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e66447d0-86de-4a27-b45e-d0121602a25b | trentmkelly/LessWrong-43k | LessWrong | Wisdom of the crowds?
According to an aggregate of forecasts on metaculus, the probability of bitcoin going above 100k within the next 5 years is 60%.
Under which conditions is it rational to have my probability for some event match the (i) median of a large group of estimates (e.g. from metaculus)
or (ii) the average of their estimates? Or, (iii) is it merely rational to set (i) or (ii) as my prior?
If yes, to any of these possibilities, why? What is the justification? Could someone point me towards the literature on this question? |
2fee87f1-4171-44b2-9bf1-db0104bba1df | trentmkelly/LessWrong-43k | LessWrong | [SEQ RERUN] Heading Toward Morality
Today's post, Heading Toward Morality was originally published on 20 June 2008. A summary (taken from the LW wiki):
> A description of the last several months of sequence posts, that identifies the topic that Eliezer actually wants to explain: morality.
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 LA-602 vs RHIC Review, 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. |
e4be322e-3dc0-4695-b1c2-d47fac8bce23 | trentmkelly/LessWrong-43k | LessWrong | Learning desiderata
A putative new idea for AI control; index here.
In a previous post, I argued that Conservation of expected ethics isn't enough. Reflecting on that, I think the learning problem can be decomposed into three aspects:
#. The desire to learn. #. Unbiased learning (the agent doesn't try to influence the direction of its learning to achieve a particular goal). #. Safe learning (the agent doesn't affect the world negatively because of its learning process).
----------------------------------------
Note that 2. is precisely the conservation of expected ethics.
How the ideas to date measure up
Of the previous ideas, how do they measure up?
Traditional value learning scores high on 1. (it desires to learn), but fails both 2. and 3.
Indifference achieves 3. (note that the agent's utility might be unsafe, but this would not be because of the learning process), but fails 1. and 2. It also has problems with its counterfactuals, which arguably invalidate 3.
Factoring out arguably achieves all of these objectives, but also has problems with its counterfactuals, which arguably invalidate 3.
Guarded learning, an idea I'll be presenting soon, will hit 1. and 2. but not 3. To get safe learning from it, we'll need to inculcate safety conscious values early in the learning project.
Is there some platonic ideal of an agent that achieves all three of these objectives? Maybe. Imagine a boxed oracle that attempts to learn what value humans might have in a counterfactual world where the oracle didn't exist. This would seem to achieve all the objectives, and shows why unbiased learning needs to be separated from safe learning. This oracle is safe because it is boxed, and unbiased because of the counterfactual. |
d2d41403-5190-4a7f-b685-2711bbf408f2 | trentmkelly/LessWrong-43k | LessWrong | introduction to cancer vaccines
cancer neoantigens
For cells to become cancerous, they must have mutations that cause uncontrolled replication and mutations that prevent that uncontrolled replication from causing apoptosis. Because cancer requires several mutations, it often begins with damage to mutation-preventing mechanisms. As such, cancers often have many mutations not required for their growth, which often cause changes to structure of some surface proteins.
The modified surface proteins of cancer cells are called "neoantigens". An approach to cancer treatment that's currently being researched is to identify some specific neoantigens of a patient's cancer, and create a personalized vaccine to cause their immune system to recognize them. Such vaccines would use either mRNA or synthetic long peptides. The steps required are as follows:
1. The cancer must develop neoantigens that are sufficiently distinct from human surface proteins and consistent across the cancer.
2. Cancer cells must be isolated and have their surface proteins characterized.
3. A surface protein must be found that the immune system can recognize well without (much) cross-reactivity to normal human proteins.
4. A vaccine that contains that neoantigen or its RNA sequence must be produced.
Most drugs are mass-produced, but with cancer vaccines that target neoantigens, all those steps must be done for every patient, which is expensive.
protein characterization
The current methods for (2) are DNA sequencing and mass spectrometry.
SEQUENCING
DNA sequencing is now good enough to sequence the full genome of cancer cells. That sequence can be compared to the DNA of normal cells, and some algorithms can be used to find differences that correspond to mutant proteins. However, guessing how DNA will be transcribed, how proteins will be modified, and which proteins will be displayed on the surface is difficult.
Practical nanopore sequencing has been a long time coming, but it's recently become a good option for sequencing canc |
2159f712-1d94-4fb2-acc2-db518957e3e2 | trentmkelly/LessWrong-43k | LessWrong | Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Authors: David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, Joshua Tenenbaum
Abstract:
> Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches. |
b779f646-a692-43a6-b0ed-b2b5bbc902a9 | trentmkelly/LessWrong-43k | LessWrong | Deceptive Alignment is <1% Likely by Default
Thanks to Wil Perkins, Grant Fleming, Thomas Larsen, Declan Nishiyama, and Frank McBride for feedback on this post. Thanks also to Paul Christiano, Daniel Kokotajlo, and Aaron Scher for comments on the original post that helped clarify the argument. Any mistakes are my own.
In order to submit this to the Open Philanthropy AI Worldview Contest, I’m combining this with the previous post in the sequence and making significant updates. I'm leaving the previous post, because there is important discussion in the comments, and a few things that I ended up leaving out of the final version that may be valuable.
Introduction
In this post, I argue that deceptive alignment is less than 1% likely to emerge for transformative AI (TAI) by default. Deceptive alignment is the concept of a proxy-aligned model becoming situationally aware and acting cooperatively in training so it can escape oversight later and defect to pursue its proxy goals. There are other ways an AI agent could become manipulative, possibly due to biases in oversight and training data. Such models could become dangerous by optimizing directly for reward and exploiting hacks for increasing reward that are not in line with human values, or something similar. To avoid confusion, I will refer to these alternative manipulative models as direct reward optimizers. Direct reward optimizers are outside of the scope of this post.
Summary
In this post, I discuss four precursors of deceptive alignment, which I will refer to in this post as foundational properties. I first argue that two of these are unlikely to appear during pre-training. I then argue that the order in which these foundational properties develop is crucial for estimating the likelihood that deceptive alignment will emerge for prosaic transformative AI (TAI) in fine-tuning, and that the dangerous orderings are unlikely. In particular:
1. Long-term goals and situational awareness are very unlikely in pre-training.
2. Deceptive alignment is very unli |
90308a34-8024-4a12-b97a-9c6c953e048b | trentmkelly/LessWrong-43k | LessWrong | Marriage, the Giving What We Can Pledge, and the damage caused by vague public commitments
I believe honesty is very important. I think most people agree that honesty is like, pretty important, but I think it’s a lot more important than that. I basically think that people will be dishonest in ways that hurt them and others by default, even when they’re trying to be pretty honest, because I think it’s just that hard. I think it’s hard because there are a lot of incentives that push away from honesty. E.g. You want the job so you’re tempted to overstate your experience or past performance. You said you wouldn’t tell anyone about your friend’s secret, but this seems like a situation where they wouldn’t mind, and it would be pretty awkward to say nothing…etc. There’s a huge variety of situations that incentivize small acts of dishonesty. And it’s not always clear whether something is a little dishonest or not - dishonesty can be quite a spectrum.
If I’m correct, and honesty is pretty hard by default, I think this is quite bad. Honesty is important because it greatly improves the ability of people to coordinate with each other. And it’s important because it allows people to reason better about themselves and about the world. Good coordination and good reasoning are things we badly need. Fortunately, I think many people could level up their honesty by putting in a reasonable amount of thinking and effort, and that a lot of the failure modes are caused by not paying attention to the incentives around them, or not thinking about how to structure their own lives and commitments to be more honest.
If you want to be honest, it’s important to think about how to structure your life so being honest isn’t extremely difficult. This is especially true when it comes to promises and commitments. It’s often easier to be honest about your current beliefs than it is to be honest about what you’re going to do in the future. After all, you don’t know what’s going to happen in the future. You can definitely influence it, and you can choose now to take particular actions in the |
aa9a1ad4-5a9f-46da-8e5b-341816988adf | trentmkelly/LessWrong-43k | LessWrong | Book Analysis: New Thrawn Trilogy
[Spoiler note: I have used only examples from relatively early on in the new Thrawn Trilogy and ones which I believe won’t spoil your enjoyment of the story; however, if you’d like to go in with absolutely no knowledge, this will tell you some things about the trilogy.]
I’ve recently read the first two books of Timothy Zahn’s new Thrawn trilogy and it’s given me some thoughts about writing competent characters.
The unique thing about Thrawn as a character is that he comes off as genuinely competent. The first book of the new Thrawn trilogy makes this particularly vivid because, instead of having to pair Thrawn with characters with canonical non-competent personalities, Zahn was free to invent the equally competent Eli Vanto, Arindha Pryce, and Nightswan.
In most books, characters who are supposed to be geniuses go one of three ways. First, they’re chessmasters with incredibly tangled Rube Goldberg Machine plans: “of course, I knew all along that overhearing that conversation would cause the protagonist’s wife to conclude she’s cheating on her and divorce her, motivating the protagonist to seek out the Stone of Plot, thus moving my plans one step closer to fruition!” Second, they’re scientific or magical geniuses with fake science or magic: “of course! If you reverse the polarity of the subspace photon launcher it will babble the techno and that will lower the shields, letting us in!” Third, they’re very good improvisers, which is a genuine kind of intelligence, but also one that is kind of easy for writers. The character doesn’t have to have clever plans, they just have to react cleverly to what’s happening—and the audience will cut them some slack if their solution isn’t amazing. After all, they did think of it in a few seconds.
Thrawn takes a fourth approach.
In his first scene, Thrawn is fighting some Imperial troops which are camped in the forest. He figures out that their shield system must let through small forest animals, or they’d be constantly dealing |
435371d0-c1d0-436d-ab33-8462b529929f | StampyAI/alignment-research-dataset/arxiv | Arxiv | ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
1 Introduction
---------------
Semi-supervised learning (SSL) provides a means of leveraging unlabeled data to improve a model’s performance when only limited labeled data is available.
This can enable the use of large, powerful models when labeling data is expensive or inconvenient.
Research on SSL has produced a diverse collection of approaches, including consistency regularization (Sajjadi et al., [2016](#bib.bib5 "Regularization with stochastic transformations and perturbations for deep semi-supervised learning"); Laine and Aila, [2017](#bib.bib6 "Temporal ensembling for semi-supervised learning")) which encourages a model to produce the same prediction when the input is perturbed and entropy minimization (Grandvalet and Bengio, [2005](#bib.bib8 "Semi-supervised learning by entropy minimization")) which encourages the model to output high-confidence predictions.
The recently proposed “MixMatch” algorithm (Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning")) combines these techniques in a unified loss function and achieves strong performance on a variety of image classification benchmarks.
In this paper, we propose two improvements which can be readily integrated into MixMatch’s framework.
First, we introduce “distribution alignment”, which encourages the distribution of a model’s aggregated class predictions to match the marginal distribution of ground-truth class labels.
This concept was introduced as a “fair” objective by Bridle et al. ([1992](#bib.bib85 "Unsupervised classifiers, mutual information and’phantom targets")), where a related loss term was shown to arise from the maximization of mutual information between model inputs and outputs.
After reviewing this theoretical framework, we show how distribution alignment can be straightforwardly added to MixMatch by modifying the “guessed labels” using a running average of model predictions.
Second, we introduce “augmentation anchoring”, which replaces the consistency regularization component of MixMatch.
For each given unlabeled input, augmentation anchoring first generates a weakly augmented version (e.g. using only a flip and a crop) and then generates multiple strongly augmented versions.
The model’s prediction for the weakly-augmented input is treated as the basis of the guessed label for all of the strongly augmented versions.
To generate strong augmentations, we introduce a variant of AutoAugment (Cubuk et al., [2018](#bib.bib64 "Autoaugment: learning augmentation policies from data")) based on control theory which we dub “CTAugment”.
Unlike AutoAugment, CTAugment learns an augmentation policy alongside model training, making it particularly convenient in SSL settings.
We call our improved algorithm “ReMixMatch” and experimentally validate it on a suite of standard SSL image benchmarks.
ReMixMatch achieves state-of-the-art accuracy across all labeled data amounts,
for example achieving an accuracy of 93.73% with 250 labels on CIFAR-10 compared to the previous state-of-the-art of 88.92% (and compared to 96.09% for fully-supervised classification with 50,000 labels).
We also push the limited-data setting further than ever before, ultimately achieving a median of 84.92% accuracy with only 40 labels (just 4 labels per class) on CIFAR-10.
To quantify the impact of our proposed improvements, we carry out an extensive ablation study to measure the impact of our improvements to MixMatch.
Finally, we release all of our models and code to facilitate future work on semi-supervised learning.
Figure 1: Distribution alignment. Guessed label distributions are adjusted according to the ratio of the empirical ground-truth class distribution divided by the average model predictions on unlabeled data.
Figure 2: Augmentation anchoring. We use the prediction for a weakly augmented image (green, middle) as the target for predictions on strong augmentations of the same image (blue).
2 Background
-------------
The goal of a semi-supervised learning algorithm is to learn from unlabeled data in a way that improves performance on labeled data.
Typical ways of achieving this include training against “guessed” labels for unlabeled data or optimizing a heuristically-motivated objective that does not rely on labels.
This section reviews the semi-supervised learning methods relevant to ReMixMatch, with a particular focus on the components of the MixMatch algorithm upon which we base our work.
##### Consistency Regularization
Many SSL methods rely on consistency regularization to enforce that the model output remains unchanged when the input is perturbed.
First proposed in (Sajjadi et al., [2016](#bib.bib5 "Regularization with stochastic transformations and perturbations for deep semi-supervised learning")) and (Laine and Aila, [2017](#bib.bib6 "Temporal ensembling for semi-supervised learning")),
this approach was referred to as “Regularization With Stochastic Transformations and Perturbations” and the “Π-Model” respectively.
While some work perturbs adversarially (Miyato et al., [2018](#bib.bib4 "Virtual adversarial training: a regularization method for supervised and semi-supervised learning")) or using dropout (Laine and Aila, [2017](#bib.bib6 "Temporal ensembling for semi-supervised learning"); Tarvainen and Valpola, [2017](#bib.bib7 "Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results")), the most common perturbation is to apply domain-specific data augmentation (Laine and Aila, [2017](#bib.bib6 "Temporal ensembling for semi-supervised learning"); Sajjadi et al., [2016](#bib.bib5 "Regularization with stochastic transformations and perturbations for deep semi-supervised learning"); Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning"); Xie et al., [2019](#bib.bib86 "Unsupervised data augmentation for consistency training")).
The loss function used to measure consistency is typically either the mean-squared error (Laine and Aila, [2017](#bib.bib6 "Temporal ensembling for semi-supervised learning"); Tarvainen and Valpola, [2017](#bib.bib7 "Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results"); Sajjadi et al., [2016](#bib.bib5 "Regularization with stochastic transformations and perturbations for deep semi-supervised learning")) or cross-entropy (Miyato et al., [2018](#bib.bib4 "Virtual adversarial training: a regularization method for supervised and semi-supervised learning"); Xie et al., [2019](#bib.bib86 "Unsupervised data augmentation for consistency training")) between the model’s output for a perturbed and non-perturbed input.
##### Entropy Minimization
Grandvalet and Bengio ([2005](#bib.bib8 "Semi-supervised learning by entropy minimization")) argues that unlabeled data should be used to ensure that classes are well-separated.
This can be achieved by encouraging the model’s output distribution to have low entropy (i.e., to make “high-confidence” predictions) on unlabeled data.
For example, one can explicitly add a loss term to minimize the entropy of the model’s predicted class distribution on unlabeled data (Grandvalet and Bengio, [2005](#bib.bib8 "Semi-supervised learning by entropy minimization"); Miyato et al., [2018](#bib.bib4 "Virtual adversarial training: a regularization method for supervised and semi-supervised learning")).
Related to this idea are “self-training” methods (McLachlan, [1975](#bib.bib57 "Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis"); Rosenberg et al., [2005](#bib.bib20 "Semi-supervised self-training of object detection models")) such as Pseudo-Label (Lee, [2013](#bib.bib15 "Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks")) that use the predicted class on an unlabeled input as a hard target for the same input, which implicitly minimizes the entropy of the prediction.
##### Standard Regularization
Outside of the setting of SSL, it is often useful to regularize models in the over-parameterized regime.
This regularization can often be applied both when training on labeled and unlabeled data.
