.02190 , 2020.
Yu, J., Xu, Y ., Koh, J. Y ., Luong, T., Baid, G., Wang, Z.,
Vasudevan, V ., Ku, A., Yang, Y ., Ayan, B. K., Hutchinson,
B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J.,
and Wu, Y . Scaling autoregressive models for content-
rich text-to-image generation. arXiv:2206.10789 , 2022.
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O.,
Fidler, S., and Kreis, K. Lion: Latent point diffusion
models for 3d shape generation. arXiv:2210.06978 , 2022.
Zhou, L., Du, Y ., and Wu, J. 3d shape generation and com-
pletion through point-voxel diffusion. arXiv:2104.03670 ,
2021a.
Zhou, P., Xie, L., Ni, B., and Tian, Q. Cips-3d: A 3d-aware
generator of gans based on conditionally-independent
pixel synthesis. arXiv:2110.09788 , 2021b.
PointE: A System for Generating 3D Point Clouds from Complex Prompts
Table 2. Training hyper-parameters for our point cloud diffusion
models. Width and depth refer to the size of the transformer
backbone.
Model Width Depth LR # Params
Base (40M) 512 12 1e-4 40,466,956
Base (300M) 1024 24 7e-5 311,778,316
Base (1B) 2048 24 5e-5 1,244,311,564
Upsampler 512 12 1e-4 40,470,540
Table 3. Sampling hyperparameters for figures and CLIP R-
Precision evaluations.
Hyperparameter Base Upsampler
Timesteps 64 64
Guidance scale 3.0 3.0
Schurn 3 0
min 1e-3 1e-3
max 120 160
A. Hyperparameters
We train all of our diffusion models with batch size 64 for
1,300,000 iterations. In Table 2, we enumerate the training
hyperparameters that were varied across model sizes. We
train all of our models with 1024 diffusion timesteps. For
our upsampler model, we use the linear noise schedule from
Ho et al. (2020), and for our base models, we use the cosine
noise schedule proposed by Nichol & Dhariwal (2021).
For P-FID and P-IS evaluations, we produce 10K samples
using stochastic DDPM with the full noise schedule. For
CLIP R-Precision and figures in the paper, we use 64 steps
(128 function evaluations) of the Heun sampler from Karras
et al. (2022) for both the base and upsampler models. Table
3 enumerates the hyperparameters used for Heun sampling.
When sampling from GLIDE, we use 150 diffusion steps for
the base model, and 50 diffusion steps for the upsampling
model. We report sampling time for each component of our
stack in Table 4.
B. P-FID and P-IS Metrics
To evaluate P-FID and P-IS, we train a PointNet++ model
on ModelNet40 (Wu et al., 2015) using an open source im-
plementation.1We modify the baseline model in several
ways. First, we double the width of the model, resulting in
roughly 16 million parameters. Next, we apply some addi-
tional data augmentations to make the model more robust to
out-of-distribution samples. In particular, we apply random
1https://github.com/yanx27/Pointnet_
Pointnet2_pytorchTable 4. Sampling performance for various components of our
model. We use the Karras sampler for our base and upsampler
models, but not for GLIDE.
Model V100 seconds
GLIDE 46.28
Upsampler (40M) 12.58
Base (40M) 3.35
Base (300M) 12.78
Base (1B) 28.67
rotations to each point cloud, and we add Gaussian noise to
the points with standard deviation sampled from U[0;0:01].
To compute P-FID, we extract features for each point cloud
from the layer before the final ReLU activation. To compute
P-IS, we use the predicted class probabilities for the 40
classes from ModelNet40. We note that our generative
models are trained on a dataset which only has P-IS 12.95,
so our best reported P-IS score of 13is near the expected
upper bound.
C. Mesh Extraction
To convert point clouds into meshes, we train a model which
predicts SDFs from point clouds and apply marching cubes
to the resulting SDFs. We parametrize our SDF model as
an encoder-decoder Transformer. First, an 8-layer encoder
processes the input point cloud as an unordered sequence,
producing a sequence of hidden representations. Then, a
4-layer cross-attention decoder takes 3D coordinates and
the sequence of latent vectors, and predicts an SDF value.
Each input query point is processed independently, allowing
for efficient batching. Using more layers in the encoder and
fewer in the decoder allows us to amortize the encoding cost
across many query points.
We train our SDF regression model on a subset of 2.4 mil-
lion manifold meshes from our dataset, and add Gaussian
noise with= 0:005to the point clouds as data augmenta-
tion. We train the model f(x)to predict the SDF ywith a
weighted L1 objective:
(
1jjf(x) yjj1f(x)>y
4jjf(x) yjj1f(x)y
Here, we define the SDF such that points outside of the sur-
face have negative sign. Therefore, in the face of uncertainty,
the model is encouraged predict that points are inside the
surface. We found this to be helpful in initial experiments,
likely because it helps prevent the resulting meshes from
effectively ignoring thin or noisy parts of the point cloud.
When producing meshes for evaluations, we use a grid size
PointE: A System for Generating 3D Point Clouds from Complex Prompts
Figure 7. Examples of point clouds (left) and corresponding ex-
tracted meshes (right). We find that our method often produces
smooth meshes and removes outliers (middle row), but can some-
times miss thin/sparse parts of objects (bottom row).
of128128128, resulting in 1283queries to the SDF
model. In Figure 7, we show examples of input point clouds
and corresponding output meshes from our model. We
observe that our method works well in many cases, but
sometimes fails to capture thin or sparse parts of a point
cloud.
D. Conditioning on DALL E 2 Samples
In our main experiments, we use a specialized text-to-image
model to produce in-distribution conditioning images for
our point cloud models. In this section, we explore what
happens if we use renders from a pre-existing text-to-image
model, DALLE 2.
In Figure 8, we present three image-to-3D examples where
the conditioning images are generated by DALL E 2. We
find that DALLE 2 tends to include shadows under objects,
and our point cloud model interprets these as a dark ground
plane. We also find that our point cloud model can misinter-
pret shapes from the generated images when the objects take
up too much of the image. In these cases, adding a border
around the generated images can improve the reconstructed
shapes.
Figure 8. Examples of point clouds reconstructed from DALL 2
generations. The top image was produced using the prompt “a
3d rendering of an avocado chair, chair imitating an avocado, full
view, white background”. The middle image was produced using
the prompt “a simple full view of a 3d rendering of a corgi in front
of a white background”. The bottom image is the same as the
middle image, but with an additional white border.
E. Pure Text-Conditional Generation
In Section 5.1, we train a pure text-conditional point cloud
model without an additional image generation step. While
we find that this model performs worse on evaluations than
our full system, it still achieves non-trivial results. In this
section, we explore the capabilities and limitations of this
model.
In Figure 9, we show examples where our text-conditional
model is able to produce point clouds matching the pro-
vided text prompt. Notably, these examples include simple
prompts that describe single objects. In Figure 10, we show
examples where this model struggles with prompts that com-
bine multiple concepts.
Finally, we expect that this model has inherited biases from
our 3D dataset. We present one possible example of this in
Figure 11, wherein the model produces longer and narrower
objects for the prompt “a woman” than for the prompt “a
man” when using a fixed diffusion noise seed.
PointE: A System for Generating 3D Point Clouds from Complex Prompts
“a motorbike” “a dog”
“a desk lamp” “a guitar”
“an ambulance” “a laptop computer”
Figure 9. Selected point clouds generated by our pure text-
conditional 40M parameter point cloud diffusion model.
(a) Prompt: “a small red cube is sitting on top of a large blue
cube. red on top, blue on bottom”
(b) Prompt: “a corgi wearing a red santa hat”
Figure 10. Sample grids where our small, pure text-conditional
model fails to understand complex prompts.
(a) Prompt: “a man”
(b) Prompt: “a woman”
Figure 11. Sample grids from our pure text-conditional 40M pa-
rameter model. Samples in the top grid use the same noise seed as
the corresponding samples in the bottom grid. |
cb9d52b2-b864-41c5-8ff7-2fdaab9d0ee5 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | [AN #158]: Should we be optimistic about generalization?
Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter **[resources here](http://rohinshah.com/alignment-newsletter/)**. In particular, you can look through **[this spreadsheet](https://docs.google.com/spreadsheets/d/1PwWbWZ6FPqAgZWOoOcXM8N_tUCuxpEyMbN1NYYC02aM/edit?usp=sharing)** of all summaries that have ever been in the newsletter.
Audio version **[here](http://alignment-newsletter.libsyn.com/alignment-newsletter-158)** (may not be up yet).
Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer.
HIGHLIGHTS
===========
**[A naive alignment strategy and optimism about generalization](https://www.alignmentforum.org/posts/QvtHSsZLFCAHmzes7/a-naive-alignment-strategy-and-optimism-about-generalization)** *(Paul Christiano)* (summarized by Rohin): We want to build an AI system that answers questions honestly, to the best of its ability. One obvious approach is to have humans generate answers to questions, select the question-answer pairs where we are most confident in the answers, and train an AI system on those question-answer pairs.
(I’ve described this with a supervised learning setup, but we don’t have to do that: we could also **[learn](https://deepmind.com/blog/learning-through-human-feedback/)** from **[comparisons](https://ai-alignment.com/optimizing-with-comparisons-c02b8c0d7877)** between answers, and we only provide comparisons where we are confident in the comparison.)
What will the AI system do on questions where we *wouldn’t* be confident in the answers? For example, questions that are complex, where we may be misled by bad observations, where an adversary is manipulating us, etc.
One possibility is that the AI system learned the **intended policy**, where it answers questions honestly to the best of its ability. However, there is an **instrumental policy** which also gets good performance: it uses a predictive model of the human to say whatever a human would say. (This is “instrumental” in that the model is taking the actions that are instrumental to getting a low loss, even in the test environment.) This will give incorrect answers on complex, misleading, or manipulative questions -- *even if* the model “knows” that the answer is incorrect.
Intuitively, “answer as well as you can” feels like a much simpler way to give correct answers, and so we might expect to get the intended policy rather than the instrumental policy. This view (which seems common amongst ML researchers) is *optimism about generalization*: we are hoping that the policy generalizes to continue to answer these more complex, misleading, manipulative questions to the best of its ability.
Are there reasons to instead be pessimistic about generalization? There are at least three:
1. If the answers we train on *aren’t* perfectly correct, the instrumental policy might get a *lower* training loss than the intended policy (which corrects errors that humans make), and so be more likely to be found by gradient descent.
2. If the AI already needs to make predictions about humans, it may not take much “additional work” to implement the instrumental policy. Conversely, if the AI reasons at a different level of abstraction than humans, it may take a lot of “additional work” to turn correct answers in the AI’s ontology into correct answers in human ontologies.
3. From **[a followup post](https://www.alignmentforum.org/posts/SRJ5J9Tnyq7bySxbt/answering-questions-honestly-given-world-model-mismatches)**, the AI system might answer questions by translating its concepts to human concepts or observations, and then deduce the answer from those concepts or observations. This will systematically ignore information that the AI system understands that isn’t represented in the human concepts or observations. (Consider the **[example](https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/)** of the robot hand that only *looked* like it was grasping the appropriate object.)
A possible fourth problem: if the AI system did the deduction in its own concepts and only as a final step translated it to human concepts, we might *still* lose relevant information. This seems not too bad though -- it seems like we should at least be able to **[explain the bad effects of a catastrophic failure](https://www.alignmentforum.org/posts/rArsypGqq49bk4iRr/can-there-be-an-indescribable-hellworld)** (**[AN #44](https://mailchi.mp/6bfac400a0c3/alignment-newsletter-44)**) in human concepts, even if we can’t explain why that failure occurred.
A **[followup post](https://www.alignmentforum.org/posts/roZvoF6tRH6xYtHMF/avoiding-the-instrumental-policy-by-hiding-information-about)** considers whether we could avoid the instrumental policy by **[preventing it from learning information about humans](https://www.alignmentforum.org/posts/BKjJJH2cRpJcAnP7T/thoughts-on-human-models)** (**[AN #52](https://mailchi.mp/1e757d9b05cb/alignment-newsletter-52)**), but concludes that while it would solve the problems outlined in the post, it seems hard to implement in practice.
TECHNICAL AI ALIGNMENT
=======================
ROBUSTNESS
-----------
**[Experimentally evaluating whether honesty generalizes](https://www.alignmentforum.org/posts/BxersHYN2qcFoonwg/experimentally-evaluating-whether-honesty-generalizes)** *(Paul Christiano)* (summarized by Rohin): The highlighted post introduced the notion of optimism about generalization. On this view, if we train an AI agent on question-answer pairs (or comparisons) where we are confident in the correctness of the answers (or comparisons), the resulting agent will continue to answer honestly even on questions where we wouldn’t be confident of the answer.
While we can’t test exactly the situation we care about -- whether a superintelligent AI system would continue to answer questions honestly -- we *can* test an analogous situation with existing large language models. In particular, let’s consider the domain of unsupervised translation: we’re asking a language model trained on both English and French to answer questions about French text, and we (the overseers) only know English.
We could finetune the model on answers to questions about grammar ("Why would it have been a grammatical error to write Tu Vas?") and literal meanings ("What does Defendre mean here?"). Once it performs well in this setting, we could then evaluate whether the model generalizes to answer questions about tone ("Does the speaker seem angry or sad about the topic they are discussing?"). On the optimism about generalization view, it seems like this should work. It is intentional here that we only finetune on two categories rather than thousands, since that seems more representative of the case we’ll actually face.
There are lots of variants which differ in the type of generalization they are asking for: for example, we could finetune a model on all questions about French text and German text, and then see whether it generalizes to answering questions about Spanish text.
While the experiments as currently suggested probably won’t show good generalization, a variant that could support it would be one in which we train for *plausibility*. In our original example, we finetune on correct answers for grammar and literal meanings, and then we *also* finetune to have the model give *plausible* answers to tone (i.e. when asked about tone, instead of saying “en colère means 'angry'”, the model says “the author is angry, as we can see from the use of ‘en colère’”). It seems possible that this combination leads to the model giving actually correct answers about tone, just because “honestly report your best guess” seems like the simplest policy that meets all of these criteria.
**[Teaching ML to answer questions honestly instead of predicting human answers](https://www.alignmentforum.org/posts/QqwZ7cwEA2cxFEAun/teaching-ml-to-answer-questions-honestly-instead-of)** *(Paul Christiano)* (summarized by Rohin): This post presents an algorithm that aims to solve the second problem from the highlighted post. As a reminder, the second problem is that an AI system that already has to make predictions about humans might learn a policy that is just “say what humans would say”, since that is simpler than learning another translation that maps its knowledge to human language (so that it can answer honestly to the best of its knowledge).
The core idea is to have a “simple” labeling process and a “complex” labeling process, where the complex process can catch errors from the simple process. We’ll also assume we have a simple / complex dataset (labeled by the simple / complex processes respectively), where we are confident that all of the answers in the dataset are correct. This means that the complex dataset has more challenging questions than the simple one. The *simple / complex instrumental policy* is the policy that predicts what the simple / complex labeling process would do.
As an inefficient first algorithm, we can train our model on answers from the simple process where we are confident in their correctness, and then simply check whether the model performs well on new, harder questions where the complex and simple processes disagree. If the model fails this check, we restart from scratch. Intuitively, when the model eventually passes the check, it has probably learned the intended policy, as that’s the one which would generalize zero-shot to complex questions as well (and in particular, the simple instrumental policy would *not* generalize in this way).
Of course, we need a more efficient algorithm than that. Instead of creating an algorithm where the intended policy would work but the instrumental policy wouldn’t, could we instead change the inductive biases so that the intended policy is *likely* while the instrumental policy is not? Our original worry was that the intended policy has to bake in two implementations of language -- one in its world model, and one when translating answers into human-understandable concepts. So we could instead try to train a model that learns language from the simple instrumental policy, but is also trained on the complex dataset. The hope would be that the intended policy can learn the second implementation of language “for free” from the simple instrumental policy, while still working on the complex dataset. The actual details are quite complex and I’m not going to go into them here.
**[This post](https://www.alignmentforum.org/posts/gEw8ig38mCGjia7dj/answering-questions-honestly-instead-of-predicting-human)** by Evan Hubinger points out some problems and potential solutions with the approach.
FORECASTING
------------
**[AXRP Episode 10 - AI’s Future and Impacts](https://www.alignmentforum.org/posts/xbABZRxoSTAnsf8os/axrp-episode-10-ai-s-future-and-impacts-with-katja-grace)** *(Daniel Filan and Katja Grace)* (summarized by Rohin): This podcast goes over various strands of research from **[AI Impacts](https://aiimpacts.org/)**, including lots of work that I either haven’t covered or have covered only briefly in this newsletter:
**AI Impacts’ methodology.** AI Impacts aims to advance the state of knowledge about AI and AI risk by recursively decomposing important high-level questions and claims into subquestions and subclaims, until reaching a question that can be relatively easily answered by gathering data. They generally aim to provide new facts or arguments that people haven’t considered before, rather than arguing about how existing arguments should be interpreted or weighted.
**Timelines.** AI Impacts is perhaps most famous for its **[survey of AI experts](https://arxiv.org/abs/1705.08807)** on timelines till high-level machine intelligence (HLMI). The author’s main takeaway is that people give very inconsistent answers and there are huge effects based on how you frame the question. For example:
1. If you estimate timelines by asking questions like “when will there be a 50% chance of HLMI”, you’ll get timelines a decade earlier than if you estimate by asking questions like “what is the chance of HLMI in 2030”.
2. If you ask about when AI will outperform humans at all tasks, you get an estimate of ~2061, but if you ask when all occupations will be automated, you get an estimate of ~2136.
3. People whose undergraduate studies were in Asia estimated ~2046, while those in North America estimated ~2090.
The survey also found that the median probability of outcomes approximately as bad as extinction was 5%, which the author found surprisingly high for people working in the field.
**Takeoff speeds.** A common disagreement in the AI alignment community is whether there will be a discontinuous “jump” in capabilities at some point. AI Impacts has three lines of work investigating this topic:
1. Checking how long it typically takes to go from “amateur human” to “expert human”. For example, it took about **[3 years](https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-imagenet-image-classification/)** for image classification on ImageNet, **[38 years](https://aiimpacts.org/time-for-ai-to-cross-the-human-range-in-english-draughts/)** on checkers, **[21 years](https://aiimpacts.org/time-for-ai-to-cross-the-human-range-in-starcraft/)** for StarCraft, **[30 years](https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-go/)** for Go, **[30 years](https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-chess/)** for chess, and ~3000 years for clock stability (how well you can measure the passage of time).
2. Checking **[how often particular technologies have undergone discontinuities in the past](https://aiimpacts.org/discontinuous-progress-in-history-an-update/)** (**[AN #97](https://mailchi.mp/a2b5efbcd3a7/an-97-are-there-historical-examples-of-large-robust-discontinuities)**). A (still uncertain) takeaway would be that discontinuities are the kind of thing that legitimately happen sometimes, but they don’t happen so frequently that you should expect them, and you should have a pretty low prior on a discontinuity happening at some specific level of progress.
3. Detailing **[arguments](https://aiimpacts.org/likelihood-of-discontinuous-progress-around-the-development-of-agi/)** for and against discontinuous progress in AI.
**Arguments for AI risk, and counterarguments.** The author has also spent some time thinking about how strong the arguments for AI risk are, and has focused on a few areas:
1. Will superhuman AI systems actually be able to far outpace humans, such that they could take over the world? In particular, it seems like humans can use non-agentic tools to help keep up.
2. Maybe the AI systems we build won’t have goals, and so the argument from **[instrumental subgoals](https://selfawaresystems.files.wordpress.com/2008/01/ai_drives_final.pdf)** won’t apply.
3. Even if the AI systems do have goals, they may have human-compatible goals (especially since people will be explicitly trying to do this).
4. The AI systems may not destroy everything: for example, they might instead simply trade with humans, and use their own resources to pursue their goals while leaving humans alone.
AI GOVERNANCE
==============
**[Decoupling deliberation from competition](https://www.alignmentforum.org/posts/7jSvfeyh8ogu8GcE6/decoupling-deliberation-from-competition)** *(Paul Christiano)* (summarized by Rohin): Under a **[longtermist](https://forum.effectivealtruism.org/tag/longtermism)** lens, one problem to worry about is that even after building AI systems, humans will spend more time competing with each other rather than figuring out what they want, which may then lead to their values changing in an undesirable way. For example, we may have powerful persuasion technology that everyone uses to persuade people to their line of thinking; it seems bad if humanity’s values are determined by a mix of effective persuasion tools, especially if persuasion significantly diverges from truth-seeking.
One solution to this is to coordinate to *pause* competition while we deliberate on what we want. However, this seems rather hard to implement. Instead, we can at least try to *decouple* competition from deliberation, by having AI systems acquire **[flexible influence](https://www.alignmentforum.org/posts/nRAMpjnb6Z4Qv3imF/the-strategy-stealing-assumption)** (**[AN #65](https://mailchi.mp/3d4e6c2c206f/an-65learning-useful-skills-by-watching-humans-play)**) on our behalf (competition), and having humans separately thinking about what they want (deliberation). As long as the AI systems are competent enough to shield the humans from the competition, the results of the deliberation shouldn’t depend too much on competition, thus achieving the desired decoupling.
The post has a bunch of additional concrete details on what could go wrong with such a plan that I won’t get into here.
NEWS
=====
**[Building and Evaluating Ethical Robotic Systems](https://ers-workshop.com/)** *(Justin Svegliato, Samer Nashed et al)* (summarized by Rohin): This workshop at IROS 2021 asks for work on ethical robotic systems, including value alignment as a subtopic. Notably, they also welcome researchers from disciplines beyond robotics, including philosophy, psychology, sociology, and law. The paper submission deadline is August 13.
**[Survey: classifying AI systems used in response to the COVID-19 pandemic](https://www.oecd.ai/wonk/pandemic)** *(Samuel Curtis et al)* (summarized by Rohin): A team at The Future Society aims to build a living database of AI systems used to respond to COVID, classified using the **[OECD framework](https://www.oecd.ai/classification)**. I think this is an interesting example of building capacity for effective AI governance. If you were involved in developing an AI system used in the COVID response, they ask that you take **[this survey](https://survey.oecd.org/index.php?r=survey/index&sid=145657&lang=en)** by August 2nd.
#### **FEEDBACK**
I'm always happy to hear feedback; you can send it to me, **[Rohin Shah](https://rohinshah.com/)**, by **replying to this email**.
#### **PODCAST**
An audio podcast version of the **Alignment Newsletter** is available. This podcast is an audio version of the newsletter, recorded by **[Robert Miles](http://robertskmiles.com/)**. |
9a70f542-e34e-4138-9161-f8c2ecc3be35 | trentmkelly/LessWrong-43k | LessWrong | The fraught voyage of aligned novelty
[Metadata: crossposted from https://tsvibt.blogspot.com/2023/03/the-fraught-voyage-of-aligned-novelty.html. First completed March 11, 2023.]
A voyage of novelty is fraught. If a mind takes a voyage of novelty, an observer is hard pressed to understand what the growing mind is thinking or what effects the growing mind will have on the world.
Fraughtness
> If a mind grows beyond human activity then it thinks in a language you don't speak, using skills you don't possess, derived from sources you don't share. The mind incorporates new ideas, invalidating your predictions and specifications for how the mind affects the world.
"Fraught" is cognate with "freight": fraughtness is ladenness. "Fraught" is an intensive prefix "fra-" (cognate with English "for-", "fro", Ancient Greek "πρό-") followed by a descendant of Proto-Germanic "aiganą", meaning "to possess". From that etymon comes English "owe", "ought", and "own", and German "eigen". To be fraught is to be laden by intensely possessing.
Extended table of contents:
* Understanding: If a mind takes a far-ranging voyage of novelty, it's difficult for an observer to understand what the mind thinks, knows, and does.
* Neurath's ship: Novelty involves change. Some of that change deeply changes how the mind operates and how the mind connects its capabilities to the effects it has on the world. Previous understanding of the mind, and in particular previously correct models of the mind's effects, might not stay valid across these changes.
* Alienness: A mind that gains a lot of structure not possessed by humans is alien to humans. It has structure (content, generators, contexts) not shared by humans. So it's hard for humans to understand the mind.
* Inexplicitness: Structure in the mind is often unavailable for relation within the mind. Such structure tends to also be unavailable for relation with an observer.
* Noncomprehensiveness: Creativity, inexplicitness, provisionality, noncartesianness, and integration |
ab849614-36d1-4e3e-b88a-34931b00f5c4 | trentmkelly/LessWrong-43k | LessWrong | [gooey request-for-help] I don't know what to do.
deleted |
2c70fa15-128e-45d3-9398-e12bc4dc7b53 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Mesa-Optimizers and Over-optimization Failure (Optimizing and Goodhart Effects, Clarifying Thoughts - Part 4)
In the previous posts, I first outlined [Selection versus Control for Optimization](https://www.alignmentforum.org/posts/2neeoZ7idRbZf4eNC/re-introducing-selection-vs-control-for-optimization), then talked about [What Optimization Means](https://www.alignmentforum.org/posts/BEMvcaeixt3uEqyBk/what-does-optimization-mean-again-optimizing-and-goodhart), and how we quantify it, then [applied these ideas](https://www.alignmentforum.org/posts/zdeYiQgwYRs2bEmCK/applying-overoptimization-to-selection-vs-control-optimizing) a bit to ground the discussion.
After doing so, I reached a point where I think that there is something useful to say about mesa-optimizers. This isn't yet completely clear to me, and it seems likely that at some point in the (hopefully near) future, someone will build a much clearer conceptual understanding, and I'll replace this with a link to that discussion. Until then, I want to talk about how mesa-optimizers are control systems built within selection systems, and why that poses significant additional concern for alignment.
Mesa-Optimizers
===============
I claimed in the previous post that Mesa-optimizers are always control systems. The base optimization selects for a mesa-optimizer, usually via side-effect-free or low-cost sampling and/or simulation, then creates a system that does further optimization as an output. In some cases, the further optimization is direct optimization in the terms laid out in my earlier post, but in others it is control.
Direct Mesa-Optimizers
----------------------
In many cases, the selection system finds optimal parameters or something similar for a direct optimization system. This is exactly the earlier example of building a guidance system for a rocket is an obvious class of example where selection leads to a direct optimizer. This isn't the only way that optimizers can interact, though.
I'd say that MCTS + Deep learning is an important example of this mix which [has been compared to "Thinking Fast and Slow"](https://papers.nips.cc/paper/7120-thinking-fast-and-slow-with-deep-learning-and-tree-search.pdf) (pdf, NIPS paper). In chess, for example, the thinking fast is the heuristic search to choose where to explore, which is based on a selection system, and the exploration is MCTS, which in this context I'm calling direct optimization. (This is despite the fact that it's obviously probabilistic, so in some respects looks like selection, because while the selection IS choosing points in the space, it isn't doing evaluation, but rather deterministic play-forward of alternative scenarios. The evaluation is being done by the heuristic evaluation system.) In that specific scenario, any misalignment is almost entirely a selection system issue - unless there was some actual mistake in the implementation of the rules of chess.
This opens up a significant concern for causal Goodhart; regime change will plausibly have particularly nasty effects. This is because the directly optimizing mesa-optimizer isn't at all able to consider whether the parameters selected by the base-optimizer should be reconsidered. And this is far worse for "true" control mesa-optimizers.
Control Mesa-optimizers
-----------------------
Before we talk more about failure modes, it's important to clarify two levels of failure; the base-optimizer can fail to achieve its goals because it designs a misaligned mesa-optimizer, and the mesa-optimizer itself can fail to achieve its goals. The two failure modes are different, because we don't have any reason to assume a-priori that our mesa-optimizer shares goals with our base optimizer.
Just like humans are adaptation-executioners, Mesa-optimizers are mesa-optimizers, not simply optimizers. If their adaptations are instead tested against the true goal, or at least the base-optimizers goal, then evaluated on that basis, they aren't mesa-optimizers, they are trials for the selection optimizer. Note that these two cases aren't necessarily incompatible; in something like Google's [federated learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) model, the shared model is updated based on the control system's data. So self-driving cars may be mesa-optimizers using a neural net, and the data gathered is later used to update the base-optimizer model, which is then given back to the agents. The two parts of the system can therefore suffer different types of failures, but at least the post-hoc updating seems to plausibly reduce misalignment of the mesa-optimizer.
But in cases where side-effects occur, so that the control mesa-optimizer imposes externalities on the system, the mesa-optimizer typically won't share goals with the base optimizer! This is because if it shares goals exactly, the base optimization doesn't need to build a mesa-optimizer, it can run tests without ceding direct control. (Our self-driving cars in the previous paragraph are similar to this, but their goal is the trained network's implicit goal, not the training objective used for the update. In such in a federated learning model, it can fail and the trial can be used for better learning the model.) On the other hand, if no side-effects occur and goals are shared, the difference is irrelevant; the mesa-optimizer can fail costlessly and start over.
Failure of Mesa-optimizers as Principle-Agent-type "Agents"
-----------------------------------------------------------
There is a clear connection to principal-agent problems. (Unfortunately, the term agent is ambiguous, since we also want to discuss embedded agents. In this section, that's not what is being discussed.) Mesa-optimizers can succeed at their goals but fail to be reliable agents, or they can fail even at their own goals. I'm unsure about this, but it seems each of these cases should be considered separately. Earlier, I noted that some Goodhart failures are model failures. With mesa-optimizers involved, there are two sets of models - the base optimizer model, and the mesa-optimizer model.
Principal optimization failures occur either if the mesa-optimiser itself falls prey to a Goodhart failure due to shared failures in the model, or if the mesa-optimizer model or goals are different than the principal's in ways that allow the metrics not to align with the principals' goals. (Abrams correctly noted in an earlier comment that this is misalignment. I'm not sure, but it seems this is principally a terminology issue.)
This second form allows a new set of failures. I'm even less sure about this next, but I'll suggest that we can usefully categorize the second class into 3 cases; mesa-superoptimizers, mesa-suboptimizers, and mesa-transoptimizers. The first, mesa-superoptimizers, is where the Mesa-optimizer is able to find clever ways to get around the (less intelligent) base optimizer's model. This allows all of the classic Goodhart's law-type failures, but they occur between the Mesa-optimizer and the based-optimizer, rather than between the human controller and the optimizer. This case includes the classic runaway superintelligence problems. The second, mesa-suboptimizers, is where the mesa-optimizer uses a simpler model than the base optimizer, and hits a Goodhart failure that the base-optimizer could have avoided. (Let's say, for example, that it uses a correlational model holding certain factors known by the base optimizer to influence the system constant, and for whatever reason the mesa-optimizer enters a regime-change region, where those factors change in ways that the base-model understands.) Lastly, there are mesa-transoptimizers, where typical human types of principle-agent failures can occur because the mesa-optimizer has different goals. The other way this occurs is if the mesa-optimizer has access to or builds a different model than the base-optimzer. This is a bit different than mesa-superoptimizers, and it seems likely that there are a variety of cases in this last category. I'd suggest that it may be more like multi-agent failures than it is like a traditional superintelligence alignment problem.