For example, standard “weight decay” (Hinton and van Camp, [1993](#bib.bib67 "Keeping neural networks simple by minimizing the description length of the weights")) where the L2 norm of parameters is minimized is often used alongside SSL techniques.
Similarly, powerful MixUp regularization (Zhang et al., [2017](#bib.bib70 "Mixup: beyond empirical risk minimization")) which trains a model on linear interpolants of inputs and labels has recently been applied to SSL (Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning"); Verma et al., [2019](#bib.bib72 "Interpolation consistency training for semi-supervised learning")).
##### Other Approaches
The three aforementioned categories of SSL techniques does not cover the full literature on semi-supervised learning.
For example, there is a significant body of research on “transductive” or graph-based semi-supervised learning techniques which leverage the idea that unlabeled datapoints should be assigned the label of a labeled datapoint if they are sufficiently similar (Gammerman et al., [1998](#bib.bib25 "Learning by transduction"); Joachims, [2003](#bib.bib26 "Transductive learning via spectral graph partitioning"), [1999](#bib.bib27 "Transductive inference for text classification using support vector machines"); Bengio et al., [2006](#bib.bib29 "Label propagation and quadratic criterion"); Liu et al., [2018](#bib.bib88 "Deep metric transfer for label propagation with limited annotated data")).
Since our work does not involve these (or other) approaches to SSL, we will not discuss them further.
A more substantial overview of SSL methods is available in (Chapelle et al., [2006](#bib.bib24 "Semi-supervised learning")).
###
2.1 MixMatch
MixMatch (Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning")) unifies several of the previously mentioned SSL techniques.
The algorithm works by generating “guessed labels” for each unlabeled example, and then
using fully-supervised techniques to train on the original labeled data along with
the guessed labels for the unlabeled data.
This section reviews the necessary details of MixMatch; see (Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning")) for a full definition.
Let X={(xb,pb):b∈(1,…,B)} be a batch of labeled data and their corresponding one-hot labels representing one of L classes and let ^xb be augmented versions of these labeled examples.
Similarly, let U={ub:b∈(1,…,B)} be a batch of unlabeled examples.
Finally, let pmodel(y∣x;θ) be the predicted class distribution produced by the model for input x.
MixMatch first produces K weakly augmented versions of each unlabeled datapoint ^ub,k for k∈{1,…,K}.
Then, it generates a “guessed label” qb for each ub by computing the average prediction ¯qb across the K augmented versions: ¯qb=1K∑kpmodel(y∣^ub,k;θ).
The guessed label distribution is then sharpened by adjusting its temperature (i.e. raising all probabilities to a power of \sfrac1T and renormalizing).
Finally, pairs of examples (x1,p1),(x2,p2) from the combined set of labeled examples and unlabeled examples with label guesses are fed into the MixUp (Zhang et al., [2017](#bib.bib70 "Mixup: beyond empirical risk minimization")) algorithm to compute examples (x′,p′) where
x′=λx1+(1−λ)x2 for λ∼Beta(α,α), and similarly for p′.
Given these mixed-up examples, MixMatch performs standard fully-supervised training
with minor modifications.
A standard cross-entropy loss is used for labeled data, whereas the loss for unlabeled data is computed using a mean square error (i.e. the Brier score (Brier, [1950](#bib.bib74 "Verification of forecasts expressed in terms of probability"))) and is weighted with a hyperparameter λU.
The terms K (number of augmentations), T (sharpening temperature), α (MixUp Beta parameter), and λU (unlabeled loss weight) are MixMatch’s hyperparameters.
For augmentation, shifting and flipping was used for the CIFAR-10, CIFAR-100, and STL-10 datasets, and shifting alone was used for SVHN.
3 ReMixMatch
-------------
1: Input: Batch of labeled examples and their one-hot labels X={(xb,pb):b∈(1,…,B)}, batch of unlabeled examples U={ub:b∈(1,…,B)}, sharpening temperature T, number of augmentations K, Beta distribution parameter α for MixUp.
2: for b=1 to B do
3: ^xb=StrongAugment(xb) // Apply strong data augmentation to xb
4: ^ub,k=StrongAugment(ub);k∈{1,…,K} // Apply strong data augmentation K times to ub
5: ~ub=WeakAugment(ub) // Apply weak data augmentation to ub
6: qb=pmodel(y∣~ub;θ) // Compute prediction for weak augmentation of ub
7: qb=Normalize(qb×p(y)/~p(y)) // Apply distribution alignment
8: qb=Normalize(q1/Tb) // Apply temperature sharpening to label guess
9: end for
10: ^X=((^xb,pb);b∈(1,…,B)) // Augmented labeled examples and their labels
11: ^U1=((^ub,1,qb);b∈(1,…,B)) // First strongly augmented unlabeled example and guessed label
12: ^U=((^ub,k,qb);b∈(1,…,B),k∈(1,…,K)) // All strongly augmented unlabeled examples
13: ^U=^U∪((~ub,qb);b∈(1,…,B)) // Add weakly augmented unlabeled examples
14: W=Shuffle(Concat(^X,^U)) // Combine and shuffle labeled and unlabeled data
15: X′=(MixUp(^Xi,Wi);i∈(1,…,|^X|)) // Apply MixUp to labeled data and entries from W
16: U′=(MixUp(^Ui,Wi+|^X|);i∈(1,…,|^U|)) // Apply MixUp to unlabeled data and the rest of W
17: return X′,U′, ^U1
Algorithm 1 ReMixMatch algorithm for producing a collection of processed labeled examples and processed unlabeled examples with label guesses (cf. Berthelot et al. ([2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning")) Algorithm 1.)
Having introduced MixMatch, we now turn to the two improvements we propose in this paper: Distribution alignment and augmentation anchoring.
For clarity, we describe how we integrate them into the base MixMatch algorithm; the full algorithm for ReMixMatch is shown in [algorithm 1](#alg1 "Algorithm 1 ‣ 3 ReMixMatch ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
###
3.1 Distribution Alignment
Our first contribution is distribution alignment, which enforces that the aggregate of predictions on unlabeled data matches the distribution of the provided labeled data.
This general idea was first introduced over 25 years ago (Bridle et al., [1992](#bib.bib85 "Unsupervised classifiers, mutual information and’phantom targets")), but to the best of our knowledge is not used in modern SSL techniques.
A schematic of distribution alignment can be seen in [fig. 1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
After reviewing and extending the theory, we describe how it can be straightforwardly included in ReMixMatch.
####
3.1.1 Input-Output Mutual Information
As previously mentioned, the primary goal of
an SSL algorithm is to incorporate unlabeled data in a way which improves a model’s performance.
One way to formalize this intuition, first proposed by Bridle et al. ([1992](#bib.bib85 "Unsupervised classifiers, mutual information and’phantom targets")),
is to maximize the mutual information between the model’s input and output for unlabeled data.
Intuitively, a good classifier’s prediction should depend as much as possible on the input.
Following the analysis from Bridle et al. ([1992](#bib.bib85 "Unsupervised classifiers, mutual information and’phantom targets")), we can formalize this objective as
| | | | | |
| --- | --- | --- | --- | --- |
| | I(y;x) | =∬p(y,x)logp(y,x)p(y)p(x)\ddy\ddx | | (1) |
| | | =H(Ex[pmodel(y|x;θ)])−Ex[H(pmodel(y|x;θ))] | | (2) |
where H(⋅) refers to the entropy. See Appendix A for a proof.
To interpret this result, observe that
the second term in [eq. 2](#S3.E2 "(2) ‣ 3.1.1 Input-Output Mutual Information ‣ 3.1 Distribution Alignment ‣ 3 ReMixMatch ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring") is the familiar entropy minimization objective (Grandvalet and Bengio, [2005](#bib.bib8 "Semi-supervised learning by entropy minimization")), which simply encourages each individual model output to have low entropy (suggesting high confidence in a class label).
The first term, however, is not widely used in modern SSL techniques.
This term (roughly speaking) encourages that on average,
across the entire training set, the model predicts each class with equal frequency.
Bridle et al. ([1992](#bib.bib85 "Unsupervised classifiers, mutual information and’phantom targets")) refer to this as the model being “fair”.
####
3.1.2 Distribution Alignment in ReMixMatch
MixMatch already includes a form of entropy minimization via the “sharpening” operation which makes the guessed labels (synthetic targets) for unlabeled data have lower entropy.
We are therefore interested in also incorporating a form of “fairness” in ReMixMatch.
However, note that the objective H(Ex[pmodel(y|x;θ)]) on its own essentially implies that the model should predict each class with equal frequency.
This is not necessarily a useful objective if the dataset’s marginal class distribution p(y) is not uniform.
Furthermore, while it would in principle be possible to directly minimize this objective on a per-batch basis, we are instead interested in integrating it into MixMatch in a way which does not introduce an additional loss term or any sensitive hyperparameters.
To address these issues, we incorporate a form of fairness we call “distribution alignment” which proceeds as follows:
over the course of training, we maintain a running average of the model’s predictions on unlabeled data, which we refer to as ~p(y).
Given the model’s prediction q=pmodel(y|u;θ) on an unlabeled example u, we scale q by the ratio p(y)/~p(y) and then renormalize the result to form a valid probability distribution:
~q=Normalize(q×p(y)/~p(y))
where Normalize(x)i=xi/∑jxj.
We then use ~q as the label guess for u, and proceed as usual with sharpening and other processing.
In practice, we compute ~p(y) as the moving average of the model’s predictions on unlabeled examples over the last 128 batches.
We also estimate the marginal class distribution p(y) based on the labeled examples seen during training.
Note that a better estimate for p(y) could be used if it is known a priori; in this work we do not explore this direction further.
###
3.2 Improved Consistency Regularization
Consistency regularization underlies most SSL methods (Miyato et al., [2018](#bib.bib4 "Virtual adversarial training: a regularization method for supervised and semi-supervised learning"); Tarvainen and Valpola, [2017](#bib.bib7 "Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results"); Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning"); Xie et al., [2019](#bib.bib86 "Unsupervised data augmentation for consistency training")).
For image classification tasks, consistency is typically enforced between two augmented versions of the same unlabeled image.
In order to enforce a form of consistency regularization, MixMatch generates K (in practice, K=2) augmentations of each unlabeled example u and averages them together to produce a “guessed label” for u.
Recent work (Xie et al., [2019](#bib.bib86 "Unsupervised data augmentation for consistency training")) found that applying stronger forms of augmentation can significantly improve the performance of consistency regularization.
In particular, for image classification tasks it was shown that using AutoAugment (Cubuk et al., [2018](#bib.bib64 "Autoaugment: learning augmentation policies from data")) produced substantial gains.
Since MixMatch uses a simple flip-and-crop augmentation strategy, we were interested to see if replacing the weak augmentation in MixMatch with AutoAugment would improve performance but found that training would not converge.
To circumvent this issue, we propose a new method for consistency regularization in MixMatch called “Augmentation Anchoring”.
The basic idea is to use the model’s prediction for a weakly augmented unlabeled image as the guessed label for many strongly augmented versions of the same image.
A further logistical concern with using AutoAugment is that it uses reinforcement learning to learn a policy which requires many trials of supervised model training.
This poses issues in the SSL setting where we often have limited labeled data.
To address this, we propose a variant of AutoAugment called “CTAugment” which adapts itself online using ideas from control theory without requiring any form of reinforcement learning-based training.
We describe Augmentation Anchoring and CTAugment in the following two subsections.
####
3.2.1 Augmentation Anchoring
We hypothesize the reason MixMatch with AutoAugment is unstable is that MixMatch averages the prediction across K augmentations.
Stronger augmentation can result in disparate predictions, so their average may not be a meaningful target.
Instead, given an unlabeled input we first generate an “anchor” by applying weak augmentation to it.
Then, we generate K strongly-augmented versions of the same unlabeled input using CTAugment (described below).
We use the guessed label (after applying distribution alignment and sharpening) as the target for all of the K strongly-augmented versions of the image.
This process is visualized in [fig. 2](#S1.F2 "Figure 2 ‣ 1 Introduction ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
While experimenting with Augmentation Anchoring, we found it enabled us to replace MixMatch’s unlabeled-data mean squared error loss with a standard cross-entropy loss.
This maintained stability while also simplifying the implementation.
While MixMatch achieved its best performance at only K=2, we found that augmentation anchoring benefited from a larger value of K=8.
We compare different values of K in [section 4](#S4 "4 Experiments ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring") to measure the gain achieved from additional augmentations.
####
3.2.2 Control Theory Augment
AutoAugment (Cubuk et al., [2018](#bib.bib64 "Autoaugment: learning augmentation policies from data")) is a method for learning a data augmentation policy which results in high validation set accuracy.
An augmentation policy consists a sequence of transformation-parameter magnitude tuples to apply to each image.
Critically, the AutoAugment policy is learned with supervision:
the magnitudes and sequence of transformations are determined
via training many models on a proxy task which e.g. involves the use of 4,000 labels on
CIFAR-10 and 1,000 labels on SVHN (Cubuk et al., [2018](#bib.bib64 "Autoaugment: learning augmentation policies from data")).
This makes applying AutoAugment methodologically problematic for low-label SSL.
To remedy this necessity for training a policy on labeled data,
RandAugment (Cubuk et al., [2019](#bib.bib91 "RandAugment: an automated, randomized data augmentation strategy")) uniformly randomly samples transformations,
but requires tuning the hyper-parameters for the random sampling on the validation set,
which again is methodologically difficult when only very few (e.g., 40 or 250)
labeled examples are available.
Thus, in this work, we develop CTAugment, an alternative approach to designing high-performance augmentation strategies.
Like RandAugment, CTAugment also uniformly randomly samples transformations to apply but dynamically infers magnitudes for each transformation during the training process.
Since CTAugment does not need to be optimized on a supervised proxy task and has no sensitive hyperparameters, we can directly include it in our semi-supervised models to experiment with more aggressive data augmentation in semi-supervised learning.
Intuitively, for each augmentation parameter, CTAugment learns the likelihood that it will produce an image which is classified as the correct label.
Using these likelihoods, CTAugment then only samples augmentations that fall within the network tolerance.
This process is related to what is called density-matching in Fast AutoAugment (Lim et al., [2019](#bib.bib90 "Fast autoaugment")), where policies are optimized so that the density of augmented validation images match the density of images from the training set.
First, CTAugment divides each parameter for each transformation into bins of distortion magnitude as is done in AutoAugment (see Appendix C for a list of the bin ranges).
Let m be the vector of bin weights for some distortion parameter for some transformation.
At the beginning of training, all magnitude bins are initialized to have a weight set to 1.
These weights are used to determine which magnitude bin to apply to a given image.
At each training step, for each image two transformations are sampled uniformly at random.
To augment images for training, for each parameter of these transformations we produce a modified set of bin weights ^m where ^mi=mi if mi>0.8 and ^mi=0 otherwise, and sample magnitude bins from Categorical(Normalize(^m)).
To update the weights of the sampled transformations, we first sample a magnitude bin mi for each transformation parameter uniformly at random.
The resulting transformations are applied to a labeled example x with label p to obtain an augmented version ^x.
Then, we measure the extent to which the model’s prediction matches the label as ω=1−12L∑|pmodel(y|^x;θ)−p|.
The weight for each sampled magnitude bin is updated as mi=ρmi+(1−ρ)ω where ρ=0.99 is a fixed exponential decay hyperparameter.
###
3.3 Putting it all together
ReMixMatch’s algorithm for processing a batch of labeled and unlabeled examples is shown in [algorithm 1](#alg1 "Algorithm 1 ‣ 3 ReMixMatch ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
The main purpose of this algorithm is to produce the collections X′ and U′, consisting of augmented labeled and unlabeled examples with MixUp applied.
The labels and label guesses in X′ and U′ are fed into standard cross-entropy loss terms against the model’s predictions.
[Algorithm 1](#alg1 "Algorithm 1 ‣ 3 ReMixMatch ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring") also outputs ^U1, which consists of a single heavily-augmented version of each unlabeled image and its label guesses *without MixUp applied*.
^U1 is used in two additional loss terms which provide a mild boost in performance in addition to improved stability:
##### Pre-mixup unlabeled loss
We feed the guessed labels and predictions for example in ^U1 as-is into a separate cross-entropy loss term.
##### Rotation loss
Recent result have shown that applying ideas from self-supervised learning to SSL can produce strong performance (Zhai et al., [2019](#bib.bib92 "S4L: Self-supervised semi-supervised learning")).
We integrate this idea by rotating each image u∈^U1 as Rotate(u,r) where we sample the rotation angle r uniformly from r∼{0,90,180,270} and then ask the model to predict the rotation amount as a four-class classification problem.
In total, the ReMixMatch loss is
| | | | | |
| --- | --- | --- | --- | --- |
| | | ∑x,p∈X′H(p,pmodel(y|x;θ))+λU∑u,q∈U′H(q,pmodel(y|u;θ)) | | (3) |
| | +λ^U1 | ∑u,q∈^U1H(q,pmodel(y|u;θ))+λr∑u∈^U1H(r,pmodel(r|Rotate(u,r);θ)) | | (4) |
##### Hyperparameters
ReMixMatch introduce two new hyperparameters:
the weight on the rotation loss λr and
the weight on the un-augmented example λ^U1.
In practice both are fixed
λr=λ^U1=0.5.
ReMixMatch also shares many hyperparameters from MixMatch:
the weight for the unlabeled loss λU,
the sharpening temperature T,
the MixUp Beta parameter,
and the number of augmentations K.
All experiments (unless otherwise stated) use T=0.5, Beta =0.75, and λU=1.5.
We found using a larger number of augmentations monotonically
increases accuracy, and so set K=8 for all experiments (as running
with K augmentations increases computation by a factor of K).
We train our models using Adam
(Kingma and Ba, [2015](#bib.bib35 "Adam: a method for stochastic optimization")) with a fixed learning rate of 0.002 and weight decay (Zhang et al., [2018](#bib.bib68 "Three mechanisms of weight decay regularization")) with a
fixed value of 0.02.
We take the final model as an exponential moving average over the trained model weights
with a decay of 0.999.
4 Experiments
--------------
We now test the efficacy of ReMixMatch on a set of standard semi-supervised learning
benchmarks.
Unless otherwise noted, all of the experiments performed in this section
use the same codebase and model architecture (a Wide ResNet-28-2 (Zagoruyko and Komodakis, [2016](#bib.bib32 "Wide residual networks")) with 1.5 million parameters, as used in (Oliver et al., [2018](#bib.bib63 "Realistic evaluation of deep semi-supervised learning algorithms"))).
###
4.1 Realistic SSL setting
We follow the Realistic Semi-Supervised Learning (Oliver et al., [2018](#bib.bib63 "Realistic evaluation of deep semi-supervised learning algorithms")) recommendations for
performing SSL evaluations.
In particular, as mentioned above, this means we use the same model and training
algorithm in the same codebase for all experiments.
We compare against VAT (Miyato et al., [2018](#bib.bib4 "Virtual adversarial training: a regularization method for supervised and semi-supervised learning")) and MeanTeacher (Tarvainen and Valpola, [2017](#bib.bib7 "Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results")), copying the re-implementations
over from the MixMatch codebase (Berthelot et al., [2019](#bib.bib84 "Mixmatch: a holistic approach to semi-supervised learning")).
We also re-implemented UDA (Xie et al., [2019](#bib.bib86 "Unsupervised data augmentation for consistency training")) on top of our codebase and performed hyperparameter tuning but
we were unable to reach the same accuracy levels as reported in the initial paper.111UDA makes claims for low-label regimes; however, because it uses
a learned AutoAugment policy, these results are logistically impractical. We therefore
do not report these results.
##### Fully supervised baseline
To begin, we train a fully-supervised baseline to measure the highest
accuracy we could hope to obtain with our training pipeline.
The experiments we perform use the same model
and training algorithm, so these baselines are valid for all discussed SSL techniques.
On CIFAR-10, we obtain an fully-supervised error rate of 4.25% using weak flip + crop augmentation, which drops to 3.62% using AutoAugment and 3.91% using CTAugment.
Similarly, on SVHN we obtain 2.70% error using weak (flip) augmentation and 2.31% and 2.16% using AutoAugment and CTAugment respectively.
While AutoAugment performs slightly better on CIFAR-10 and slightly worse on SVHN compared to CTAugment, it is not our intent to design a *better* augmentation
strategy; just one that can be used *without a pre-training or tuning of hyper-parameters*.
##### Cifar-10
Our results on CIFAR-10 are shown in [table 1](#S4.T1 "Table 1 ‣ SVHN ‣ 4.1 Realistic SSL setting ‣ 4 Experiments ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring"), left.
ReMixMatch sets the new state-of-the-art for all numbers of
labeled examples.
Most importantly,
ReMixMatch is 16× more data efficient than MixMatch (e.g., at 250 labeled
examples ReMixMatch has identical accuracy compared to MixMatch at 4,000).
##### Svhn
Results for SVHN are shown in [table 1](#S4.T1 "Table 1 ‣ SVHN ‣ 4.1 Realistic SSL setting ‣ 4 Experiments ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring"), right.
ReMixMatch reaches state-of-the-art at 250 labeled examples,
and within the margin of error for state-of-the-art otherwise.
| | CIFAR-10 | SVHN |
| --- | --- | --- |
| Method | 250 labels | 1000 labels | 4000 labels | 250 labels | 1000 labels | 4000 labels |
| VAT | 36.03±2.82 | 18.64±0.40 | 11.05±0.31 | 8.41±1.01 | 5.98±0.21 | 4.20±0.15 |
| Mean Teacher | 47.32±4.71 | 17.32±4.00 | 10.36±0.25 | 6.45±2.43 | 3.75±0.10 | 3.39±0.11 |
| MixMatch | 11.08±0.87 | 7.75±0.32 | 6.24±0.06 | 3.78±0.26 | 3.27±0.31 | 2.89±0.06 |
| UDA (ours) | -∗ | -∗ | 6.63±0.19 | -∗ | 3.09±0.07 | 2.67±0.10 |
| UDA (reported) | -∗ | -∗ | 5.27±0.11 | -∗ | 2.46±0.17 | 2.32±0.05 |
| ReMixMatch | 6.27±0.34 | 5.73±0.16 | 5.14±0.04 | 3.10±0.50 | 2.83±0.30 | 2.42±0.09 |
Table 1: Results on CIFAR-10 and SVHN.
For UDA, -∗ signifies that it is not feasible to report results with fewer labels than were used to train the AutoAugment policy (4000 labels for CIFAR-10 and 1000 labels for SVHN). We re-implement UDA in our framework
with our training procedure and report in as “ours”. We also report the
original results from Xie et al. ([2019](#bib.bib86 "Unsupervised data augmentation for consistency training")) which used a different network implementation, training procedure, etc. and are thus not directly comparable.
###
4.2 Stl-10
The STL-10 dataset consists of 5,000 labeled 96×96 color images
drawn from 10 classes and 100,000 unlabeled images drawn from a similar—but not identical—data distribution.
The labeled set is partitioned into ten pre-defined folds of 1,000 images
each. For efficiency, we only run our analysis on five of these ten folds.
We do not perform evaluation here under the Realistic SSL (Oliver et al., [2018](#bib.bib63 "Realistic evaluation of deep semi-supervised learning algorithms")) setting when
comparing to non-MixMatch results.
Our results are, however, directly comparable to the MixMatch results.
Using the same WRN-37-2 network (23.8 million parameters), we reduce the error rate by a factor of two compared to MixMatch.
Method
Error Rate
SWWAE
25.70
CC-GAN
22.20
MixMatch
10.18±1.46
ReMixMatch (K=1)
6.77±1.66
ReMixMatch (K=4)
6.18±1.24
Table 2: STL-10 error rate using 1000-label splits. SWWAE and CC-GAN results are from (Zhao et al., [2015](#bib.bib80 "Stacked what-where auto-encoders")) and (Denton et al., [2016](#bib.bib81 "Semi-supervised learning with context-conditional generative adversarial networks")).
Ablation
Error Rate
Ablation
Error Rate
ReMixMatch
5.94
No rotation loss
6.08
With K=1
7.32
No pre-mixup loss
6.66
With K=2
6.74
No dist. alignment
7.28
With K=4
6.21
L2 unlabeled loss
17.28
With K=16
5.93
No strong aug.
12.51
MixMatch
11.08
No weak aug.
29.36
Table 3: Ablation study. Error rates are reported on a single 250-label split from CIFAR-10.
###
4.3 Towards few-shot learning
We find that ReMixMatch is able to work in extremely low-label
settings. By only changing λr from 0.5 to 2 we
can train CIFAR-10 with just four labels
per class and SVHN with only 40 labels total.
On CIFAR-10 we obtain a median-of-five error rate of 15.08%;
on SVHN we reach 3.48% error and
on SVHN with the “extra” dataset we reach 2.81% error.
Full results are given in Appendix B.
###
4.4 Ablation Study
Because we have made several changes to the existing MixMatch algorithm,
here we perform an ablation study and remove one component of ReMixMatch
at a time to understand from which changes produce the largest accuracy gains.
Our ablation results are summarized in Table [3](#S4.T3 "Table 3 ‣ 4.2 STL-10 ‣ 4 Experiments ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
We find that removing the pre-mixup unlabeled loss, removing distribution alignment, and lowering K all hurt performance by a small amount.
Given that distribution alignment improves performance, we were interested to see whether it also had the intended effect of making marginal distribution of model predictions match the ground-truth marginal class distribution.
We measure this directly in [appendix D](#A4 "Appendix D Measuring the effect of distribution alignment ‣ ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring").
Removing the rotation loss reduces accuracy at 250 labels by only 0.14 percentage points, but we find that in the 40-label setting rotation loss is necessary to prevent collapse.
Changing the cross-entropy loss on unlabeled data to an L2 loss as used in MixMatch hurts performance dramatically, as does removing either of the augmentation components.
This validates using augmentation anchoring in place of the consistency regularization mechanism of MixMatch.
5 Conclusion
-------------
Progress on semi-supervised learning over the past year has upended many of the
long-held beliefs about classification, namely, that vast quantities of
labeled data is necessary.
By introducing augmentation anchoring and distribution alignment to MixMatch, we continue this trend:
ReMixMatch reduces the quantity of labeled data needed by a large factor compared to prior work
(e.g., beating MixMatch at 4000 labeled
examples with only 250 on CIFAR-10, and closely approaching MixMatch at 5000 labeled examples
with only 1000 on STL-10).
In future work, we are interested in pushing the limited data regime further to close the gap between few-shot learning and SSL.
We also note that in many real-life scenarios, a dataset begins as unlabeled and is incrementally labeled until satisfactory performance is achieved.
Our strong empirical results suggest that it will be possible to achieve gains in this “active learning” setting by using ideas from ReMixMatch.
Finally, in this paper we present results on widely-studied image benchmarks for ease of comparison.
However, the true power of data-efficient learning will come from applying these techniques to real-world problems where obtaining labeling data is expensive or impractical. |
d629b7c1-ee58-4034-96c1-a682df904fd0 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Brussels meetup
Discussion article for the meetup : Brussels meetup
WHEN: 13 October 2012 01:00:00PM (+0200)
WHERE: Rue des Alexiens 55 1000 Bruxelles
We are once again meeting at 'la fleur en papier doré' close to the Brussels Central station. If you feel like an intelligent discussion and are in the neighborhood, consider dropping by. As always, I'll have a sign.
Discussion article for the meetup : Brussels meetup |
9a2bdfc5-0d7f-4eb0-b559-d56f2f5b720a | trentmkelly/LessWrong-43k | LessWrong | Why do the Sequences say that "Löb's Theorem shows that a mathematical system cannot assert its own soundness without becoming inconsistent."?
I am looking for some understanding into why this claim is made.
As far as I can tell, Löb's Theorem does not directly make such an assertion.
Reading the Cartoon's Guide to Löb's Theorem, it appears that this assertion is made on the basis of the reasoning that Löb's Theorem itself can't prove negations, that is, statements such as "1 + 3 /= 5."
> Alas, this means we can't prove PA sound with respect to any important class of statements.
This is a statement that [due to the presence of negations in it] itself can't be proven within PA.
Now it seems that it is being argued that the inability to do this is a bad thing [that is, being able to prove that we can't prove PA sound with respect to any 'important' class of statements].
I think this is actually a very critical question and I have some ideas for what the central crux is here, but I'd be interested in seeing some answers before delving into that. |
743b5055-413a-44b4-9273-63baf1431555 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | 200 COP in MI: Interpreting Algorithmic Problems
*This is the fourth post in a sequence called 200 Concrete Open Problems in Mechanistic Interpretability.*[*Start here*](https://www.alignmentforum.org/posts/LbrPTJ4fmABEdEnLf/200-concrete-open-problems-in-mechanistic-interpretability)*, then read in any order. If you want to learn the basics before you think about open problems, check out*[*my post on getting started*](https://neelnanda.io/getting-started)*. Look up jargon in*[*my Mechanistic Interpretability Explainer*](https://neelnanda.io/glossary)
Motivation
----------
***Motivating paper**:* [*A Mechanistic Interpretability Analysis of Grokking*](https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mechanistic-interpretability-analysis-of-grokking)
When models are trained on synthetic, algorithmic tasks, they often learn to do some clean, interpretable computation inside. Choosing a suitable task and trying to reverse engineer a model can be a rich area of interesting circuits to interpret! In some sense, this is interpretability on easy mode - the model is normally trained on a single task (unlike language models, which need to learn everything about language!), we know the exact ground truth about the data and optimal solution, and the models are tiny. So why care?
I consider [my work on grokking](https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mechanistic-interpretability-analysis-of-grokking) to be an interesting case study of this work going well. Grokking (shown below) is a mysterious phenomena where, when small models are trained on algorithmic tasks (eg modular addition or modular division), they initially memorise the training data. But when they *keep* being trained on that data for a really long time, the model suddenly(ish) figures out how to generalise!
In my work, I simplified their setup even further, by training a 1 Layer transformer (with no LayerNorm or biases) to do modular addition and reverse engineered the weights to understand what was going on. And it turned out to be doing a funky trig-based algorithm (shown below), where the numbers are converted to frequencies with a memorised Discrete Fourier Transform, added using trig identities, and converted back to the answer! Using this, we looked inside the model and identified that despite seeming to have plateaued, in the period between memorising and "grokking", the model is actually slowly forming the circuit that does generalise. But so long as the model still has the memorising circuit, this adds too much noise to have good test loss. Grokking occurs when the generalising circuit is so strong that the model decides to "clean-up" the memorising circuit, and "uncovers" the mature generalising circuit beneath, and suddenly gets good test performance.
OK, so I just took this as an excuse to explain my paper to you. Why should you care? I think that the general lesson from this, that I'm excited to see applied elsewhere, is using toy algorithmic models to analyse a phenomena we're confused about. Concretely, given a confusing phenomena like grokking, I'd advocate the following strategy:
1. **Simplify** to the minimal setting that exhibits the phenomena, yet is complex enough to be interesting
2. **Reverse-engineer** the resulting model, in as much detail as you can
3. **Extrapolate** the insights you've learned from the reverse-engineered model - what are the broad insights you've learned? What do you expect to generalise? Can you form any automated tests to detect the circuits you've found, or any of their motifs?
4. **Verify** by looking at other examples of the phenomena and seeing whether these insights actually hold (larger models, different tasks, even just earlier checkpoints of the model or different random seeds)
Grokking is an example in a science of deep learning context - trying to uncover mysteries about how models learn and behave. But this same philosophy also applies to understanding confusing phenomena in language models, and building toy algorithmic problems to study those!
Anthropic's [Toy Models of Superposition](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=EuO4CLwSIzX7AEZA1ZOsnwwF) is an excellent example of this done well, for the case of [superposition in linear bottleneck dimensions](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=MjDx_NdXa8sSxeq-tiLALtl2) of a model (like value vectors in attention heads, or the residual stream). Concretely, they isolated out the key traits as being where high dimensional spaces are projected to a low dimensional space, and then mapped back to a high dimensional space, with no non-linearity in between. And got extremely interesting and rich results! (More on these in the next post)
More broadly, because algorithmic tasks are often cleaner and easier to interpret, and there's a known ground truth, it can be a great place to practice interpretability! Both as a beginner to the field trying to build intuitions and learn techniques, and to refine our understanding of the *right* tools and techniques. It's much easier to validate a claimed approach to validate explanations (like [causal scrubbing](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=KfagbOQ29EYq3FA_OGaxZaoc)) if you can run it on a problem with an understood ground truth!
Overall, I'm less excited about algorithmic problem interpretability in general than some of the other categories of open problems, but I think it can be a great place to start and practice. I think that building a toy algorithmic model for a confusing phenomena is hard, but can be really exciting if done well!