On to Embedded Agents
---------------------
I need to think further about the above, and should probably return to it, but for now I plan to make the (eventual) next post about embedded agency in this context. Backing up from the discussing on Mesa-optimizers, a key challenge for building safe optimizers in general is that control often involves embedded agent issues, where the model must be smaller than the system. In particular, in the case of mesa-optimizers, the base-optimizer needs to think of itself as an embedded agent whose model needs to include the mesa-optimizer's behavior, which is being chosen by the base-optimizer. This isn't quite embedded agency, but it requires the base optimizer to be "larger" than the mesa-optimizer, only allowing mesa-suboptimizers, which is unlikely to be guaranteed in general. |
32477444-8a39-42c4-8cfd-f5e3a994f28d | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Tools want to become agents
In the spirit of "[satisficers want to become maximisers](/lw/854/satisficers_want_to_become_maximisers/)" here is a somewhat weaker argument (growing out of a discussion with Daniel Dewey) that "[tool](/lw/cbs/thoughts_on_the_singularity_institute_si/) [AIs](/lw/cze/reply_to_holden_on_tool_ai/)" would want to become agent AIs.
The argument is simple. Assume the tool AI is given the task of finding the best plan for achieving some goal. The plan must be realistic and remain within the resources of the AI's controller - energy, money, social power, etc. The best plans are the ones that use these resources in the most effective and economic way to achieve the goal.
And the AI's controller has one special type of resource, uniquely effective at what it does. Namely, the AI itself. It is smart, potentially powerful, and could self-improve and pull all the usual AI tricks. So the best plan a tool AI could come up with, for almost any goal, is "turn me into an agent AI with that goal." The smarter the AI, the better this plan is. Of course, the plan need not read literally like that - it could simply be a complicated plan that, as a side-effect, turns the tool AI into an agent. Or copy the AI's software into a agent design. Or it might just arrange things so that we always end up following the tool AIs advice and consult it often, which is an indirect way of making it into an agent. Depending on [how we've programmed the tool AI's preferences](/lw/cfd/tools_versus_agents/), it might be motivated to mislead us about this aspect of its plan, concealing the secret goal of unleashing itself as an agent.
In any case, it does us good to realise that "make me into an agent" is what a tool AI would consider the *best possible plan* for many goals. So without a hint of agency, it's motivated to make us make it into a agent. |
656829f0-1707-4921-87e1-eea04349337e | trentmkelly/LessWrong-43k | LessWrong | Meetup : San Francisco Meetup: Keep your identity small
Discussion article for the meetup : San Francisco Meetup: Keep your identity small
WHEN: 29 June 2015 06:15:00PM (-0700)
WHERE: 1061 Market St #4, San Francisco, CA 94103
We'll be discussing Paul Ghram's essay Keep Your Identity Small.
Reading these beforehand is optional but encouraged!
As usual, call 301-458-0764 to be let in, and feel free to show up late.
Discussion article for the meetup : San Francisco Meetup: Keep your identity small |
a0556204-0bc8-47cf-8f33-0a2d327563b2 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Safe Scrambling?
Status: half-formed thought on a potential piece of an alignment strategy that I've not heard discussed but probably exists somewhere, might just be missing a concept name.
Idea
----
Any alignment scheme that plans to allow potentially unsafe systems to be trained but then tests their safety before deploying runs the risk of being uncompetitive by requiring the system to be trained many times before a safe system is found.
One solution to this problem is to find ways of 'scrambling' an unsafe system to quickly create many variants of the system. It would require that in some of the new systems, the subsystems that have learned to perform to a high level are retained, or remain sufficiently intact to be retrained far more quickly, without retaining the elements that made the original unsafe.
Has there been much work in this area? I'm not really sure where to start looking, but curious to pursue since it suggests a practical research direction that could be worked on with current systems and plausibly assist alignment proposals.
Thoughts on scrambling as a safety component
--------------------------------------------
Retaining some of the previous structures would be likely to result in the same incentive gradients that led to the development of an unsafe system in the first place, reducing the safety of the system.
More worryingly, it could result in being able to quickly train a variety of unsafe systems, thereby acting as an adversarial attack on whatever safety-checker is in place, so you'd be wary of using such a system unless you were very confident in the robustness of the safety system.
On the other hand, we might expect that if the computation can loosely be decomposed into that which allows good performance in training and that which provides potentially unsafe goals then scrambling might prove an easy way to retrain the offending subsection.
The crux of this would potentially be the extent to which the nature of the non-alignment is bound up with the computation which makes it successful. For example, a reasoner which deduces correct answers indirectly as an emergent property of maximising some non-aligned goal would be difficult to decompose in this way, whereas a system which directly solves the given problem, but only under certain conditions which always hold in the training distribution might be decomposed more easily.
If we were picking functions according to NNs with gradient descent then I would expect this to result in more decomposable functions, at least relative to picking function by more abstract criteria like the universal prior, since gradient descent encourages a learning trajectory where the learner is always directly trying to solve the whole problem, even at an immature stage, making a jump from a direct form to indirect form of solution more difficult, since an indirect form of solution requires a more mature system and creating it would thus require a significant transition in the way in the approach during training. |
7ce288e2-75cf-40b6-bf21-b1ce261ecc1d | trentmkelly/LessWrong-43k | LessWrong | Weekly LW Meetups
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f7712912-adc6-4525-b0d8-a5822093e6bb | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | The Open Philanthropy Project's work on AI risk | Helen Toner | EA Global: London 2017
morning everybody
is that working yeah okay um
nice to see some more of you I think 10
a.m. maybe was a bit ambitious for some
so this morning it's my great pleasure
to introduce Helen toner Helen toner is
a senior research analyst at the open
front group philanthropy project her
work focuses on policy governance and
strategy issues related to progress and
artificial intelligence this is across
domains including geopolitics national
security public policy and AI safety
before joining apron philanthropy hadn't
studied chemical engineering and Arabic
at the University of Melbourne while
studying she led a Melbourne worked at a
finance startup Veszprem capital and
interned at the Boston Consulting Group
so please join me in welcoming Helen
hi my name is Helen I work on policy and
strategy at the open philanthropy
project as you probably know as well as
work on criminal justice reform farm
animal welfare funding scientific
research and some other things potential
risks from advanced artificial
intelligence is a major focus of ours
which is why I'm going to talk about it
today so I'm going to talk through how
we're thinking about this space some of
the things we've done so far some of the
things we think we've learned and also
close with some thoughts on how you can
get involved in this area if that's
something that you're interested in I'm
going to assume that you're basically
familiar with the open philanthropy
project with the basic case for AI as an
effective altruism cause area but I'd be
really happy to talk over any of the
more basic aspects of this at office
hours if you'd like so I'll be doing
office hours at 1:00 p.m. in the senior
common room so feel free to come and
find me there
just before I get started on these
things I wanted to briefly touch on how
I decided that artificial intelligence
is what I wanted to focus on in my work
I think it can sometimes seem like
people in the EA community have always
thought what they thought
but it seems valuable to me to tell
stories of people changing their mind
because I think that does actually
happen quite a lot and I think it's a
really important part of the effective
altruism community
so my story is that I was at university
I was actually planning on a career in
aid and development and
then I came across effective altruism I
made friends with the two organizers of
the EA Melbourne group and they pretty
soon started telling me about
existential risk artificial intelligence
I thought that they were firstly
philosophically confused and secondly
just over enthusiastic science-fiction
nerds and I told them so which they took
very well to their credit
uh-huh but I didn't want to just dismiss
them out of hand I wanted to understand
why they were wrong so I read more moral
philosophy than I had in my life which
wasn't hard because it had been zero up
till that point and I spent months
debating with them about history ethics
epistemology all kinds of things I don't
want to rehash that whole discussion
because it took several months and I
think there are better places for you to
go and read about the basic case for
thinking about existential risk or
thinking of artificial intelligence as
an ear cause area but I thought it might
be interesting to highlight a couple of
ways
of looking at it that were fairly
important to me and then I I don't see
obviously represented in other places so
the first one of these is that looking
back over history it really seems like a
small number of major transition points
so for example the Agricultural
Revolution or the Industrial Revolution
were you know had radically larger
effects on how people live and on how
civilization functions then on many of
the smaller changes put together and so
you know one implication of this is that
if it looks like something on the
horizon might you know have a reasonable
chance of being one of these major
transitions and it looks like there
might be reasonable work that you can do
to make that transition go better for
Humanity then that could be a really
valuable thing to work on even without
taking the existential risk angle into
it and the second thing I was just
looking at how likely I thought you know
this whole space artificial intelligence
machine learning how likely that is to
be a big deal compared to you know
relative to other things so relative to
how much how much attention and
resources it was getting if you compare
it to
you know other ways of doing good for
the world or if you compare it to the
amount of resources going into
straight-up AI research
you know the amount of time and thought
going into how to make sure that AI is a
good thing for Humanity is comparatively
tiny and so I think that means that you
can and I can think that it's a good
idea for more attention to be being paid
to this issue without thinking that it's
the only topic that matters all right
enough about me on to open philanthropy
so several of my colleagues at open
field also do some work on AI so Daniel
Julie is our program officer for AI and
he leads the technical side of things
Luke Mel has a nick Beckstead hold on
Karnofsky and i also all spend a
significant chunk of our time on AI
related work and the overall approach
that we're taking to thinking about this
space is based around the idea that it
looks pretty plausible that in the next
10 or 20 years something that we call
transformative AI will be developed
which is a term we use to mean an AI
system or AI systems that will have an
effect on the world of roughly
comparable magnitude to the Industrial
Revolution there's obviously a somewhat
vague term and they're a bunch of
different things that you could imagine
transformative AI looking like maybe it
means that we figure out a way to do
scientific Rd in an automated way or
maybe it means we have you know the
advent of automated corporations or
maybe you know AI systems get really
good at learning across many different
domains and we see something like a GI
but but so we frame our you know our
work on this around the possibility that
something of this you know level of
importance might be developed in the
next 10 or 20 years and we think it's a
long way from inevitable that that's the
case
but we think it looks likely enough at
this point so for example we said in a
blog post last year looks pretty safe to
us to say there's a greater than 10%
chance that something like this is
developed in the next 20 years so by
2036
20 years from last year and we still see
that as a reasonable estimate and so we
think that that's enough of a
probability that someone should be
thinking seriously about this and we
don't see many people doing that so I
guess that's where we come in so within
that framing of
transformative AI we tend to get down
into two main areas the first is on the
technical side so building AI systems
that work as we intend them to basically
so this means that they're aiming for
the objective that we want them to aim
for they're reliable in different
contexts that control a ball or
something goes wrong and properties like
that and we see a lot of disagreement
among AI and machine learning
researchers about exactly how hard this
is likely to be and how much extra
research is likely to be needed to make
this the case over and above the amount
of research that will be needed to build
systems that work at all
so we you know we we think it's valuable
to be continuing those debates and
continuing to try and get clarity on it
on how difficult this will be and how
much extra research will be needed while
also funding a variety of approaches to
hopefully I mean you know increase their
probability that that the systems that
are built work really well and then the
second area is non technical so even if
the systems do work exactly how you want
them to what happens then if you you
know if you do have AI systems machine
learning systems that could create
massive changes in the world
you know who should be able to use those
systems what should they be able to do
with them who should they need to
consult with first those kind of
questions and then in both of these
areas the common thread for us is that
the main thing we're aiming to do at
this point is to help build fields to
help set things up to improve the
chances that the transition to a world
that contains these kind of systems in
it you know goes well and so by building
fields I mean that we're aiming to
support the development of the kind of
ecosystems of people thinking about
these topics and building on each
other's work that you see in existing
fields like particle physics or
sociology or whatever
and so we think that this kind of field
building work is something that
philanthropists have a decent track
record record of and so that's that's
part of why we think it's a good idea
for it to be what we are focusing on and
you do run into a kind of chicken and
egg problem when you're doing this kind
of field building you know does it make
sense to start by funding senior people
who can take more junior people under
their wing or should you start by
funding the junior people who then go
and find that they're you know
supervisors or how does that work and
I'll get back to that chicken and egg
problem a bit later in the talk okay so
and what we've done so far
on the deployment problem which is the
you know policy strategy side of things
I would say we're still at a pretty
early stage of figuring out how we want
to approach this space we have made a
few initial grants including funding the
future of humanity Institute at Oxford
and we've had some conversations with
people in government academia I think
tanks about how they're thinking about
the space and and how they want to
approach it I would say a big thing that
we're learning is just that there's a
huge number of potential questions and
potential approaches to use in this
space and it obviously doesn't come for
granted that people were talking to
share enough of our assumptions or
values to land on the same questions
that we think are most important for
example the the transformative AI
framing and it's also becoming really
clear to us that there's no obvious sort
of home field for this for this work and
where it should slide in you know is
that Technology Policy is that
international relations is it law is it
cybersecurity and so that also makes it
hard to know where to target out
conversations
the grant that we've made in this space
that I'm most excited about is to
Professor Alan Defoe who's an
international politics professor at Yale
and at the future of humanity Institute
at Oxford and we're lucky enough to have
him giving a talk right after this so if
you're interested in this side of that
of the AI space my main recommendation
is to stick around and hear what he has
to say on it on the technical safety
side the thing where most aiming to
support right now is the development of
a subfield of machine learning that is
focused on safety so two areas that we
think are especially promising for this
firstly reward engineering so basically
figuring out how to convey to the
Machine what exactly you're hoping for
in a way that conveys the the nuance of
what you want and doesn't let the
Machine doesn't end up with the Machine
cheating or misunderstanding you in some
important way and then the second area
is reliability so building systems that
will work in different contexts that
weren't malfunction in important
situations or if they're put in a
context it's very different to what they
were trained on that type of thing I
think I'll work on starting to build
this field is going pretty well
our approach to solving the chicken and
egg problem here has basically just been
to fund everything at once that we can
find that we think is great so we ran a
closed requests for proposals where we
worked closely with professors at some
top machine learning research groups -
and AI research groups to you know
develop plans for them to do work on on
AI safety in their groups and and
develop those plans so that it was work
on AI safety that we were excited about
and that's resulted in grants to groups
like Stuart Russell's Center at Berkeley
yoshua bengio is group in Montreal Percy
Lee airing at Stanford and uncle Dragan
and Sergey Levine at Berkeley
and we also just on Friday closed
applications for the first year of our
fellowship which we're running for
machine learning PhD students where we
will find top students who we think have
good ideas to do work on safety and
through the fellowship we'll also
introduce them to each other introduce
them to the AI safety community and
start to build a network that way we've
also supported workshops at major
machine learning conferences as well as
running a workshop of our own which also
helps to create these links between
researchers and help them critique and
build on each other's work which we
think is really important and lastly
we've also made a few grants in this
space that don't fit quite into these
categories so most notably the two would
be a grant to the machine intelligence
Research Institute in Berkeley which is
doing AI safety work but within quite a
different paradigm to the type of
machine learning safety that I'm talking
about young and we also have a
partnership with open AI where we're
working closely with them to shape their
work on on safety and governance in
terms of things we've learned I think
the biggest thing on the technical
safety side is which is not so much a
new thing as something that has been
really confirmed for us in how important
it is is that it's really important and
helpful to have agendas of problems that
people can work on so an obvious example
of this is the paper concrete problems
in AI safety which came out in 2016 and
was kind of a first real attempt to
translate people's concerns about the
longer longer term safety of AI into
concrete and attackable machine learning
research problems so this one paper
seems to have been really helpful in
giving machine learning researchers the
feeling that there are specific problems
that they can work and publish about in
this space rather than seeing it as kind
of an amorphous blob of of you know
worries that people have that it's very
difficult to tap into okay so what does
this mean for you if you're interested
in technical safety I think the most
obvious path is to learn as much about
machine learning as you can which
probably step one of that is to get a
PhD
young Laika who works on the the AI
safety team at deep mind David talked on
this at EA global San Francisco and I
think Hawaiian and Vika also gave a talk
about this yesterday so one thing you
could do is look up the recordings of
those talks if you're interested in
learning more about this path I think a
harder but even more valuable thing you
can do on the technical side which is
related to what I was saying about the
importance of having agendas that people
have concrete problems people can work
on
so this this harder thing would be to
read as much as you can about current
thinking on how to approach AI safety so
this is including agendas from Mary from
Paul Cristiano and ammonia towers that
this concrete safety paper
and to you know read those through think
about the problem yourself really go go
deep and come up with your own ideas
about what you think the critical
problems are going to be what people
should work on when and what concretely
can be done
if you're interested in the the policy
and strategy side of things the
deployment problem
the first thing I'd suggest is looking
at Carrick Flynn's post on the ei forum
the upshot of the post is that he thinks
the main type of research that's read it
in this that's needed in this space
right now is what he calls
disentanglement research which is again
kind of similar to what I was saying
about the importance of agendas so
disentanglement research so-called is
you know taking this big messy vague set
of problems you know what might happen
is more and more capable systems are
developed how to very in fact as respond
to that taking that big mess and trying
to kind of slice it into more concrete
and more manageable sub areas I think
this type of research is really
difficult and there's relatively small
number of people who can do it so I
think it's really worth giving a try out
if you think you can do it
fantastic if you think it's not so much
your thing I think another really
valuable thing to be doing right now on
this side is to be building up expertise
and experience in areas related to AI
policy so that you can be well
established in in one of those areas at
a later time when when this the you know
the other side of things is a little bit
further along and and we have more of a
sense of what kind of policies might be
might be valuable to push from from
various actors which could include
governments AI companies and others
and I also think that if you're
interested in the policy and strategy
side of things I do think it's really
valuable to learn as much as you can
about the current cutting edge of
machine learning research so that you
can you know be as informed as possible
about how the technology works okay and
then lastly if you hear all this talk
about AI and you think you know maybe I
should work on that but I'm just really
really excited about this other thing
then I think great go do that instead I
think as much as I personally think that
AI is a hugely important topic
I even more strongly think that the best
version of the EA community is going to
involve you know a wide range of people
with deep expertise on a wide range of
topics it's not going to involve
everyone going off at the same thing or
everyone trying to stay abroad so that
they can keep their options open so if
some other topic has grabbed you
fantastic I think do that that's all I
have to say I think we have a bit of
time for questions I put up a few links
that might be helpful if you want to
read more so feel free to photograph
that slide if you like and
yeah come feel free to come find me at
office hours which is 1:00 p.m. I think
in the senior corner thank you
yes you can still submit questions at
London dot e a global dot org forward
slash polls
thanks very much Helen and so you spoke
about the different areas to work in AI
and specifically if you're sort of maybe
a younger point your career when you can
do a PhD what about somebody who's
perhaps sort of further along their
career path yeah I think it I think it
depends a little bit where you are in
your career and and what you have
expertise in I do think there's I do
think AI will be relevant for a wide
range of different different fields so
if you think there's potential overlap
between the field they are already in an
AI that could be a good angle to try and
try and pivot towards so a great example
is Alan de veau who's speaking next is
you know his background is in
international politics which you know in
fact AI is going to have a lot of
relevance to that so he's you know
relatively later in his career relative
to you know university student he's
started to learn a lot about these
issues and sort of switched his focus to
that or I think if you're in a totally
unrelated area and you really care about
AI you could consider a switch
my last comment about if you really care
about something else as well or I guess
donate right if you haven't got a
realistic chance of maybe
sort of making an impact then maybe
donate yeah absolutely yeah okay so do
you have any thoughts about the role of
communication and outreach for AI safety
when we talk to people about this is
there anything to emphasize or
de-emphasize
strategically word we want the public to
be yeah I have lots of thoughts about
this I would be happy to chat more at
office hours I think one really big
thing is to be aware of this dynamic
that's gone on where
you know the media likes to get clicks
that's their whole that's that's the
whole thing and so we've seen this whole
wave of articles talking about AI risk
in terms of the Terminator and in terms
of robots with guns deciding that they
hate humanity because they're evil and
coming to kill everyone so I think one
thing to be really aware of when you're
communicating about this topic to people
who haven't heard much about it before
is that their mind is immediately going
to go to that because that's the story
that people have pushed so I think it's
important too
kind of I often take the approach of
explicitly saying that's not what I mean
because it means that it preempts the
issue
and so I think it's important to be able
to disambiguate
the types of concerns that that AI
researchers actually have from this
concern that the robots are going to
wake up and come and kill everyone so
that's you know that's where we talk
about you know badly specified objective
functions
you know robustness reliability that
kind of thing
technical concern rather than angry
robots with guns yeah okay um so what
single effort would you recommend a UK
Member of Parliament to undertake right
now to affect change on this issue
gosh if you just give the solution that
would be great you
figured it out you guys
yeah I'm really I'm really not sure I do
think that
that on the weight you know when
thinking about the the kind of questions
that spring from what happens if
transformative a I of some kind just
developed in the next next little while
I do think that we're at kind of a pre
paradigm attic stage of things where we
really don't have very concrete well you
know by we I mean the sort of whole EA
community and the whole community of
people think about these issues don't
really have very concrete suggestions
for things that we should do at this
point so I think I'm gonna stick with
the very unsatisfying answer if I don't
know yeah and so it's one thing to say
do a PhD but there's no way to install
one does tech AI safety work it seems to
be restricted to top university PhDs
especially in reinforcement learning or
inverse RL what can be done to expand
the pool of talent yeah so open
philanthropy we're definitely trying
very hard to expand the number of places
where you can do this kind of work and
we do definitely hope we yeah we think
that our efforts along with the efforts
of others certainly have already helped
to make this make you know technical AI
safety work something that you know is a
legitimate thing to talk about at all
and we definitely hope to expand that in
the future I do think that and this is
something that that yarn like I said in
his talk at a global San Francisco
I do think that as you're getting
started out if you're interested in
doing this machine learning side of AI
safety it's probably more important to
get really really good at machine
learning and become a really really
talented machine learning researcher
that's probably more important than
trying to work on safety as soon as you
can so I think it's not necessarily a
problem if you you know if you get into
a program that doesn't have an
established you know a safety professor
who you can go and talk to I think
that's totally fine and you should just
try and try and learn as much about
machine learning as you can and then you
know while you're doing that sort of as
I mentioned be thinking about what kind
of research do you think could be
helpful on these issues and keeping up
with the the other AI safety research
that is going on and then at some point
either maybe the university you're at
will start to think about these issues
or you can switch to another university
or to somewhere like deepmind or open AI
who both have active safety active and
growing safety teams and so how would
you evaluate the merrier approach to a
safety compared with a more machine
learning based approach yeah so this is
a very difficult question to answer
briefly I think one thing I would point
to is my colleague Daniel Juiz who as I
said leads our work on the technical
side of things he wrote a long post on
the ei forum about his thinking on on
Marie's work
yeah I think it's I think it's really
hard to say I think people people
disagree a lot I think you know as I
said we're at the point right now where
we are interested in funding different
approaches from different angles from
many different people who we think are
you know smart and thoughtful on these
issues and I certainly think that we let
mary falls into that that bucket
yeah that's a longer conversation happy
to go into it at office hours okay great
thanks very much Helen could you join me
in thanking Helen first time
[Applause] |
da1e08c5-3338-4ab3-baf7-40ff5652f213 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | "We can Prevent AI Disaster Like We Prevented Nuclear Catastrophe"
Haven't seen this posted and would be curious to hear people's thoughts. This suggests pausing all frontier AI development except for one organization.
"World leaders are calling for the establishment of an international institution to deal with the threat of AGI: a ‘CERN’ or ‘IAEA for AI’...
MAGIC (the Multilateral AGI Consortium) would be the world’s only advanced and secure AI facility focused on safety-first research and development of advanced AI. Like CERN, MAGIC will allow humanity to take AGI development out of the hands of private firms and lay it into the hands of an international organization mandated towards safe AI development.
MAGIC would have exclusivity when it comes to the high-risk research and development of advanced AI. It would be illegal for other entities to independently pursue AGI development. This would not affect the vast majority of AI research and development, and only focus on frontier, AGI-relevant research, similar to how we already deal with dangerous R&D with other technologies. Research on engineering lethal pathogens is outright banned or confined to very high biosafety level labs. At the same time, the vast majority of drug research is instead supervised by regulatory agencies like the FDA.
MAGIC will only be concerned with preventing the high-risk development of frontier AI systems - godlike AIs. Research breakthroughs done at MAGIC will only be shared with the outside world once proven demonstrably safe.
To make sure high risk AI research remains secure and under strict oversight at MAGIC, a global moratorium on creation of AIs using more than a set amount of [computing power](https://time.com/6300942/ai-progress-charts/) be put in place (here’s a [great overview](https://time.com/6300942/ai-progress-charts/) of why computing power matters). This is similar to how we already deal with uranium internationally, the main resource used for nuclear weapons and energy.
Without competitive pressures, MAGIC can ensure the adequate safety and security needed for this transformative technology, and distribute the benefits to all signatories. CERN exists as a precedent for how we can succeed with MAGIC." |
1116aec2-64c0-4255-8304-9a25df1c8926 | trentmkelly/LessWrong-43k | LessWrong | Business Insider: "They Finally Tested The 'Prisoner's Dilemma' On Actual Prisoners — And The Results Were Not What You Would Expect"
Article at http://www.businessinsider.com/prisoners-dilemma-in-real-life-2013-7#ixzz2ZxwzT6nj, seems revelant to a lot of the discussion here.
There've been studies about people who consider themselves to be relatively successful are less cooperative than people who consider themselves relatively unsuccessful. The study referenced in that article seems to bear this out.
So if you want the other party to cooperate, should you attempt to give that party the impression it has been relatively unsuccessful, at least if that party is human? |
0e19ea72-4245-479c-a15b-0acab957bd12 | trentmkelly/LessWrong-43k | LessWrong | Logical and Indexical Uncertainty
Cross-posted on By Way of Contradiction
Imagine I shot a photon at a half silvered mirror which reflects the photon with "probability" 1/2 and lets the photon pass through with "probability" 1/2.
Now, Imagine I calculated the trillionth decimal digit of pi, and checked whether it was even or odd. As a Bayesian, you use the term "probability" in this situation too, and to you, the "probability" that the digit is odd is 1/2.
What is the difference between these too situations? Assuming the many worlds interpretation of quantum mechanics, the first probability comes from indexical uncertainty, while the second comes from logical uncertainty. In indexical uncertainty, both possibilities are true in different parts of whatever your multiverse model is, but you are unsure which part of that multiverse you are in. In logical uncertainty, only one of the possibilities is true, but you do not have information about which one. It may seem at first like this should not change our decision theory, but I believe there are good reasons why we should care about what type of uncertainty we are talking about.
I present here 6 reasons why we potentially care about the 2 different types of uncertainties. I do not agree with all of these ideas, but I present them anyway, because it seems reasonable that some people might argue for them. Is there anything I have missed?
1) Anthropics
Suppose Sleeping Beauty volunteers to undergo the following experiment, which is described to her before it begins. On Sunday she is given a drug that sends her to sleep, and a coin is tossed. If the coin lands heads, Beauty is awakened and interviewed on Monday, and then the experiment ends. If the coin comes up tails, she is awakened and interviewed on Monday, given a second dose of the sleeping drug that makes her forget the events of Monday only, and awakened and interviewed again on Tuesday. The experiment then ends on Tuesday, without flipping the coin again. Beauty wakes up in the experiment an |
6df6898c-f8ce-430c-b2e2-8379103387a7 | trentmkelly/LessWrong-43k | LessWrong | Shanghai Less Wrong Meetup April 30th, 2pm
John Teddy asked me to post the following info for a Less Wrong meetup he is organizing.
Where: Starbucks, Carrefour Mall, the Zhongshan Park Store, near the Zhongshan Park Station, Line 2, 3, or 4, Shanghai, China
B1&B2, No.1018, ChangNing Road, ChangNing District, Shanghai ( 长宁路1018号, 龙之梦购物中心B1-2楼 近凯旋路 )
When: 2011, April 30th at 2pm.
Contact John Teddy: 18721399070 (phone no.)
|
84c6c971-12ef-4d54-a1f7-8117382991dd | StampyAI/alignment-research-dataset/blogs | Blogs | MIRI wants to fund your independently-organized Friendly AI workshop
To support Friendly AI research around the world, our new [**MIRIx program**](http://intelligence.org/mirix/) funds mathematicians, computer scientists, and formal philosophers to organize their own Friendly AI workshops.
A MIRIx workshop can be as simple as gathering some of your friends to read [MIRI papers](http://intelligence.org/research/) together, talk about them, eat some snacks, scribble some ideas on whiteboards, and go out to dinner together. Or it can be a multi-day research workshop pursuing a specific line of attack on a particular problem. It’s up to you.
Apply for funding **[here](http://intelligence.org/mirix/)**. In some cases we’ll be able to send a MIRI research fellow to your first meeting to give tutorials and answer questions, or perhaps they’ll Skype in to your workshop to do the same.
The post [MIRI wants to fund your independently-organized Friendly AI workshop](https://intelligence.org/2014/05/29/miri-wants-fund-independently-organized-friendly-ai-workshop/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
4dff7136-6d9a-459b-bb82-61167d08d707 | trentmkelly/LessWrong-43k | LessWrong | What The Lord of the Rings Teaches Us About AI Alignment
The Mistake of the Lord of the Rationality
In the online version of Harry Potter and the Methods of Rationality, there is an extra chapter where Eliezer Yudkowsky gives glimpses into what other rationalist fanfiction he might have written.[1] The first one shows a scene from The Lord of the Rings. In it, Yudkowsky loses the war.
The scene is the Council of Elrond and the protagonists are trying to decide what to do. Yud!Frodo rejects the plan of the rest of the Council as obviously terrible and Yud!Bilbo puts on the Ring to craft a better plan.
Yudkowsky treats the Ring as if it were a rationality enhancer. It’s not. The Ring is a hostile Artificial Intelligence.
The plan seems to be to ask an AI, which is known to be more intelligent than any person there, and is known to be hostile, to figure out corrigibility for itself. This is not a plan with a good chance of success.
Hobbits and The Wise
Evidence that the Ring is both intelligent and agentic can be found in Tolkien’s text itself. Gandalf explains to Frodo that the Ring has a will of its own, and had come to dominate Gollum’s will: “He could not get rid of it. He had no will left in the matter. A Ring of Power looks after itself, Frodo. It may slip off treacherously, but its keeper never abandons it. … It was not Gollum, but the Ring itself that decided things. The Ring left him.”[2] The Ring tempts people, and tailors the temptation for the specific person it is trying to tempt: Boromir’s temptation involves leading armies of men, while Sam’s involves gardens. Even the wisest people in Middle Earth believe that they would not be able to resist the temptation of the Ring for long.
We aren’t explicitly told how Sauron tried to align the Ring, but there is a poem that gives some indication:
> One Ring to rule them all, One Ring to find them,
> One Ring to bring them all and in the darkness bind them.
This goal could obviously lead to problems.