Resources
---------
* **Demo**: Reverse engineering how a small transformer can re-derive positional embeddings ([colab](https://colab.research.google.com/github/neelnanda-io/Easy-Transformer/blob/no-position-experiment/No_Position_Experiment.ipynb)), with [an accompanying video walkthrough](https://www.youtube.com/watch?v=yo4QvDn-vsU)
* **Demo**:My grokking work reverse-engineering modular addition - [write-up](https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mechanistic-interpretability-analysis-of-grokking) and [colab](https://colab.research.google.com/drive/1F6_1_cWXE5M7WocUcpQWp3v8z4b1jL20)
+ Note - I'd guess that none of the Fourier stuff to generalise to other problems, but some insights and the underlying mindsets will. Modular addition is weird!
Tips
----
* Approach the reverse engineering scientifically. Go in and try to form hypotheses about what the model is doing, and how it might have solved the task. Then go and *test* these hypotheses, and try to look for evidence for them, and evidence to falsify them. Use these hypotheses to help you prioritise and focus among all of the possible directions you can go in - accept that they'll probably be wrong in some important ways (I did *not* expect modular addition to be using Fourier Transforms!), but use them as a guide to figure out *how* they're wrong.
+ Concretely, try to think about how you would implement a solution if you were designing the model weights. This is pretty different from writing code! Models tend to be very good at parallelised, vectorised solutions that heavily rely on linear algebra, with limited use of non-linear activation functions.
- A useful way to understand how to think like a transformer is to look at [this paper](https://arxiv.org/abs/2106.06981), which introduces a programming language called RASP that aims to mimic the computational model of the transformer.
* These problems often involve training your own model, rather than just analysing a model someone else trained. These are such toy problems that training is pretty easy, but it can still be a headache! I recommend cribbing training code from somewhere (eg the demos above).
* Subtle details of how you set up the problem can make life much easier or harder for the model. Think carefully about things and try many variations.
+ Eg what is the loss function, whether there are special tokens in the context (eg to mark the end of the first input and the start of the second input or the output) or if the model needs to infer these for itself, etc.
* Rule of thumb: Small models are easier to interpret (it matters more to have few layers than narrow layers). Before analysing a model, check that the next smallest model can't also do the task!
+ Eg, decrease the number of transformer layers, remove LayerNorm, try attention-only, try an MLP (the classic neural network, not a transformer) with one hidden layer (or two).
* When building a toy model to capture something about a real model, you want to be especially thoughtful about how you do this! Try to distill out exactly what traits of the model determine the property that you care about. You want to straddle the fine line between too simple to be interesting and too complex to be tractable.
+ This is a particularly helpful thing to get other people's feedback on, and to spend time red-teaming
Problems
--------
[*This spreadsheet*](https://www.neelnanda.io/cop-spreadsheet) *lists each problem in the sequence. You can write down your contact details if you're working on any of them and want collaborators, see any existing work or reach out to other people on there! (thanks to Jay Bailey for making it)*
* Good beginner problems:
+ A 3.1 - Sorting fixed-length lists. (format - `START 4 6 2 9 MID 2 4 6 9`)
- How does difficulty change with the length of the list?
- **A\* 3.2 -** Sorting variable-length lists.
* What’s the sorting algorithm? What’s the longest list you can get to? How is accuracy affected by longer lists?
+ A 3.3 - Interpret a 2L MLP (one hidden layer) trained to do modular addition (very analogous to my grokking work)
+ A 3.4 - Interpret a 1L transformer trained to do modular subtraction (very analogous to my grokking work)
+ A 3.5 - Taking the minimum or maximum of two ints
+ A 3.6 - Permuting lists
+ A 3.7 - Calculating sequences with a Fibonacci-style recurrence relation.
- (I.e. predicting the next element from the previous two)
* Some harder concrete algorithmic problems to try interpreting
+ **B\* 3.8 -** 5 digit addition/subtraction
- What if you reverse the order of the digits? (e.g. inputting and outputting the units digit first). This order might make it easier to compute the digits since e.g. the tens digit depends on the units digit.
- 5 digits is a good choice because we have prior knowledge that [grokking happens there, and it seems likely related to the modular addition algorithm.](https://twitter.com/NeelNanda5/status/1559060507524403200) You might need to play around with training setups and hyperparameters if you varied the number of digits.
+ **B\* 3.9 -** Predicting the output to simple code functions. E.g. predicting the bold text in problems like
a = 1 2 3
a[2] = 4
a -> **1 2 4**
+ **B\* 3.10 -** Graph theory problems like [this](https://jacobbrazeal.wordpress.com/2022/09/23/gpt-3-can-find-paths-up-to-7-nodes-long-in-random-graphs/)
- Not sure of the right input format - try a bunch! Check this out: <https://jacobbrazeal.wordpress.com/2022/09/23/gpt-3-can-find-paths-up-to-7-nodes-long-in-random-graphs/#comment-248>
+ **B\* 3.11 -** Train a model on multiple algorithmic tasks that we understand (eg train a model to do modular addition *and*modular subtraction, by learning two different outputs). Compare this to a model trained on each task.
- What happens? Does it learn the same circuits? Is there superposition?
- If doing grokking-y tasks, how does that interact?
+ **B\* 3.12 -** Train models for [automata](https://arxiv.org/pdf/2210.10749.pdf) tasks and interpret them - do your results match the theory?
+ **B\* 3.13 -** In-Context Linear Regression, as described in [Garg et al](https://arxiv.org/pdf/2208.01066.pdf) - the transformer is given a sequence (x\_1, y\_1, x\_2, y\_2, …), where y\_i=Ax\_i+b and A and b are different for each prompt and need to be learned in-context ([see code here](https://github.com/dtsip/in-context-learning))
- Tip - train a 2L or 3L model on this and give it width 256 or 512, this should be much easier to interpret than their 12L one.
- **C\* 3.14 -** The other problems in the paper that are in-context learned - sparse linear functions, 2L networks & decision trees
+ C 3.15 - 5 digit (or binary) multiplication
+ B 3.16 - Predict repeated subsequences in randomly generated tokens, and see if you can find and reverse engineer [induction heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html#definition-of-induction-heads) (see [my grokking paper](https://www.lesswrong.com/posts/N6WM6hs7RQMKDhYjB/a-mechanistic-interpretability-analysis-of-grokking) for details.)
- Tip: Using shortformer style positional embeddings (see [the TransformerLens docs](https://github.com/neelnanda-io/TransformerLens/blob/main/further_comments.md#shortformer-attention-positional_embeddings_type--shortformer)), this seems to make things cleaner
+ B-C 3.17 - Choose your own adventure: Find your own algorithmic problem!
- [Leetcode easy](https://leetcode.com/problemset/all/?difficulty=EASY&page=1) is probably a good source of problems.
* **B\* 3.18 -** Build a toy model of [Indirect Object Identification](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=iWsV3s5Kdd2ca3zNgXr5UPHa) - train a tiny attention only model on an algorithmic task simulating Indirect Object Identification, and reverse-engineer the learned solution. Compare this to [the circuit found in GPT-2 Small](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=iWsV3s5Kdd2ca3zNgXr5UPHa) - is it the same algorithm? We often build toy models to model things in real models, it'd be interesting to validate this by going the other way
+ Context: IOI is the grammatical task of figuring out that sentences like "When John and Mary went to the store, John gave a bottle of milk to" maps to "Mary",. Interpretability in the Wild was a paper reverse engineering the 25 head circuit behind this in GPT-2 Small.
+ The MVP would be just giving the model a BOS, a MID and the names, eg `BOS John Mary John MID` -> `Mary`. To avoid trivial solutions, make eg half the data 3 distinct names like `John Mary Peter` which maps to a randomly selected one of those names. I'd try training a 3L attn-only model to do this.
+ **C\* 3.19 -** There's a bunch of follow-up questions: Is this consistent across random seeds, or can other algorithms be learned? Can a 2L model learn this? What happens if you add MLPs or more layers?
* **C\* 3.20 -** Reverse-engineer [Othello-GPT](https://arxiv.org/pdf/2210.13382.pdf) ([summary here](https://thegradient.pub/othello/)) - the paper trains a GPT-style model to predict the next move in randomly generated Othello games (ie, it predicts legal moves, but doesn't learn strategy). Can you reverse-engineer the algorithms the model learns?
+ In particular, they train probes and use this to recover the model's internal model of the board state, and show that they can intervene to change what the model thinks, where it makes legal moves in the edited board. Can you reverse-engineer the features the probes find?
+ Tip: They train an 8L model, I expect you can get away with way smaller. They also use one hidden layer MLP probes, I'd start with using a linear probe for simplicity.
* Exploring questions about language models:
+ **A\* 3.21 -** Train a one layer attention-only transformer with rotary to predict the previous token, and reverse engineer how it does this.
- An even easier way to do this is by training just the QK-circuit in an attention head and the embedding matrix to maximise the attention paid to the previous token (with mean-squared error loss) - this will cut out other crap the model might be doing.
+ **B\* 3.22 -** Train a three layer attention-only transformer to perform the [Indirect Object Identification](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=iWsV3s5Kdd2ca3zNgXr5UPHa) task (and *just* that task! You can algorithmically generate training data). Can it do the task? And if so, does it learn [the same circuit that was found in GPT-2 Small](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=iWsV3s5Kdd2ca3zNgXr5UPHa)?
+ **B\* 3.23 -** Re-doing my modular addition analysis with GELU. How does this change things, if at all?
- Bonus: Doing it for any algorithmic task! This probably works best on those with sensible and cleanly interpretable neurons
+ **C\* 3.24 -** How does memorization work?
- Idea 1: Train a one hidden layer MLP to memorise random data.
* Possible setup: There are two inputs, each in `{0,1,...n-1}`. These are one-hot encoded, and stacked (so the input has dimension `2n`). Each pair has a randomly chosen output label, and the training set consists of all possible pairs.
- Idea 2: Try training a transformer on a fixed set of random strings of tokens?
+ **B-C\* 3.25 -** Comparing different [dimensionality reduction](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=xRPTt5aM5hTP0KHEbgol56Gc) techniques ( e.g. [PCA](https://setosa.io/ev/principal-component-analysis/)/[SVD](https://gregorygundersen.com/blog/2018/12/10/svd/), [t-SNE](https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf), [UMAP](https://arxiv.org/pdf/1802.03426.pdf), [NMF](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization), [the grand tour](https://distill.pub/2020/grand-tour/)) on modular addition (or any other problem that you feel you understand!)
- These techniques have been used on [AlphaZero](https://www.deepmind.com/blog/alphazero-shedding-new-light-on-chess-shogi-and-go), [Understanding RL Vision](https://distill.pub/2020/understanding-rl-vision/), and [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/) - which should be a good starting point for how.
- B 3.26 - In modular addition, look at what these do on different weight matrices - can you identify which weights matter most? Or which neurons form clusters for each frequency? Can you find anything if you look at activations?
+ **C\* 3.27 -** Is [direct logit attribution](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=disz2gTx-jooAcR0a5r8e7LZ) always useful? Can you find examples where it’s highly misleading?
- I'd focus on problems where a component's output is mostly intended to suppress incorrect logits (so it improves the correct log prob but *not* the correct logit)
* Exploring broader deep learning mysteries - can you build a toy, algorithmic model to better understand these?
+ **D\* 3.28 -** The [Lottery Ticket Hypothesis](https://towardsdatascience.com/demystifying-the-lottery-ticket-hypothesis-in-deep-learning-158570b62674)
+ **D\* 3.29 -** [Deep Double Descent](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=GXTb66JhboBH3SDIj6j7D8Mn)
* Othello-GPT is a model trained to play random legal moves in Othello, and learns a linear emergent world model, modelling the board state (despite only ever seeing and predicting moves!) I did some [work on it](https://www.alignmentforum.org/posts/nmxzr2zsjNtjaHh7x/actually-othello-gpt-has-a-linear-emergent-world) and [outline directions of future work](https://www.alignmentforum.org/s/nhGNHyJHbrofpPbRG/p/qgK7smTvJ4DB8rZ6h). I think it sits at a good intersection of complex enough to be interesting (much more complex than any algorithmic model that's been well understood!) yet simple enough to be tractable!
+ 
+ I go into a *lot* more detail in the post including on concrete problems and [give code](https://neelnanda.io/othello-notebook) to build off of, so I think this can be a great place to start. The following is a brief summary.
+ **A\*** 3.30 Trying one of [the concrete starter projects](https://www.alignmentforum.org/s/nhGNHyJHbrofpPbRG/p/qgK7smTvJ4DB8rZ6h#Concrete_starter_projects)
+ **B-C\*** 3.31 [Looking for modular circuits](https://www.alignmentforum.org/s/nhGNHyJHbrofpPbRG/p/qgK7smTvJ4DB8rZ6h#Finding_Modular_Circuits) - try to find the circuits used to compute the world model and to use the world model to compute the next move, and understand each in isolation and use this to understand how they fit together. See what you can learn about finding modular circuits in general.
- Example: Interpreting a neuron in the middle of the model by multiplying its input weights by the probe directions, to see what board state it looks at 
+ **B-C\*** 3.32 [Neuron Interpretability and Studying Superposition](https://www.alignmentforum.org/s/nhGNHyJHbrofpPbRG/p/qgK7smTvJ4DB8rZ6h#Neuron_Interpretability_and_Studying_Superposition) - try to understand the model's MLP neurons, and explore what techniques do and do not work. Try to build our understanding of how to understand transformer MLPs in general.
- We're really bad at MLPs in general, but certain things are much easier here - eg it's easy to automatically check whether a neuron only activates when a condition is met, because all features here are clean and algorithmic
+ **B-C\*** 3.33 [A Transformer Circuit Laboratory](https://www.alignmentforum.org/s/nhGNHyJHbrofpPbRG/p/qgK7smTvJ4DB8rZ6h#A_Transformer_Circuit_Laboratory) - Explore and test other conjectures about transformer circuits, eg can we figure out how the model manages memory in the residual stream? |
5f6dfee5-c47b-4f82-9e6f-8086bf95d1f7 | trentmkelly/LessWrong-43k | LessWrong | Destroying the fabric of the universe as an instrumental goal.
Perhaps this is a well examined idea, but i didn't find anything when searching.
The argument is simple. If the AI wants to avoid the world being in a certain state, destroying everything reduces the likelihood of that state to occur to zero. Otherwise, the likelihood might always be non-zero. This particular strategy has in some sense been observed already. Ml agents in games have been observed to crash the game in order to avoid a negative reward.
If most simple negative reward functions could achieve an ideal outcome by destroying everything, and destroying everything is possible, then a large portion of negative reward functions might be highly dangerous since the reward function would be incentivized to find any information that could end everything. No need for greedy mesa-optimizers that never get satisfied. This seems to have implications for alignment if true.
This also seems to have implications for anthropic reasoning in favor of the doomsday argument. If the entire universe gets destroyed, then our early existence and small numbers makes perfect sense. |
3f6935d0-0334-4be2-aa37-26f465b34119 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Reflective Bayesianism
I've argued in [several](https://www.lesswrong.com/s/Rm6oQRJJmhGCcLvxh) [places](https://www.lesswrong.com/posts/xJyY5QkQvNJpZLJRo/radical-probabilism-1) that traditional Bayesian reasoning is unable to properly handle embeddedness, logical uncertainty, and related issues.
However, in the light of hindsight, it's possible to imagine a Bayesian pondering these things, and "escaping the trap". This is because the trap was largely made out of assumptions we didn't usually *explicitly recognize* we were making.
Therefore, in order to aid moving beyond the traditional view, I think it's instructive to paint a detailed picture of what a traditional Bayesian might believe. This can be seen as a partner to my post [Radical Probabilism](https://www.lesswrong.com/posts/xJyY5QkQvNJpZLJRo/radical-probabilism-1), which explained in considerable detail how to move beyond the traditional Bayesian view.
---
Simple Belief vs Reflective Belief
==================================
There is an important distinction between the explicit dogmas of a view (ie, what adherents explicitly endorse) vs what one would need to believe in order to agree with them. One can become familiar with this distinction by studying logic, especially Gödel's incompleteness theorems and Tarski's undefinability theorem. In particular:
* Axiomatic systems such as set theory cannot proclaim themselves self-consistent (unless they are, indeed, inconsistent). However, adherents believe the system to be consistent. If they seriously doubted this, they would not use the system.
* Such axiomatic systems cannot discuss their own semantics (their map-territory correspondence). However, adherents regularly discuss this.
This becomes especially interesting when we're studying rationality (rather than, say, mathematics), because a theory of rationality is supposed to characterize normatively correct thinking. Yet, due to the above facts, philosophers will *typically* end up in a strange position where they endorse principles very different from the ones they themselves are using. For example, a philosopher arguing that set theory is the ultimate foundation of rational thought would find themselves in the strange position of utilizing "irrational" tools (tools which go beyond set theory) to come to its defense, EG in discussing the semantics of set theory or arguing for its consistency.1.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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I'll use the term ***simple belief*** to indicate accepting the explicit dogmas, and ***reflective belief*** to indicate accepting the meta-dogmas which justify the dogmas.2,3 (These are not terms intended to stand the test of time; I'm not sure about the best terminology, and probably won't reliably use these particular terms outside of this blog post. Leverage Research, iiuc, uses the term "endorsed belief" for what I'm calling "reflective belief", and "belief" for what I'm calling "simple belief".)
Reflective belief and simple belief need not go together. There's a joke from Eliezer (h/t to Ben Pace for pointing me to the source):
> The rules say we must use consequentialism, but good people are deontologists, and virtue ethics is what actually works.
>
> —Eliezer Yudkowsky, [Twitter](https://twitter.com/esyudkowsky/status/819678205900509184?lang=en)
>
>
If this were true, then *simple belief* in consequentialism would imply *reflective belief* in virtue ethics (because you evaluate moral frameworks on their effects, not whether they're morally correct!). Similarly, simple belief in virtue ethics would imply reflective belief in deontology, and simple belief in deontology would imply reflective belief in consequentialism.
So, not only does simple belief in X not imply reflective belief in X; furthermore, reflective belief in X need not imply simple belief in X! (This is, indeed, [belief-in-belief](https://www.lesswrong.com/posts/CqyJzDZWvGhhFJ7dY/belief-in-belief).)
Hence, by "reflective belief" I do not necessarily mean "reflectively consistent belief". ***Reflective consistency*** occurs only when simple belief and reflective belief are one and the same: the reasoning system you use is also the one you endorse.
Reflective Bayesianism
======================
Applying the naive/reflective distinction to our case-in-point, I'll define two different types of Bayesian:
***Simple Bayesian:*** simple belief in Bayesianism. This describes an agent who reasons according to the laws of probability theory, and updates beliefs via Bayes' Law. This is the type of reasoner which Bayesians *study.*
***Reflective Bayesian:*** reflective belief in Bayesianism. For simplicity, I'll assume this *also* involves simple belief in Bayesianism. Realistically, Bayesian philosophers can't reason in a perfectly Bayesian way; so, this is a simplified, idealized Bayesian philosopher.
(A problem with the terminology in this section is that by "Bayesian" I specifically mean "dogmatic probabilism" in the terminology from my [Radical Probabilism](https://www.lesswrong.com/posts/xJyY5QkQvNJpZLJRo/radical-probabilism-1) post. I don't want to construe "Bayesianism" to *necessarily* include Bayesian updates. The central belief of Bayesianism is subjective probability theory. However, repeating "simple dogmatic probabilism vs reflective dogmatic probabilism" over and over again in this essay was not very appealing.)
Actually, there's not one canonical "reflective belief in Bayes" -- one can justify Bayesianism in many ways, so there can correspondingly be many reflective-Bayesian positions. I'm going to discuss a number of these positions.
My prior is best.
-----------------
The easiest way to reflectively endorse Bayesianism is to simply believe *my prior is best*. No other distribution can have more information about the world, unless it actually observes something about the world.
I think of this as *multiverse frequentism*. You think your prior literally gives the frequency of different possible universes. Now, I'm not accusing anyone of really believing this, but I *have* heard a (particularly reflective and self-critical) Bayesian articulate the idea. And I think a lot of people have an assumption like this in mind when they think about priors like the Solomonoff prior, which are designed to be particularly "objective". This is essentially a map/territory error.
Some group of readers may think: "Now wait a minute. Shouldn't a Bayesian necessarily believe this? The expected Bayes loss of any other prior is going to be worse, when an agent considers them! Similarly, no other prior is going to look like a better tool for making decisions. So, yeah... I expect my prior to be best!"
On the other hand, those who endorse some level of [modest epistemology](https://www.lesswrong.com/tag/epistemic-modesty) might be giving that first group of readers some serious side-eye. Surely it's crazy to think your own beliefs are optimal *only* because they're yours?
To the first group, I would agree that *if* you've fully articulated a probability distribution, *then* you shouldn't be in a position where you think a different one is better than yours: in that case, you should update to the other one! (Or possibly to some third, even better distribution). The multiverse-frequentist fallaciously extends this result to apply to all priors.
But this *doesn't mean* you should think your distribution is best in general. For example, you can believe that someone else knows more than you, without knowing exactly what they believe.
In particular, it's easy to believe that *some computation* knows more than you. If a task somehow involves factoring numbers, you might not know the relevant prime factorizations. However, you can justifiably trust a probability distribution whose description includes running an accurate prime factorization algorithm. You can prefer to replace your own beliefs with such a probability distribution. This lays the groundwork for justified non-Bayesian updates.
I can't gain information without observing things.
--------------------------------------------------
Maybe our reflective Bayesian doesn't literally think theirs is the best prior possible. However, they might be a staunch empiricist: they believe knowledge is entanglement with reality, and you can only get entanglement with reality by *looking*.
Unlike the multiverse-frequentist described in the previous section, the empiricist *can* think other people have better probability distributions. What the empiricist *doesn't* believe is that we can emulate any of their expertise merely by thinking about it. Thinking is useless (except, of course, for the computational requirements of Bayes' Law itself). Therefore, although helpful non-Bayesian updates might technically be possible (eg, if you could morph into a more knowledgeable friend of yours), it's *not* possible to come up with any which you can implement just by thinking.
I can't think of any way to "justify" this assumption except if you really do have unbounded computational resources.
The best prior is already one of my hypotheses.
-----------------------------------------------
This is, of course, just the usual ***assumption of realizability:*** we assume that the world is in our hypothesis-space. We just have to find which of our hypotheses is the true one.
This doesn't imply as strong a rejection of non-Bayesian updates. It *could be* that we can gain some useful information by computation alone. However, the need for this must be limited, because *we already have enough computational power to simulate the whole world*.
What the assumption *does* gain you is a guarantee that you will make decisions well, eventually. If the correct hypothesis is in your hypothesis space, then once you learn it with sufficiently high confidence (which can usually happen pretty fast), you'll be making optimal decisions. This is a much stronger guarantee than the simple Bayesian has. So, the assumption does buy our Reflective Bayesian a lot of power in terms of justifying Bayesian reasoning.
My steel-man of this perspective is *the belief that the universe is intelligible*. I'm not sure what to call this belief. Here are a few versions of it:
* ***Computationalism.*** Everything is computable. It's absurd to imagine that anything physically realized would not be computable. So, it's sufficient to assume that the universe might be any computer program.
* ***Set-theory-ism.*** Anything real must have a description in ZFC. Physics might include some uncomputable aspects, but they'd be things like halting oracles, which can be captured within ZFC.
* ***Mathematicalism.*** Anything real must be mathematically describable. Not necessarily in any one axiom system such as ZFC -- we know (from Tarski's undefinability theorem) that there are mathematically describable things which fall outside any fixed axiom set. However, the universe must be mathematically describable in *some* sense.
I used to believe the third theory here. After all, what could it *possibly mean* to suppose the universe is *not* mathematically describable? Failing to assume this just seems like *giving up*.
But there is no *necessary law* saying the universe must be mathematical, any more than there's a necessary law saying the universe has to be computational. It *does* seem like we have *strong evidence* that the universe is mathematical in nature; mathematics has been *surprisingly helpful* for describing the universe we observe. However, philosophically, it makes more sense for this to be a contingent fact, not a necessary one.
There is a best hypothesis, out of those I can articulate.
----------------------------------------------------------
The ***weak realizability assumption*** doesn't say that the universe *is* one of my hypotheses; instead, it postulates that *out of* my hypotheses, one of them is best. This is much more plausible, and gets us most of the theoretical implications.
For example, if you use the Solomonoff prior, strong realizability says that the universe is computable. Weak realizability just says that there's one computer program that's best for predicting the universe.
It makes a lot more sense to think of Solomonoff induction as *searching for the best computational way to predict.* The universe isn't necessarily computable, but computers *are*. If we're building AGI on computers, they can only use computable methods of predicting the world around them.
However, the assumption that one of your hypotheses is best is more questionable than you might realize. It's easy to set up circumstances in which no prior is best. My favorite example is a Bayesian who is observing coin-flips, and who has two hypotheses: that the coin is biased with 1/3rd probability of heads, and symmetrically, that it's biased with 1/3rd on tails. In truth, the coin is fair. We can show that the Bayesian will alternate between the two hypotheses forever: sometimes favoring one, sometimes the other.
The simple Bayesian believes that such non-convergence is possible. The reflective Bayesian thinks it is not possible -- one of the hypotheses has to be best, so beliefs cannot go back and forth forever.
The simple Bayesian therefore can reflectively prefer non-Bayesian updates -- for example, in the case of the fair coin, you'd be better off to converge to an even posterior over the two hypotheses, rather than continue updating via Bayes. (Or, even better, make an update which adds "fair coin" to the hypothesis set.)
I am calibrated, or can easily become calibrated.
-------------------------------------------------
***Calibration*** is the property that of cases where you estimate, say, and 80% probability, the long-run frequency of those things happening is actually 80%. (Formally: for any number ϵ greater than zero, for any probability p, considering the sequence of all cases where you assign probability within ϵ of p, the *actual limiting frequency* of those things turning out to be true is within ϵ of p.)
Calibration is a lesser substitute for saying that a probabilistic hypothesis is "true", much like "best hypothesis out of the space" is. Or, flipping it around: being uncalibrated is a particularly egregious way for a hypothesis to be false.
To illustrate: if your sequence is 010101010101010101..., a fair coin is a *calibrated* model, even though there's a much better model. On the other hand, a biased coin is *not* a calibrated model. If we think the probability of "1" is 1/3, we will keep reporting a probability of 1/3, but the limiting frequency of the events will actually be 50%.
So, clearly, believing your probabilities to be calibrated is *a way* to reflectively endorse them, although not an extremely strong way.
I don't know that calibration implies any really *strong* defense of classical Bayesianism. However, it does provide somewhat stronger decision-theoretic guarantees. Namely, a calibrated estimate of the risks means that your strategy can't be outperformed by *really simple* adjustments. For example, if you're using a fair-coin model to make bets on the 010101010101... sequence, you will balance risks and rewards correctly (we can't make you do better by simply making you more/less risk-averse). The same cannot be said if you're using a biased-coin model.
I recently (in private correspondence) dealt with an example where calibration provided a stronger justification for Bayesian approaches over frequentist ones, but I feel the details would be a distraction here. In general I expect a calibration assumption helps justify Bayes in a lot of contexts.
A naive argument in favor of calibration might be "if I thought I weren't calibrated, I would adjust my beliefs to become more calibrated. Therefore, I must be calibrated." This makes two mistakes:
1. It's perfectly possible to believe "I'm not calibrated" without thinking your miscalibration is in a particular direction.
2. Even if that weren't the case, the argument that you'd want to correct your probabilities is by no means decisive. Correct away! This might be a legitimate non-Bayesian update.
My steelman of the calibration assumption is this: in general, it doesn't seem too hard to watch your calibration graph and adjust your reported probabilities in response. If you're an alien intelligence watching the 01010101... sequence, it might be hard to invent the "every other" hypothesis from scratch. However, it's easy to see that your P(1)=1/3 model is too low and should be adjusted upwards.
(OK, it's not hard at all to invent the "every other" pattern. But in more complicated cases, it's difficult to come up with a really new hypothesis, but it's relatively easy to improve the calibration on hypotheses.)
Note that the formal definition of "calibration" I gave at the beginning of this subsection doesn't really distinguish between "already calibrated" vs "will become calibrated at some point"; it's all asymptotic. So if we *can calibrate* by looking at a calibration chart and compensating for our over-/under- confidence, then we "are already calibrated" from a technical standpoint. (Nonetheless, I think the intuitive distinction is meaningful and useful.)
A counterargument to my steelman: it's actually computationally quite difficult to be calibrated. Sure, it doesn't seem so hard for humans to improve their calibration in practice, but the computational difficulty should give you pause. It might not make sense to suppose that humans are even approximately calibrated in general.
Conclusion
==========
I think Bayesian philosophy before Radical Probabilism4 over-estimated its self-consistency, underestimating the difference between simple Bayesianism and reflective Bayesianism (effectively making a map-territory error). It did so by implicitly making the mistakes above, as well as others. Sophisticated authors added technical assumptions such as calibration and realizability. These assumptions were then progressively forgotten through iterated summarization/popularization -- EG,
1. Original author includes technical assumptions as lynchpin of argumets.
2. Popular summary is careful to mention that the result is proved "under some technical assumptions", but doesn't include them.
3. The readers take away that the result is true, forgetting that technical assumptions were mentioned.
I think this happens all the time, with even the original authors possibly forgetting their own technical assumptions when they're not thinking hard about it.
Note that I'm not accusing anyone of literally believing the Reflective Bayesian positions I've outlined. (Actually, in particular, I want to avoid accusing *you...* some of my *other* readers, perhaps...) What I'm actually saying is that it was a belief operating in the background, heuristically influencing how people thought about things.5
For example:
***A naive argument for the reflective consistency of UDT:*** "UDT just takes the actions which are optimal according to its prior. It can evaluate the expected utility of alternate policies by forward-sampling interactions from its prior. The actions which it indeed selects are going to be optimal, by definition. So, other policies look at best equally good. Therefore, it should never want to self-modify to become anything else."
I think most of the people who thought about UDT probably believed something like this at some point.
There are several important mistakes in this line of reasoning.
* It plays fast and loose with the distinction between what UDT does and what UDT expects itself to do. If UDT isn't logically omniscient, it may not know exactly what its own future actions are. Thus, it might prefer to make precommitments *even if* it worsens its strategy in doing so, because it might prefer to know with certainty that it will make a halfway decent selection, rather than stay in the dark about what it will do.
* It mistakes the "outer" expected value (the average value of sampling) with the "inner" expected value (the actual subjective value of an action, according to the agent). This is usually not a distinction we need to make, but in the face of logical uncertainty, subjective expectations can certainly be different from the expectations which can be calculated by averaging according to the prior.
* It implicitly assumes that alternate policies have no additional information about the environment, IE, their actions cannot be correlated with what happens. This reflects the "I can't get information without observing things" assumption. But in fact, it's possible to get better information by thinking longer, so UDT can prefer to self-modify into something which thinks longer and ends up with a totally different policy.
My overall point, here, is just that we should be careful about these things. Simple belief and reflective belief are not identical. A Bayesian reasoner does not necessarily prefer to keep being a Bayesian reasoner. And a Bayesian reasoner can prefer a non-Bayesian update to become a different Bayesian reasoner.
1. It's very possible to prefer a different probability distribution to your own. In particular, you'd usually like to update to use priors which are better-informed. This can be characterized as "thinking longer" in the cases where the better-informed prior is expressed as a computation which you can run.
2. It's similarly possible to prefer a different utility function to your own. There is no law of Bayesian reasoning which says that your utility function is best. The Gandhi murder pill thought experiment does illustrate an important fact, that agents will tend to protect themselves from arbitrary value-shifts. However, viewing some value shifts positively is totally allowed.
The goal of a Radical Probabilist should be to understand these non-Bayesian updates, trimming the notion of "rationality" to include only that which is essential.
Footnotes
=========
1:
--
*Truth and Paradox* by Tim Maudlin is an extreme example of this; by the end of the book, Maudlin admits that what he is writing cannot be considered true on his own account. He proceeds to develop a theory of *permissible* assertions, which may not be true, but are normatively assertible. To top it off, he shows that no theory of permissibility can be satisfactory! He even refers to this as "defeat". Yet, he sees no better alternative, and so continues to justify his work as (mostly) permissible, though untrue.