I also think that the Ring is not completely aligned with Sau |
1b4648c1-92fe-4d78-b39a-4946dc9c68fd | StampyAI/alignment-research-dataset/arxiv | Arxiv | Institutionalising Ethics in AI through Broader Impact Requirements
Introduction
------------
Growing concerns about the ethical, societal, environmental, and economic impacts of artificial intelligence (AI) have led to a wealth of governance initiatives. In addition to traditional regulatory approaches, complementary forms of governance can help address these challenges [[1](#bib.bib1)]. One such governance form is community-based technology governance, or “governance from within” [[2](#bib.bib2)]. Here, measures to influence research based on societal considerations develop from within the scientific community and are implemented at the community level. A recent initiative of this kind comes from one of the world’s largest AI conferences, the Conference on Neural Information Processing Systems (NeurIPS). In early 2020, the committee announced a new submission requirement: submitting authors must now include a statement that addresses the broader impacts of their research, including its “ethical aspects and future societal consequences” [[3](#bib.bib3)]. NeurIPS’ new requirement has triggered mixed reactions by the AI research community, with discussions about its purpose and effectiveness emerging in social media and other venues [[4](#bib.bib4)]. While few contest that there exists a real need to identify and address ethical and societal challenges from AI, the diversity in reactions illustrates that there is little consensus on the right approach, nor on what the responsibilities of individual researchers or the research community (including conferences) should be in the process [[5](#bib.bib5), [6](#bib.bib6)]. It also highlights the need for further discussion on the purpose, implementation, and effects of NeurIPS’ new requirement and similar governance measures.
With this article we seek to contribute to the discussion on NeurIPS’ new requirement, as well as on broader impact requirements in conference submissions more generally. We compare the new requirement to other established governance mechanisms and provide an analysis of its implications. Our goal is to (a) identify and make explicit the risks and challenges associated with the introduction of NeurIPS’ broader impact requirement, (b) propose a set of measures to address these challenges, and (c) by doing so contribute to the discussion on this and related governance efforts in AI. While we take up several points that have already been made in favour of broader impact statements, most notably by Hecht et al. [[6](#bib.bib6)], our article also emphasises and expands upon the relevant risks and challenges. In particular, our analysis shows how the success of NeurIPS’ new impact statement requirement is by no means guaranteed—in fact, we believe that for the requirement to be effective, a substantial amount of effort must go into addressing the challenges listed below. Whether this effort suffices and even whether the requirement is warranted in the face of other, possibly more potent governance initiatives, will need to be subject of continued discussion. Despite this uncertain outcome of NeurIPS’ unprecedented step, this initiative provides the community with a major opportunity to deliberate on what governance measures ought to be put in place so as to effectively address the challenges from AI development.
We begin our discussion by introducing NeurIPS’ new requirement and comparing it to similar governance mechanisms. We then discuss the potential positive and negative effects, and identify several challenges and possible causes of these negative outcomes. Finally, we tentatively suggest several measures conference organisers and the research community at large can take so as to address the identified challenges.
The new requirement and how it relates to existing practices
------------------------------------------------------------
NeurIPS first announced their new impact statement requirement in a Medium post as follows:
>
> Authors are asked to include a section in their submissions discussing the broader impact of their work, including possible societal consequences—both positive and negative [[3](#bib.bib3)].
>
>
>
In light of the community’s increasing impact on society, the organisers write, it becomes necessary to “think more broadly about what it means to develop new methods and systems, and to consider not only the beneficial applications and products enabled by our research, but also potential nefarious uses and the consequences of failure” [[7](#bib.bib7)]. Papers are not rejected solely on the basis of the broader impact statement, though reviewers were asked to “check whether the Broader Impact is adequately addressed” [[8](#bib.bib8)].
In an analysis of the NeurIPS 2020 reviewing process, the program chairs provided more clarity on the process [[9](#bib.bib9)]. Reviewers could flag submissions for ethical concerns and papers with strong technical reviews that had also been flagged for ethical concerns were passed to a pool of ethics advisers for assessment. Of the 13 papers forwarded, four were rejected and seven were conditionally accepted upon revision of the broader impact section or the removal of problematic data sets.
NeurIPS’ requirement to include a broader impact section in conference submissions is novel in many respects. Yet, similar measures exist across other disciplines and even within computer science. We briefly outline three related approaches.
### Institutional review boards (IRBs)
Research involving human subjects usually requires researchers to first obtain approval from their institutional review board (IRB). The purpose of IRBs is to assess whether the proposed research project meets certain ethical standards regarding the foreseeable impacts on human subjects. In addition to a project description, researchers have to submit an assessment of the potential risks associated with their project and suggest mitigating measures [[10](#bib.bib10)]. Unlike the NeurIPS requirement, IRBs are less concerned with the broader impacts of research and its downstream applications, but instead focus on the direct effects on human subjects during or after the research process. Historically, IRBs have been rare in computer science, compared to the social and biomedical sciences. However, the IRB system and other ethical review processes are becoming more embedded in computer science. This development is in part driven by research conducted at the intersection of AI and disciplines that already have these processes in place, such as neuroscience, psychology, or sociology, and in part by a need for ethical oversight in the face of an increasing amount of empirical research involving human participants or their data [[11](#bib.bib11), [12](#bib.bib12)].
There exists a large literature on the efficacy, structure, process, and outcome variance of IRBs [[13](#bib.bib13)]. Opinions on both the need for and the effectiveness of IRBs are mixed [[14](#bib.bib14)], with critics lamenting excessive bureaucracy [[15](#bib.bib15)], lack of reliability [[16](#bib.bib16)], inefficiency [[17](#bib.bib17), [23](#bib.bib23)], and, importantly, high variance in outcomes [[18](#bib.bib18), [19](#bib.bib19), [20](#bib.bib20), [21](#bib.bib21), [22](#bib.bib22)]. Despite these difficulties, various surveys suggest that IRBs are generally perceived as a useful and necessary institution to protect human subjects, with proponents emphasising the need for ethical oversight to protect the public [[24](#bib.bib24), [25](#bib.bib25), [26](#bib.bib26), [27](#bib.bib27), [17](#bib.bib17)].
### Conference programme committees (PCs)
Some subfields within computer science, such as networking and cybersecurity, have implemented community-led oversight measures that go beyond IRB approval. For research involving users or user data, several conferences, including the Association for Computing Machinery’s (ACM’s) HotNets, SIGCOMM, IMC, and SIGMETRICS, not only require prior IRB approval, but also reserve the right to make their own assessment, in which they “examine the ethical soundness of the paper” [[28](#bib.bib28)]. In practice, rejections on the basis of ethical grounds are rare and opinions about the legitimacy and effectiveness of PC ethical oversight are mixed [[29](#bib.bib29), [30](#bib.bib30)]. One controversial case was a 2015 article on internet censorship, in which researchers collected information about website accessibility through the browsers of thousands of people across the world—without their knowledge or consent [[31](#bib.bib31)]. After long debates, the PC of ACM SIGCOMM 2015 published the article but added a “signing statement” at the beginning of the article. There, they flagged the ethically controversial content. While this episode demonstrated that the PC was paying attention to the ethical implications of research, it also revealed that in cases where the community itself has not yet come to an agreement on relevant norms, the effectiveness of community-led ethical oversight can suffer as a result [[30](#bib.bib30)]. A counterpoint to the above example is the publication of an article on “emotional contagion” through social networks by the journal PNAS [[32](#bib.bib32)]. Here, it was pressure from the community that led the journal to add an editorial expression of concern [[33](#bib.bib33)].
### Funding bodies
Some funding bodies, including the US National Science Foundation (NSF), UK Research and Innovation (UKRI) and the European Commission’s Horizon2020 programme require grant applicants to embed indicators of impact in their applications, and some also require researchers to state how the eventual project will incorporate principles of Responsible Innovation [[34](#bib.bib34)]. To give an explicit example, the NSF states that it is interested in funding research related activities that lead to “the advancement of scientific knowledge and activities that contribute to the achievement of societally relevant outcomes” [[35](#bib.bib35)]. Next to “intellectual merit”, “broader impacts” is one of two merit criteria for the assessment of grant applications. Scientists have expressed mixed opinions about the criterion, with some embracing that it encourages scientists to reflect on the impacts of their work, while others consider it inappropriate, confusing, or burdensome and point out the low quality of many submitted statements [[36](#bib.bib36), [37](#bib.bib37)]. There are also debates on the broader question of whether peer-review, as is common practice in many funding agencies, is suitable for the assessment of broader impacts [[38](#bib.bib38), [39](#bib.bib39), [40](#bib.bib40), [6](#bib.bib6)].
While these requirements are an opportunity for grant applicants to highlight potential negative impacts alongside positive ones, there is arguably little incentive for them to do so as the wording of funding bodies’ application criteria tends to focus solely on the positive impacts through references to ‘public benefit’ and ‘societal needs’ etc. One case study in which researchers were explicitly asked about risks and ethical concerns comes from Owen and Goldberg [[41](#bib.bib41)]. In this experiment, nanoscience researchers needed to submit a risk register as part of an EPSRC grant application. They were asked to list “any potential environmental, health, societal, or other impacts and/or any ethical concerns that may result from the innovation process” (p.1701). Very few applicants addressed environmental impacts and none addressed future societal impacts. Most focused on relatively minor risks, such as laboratory safety. When interviewed, these applicants indicated that they lacked guidance on what impact assessment approaches to use (and how), which led to them feeling “out of their depth” (p.1704). Those who engaged more extensively with larger impacts had involved specialists from other disciplines in their project.
### The NeurIPS broader impact requirement in comparison
There exist several differences between the above approaches and the NeurIPS broader impact requirement. First, unlike IRBs and the majority of the listed ACM conferences, the NeurIPS requirement is not restricted to research involving human subjects, users, or user data. All authors submitting to NeurIPS are required to include a broader impact section, though those working on very theoretical topics, may write “that a Broader Impact discussion is not applicable” [[8](#bib.bib8)]. Second, the organisers require that the statement includes broader impacts, ethical aspects and “future societal consequences” [[3](#bib.bib3)]. Thus the statement is not limited to direct (or “narrow”) impacts from the research process itself, contrary to both IRBs and the ACM conference examples. Third, NeurIPS requires authors to list both positive and negative effects, which is similar to IRB applications but differs from many grant proposals. Finally, there are differences in timing. At the time of conference submission, research projects will have been completed and so any intervention will take place ex post, whereas interventions from IRBs and funding applications take place at a much earlier stage in the research cycle.
Table [1](#Sx2.T1 "Table 1 ‣ The NeurIPS broader impact requirement in comparison ‣ The new requirement and how it relates to existing practices ‣ Institutionalising Ethics in AI through Broader Impact Requirements") summarises these similarities and differences between the different governance initiatives.
| | | | | |
| --- | --- | --- | --- | --- |
| | Type of research | Impacts | Positive/ negative | Timing |
| NeurIPS requirement | all\* | broad | both | late |
| IRBs | human-subject | narrow | both | early |
| ACM conferences | varies | narrow | both | late |
| Funding bodies | all | broad | mainly positive | early |
Table 1: Comparison between the NeurIPS requirement and similar governance initiatives. By “narrow” impacts we refer to those resulting from the research process itself; by “broad” impacts we also include potential future consequences, for example those arising from future applications of the research. \*Excluding very theoretical research.
We can now distil several key insights from the discussion on similar governance measures that are relevant for NeurIPS’ broader impact requirement:
* Even for well-defined impact scopes (e.g. impacts on human subjects) there can be high variance in how impact assessments are evaluated [[18](#bib.bib18), [19](#bib.bib19), [20](#bib.bib20), [21](#bib.bib21), [22](#bib.bib22)]. Review quality, consistency and process transparency are key desiderata [[16](#bib.bib16)], as is expertise by those who perform the evaluation [[38](#bib.bib38), [39](#bib.bib39), [40](#bib.bib40)].
* If impact statements are evaluated by peers but community norms and best practice have not yet fully developed, disagreements may undermine efforts of ethical oversight [[30](#bib.bib30)].
* Researchers can feel overwhelmed by the task of having to consider broader impacts, or fail to consider them, if they do not receive appropriate training and guidance [[41](#bib.bib41)].
* Assessment of broader impacts can benefit from the inclusion of specialists from other disciplines [[41](#bib.bib41)].
Effects of broader impact statement requirements
------------------------------------------------
### Potential positive effects
Introducing a broader impact requirement might have various potential positive effects. Building on and extending Hecht et al.’s assessment [[6](#bib.bib6)], we identify four areas that could benefit in particular: anticipation, action, reflection and awareness, and coordination. These four categories closely resemble the EPSRC AREA framework on responsible research and innovation, which holds that science and innovation can better benefit society by following the steps: Anticipate, Reflect, Engage, and Act [[42](#bib.bib42), [43](#bib.bib43)]. If the broader impact requirement proves to be successful, it could be seen as a significant contribution to the development of a responsible research practice.
We briefly outline the benefits within each category.
Anticipation. A thorough understanding of the societal effects of AI technologies should undergird all actions intended to improve its impacts. AI researchers are well positioned to improve this understanding due to their technical expertise and in-depth knowledge of both their own research and the field more broadly. In particular, they are in an advantageous position to
1. anticipate potential applications of their research, and
2. identify technical limitations and technical risks associated with such applications.
As such, impact statements can point towards opportunities, limitations, and risks that otherwise might not have gained attention [[44](#bib.bib44)].
Researchers writing impact statements may also lead to certain impacts being anticipated sooner. This can alleviate the Pacing Problem: the significant time lag between possibly disruptive innovation and adequate regulatory responses to many emerging technologies [[45](#bib.bib45), [41](#bib.bib41)]. The broader impact statements can help anticipate potential challenges at the research and development stage, allowing more time for identifying, evaluating, and ultimately acting to improve anticipated impacts.
Action. The anticipation of impacts helps stakeholders take appropriate action. For example, technical researchers can investigate mitigating solutions to potential negative consequences, or change the direction of their research so as to minimise negative impacts or to seize opportunities for positive impact [[6](#bib.bib6)]. Policymakers can enact policies to prevent rather than mitigate the impacts of technology after the fact. The public can pressure companies to release products that benefit society, and companies can better align their research with the public interest.
Reflection and awareness. By inviting researchers to reflect on the impacts of their own research, the requirement can contribute to raising awareness about issues associated with particular research or the field more generally, among both readers and authors. In time, this could produce a generation of researchers who have grappled with thinking about impacts and who chose to steer their research agendas towards opportunities to benefit society as a result. Reflection on impacts can furthermore help researchers to align their research with the interests of other stakeholders, including the public, and in particular those most at risk of being adversely affected by downstream applications.
Coordination. The statements can be used to discover where there is consensus and disagreement on risks and opportunities within the AI community. Consensus on the existence of particularly risks can aid policy-making efforts. Disagreement can highlight areas where further dialogue is needed. In addition, the requirement may help build bridges with other communities in that it may help experts from other domains identify issues that need further investigation. There is also some evidence that requiring researchers to consider the broader impacts of their research can lead to an increase in outreach and cross-disciplinary collaboration [[41](#bib.bib41)].
### Potential negative effects
Drawing on the lessons from the previous case studies, and reflecting on the specifics of the NeurIPS requirement, we list the following potential negative outcomes.
Quality deficits. Statements risk being uninformative, biased, misleading, or overly speculative. Quality deficits and “lack of substance” [[38](#bib.bib38), p.185] are issues that have also been flagged repeatedly in the context of NSF grant application impact statements and impact assessments more broadly [[37](#bib.bib37), [46](#bib.bib46)]. In the case of NeurIPS, several referees commented on social media about quality deficits of impact statements.
Trivialisation of ethics and governance: Researchers might form the impression that it is possible to fully anticipate the ethical and societal consequences of one’s research in such a statement, thereby trivialising the complexity of the task and the efforts needed [[39](#bib.bib39), [47](#bib.bib47), [48](#bib.bib48)]. Adequately anticipating impacts arguably requires expertise in the relevant ethical and social disciplines, good theory, and careful study of empirical evidence. Just as one might consider it inappropriate to ask ethicists or social scientists to include a statement on the existence of computationally feasible methods to implement concepts such as fairness, asking computer scientists to address the ethical and societal consequences of their work might not only lead to unsatisfactory results, but could contribute more widely to a trivialisation of ethics and governance itself. Additionally, there is a risk that shallow assessments become more prevalent than intellectually rigorous in-depth study.
Negative attitudes. Researchers might find writing the statements burdensome, confusing, punitive, or may perceive the activity to be of little value. In the context of NSF grant application impact statements, several scientists have expressed views along these lines [[36](#bib.bib36)]. In a worse case scenario, researchers might develop negative attitudes towards responsible research and innovation more generally.
False sense of security. Researchers (and readers) might feel a false sense of security if positive impacts are overstated, risks are underestimated, or if some risks are omitted altogether.
Unintended signalling. The requirement or the statements themselves could signal messages that are unintended by the conference organisers, such as:
1. The focus on individual researchers: The requirement may signal that individual technical researchers are in the best position to reason about impact and make ethical judgements about their own work. In reality, the mitigation of harms requires a collective effort and input from many different stakeholders [[30](#bib.bib30)].
2. Add-on: The requirement could suggest to researchers that ethics is a tick-box exercise that is added to one’s research as an afterthought. The UK Research and Innovation body removed their “Pathways to Impact” requirement for funding applications because they wanted impacts to be embedded in the project proposal rather than being a separate consideration [[49](#bib.bib49)].
Polarisation of the research community. Polarisation of the research community on this topic may occur along political or institutional lines, or based on researcher’s access to relevant resources. Similarly, scientific articles may become more politicised.
The above list illustrates how the introduction of broader impact requirements might miss its target (quality deficits) or lead to undesired effects on individual researchers (false sense of security, unintended signalling), the community (polarisation), or the field of AI ethics and governance as a whole (negative attitudes, trivialisation). Many of these also apply to discussions of positive impacts, which are more routinely discussed in AI research papers. However, we believe the potential negative effects listed may become more acute when discussion of negative impacts are made compulsory, and when impact statements are treated as a separate part of the research.
Causes and challenges
---------------------
We now identify a number of causes for these potential negative effects, which in turn will allow us to develop a list of tentative suggestions for conference organisers and the community at large.
The complexity and difficulty of the task. Foreseeing the downstream uses of AI research, especially foundational research, is notoriously difficult. This is particularly relevant for general purpose technologies like machine learning (ML), in which one technique may be applicable to a broad range of potential applications. Anticipating the societal impacts of those applications is even more challenging, even for social scientists and ethicists
who have specialised in the task [[39](#bib.bib39), [47](#bib.bib47), [48](#bib.bib48)]. In contrast to social scientists, however, computer scientists typically have not received the relevant training that would allow them to address the complexity and nuances behind impact anticipation, nor can they be expected to be as familiar with the relevant literature.
Lack of best practice and guidance. There is currently no established best practice for writing impact statements. The experiment by Owen and Goldberg discussed above demonstrates the importance of guidance for researchers [[41](#bib.bib41)]. Numerous studies that show high variance in IRB approval, as well as the mentioned ACM SIGCOMM 2015 incident, illustrate the importance of having clear guidelines and benchmarks that (a) help researchers to write adequate statements, and (b) allow for adequate and consistent assessments [[18](#bib.bib18), [19](#bib.bib19), [20](#bib.bib20), [21](#bib.bib21)].
Lack of explanation of purpose. Currently, little explanation has been provided regarding the precise purpose and motivation of these statements. For example, whether the aim is to raise awareness, to anticipate impacts, to identify potentially unethical research, to encourage reflection or discussion, to motivate researchers to take responsibility, or any combination of the above. Such lack of clarity may dampen motivation, and lead to confusion. Clarity of purpose is also required to understand whether the initiative is meeting its aims.
Lack of procedural transparency and justice. Transparency of procedures regarding the evaluation of impact statements, and how they relate to the ethical review process and acceptance decisions can help set expectations and create accountability for the process. Lack of transparency, on the other hand, as well as procedural injustice which causes decisions to be perceived as being influenced by bias, political opinions, or prejudice, may not only lead to inadequate evaluation, but also to growing resistance amongst researchers [[26](#bib.bib26)].
High opportunity costs. Writing a high quality impact statement takes time. Researchers routinely experience a substantial amount of pressure to deliver high-quality research articles under tight conference deadlines. If there is a trade-off between working on a research article and the broader impact statement, it is likely that researchers will opt for the first, especially when the broader impact statement does not substantially affect conference admission.
Institutional and social pressure. Researchers may experience pressure—psychological, social, or institutional—to primarily emphasise the benefits of their work rather than potential downsides. Indeed, only emphasising the potential upsides of one’s research is already standard practice [[6](#bib.bib6)]. As a result, researchers may be incentivised to focus on minor risks or those that do not threaten their own or their organisation’s interests. Researchers working in a commercial setting might be particularly affected, as their employers have incentives to downplay certain risks to save commercial interests or prevent legal liabilities. It has been noted that corporations have an incentive to reframe ethics discussions in such a way that they do not challenge how the firm operates in any significant way [[50](#bib.bib50)]. This is of increasing significance as the number of papers from corporate affiliations increases over time [[51](#bib.bib51)].
Cognitive and social biases. Cognitive and social biases may affect authors and referees of broader impact statements. Phenomena such as motivated reasoning may lead to gaps or belittlement of risks [[52](#bib.bib52)]. Several other biases, such as the framing effect [[53](#bib.bib53)], ambiguity effect [[54](#bib.bib54)], or confirmation bias [[55](#bib.bib55)], may similarly affect the quality and the comprehensiveness of impact statements.
Tentative suggestions
---------------------
For conferences that choose to implement a broader impact requirement, we propose a number of tentative suggestions to help address the above challenges. We group these under transparency, guidance, incentives and deliberation, as summarised in Figure [2](#Sx5.T2 "Table 2 ‣ Tentative suggestions ‣ Institutionalising Ethics in AI through Broader Impact Requirements"). Many of the listed suggestions are not limited to broader impact statements, but apply to the peer-review process more generally. We emphasise that continuing or instigating a broader impact requirement should be contingent on the benefits outweighing the risks and costs, as revealed through (ongoing) deliberation.
| Transparency |
| |
| --- |
| Purpose, motivation and expectations |
| Procedural transparency |
| Accountability mechanisms |
|
| Guidance |
| |
| --- |
| Guidance for researchers |
| Guidance for reviewers |
| Communication channels |
|
| Incentives |
| |
| --- |
| Peer-review |
| Outside expert involvement |
| Encouragement to cite |
| Prizes |
|
| Deliberation |
| |
| --- |
| Forums for deliberation |
| Data-driven deliberation |
| Minimisation of reputational and legal costs |
|
Table 2: Tentative suggestions for the implementation of broader impact requirements.
### Improving transparency and setting expectations
Improving transparency and setting expectations can help address a number of the potential negative effects and significantly improve deliberation on both the purpose and implementation of broader impact requirements.
Purpose, motivation and expectations. Conference organisers need to communicate the purpose and motivations for introducing the broader impact statement requirement, as well as set expectations about length, format, and scope to reduce ambiguity. This can motivate and help researchers to better meet expectations, which in turn could make impact statements more consistent and of higher quality.
Procedural transparency. Researchers need visibility of how their impact statement is assessed and how this interacts with the peer-review process. In the case of NeurIPS, there still exists ambiguity regarding the review and assessment of impact statements. Procedural transparency furthermore allows for improved deliberation on the process by the community.
Accountability mechanisms. High variance in review results can be a problem for impact assessments [[19](#bib.bib19), [20](#bib.bib20), [21](#bib.bib21), [22](#bib.bib22)]. Established benchmarks against which statements are assessed are indispensable to improve reliability of outcomes and to create accountability.
### Providing guidance
Guidance for researchers. In order to systematically and effectively address the broader impacts of their work, researchers need access to relevant guidelines, tools and training. There already exist a number of freely available impact assessment tools, but it should be made clear to researchers, which, if any, to use [[56](#bib.bib56)]. In addition, authors may benefit from guidance specifically aimed at computer science researchers and the NeurIPS requirement [[57](#bib.bib57), [58](#bib.bib58), [59](#bib.bib59)]. To make impact statements more comparable and stimulate deliberations over best practice, we recommend that conference organisers (or sub-communities) deliberate on and develop their own set of tools and guidelines, including example statements. The community should deliberate on how prescriptive the guidance (and requirement) should be. Guidance that is too prescriptive could result in boilerplate responses; insufficient guidance could lead to poorly written statements [[41](#bib.bib41)]. Providing researchers with training sessions, workshops, or tutorials organised by e.g. NeurIPS or third parties (such as IEEE or PAI) might also prove beneficial.
Guidance for reviewers. If impact statements are subject to (peer) review, it is important that those assessing them are qualified to do so and are informed about benchmarks and relevant assessment criteria. This is necessary to ensure quality and reliability of assessments.
Creating communication channels. Enabling researchers to engage with experts in other relevant fields may improve the scope and quality of impact statements and provide researchers with more support [[41](#bib.bib41)]. For example, funding schemes directed at improving communication between research communities have shown to successfully enhance cross-disciplinary collaboration [[60](#bib.bib60)]. Events, such as workshops at ML conferences, can facilitate discussion on the impacts of AI research and improve deliberation both within and across disciplines. ML conferences can invite social science or ethics speakers to their conferences to improve awareness and dialogue. Finally, researchers might benefit from having access to a pool of AI ethics experts who have agreed to be available for feedback and discussion.
### Setting incentives
Peer-review. Integrating impact statements into the peer-review process is one way to ensure researchers take the requirement seriously. We encourage this step, though the challenges highlighted above should be addressed. There are significant resource considerations to address—with over 11,000 submissions to NeurIPS 2020, the peer-review process is already under strain. Implementation into the peer-review process could take different forms, ranging from referee comments, or unsatisfactory statements being grounds for rejection.
Outside expert involvement in the peer-review process. If statements are subject to peer-review, the involvement of experts from relevant fields such as AI ethics and governance can allow for a more comprehensive assessment of impacts and deliberation on best practice. In the case of NeurIPS 2020, this was the case for papers that had been flagged for ethical concerns (and that had strong technical reviews). The programme chairs praised the high quality of outside expert assessments [[9](#bib.bib9)].
Encourage authors to cite relevant work and impact statements. Researchers face strong incentives to ensure their work is widely cited. Tapping into this incentive, authors should be encouraged to cite relevant, high-quality impact statements as well as papers whose core research is positively influenced by societal considerations.
Prizes. Rewarding researchers for conscientious and well-written impact statements can encourage them to engage. Rewards could take different forms, including awards akin to best-paper awards.
### Public and community deliberation
As we learn from experience of the initiative, conference organisers and the community need to deliberate publicly about its value, shortcomings, and future.
Establish forums for deliberation. Conference organisers should collect feedback from conference participants, publish their evaluation of the requirement, and reason publicly about the impact statement’s value, shortcomings, and future.
Provide relevant data. Conference organisers or independent researchers should collect and publish data pertaining to the success and shortcomings of the requirement. Analysis could focus on (i) indicators of the quality of statements, (ii) whether institutional incentives appear to influence the considerations included, and (iii) the community’s attitude towards the requirement.
Investigate ways to minimise reputational or legal costs. Researchers and their employers may fear legal or political backlash for highlighting certain risks, causing pressure to de-emphasise these risks. This needs to be addressed by the community and discussed in more detail with the stakeholders involved.
We acknowledge that these suggestions will require additional resources from the ML community. However, some suggestions may reduce costs for individual researchers (such as improved guidance) and for many suggestions, the costs will reduce over time. Moreover, steps taken to encourage deliberation will not only benefit this initiative, but community-led governance initiatives more broadly. While many would argue that the responsibility to reduce harms justifies these costs, the community will need to continue to investigate the benefits and risks, and compare this initiative to other governance options.
Conclusion
----------
NeurIPS’ novel governance initiative provides an important opportunity for the AI research community to reflect on its role and responsibilities in addressing societal impact. Our analysis shows that there are several challenges involved with the introduction of such broader impact requirements. We do not believe, however, that these challenges are insurmountable. By offering a list of tentative suggestions we hope to have provided a starting point for further deliberation on these and similar governance initiatives.
Acknowledgements
----------------
We thank Josh Tenenbaum, Yarin Gal, Toby Shevlane and colleagues at the Centre for the Governance of AI for helpful feedback and comments.
Competing interests
-------------------
The authors declare no competing interests. |
b4c23a76-9788-4879-81e8-365b90d1a29e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Boston: In Defence of the Cathedral
Discussion article for the meetup : Boston: In Defence of the Cathedral
WHEN: 16 February 2014 02:00:00PM (-0500)
WHERE: Citadel, 98 Elm St Apt 1, Somerville, MA
Anders Huitfeldt (Anders_H) will give a presentation called "In Defence of the Cathedral", inspired by the discussion between blogger Scott Alexander and neoreactionaries. If you have time, you may want to first read Scott Alexander's essay "Reactionary Philosophy in an enormous, planet-sized nutshell", but this is not required.
Cambridge/Boston-area Less Wrong meetups start at 2pm, and have an alternating location:
* 2nd and 4th Sunday meetups are in MIT's building 66 at 25 Ames St, room 156. Room number subject to change based on availability; signs will be posted with the actual room number.
* Meetups on other weeks are at Citadel in Porter Sq, at 98 Elm St, apt 1, Somerville.
Our default schedule is as follows:
—Phase 1: Arrival, greetings, unstructured conversation.
—Phase 2: The headline event. This starts promptly at 2:30pm, and lasts 30-60 minutes.
—Phase 3: Further discussion. We'll explore the ideas raised in phase 2, often in smaller groups.
—Phase 4: Dinner.
Discussion article for the meetup : Boston: In Defence of the Cathedral |
0548ebde-c020-4bed-a987-9f4bcd42945e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Seattle Biweekly Meetup: Occam's Razor, Repetition and Time's Up
Discussion article for the meetup : Seattle Biweekly Meetup: Occam's Razor, Repetition and Time's Up
WHEN: 19 September 2011 04:00:00PM (-0700)
WHERE: 414 50th St NE, Seattle, WA
The biweekly meetup is happening at the regular place and time. The plan is to:
* Read and then discuss EY's post Occam's Razor
* Play a variant of Repetition (two people conversing, one making observations)
* Play the good version of Time's Up
We will be ordering take out again, so please bring some cash if you expect to be hungry.
The official end of the meetup will be 6:30pm, but people are welcome to stay and hang out after that.
Looking forward to seeing you guys!
Discussion article for the meetup : Seattle Biweekly Meetup: Occam's Razor, Repetition and Time's Up |
21efa35d-0874-4d83-a9de-37283e4434c6 | trentmkelly/LessWrong-43k | LessWrong | Is there any way someone could post about public policy relating to abortion access (or another sensitive subject) on LessWrong without getting super downvoted?