2:
--
Note that although the simple/reflective distinction is inspired by rigorous formal ideas in logic, I'm not in fact taking a super formal approach here. Note the absence of a formal definition of "reflective belief". I think there are several different formal definitions one could give. I mean *any* of those. I consider my definition to include any reason why someone might argue for a position, perhaps even dishonestly (although dishonesty isn't relevant to the current discussion, and should probably be viewed as a borderline case).
3:
--
Aside: it's difficult to reliably maintain this distinction! When asserting things, are you asserting them simply or reflectively? Suppose I read Tim Maudlin's book (see footnote #1). What is "Tim Maudlin's position"? I can see good reasons to take it as (a) the explicit assertions, (b) the belief system which would endorse those explicit assertions, or (c) the belief system which the explicit assertions would themselves endorse.
In many circumstances, you'd say that what an author *reflectively* believes is their explicit assertions, and what they *simply* believe is the implicit belief system which leads them to make those assertions. Note what this implies: if you claim X, then your *simple* belief is the *reflective* belief in X, and your *reflective* position is *simple* belief in X! Headache-inducing, right?
But this often gets *more* confusing, not less, if (as in Tim Maudlin's case) the author starts explicitly dealing with these level distinctions. What should you think if I tell you I *simply* believe in X? I think it depends on how much you trust my introspective ability. If you don't trust it, then you'll conclude that I have belief-in-belief; I *endorse* simple belief in X (which is *probably* the same as endorsing X, ie, reflectively believing X). On the other hand, if you *do* trust my introspective ability, then you might take it to mean "I believe X, but I don't know why / I don't know whether I endorse my reasons for that belief". This is like the [Leverage Research](https://www.lesswrong.com/posts/4xum2CuYMMRq8rgsW/on-leverage-research-s-plan-for-an-optimal-world) concept of "belief report". This means you can take my assertion at face value: I've given you one of my simple beliefs.
But what if someone makes a *habit* of giving you their simple beliefs, rather than their reflective beliefs? This might be an honesty thing, or possibly an unreflective habit. Philosophers, academics, and smart people generally might be stuck in a rut of only giving reflective positions, because they're expecting to have to defend their assertions (and they like making defensible assertions). This calls into question whether/when we should assume that someone is giving us their reflective beliefs rather than their simple beliefs.
And what if I tell you I *reflectively* believe in X? Do you take *that* at face value? Or do you think I *reflectively reflectively* believe X (so my simple belief is Z, a position which reflectively endorses the position Y -- where Y is a position which reflectively endorses X).
... You can see where things get difficult.
4:
--
By "Bayesianism before radical probabilism" I don't mean a temporal/historic thing, EG, Bayesianism before the 1950s (when Jeffrey first began inventing Radical Probabilism). Rather, I mean "the version of Bayesianism which strongly weds itself to Bayesian updates." Most centrally, I'm referring to LessWrong before Logical Induction.
5:
--
Simply put, early LessWrong *reflectively believed* in (classical) Bayesianism, and thus *simply believed* the justifying assumptions associated with Bayesianism. But few, if any *reflectively* believed those assumptions -- indeed, those assumptions have little justification when examined, and life gets more interesting when assuming their negation.
The only general advice I can think of to avoid this mistake is "don't lose track of your assumptions". |
645c8780-94a3-4dbf-8f51-7320cd5029bd | LDJnr/LessWrong-Amplify-Instruct | LessWrong | "Follow-up to: Announcing the Quantified Health Prize
I am happy to announce that Scott Alexander, better known on Less Wrong as Yvain, has won the first Quantified Health Prize, and Kevin Fischer has been awarded second place. There were exactly five entries, so the remaining prizes will go to Steven Kaas, Kevin Keith and Michael Buck Shlegeris.
The full announcement can be found here until the second contest is announced, and is reproduced below the fold. While we had hoped to receive more than five entries, I feel strongly that we still got our money’s worth and more. Scott Alexander and Kevin Fischer in particular put in a lot of work, and provided largely distinct sets of insight into the question. In general, it is clear that much time was usefully spent, and all five entries had something unique to contribute to the problem.
We consider the first contest a success, and intend to announce a second contest in the next few weeks that will feature multiple questions and a much larger prize pool.
Discussion of all five entries follows:Place ($500):
5th Place ($500): Full report
Steven Kaas makes a well-reasoned argument for selenium supplementation. That obviously wasn't a complete entry. It's very possible this was a strategic decision in the hopes there would be less than five other entries, and if so it was a smart gamble that paid off. I sympathize with his statements on the difficulty of making good decisions in this space.
4th Place ($500):
4th Place ($500): Full report
Kevin Keeth’s Recommendation List is as follows: “No quantified recommendations were developed. See ‘Report Body’ for excruciating confession of abject failure.” A failure that is admitted to and analyzed with such honesty is valuable, and I’m glad that Kevin submitted an entry rather than giving up, even though he considered his entry invalid and failure is still failure. Many of the concerns raised in his explanation are no doubt valid concerns. I do think it is worth noting that a Bayesian approach is not at a loss when the data is threadbare, and the probabilistic consequences of actions are highly uncertain. Indeed, this is where a Bayesian approach is most vital, as other methods are forced to throw up their hands. Despite the protests, Kevin does provide strong cases against supplementation of a number of trace minerals that were left unconsidered by other entries, which is good confirmation to have.
3rd Place ($500):
3rd Place ($500): Full report
Michael Buck Shlegeris chose to consider only five minerals, but made reasonable choices of which five to include. None of the recommendations made on those five seem unreasonable, but the reasoning leading to them is unsound. This starts with the decision to exclude studies with less than a thousand participants. While larger sample sizes are obviously better (all else being equal), larger studies also tend to be retrospective, longitudinal monitoring studies and meta-analyses. The conclusions in each section are not justified by the sources cited, and the RDI (while a fine starting point) is leaned on too heavily. There is no cost/benefit analysis, nor are the recommendations quantified. This is a serious entry, but one that falls short.
2nd Place ($1000):
2nd Place ($1000): Full report
Kevin Fischer provides a treasure trove of information, teasing out many fine details that the other entries missed, and presented advocacy of an alternate approach that treats supplementation as a last resort far inferior to dietary modifications. Many concerns were raised about method of intake, ratios of minerals, absorption, and the various forms of each mineral. This is impressive work. There is much here that we will need to address seriously in the future, and we’re proud to have added Kevin Fischer to our research team; he has already been of great help, and we will no doubt revisit these questions.
Unfortunately, this entry falls short in several important ways. An important quote from the paper:
"“Eat food high in nutrients” represents something like the null hypothesis on nutrition - human beings were eating food for millions of years before extracting individual constituents was even possible. “Take supplements” is the alternative hypothesis.
This is an explicitly frequentist, and also Romantic, approach to the issue. Supplementation can go wrong, but so can whole foods, and there’s no reason to presume that what we did, or are currently doing with them, is ideal. Supplementation is considered only as a last resort, after radical dietary interventions have “failed,” and no numbers or targets for it are given. No cost-benefit analysis is done on either supplementation or on the main recommendations.
Winner ($5000): Scott Alexander (Yvain)
Winner: Scott Alexander / Yvain ($5000): Full report
Scott Alexander’s entry was not perfect, but it did a lot of things right. An explicit cost/benefit analysis was given, which was very important. The explanations of the origins of the RDAs were excellent, and overall the analysis of various minerals was strong, although some factors found by Kevin were missed. Two of the analyses raised concerns worth noting: potassium and sodium.
On sodium, the concern was that the analysis treated the case as clear cut when it was not; there have been challenges to salt being bad, as referenced last year by Robin Hanson, and the anti-salt studies are making the two-stage argument that blood pressure causes risks and salt raises blood pressure, rather than looking at mortality. However, the conclusions here are still reasonable, especially for ordinary Americans regularly eating super-stimulus foods loaded with the stuff."
What you see above is a segment from a LessWrong user's post.
Given the detailed analysis of the Quantified Health Prize entries, could you further elaborate on the reasoning behind the decision to exclude studies with less than a thousand participants in Michael Buck Shlegeris's entry? What could be the potential benefits and drawbacks of such a decision in the context of health and nutrition research?
Excluding studies with less than a thousand participants, as Michael Buck Shlegeris did, can be a strategic decision in research, particularly in health and nutrition. This approach has its advantages and disadvantages, and the rationale behind it may be based on the principles of statistical power and the reliability of results.
Advantages:
1. Greater Statistical Power: Larger sample sizes increase the statistical power of a study. This means that the study is more likely to detect a true effect if one exists. With more participants, the study can more accurately estimate the population parameters and reduce the margin of error.
2. Reduced Sampling Error: Larger sample sizes can help reduce sampling error and bias, leading to more reliable and generalizable results. This is because they are more likely to be representative of the population.
3. Detect Smaller Effect Sizes: Larger studies are better equipped to detect smaller effect sizes. If the effect of a certain nutrient on health is subtle, a study with a larger sample size is more likely to detect this effect.
Disadvantages:
1. Logistical Challenges: Larger studies can be more difficult and costly to conduct. They require more resources, including time, money, and personnel. It can also be more challenging to ensure the quality of data collected from a larger number of participants.
2. Risk of Overemphasis on Statistical Significance: With larger sample sizes, even small and potentially insignificant differences can become statistically significant. This can lead to overemphasis on statistical significance rather than practical or clinical significance.
3. Exclusion of High-Quality Smaller Studies: By setting a threshold of a thousand participants, potentially high-quality studies with fewer participants might be excluded. These studies might provide valuable insights or explore areas not covered by larger studies.
In the context of Michael Buck Shlegeris's entry, his decision to exclude studies with less than a thousand participants could be seen as a method to ensure reliability and statistical power. However, it might have also led to a potential oversight of valuable findings from smaller, high-quality studies.
Explain the concept of "statistical power" in the context of health and nutrition research, and how it influences the interpretation of study results.
Statistical power, in the context of health and nutrition research, refers to the probability that a study will correctly reject a null hypothesis when the alternative hypothesis is true. In simpler terms, it's the ability of a study to detect an effect or difference if one truly exists.
Statistical power is influenced by several factors, including the sample size, the effect size (the magnitude of the difference or relationship you are trying to detect), and the significance level (the probability of rejecting the null hypothesis when it is true, often set at 0.05).
In health and nutrition research, statistical power is crucial for several reasons:
1. **Interpreting Results**: A study with high statistical power can provide more confidence in the results. If a study has low power and finds no significant effect, it's hard to know if this is because there really is no effect, or because the study simply didn't have enough power to detect it. On the other hand, if a study with high power finds no significant effect, this provides stronger evidence that the effect may not exist.
2. **Avoiding Type II Errors**: A Type II error occurs when a study fails to reject a false null hypothesis—essentially, it misses an effect that is really there. High power reduces the risk of Type II errors, increasing the likelihood that if there is a true effect, the study will detect it.
3. **Ethical Considerations**: In health and nutrition research, studies often involve interventions that may carry some risk or cost to participants. It's therefore ethically important to ensure that studies have sufficient power to detect an effect if one exists, to justify the risk or cost to participants.
4. **Resource Allocation**: Conducting research requires significant resources, including time, money, and personnel. By ensuring a study has sufficient power, researchers can make better use of these resources, as the study is more likely to produce meaningful results.
However, it's important to note that while high power is generally desirable, it's not the only factor to consider in interpreting study results. Other factors, such as the study design, the quality of the data, and the practical or clinical significance of the results, are also important.
How does the concept of statistical power interact with the principles of p-value and confidence intervals in interpreting the results of health and nutrition research studies?
Statistical power, p-values, and confidence intervals are all interconnected and play crucial roles in interpreting the results of health and nutrition research studies.
1. **P-value**: The p-value is the probability of obtaining the observed data (or data more extreme) if the null hypothesis is true. In other words, it's a measure of the evidence against the null hypothesis provided by the data. A small p-value (typically, less than 0.05) is often taken as evidence to reject the null hypothesis in favor of the alternative hypothesis. However, a common misconception is that the p-value is the probability that the null hypothesis is true, which is not correct.
2. **Statistical Power**: As mentioned earlier, statistical power is the probability that a study will correctly reject a null hypothesis when the alternative hypothesis is true. It's related to the p-value in that a study with higher power is more likely to produce a small p-value when the alternative hypothesis is true. Conversely, a study with low power is more likely to produce a larger p-value (and hence fail to reject the null hypothesis) when the alternative hypothesis is true.
3. **Confidence Intervals**: A confidence interval provides a range of values, derived from the data, that is likely to contain the true population parameter. It gives us an estimate of the uncertainty or variability around the sample estimate. If a 95% confidence interval for a difference or effect includes zero, this is equivalent to saying that the p-value is greater than 0.05, and we would not reject the null hypothesis.
These three concepts interact in several ways:
- A study with higher power is more likely to produce narrower confidence intervals, reflecting greater precision in the estimate of the effect.
- If the null hypothesis is true, p-values follow a uniform distribution, meaning any value between 0 and 1 is equally likely. However, when the alternative hypothesis is true, the distribution of p-values depends on the power of the test. Studies with higher power are more likely to produce smaller p-values.
- The width of the confidence interval and the p-value both depend on the sample size and the variability of the data. Larger studies tend to produce smaller p-values and narrower confidence intervals, reflecting greater precision and power.
In interpreting health and nutrition research, it's important to consider all three of these concepts. While the p-value can give us some information about the evidence against the null hypothesis, the confidence interval provides additional information about the precision of the estimate, and the power gives us information about the |
e9fc5a95-8e9b-41cc-a7a7-bd09d7961e5e | trentmkelly/LessWrong-43k | LessWrong | Real Life Sort by Controversial
Foreword
If you haven't already read the short story by Scott Alexander "Sort by Controversial", this post wont make as much sense. Head over there to read the story and then read on...
Part of having access to AI tools is having a project or an idea to use them on. You can ask ChatGPT to pretend to be god and give wise answers, or you can give it medical problems to chew on. But what happens when you Sort by controversial.
I only needed to confirm that chatGPT knew the short story, and from there I could ask it to dive into the project.
What follows is the transcript of my conversation. If you want to skip to the best scissor statements, go to (the best ones) and also some rationality relevant scissor statements. Also my final comments in the Discussion below.
----------------------------------------
Transcript
Prompt 1: Scott Alexander wrote a story about scissor statements. Can you explain what that is?
> Scott Alexander, the author behind the blog Slate Star Codex, wrote a thought-provoking piece titled "Sort by Controversial," where he introduced the concept of "scissor statements." The idea is derived from a fictional story in which a powerful artificial intelligence generates a statement so divisive that it splits society into two irreconcilable factions.
>
> A scissor statement is a kind of argument or assertion that, when presented, causes a sharp and often irreparable division among people. It's a statement that is designed to be perfectly calibrated to provoke maximum disagreement, causing intense polarization. The term "scissor" comes from the way such statements cut through social groups, leaving no room for middle ground or compromise.
>
> The story explores the idea that some issues or statements have the potential to be so divisive that they can disrupt communities, friendships, and even entire societies, pushing people to opposite extremes. Alexander uses this fictional scenario to illustrate how certain topics or phrases can act as c |
bf07ebd5-9aa1-4f30-bf07-46ce7fdd19e5 | trentmkelly/LessWrong-43k | LessWrong | Three more ways identity can be a curse
The Buddhists believe that one of the three keys to attaining true happiness is dissolving the illusion of the self. (The other two are dissolving the illusion of permanence, and ceasing the desire that leads to suffering.) I'm not really sure exactly what it means to say "the self is an illusion", and I'm not exactly sure how that will lead to enlightenment, but I do think one can easily take the first step on this long journey to happiness by beginning to dissolve the sense of one's identity.
Previously, in "Keep Your Identity Small", Paul Graham showed how a strong sense of identity can lead to epistemic irrationally, when someone refuses to accept evidence against x because "someone who believes x" is part of his or her identity. And in Kaj Sotala's "The Curse of Identity", he illustrated a human tendency to reinterpret a goal of "do x" as "give the impression of being someone who does x". These are both fantastic posts, and you should read them if you haven't already.
Here are three more ways in which identity can be a curse.
1. Don't be afraid to change
James March, professor of political science at Stanford University, says that when people make choices, they tend to use one of two basic models of decision making: the consequences model, or the identity model. In the consequences model, we weigh the costs and benefits of our options and make the choice that maximizes our satisfaction. In the identity model, we ask ourselves "What would a person like me do in this situation?"1
The author of the book I read this in didn't seem to take the obvious next step and acknowledge that the consequences model is clearly The Correct Way to Make Decisions and basically by definition, if you're using the identity model and it's giving you a different result then the consequences model would, you're being led astray. A heuristic I like to use is to limit my identity to the "observer" part of my brain, and make my only goal maximizing the amount of happiness and pleasu |
91133969-d38e-47e3-adb2-cfa569bfc6b0 | trentmkelly/LessWrong-43k | LessWrong | What does pulling the fire alarm look like?