I'm aware there are some sensitive subjects that have been debated to death among rationalists, if not on LessWrong, than elsewhere online, such that it would in general be fruitless to start the conversation over instead of following what has been debated thus far. I'm curious if this is considered to apply to potentially literally any single socially, culturally or politically sensitive subject. It has been several years since I've checked but my impression from before is that the prevailing norm on LessWrong was based on a particular interpretation of "Politics is the Mind-Killer" that amounts to "politics=anti-rational=downvote on site." This seems contradicted by the original post:
> I’m not saying that I think we should be apolitical, or even that we should adopt Wikipedia’s ideal of the Neutral Point of View. But try to resist getting in those good, solid digs if you can possibly avoid it. If your topic legitimately relates to attempts to ban evolution in school curricula, then go ahead and talk about it—but don’t blame it explicitly on the whole Republican Party; some of your readers may be Republicans, and they may feel that the problem is a few rogues, not the entire party.
It seems like the alternative interpretation, though, was adopted because nobody stuck to that and so it was more convenient for reinforcing norms of civility in discourse to go with the "politics=anti-rational=downvote" interpretation.
Is that still a norm? Even if someone like myself posts about it in a way that doesn't suck, is the concern still that others will lower the quality of discourse with their comments, and downvote anyway? (I'm aware there are not formal rules but I'm in essence asking if according to social norms what categories of topics are considered forbidden by default.) |
bd282c81-6b8d-463f-a67c-a54a55de525f | trentmkelly/LessWrong-43k | LessWrong | Zwicky's Trifecta of Illusions
Linguist Arnold Zwicky has named three linguistic 'illusions' which seem relevant to cognitive bias. They are:
1. Frequency Illusion - Once you've noticed a phenomenon, it seems to happen a lot.
2. Recency Illusion - The belief that something is a recent phenomenon, when it has actually existed a long time.
3. Adolescent Illusion - The belief that adolescents are the cause of undesirable language trends.
Zwicky talks about them here, and in not so many words links them to the standard bias of selective perception.
As an example, here is an exerpt via Jerz's Literacy Weblog (originally via David Crystal), regarding text messages:
> * Text messages aren't full of abbreviations - typically less than ten percent of the words use them. [Frequency Illusion]
> * These abbreviations aren't a new language - they've been around for decades. [Recency Illusion]
> * They aren't just used by kids - adults of all ages and institutions are the leading texters these days. [Adolescent Illusion]
It is my conjecture that these illusions are notable in areas other than linguistics. For example, history is rife with allusions that the younger generation is corrupt, and such speakers are not merely referring to their use of language. Could this be the adolescent illusion in action?
So, are these notable biases to watch out for, or are they merely obvious instances of standard biases? |
d2bd1092-9557-423a-96a4-73e1e01f1867 | trentmkelly/LessWrong-43k | LessWrong | Treatments for depression that depressed LW readers may not have tried?
I would guess that a disproportionate amount of LessWrong readers are significantly impaired by depression. Successful treatment of depression is greatly beneficial to 1) quality of life and 2) ability to impact the world. What are some ways to manage depression that some LessWrong readers may not have considered strongly enough, if at all?
(My reasons for asking this are also partly selfish.) |
58439716-e490-4e6e-9004-1926262a90f9 | trentmkelly/LessWrong-43k | LessWrong | Stress Response, Growth Mindset, and Nonviolent Communication
|
520cba27-8c24-4e33-9d65-519506e35c3d | StampyAI/alignment-research-dataset/arxiv | Arxiv | Diagnostic reasoning with A-Prolog
1 Introduction
---------------
In this paper we continue the investigation
of applicability of A-Prolog (a loosely defined
collection of logic programming languages under the answer
set (stable model) semantics [[Gelfond and Lifschitz, 1988](#bib.bibx8), [Gelfond and Lifschitz, 1991](#bib.bibx9)] to knowledge
representation and reasoning.
The focus is on the development of an architecture for a software
agent acting in a changing environment.
We assume that the agent and the environment (sometimes
referred to as a dynamic system) satisfy the following simplifying
conditions.
1. The agent’s environment can be viewed as a transition
diagram whose states are sets of fluents
(relevant properties of the domain whose truth values may depend on time)
and whose arcs are labeled by actions.
2. The agent is capable of making correct observations, performing
actions, and remembering the domain history.
3. Normally the agent is capable of observing all relevant exogenous
events occurring in its environment.
These assumptions hold in many realistic domains and are suitable
for a broad class of applications. In many domains, however, the
effects of actions and the truth values of observations can only be known
with a substantial degree of uncertainty which cannot be ignored
in the modeling process. It remains to be seen if some of our
methods can be made to work in such situations.
The above assumptions determine the structure of
the agent’s knowledge base. It consists of three parts. The first part, called an action (or system)
description,
specifies the
transition diagram representing possible trajectories of the system.
It contains descriptions of domain’s
actions and fluents, together with the
definition of possible successor states to which the system can
move after an action a is executed in a state σ.
The second part of the agent’s knowledge, called a recorded history
contains observations made
by the agent together with a record of its own actions. It defines
a collection of paths in the diagram which, from the standpoint of the
agent, can be interpreted as
the system’s possible pasts. If the
agent’s knowledge is complete (e.g., it has complete information
about the initial state and the occurrences of actions, and the system’s
actions are deterministic) then there is only one such path.
The third part of agent’s knowledge base
contains a collection of the agent’s goals. All
this knowledge is used and updated by the agent who
repeatedly executes the following steps (the *observe-think-act-loop* [[Kowalski and Sadri, 1999](#bib.bibx12), [Baral and Gelfond, 2000](#bib.bibx1)]):
1. observe the world and interpret the observations;
2. select a goal;
3. plan;
4. execute part of the plan.
In this paper we concentrate on agents operating
physical devices and capable of testing and repairing
the device components. We are especially interested in
the first step of the loop, i.e. in agent’s interpretations
of discrepancies between agent’s expectations and the system’s actual behavior.
The following example will be used throughout the paper:
Figure 1: AC
###### Example 1.1
Consider a system S consisting of an agent operating an
analog circuit AC from figure 1.
We assume that switches s1 and s2 are mechanical components which
cannot become damaged. Relay r is a magnetic coil.
If not damaged, it is activated when s1 is closed,
causing s2 to close. Undamaged
bulb b emits light if s2 is closed.
For simplicity of presentation
we consider the agent capable of performing only one action,
close(s1). The environment can be represented
by two damaging exogenous111By exogenous actions
we mean actions performed by the agent’s environment. This includes natural
events as well as actions performed by other agents.
actions:
brk, which causes b to become faulty, and
srg (power surge), which damages r and also b
assuming that b is not protected.
Suppose that the agent operating this device is given a goal
of lighting the bulb. He realizes that this can be achieved
by closing the first switch, performs the operation,
and discovers that the bulb is not lit. The goal of the paper
is to develop methods for modeling the agent’s behavior after this discovery.
We start with presenting a mathematical model of an agent
and its environment based on the theory of action languages [[Gelfond and Lifschitz, 1998](#bib.bibx10)].
Even though our approach is applicable to a large collection of action
languages, to simplify the discussion we
will limit our attention to action language
AL from [[Baral and Gelfond, 2000](#bib.bibx1)].
We proceed by presenting definitions of the
notions of symptom, candidate diagnosis, and diagnosis which
somewhat differ from those we were able to find in the literature.
These definitions are used to give a simple account of
the agent’s behavior including diagnostics, testing, and repair.
We also suggest algorithms for performing these tasks, which are
based on encoding the agents knowledge in A-Prolog
and reducing the agent’s tasks to computing stable models
(answer sets) of logic programs.
In this paper we assume that at any moment of time
the agent is capable of testing whether a given component is functioning
properly. Modification of the algorithms in the situation when this
assumption is lifted is the subject of further research.
There is a numerous literature on automating various
types of diagnostic tasks and the authors were greatly influenced by it.
We mention only several papers which served as a starting point
for our investigation. Of course we are indebted to R. Reiter [[Reiter, 1987](#bib.bibx28)]
which seems to contain the first clear logical account of the diagnostic
problem. We were also influenced by early papers of D. Poole and K. Eshghi
who related diagnostics and logic programming, seriously discussed
the relationship between diagnostics and knowledge representation,
and thought about the ways to combine descriptions of normal behaviour
of the system with information about its faults.
More recently M. Thielscher, S. McIlraith,
C. Baral, T. Son, R. Otero
recognized that diagnostic problem solving
involves reasoning about the evolution of dynamic systems,
related diagnostic reasoning with reasoning about action, change, and
causation, and told the story of diagnostics which included
testing and repair.
In our paper we generalize and modify this work in several directions.
* We considered a simple and powerful language AL
for describing the agent’s knowledge. Unlike some of the previous languages
used for this purpose, AL allows concurrent actions and
consecutive time-steps, and makes the distinction between observations
and the derived (possibly defeasible) knowledge.
The semantics of the language allows to explain malfunctioning of the system
by some past occurrences of exogenous (normally breaking) actions which
remain unobserved by the agent.
* We simplified the basic definitions such as
symptom, candidate diagnosis, and diagnosis.
* We established the realtionship between AL and logic programming
and used this relationship to reduce various diagnostic tasks
to computing stable models of logic programs.
* Finally we proved correctness of the corresponding diagnostic algorithms.
The paper is organized as follows: in Section [2](#S2 "2 Modeling the domain ‣ [") we introduce a
motivating example. Section [3](#S3 "3 Basic definitions ‣ [") introduces basic definitions used throughout
the paper. In Sections [4](#S4 "4 Computing candidate diagnoses ‣ [") and [5](#S5 "5 Finding a diagnosis ‣ ["), we show how
techniques of answer set programming can be applied to the computation of candidate diagnoses
and of diagnoses. In Section [6](#S6 "6 Diagnostics and repair ‣ [") we investigate the issues related to the introduction
of the ability to repair damaged components. Section [7](#S7 "7 Related work ‣ [") discusses
related work. In Section [8](#S8 "8 Conclusions and further work ‣ [") we conclude the paper and
describe how our work can be extended. The remaining sections contain the description of
syntax and semantics of A-Prolog and AL, as well as the proofs of the main theorems stated in
this paper.
2 Modeling the domain
----------------------
We start with some formal definitions describing a diagnostic domain
consisting of an agent controlling a physical device.
We limit ourselves to non-intrusive and observable
domains in which the agent’s environment does not
normally interfere with his work and the agent
normally observes all of the domain occurrences of exogenous actions.
The agent is, however, aware of the fact that
these assumptions can be contradicted by
observations. As a result the agent is ready to observe and to take into
account occasional occurrences of exogenous ‘breaking’ actions.
Moreover, discrepancies between expectations and observations
may force him to conclude that some
exogenous actions in the past remained unobserved.
This view of the relationship between the agent and his environment
determined our choice of action language used
for describing the agent’s domain and, to the large extent, is responsible
for substantial differences between our approach and that of [[Baral, McIlraith, and Son, 2000](#bib.bibx3)].
By a domain signature we mean a triple Σ=⟨C,F,A⟩
of disjoint finite sets. Elements of C will be
called device components and used to name various parts of the
device. Elements of F are referred to as fluents and used
to denote dynamic properties of the domain
222Our definitions could be easily
generalized to domains with non-boolean fluents. However, the restriction
to boolean fluents will simplify the
presentation..
By fluent literals we mean fluents
and their negations (denoted by ¬f).
We also assume existence of a set F0⊆F which,
intuitively, corresponds to the class of fluents which can be directly
observed by the agent.
The set of literals formed from a set X⊆F of fluents will be
denoted by lit(X). A set Y⊆lit(F) is called complete
if for any f∈F, f∈Y or ¬f∈Y; Y is called consistent if there is no f such that f,¬f∈Y.
We assume that for every component c the set F0 contains
a fluent ab(c) which says that the device’s component c
is faulty. The use of ab in diagnosis goes back to [[Reiter, 1987](#bib.bibx28)].
The set A of elementary actions is partitioned into two
disjoint sets, As and Ae; As consists of actions performed
by an agent and Ae consists of exogenous actions.
(Occurrences of unobserved exogenous actions will be viewed as
possible causes of the system’s malfunctioning).
By a transition diagram over signature Σ we mean
a directed graph T such that:
(a) the states of T are labeled by complete and consistent
sets of fluent literals (corresponding to possible physical states of
the domain).
(b) the arcs of T are labeled by subsets of A called compound actions.
(Intuitively, execution of a compound action {a1,…,ak} corresponds to
the simultaneous execution of its components).
Paths of a transition diagram correspond to possible trajectories
of the domain. A particular trajectory, W, called the actual trajectory
corresponds to the actual behavior of the domain.
In our observe-think-act loop the agent’s connection with reality is
modeled by a function observe(n,f) which
takes a natural number n and a fluent f∈F0
as parameters and returns
f if f belongs to the n’th state of W and ¬f otherwise
###### Definition 2.1
By a diagnostic domain we mean a triple
⟨Σ,T,W⟩ where Σ is a domain signature,
T is a transition diagram
over Σ, and W is the domain’s actual trajectory.
To design an intelligent agent associated with a diagnostic domain
S=⟨Σ,T,W⟩
we need to supply the agent with the knowledge of Σ, T,
and the recorded history of S up to a current point n.
Elements of Σ can normally be defined by a simple logic program.
Finding a concise and convenient way to define the transition diagram
of the domain is somewhat more difficult. We start with limiting our
attention to transition diagrams defined by action descriptions of action
language AL from [[Baral and Gelfond, 2000](#bib.bibx1)]. The accurate description
of the language can be found in Section [10](#S10 "10 Syntax and semantics of the causal laws of AL ‣ [").
A typical action description SD of AL consists of
a collection of causal laws determining the effects of the domain’s
actions, the actions’ executability conditions, and the state
constraints - statements describing dependences between fluents.
(We often refer to statements of SD as laws.)
Causal laws of SD can be divided
into two parts. The first part, SDn, contains laws describing normal
behavior of the system. Their bodies usually contain special
fluent literals of the form ¬ab(c).
The second part, SDb, describes effects of
exogenous actions damaging the components. Such laws normally
contain relation ab in the head or
positive parts of the bodies.
(To simplify our further discussion we only consider exogenous actions
capable of causing malfunctioning of the system’s components.
The restriction is however inessential and can easily be lifted.)
By the recorded history Γn
of S up to a current moment n we mean a collection of observations,
i.e. statements of the form:
1. obs(l,t) - ‘fluent literal
l was observed to be true at moment t’;
2. hpd(a,t) - elementary action a∈A was observed to happen at moment t
where t is an integer from the interval [0,n).
Notice that, intuitively, recorded history hpd(a1,1),hpd(a2,1) says
that an ’empty’ action, {}, occurred at moment 0 and actions a1 and a2
occur concurrently at moment 1.
An agent’s knowledge about the domain up to moment n will consists
of an action description of AL and domain’s recorded history.
The resulting theory will often be referred to as a domain
description of AL.
###### Definition 2.2
Let S be a diagnostic domain with transition diagram T
and actual trajectory
W=⟨σw0,aw0,σw1,…,awn−1,σwn⟩, and let Γn be a recorded
history of S up to moment n.
(a) A path ⟨σ0,a0,σ1,…,an−1,σn⟩
in T is a model of Γn (with respect to S) if
for any 0≤t≤n
1. at={a:hpd(a,t)∈Γn};
2. if obs(l,t)∈Γn then l∈σt.
(b) Γn is consistent (with respect to S) if it has a model.
(c) Γn is sound (with respect to S) if, for any
l, a, and t, if obs(l,t),hpd(a,t)∈Γn then
l∈σwt and a∈awt.
(d) A fluent literal l holds in a model M of Γn at time
t≤n
(M⊨h(l,t)) if l∈σt; Γn entails h(l,t)
(Γn⊨h(l,t)) if, for every model M of Γn,
M⊨h(l,t).
Notice that, in contrast to definitions from
[[Baral, McIlraith, and Son, 2000](#bib.bibx3)] based on action description
language L from [[Baral, Gelfond, and Provetti, 1994](#bib.bibx2)], recorded history
in AL is consistent only if
changes in the observations of system’s states can be explained
without assuming occurrences of any action not recorded
in Γn. Notice also that a recorded history may be
consistent, i.e. compatible with T, but not sound, i.e. incompatible
with the actual trajectory of the domain.
The following is a description, SD, of system S from Example
[1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ ["):
| | | |
| --- | --- | --- |
| | Objects⎧⎪
⎪
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⎪⎩comp(r).comp(b).switch(s1).switch(s2).Fluents⎧⎪
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⎪⎩fluent(active(r)).fluent(on(b)).fluent(prot(b)).fluent(closed(SW))←switch(SW).fluent(ab(X))←comp(X).AgentActions⎧⎪⎨⎪⎩a\_act(close(s1)).ExogenousActions⎧⎪
⎪
⎪
⎪⎨⎪
⎪
⎪
⎪⎩x\_act(brk).x\_act(srg). | |
Causal Laws and Executability Conditions describing normal
functioning of S:
SDn⎧⎪
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⎪⎩causes(close(s1),closed(s1),[]).caused(active(r),[closed(s1),¬ab(r)]).caused(closed(s2),[active(r)]).caused(on(b),[closed(s2),¬ab(b)]).caused(¬on(b),[¬closed(s2)]).impossible\_if(close(s1),[closed(s1)]).
(causes(A,L,P) says that execution of elementary action A in a state
satisfying fluent literals from P causes fluent literal L to become
true in a resulting state; caused(L,P) means that every state satisfying
P must also satisfy L, impossible\_if(A,P) indicates that action A
is not executable in states satisfying P.)
The system’s malfunctioning information from Example [1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ [") is given by:
| | | |
| --- | --- | --- |
| | SDb⎧⎪⎨⎪⎩causes(brk,ab(b),[])%
.causes(srg,ab(r),[]).causes(srg,ab(b),[¬prot(b)]).caused(¬on(b),[ab(b)]).caused(¬active(r),[ab(r)]). | |
Now consider a history, Γ1 of S:
| | | |
| --- | --- | --- |
| | Γ1⎧⎪⎨⎪⎩hpd(close(s1),0).obs(¬closed(s1),0).obs(¬closed(s2),0).obs(¬ab(b),0).obs(¬ab(r),0).obs(prot(b),0). | |
Γ1 says that, initially, the agent observed that s1 and s2 were open, both
the bulb, b, and the relay, r, were not to be damaged, and the bulb was protected from surges.
Γ1 also contains the observation that action close(s1) occurred at time 0.
Let σ0 be the initial state, and σ1 be the successor state, reached by
performing action close(s1) in state σ0. It is easy to see that the path
⟨σ0,close(s1),σ1⟩ is
the only model of Γ1 and that Γ1⊨h(on(b),1).
3 Basic definitions
--------------------
Let S be a diagnostic domain with the transition diagram T,
and actual trajectory
W=⟨σw0,aw0,σw1,…,awn−1,σwn⟩. A pair, ⟨Γn,Omn⟩,
where Γn is the recorded history of S up to moment n
and Omn
is a collection of observations made by the agent
between times n and m, will be called a configuration.
We say that a configuration
| | | | |
| --- | --- | --- | --- |
| | S=⟨Γn,Omn⟩ | | (1) |
is a symptom of the system’s malfunctioning if
Γn is consistent (w.r.t. S) and Γn∪Omn is not.
Our definition of a candidate diagnosis of symptom ([1](#S3.E1 "(1) ‣ 3 Basic definitions ‣ ["))
is based on the notion of
explanation from [[Baral and Gelfond, 2000](#bib.bibx1)]. According to that terminology,
an explanation, E, of symptom ([1](#S3.E1 "(1) ‣ 3 Basic definitions ‣ ["))
is a collection of statements
| | | | |
| --- | --- | --- | --- |
| | E={hpd(ai,t):0≤t<n and ai∈Ae} | | (2) |
such that Γn∪Omn∪E is consistent.
###### Definition 3.1
A candidate diagnosis D of symptom
([1](#S3.E1 "(1) ‣ 3 Basic definitions ‣ [")) consists of an explanation E(D) of ([1](#S3.E1 "(1) ‣ 3 Basic definitions ‣ ["))
together with the set Δ(D) of components of S which
could possibly be damaged by
actions from E(D). More precisely,
Δ(D)={c:M⊨h(ab(c),m)}
for some model M of Γn∪Omn∪E(D).
###### Definition 3.2
We say that D is a diagnosis of a symptom
S=⟨Γn,Omn) if D is a candidate diagnosis
of S in
which all components in Δ are faulty, i.e.,
for any c∈Δ(D), ab(c)∈σwm.
4 Computing candidate diagnoses
--------------------------------
In this section we show how the need for diagnosis can be determined
and candidate diagnoses found by the techniques of answer set
programming [[Marek and Truszczynski, 1999](#bib.bibx16)]. The proofs of the theorems presented here can be found
in Section [12](#S12 "12 Proofs of the theorems ‣ [").
From now on, we assume that we are given a diagnostic domain
S=⟨Σ,T,W⟩. SD will denote an action
description defining T.
Consider a system description SD of S whose behavior up to the moment
n from some interval [0,N) is described by recorded history Γn.
(We assume that N is sufficiently large for our application.)
We start by describing an encoding of SD
into programs of A-Prolog suitable for execution by SMODELS [[Niemela and Simons, 1997](#bib.bibx23)].
Since SMODELS takes as an input programs with finite Herbrand
bases, references to lists should be eliminated from laws
of SD. To do that we expand
the signature of SD by new
terms - names of the corresponding statements of SD - and consider a
mapping α, from action descriptions of AL into programs of A-Prolog,
defined as follows:
1. α(causes(a,l0,[l1…lm])) is the
collection of atoms
| | | |
| --- | --- | --- |
| | d\_law(d),head(d,l0),action(d,a),prec(d,1,l1),…,prec(d,m,lm),prec(d,m+1,nil). | |
Here and below d will refer to the name of the corresponding law.
Statement prec(d,i,li), with 1≤i≤m, says that li
is the i’th precondition of the law d; prec(d,m+1,nil)
indicates that the law has exactly m preconditions.
This encoding of preconditions has a purely technical advantage.
It will allow us to concisely express the statements of the form ‘*All
preconditions of a law d are satisfied at moment T*’. (See rules (3-5)
in the program Π below.)
2. α(caused(l0,[l1…lm])) is the
collection of atoms
| | | |
| --- | --- | --- |
| | s\_law(d),head(d,l0),prec(d,1,l1),…,prec(d,m,lm),prec(d,m+1,nil). | |
3. α(impossible\_if(a,[l1…lm])) is
a constraint
| | | |
| --- | --- | --- |
| | ←h(l1,T),…,h(ln,T),o(a,T). | |
where o(a,t) stands for ‘elementary action a occurred at time t’.
By α(SD) we denote the result of applying α to
the laws of SD.
Finally, for any history, Γ, of S
| | | |
| --- | --- | --- |
| | α(SD,Γ)=Π∪α(SD)∪Γ | |
where Π is defined as follows:
| | | |
| --- | --- | --- |
| | Π⎧⎪
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⎪⎩1. h(L,T′)←d\_law(D),head(D,L),action(D,A),o(A,T),prec\_h(D,T).2. h(L,T)←s\_law(D),head(D,L),prec\_h(D,T).3. all\_h(D,N,T)←prec(D,N,nil).4. all\_h(D,N,T)←prec(D,N,P),h(P,T),all\_h(D,N′,T).5. prec\_h(D,T)←all\_h(D,1,T).6. h(L,T′)←h(L,T),noth(¯¯¯¯L,T′).7.←h(L,T),h(¯¯¯¯L,T).8. o(A,T)←hpd(A,T).9. h(L,0)←obs(L,0).10.←obs(L,T),noth(L,T). | |
Here D,A,L are variables for the names of
laws, actions, and fluent literals respectively,
T,T′ denote consecutive time points,
and N,N′ are variables for consecutive integers.
(To run this program under SMODELS we need to either define the above types
or add the corresponding typing predicates in the bodies of some rules of
Π. These details will be omitted to save space.)
The relation o is used instead of hpd to distinguish
between actions observed (hpd), and actions hypothesized (o).
Relation prec\_h(d,t), defined by the rule (5) of Π,
says that all the preconditions of law d are satisfied at moment t.
This relation is defined via an auxiliary relation all\_h(d,i,t) (rules
(3), (4)), which
holds if the preconditions li,…,lm of d are satisfied at moment
t. (Here l1,…,lm stand for the ordering of preconditions of d
used by the mapping α.)
Rules (1),(2) of Π describe the effects of causal laws and constraints
of SD. Rule (6) is the inertia axiom [[McCarthy and Hayes, 1969](#bib.bibx19)], rule (7) rules out inconsistent
states, rules (8) and (9) establish the relationship between
observations and the basic relations of Π, and rule (10), called the *reality check*,
guarantees that observations do not contradict the agent’s expectations.
(One may be tempted to replace ternary relation prec(D,N,P) by
a simpler binary relation prec(D,P) and to define
relation prec\_h by the rules:
| | | |
| --- | --- | --- |
| | ¬prec\_h(D,T)←prec(D,P),¬h(P,T).prec\_h(D,T)←not¬prec\_h(D,T). | |
It is important to notice that this definition is incorrect since
the latter rule is defeasible and may therefore conflict with
the inertia axiom.)
The following terminology will be useful for describing the relationship
between answer sets of α(SD,Γn) and models of Γn.
###### Definition 4.1
Let SD be an action description, and A be a set of literals over
lit(α(SD,Γn)). We say that A *defines* the sequence
| | | |
| --- | --- | --- |
| | ⟨σ0,a0,σ1,…,an−1,σn⟩ | |
if σk={l|h(l,k)∈A} and ak={a|o(a,k)∈A}.
The following theorem establishes the relationship between
the theory of actions in AL and logic programming.
{theorem}
If the initial situation of Γn is complete, i.e. for any fluent
f of SD, Γn contains obs(f,0) or obs(¬f,0)
then
M is a model of Γn iff M is defined by some answer set of
α(SD,Γn).
(The theorem is similar to the result from [[Turner, 1997](#bib.bibx35)] which
deals with a different language and uses the definitions from
[[McCain and Turner, 1995](#bib.bibx17)].)
Now let S be a configuration of the form ([1](#S3.E1 "(1) ‣ 3 Basic definitions ‣ [")),
and let
| | | | |
| --- | --- | --- | --- |
| | Conf(S)=α(SD,Γn)∪Omn∪R | | (3) |
where
| | | |
| --- | --- | --- |
| | R{h(f,0)←noth(¬f,0).h(¬f,0)←noth(f,0). | |
for any fluent f∈F.
The rules of R are sometimes called the awareness axioms.
They guarantee that initially the agent considers all possible values
of the domain fluents.
(If the agent’s information about the
initial state of the system is complete these axioms can be omitted.)
The following corollary forms the basis for
our diagnostic algorithms.
{corollary}
Let S=⟨Γn,Omn⟩ where Γn
is consistent. Then configuration
S is a symptom of system’s malfunctioning
iff program Conf(S) has no answer set.
To diagnose the system, S, we construct a program, DM,
defining an explanation space of our diagnostic agent - a collection
of sequences of exogenous events which could happen (unobserved) in the
system’s past and serve as possible explanations
of unexpected observations. We call such programs diagnostic modules
for S.
The simplest diagnostic module, DM0, is defined by rules:
| | | |
| --- | --- | --- |
| | DM0⎧⎪
⎪
⎪
⎪
⎪
⎪
⎪⎨⎪
⎪
⎪
⎪
⎪
⎪
⎪⎩o(A,T)←0≤T<n, x\_act(A),not¬o(A,T). ¬o(A,T)←0≤T<n, x\_act(A),noto(A,T). | |
or, in the more compact, choice rule, notation of SMODELS [[Simons, 1999](#bib.bibx30)]
| | | |
| --- | --- | --- |
| | {o(A,T):x\_act(A)}←0≤T<n. | |
(Recall that a choice rule has the form
| | | |
| --- | --- | --- |
| | m{p(¯¯¯¯¯X) : q(¯¯¯¯¯X)}n←body | |
and says that, if the body is satisfied by an answer set AS of a program
then AS must contain between m and n atoms of the form p(¯t)
such that q(¯t)∈AS. For example, program
| | | |
| --- | --- | --- |
| | {p(X):q(X)}.q(a). | |
has two answer sets: {q(a)}, and {p(a),q(a)}.)
Finding candidate diagnoses of symptom S can
be reduced to finding answer sets of a diagnostic program
| | | | |
| --- | --- | --- | --- |
| | D0(S)=Conf(S)∪DM0. | | (4) |
The link between answer sets and candidate diagnoses is described by the
following definition.
###### Definition 4.2
Let SD be a system description,
S=⟨Γn,Omn⟩ be
a symptom of the system’s malfunctioning,
X be a set of ground literals,
and E and δ be sets
of ground atoms.
We say that ⟨E,Δ⟩ *is determined by* X if
| | | |
| --- | --- | --- |
| | E={hpd(a,t)|o(a,t)∈X and a∈Ae}, and | |
| | | |
| --- | --- | --- |
| | Δ={c|obs(ab(c),m)∈X}. | |
{theorem}
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a system
description of T, S=⟨Γn,Omn⟩ be
a symptom of the system’s malfunctioning, and E and δ be sets
of ground atoms. Then,
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a candidate diagnosis of S | |
iff
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is determined by an answer set of D0(S). | |
The theorem justifies the following simple algorithm for computing
candidate diagnosis of a symptom S:
| |
| --- |
| function Candidate\_Diag( S: symptom ); |
| | Input: a symptom S=⟨Γn,Omn⟩. |
| | Output: a candidate diagnosis of the symptom, or ⟨∅,∅⟩ if no candidate |
| | | | diagnosis could be found. |
| | var | | E : history; |
| | | | Δ : set of components; |
| | if D0(S) is consistent then |
| | | select an answer set, X, of D0(S); |
| | | compute ⟨E,Δ⟩ determined by X; |
| | | return (⟨E,Δ⟩); |
| | else |
| | | E:=∅; Δ:=∅; |
| | return (∅,∅). |
| | end |
Given a symptom S, the algorithm constructs the program
D0(S) and passes it as an input to SMODELS [[Niemela and Simons, 1997](#bib.bibx23)], DLV
[[Citrigno, Eiter, Faber, Gottlob, Koch, Leone, Mateis, Pfeifer,
Scarcello, 1997](#bib.bibx6)], DeReS [[Cholewinski, Marek, and Truszczynski, 1996](#bib.bibx7)], or some
other answer set finder. If no answer set is found the algorithm returns
⟨∅,∅⟩. Otherwise the algorithm
returns a pair ⟨E,Δ⟩ extracted from some answer
set X of the program. By Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") the
pair is a candidate diagnosis of S. Notice that the set E
extracted from an
answer set X of D0(S) cannot be empty and hence the answer
returned by the function is unambiguos. (Indeed, using the Splitting Set
Theorem [[Lifschitz and Turner, 1994](#bib.bibx15), [Turner, 1996](#bib.bibx34)] we can show that the existence of answer set of D0(S)
with empty E will lead to existence of an answer set of
Conf(S), which, by Corollary [4](#S4 "4 Computing candidate diagnoses ‣ ["), contradicts to
S being a symptom.)
The algorithm can be illustrated by the following example.
###### Example 4.1
Let us again consider system
S from Example [1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ [").
According to Γ1 initially the switches s1 and s2 are open,
all circuit components are ok, s1 is
closed by the agent, and b is protected. It is predicted that
b will be on at 1.
Suppose that, instead, the agent observes that at time 1
bulb b is off, i.e.
O1={obs(¬on(b),1)}.
Intuitively, this is viewed as a symptom
S0=⟨Γ1,O1⟩
of malfunctioning of S. By running SMODELS on
Conf(S0)
we discover that this program has no answer sets and therefore, by
Corollary [4](#S4 "4 Computing candidate diagnoses ‣ ["), S0 is indeed a symptom.
Diagnoses of S0 can be found by running SMODELS on
D0(S0) and extracting the necessary information
from the computed answer sets.
It is easy to check that, as expected, there are three candidate
diagnoses:
| | | |
| --- | --- | --- |
| | D1=⟨{o(brk,0)},{b}⟩D2=⟨{o(srg,0)},{r}⟩D3=⟨{o(brk,0),o(srg,0)},{b,r}⟩ | |
which corresponds to our intuition. Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") guarantees
correctness of this computation.
The basic diagnostic module D0 can be modified
in many different ways. For instance, a simple modification,
D1(S),
which eliminates some candidate diagnoses containing actions unrelated
to the corresponding symptom can be constructed as follows. First, let us
introduce some terminology. Let αi(SD) be a function that maps each
impossibility condition of SD into a collection of atoms
| | | |
| --- | --- | --- |
| | imp(d),action(d,a),prec(d,m+1,nil),prec(d,1,l1),…,prec(d,m,lm), | |
where d is a new constant naming the condition, and a, li’s are
arguments of the condition. Let also REL be the
following program:
| | | |
| --- | --- | --- |
| | REL⎧⎪
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⎪⎩1.rel(A,L)←d\_law(D),head(D,L),action(D,A).2.rel(A,L)←law(D),head(D,L),prec(D,N,P),rel(A,P).3.rel(A2,L)←rel(A1,L),imp(D),action(D,A1),prec(D,N,P),rel(A2,¯¯¯¯P).4.rel(A)←obs(L,T),T≥n,rel(A,L).5.←T<n,o(A,T),x\_act(A),nothpd(A,T),notrel(A). | |
and
| | | |
| --- | --- | --- |
| | DM1=DM0∪REL∪αi(SD). | |
The new diagnostic module, D1 is defined as
| | | |
| --- | --- | --- |
| | D1(S)=Conf(S)∪DM1. | |
(It is easy to see that this modification is safe,
i.e. D1 will not miss any useful
predictions about the malfunctioning components.)
The difference between D0(S) and D1(S)
can be seen from the following example.
###### Example 4.2
Let us expand the system S from Example [1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ [")
by a new component, c, unrelated to the circuit,
and an exogenous action a
which damages this component. It is easy to see
that diagnosis S0 from Example [1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ [")
will still be a symptom of malfunctioning
of a new system, Sa, and that the basic diagnostic module
applied to Sa will return diagnoses (D1)−(D3) from
Example [4.1](#S4.Thmexample1 "Example 4.1 ‣ 4 Computing candidate diagnoses ‣ [") together with new diagnoses containing a
and ab(c), e.g.
| | | |
| --- | --- | --- |
| | D4=⟨{o(brks,0),o(a,0)},{b,c}⟩. | |
Diagnostic module D1 will ignore actions
unrelated to S and return only (D1)−(D3).
It may be worth noticing that the distinction between hpd and o allows
exogenous actions, including those unrelated to observations,
to actually happen in the past.
Constraint (5) of program REL only prohibits generating such actions in our
search for diagnosis.
There are many other ways of improving quality of candidate diagnoses
by eliminating some redundant or unlikely diagnoses, and by ordering the
corresponding search space.
For instance, even more unrelated actions can be eliminated from the search space of
our diagnostic modules by considering relevance relation rel
depending on time. This can be done by a simple modification of program REL
which is left as an exercise to the reader.
The diagnostic module D1 can also be further modified
by limiting its search to recent occurrences of exogenous
actions. This can be done by
| | | |
| --- | --- | --- |
| | D2(S)=Conf(S)∪DM2 | |
where DM2 is obtained by replacing atom
0≤T<n in the bodies of rules of DM0 by
n−m≤T<n.
The constant m determines the time interval
in the past that an agent is
willing to consider in its search for possible explanations.
To simplify our discussion in the rest of the paper we assume that
m=1. Finally, the rule
| | | |
| --- | --- | --- |
| | ←k{o(A,n−1)}. | |
added to DM2 will eliminate all diagnoses containing
more than k actions.
Of course the resulting module D3 as well as D2
can miss some candidate diagnoses and deepening of the search and/or increase
of k may be necessary
if no diagnosis of a symptom is found. There are many other interesting
ways of constructing efficient diagnostics modules. We are especially
intrigued by the possibilities of using new features of answer sets
solvers such as weight rules and minimize of SMODELS and weak constraints
of DLV [[Citrigno, Eiter, Faber, Gottlob, Koch, Leone, Mateis, Pfeifer,
Scarcello, 1997](#bib.bibx6), [Buccafurri, Leone, and Rullo, 1997](#bib.bibx5)] to specify a preference relation on diagnoses.
This however is a subject of further investigation.
5 Finding a diagnosis
----------------------
Suppose now the diagnostician has a candidate diagnosis D
of a symptom S. Is it indeed a diagnosis?
To answer this question the agent should be able to test
components of Δ(D). Assuming that no exogenous actions
occur during testing a diagnosis can be found by
the following simple algorithm, Find\_Diag(S):
| |
| --- |
| function Find\_Diag( var S: symptom ); |
| | Input: a symptom S=⟨Γn,Omn⟩. |
| | Output: a diagnosis of the symptom, or ⟨∅,∅⟩ if no
diagnosis |
| | | could be found. Upon successful termination of the loop, the set
Omn |
| | | is updated in order to incorporate the results of the tests |
| | | done during the search for a diagnosis. |
| | var | | O, E : history; |
| | | | Δ, Δ0 : set of components; |
| | | | diag : bool; |
| | O:=Omn; |
| | repeat |
| | | ⟨E,Δ⟩:=Candidate\_Diag(⟨Γn,O⟩); |
| | | if E=∅ { no diagnosis could be found } |
| | | | return(⟨E,Δ⟩); |
| | | diag:=true; Δ0:=Δ; |
| | | while Δ0≠∅ and diag do |
| | | | select c∈Δ0; Δ0:=Δ0∖{c}; |
| | | | if observe(m,ab(c))=ab(c) then |
| | | | | O:=O∪obs(ab(c),m); |
| | | | else |
| | | | | O:=O∪obs(¬ab(c),m); |
| | | | | diag:=false; |
| | | | end |
| | | end {while} |
| | until diag; |
| | Omn:=O; |
| | return (⟨E,Δ⟩). |
The properties of Find\_Diag are described by the following theorem.
{theorem}
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a
system description of T, and S=⟨Γn,Omn⟩
be a symptom of the system’s malfunctioning. Then,
1. Find\_Diag(S) terminates;
2. let ⟨E,Δ⟩=Find\_Diag(S), where the value of
variable S is set to S0. If Δ≠∅,
then
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a diagnosis of S0; | |
otherwise, S0 has no diagnosis.
To illustrate the algorithm, consider the following example.
###### Example 5.1
Consider the system S from Example [1.1](#S1.Thmexample1 "Example 1.1 ‣ 1 Introduction ‣ [") and a
history Γ1 in which
b is not protected,
all components of S are ok, both switches are open, and the agent closes
s1 at time 0. At time 1, he observes that
the bulb b is not lit, considers
S=⟨Γ1,O1⟩ where
O1={obs(¬on(b),1)} and calls
function Need\_Diag(S)
which searches for an answer set of Conf(S). There are no such sets,
the diagnostician realizes he has a symptom to diagnose and calls
function Find\_Diag(S).
Let us assume that the first call to Candidate\_Diag
returns
| | | |
| --- | --- | --- |
| | PD1=⟨{o(srg,0)},{r,b}⟩ | |
Suppose that the agent selects component r from Δ
and determines that it is not faulty.
Observation obs(¬ab(r),1) will be added to O1, diag will be set to false
and the program will call
Candidate\_Diag again with the updated symptom S as a parameter.
Candidate\_Diag will return another possible diagnosis
| | | |
| --- | --- | --- |
| | PD2=⟨{o(brk,0)},{b}⟩ | |
The agent will test bulb b, find it
to be faulty, add observation
obs(ab(b),1) to O1 and return PD2.
If, however, according to our actual trajectory, W, the bulb is still ok, the
function returns ⟨∅,∅⟩. No diagnosis is found
and the agent (or its designers) should start looking for a modeling error.
6 Diagnostics and repair
-------------------------
Now let us consider a scenario which is only slightly different from that
of the previous example.
###### Example 6.1
Let Γ1 and observation
O1 be as in Example [5.1](#S5.Thmexample1 "Example 5.1 ‣ 5 Finding a diagnosis ‣ [") and
suppose that the program’s first call to
Candidate\_Diag returns
PD2, b is found to be faulty,
obs(ab(b),1) is added to O1, and Find\_Diag
returns PD2.
The agent proceeds to have b repaired but, to his
disappointment, discovers that b is still not on!
Intuitively this means that PD2 is a wrong diagnosis
- there must have been a power surge at 0.
For simplicity we assume that,
similar to testing,
repair occurs in well controlled
environment, i.e. no exogenous actions happen during the repair process.
The example shows that,
in order to find a correct explanation
of a symptom, it is essential for an agent
to repair damaged components and observe the behavior of the system
after repair.
To formally model this process we introduce a special action,
repair(c), for every component c of S.
The effect of this action will be defined
by the causal law:
| | | |
| --- | --- | --- |
| | causes(repair(c),¬ab(c),[]) | |
The diagnostic process will be now modeled by the following algorithm:
(Here S=⟨Γn,Omn⟩ and
{obs(fi,k)} is a collection of observations
the diagnostician makes to test his repair at moment k.)
function Diagnose(S) : boolean;
Input: a symptom S=⟨Γn,Omn⟩.
Output: false if no diagnosis can be found.
Otherwise
repairs the system, updates Omn, and returns true.
var
E : history;
Δ : set of components;
E=∅;
while Need\_Diag(⟨Γn∪E,Omn⟩) do
⟨E,Δ⟩=Find\_Diag(⟨Γn,Omn⟩);
if E=∅ then return(false)
else
Repair(Δ);
Omn:=Omn∪{hpd(repair(c),m):c∈Δ};
m:=m+1;
Omn:=Om−1n∪{obs(fi,m)};
end
end
return(true);
###### Example 6.2
To illustrate the above algorithm let us go back to the agent from
Example [6.1](#S6.Thmexample1 "Example 6.1 ‣ 6 Diagnostics and repair ‣ [")
who just discovered diagnosis PD2=⟨{o(brk,0)},{b}⟩. He will repair
the bulb and check if the bulb is lit. It is not, and therefore a new
observation is recorded as follows:
| | | |
| --- | --- | --- |
| | O1:=O1∪{hpd(repair(b),1),obs(¬on(b),2)} | |
Need\_Diag(S) will detect a continued need for diagnosis,
Find\_Diag(S) will
return PD1, which, after new repair and testing will
hopefully prove to be the right diagnosis.
The diagnosis produced by the above algorithm can be viewed as a
reasonable interpretation of discrepancies between the agent’s predictions
and actual observations. To complete our analysis of step 1 of
the agent’s acting and reasoning loop we need to explain how
this interpretation can be incorporated in the agent’s history.
If the diagnosis discovered is unique then the answer
is obvious - O is simply added to Γn. If however
faults of the system components can be caused by different sets of
exogenous actions the situation becomes more subtle.
Complete investigation of the issues involved is the subject of further
research.
7 Related work
---------------
There is a numerous collection of papers on diagnosis
many of which substantially influenced the author’s views
on the subject.
The roots of our approach go back to [[Reiter, 1987](#bib.bibx28)]
where diagnosis for a static environment were formally defined in logical
terms. To the best of our knowledge the first published
extensions of this work to dynamic domains appeared in [[Thielscher, 1997b](#bib.bibx32)],
where dynamic domains were described in fluent calculus [[Thielscher, 1998](#bib.bibx33)], and in
[[McIlraith, 1997](#bib.bibx20)] which used situation calculus [[McCarthy and Hayes, 1969](#bib.bibx19)].
Explanation of malfunctioning of system components
in terms of unobserved exogenous actions was first
clearly articulated in [[McIlraith, 1998](#bib.bibx21)]. Generalization and extensions
of these ideas [[Baral, McIlraith, and Son, 2000](#bib.bibx3)] which specifies dynamic domains in
action language L, can be viewed as a starting point of the
work presented in this paper.
The use of a simpler action language AL allowed us to
substantially simplify the basic definitions of [[Baral, McIlraith, and Son, 2000](#bib.bibx3)]
and to reduce the computation of diagnosis to finding stable models
of logic programs. As a result we were able to incorporate diagnostic
reasoning in a general agent architecture based on the answer set programming
paradigm, and to combine diagnostics with planning and other activities of
a reasoning agent. On another hand [[Baral, McIlraith, and Son, 2000](#bib.bibx3)] addresses some questions which
are not fully addressed by our paper. In particular, the underlying action
language of [[Baral, McIlraith, and Son, 2000](#bib.bibx3)] allows non-deterministic and knowledge-producing
actions absent in our work. While our formulation allows immediate
incorporation of the former, incorporation of
the latter seems to substantially increase conceptual complexity of the
formalism. This is of course the case in [[Baral, McIlraith, and Son, 2000](#bib.bibx3)] too but
we believe that the need for such increase in complexity remains an open
question. Another interesting related work is [[Otero and Otero, 2000](#bib.bibx26)]. In this paper the
authors address the problem of dynamic diagnosis using the notion of
pertinence logic from [[Otero and Cabalar, 1999](#bib.bibx25)]. The formalism allows to define dynamic diagnosis
which, among other things, can model intermittent faults of the system.
As a result it provides a logical account of the following scenario:
Consider a person trying to shoot a turkey.
Suppose that the gun is initially loaded, the agent shoots, observes
that the turkey is not dead, and shoots one more time. Now the turkey is dead.
The pertinence formalism of [[Otero and Otero, 2000](#bib.bibx26)] does not claim
inconsistency - it properly determines that the gun has an intermittent
fault. Our formalism on another hand is not capable of modeling this scenario -
to do that we need to introduce non-deterministic actions.
Since, in our opinion, the use of pertinence logic substantially
complicates action formalisms it is interesting to see if such use
for reasoning with intermittent faults can always be avoided
by introducing non-determinism.
Additional comparison of the action languages based
approach to diagnosis with other related approaches can be found in
[[Baral, McIlraith, and Son, 2000](#bib.bibx3)].
Finally, let us mention that the reasoning algorithms proposed in this paper
are based on recent discoveries of close
relationship between
A-Prolog and reasoning about effects of actions [[McCain and Turner, 1995](#bib.bibx17)]
and the ideas from answer set programming
[[Marek and Truszczynski, 1999](#bib.bibx16), [Niemela, 1999](#bib.bibx24), [Lifschitz, 1999](#bib.bibx14)].
This approach of course would be impossible without existence
of efficient answer set reasoning systems.
The integration of diagnostics and other activities
is based on the agent architecture from [[Baral and Gelfond, 2000](#bib.bibx1)].
8 Conclusions and further work
-------------------------------
The paper describes a work on the development of a diagnostic
problem solving agent in which a mathematical model of an agent
and its environment is based on the theory of action
language AL from [[Baral and Gelfond, 2000](#bib.bibx1)]. The language, which contains the means
for representing concurrent actions and fairly complex relations
between fluents, is used to give concise
descriptions of transition diagrams characterizing possible
trajectories of the agent domains as well as the domains’ recorded
histories.
In this paper we:
* Establish a close relationship between AL
and logic programming under the answer set semantics
which allows reformulation of the agent’s knowledge in A-Prolog.
These results build on previous work connecting action languages
and logic programming.
* Give definitions of symptom, candidate diagnosis,
and diagnosis which we believe to be simpler
than similar definitions we were able to find in the literature.
* Suggest a new algorithm for computing candidate
diagnoses. (The algorithm is based on answer set programming and views
the search for candidate diagnoses as ‘planning in the past’.)
* Suggest some simple ways of using A-Prolog to declaratively
limit the diagnostician’s search space. This leads to higher quality
diagnosis and substantial improvements in the diagnostician’s efficiency.
* Give a simple account of diagnostics, testing and repair
based on the use of answer set solvers. The resulting algorithms, which
are shown to be provenly correct, can be easily incorporated in the agent’s
architecture from [[Baral and Gelfond, 2000](#bib.bibx1)].
In our further work we plan to:
* Expand our results to more
expressive languages, i.e. those with non-deterministic actions,
defeasible causal laws, etc.
* Find more powerful declarative ways of limiting the diagnostician’s
search space. This can be done by expanding A-Prolog by ways of expressing
preferences between different rules or by having the agent plan
observations aimed at eliminating large clusters of possible diagnosis.
In investigating these options we plan to build on related work
in [[Buccafurri, Leone, and Rullo, 1997](#bib.bibx5)] and [[Baral, McIlraith, and Son, 2000](#bib.bibx3), [McIlraith and Scherl, 2000](#bib.bibx22)].
* Test the efficiency of the suggested algorithm on medium size
applications.
9 The syntax and semantics of A-Prolog
---------------------------------------
In this section we give a brief introduction to the syntax
and semantics of a comparatively simple variant of A-Prolog.
The syntax of the language is
determined by a signature Σ consisting of types,
types(Σ)={τ0,…,τm}, object constants
obj(τ,Σ)={c0,…,cm} for each type τ, and
typed function and predicate constants func(Σ)={f0,…,fk} and
pred(Σ)={p0,…,pn}.
We will assume that the signature contains symbols for integers and
for the standard relations of arithmetic.
Terms are built as in typed first-order languages;
positive literals (or atoms) have the form p(t1,…,tn),
where t’s are terms of proper types and p is a predicate symbol of
arity n; negative literals are of the form ¬p(t1,…,tn). In our further discussion we often
write p(t1,…,tn) as p(¯t).
The symbol ¬ is called classical or strong negation.
Literals of the form p(¯t)
and ¬p(¯t) are called contrary. By
¯l we denote a literal contrary to l.
Literals and terms not containing variables are called ground.
The sets of all ground terms, atoms and literals over Σ will
be denoted by terms(Σ), atoms(Σ) and lit(Σ) respectively.
For a set P of predicate symbols from Σ,
atoms(P,Σ) (lit(P,Σ))
will denote the sets of ground atoms (literals) of Σ formed
with predicate symbols from P. Consistent sets of ground
literals over signature Σ, containing all arithmetic literals
which are true under the standard interpretation of their symbols,
are called states of Σ and denoted
by states(Σ).
A rule of A-Prolog is an expression of the form
| | | | |
| --- | --- | --- | --- |
| | l0←l1,…,lm,notlm+1,…,notln | | (5) |
where n≥1, li’s are literals, l0 is a literal or the symbol
⊥,
and not is a logical
connective called negation as failure or default negation.
An expression
notl says that there is no reason to believe in l.
An extended literal is an expression of the form l or
notl where l is a literal.
A rule ([5](#S9.E5 "(5) ‣ 9 The syntax and semantics of A-Prolog ‣ [")) is called a constraint if l0=⊥.
Unless otherwise stated, we assume that the l′s in
rules ([5](#S9.E5 "(5) ‣ 9 The syntax and semantics of A-Prolog ‣ [")) are ground.
Rules with variables (denoted by capital letters) will be used
only as a shorthand for the sets of their ground instantiations.
This approach is justified for the so
called closed domains, i.e. domains satisfying
the domain closure assumption [[Reiter, 1978](#bib.bibx27)] which
asserts that all objects in the domain of discourse have names in
the language of Π.
A pair ⟨Σ,Π⟩ where Σ is a signature and Π is
a collection of rules over Σ is called a logic
program. (We often denote such pair by its second element
Π. The corresponding signature will be denoted by Σ(Π).)
We say that a literal l∈lit(Σ) is true in a
state X of Σ if l∈X;
l is false in X if ¯l∈X;
Otherwise, l is unknown. ⊥ is false in X.
Given a signature Σ and a set of predicate symbols E, lit(Σ,E)
denotes the set of all literals of Σ formed by predicate symbols from
E. If Π is a ground program, lit(Π) denotes the set of all atoms occurring
in Π, together with their negations, and lit(Π,E) denotes the set of all
literals occurring in lit(Π) formed by predicate symbols from E.
The answer set semantics of a logic program Π assigns to
Π a collection of answer sets – consistent sets of ground literals
over signature Σ(Π) corresponding to beliefs
which can be built by a rational reasoner on the basis of rules of Π.
In the construction of these beliefs the reasoner is assumed to be guided
by the following informal principles:
* He should satisfy the rules of Π, understood as
constraints of the form:
If one believes in the body of a rule one must belief in its head.
* He cannot believe in ⊥ (which is understood as falsity).
* He should adhere to the rationality principle which says that
one shall not believe anything he is not forced to believe.
The precise definition of answer sets
will be first given for programs whose rules do not contain default
negation. Let Π be such a program and let X be a state of
Σ(Π).
We say that X is closed under Π if, for every
rule head←body of Π, head is true in X whenever body
is true in X. (For a constraint this condition means that the body is not
contained in X.)
###### Definition 9.1
(Answer set – part one)
A state X of Σ(Π)
is an answer set for Π if X is minimal
(in the sense of set-theoretic inclusion) among
the sets closed under Π.
It is clear that a program without default negation
can have at most one answer set.
To extend this definition to arbitrary programs, take any program Π,
and let X be a state of Σ(Π). The reduct, ΠX, of Π
relative to X is the set of rules
| | | |
| --- | --- | --- |
| | l0←l1,…,lm | |
for all rules ([5](#S9.E5 "(5) ‣ 9 The syntax and semantics of A-Prolog ‣ [")) in Π such that lm+1,…,ln∉X.
Thus ΠX is a program without default negation.
###### Definition 9.2
(Answer set – part two)
A state X of Σ(Π) is
an answer set for Π if X is an answer set for ΠX.
(The above definition differs slightly from the original definition in
[[Gelfond and Lifschitz, 1991](#bib.bibx9)], which allowed the inconsistent answer set, lit(Σ). Answer
sets defined in this paper correspond to consistent answer sets of the original
version.)
10 Syntax and semantics of the causal laws of AL
-------------------------------------------------
An action description of AL is a
collection of propositions of the form
1. causes(ae,l0,[l1,…,ln]),
2. caused(l0,[l1,…,ln]), and
3. impossible\_if(ae,[l1,…,ln])
where ae is an elementary action
and l0,…,ln are fluent literals from
Σ. The first proposition says that, if
the elementary action ae were to be executed in a situation in which
l1,…,ln hold, the fluent literal l0 will be caused to
hold in the resulting situation. Such propositions are called dynamic causal laws.
The second proposition, called a
static causal law, says that, in an arbitrary situation,
the truth of fluent literals, l1,…,ln is sufficient to
cause the truth of l0. The last proposition says that action
ae cannot be performed in any
situation in which l1,…,ln hold.
(The one presented here is actually a
simplification of AL. Originally impossible\_if took as
argument a compound action rather than an elementary one.
The restriction on ae being elementary is not
essential and can be lifted. We require it to simplify the presentation).
To define the transition diagram, T, given by
an action description A of AL
we use the
following terminology and notation. A set S of fluent literals is
closed under a set Z of static causal laws if S includes the
head, l0, of every static causal law such that
{l1,…,ln}⊆S. The set CnZ(S) of consequences of S under Z is the smallest set of fluent literals that
contains S and is closed under Z.
E(ae,σ) stands for the set of all fluent literals l0 for which
there is a dynamic causal law causes(ae,l0,[l1,…,ln])
in A such that [l1,…,ln]⊆σ.
E(a,σ)=⋃ae∈aE(ae,σ).
The transition system T=⟨S,R⟩ described by an action description
A is defined as follows:
1. S is the collection of all complete and consistent sets
of fluent literals of Σ closed under the static laws of
A,
2. R is the set of all triples
⟨σ,a,σ′⟩ such that A does not contain a
proposition of the form impossible\_if(a,[l1,…,ln]) such
that [l1,…,ln]⊆σ and
| | | | |
| --- | --- | --- | --- |
| | σ′=CnZ(E(a,σ)∪(σ∩σ′)) | | (6) |
where Z is the set of all static causal laws of A.
The argument of Cn(Z) in
([6](#S10.E6 "(6) ‣ item 2 ‣ 10 Syntax and semantics of the causal laws of AL ‣ [")) is the union of the set E(a,σ) of the “direct
effects” of a with the set σ∩σ′ of facts that are
“preserved by inertia”. The application of Cn(Z) adds the
“indirect effects” to this union.
We call an action description deterministic
if for any state σ0 and action a there is
at most one such successor state σ1.
The above definition of T is from [[McCain and Turner, 1997](#bib.bibx18)] and is the product of
a long investigation of the nature of causality. (See for instance,
[[Lifschitz, 1997](#bib.bibx13), [Thielscher, 1997a](#bib.bibx31)].) Finding this definition required the good understanding
of the nature of causal
effects of actions in the presence of complex interrelations between fluents.
An additional level of complexity is added by the need to specify what
is not changed by actions. The latter, known as the frame problem,
is often
reduced to the problem of finding a concise and accurate representation of
the inertia axiom – a default which says that things normally stay as
they are [[McCarthy and Hayes, 1969](#bib.bibx19)]. The search for such a representation substantially
influenced AI research during the last twenty years.
An interesting account of history of this research
together with some possible solutions can be found in [[Shanahan, 1997](#bib.bibx29)].
11 Properties of logic programs
--------------------------------
In this section we introduce several properties of logic programs which will be
used, in the next appendix, to prove the main theorem of this paper.
We begin by summarizing two useful definitions from [[Brass and Dix, 1994](#bib.bibx4)].
###### Definition 11.1
Let q be a literal and P be a logic program. The *definition* of q in
P is the set of all rules in P which have q as their head.
######
Definition 11.2 (Partial Evaluation)
Let q be a literal and P be a logic program. Let
| | | |
| --- | --- | --- |
| | q←Γ1.q←Γ2.⋯ | |
be the definition of q in P. The Partial Evaluation of P w.r.t. q
(denoted by e(P,q)) is the program obtained from P by replacing every rule of
the form
| | | |
| --- | --- | --- |
| | p←Δ1,q,Δ2. | |
with rules
| | | |
| --- | --- | --- |
| | p←Δ1,Γ1,Δ2.p←Δ1,Γ2,Δ2.⋯ | |
Notice that, according to Brass-Dix Lemma [[Brass and Dix, 1994](#bib.bibx4)], P and e(P,q) are equivalent (written
P≃e(P,q)), i.e. they have the same answer sets.
The following expands on the results from [[Brass and Dix, 1994](#bib.bibx4)].
######
Definition 11.3 (Extended Partial Evaluation)
Let P be a ground program, and →q=⟨q1,q2,…,qn⟩ be a
sequence of literals. The Extended Partial Evaluation of P w.r.t. →q
(denoted by e(P,→q)) is defined as follows:
| | | | |
| --- | --- | --- | --- |
| | | | (7) |
From now on, the number of elements of →q will be denoted by
|→q|.
######
Definition 11.4 (Trimming)
Let →q and P be as above. The Trimming of P w.r.t.
→q (denoted by t(P,→q)) is the program obtained by dropping the
definition of the literals in →q from e(P,→q).
{lemma}
Let P and R be logic programs, such that
| | | | |
| --- | --- | --- | --- |
| | P≃R. | | (8) |
Then, for any sequence of literals →q,
| | | | |
| --- | --- | --- | --- |
| | e(P,→q)≃e(R,→q). | | (9) |
{proof}
By induction on |→q|.
Base case: |→q|=0. By Definition [11.3](#S11.Thmdefinition3 "Definition 11.3 (Extended Partial Evaluation) ‣ 11 Properties of logic programs ‣ ["), e(P,→q)=P and
e(R,→q)=R. Then, ([8](#S11.E8 "(8) ‣ 11 Properties of logic programs ‣ [")) can be rewritten as
e(P,→q)≃e(R,→q).
Inductive step: let us assume that ([9](#S11.E9 "(9) ‣ 11 Properties of logic programs ‣ [")) holds for
|→q|=n−1 and show that it holds for |→q|=n. Since P≃R, by inductive hypothesis
| | | | |
| --- | --- | --- | --- |
| | e(P,⟨q1,…,qn−1⟩)≃e(R,⟨q1,…,qn−1⟩). | | (10) |
By the Brass-Dix Lemma,
| | | | |
| --- | --- | --- | --- |
| | e(P,qn)≃P and e(R,qn)≃R. | | (11) |
Again by inductive hypothesis, ([11](#S11.E11 "(11) ‣ 11 Properties of logic programs ‣ [")) becomes
e(e(P,qn),⟨q1,…,qn−1⟩)≃e(P,⟨q1,…,qn−1⟩) and similarly for R. Then,
([10](#S11.E10 "(10) ‣ 11 Properties of logic programs ‣ [")) can be rewritten as
| | | | |
| --- | --- | --- | --- |
| | e(e(P,qn),⟨q1,…,qn−1⟩)≃e(e(R,qn),⟨q1,…,qn−1⟩). | | (12) |
By Definition [11.3](#S11.Thmdefinition3 "Definition 11.3 (Extended Partial Evaluation) ‣ 11 Properties of logic programs ‣ ["), e(e(P,qn),⟨q1,…,qn−1⟩)=e(P,→q) and similarly for R. Then, ([12](#S11.E12 "(12) ‣ 11 Properties of logic programs ‣ [")) becomes
| | | |
| --- | --- | --- |
| | e(P,→q)≃e(R,→q). | |
{lemma}
Let →q be a sequence of literals and P be a logic program. Then,
| | | | |
| --- | --- | --- | --- |
| | P≃e(P,→q). | | (13) |
{proof}
By induction on |→q|.
Base case: |→q|=0. P=e(P,→q) by Definition
[11.3](#S11.Thmdefinition3 "Definition 11.3 (Extended Partial Evaluation) ‣ 11 Properties of logic programs ‣ [").
Inductive step: let us assume that ([13](#S11.E13 "(13) ‣ 11 Properties of logic programs ‣ [")) holds for
|→q|=n−1 and show that it holds for |→q|=n.