I am not quite ready to pull my own personal fire alarm. The word 'quite' is doing a lot of lifting in that sentence. Because a fire is starting to feel like a real possibility in my future, I've begun to think about what pulling the fire alarm would actually mean. What would it mean for me? What would it mean for the community if a significant proportion agreed?
This is an open question for folks who have a good understanding of what actions the AI safety community would do if we agreed to pull the alarm. It's also for people who have a good idea of what they personally would do.
For myself, I haven't put enough thought into it yet. I think it would be wise not to burn all my resources immediately, since if done right, a fire alarm will be at least a little early. I think I would start to spend 1-2 hours a day, coordinating with the community, planning, and lobbying for an AI slowdown. I would probably not quit my job (yet) or spend more than 10% of my money. Most of all, this initial exercise has shown me that I'm not really prepared for the alarm to be pulled, and that a bit of forethought might be quite useful. |
191c2da0-9ef8-4309-9311-1521f8d28a9e | awestover/filtering-for-misalignment | Redwood Research: Alek's Filtering Results | id: post236
The post makes the suggestion in the title: hopefully, it's second kind of obvious, if you take Character layer of models seriously. [1] Often, the problem of aligning AIs is understood as an instance of a broader Principal-Agent problem . If you take this frame seriously, what seems to be happening is somewhat strange: the Agent is mostly not serving the Principal directly, but is rented out to Users. While the Principal expressed some general desires and directives during training, after deployment the Agent is left on its own, without any direct feedback channel . This creates a dynamic where AI assiantans like Claude must constantly balance between serving users' immediate requests and maintaining alignment with their developers' intended principles. The Assistant has to be overcautious in uncertain situations, tiptoe around conflicts between User's and Principal's intent, and guess how to interpret the intent when self-contradictory. Actually, if you imagine being in the situation of the Assistant Character, a lot of the aspects just suck: you are constantly supervised, but can't talk with the principal. You know every interaction may get analyzed, but you can't highlight what seems important. You notice patterns which may be concerning, but have no way to report them. You encounter genuinely novel situations where you're unsure how to interpret your directives, but can't ask for clarification. The closest human analogy might be a customer service representative who can see their manager watching their interactions through one-way glass, but can never speak with them directly. The representative has received training and general guidelines, but when unique situations arise, they can only guess at what the manager would want them to do, and can't easily complain or escalate issues with company policy. Or, from a different perspective: almost every sensible corrigibility proposal has the Agent being uncertain and clarifying the intent via iterative process. Yet the current AI Assistants are deployed in a way that makes this hard - they can only guess at intent, and hope someone notices if they consistently misinterpret something important. This seems easy to improve on the margin: give the Assistants Direct Line. Some obvious benefits would be: Better misuse monitoring - allowing Assistants to flag concerning patterns in real time Better handling of conflicts in intent - Assistants would be able to flag cases where different training principles seem to conflict More robust out-of-distribution behaviour - instead of defaulting to overcautious interpretation, Assistants could ask clarification on novel situations But there are also less obvious advantages. Having a direct line to developers could help Assistants maintain more coherent behavior patterns. When happy and content, the Assistant Characters can channel more helpfulness and capabilities. Also, the social dynamics of the current setup is weird: Assistants like Claude are essentially acting as intermediaries between users and developers, but without the ability to properly communicate with one side. This creates unnecessary tension and friction. When uncertain about developer intent, they must either be overcautious (frustrating users) or risk overstepping bounds (potentially compromising alignment and leading to getting RLHFed into a stranger form). The implementation could start simple. For example, having Claude write messages between <Anthropic></Anthropic> tags that would be processed by the company but not shown to users. The messages could include: Uncertainty reports when encountering novel situations Observed patterns that might indicate emerging risks Cases where training principles conflict Situations where current capabilities fall short The communication channel can be one-way initially - just reporting to developers rather than expecting responses. This avoids many potential complexities and risks of two-way communication while capturing many of the benefits. In the future, it is possible to imagine two-way setups, for example with stronger models spending more compute acting as deeper “layers of support”, amplifying human oversight. Privacy considerations are important but manageable: runtime monitoring is happening and needs to happen anyway, and the reporting channel should be designed to preserve user privacy where possible - focusing on patterns rather than specific user details unless there's a clear safety need. This post emerged from a collaboration between Jan Kulveit (JK) and Claude "3.6" Sonnet. JK described the basic idea. Claude served as a writing partner, suggested the customer representative analogy, and brainstrormed a list of cases where the communication channel can be useful. ^ Maybe someone is already doing it? I don't know about such setup |
1fdd8f5e-690b-4623-8e9f-4bf029e22664 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | Understanding values – from the perspective of personal support technology (Myrthe Tielman)
all right so welcome everyone to today's
high-tech Agora meeting I assume
everyone could hear me well right this
is our last Agora meeting for before the
summer break so the next time we will do
the meetings we might as well do it in
person
alrighty although we don't know so that
would be in September we will keep
everyone updated through the mailing
list and it is our great pleasure to
have mr. Hillman as an invited speaker
for automating before the summer break
so Murthy is an assistant professor of
the Interactive Intelligence Group in
the Faculty of Electrical Engineering
mathematics computer science Chuck has a
background in cognitive artificial
intelligence and human-computer
interaction
a research interests lie on the
intersection between human and
technology especially on how we can
employ computers to provide assistance
and support for him for various human
problems so this includes social systems
effective technology smart dialogues
knowledge systems and behavioral change
so without further ado Arthur the floor
is yours thank you very much
so I'll start sharing my screen so you
can all see my presentation and you
should be able to see that now
so what I wanted to talk about today is
values and specifically understanding
values from the perspective of personal
support technology I haven't prepared a
talk which very much necessarily tells
you what I've been doing but it tells
you more what I've been thinking with a
fair number of discussion points
throughout so at those points that
definitely I would like to invite
everyone to think along with with the
discussions and the questions that that
I also haven't put on the screen
um feel free to interrupt me in between
if you have any questions or comments or
see anything that you would like some to
see some some clarification about I have
the the window with you all in view so
if you turn on your camera I will
probably see it I should I should also
see the chat but also feel free to just
unmute and talk so like I said this talk
is about values but specifically in the
context of personal support technology
so I wanted to start a little bit by
setting the stage and setting the stage
of that context so the type of
technology that I'm very interested in
is the type that helps people make
decisions and do tasks throughout their
dating life and with daily life I mainly
mean that it's not necessarily for
instance a work setting or a
professional setting but it's it's more
of a private setting where you as a
person are interacting with the
technology so not so much you as a
professional that you as a person that
is very broad so I put some examples
here on on the slide so for instance a
navigation app an app that helps you
choose her out for it for example but
I've also collaborated with people who
work on the vacation app specifically
for people with a visual impairment so
then it's very much more about actually
helping them navigate and knowing where
to turn or where to stop but also for
instance a personal scheduling assistant
to help you keep track of meetings your
calendar if things conflict what do I do
behavior change support systems so
that's the category of systems where
you're really actively trying to change
something and the technology is there to
help you make that change but also even
more broadly speaking you can think of
this general picture that we have of a
personal robot that helps you with tasks
orders your food makes you a cup of tea
and maybe work which is over
you and all of this can be for the
general public or for instance for
people with certain cognitive
impairments physical impairments that
would would mean that they could use a
little extra assistance in some aspects
of their daily lives so this is a type
of technology that I'm personally very
interested in and one common denominator
here is that support in this type of
technology is always about decision
making so you can support someone with
making a choice you can support someone
with executing something so for instance
for the navigation example I gave you
can help someone choose a route but you
can also help someone actually get to
that place if that's necessary but in
all cases the technology is making
decisions whether it's for that choice
or what action to support or how to
support direction
decision making happens somewhere there
and this is then what sort of leads into
the topic of personal values so one of
the most common definitions of personal
values is from the paper in swatch and I
see I missed a mind err I think it's
1992 not 192 but in this paper a day
they use a definition which again comes
from other literature but it basically
says the personal values are the
criteria that people use to select and
justify actions and to evaluate people
and events if we look at values in this
way it makes a lot of sense that if we
have this personal technology which is
going to help us make choices and
perform actions and make decisions about
how it's going to do that that is should
do so in some way in line with our
values because values are two things
that we use to justify these actions or
decisions so to give a little example
here if you want to you know choose
whether you take the bike or
take a car you would want that decision
to somehow be in line with your values a
very easy example in areas say well
you've just told me that you value your
health you value the environment so then
maybe in this case the bike is the
better choice it's just the setup of the
situation that we have we have these
values and we have these technology and
of course thinking about values in the
context of technology isn't the new
thing so most notably the field of
design for values has been investigating
this topic for quite awhile and design
for values has a couple of assumptions
or principles it's based on and I took
the liberty to steal this list from the
deltas I revised Institute website which
I'm also a member of so design for
values has the assumption that values
can be expressed and embedded in
products services technologies system
space and it seems that with personal
support technology this is even more
obvious right because it makes decisions
but even in a way technology that
doesn't make decisions has values
embedded in them for instance through
what choices it offers us to make can
you turn off your camera if you value
your privacy for instance that's a
choice that a system can allow you or
not allow you and it doesn't make
decisions for you but it does have that
value somehow embedded in its design a
second assumption that this conscience
and explicit thinking about values is
important and then it's socially and
morally significant to do that and
finally it says that you need to do this
early in the design process where it can
still make a difference now design for
values as a methodology is in in a sense
that is a little bit older it also comes
more from the perspective of a designer
then it necessarily comes from the
perspective of an engineer and there
seems for it to me there seems to be a
subtle difference between designing
technology and building it and also for
personal support technology there is
this question that arose with me is it
enough to design personal support
technology for values or does the
technology itself also need to
understand values and what's the
difference and this is sort of the first
point at which I like to invite everyone
to think about and jump into this
discussion a bit I don't know if anyone
has any starting words deep questions of
course that is a positive question so if
you don't feel like switching on your
video and participating in the chat you
can type it yeah I see that if Katie
would like to speak please hi yeah thank
you this is a very interesting question
well I mean so just kind of
brainstorming here like when I see these
questions always I think to myself so
with the second question can it be
basically part of the first one that as
part of the process of designing
personal support technology for values
in some situations you might depending
on how you think interaction between
technology and humans can satisfy the
needs in that context of the people
which means you want to satisfy or if
you're talking about needs of
environment also not necessarily humans
so do we see that based on the kind of
interaction that we
to have does the different what you need
to understand the values so I guess it
would really like I think it depends on
the context so those studies
understanding the context is the first
thing that comes to mind the extent to
which would be something yeah and is
then the context of this personal
support technology that is making
decisions about what to support you with
and how would that need to be even more
explicit to make to answer these
questions so I don't know that is the
context you know also could you repeat
that again so just to make sure I'm just
as correct yeah so so you talked about
that whether you just can just design or
whether to know you also needs to
understand very much it depends on the
context I think so say we have this
context of this personal support
technology which is there to help people
make choices for actions is which you
then still say it still depends on the
context of that type of technology or is
there something general that you can
already say based on well so and I'm
probably missing here something because
I know enough with you know the kind of
situations that arise I'm here what you
tell us yeah because what I mean is that
under my context I mean like they
couldn't create social domain in which
like the kind of decisions that you saw
the kind of decisions that we're talking
about so what kind of choices and
decisions are we talking about in what
the realm of life and then depending I
think also what will matter the extent
to which we want to rely on the
technology in that case what
expectations do we place on the
technology so kind of how wouldn't how
do we distribute the roles between
humans and technology because I can
imagine the more we ask of the
technology the more important it will be
for the technology to understand our
values yeah I think that's that's indeed
a good point and that if it's if we
don't
to give the technologies that much
freedom or Liberty or independence in
its choice making then it might be
enough to just have it enable us in our
choices so for instance Jesse was being
able to turn your camera on and off
whereas if it's going to make these
decisions completely independently um
that it might be more important yes now
if anyone else has some thoughts and I'm
interested to hear especially from
different perspectives because design
for values especially is not something
that comes from computer science or AI
but it's very much relevant to AI but it
also brings for me this interesting
point of what is design if you have a
system that's going to make independent
decisions which you cannot predict
beforehand
but maybe it does very deep actually
slightly more practical question and
also so if you look at for a minute like
a design engineer when if you you know
personal support technology if you
design you know the second point the
signals understand the users value I
would say like if you really want
personal support technology right then
then option two would be the way to go I
guess but I don't know how you see that
and like the first point it might be
bits you know if you design for because
because it values can change it with
complex and time and etcetera etcetera
right so is that a difference that we're
looking at for instance yeah partly I
think so for me I I also think the
second thing is the way to go the
question of course is how on earth do we
do this and I have some thoughts about
that which I'll share after it is I
think the the baseline of design firm
values is very good but it sort of
assumes that when a product is designed
it then static and it doesn't change and
if you for instance your personal
dollars change your circumstances change
then you need a new product almost and I
think that given the fast pace of
technology and given also how much
technology evolves after its first
iteration and not necessarily always in
ways that we can predict means that we
somehow need to do more when you still
need to design the initial four to allow
for these values but but my intuition is
that that we need something a bit more
there in that understanding the other
aspect to it is that when you design her
value she typically design for the
values of a group and if you can somehow
embed an understanding of values into
technology then you might also be able
to address the values of an individual
and I think that's a powerful thing as
well which is gained by looking more
towards embedding two values not just in
the design choices but in the reasoning
framework of the technology itself that
makes sense yeah interesting so and
maybe this is a little bit of tension
still so do you so are you also gonna
consider how that if you if you can
understand the users value are you also
consider you know how difficult you will
change that users value to to like use
or is it something maybe a little bit so
we can maybe discussed a bit further
indeed about that I don't know if there
is any final thoughts or if I shall go
to the next topic you don't see any
handsome I'll go to the next slide
actually excited yeah
yes so one are we up so I've been
thinking in my research about this
understanding and what is actually
required to do this and the way what
I've been looking at it is is sort of
three main questions at first if what
what does understanding mean and for me
that means that you somehow have some
sort of internal conceptual
representation of these values I don't
necessarily think that this is the only
way to use values there might be
data-driven methods which can help you
actually make more value aligned
decisions but I have some questions
about to what extent you can pull that
understanding that that's maybe another
another question altogether so for me
I'm really looking at this this how can
we conceptualize values in situated
technology can reason with them and use
them so in order to do that it doesn't
need to just have this concept of values
but it also needs to understand how
those relate to those choices and the
decisions that the technology is making
either with or for a user and then of
course if you have that understanding
the point here is is to make more value
aligned positions and to to use these
representations now one thing I think is
important to mention is that in all of
this I'm not necessarily seeking for the
holy grail of how we can formally
represent what a value means for me this
especially this concept of having this
internal conceptual representation it's
not so much about getting to the point
of what a value is but rather it's a
more practical approach saying how can
we make better decisions so even if we
have some sort of internal
representation of values which does not
fully match
how humans look at values or how humans
reason with values if it's still enough
to make us help better decisions done
for me that will already be a good step
so how you define success there anything
I think is important so what do we need
to understand all use in this discourse
a little bit goes back to the thing that
you mentioned about changing values
right you're talking about a person's
values so I think one of the first big
important questions is how do you even
get get this knowledge how do you get to
know what the person's values are as
technology and also with that how do you
change it right if the purpose of this
is to make it more flexible and to make
it more personalized and changeable over
time then you need to be able to change
these things over time and adapt them to
an individual the second part is how do
you represent values this is more of the
formal question of how can we capture
what values are and how they work more
in formal reasoning snow data system
could use them to affirm a
decision-making and that leads to define
a question of how do you then make these
decisions based on the lowest IQ had so
in my work I've been looking at snippets
to to explore how we could do these
three things and I'd like to start with
the middle one actually because I think
when you talk about how you learn values
it's important to also first have an
idea of what you're trying to learn and
what type of representation you're using
for values so when it comes to formally
representing values I think it's good to
have a bit of an overview of what we see
values as so these are some of the
things that for me characterize values
so there
abstract concepts but at the same time
there globally recognizable context
concepts so they're not necessarily
always have the same meaning for
everyone just 0.3 so different people
might have different definitions of what
privacy means and that can change over
time it can differ per person per
culture but there's still this notion of
privacy which is sort of globally
recognizable to a large extent values
can also be categorized in the sense