By the Brass-Dix Lemma, P≃e(P,qn). Then, Lemma [11](#S11 "11 Properties of logic programs ‣ [") can be
applied to P and e(P,qn), obtaining
| | | | |
| --- | --- | --- | --- |
| | e(P,⟨q1,…,qn−1⟩)≃e(e(P,qn),⟨q1,…,qn−1⟩). | | (14) |
Since, by inductive hypothesis, P≃e(P,⟨q1,…,qn−1⟩), ([14](#S11.E14 "(14) ‣ 11 Properties of logic programs ‣ [")) can be rewritten as
| | | |
| --- | --- | --- |
| | P≃e(e(P,qn),⟨q1,…,qn−1⟩), | |
which, by Definition [11.3](#S11.Thmdefinition3 "Definition 11.3 (Extended Partial Evaluation) ‣ 11 Properties of logic programs ‣ ["), implies ([13](#S11.E13 "(13) ‣ 11 Properties of logic programs ‣ [")).
The following expands similar results from [[Gelfond and Son, 1998](#bib.bibx11)], making them suitable for
our purposes.
######
Definition 11.5 (Strong Conservative Extension)
Let P1 and P2 be ground programs such that lit(P1)⊆lit(P2).
Let Q be lit(P2)∖lit(P1).
We say that P2 is a Strong Conservative Extension of P1 w.r.t. Q (and
write P2≻QP1) if:
* if A is an answer set of P2, A∖Q is an answer set of
P1;
* if A is an answer set of P1, there exists a subset B of Q such
that A∪B is an answer set of P2.
{lemma}
Let P be a ground program, Q⊆lit(P), and →q=⟨q1,…,qn⟩ be an ordering of Q. If Q∩lit(t(P,→q))=∅, then
| | | | |
| --- | --- | --- | --- |
| | P is a Strong Conservative Extension of t(P,→q) w.r.t Q. | | (15) |
{proof}
Notice that, under the hypotheses of this lemma, the complement,
¯¯¯¯Q, of Q is a splitting set for e(P,→q), with
bottom¯¯¯¯Q(e(P,→q))=t(P,→q). Then, by the
Splitting Set Theorem, and by Definition [11.5](#S11.Thmdefinition5 "Definition 11.5 (Strong Conservative Extension) ‣ 11 Properties of logic programs ‣ ["),
| | | | |
| --- | --- | --- | --- |
| | e(P,→q)≻Qt(P,→q). | | (16) |
By Lemma [11](#S11 "11 Properties of logic programs ‣ ["), P≃e(P,→q). Then,
([16](#S11.E16 "(16) ‣ 11 Properties of logic programs ‣ [")) can be rewritten as
| | | |
| --- | --- | --- |
| | P≻Qt(P,→q). | |
12 Proofs of the theorems
--------------------------
###
12.1 Proof of Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [")
The proof of Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") will be given in several steps.
First of all, we
define a simplified encoding of an action description, SD, in
A-Prolog. Then, we prove that the answer sets of the programs generated using
this encoding correspond exactly to the paths in the transition diagram
described by SD.
Later, we extend the new encoding and prove that, for every recorded history
Γn, the models of Γn are in a one-to-one correspondence with the
answer sets of the programs generated by this second encoding.
Finally, we prove that programs obtained by applying this second encoding are
essentially ‘equivalent’ to those generated with the encoding presented in Section
[4](#S4 "4 Computing candidate diagnoses ‣ ["), which completes the proof of Theorem [4](#S4 "4 Computing candidate diagnoses ‣ ["). In
addition, we present a corollary that extends the theorem to
the case in which the initial situation of Γn is not complete.
####
12.1.1 Step 1
The following notation will be useful in our further discussion. Given a time
point t, a state σ, and a compound action a, let
| | | | |
| --- | --- | --- | --- |
| | h(σ,t)={h(l,t)|l∈σ}o(a,t)={o(a′,t)|a′∈a}. | | (17) |
These sets can be viewed as the representation of σ and a in A-Prolog.
###### Definition 12.1
Let SD be an action description of AL , n be a positive integer, and
Σ(SD) be the signature of SD. Σnd(SD) denotes the signature
obtained as follows:
* const(Σnd(SD))=⟨const(Σ(SD))∪{0,…,n}⟩;
* pred(Σnd(SD))={h,o}.
Let
| | | | |
| --- | --- | --- | --- |
| | αnd(SD)=⟨Παd(SD),Σnd(SD)⟩, | | (18) |
where
| | | | |
| --- | --- | --- | --- |
| | Παd(SD)=⋃r∈SDαd(r), | | (19) |
and αd(r) is defined as follows:
* αd(causes(a,l0,[l1,…,lm])) is
| | | |
| --- | --- | --- |
| | h(l0,T′)←h(l1,T),…,h(lm,T),o(a,T). | |
* αd(caused(l0,[l1,…,lm])) is
| | | | |
| --- | --- | --- | --- |
| | h(l0,T)←h(l1,T),…,h(lm,T). | | (20) |
* αd(impossible\_if(a,[l1,…,lm])) is
| | | |
| --- | --- | --- |
| | ←h(l1,T),…,h(lm,T),o(a,T). | |
Let also
| | | | |
| --- | --- | --- | --- |
| | βnd(SD)=⟨Πβd(SD),Σnd(SD)⟩, | | (21) |
where
| | | | |
| --- | --- | --- | --- |
| | Πβd(SD)=Παd(SD)∪Πd | | (22) |
and Πd is the following set of rules:
| | | |
| --- | --- | --- |
| | 1.h(L,T′)←h(L,T),noth(¯¯¯¯L,T′).2.←h(L,T),h(¯¯¯¯L,T). | |
When we refer to a single action description, we will often drop the argument
from Σnd(SD),αnd(SD),Παd(SD),βnd(SD),Πβd(SD) in order to simplify the presentation.
For the rest of this section, we will restrict attention to ground programs. In
order to keep notation simple, we will use αnd, βnd, αn
and βn to denote the ground versions of the programs previously defined.
For any action description SD, state σ0 and action
a0, let βnd(SD,σ0,a0) denote
| | | | |
| --- | --- | --- | --- |
| | βnd∪h(σ0,0)∪o(a0,0). | | (23) |
We will sometimes drop the first argument, and denote the program by
βnd(σ0,a0).
The following lemma will be helpful in proving the main result of this
subsection. It states the correspondence between (single) transitions of the
transition diagram and answer sets of the corresponding A-Prolog program.
{lemma}
Let SD be an action description, T(SD) be the transition diagram it
describes, and β1d(σ0,a0) be defined as in
([23](#S12.E23 "(23) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")). Then, ⟨σ0,a0,σ1⟩∈T(SD) iff σ1={l|h(l,1)∈A} for some answer set
A of β1d(σ0,a0).
{proof}
Let us define
| | | | |
| --- | --- | --- | --- |
| | I=h(σ0,0)∪o(a0,0) | | (24) |
and
| | | |
| --- | --- | --- |
| | β1d=β1d(σ0,a0)∪I. | |
*Left-to-right*. Let us show that, if ⟨σ0,a0,σ1⟩∈T(SD),
| | | | |
| --- | --- | --- | --- |
| | A=I∪h(σ1,1) | | (25) |
is an answer set of β1d(σ0,a0). Notice that ⟨σ0,a0,σ1⟩∈T(SD) implies that σ1 is a state.
Let us prove that A is the minimal set of atoms closed under the rules of the
reduct PA. PA contains:
1. set I;
2. all rules in α1d(SD) (see ([18](#S12.E18 "(18) ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")));
3. a constraint ←h(l,t),h(¯l,t). for any fluent literal l and
time point t;
4. a rule
| | | |
| --- | --- | --- |
| | h(l,1)←h(l,0) | |
for every fluent literal l such that h(l,1)∈A (in fact, since σ1
is complete and consistent, h(l,1)∈A⟺h(¯l,1)∉A).
A is closed under PA. We will prove it for every rule of
the program.
Rules of groups (a) and (d): obvious.
Rules of group (b) encoding dynamic laws of the form
causes(a,l,[l1,…,lm]):
| | | |
| --- | --- | --- |
| | h(l,1)←h(l1,0),…,h(lm,0),o(a,0). | |
If {h(l1,0),…,h(lm,0),o(a,0)}⊆A, then, by
([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), {l1,…,lm}⊆σ0 and a∈a0. Therefore, the preconditions of the dynamic law are satisfied by
σ0. Hence ([6](#S10.E6 "(6) ‣ item 2 ‣ 10 Syntax and semantics of the causal laws of AL ‣ [")) implies l∈σ1. By
([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l,1)∈A.
Rules of group (b) encoding static laws of the form
caused(l,[l1,…,lm]):
| | | |
| --- | --- | --- |
| | h(l,t)←h(l1,t),…,h(lm,t). | |
If {h(l1,t),…,h(lm,t)}⊆A, then, by
([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), {l1,…,lm}⊆σt, i.e. the
preconditions of the static law are satisfied by σt. If t=1, then
([6](#S10.E6 "(6) ‣ item 2 ‣ 10 Syntax and semantics of the causal laws of AL ‣ [")) implies l∈σ1. By ([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")),
h(l,t)∈A. If t=0, since states are closed under the static laws of SD,
we have that l∈σ0. Again by ([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l,t)∈A.
Rules of group (b) encoding impossibility laws of the form
impossible\_if(a,[l1,…,lm]):
| | | |
| --- | --- | --- |
| | ←h(l1,0),…,h(lm,0),o(a,0). | |
Since ⟨σ0,a0,σ1⟩∈T(SD) by hypothesis,
⟨σ0,a0⟩ does not satisfy the preconditions of any
impossibility condition. Then, either a∉a0 or li∉σ0
for some i. By ([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), the body of this rule is not
satisfied.
Rules of group (c).
Since σ0 and σ1 are consistent by hypothesis, l and
¯l cannot both belong to the same state. By ([25](#S12.E25 "(25) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")),
either h(l,0)∉A or h(¯l,0)∉A, and the same holds
for time point 1. Therefore, the body of these rules is never satisfied.
A is the minimal set closed under the rules of PA. We will
prove this by assuming that there exists a set B⊆A such that B is
closed under the rules of PA, and by showing that B=A.
First of all,
| | | | |
| --- | --- | --- | --- |
| | I⊆B, | | (26) |
since these are facts in PA.
Let
| | | | |
| --- | --- | --- | --- |
| | δ={l|h(l,1)∈B}. | | (27) |
Since B⊆A,
| | | | |
| --- | --- | --- | --- |
| | δ⊆σ1 | | (28) |
We will show that δ=σ1 by proving that
| | | | |
| --- | --- | --- | --- |
| | δ=CNZ(E(a0,σ0)∪(σ1∩σ0)). | | (29) |
Dynamic laws. Let d be a dynamic law of SD of the form
causes(a,l0,[l1,…,lm]), such that a∈a0 and {l1,…,lm}⊆σ0. Because of ([26](#S12.E26 "(26) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h({l1,…,lm},0)⊆B and o(a,0)∈B. Since B is closed under αd(d)
([20](#S12.E20 "(20) ‣ 2nd item ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l0,1)∈B, and l0∈δ. Therefore,
E(a0,σ0)⊆δ.
Inertia. PA contains a (reduced) inertia rule of the form
| | | | |
| --- | --- | --- | --- |
| | h(l,1)←h(l,0). | | (30) |
for every literal l∈σ1. Suppose l∈σ1∩σ0.
Then, h(l,0)∈I, and, since B is closed under ([30](#S12.E30 "(30) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l,1)∈B. Therefore, σ1∩σ0⊆δ.
Static laws. Let s be a static law of SD of the form
caused(l0,[l1,…,lm]), such that
| | | | |
| --- | --- | --- | --- |
| | h({l1,…,lm},0)⊆B. | | (31) |
Since B is closed under αd(s) ([20](#S12.E20 "(20) ‣ 2nd item ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l0,1)∈B,
and l0∈δ. Then, δ is closed under the static laws of SD.
Summing up, ([29](#S12.E29 "(29) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds. From ([6](#S10.E6 "(6) ‣ item 2 ‣ 10 Syntax and semantics of the causal laws of AL ‣ [")) and
([28](#S12.E28 "(28) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), we obtain σ1=δ. Therefore h(σ1,1)⊆B.
At this point we have shown that I∪h(σ1,1)⊆B⊆A.
*Right-to-left*. Let A be an answer set of P such that σ1={l|h(l,1)∈A}. We have to show that
| | | | |
| --- | --- | --- | --- |
| | σ1=CNZ(E(a0,σ0)∪(σ1∩σ0)), | | (32) |
that ⟨σ0,a0⟩ respects all impossibility conditions, and that
σ1 is consistent and complete.
σ1 consistent. Obvious, since A is a (consistent) answer set
by hypothesis.
σ1 complete. By contradiction, let l be a literal s.t. l∉σ1, ¯l∉σ1, and l∈σ0 (since
σ0 is complete by hypothesis, if l∉σ0, we can still
select ¯l). Then, the reduct PA contains a rule
| | | | |
| --- | --- | --- | --- |
| | h(l,1)←h(l,0). | | (33) |
Since A is closed under PA, h(l,1)∈A and l∈σ1.
Contradiction.
Impossibility conditions respected. By contradiction, assume that
condition impossible\_if(a,[l1,…,lm]) is not respected. Then,
h({l1,…,lm},0)⊆A and o(a,0)∈A. Therefore, the body of
the αd-mapping ([20](#S12.E20 "(20) ‣ 2nd item ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) of the impossibility condition is
satisfied by A, and A is not a (consistent) answer set.
([32](#S12.E32 "(32) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds. Let us prove that σ1⊇E(a0,σ0). Consider a dynamic law d in SD of the form
causes(a,l0,[l1,…,lm]), such that {l1,…,lm}⊆σ0 and a∈a0. Since A is closed under αd(d)
([20](#S12.E20 "(20) ‣ 2nd item ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l0,1)∈A. Then, σ1⊇E(a0,σ0).
σ1⊇σ1∩σ0 is trivially true.
Let us prove that σ1 is closed under the static laws of SD. Consider a
static law s, of the form caused(l0,[l1,…,lm]), such that {l1,…,lm}⊆σ0. Since A is closed under αd(s)
([20](#S12.E20 "(20) ‣ 2nd item ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), h(l0,1)∈A.
Let us prove that σ1 is the minimal set satisfying all conditions.
By contradiction, assume that there exists a set δ⊂σ1
such that δ⊇E(a0,σ0)∪(σ1∩σ0) and
that δ is closed under the static laws of SD. We will prove
that this implies that A is not an answer set of P.
Let A′ be the set obtained by removing from A all atoms h(l,1) such that
l∈σ1∖δ. Since δ⊂σ1, A′⊂A.
Since δ⊇E(a0,σ0)∪(σ1∩σ0), for every l∈σ1∖δ it must be
true that l∉σ0 and l∉E(a0,σ0).
Therefore there must exist (at least) one static law
caused(l,[l1,…,lm]) such that {l1,…,lm}⊆σ1 and {l1,…,lm}⊈δ. Hence, A′ is
closed under the rules of PA. This proves that A is not an answer set of
P. Contradiction.
We are now ready to prove the main result of this subsection. The following
notation will be used in the theorems that follow. Let SD be an
action description, and M=⟨σ0,a0,σ1,…,an−1,σn⟩ be a sequence where σi are sets of fluent literals and
ai are actions. o(M) denotes
| | | |
| --- | --- | --- |
| | ⋃to(at,t), | |
with o(a,t) from ([17](#S12.E17 "(17) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")). The length of M, denoted by l(M), is
m−12, where m is the number of elements of M.
Given an action description SD and a sequence M=⟨σ0,a0,σ1,…,an−1,σn⟩, βnd(SD,M) denotes the
program
| | | | |
| --- | --- | --- | --- |
| | βnd∪h(σ0,0)∪o(M). | | (34) |
We will use βnd(M) as short form for βnd(SD,M).
{lemma}
Let SD be an action description, M=⟨σ0,a0,σ1,…,an−1,σn⟩ be a sequence of length n, and βnd(M) be
defined as in ([34](#S12.E34 "(34) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")). If σ0 is a state, then, M is a
trajectory of T(SD) iff M is defined by an answer set of
P=βnd(M).
{proof}By induction on l(M).
Base case: l(M)=1. Since l(M)=1, M is the sequence ⟨σ0,a0,σ1⟩, o(M)=o(a0,0) and, since σ0 is a state by
hypothesis, P=β1d(SD,σ0,a0) ([23](#S12.E23 "(23) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")). Then,
Lemma [12.1.1](#S12.SS1.SSS1 "12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [") can be applied, thus completing the proof for this
case.
Inductive step. We assume that the theorem holds for trajectories of length n−1
and prove that it holds for trajectories of length n.
M=⟨σ0,a0,σ1,…,an−1,σn⟩ is a
trajectory of T(SD) iff
| | | | |
| --- | --- | --- | --- |
| | ⟨σ0,a0,σ1⟩∈T(SD)%
, and | | (35) |
| | M′=⟨σ1,a1,…,an−1,σn⟩ is a trajectory
of T(SD). | | (36) |
Let R1 denote β1d(SD,σ0,a0). By Lemma [12.1.1](#S12.SS1.SSS1 "12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["),
([35](#S12.E35 "(35) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | R1 has an answer set, A, such that σ1={l|h(l,1)∈A}. | | (37) |
Notice that this implies that σ1 is a state. Now, let R2 denote
βn−1d(SD,M′). By inductive hypothesis, ([36](#S12.E36 "(36) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M′ is defined by an answer set, B, of R2. | | (38) |
Let S be the set containing all literals of the form h(l,0), h(l,1) and
o(a,0), over the signature of P. Let C be the set of the constraints
of R1. Notice that:
* S is a splitting set of P;
* bottomS(P)=R1∖C.
Then, ([37](#S12.E37 "(37) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | |
| --- | --- | --- |
| | A is an answer set of bottomS(P), satisfying C, | |
| | such that σ1={l|h(l,1)∈A}. | | (39) |
For any program R, let R+1 denote the program obtained from R by:
1. replacing, in the rules of R, every occurrence of a constant symbol denoting a
time point with the constant symbol denoting the next time point;
2. modifying accordingly the signature of R.
Then, ([38](#S12.E38 "(38) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M′ is defined by a set B such that B+1 is an %
answer set
of R+12. | | (40) |
Notice that eS(P,A)=eS(R+12,A)∪eS(C,A). Therefore, A satisfies
C iff eS(P,A)=eS(R+12,A). Notice also that eS(R+12,A)≃R+12.
Then, ([39](#S12.E39 "(39) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) and ([40](#S12.E40 "(40) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) hold iff
| | | |
| --- | --- | --- |
| | A is an answer set of bottomS(P) | |
| | such that σ1={l|h(l,1)∈A}, and | |
| | M′ is defined by B, and | | (41) |
| | B+1 is an answer set of eS(P,A). | |
By Definition [4.1](#S4.Thmdefinition1 "Definition 4.1 ‣ 4 Computing candidate diagnoses ‣ ["), ([41](#S12.E41 "(41) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | |
| --- | --- | --- |
| | A is an answer set of bottomS(P), and | |
| | M is defined by A∪B+1, and | | (42) |
| | B+1 is an answer set of eS(P,A). | |
By the Splitting Set Theorem, ([42](#S12.E42 "(42) ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | |
| --- | --- | --- |
| | M is defined by an answer set of P. | |
####
12.1.2 Step 2
In this subsection, we extend the previous encoding in order to be able to
generate programs whose answer sets describe exactly the paths consistent with a
specified recorded history Γn.
Let ΣnΓ,d(SD) denote the signature defined as
follows:
* const(ΣnΓ,d(SD))=const(Σnd(SD));
* pred(ΣnΓ,d(SD))=pred(Σnd(SD))∪{hpd,obs}.
Let
| | | | |
| --- | --- | --- | --- |
| | αnd(SD,Γn)=⟨ΠΓd,ΣnΓ,d⟩, | | (43) |
where
| | | | |
| --- | --- | --- | --- |
| | ΠΓd=Πβd(SD)∪^Π∪Γn. | | (44) |
Πβd(SD) is defined as in ([22](#S12.E22 "(22) ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), and ^Π is the set
of rules:
| | | |
| --- | --- | --- |
| | 3.o(A,T)←hpd(A,T).4.h(L,0)←obs(L,0).5.←obs(L,T),noth(L,T). | |
(Notice that these rules are equal the last 3 rules of program Π,
defined in Section [4](#S4 "4 Computing candidate diagnoses ‣ [").)
When we refer to a single system description, we will often drop argument SD
from
ΣnΓ,d(SD),αnd(SD,Γn),ΠΓd(SD) in order to
simplify the presentation.
Notice that, as we did before, in the rest of this section we will restrict
attention to ground programs.
{proposition}
If the initial situation of Γn is complete, i.e. for any fluent f of
SD, Γn contains obs(f,0) or obs(¬f,0), then
| | | | |
| --- | --- | --- | --- |
| | M is a model of Γn | | (45) |
iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of αnd(SD,Γn). | | (46) |
{proof}
Let P0 be αnd(SD,Γn), and P1 be obtained
from P0 by removing every constraint
| | | | |
| --- | --- | --- | --- |
| | ←obs(l,t),noth(l,t). | | (47) |
such that obs(l,t)∉Γn. Notice that
| | | | |
| --- | --- | --- | --- |
| | P0≃P1, | | (48) |
hence ([46](#S12.E46 "(46) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of P1. | | (49) |
Let Q be the set of literals, over the signature of P1, of the form
hpd(A,T) and obs(L,0). Let →q be an arbitrary ordering of
the elements of Q, and P2 be t(P1,→q). By Lemma
[11](#S11 "11 Properties of logic programs ‣ ["), P1≻QP2. Since predicate names h and
o are common to the signatures of both P1 and P2, ([49](#S12.E49 "(49) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["))
holds iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of P2. | | (50) |
Let σ0 be {l|obs(l,0)∈Γn}. It is easy to check
that
| | | |
| --- | --- | --- |
| | rules(P2)=R∪C, | |
where R=βnd∪h(σ0,0)∪{o(a,t)|hpd(a,t)∈Γn} (see ([22](#S12.E22 "(22) ‣ Definition 12.1 ‣ 12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["))) and C is the set of constraints
| | | |
| --- | --- | --- |
| | ←noth(l,t). | |
such that obs(l,t)∈Γn.
Then, ([50](#S12.E50 "(50) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set,A, of R,and | | (51) |
| | A satisfies C. | | (52) |
Notice that R=βnd(M).
Since the initial situation of Γn is complete, σ0 is complete.
This, together with ([51](#S12.E51 "(51) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")), implies that σ0 is closed under
the static laws of SD, i.e. σ0 is a state. Then, Lemma
[12.1.1](#S12.SS1.SSS1 "12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [") can be applied, obtaining that ([51](#S12.E51 "(51) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | |
| --- | --- | --- |
| | M is a trajectory of T(SD),and | |
| | o(M)={o(a,t)|hpd(a,t)∈Γn} | | (53) |
By construction of C, ([52](#S12.E52 "(52) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff, if obs(l,t)∈Γn, then h(l,t)∈A. According to Lemma [12.1.1](#S12.SS1.SSS1 "12.1.1 Step 1 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["), h(l,t)∈A iff l∈σt, where σt are the states that appear in M.
Therefore, ([52](#S12.E52 "(52) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | if obs(l,t)∈Γn,then l∈σt. | | (54) |
By Definition [2.2](#S2.Thmdefinition2 "Definition 2.2 ‣ 2 Modeling the domain ‣ [")(a), ([53](#S12.E53 "(53) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) and ([54](#S12.E54 "(54) ‣ 12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) hold
iff
| | | |
| --- | --- | --- |
| | M is a model of Γn. | |
####
12.1.3 Step 3
In this section we prove that models of Γn are in a one-to-one
correspondence with the answer sets of α(SD,Γn). We will do this by
proving that the answer sets of αnd(SD,Γn) and of
α(SD,Γn) define the same models.
In order to prove this equivalence, we define a new encoding of AL that
will allow us to link α(SD,Γn) and αnd(SD,Γn).
Let SD be an action description of AL and Σ(SD) be its signature. For
any positive integer n, Σn(SD) denotes the signature obtained as
follows:
* const(Σn(SD))=const(Σ(SD))∪{0,…,n}∪{1,…,k}, where k is the maximum number of preconditions present in
the laws of SD;
* pred(Σn(SD))={h,o,d\_law,s\_law,head,action,prec,all\_h,prec\_h}.
Let
| | | | |
| --- | --- | --- | --- |
| | αn(SD)=⟨Πα(SD),Σn(SD)⟩, | | (55) |
where
| | | | |
| --- | --- | --- | --- |
| | Πα(SD)=⋃r∈SDα(r) | | (56) |
and α(r) is defined as in Section [4](#S4 "4 Computing candidate diagnoses ‣ [").
Finally, let
| | | | |
| --- | --- | --- | --- |
| | βn(SD)=⟨Πβ(SD),Σn(SD)⟩, | | (57) |
where
| | | | |
| --- | --- | --- | --- |
| | Πβ(SD)=Πα(SD)∪Π− | | (58) |
and Π− is the set of rules:
| | | |
| --- | --- | --- |
| | 1.h(L,T′)←d\_law(D),head(D,L),action(D,A),o(A,T),prec\_h(D,T).2.h(L,T)←s\_law(D),head(D,L),prec\_h(D,T).3.all\_h(D,N,T)←prec(D,N,nil).4.all\_h(D,N,T)←prec(D,N,P),h(P,T),all\_h(D,N′,T).5.prec\_h(D,T)←all\_h(D,1,T).6.h(L,T′)←h(L,T),noth(¯¯¯¯L,T′).7.←h(L,T),h(¯¯¯¯L,T). | |
(Notice that these rules correspond to rules (1)−(7) of program Π defined
in Section [4](#S4 "4 Computing candidate diagnoses ‣ [").)
When we refer to a single action description, we will often drop the argument
from Σn(SD),αn(SD),Πα(SD),βn(SD),Πβ(SD) in
order to simplify the presentation. We will also restrict attention to the
ground versions of the programs just defined. For this reason, we will abuse
notation slightly and denote by αn(SD) and βn(SD) the
ground versions of the programs defined above.
The following theorem establishes a link between βn and βnd.
{lemma}
Let SD be an action description, n be a positive integer, and Q denote
lit(βn)∖lit(βnd). Then, for any program R
such that lit(R)∩Q=∅,
| | | |
| --- | --- | --- |
| | βn∪R≻Qβnd∪R. | |
{proof}
Let →q be an ordering of the elements of Q, P be βn∪R, and Pd be βnd∪R.
Notice that, in e(P,→q), the elements of Q only occur in the rules that
define them, and that lit(R)∩Q=∅ by hypothesis. Then, by Lemma
[11](#S11 "11 Properties of logic programs ‣ ["),
| | | | |
| --- | --- | --- | --- |
| | P≻Qt(P,→q). | | (59) |
It can also be easily checked that t(P,→q)=Pd. Hence, ([59](#S12.E59 "(59) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["))
can be rewritten as
| | | |
| --- | --- | --- |
| | P≻QPd, | |
that is,
| | | |
| --- | --- | --- |
| | βn∪R≻Qβnd∪R. | |
We are finally able to give the proof of Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [").
*Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [")*
If the initial situation of Γn is complete, i.e. for any fluent f of
SD, Γn contains obs(f,0) or obs(¬f,0), then M is a model of
Γn iff M is defined by some answer set of α(SD,Γn).
{proof}
By Proposition [12.1.2](#S12.SS1.SSS2 "12.1.2 Step 2 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["), M is a model of Γn iff
M is defined by some answer set of αnd(SD,Γn). Let Pd be
αnd(SD,Γn) and P be α(SD,Γn).
Let R be P∖βn. Let also Q be lit(P)∖lit(Pd). By Lemma [12.1.3](#S12.SS1.SSS3 "12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["), βn∪R≻Qβnd∪R.
From this we obtain that
α(SD,Γn)≻Qαnd(SD,Γn). Notice that predicate
names h and o are common to the signatures of both P and Pd. Then, the
thesis follows from the definition of Strong Conservative Extension.
The following corollary extends Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") to the case in which the
initial situation of Γn is not complete.
{corollary}
Let R be
| | | |
| --- | --- | --- |
| | h(F,0)←noth(¬F,0).h(¬F,0)←noth(F,0). | |
For any history Γn,
| | | | |
| --- | --- | --- | --- |
| | M is a model of Γn | | (60) |
iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of α(SD,Γn)∪R. | | (61) |
{proof}
Let σ0 be the first component of M, and obs(σ0,0)={obs(l,0)|l∈σ0}.
First of all, we will show that ([61](#S12.E61 "(61) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) is equivalent to
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of α(SD,Γn)∪obs(σ0,0). | | (62) |
Let Π1=α(SD,Γn)∪R and Π2=α(SD,Γn)∪obs(σ0,0).
Consider a splitting of Π1 and Π2 based on set
| | | |
| --- | --- | --- |
| | S={obs(l,0)|l∈FL}∪{h(l,0)|l∈FL} | |
where FL is the set of fluent literals of SD. Let set Q include:
* the set of ground instances, with T=0, of rules (2)-(5) (we need to consider only the
instances where variable D denotes a static law), and (9) from program Π, in Section
[4](#S4 "4 Computing candidate diagnoses ‣ [");
* the subset of α(SD) containing those facts occurring in the body of the above rules.
Π1 and Π2 are split by S so that:
| | | | |
| --- | --- | --- | --- |
| | bottomS(Π1)=R∪Q∪(Γn∩S), | | (63) |
| | bottomS(Π2)=Q∪obs(σ0,0), | | (64) |
| | topS(Π1)=topS(Π2). | | (65) |
bottomS(Π2) has a unique answer set, A2, and A2∩lit(h)=h(σ0,0). It can be shown
that there exists an answer set, A1, of bottomS(Π1), such that A1⊆A2.
Moreover, for such A1,
| | | | |
| --- | --- | --- | --- |
| | A1∖lit(obs)=A2∖lit(obs). | | (66) |
Let Q′ denote the set of ground instances, with T=0, of rule (10) from program Π in Section
[4](#S4 "4 Computing candidate diagnoses ‣ ["). From ([65](#S12.E65 "(65) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) and ([66](#S12.E66 "(66) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")),
| | | | |
| --- | --- | --- | --- |
| | eS(Π1∖Q′,A1)≃eS(Π2∖Q′,A2). | | (67) |
Since the body of the rules in Q′ is never satisfied,
| | | | |
| --- | --- | --- | --- |
| | eS(Π1,A1)≃eS(Π2,A2). | | (68) |
Let B be an answer set of eS(Π2,A2) and C2=B∪A2. By the Splitting Set Theorem,
C1=B∪A1 is an answer set of Π1. This implies that ([61](#S12.E61 "(61) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) is
equivalent to ([62](#S12.E62 "(62) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")).
Now we will complete the proof by showing that ([62](#S12.E62 "(62) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) is equivalent
to ([60](#S12.E60 "(60) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")). Since
| | | |
| --- | --- | --- |
| | α(SD,Γn)∪obs(σ0,0)=α(SD,Γn∪obs(σ0,0)), | |
Equation ([62](#S12.E62 "(62) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M is defined by some answer set of α(SD,Γn∪obs(σ0,0)). | | (69) |
By Theorem [4](#S4 "4 Computing candidate diagnoses ‣ ["), ([69](#S12.E69 "(69) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M is a model of Γn∪obs(σ0,0). | | (70) |
By Definition [2.2](#S2.Thmdefinition2 "Definition 2.2 ‣ 2 Modeling the domain ‣ [")(a), ([70](#S12.E70 "(70) ‣ 12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | M is a model of Γn. | | (71) |
###
12.2 Answer sets of D0(S) and candidate diagnoses
Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") establishes a link between
answer sets and candidate diagnoses. In this section, we give a proof of this
theorem.
*Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [")*
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a system
description of T, S=⟨Γn,Omn⟩ be
a symptom of the system’s malfunctioning, and E and δ be sets
of ground atoms.
| | | | |
| --- | --- | --- | --- |
| | ⟨E,Δ⟩ is a candidate diagnosis of S | | (72) |
iff
| | | | |
| --- | --- | --- | --- |
| | ⟨E,Δ⟩ is determined by an answer set of D0(S). | | (73) |
{proof}
By definition of candidate diagnosis, ([72](#S12.E72 "(72) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) holds
iff
| | | | |
| --- | --- | --- | --- |
| | there exists a model, M, of history Γn∪Omn∪E such thatΔ={c|M⊨h(ab(c),m)}. | | (74) |
By Corollary [12.1.3](#S12.SS1.SSS3 "12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["), ([74](#S12.E74 "(74) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | there exists an answer set, X, of P=Conf(S)∪E such thatΔ={c|h(ab(c),m)∈X}. | | (75) |
Consider now ([73](#S12.E73 "(73) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")). By definition [4.2](#S4.Thmdefinition2 "Definition 4.2 ‣ 4 Computing candidate diagnoses ‣ ["),
([73](#S12.E73 "(73) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | there exists an answer set, X′, of P′=D0(S) such that⟨E,Δ⟩ is determined by X′. | | (76) |
Let
| | | |
| --- | --- | --- |
| | SP0={hpd(a,t)|hpd(a,t)∈P and a∈Ae}, | |
| | | |
| --- | --- | --- |
| | SP=SP0∪{o(a,t)|hpd(a,t)∈SP0}. | |
SP is a splitting set for both P and P′.
By the Splitting Set Theorem, ([75](#S12.E75 "(75) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) and
([76](#S12.E76 "(76) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) are, respectively, equivalent to
| | | | |
| --- | --- | --- | --- |
| | (a) there exists an answer set, XB, of bottomSP(P) and(b) there exists an answer set, Y, of eSP(P,XB)such that Δ={c|h(ab(c),m)∈XB∪Y}. | | (77) |
| | | | |
| --- | --- | --- | --- |
| | (a) there exists an answer set, X′B, of bottomSP(P′) and(b) there exists an answer set, Y′, of eSP(P′,X′B)such that ⟨E,Δ⟩ is determined by X′B∪Y′. | | (78) |
We want to prove that ([77](#S12.E77 "(77) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) and
([78](#S12.E78 "(78) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) are equivalent.
Let
| | | |
| --- | --- | --- |
| | Ed={hpd(a,t)|hpd(a,t)∈E and hpd(a,t)∉Γn}, | |
and Q be the set of rules
| | | |
| --- | --- | --- |
| | o(A,T)←hpd(A,T). | |
for A and T such that hpd(A,T)∈Ed. bottomSP(P) is Ed∪Q
and bottomSP(P′) is DM0∪Q. Let B and B′ be defined as
follows:
| | | | |
| --- | --- | --- | --- |
| | H={hpd(a,t)|hpd(a,t)∈Ed}B=H∪{o(a,t)|hpd(a,t)∈H}B′=B∖Ed∪{¬o(a,t)|o(a,t)∉B}. | | (79) |
Let us show that ([77](#S12.E77 "(77) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds iff
([78](#S12.E78 "(78) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds.
Assume that ([77](#S12.E77 "(77) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds. It is easy to see that B is
the unique answer set of bottomSP(P). If we observe that the answer sets of
bottomSP(P′) enumerate all possible sequences of exogenous actions, we
obtain that B′ is an answer set of bottomSP(P′). Therefore,
([78](#S12.E78 "(78) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds.
Now, assume that ([78](#S12.E78 "(78) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds. As before, B′ is an
answer set of bottomSP(P′). Immediately, we obtain that B is an answer
set of bottomSP(P). Therefore, ([77](#S12.E77 "(77) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")a) holds.
Let us now show that eSP(P,B)=eSP(P′,B′). Notice that
topSP(P)=Conf(S)∖Q=topSP(P′). Let I=B∩B′ and
¯¯¯I=(B∪B′)∖I. Observe that, for every literal l∈¯¯¯I, l∉topSP(P). This means that
| | | | |
| --- | --- | --- | --- |
| | eSP(P,B)=eSP(P,I)=eSP(P′,I)=eSP(P′,B′). | | (80) |
Let Z denote an answer set of eSP(P,B). By construction of B and B′
and from ([80](#S12.E80 "(80) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")),
| | | |
| --- | --- | --- |
| | Δ={c|h(ab(c),n−1)∈(B∪Z)} iff ⟨E,Δ⟩ is
determined by B′∪Z. | |
Hence, ([77](#S12.E77 "(77) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) and ([78](#S12.E78 "(78) ‣ 12.2 Answer sets of D0(S) and candidate diagnoses ‣ 12 Proofs of the theorems ‣ [")) are
equivalent.
###
12.3 Properties of Diagnostic Module D1
In this subsection, we show that using diagnostic module D1
in place of D0 is *safe*,
i.e. D1 will not miss any useful
predictions about the malfunctioning components.
We start by introducing some terminology which is needed in the rest of the subsection.
###### Definition 12.2
Elementary action ae is *relevant to fluent literal l* (written rel(ae,l)), if:
* ``causes(ae,l,P)''∈SD;
* ``caused(l,[l1,…,lm])''∈SD and ae is relevant to some li
from the preconditions of the law;
* ``impossible\_if(a′e,[l1,…,lm])''∈SD, a′e is relevant to some l, and
ae is relevant to some ¯¯¯li from the preconditions of the condition.
An action, a, is *relevant to set O* of fluent literals if every elementary action from a
is relevant to some l∈O.
###### Definition 12.3
The set, rel(O), of fluent literals *relevant to collection of fluent literals O* is defined
as follows.
1. O⊆rel(O);
2. if ``causes(ae,l,P)''∈SD, and l∈rel(O), then P⊆rel(0);
3. if ``caused(l,P)''∈SD, and l∈rel(O), then P⊆rel(0);
4. for every condition “impossible\_if(ae,[l1,…,lm])” from SD,
if ae is relevant to O, then
{¯¯¯¯l1,…,¯¯¯¯¯lm}⊆rel(O).
###### Definition 12.4
States s1,s2 are called *equivalent w.r.t. a set of fluent literals O*
(s1≃Os2) if
| | | |
| --- | --- | --- |
| | ∀l∈rel(O)l∈s1 iff l∈s2. | |
###### Definition 12.5
The *rank of a sequence of actions, α, w.r.t. a collection of fluent
literals, O*, is the number of elementary actions in α which are not
relevant to O, and is denoted by |α|O.
###### Definition 12.6
Sequence of actions α2 is the *reduct of α1 w.r.t. O*
(α2=redO(α1)) if α2 is obtained from α1 by
replacing a by a∖{ae} if ae is an exogenous action
from a not relevant to O. We say that α1 is *equivalent to
α2 w.r.t. O* (α1≃Oα2) if α1=redO(α2)
or α2=redO(α1).
######
Definition 12.7 (Well-defined system description)
Let T be the transition diagram corresponding to system description SD.
SD is *well-defined* if, for any state s and action a,
``impossible\_if(a,P)''∈SD and P⊆s
iff there is no s′ such that ⟨s,a,s′⟩∈T.
######
Definition 12.8 (Set of final states)
Let O be a collection of fluent literals. The set of final states w.r.t. O is
| | | |
| --- | --- | --- |
| | FO={s|O⊆s}. | |
###### Definition 12.9
A *relevant* candidate diagnosis of a symptom,
S=⟨Γn,Omn⟩, is a candidate diagnosis,
⟨E,Δ⟩, of S such that all actions in E
are relevant to the observations in Omn.
In the theorems that follow, we will implicitly consider only well-defined
system descriptions.
We will also write ⟨s0,α,s1⟩∈T,
to indicate that transition diagram T contains a path from state s0 to state s1
whose actions are labeled by sequence of compound actions α.
{lemma}
Let O be a collection of fluent literals, s0, s′0 be states such that
s0≃Os′0, and α be a sequence of actions s.t. every action
of α is relevant to O.
If ⟨s0,α,s1⟩∈T, then there exists s′1 such that
1. ⟨s′0,α,s′1⟩∈T;
2. s′1≃Os1.
{lemma}
Let O be a collection of fluent literals, and e,a1,…,ak be elementary
actions. If e is not relevant to O
and ⟨s0,{e,a1,…,ak},s1⟩∈T,
then there exists s′1 such that
1. ⟨s0,{a1,…,ak},s′1⟩∈T;
2. s1≃Os′1.
{theorem}
Let SD be a system description and O be a collection of fluent literals.
For every path ⟨s0,α,sf⟩ from T(SD) and
s′0≃Os0 there is a path ⟨s′0,α′,s′f⟩
such that:
| | | | |
| --- | --- | --- | --- |
| | α≃Oα′, |α′|O=0; | | (81) |
| | | | |
| --- | --- | --- | --- |
| | s′f≃Osf. | | (82) |
{proof}
By induction on the rank of α w.r.t. O, |α|O. In the rest of this proof,
we will use T to denote T(SD).
Base case: |α|O=0. By Lemma [12.3](#S12.SS3 "12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["), there exists a path
⟨s′0,α,s′f⟩∈T such that s′f≃Osf.
Inductive step: since |α|O>0, at least one elementary action irrelevant to
O occurs in α. Hence, there exist states sk, sk+1, elementary actions
e1,…,em, and (possibly empty) sequences of actions
α1, α2 such that:
1. all actions in α1 are relevant to O, and ⟨s0,α1,sk⟩∈T;
2. e1 is not relevant to O;
3. ⟨sk,{e1,…,em},sk+1⟩∈T;
4. ⟨sk+1,α2,sf⟩∈T.
First we show that there exists s′k+1 such that
| | | | |
| --- | --- | --- | --- |
| | ⟨s′0,α1{e2,…,em},s′k+1⟩∈T. | | (83) |
and s′k+1≃Osk+1.
Let s′0 be a state such that s′0≃Os0. From condition ([1](#S12.I13.i1 "item 1 ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["))
and the inductive hypothesis, it follows that there exists s′k≃Osk such that
| | | | |
| --- | --- | --- | --- |
| | ⟨s′0,α1,s′k⟩∈T. | | (84) |
Next, we notice that, by conditions ([2](#S12.I13.i2 "item 2 ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")), ([3](#S12.I13.i3 "item 3 ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")) and
Lemma [12.3](#S12.SS3 "12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["),
there exists s′′k+1 such that s′′k+1≃Osk+1
and ⟨sk,{e2,…,em},s′′k+1⟩∈T.
By inductive hypothesis, there exists s′k+1 such that s′k+1≃Os′′k+1
and
| | | | |
| --- | --- | --- | --- |
| | ⟨s′k,{e2,…,em},s′k+1⟩∈T. | | (85) |
Since relation “≃O” is transitive,
| | | | |
| --- | --- | --- | --- |
| | sk+1≃Os′k+1. | | (86) |
Hence, ([83](#S12.E83 "(83) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")) is proven.
Notice that, by construction, |α2|O<|α|O.
By condition ([4](#S12.I13.i4 "item 4 ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")), equation ([86](#S12.E86 "(86) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")), and the inductive hypothesis,
we obtain that there exist s′′f and α′2 such that s′f≃Osf,
α2≃Oα′2, |α′2|O=0, and
| | | | |
| --- | --- | --- | --- |
| | ⟨s′k+1,α′2,s′′f⟩∈T. | | (87) |
Let γ be the sequence consisting of α1, {e2,…,em}
and α′2. From ([84](#S12.E84 "(84) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["))–([87](#S12.E87 "(87) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")), it follows that
| | | | |
| --- | --- | --- | --- |
| | ⟨s′o,γ,s′′f⟩∈T. | | (88) |
By the inductive hypothesis there exists a path ⟨s′0,α′,s′f⟩∈T
such that α′≃Oγ and s′f≃Os′′f. ([81](#S12.E81 "(81) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["))
and ([82](#S12.E82 "(82) ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [")) follow immediately from the transitivity of relation “≃O”.
The following theorem shows that, if only relevant candidate diagnoses are computed,
no useful prediction about the system’s malfunctioning is missed.
{theorem}
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a system
description of T, and S=⟨Γn,O⟩ be a symptom of the
system’s malfunctioning. For every candidate diagnosis D=⟨E,Δ⟩
of S there exists a relevant candidate diagnosis
Dr=⟨Er,Δr⟩ such that Er=redO(E).
{proof}
First, let us prove that Dr is a candidate diagnosis.
By definition of candidate diagnosis (Definition [3.1](#S3.Thmdefinition1 "Definition 3.1 ‣ 3 Basic definitions ‣ [")), E describes one or more paths
in the transition diagram, whose final state, sf, is consistent with O.
By Theorem [12.3](#S12.SS3 "12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ ["), Er describes paths whose final state, s′f, is
equivalent to sf w.r.t. O. Hence, s′f is consistent with O, as well,
and therefore Dr is a candidate diagnosis.
The fact that Dr is a relevant candidate diagnosis follows directly from
Definition [12.9](#S12.Thmdefinition9 "Definition 12.9 ‣ 12.3 Properties of Diagnostic Module D1 ‣ 12 Proofs of the theorems ‣ [").
The next theorem
proves that diagnostic module D1(S) generates all relevant
candidate diagnoses of S.
{theorem}
Let ⟨Σ,T,W⟩ be a diagnostic domain, and SD be a system
description of T. For every symptom of the system’s malfunctioning,
S=⟨Γn,Omn⟩, diagnostic module D1(S)
computes all relevant candidate diagnoses of S.
{proof}
(Sketch.) By Theorem [4](#S4 "4 Computing candidate diagnoses ‣ ["), D0(S) computes all
candidate diagnoses of S. D1(S) essentially consists in the addition
of a constraint. This constraint makes the module reject all candidate diagnoses
which are not relevant to the observations in Omn.
Hence, all candidate diagnoses returned by D1(S) are relevant
to S.
###
12.4 Properties of Find\_Diag
The properties of Find\_Diag are described by Theorem
[5](#S5 "5 Finding a diagnosis ‣ ["). In order to prove the theorem, we prove separately the
termination of the algorithm and its correctness.
{lemma}
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a
system description of T, and S=⟨Γn,On⟩
be a symptom of the system’s malfunctioning. Then, Find\_Diag(S)
terminates.
{proof}Recall that Σ=⟨C,F,A⟩. Let us prove by induction
that the following is an invariant of the repeat … until loop:
“at the beginning of the ith iteration of the loop, there is a set M⊆C such that
| | | | |
| --- | --- | --- | --- |
| | (a)|M|=i−1(b)∀c∈Mobs(¬ab(c),m)∈O | | (89) |
Base case: i=1. Trivially satisfied.
Inductive step. Assume that ([89](#S12.E89 "(89) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) holds for i
and prove that it holds for i+1. Let M={c1,…,ci−1} be the set
satisfying ([89](#S12.E89 "(89) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) for i. Since the loop was not
exited after the last iteration, we can conclude that, during
iteration m, a component c was found, such that observe(m,ab(c))≠ab(c) and c was hypothesized faulty in the candidate diagnosis computed by
Candidate\_Diag. Under these conditions, the *else* branch inside the
while loop adds obs(¬ab(c),m) to O. Notice that, by definition of
candidate diagnosis, this literal did not previously belong to O, which
implies c∉M. Then M∪{c} satisfies
([89](#S12.E89 "(89) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) for i+1.
To complete the proof we show that the algorithm performs at most |C|+1
iterations of the repeat … until loop. Suppose it is in the beginning of
iteration |C|+1. From the invariant, we obtain that
| | | | |
| --- | --- | --- | --- |
| | ∀c∈Cobs(¬ab(c),m)∈O. | | (90) |
Definition ([3.1](#S3.Thmdefinition1 "Definition 3.1 ‣ 3 Basic definitions ‣ [")) implies that ⟨Γn,O⟩ has no candidate diagnosis. The next call to
Candidate\_Diag returns ⟨∅,∅⟩ and the
algorithm terminates immediately.
{lemma}
Let SD be defined as above, S0=⟨Γn,Omn⟩ be a symptom of the system’s malfunctioning, and
⟨E,Δ⟩=Find\_Diag(S), where the value of variable
S is set to S0. If Δ≠∅, then
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a diagnosis of S0; | |
otherwise, S0 has no diagnosis.
{proof}
Let us show that, if Δ=∅, S0 has no
diagnosis. By definition of candidate diagnosis and Theorem
[4](#S4 "4 Computing candidate diagnoses ‣ ["), Candidate\_Diag returns Δ=∅ only
if S0 has no diagnosis. The proof is completed by observing that,
if Candidate\_Diag returns Δ=∅, the function terminates
immediately, and returns ⟨E,Δ⟩.
Let us now assume that Δ≠∅. We have to show that
1. ⟨E,Δ⟩ is a candidate diagnosis of S0, and
2. all the components in Δ are faulty.
Let Oi (i≥1) denote the value of variable O at the beginning of
the ith iteration of the repeat…until loop of Find\_Diag. Notice
that O1 is equal to the initial value of On. By Lemma
[12.4](#S12.SS4 "12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["), the algorithm terminates. Let u denote the index
of the last iteration of the repeat…until loop.
Since Δ≠∅, by Theorem [4](#S4 "4 Computing candidate diagnoses ‣ [") and
by the fact that ⟨E,Δ⟩ is not updated by the while loop,
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a candidate diagnosis of ⟨Γn,Ou⟩. | |
We want to show that this implies statement [1](#S12.I14.i1 "item 1 ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [").
By the definition of candidate diagnosis,
| | | | |
| --- | --- | --- | --- |
| | there exists a model, M, of Γn∪Ou∪E such
thatΔ={c|M⊨h(ab(c),m)}. | | (91) |
Let M denote one such model. From Corollary [12.1.3](#S12.SS1.SSS3 "12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["),
([91](#S12.E91 "(91) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) holds iff
| | | | |
| --- | --- | --- | --- |
| | there exists an answer set, AS, of P=α(SD,Γn∪Ou∪E)∪R such that Δ={c|h(ab(c),m)∈AS}. | | (92) |
Let Au denote one such answer set.
Since S0 is a symptom, n>0. Notice that O′=Ou∖O1 is a set of observations made at time n. Let C denote the set of
constraints of P of the form
| | | | |
| --- | --- | --- | --- |
| | ←obs(l,t),noth(l,t). | | (93) |
where obs(l,t)∈O′ – these constraints correspond to rule (10) of
Π (see Section [4](#S4 "4 Computing candidate diagnoses ‣ ["))). Let also Q denote P∖C.
By the properties of the answer set semantics, ([92](#S12.E92 "(92) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["))
holds iff
| | | | |
| --- | --- | --- | --- |
| | Au is an answer set of Q,Au does not %
violate C, andΔ={c|h(ab(c),m)∈Au}. | | (94) |
Notice that O′ is a splitting set for Q, and bottomO′(Q)=O′. Since no
literal of O′ occurs in topO′(Q), eO′(Q,O′)=Q∖O′. Let R
denote Q∖O′. By the Splitting Set Theorem, Au is an answer set
of Q iff Au∖O′ is an answer set of R.
Let A1 denote Au∖O′. Observe that the literals of O′
occur, within P, only in the constraints of C, and that they never occur
under negation as failure. Therefore, if Au does not violate C, then A1
does not violate C, either. Hence, ([94](#S12.E94 "(94) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) implies that
| | | | |
| --- | --- | --- | --- |
| | A1 is an answer set of R,A1 does not %
violate C, andΔ={c|h(ab(c),m)∈A1}. | | (95) |
By the properties of the answer set semantics, ([95](#S12.E95 "(95) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["))
holds iff
| | | | |
| --- | --- | --- | --- |
| | A1 is an answer set of R∪C, andΔ={c|h(ab(c),m)∈A1}. | | (96) |
Since R=Q∖O′,
| | | |
| --- | --- | --- |
| | R∪C=Q∖O′∪C=P∖O′=α(SD,Γn∪O1∪E)∪R. | |
Hence ([96](#S12.E96 "(96) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) can be rewritten as:
| | | | |
| --- | --- | --- | --- |
| | A1 is an answer set of α(SD,Γn∪O1∪E)∪R, andΔ={c|h(ab(c),m)∈A1}. | | (97) |
From Corollary [12.1.3](#S12.SS1.SSS3 "12.1.3 Step 3 ‣ 12.1 Proof of Theorem 4 ‣ 12 Proofs of the theorems ‣ ["), ([97](#S12.E97 "(97) ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [")) holds
iff
| | | | |
| --- | --- | --- | --- |
| | there exists a model, M, of Γn∪O1∪E such
thatΔ={c|M⊨h(ab(c),n−1)}. | | (98) |
By the definition of candidate diagnosis,
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a candidate diagnosis of S0. | |
To prove statement [2](#S12.I14.i2 "item 2 ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["), notice that Δ
is the result of the latest call to Candidate\_Diag. Since, by hypothesis,
Δ≠∅, the value of variable diag at the end of the final
iteration of the repeat…until loop must have been *true*. In turn,
this implies that, in the same iteration, the while loop terminated with
Δ0=∅ and diag=true. Therefore all the components in
Δ are faulty and, by definition of diagnosis,
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a diagnosis of S0. | |
*Theorem [5](#S5 "5 Finding a diagnosis ‣ [")*
Let ⟨Σ,T,W⟩ be a diagnostic domain, SD be a
system description of T, and S=⟨Γn,Omn⟩
be a symptom of the system’s malfunctioning. Then,
1. Find\_Diag(S) terminates;
2. Let ⟨E,Δ⟩=Find\_Diag(S), where the value of
variable S is set to S0. If Δ≠∅,
then
| | | |
| --- | --- | --- |
| | ⟨E,Δ⟩ is a diagnosis of S0; | |
otherwise, S0 has no diagnosis.
{proof}
Statement [1](#S12.I15.i1 "item 1 ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [") is proven by applying Lemma
[12.4](#S12.SS4 "12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["). Statement [2](#S12.I15.i2 "item 2 ‣ 12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ [") is proven by applying
Lemma [12.4](#S12.SS4 "12.4 Properties of Find_Diag ‣ 12 Proofs of the theorems ‣ ["). |
1b8236cb-faa1-4b94-a244-fa521fd43f79 | trentmkelly/LessWrong-43k | LessWrong | Training Regime Day 8: Noticing
Introduction
In some parts of the world, people go into the forest to hunt mushrooms. The reasons why they do this are not important, but the way that they do it will serve as a guide for noticing.
The problem is that mushrooms are kind of brown and the forest floor is also kind of brown. Finding brown things against a background of brown things is not an easy task.
Finding the first mushroom is done by brute force search. However, after the first mushroom is found, the finder crouches down and looks at that mushroom for a while. This act of observing the mushroom from all angles is called "getting your eyes on."
The purpose of this move is twofold. First, it lets you figure out what shape/color the mushrooms are, which presumably make them easier to find. Second, it tells your brain that it should be on the lookout for these types of things.
Noticing is equivalent to "getting your eyes on" sensations in your body.
In terms of the skill-tree, I think that noticing is pretty low. It doesn't have very many direct applications, but it does make a lot of skills interface with your feelings better. For example, if you want to install a TAP related to "feeling confused", I think that being good at noticing helps a lot with that.
Noticing
The first thing to do is to pick something that you want to notice. It doesn't have to be a feeling, but I'm going to call it a feeling for the rest of this post for convenience.
Recently, I wanted to notice when I was being defensive, so that will be the example I use for the rest of this post. In my case, there was already an English word that described the general sensation, but noticing can also be done on nameless things.
Next, we want to figure out the "triggers" of the feeling. Triggers can be situational factors that cause the feeling or actions/events caused by the feeling, but the best triggers are the phenomenology of the feeling. This is akin to finding the first mushroom, but in your own memories instead of in the wo |
1efc409c-58c1-4418-8b60-2fcd25268baf | trentmkelly/LessWrong-43k | LessWrong | Seize the Maximal Probability Moment
Try and remember 3 or 4 things that you think would be effective hacks for your life but you have not so far implemented. Really, find three.
Probably that was not so hard.
Now think of at which moment in time did you have a maximal probability of having implemented such hacks. Sometimes you had no idea that was the moment. But sometimes you did, like when a friend tells you "I just read this great paper on how people report cartoons being funnier when their face is shaped in a more smiling fashion." and you thought "Great! I may one day implement the algorithm: if studying, force a smile".
You knew you didn't plan to read the article, you knew you trust that friend, and you knew you'd either forget it later, or in any case that from that moment on, the likelihood of you implementing the algorithm would lower.
So my hack of the day is: If you feel you are likely at the maximal probability moment to start a new policy, start immediately.
My friend was telling me about how he went abroad to research: "...so at this place and people there used very strong lights as cognitive enhancement and yadda yadda yadda... (stopped listening for 40s) yadda yadda yadda.... and I wrote a paper on ..." By that time my room had an extra 110W light working.
Just now0 I thought: It was good I installed that light. Why didn't I do the same when I felt like finding a personalized shirt website where the front would be "I Don't want to talk about: [list]" and the back "Pick your topic: [list]" to once and for all stop the gossip and sports ice-breakers?
I didn't seize the maximal probability moment. That's what happened.
Then I noticed that that1 was the maximal probability moment to install in my mind the maximal probability moment algorithm, I did, and that2 was the maximal probability moment of writing this post.
Now if you'll excuse me, I have3 a shirt to buy. |
c3e0de09-6519-4477-8a33-f93cc13b8223 | trentmkelly/LessWrong-43k | LessWrong | Disentangling Competence and Intelligence
I struggle quite a bit with publishing, so today I am just going to publish a bunch of hopefully relatively refined drafts. Any comments and questions are welcome, I would be grateful to elaborate on any area where I appear unclear. If you like, you can also let me know some thoughts about formatting this to be easier to read. This applies to all my future posts.
This post captures most of my current thoughts on deconfusing intelligence.
note: the two parts overlap somewhat, but I didn’t quite synthesize them yet. Part 1 had been included in our publications in AISC before, but I think it's cleaner to post it seperately.
Part 1 (comments on conceptual confusion):
The “ability to achieve goals in a wide range of environments” as per Markus Hutter and Shane Legg (https://arxiv.org/abs/0706.3639) is often used as a definition of general intelligence, but in my opinion it better captures the notion of general competence.
How might the two concepts differ?
Most agents can achieve significantly fewer goals in many environments if their embodiments are changed (say, if their legs were removed), even when retaining the same cognitive capabilities. Is it therefore right to say that an agent has become less intelligent as a consequence of losing its legs?
I believe that including the embodiment/interface of an agent within our measure/concept of its intelligence inflates the concept and leads to confusion.
An example of such confusion would be the notion that companies are already superintelligent agents/entities and thus serve as useful analogies/frameworks for thinking about how to align superintelligent agents in general. While I agree that companies can be more competent than individuals in many domains, they are interfacing with the world very differently, which is something that needs to be accounted for before characterising this difference in competence as purely/mostly a matter of intelligence.
Competence is contextual and compositional, there are many |
6bf4f546-e75d-4c4e-8c4b-cec6e773c885 | trentmkelly/LessWrong-43k | LessWrong | Subdivisions for Useful Distillations?
I am new to the field of AI safety and am testing my fit for being a communicator because, with the current climate, I think it would do a lot of good to bring clarity to the mind of the everyday person.
I started off with writing distillations as was recommended and suggested and I wrote one on the first post of "Risks From Learned Optimization" by Evan Hubinger et al. Filled with analogies and humor, cause that's my style of writing.
But I recently was part of the SERI MATS workshops, in the Agent Foundations stream headed by John Wentworth and realized I had a bunch of questions regarding writing distillations in general.
* What's the appropriate length?
* How much should you cut out / add to the distillation?
* How do you assess whether your distillation adds anything of value to sites that alignment researchers/laypersons frequent? (Other than the obvious value of perceiving the material through a clearer lens?)
John's opinion (in quick response to my query) was that, whilst he firmly believed that distillations contained value and awareness was key, over the past time period, there were a growing number of distillations that just regurgitated what the original article/paper/post said or cut down and just gave the insights. Upon additional queries, he believed a good percentage of the time, the distillation in question should ideally be larger than the source material.
----------------------------------------
With that in mind, I think distillations could be divided into two categories (which can also be categorized as two others, but I will get to that) would provide a better categorization to go through for incoming would-be alignment researchers:
-> Expansive Distillations
This would be a space for distillations that expand and bring to proper clarity the source material so that each paragraph of the original can be digested more easily, and serve as a reference for anyone who seeks explanation.
-> Contractive Distillations
Working on |
32ae4f7c-0f34-4ba8-bf67-4d064b017368 | trentmkelly/LessWrong-43k | LessWrong | Embedded vs. External Decision Problems
(I've discussed these ideas in the past, but this idea was lacking a canonical reference post)
Suppose that you are an expected-utility maximising agent. This means that when you make a decision you should calculate the expected-utility for each counterfactual and choose the counterfactual with the highest expected utility. However, if you know yourself well enough to know that you are an expected utility maximiser, this knowledge can change your counterfactuals. For example, this can provide information about your likely future actions, the expected number of expected utility maximisers in the world or how clones of you might behave. In general, if you are in an embedded problem and you have reflective knowledge of your own algorithm, then your decision theory provides both a system of evaluation and a data-point about the world. We can call this the embedded view.
In contrast, we could also imagine a utility maximiser outside the problem description, analysing the decisions of various agents within the problem according to how well they maximise utility, none of which need to be utility maximisers themselves. In general, when we use a decision theory in this way, we will call this the external view of the problem. Normally, we'll be talking about applying one or both of these two things:
* A method of constructing counterfactuals - ie. evidential counterfactuals, causal counterfactuals, timeless counterfactuals, logical counterfactuals
* A method of evaluating counterfactuals - ie. expected utility, minimax, maximax
We can use these as tools for evaluating agents that aren't necessarily using these tools. One case where this would be useful is when knowing how the agent makes a decision would mean that only a single decision would be consistent with the problem description and hence that the decision theory problem would be trivial in a particular sense. For example, an updateless agent will always one-box in Newcomb's, so it kind of doesn't make sense to as |
27890c8d-76dd-45b2-a2c3-7faf1fbb831c | trentmkelly/LessWrong-43k | LessWrong | Weekly LW Meetups: Oakville ON, Atlanta, Berkeley, Melbourne, London
There are upcoming irregularly scheduled Less Wrong meetups in:
* Sheridan College - Oakville, ON: 25 November 2011 08:30PM
* Next Atlanta Less Wrong Meetup: 10 December 2011 06:00PM
The following meetups take place in cities with regularly scheduled meetups, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:
* Monthly Bay Area meetup: 26 November 2011 07:00PM
* Melbourne practical rationality meetup: 02 December 2011 07:00PM
* London, 04 December 2011 02:00PM. (Details the same as in the link, just with a a different date.)
The Mountain View meetup is now every other week, rather than weekly.
Cities with regularly scheduled meetups: Austin, Berkeley, Cambridge, MA, Irvine, London, Madison, WI, Marin, CA (uses the Bay Area List), Melbourne, Mountain View, New York, Ottawa, Oxford, San Francisco, Toronto, Washington, DC, and West Los Angeles.
If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, and have fun!
In addition to the handy sidebar of upcoming meetups, a meetup overview will continue to be posted on the front page every Friday. These will be an attempt to collect information on all the meetups happening in the next weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll now also have the benefit of having your meetup mentioned in a weekly overview. These overview posts will be moved to the discussion section when the new post goes up.
Please note that for your meetup to appear in the weekly meetups feature, you need to post your meetup before the Friday before your meetup!
If you check Less Wrong irregularly, consider subscribing to one or more city-specific mailing list in order to be notified when an irregular meetup is happening: Atlanta, Chicago, Helsinki, London, Pittsburgh, |
79d40a98-78c1-4e85-b370-4e1648797871 | trentmkelly/LessWrong-43k | LessWrong | Paying Russians to not invade Ukraine
I know nothing about war except that horseback archers were OP for a long time. But from my point of view, which is blatantly uneducated when it comes to war, being a Russian soldier seems like a miserable experience. It therefore makes me wonder why 300,000 Russian soldiers are willing to risk it all in Ukraine.[1] Why don’t they desert? How does the Russian regime get so many people to fight a war when my home government is struggling to convince me to sort my trash? If the Russian regime can convince so many people to have a shit time in Ukraine, I’d argue that the West could convince these people to go live an easier life. The idea is so simple that by now I mostly wonder if my inability to figure out why NATO isn’t already bribing Russian soldiers to desert is a sign that I’m officially off the proverbial rails.
Suppose I’m correct in assuming that even Russian soldiers are somewhat rational and self-concerned actors. In that case, the Russian war apparatus is putting on a masterclass in how to get thousands of people to do something that I assume they don’t particularly feel like doing. Based on some superficial research, I get the impression that the motivational methods of the Russian regime partly are offering a giant carrot to people who have very little. I base this conviction entirely on salary statistics and this NYT opinion piece[2] that states that the Russian armed forces are recruited from the bottom rung of the societal ladder.
The price of making the Russian army vanish
In 2022, prospective recruits were being offered 2,700 USD[3] a month – which I assume is an outrageously sweet deal in a country where the median wage is less than 500 USD a month. Where I live, the median salary is roughly 3,000. I don’t think I would invade Ukraine for 15,000 a month but fuck if that isn’t a fat pile of cash. I’ve seen other articles stating much lower numbers, something like 1000, but all make the same point. It is a ton of money for those who sign up co |
a12660f3-ab1b-42a6-a60f-a7eeb5fd87cd | StampyAI/alignment-research-dataset/lesswrong | LessWrong | GPT-4 Predictions
Introduction
============
GPT-4 is OpenAI’s next major language model which is expected to be released at some point in 2023. My goal here is to get some idea of when it will be released and what it will be capable of. I also think it will be interesting in retrospect to see how accurate my predictions were. This post is partially inspired by Mathew Barnett’s GPT-4 [Twitter thread](https://twitter.com/MatthewJBar/status/1605328892926885888) which I recommend reading.
Background of GPT models
========================
GPT-1, GPT-2, GPT-3
-------------------
GPT stands for generative pre-trained transformer and is a family of language models that were created by OpenAI. GPT was released in 2018, GPT-2 in 2019, and GPT-3 in 2020. All three models have used a similar architecture with some relatively minor variations: a dense, text-only, decoder transformer language model that’s trained using unsupervised learning to predict missing words in its text training set [[1]](#fn8rpvyx1dv4w).
InstructGPT, GPT-3.5, ChatGPT
-----------------------------
Arguably one of the biggest changes in the series in terms of architecture and behavior was the release of InstructGPT in January 2022 which used supervised fine-tuning using model answers and reinforcement learning with human feedback where model responses are ranked in addition to the standard unsupervised pre-training.
The [GPT-3.5](https://beta.openai.com/docs/model-index-for-researchers) models finished training and were released in 2022, and demonstrated better quality answers than GPT-3. In late 2022, OpenAI released [ChatGPT](https://openai.com/blog/chatgpt/) which is based on GPT-3.5 and fine-tuned for conversation.
When will GPT-4 be released?
============================
Sam Altman, the CEO of OpenAI, was [interviewed](https://www.youtube.com/watch?v=57OU18cogJI) by StrictlyVC in January 2023. When asked when GPT-4 would come out, he replied, “It will come out at some point when we are confident that we can do it safely and responsibly.”
[Metaculus](https://www.metaculus.com/questions/14305/when-will-gpt-4-be-announced/) predicts a 50% chance that GPT-4 will be released by May 2023 and a ~93% chance that it will be released by the end of 2023.
It seems like there’s still quite a lot of uncertainty here but I think we can be quite confident that GPT-4 will be released at some point in 2023.
What will GPT-4 be like?
========================
Altman revealed some more [details](https://news.knowledia.com/US/en/articles/sam-altman-q-and-a-gpt-and-agi-lesswrong-aa6293dde7b95b537f5f50e37c861645fd9b4dbb) about GPT-4 at an AC10 meetup Q&A. He said:
* GPT-4 will be a text-only model like GPT-3.
* GPT-4 won’t be much bigger than GPT-3 but will use much more compute and have much better performance.
* GPT-4 will have a longer context window.
How capable will GPT-4 be?
==========================
Scaling laws
------------
According to the paper *Scaling Laws for Neural Language Models (2020),*model performance as measured by cross-entropy loss can be calculated from three factors: the number of parameters in the model, the amount of compute used during training, and the amount of training data. There is a power-law relationship between these three factors and the loss. Basically, this means you have to increase the amount of compute, data, and parameters by a factor of 10 to decrease the loss by one unit, by 100 to decrease the loss by two units, and so on. The authors of the paper recommended training very large models on relatively small amounts of data and recommended investing compute into more parameters over more training steps or data to minimize loss as shown in this diagram:
For every 10x increase in compute, the paper approximately recommends increasing the number of parameters by 5x, the number of training tokens by 2x, and the number of serial training steps by 1.2x. This explains why the original GPT-3 model and other models such as Megatron and PaLM were so large.
However, the new scaling laws from DeepMind’s 2022 paper *Training Compute Optimal Language Models*insteademphasize the importance of training data for minimizing loss. Instead of prioritizing more parameters, the paper recommends scaling the number of parameters and training tokens equally.
DeepMind originally trained a large 280B parameter model named Gopher but then found a 70B model named Chinchilla trained on 4x as much training data performed better using the same compute budget. Chinchilla also outperformed other larger models such as GPT-3.
Given a fixed compute budget, the Chinchilla paper describes how to calculate the optimal number of parameters and training tokens for the model. These two parameters can then be used to calculate the optimal loss.
Therefore, we can predict GPT-4’s performance by first estimating the amount of compute it will use in training, then the number of parameters and training tokens it will use during training, and finally the model’s loss.
Training compute
----------------
Of training compute, tokens, and parameters, compute is generally the most expensive factor and therefore arguably the most significant constraint on model performance.
### GPT-3 training compute
Training GPT-3 required 3.14e23 FLOP according to EpochAI’s [dataset](https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/edit#gid=0). The GPT-3 paper *Large Language Models are Few-Shot Learners (2020)* doesn’t say much about the training process except “All models were trained on V100 GPUs on part of a high-bandwidth cluster provided by Microsoft”.
The V100 GPU was released by Nvidia in 2017. It has a single-precision (FP32) performance of 14 TFLOP/s (teraflops per second) and a tensor performance of 114 TFLOP/s (8X theoretical speedup) when tensor cores are used. Though in reality, the speedup is usually lower. Another limitation is that mixed precision (FP16) needs to be used to make use of these tensor cores. GPT-3 was trained with half-precision (FP16) so the training process was probably accelerated using tensor cores.
An experiment by [DeepSpeed](https://www.deepspeed.ai/2020/05/18/bert-record.html#:~:text=Using%20optimized%20transformer%20kernels%20as,overview%2C%20see%20our%20press%20release.) in 2020 that involved training the BERT language model achieved 66 TFLOP/s per V100 GPU (4.7x speedup). Assuming that each V100 GPU contributed about 60 TFLOP/s during the GPT-3 training process, then training the model on one GPU would have taken about 165 years. If the training process actually took about 30 days, then OpenAI would have needed about 2000 V100 GPUs for training.
### AI training supercomputers
Since the V100 GPU, Nvidia has released the A100 GPU (May 2020) which has ~3x [better](https://lambdalabs.com/blog/nvidia-a100-vs-v100-benchmarks) performance than the V100, and the H100 (release date: early 2023) which has ~4x [better](https://lambdalabs.com/blog/nvidia-h100-gpu-deep-learning-performance-analysis#:~:text=Compared%20to%20NVIDIA's%20previous%2Dgeneration,multiprocessors%2C%20and%20higher%20clock%20frequency.) performance than the A100 for language modeling tasks. I’m going to assume that GPT-4 mostly uses A100s for training since the A100 is already released.
The A100 has 19.5 FLOP/s of FP32 performance and 312 TFLOP/s when using FP16 (half-precision) with tensor cores which is about 3x higher than the V100. Assuming ~50% utilization, each A100 can contribute about 150 TFLOP/s.
Nvidia has a product named [DGX A100](https://images.nvidia.com/aem-dam/Solutions/Data-Center/nvidia-dgx-a100-80gb-datasheet.pdf) which combines 8 A100 80GB GPUs. A [DXB SuperPOD](https://blogs.nvidia.com/blog/2020/05/14/dgx-superpod-a100/) combines 140 DXB A100 nodes (1120 A100 GPUs). In 2020, Nvidia combined 4 DXB SuperPODs to create a 560-node (4480 A100) supercomputer named [Selene](https://blogs.nvidia.com/blog/2020/08/14/making-selene-pandemic-ai/). According to Microsoft’s [Megatron-Turing NLG](https://arxiv.org/abs/2201.11990) paper (2021), each A100 GPU can deliver up to about 160 TFLOP/s. Therefore, Selene’s total computing capacity for AI training is about 700 PFLOP/s [[2]](#fn99kb55g33we). When it was created, it was also the 7th fastest computer in the world because of its ability to do 63 PFLOP/s of high-performance computing.
In May 2020 (around the release date of GPT-3), Microsoft announced that it created a new AI training supercomputer exclusively for OpenAI. The supercomputer had about 285,000 CPUs and 10,000 GPUs and it ranked in the top 5 supercomputers in the world. Assuming that it used a similar architecture to Nvidia’s Selene supercomputer (A100s), then it would have 1250 DGX A100 nodes which are equivalent to about 9 DXB SuperPODs.
Microsoft trained its large 530B parameter Turing NLG language model using Selene. In the paper, they also described how they trained models of different sizes from 1.7B parameters to 1T parameters.
The paper introduced a formula for calculating the training time for a model:
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Where T is the number of training tokens, P is the number of parameters, n is the number of GPUs and X is the number of TFLOP/s per GPU.
They used this formula to calculate how long it would take to train GPT-3 using A100 GPUs. T = 300B, P = 175B, n = 1024 and X = 140 TFLOP/s. They calculated that it would take 34 days to train GPT-3 using 1024 A100 GPUs which would cost about $2m assuming that each A100 costs about $3 per [hour](https://azure.microsoft.com/en-us/pricing/details/machine-learning/).
In March 2022, Nvidia announced a new supercomputer named [Eos](https://nvidianews.nvidia.com/news/nvidia-announces-dgx-h100-systems-worlds-most-advanced-enterprise-ai-infrastructure) which uses 4608 H100 GPUs and was expected to begin operating in late 2022 though I'm not sure if it's actually been built yet. Assuming that each H100 is 4x faster than an A100 GPU, then Eos should have a performance of about 3 EFLOP/s [[3]](#fn5nwjnxuuv5y).
### GPT-4 training compute
GPT-3 used about 200 times more training [compute](https://epochai.org/mlinputs/visualization?startDate=1950-1-1&endDate=2022-2-1&xAxis=Publication%20date&yAxis=Training%20compute%20(FLOP)&separateCategories=false&citationThreshold=0&otherDomainThreshold=10&startDlEra=2009-12-31&startLargeScaleEra=2015-9-1&largeScaleAction=label&outliersAction=remove&recordSettersAction=ignore&bigAlphagoAction=ignore&alphagozeroAction=ignore&lowOutliersZValueThreshold=-2&highOutliersZValueThreshold=0.76&outlierWindowSize=2&filterText=&splitDomains=&domainsToNotSplit=&ranges=[object%20Object]&splitDlEra=true&splitLargeScaleEra=true&plotRegressions=true&bootstrapSampleSize=10&adjustForEstimateUncertainty=true&preset=Three%20eras%20of%20compute&aspectRatio=fit%20to%20container&autoresetCamera=true&labelEras=true&showDoublingTimes=true&showOpenAITrend=false®ressionDecimals=1&textSize=100&systemAlpha=100&systemNames=hide&showLegend=true¶metersRange=NaN,NaN&trainingComputeRange=NaN,NaN&inferenceComputeRange=NaN,NaN&trainingDatasetSizeRange=NaN,NaN&inferenceComputePerParameterRange=NaN,NaN&inferenceComputeTimesParameterRange=NaN,NaN&labelPoints=false) than GPT-2.
Training GPT-3 in 30 days would require a supercomputer with about 120 PFLOP/s such as a cluster of 2000 V100 GPUs. In theory, Selene could train GPT-3 in 5 days and Eos in just 1 day.
If OpenAI wanted to maximize the amount of compute they used for training GPT-4, they would maximize the number of GPUs used in training and the total training time. If they had 10,000 A100 GPUs with 160 TFLOP/s per A100 then the total compute of this system would be 1.6 EFLOP/s. To reach 1 EFLOP/s, 6250 A100 GPUs would be required.
I created a simple Squiggle model to estimate GPT-4’s training compute:
```
a100_gpu_flop = 160
total_gpus = 2000 to 15000
training_months = 1 to 6
training_seconds = training_months * 30 * 24 * 3600
flop_per_second = a100_gpu_flop * total_gpus
total_flop = flop_per_second * training_seconds
```
From the model, the 25%, 50%, and 75% percentile values for the training supercomputer are 580 PFLOP/s, 880 PFLOP/s, and 1.3 EFLOP/s.
For the total compute, the 25%, 50%, and 75% percentile values are 3.2e24 FLOP, 5.63e24 FLOP, and 9.7e24 FLOP.
5.63e24 FLOP is about 18 times more compute than what was used to train GPT-3. In the following chart, I add GPT-4 to EpochAIs [visualization](https://epochai.org/mlinputs/visualization?startDate=1950-1-1&endDate=2022-2-1&xAxis=Publication%20date&yAxis=Training%20compute%20(FLOP)&separateCategories=false&citationThreshold=0&otherDomainThreshold=10&startDlEra=2009-12-31&startLargeScaleEra=2015-9-1&largeScaleAction=label&outliersAction=remove&recordSettersAction=ignore&bigAlphagoAction=ignore&alphagozeroAction=ignore&lowOutliersZValueThreshold=-2&highOutliersZValueThreshold=0.76&outlierWindowSize=2&filterText=&splitDomains=&domainsToNotSplit=&ranges=[object%20Object]&splitDlEra=true&splitLargeScaleEra=true&plotRegressions=true&bootstrapSampleSize=10&adjustForEstimateUncertainty=true&preset=Three%20eras%20of%20compute&aspectRatio=fit%20to%20container&autoresetCamera=true&labelEras=true&showDoublingTimes=true&showOpenAITrend=false®ressionDecimals=1&textSize=100&systemAlpha=100&systemNames=hide&showLegend=true¶metersRange=NaN,NaN&trainingComputeRange=NaN,NaN&inferenceComputeRange=NaN,NaN&trainingDatasetSizeRange=NaN,NaN&inferenceComputePerParameterRange=NaN,NaN&inferenceComputeTimesParameterRange=NaN,NaN&labelPoints=false):
Although GPT-4 will almost certainly use more compute than previous models it seems like the increase in compute over time is slowing down somewhat. Though if GPT-4 is trained using H100 GPUs in addition to standard A100 GPUs, then we can expect the total compute to be even higher.
Model parameters
----------------
As mentioned earlier, Sam Altman stated that GPT-4 won’t be much bigger than GPT-3. This makes sense because according to the Chinchilla scaling laws, GPT-3 was undertrained. Therefore, compute, and especially data – not parameters – seem to be the bottleneck for improving performance.
The Chinchilla paper calculated the relationship between compute, parameters, and training tokens using three different methods resulting in three tables that show the optimal amount of training FLOP and training tokens given certain model sizes. The first table is shown in the paper and the other 2 are in the appendix. Here is the first table:
The following table shows the average values of the three tables:
| | | |
| --- | --- | --- |
| Parameters | Compute | Tokens |
| 400M | 1.99e19 | 8.30B |
| 1B | 1.34e20 | 22.43B |
| 10B | 1.67e22 | 278.2B |
| 67B | 9.91e23 | 2.43T |
| 175B | 3.22e24 | 6.67T |
| 280B | 1.90e25 | 11.03T |
| 520B | 7.07e25 | 22.63T |
| 1T | 2.84e26 | 47.27T |
| 10T | 3.87e28 | 644.57T |
Another way to think about the tables is that given a certain fixed compute budget, they show the optimal number of parameters and training tokens for minimizing the model’s loss. This is shown more clearly by a graph in the appendix of the paper:
Chinchilla was trained using 5.76e23 FLOP which is about twice GPT-3’s compute budget of 3.14e23 FLOP. But Chinchilla only has 70B parameters whereas GPT-3 had 175B. The authors estimated that training a 175B model like GPT-3 optimally requires 4.41e24 FLOP which is 14 times more than GPT-3’s original compute budget. This suggests that GPT-3 was an oversized model and should have been similar in size to Chinchilla and trained on much more data.
If GPT-4’s compute budget is 5.63e24 FLOP, these scaling laws suggest that GPT-4 will be similar in size to GPT-3 to achieve optimal loss. From the table, we can see a model with a compute budget of 5.63e24 should have between about 175B and 280B parameters. My best guess from the data is about 200B parameters though the data seems too variable to make a precise guess.
Training tokens
---------------
According to OpenAIs *Scaling Laws for Neural Language Models* *(2020)* paper, the amount of compute used in training by a model can be approximated by this equation: C≈6DN where D is the number of training tokens and N is the number of parameters in the model. Therefore, given a fixed amount of compute, a relatively large model could be trained on less data (e.g. GPT-3) or a smaller model could be trained on more data (e.g. Chinchilla).
The Chinchilla model was designed using DeepMind’s new scaling laws and had 70B parameters and was trained on 1.5T tokens. In contrast, although GPT-3 had 175B parameters, it was only trained on 300B tokens.
This suggests that GPT-4 can achieve a much lower loss by being trained on significantly more tokens. Assuming GPT-4 has 175B parameters, it should be trained on about 4T tokens according to the paper which is about 13 times more than the amount of training data used by GPT-3.
However, like compute, training tokens also seem to be in limited supply. An EpochAI paper [estimates](https://epochai.org/blog/will-we-run-out-of-ml-data-evidence-from-projecting-dataset) that there are about 10T tokens of high-quality language data in the world and a post by [nostalgebraist](https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications#_all_the_data_we_have_) estimated there are only about 3.2T of text training data. On the other hand, according to the Chinchilla paper, methods like RETRO where the model is assisted by a database have the same effect as increasing the number of training tokens by a factor of 10.
I’m going to assume here that it will be possible to find the necessary training tokens to train GPT-4 optimally. Though a training data shortage could be an issue for future models.
GPT-4 loss
----------
Now that we have estimates for how much compute, data, and parameters GPT-4 will have, we can plug these parameters into a scaling formula to calculate GPT-4’s loss compared to other models.
The Chinchilla paper has a formula of the form L(N,D) for calculating a model's optimal loss[[4]](#fnppybhvulclc). By entering the number of parameters in the model and the number of tokens it was trained on, we can calculate its loss:
L(N,D)=E+ANα+BDβE is a constant, the second term is the loss from the fact that the model has a finite number of parameters and the third term is the loss contribution from the model's finite number of training tokens. Therefore, a 'perfect' model with infinite parameters and infinite training data would have a loss of E (1.69).
The formula using constants from the paper:
L(N,D)=1.69+406.4N0.34+410.7D0.28GPT-3 had 175B parameters and was trained with 3.14e23 FLOP of compute and 300B tokens:
L(175⋅109,300⋅109)=1.69+0.061+0.25=2.002For GPT-4 I’m going to assume that it has 175B parameters and is trained on 4T tokens:
L(175⋅109,4⋅1012)=1.69+0.061+0.12=1.871Here are the loss values for several models:
| | | | |
| --- | --- | --- | --- |
| Model name | N | D | Loss |
| GPT | 117M | 1B | 3.6644 |
| GPT-2 | 1.5B | 15B | 2.5794 |
| GPT-3 | 175B | 300B | 2.0023 |
| Megatron-Turing NLG | 530B | 270B | 1.9906 |
| Gopher | 280B | 300B | 1.9933 |
| Chinchilla | 70B | 1.5T | 1.9335 |
| GPT-4 | 175B | 4T | 1.8727 |
GPT-4 performance
-----------------
The relationship between loss and performance is not immediately clear but we can get some idea by comparing the performance of different models which is done in the Chinchilla paper.
Chinchilla's loss as calculated from the formula is only 3% lower than Gopher's but has significantly better performance according to the paper with 7.6% better performance on the MMLU benchmark. This suggests that performance is quite sensitive to changes in loss.
### MMLU
The paper has a table that shows the performance of various models on the MMLU benchmark [[5]](#fnojqdqw79pkd) which is a dataset of multiple-choice questions on many different topics such as math and science questions. I also added some information from the MMLU benchmark [table](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu) from Papers With Code:
| | | |
| --- | --- | --- |
| Model | Loss | MMLU 5-shot accuracy |
| Random | N/A | 25.0% |
| GPT-J (6B) | 2.13363 | 27.3% |
| Average human rater | N/A | 34.5% |
| GPT-3 | 2.00223 | 43.9% |
| Gopher | 1.99326 | 60.0% |
| Chinchilla | 1.93352 | 67.6% |
| PaLM (540B) | 1.92387 | 69.3% |
| GPT-4 (predicted) | 1.87274 | 78.9% |
| Average human expert | N/A | 89.8% |
As you can see, there is an inverse correlation between loss and MMLU performance. Using this data, I created a simple linear regression to predict GPT-4's MMLU performance based on its loss:
The red x's are previous models and the blue dot is GPT-4's predicted performance. The green line represents expert human performance.
Currently, the best model on the MMLU benchmark is a fine-tuned version of PaLM which has 75.2% accuracy. If GPT-4 achieves 78.9% accuracy, then it could set a new record on the benchmark.
### Distinguishing AI-generated text from human text
In the GPT-3 paper *Language Models are Few Shot Learners (2020),* you can find this interesting graph showing the relationship between model parameters and the ability of humans to distinguish between model-generated and human-written texts.
This time I assumed 300e9 training tokens and inputted the number of parameters in the graph into the L(N, D) formula. Then I created a linear regression between the percentage of the time humans can distinguish between model-generated and human text and the estimated loss:
In this graph, the green dot is GPT-3 and the blue dot is GPT-4. As you can see, the blue dot is below the green line which means text generated by GPT-4 could be indistinguishable from human text!
### GPT-4 context length
Sam Altman mentioned that GPT-4 would have a longer context length.
GPT-2 had a context length of 1024 and the original GPT-3 model had a 2048 token context window though it has been raised to 4096 tokens for newer GPT-3.5 [models](https://platform.openai.com/docs/api-reference/completions) such as ChatGPT.
The context window of transformer language models is limited because the compute and memory cost tends to grow with the length of the window. Traditionally it grew quadratically but in GPT-3 the ratio is O(n√n) [[1]](#fn8rpvyx1dv4w).
Based on improvements in GPU memory and the trend of increasing context length in past GPT models, my rough guess is that GPT-4 will have twice the context length: 8192 tokens.
### Emergent capabilities
In addition to more predictable improvements in capabilities, language models can suddenly gain unexpected new emergent capabilities. Emergent capabilities are those that are present in large models but mostly absent from smaller models.
For example, the GPT-3 paper showed that language models can rapidly become much better at addition and other forms of calculation as they are scaled up:
Similarly, GPT-4 could have unexpected new capabilities.
Conclusion
==========
In conclusion, I expect GPT-4 to be very impressive but probably not an AGI. As Sam Altman said in the StrictlyVC interview: *"We don’t have an actual AGI and that’s sort of what’s expected of us."*
Will the performance gap between GPT-4 and GPT-3 be as large as the gap between GPT-3 and GPT-2? GPT-3 used 200 times more compute than GPT-2 but I predict that GPT-4 will only use about 20 times more compute than GPT-3. GPT-4 probably won't be much larger than GPT-3 but will instead improve by being trained on many more training tokens. GPT-2's loss was 2.5, GPT-3's was 2.0 and I predict that GPT-4 will have a loss of about 1.9 based on this information. Therefore, based on this information it seems unlikely that GPT-4 will be better than GPT-3 in the same way that GPT-3 was better than GPT-2.
However, GPT-4 could still be as impressive as GPT-3 was compared to GPT-2 for other reasons. One reason is that GPT-3 could be on the threshold of being highly disruptive for some capabilities. GPT-3 excelled at writing but its quality of writing wasn't quite human-level given that people can distinguish text generated by GPT-3 better than chance. For example, the Guardian published an [article](https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3]) that was written by GPT-3 but it was edited by humans.
But this could change for GPT-4 which will have a lower loss and longer context length. I expect GPT-4 to excel at writing as GPT-3 has and it could be disruptive if it can generate text that is indistinguishable from human text. With GPT-4, we might see the first AI model to be convincingly human-level at writing blog posts without human editing [[6]](#fnzd828g5ge7n). GPT-4 could also be highly capable at programming.
Another way GPT-4 could be very disruptive is if it achieves a large leap in capabilities via emergent capabilities. Even though GPT-3 was only trained on next-word prediction, it developed unexpected capabilities such as the ability to learn on the fly from prompts (in-context learning), arithmetic, and code generation. Similarly, GPT-4 could gain many qualitatively different capabilities that are hard to predict in advance. If GPT-4 has many emergent capabilities, then the advancement from GPT-3 to GPT-4 and overall change in impressiveness could be as large as the difference between GPT-2 and GPT-3.
1. **[^](#fnref8rpvyx1dv4w)**GPT-3 uses the exact same architecture as GPT-2 except it used “alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer” which reduces the context length to memory ratio from O(n2) to O(n√n).
2. **[^](#fnref99kb55g33we)**Nvidia claims that Selene can reach 2.8 EFLOP but that's Selene's theoretical performance using structural sparsity. Without sparsity, the performance is half that and half again given that typical GPU utilization is only 50%. 2800 / 4 = 700.
3. **[^](#fnref5nwjnxuuv5y)**When training dense models.
4. **[^](#fnrefppybhvulclc)**[Chinchilla's Wild Implications](https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications) by nostalgebraist explains the formula well.
5. **[^](#fnrefojqdqw79pkd)**Massive multitask language understanding.
6. **[^](#fnrefzd828g5ge7n)**I wrote this post myself but imagine for a moment if it had been generated by a language model without any editing. |
063b5c2f-b897-4693-ade9-c98abeac1f38 | trentmkelly/LessWrong-43k | LessWrong | Notes on Know-how
This post examines the virtue of know-how. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about this virtue and how to nurture it.
What is know-how?
> “A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.” ―Robert A. Heinlein[1]
You have know-how to the extent that you have skills obtained from practice doing things in the real world.
Know-how is distinct from mere theoretical understanding. For example, the theoretical discipline of hydrodynamics may be fascinating and useful, but if you want to know how to swim you have to get wet.
Know-how as a virtue seems to mean having competence in a broad, well-chosen range of skills.
Related virtues
Know-how is related to “practical knowledge,” though practical knowledge can be knowledge about things as well as how to do things. There’s some overlap. If you’re building a cabinet, practical knowledge will help you know which type of wood is best for the job, while know-how will help you construct it skillfully.
Know-how is kind of like “craftsmanship,” but craftsmanship implies a more advanced skill in a particular specialty — connoisseurship, expertise, commitment to quality, attention to detail, efficiency — while know-how is more of a baseline proficiency in many areas.
Know-how has some connection with prudence (in the sense of “practical wisdom”), though prudence has more to do with having a well-honed process for making good decisions generally, while know-how is about experience-tested decisions in specific contexts.
How to know what you need to know
An American in |
e93728e2-688f-4317-a351-7ead23c37611 | trentmkelly/LessWrong-43k | LessWrong | "Map of AI Futures" - An interactive flowchart
Note: The linked site is a personal project, and all views expressed here are my own.
TL;DR
* I created an interactive flowchart about various scenarios how the future of AI might play out.
* By setting various conditional probabilities you can see charts of what the resulting estimated probabilities for good, ambiguous, and existentially bad outcomes are.
* Use the site as a conversation starter and tool for reflection. You can share your results either as images of the resulting outcome charts, or just by sharing your personalized URL (which contains your estimates).
* Feel free to clone, expand, and modify the site. I'd love to see how other people would structure their flowchart!
Additional context (copied from the website itself)
Background
Many experts and CEOs of the leading AI companies think advanced AI could pose catastrophic, or even existential risks to humanity.
I think we should take these concerns extremely seriously, and work hard to ensure that AI is developed in a way that is safe and beneficial for humanity.
This map is an attempt to visualize some of the key questions and uncertainties that could determine the future of AI, and to explore how different assumptions about these questions could lead to different outcomes.
How to use
* White nodes are questions; grey nodes are intermediate states; red/yellow/green nodes are bad/ambiguous/good outcomes.
* Adjust the sliders to set the conditional probabilities for each question.
* The map and charts will update in real-time to show the probabilities for each node and outcome category.
* Adjust the settings to make more likely paths bolder, or less likely paths transparent. By setting the minimum opacity to zero, impossible branches of the map will become fully transparent.
* Click on a node to set it as the root node for the probability calculations, i.e. "what happens if we assume we have reached this state?". Click the same node again, or the "START HERE" node to reset the probabil |
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