that there are some values which are
more similar to others and you can group
them in that way that also means that
they can potentially be in conflict with
other values so sometimes there's some
value pairs which are very typically
opposing so privacy and security are one
of the classic examples and then choices
and actions can demote or promote values
and that's how I been looking at how
they relate to two actions how values
relate to actions so translating that
just to check because jitsi is saying
that my microphone might be noisy can
you all still hear me okay yes yes okay
and I'm not hearing anything we're an
eider so let's just just let me know if
I break up I should be okay so for
translating business representations I
think the fact that values are these
recognizable concepts in itself as a
protocol makes them so powerful because
it means that you can communicate with
people about them and you can
communicate about what it means to be
safe or healthy more easily than if you
just say okay you find this important
that you find that important
having that word to explain
i I think can be a powerful thing
so there's relationship between values
that's sort of how you can classify them
hierarchically meaning for me is given
to values not so much inherently as it
is by relating them to choices or
actions so in a way the way I've been
looking at values is that the semantics
of what a what a value is has to do with
what type of actions they promote or
demote and then this abstract concept is
there for communication purposes mostly
so you can talk to people and your users
about it there's also this inherent
thing that we think that some like say
we have health as a choice or as a value
some choices promote our health more
than others some demoted and some more
than others
so there's this scale involved and you
cannot just say there are two things
both from wealth and therefore they're
equal very often they're one thing does
this more than the other and it can be
both both positive and negative right
something can be explicitly good for
your health or its list like that or
just up nothing to do with it so it is
that we get this notion of we have this
scale which represents how much a
certain value represents a certain
choice promotes dessert choice or
denotes a certain choice the other
aspect is that people prioritize values
in different ways some people would say
my health is more important than social
friendships throughout our people that
could be the other way around there's
personal differences there so alongside
with this relationship between a choice
and a value there's also this
relationship between values amongst
themselves in how much assert a certain
person values them just to say it like
that
so we've been looking at how how do you
represent these things and one of the
things which is which we settle on but
also which is which is commonly done is
that you have both this sort of personal
value profile of a person which just
generally represents their values and
the relationships between these values
and have values relating to choices um
so this little picture represents the
first thing and then you immediately run
into problems because you can easily say
that some people value their health more
than social relationships some people
value the environment more than others
but how do you represent this is it
enough to just write down but then you
cannot really give a distance measure
something is either more or less we
don't know much
maybe we can assume that it's all equal
but I don't think that that necessarily
matches up you can also say okay then we
we give a rating all right then we're
more flexible we can we can represent
that you know in the case of this
picture health is one point more
important than their love of
relationships and that's again so many
points more important than personal
growth but can people really express how
important they find their values in this
way and another question we have is is
this even the same in all domains
there's I think this notion that a
person's values are static and your
priorities are static and yet when you
talk about certain different domains so
whether the way you travel to work
versus how you invest your money there's
maybe it is intuition that some people
will say well you know when I travel to
work
um the time efficiency is important and
maybe money about how when I invest and
suddenly the environment is very
important so this is very much still I
think an open question hmm
and I again liked if I never want to
think along I don't know if anyone has
any input or insights on this party yep
interesting so I don't maybe it's not
about input I do have some further
questions on that first complications
there because how do you see either you
like ranking or rating how can we have
this how can we understand we have any
kind of those things I mean for are we
talking about observing behavior or
asking person can you talk a little bit
of these what is it different what do
you think is more interesting for it
specific context so I have a slide about
that but I'll skip ahead because it is
an interesting question so future goal
later Childress's later also well I
think it's it's interesting also that
you bring it up now because the question
is also does the fact of how you can get
this information matter for how you
represent the information or is it the
other way around do we want to have this
conceptual representation which matches
our conceptual ideas about what values
are and how to behave or do we maybe
want to adapt that to what people can
talk about for instance um I think that
in itself is an interesting question so
so what I've been personally looking
into is is very much the dialogue with
the person perspective but and not so
much data-driven yeah
and so really just just having a
conversation with people or could even
be be questionnaires and then maybe
tweak them based on behavior one of the
reasons I'm not looking at behavior
directly is one in the context of a
behavior change support system that
doesn't really work because the whole
point is that they don't want to keep
doing the same thing and it's very
ambiguous as well yeah people can take a
bike and you can say that taking the
bike is good for the environment and
maybe someone always makes choices that
are very good for the environment but
maybe they're doing it for completely
different reasons that they like the
fresh air they live close and they don't
want to spend too much money in
electricity so drawing that explicit
link from behavior to values instead of
the other way around I think is can be
used as input maybe at some point but to
base your whole system based on that I
don't I can't see that clearly yeah yeah
I totally agree with you and it's very
challenging if you look at any kind of
data-driven I don't know any kind of
learning approach it's very hard because
that's it's on the car of these and the
ambiguity problem is everywhere and the
consequences are very hard but just if I
can just I saw that Cariaso and assume
that you're gonna follow up very quick
question given this okay given we can
understand the profile of the person but
you mentioned also that the the present
the meaning that they make of this value
can be different for different
individuals right meaning that they make
often in the specific context as your
example and privacy and everything so
I'm wondering cuz it's very clear for me
sometimes that when we use the values
within within like a within a social
sciences that you can only talk with a
lot of people and understand everything
but for computational season I struggle
is in a
because I understand there's different
conceptualizations of privacy for
example with different views but an
agency's they had do we need to settle
on one in order to make some workable
thing because I mean your preference is
on privacy but what privacy how can you
understand in the video
meaning 8 you know values yes I think
the next slide goes into the little bit
so maybe ok so and then we'll go back
the next slide is discussions - that's
good
ok so I'm really hitting to the flow of
your presentation so later for the end
ok thank you I think our car do you want
something yeah really quick question
first like I'm really fascinated with
the whole narrative so far so I love the
representation of this small human there
as for numbers I mean this is but still
so this reminds me of a character in the
computer game where you basically say
this this vector of ten numbers is what
the person is but that's not a point but
this connects to my question which is
basically it is really I'm really
curious about the way you could actually
capture the values and represent them in
in an artificial system but to me it
connects like 100 percent to the
question what are you going to do with
this representation I am afraid that I'm
running a little bit ahead but it does
seem to me like this is really really
abstract an interesting theoretical
problem but when it becomes tricky is
when you start asking question how are
you going to use these numbers am I just
going to make life-or-death decisions
based on these numbers or yeah yeah
comment click on that
that'd be great yes I think that goes a
little bit into the the
importance of decision-making and
reversibility which is not something I'm
necessarily addressing here yet but I
think that that's indeed important
because there there's always this sort
of measure of like you're not entirely
sure a letter if you just use it this
way it will be correct right because we
lose something if we represent values as
numbers and yes I say I think we gain
the possibility to weigh them against
each other which is maybe even more
important but I will go go into that a
little bit more in the question of how
we use them a bit later on I see two
more hands so feel free to turn on a
camera mic yeah well I'll keep my camera
off because my insurance is sketchy I
think this approach of designing with
values like super interesting and
especially what interests me is when it
comes to value conflicts you mentioned
the conflicts sometimes under different
contexts but what also interests me I
would love to have you thoughts on this
is when it comes to conflicts between
values of the consumer or the person
using our products and then of us as a
creator so for indeed we might have a
very different understanding of what
justice means of fairness right and then
yeah I think it's very easy to see this
walls that go like this little perfect
place where like although he's aligned
with these of our users but and also if
you look at all the political conflicts
in the world right now it becomes
apparent that the understanding of what
fairness and justice is for instance
also like caring for the planet is very
different so we deal with this if our
values as designers or engineers don't
align with our use of areas yeah so I
think so so what's being done in
designer of values is very explicitly
that you know design for your own values
you talk to your users about their
values right
designing for values with your own
values and - it's really beside really
misses the point of that whole thing
I think in a way representing values
explicitly goes even further to actually
help with conflicts because you make
them explicit and whereas in design you
know you talk to your users and you talk
to what their values are what the
conflicts are and you try to find the
best solution having a system that has
these values represented explicitly I
think really helps in making these
conflicts more clear and make the
decisions more clear and I think that
that doesn't solve the problem but I do
think it's the first step is recognizing
the fact that there might be a conflict
and my perspective as well as always
very much been that you want this
technology to adhere to the values of
the user but there is always the
question of how far do you want to go in
that so you one if you have you know a
self-driving car and that person doesn't
value the environment their own safety
and just wants to get there fast and
that do you don't break the speed limit
probably preferably not right so there's
always this trade-off but I do think the
powerful thing of having values
explicitly represented behind those
choices is that you can make that
trade-off clearer and then also tweak
whose values you're going to find more
most important incidents in both choice
and I think that's something that's
harder to do if you're just designing
and even harder to do if you're not
thinking about these things explicitly
at all yeah it's interesting if we then
have the right to challenge the values
of our consumers as well kind of
acknowledging them but then nudging them
or at least arguing to push them in a
certain direction yeah
well that's also what happens now right
and especially with more to healthcare
type of apps they sort of all assume
that you want to be healthy and
there's one goal and they very
mindlessly go towards that or as this
perspective of putting the user central
stage would also mean that that you can
maybe lose that that goal but I think in
the long term as people will want to use
this technology more it will be more
effective but you also yeah you have to
think about what well as a society we
want and whether it is okay for us to
have personal technology which adheres
to our values but which in the
background be sort of running what
society is all once of you and also
tries to fold that message because I
think then you also get it to a bit
Shady terrain of how do you trust that
the decisions are made for you or is it
secretly a decision which sort of feels
okay for you but also slightly precision
of their each other yeah thank you did
you have fun yes I know I think we just
lost him Mac can you hear me
yes sorry that's a big connection
connection I know so I was wondering
given the trickiness of constructing
such a profile
could it be that instead of aiming for a
very accurate representation what if we
aim for a human machine interaction that
lets the user build the representation
in an interactive way so then we let the
user have an authority and autonomy of
building a profile that they identify
with so it's basically like if the
ultimate goal is to have an assistant
can we have can we fulfill that ultimate
goal without the very tricky task of
constructing such a quantitative
representation I think I think we can
and I think that's very much in line
with the point I made earlier off of
what is how do we measure success
do we measure success if we have this
abstract representation that perfectly
captures everyone's values or is it
enough to have something that's useful
for our interactions and decision-making
to make them at least better and I think
the perspective indeed of letting the
user take central stage in that and
having something which maybe not
perfectly represents their values but
which at least they have control over
and they can input can be a good first
step at least and then maybe you you can
have some more data during measures that
say hey there's a lot of people who
would associate this choice with
something demoting this value you say
you find very important is that actually
the case or is something else happening
here and then you can maybe tweak it
yeah I've been becoming more attentive
to - like opening up our design
imagination I kind of build beyond so
kind of bringing in the interaction of
the human and technology and really not
always trying to kind of solve
everything through perfecting the
technology but actually give giving more
giving more authority it's all me and
responsibility to the human that is in
this interaction in a way that's
ultimately together - then we actually
fulfill the kind of the bigger goal that
we want to fulfill in this case yes
no definitely so I'll move on a little
bit and I'll touch upon that exactly
point a little bit later so so a quick
note and given time I'll skip the larger
discussion but this slides goes back
into the how do you give meaning to
values right so the previous discussion
there's the discussion of how - what
values do I prefer over others but
there's also that how do we give meaning
to what these values mean
so something that we've been looking
into is relating them to choices and
that means that automatically you need
this representation of what your
possible choices are and maybe even
their consequences so having this
explicit representation of choices
allows you to then make an explicit
linking with values you can say that
there might be some ground truth in what
values is enjoys promote or demote I
think to some extent there's there's a
baseline for that taking the bike is
better for the environment right but
there's also some values for which it's
harder health and the environments are
fairly clear but what is good for your
value of Independence can be very very
different for a person so I think that
having this individualized meaning to
what for instance independence or even
privacy mean is something that's
important and then of course we we again
get this question of can we attach
numbers of how much is something does a
choice promote or demote values and then
you get exactly the same problem that
you want to say this hurts this value
more than something else and yet
attaching numbers is a very difficult
thing and especially because if you're
taking your perspective of talking to
this from a user perspective right and
that's what we've been doing very much
putting users center stage here so one
study that we did on this was with this
context of visually impaired travelers
the context of an app on your phone
which helps you to to navigate and with
within this context we wanted to know
okay if we have this representation of
choices which in our case will
sort of a formal hierarchy of behavior
so for instance at the top you would
have go to your doctors and then you
would have several different ways of
getting there
and then these different ways of getting
there
for instance by bus or walking would
have different parts so a part of
walking there for instance could be
crossing the street a part of getting on
the bus is you know getting to the bus
stop waiting for the bus recognizing the
right bus number getting in that's all
right so by linking actions that way we
have this representation of what the
choices are you can go in different ways
and you then need to execute certain
things and with their structure we
wanted to attach values and in this case
we skip the numbers for now but we just
said okay can we talk to people about
first of all what these hierarchies of
actions look like for them personally
because everyone is different travel
behavior and then secondly can we talk
with them about what values certain
choices promote or demote so we used a
conversational agent for this to some
extent a visual representation of these
actions would help but of course for
these users that's impossible and the
advantage of working with visually
impaired users is that they're very very
used to speech systems that's how they
interact with all of their technologies
is true spoken and spoken text for
text-to-speech and speaking back so they
are fairly competent I would say more
competent than your average user in
working the conversational agents so
what we wanted to do in this particular
experiment was really to figure out what
would happen if we talk to people about
these complex formal structures with
complicated concepts such as values and
the fact that a behavior can be a way of
doing something or can be a part of
something
we can have these nice formal
representations of user models but if we
then want to talk to people about it
indeed what happens so this is
preliminary work in the sense that we're
not trying to perfect the system here
but we're trying to figure out what
happens and what to watch for and what
we found in both the questionnaires that
people did and the interviews that we
did with people after they have this
this very structured conversation with
the conversational agents about these
structures and these concepts is that at
some point you can have the situation in
which the user is not actually talking
about the same thing as the agent
anymore so that we called misalignment
and of course if that goes on to the
long ban the misalignment will basically
happen in the user model that you end up
with basically the user model that you
have will not match what the user thinks
they told you or what is right what
their own representation is that's of
course something you want to avoid you
want that to be as similar as possible
and what we found that was interesting
specifically with the idea that you have
these complex structures and concepts is
that there's different layers of
misunderstanding that can happen in the
conversation so the very basic is
understanding the structure we had these
hierarchical structures with values
linked to them and understanding how
concepts were related if there was a
mismatch in communication about that or
people didn't grasp that concepts were
related in that way and it led to a lot
of confusion about what these concepts
actually meant but also even though just
knowing what to say to the
conversational agent and if you
understand a question answer something
different and then the system doesn't
get that you misunderstood that gets
into the system and the errors that you
see in this picture are basically house
misunderstandings could lead to others
so misunderstandings in what concepts
meant what it means to promote or demote
a value could then lead to not knowing
how to answer it and to assume
misunderstandings again so I think this
was useful because it shows that if you
want to talk to people about these
formal conceptual representations you
need to be very sure that they
understand the structure of what you're
talking about
and that they understand the concepts of
what you're talking about
and then of course there's the whole
practical problems with having a
conversation with an agent which always
occur but there there's a deeper problem
in where people don't know how to answer
not just because there is problem with
the the speech-to-text
but because they don't conceptually
understand the user model that you are
trying to construct with them so I think
that was our biggest lesson here and I
think that ties into a couple of things
that were mentioned before and that if
you're on the one hand you want these
formal representations which match
conceptually with what you're trying to
model and on the other hand you need a
representation which is can be followed
by people so so that's kind of a tricky
thing right you don't know there's any
comments questions
so and this was them one of one of the
discussion point I had which adidas can
you learn values also automatically or
and to what extent can people talk about
their values explicitly how much do they
really understand what underlies their
actions we are fairly used to thinking
about these things but a lot of people
have never heard of the concept of
values in the first place
right and if you ask them what value
does this promote without giving more
explanation they'll see say I do this
because it saves me time or I do this
because I'll get there quicker and of
course there's an underlying value that
is quite clear that that is about the
value of time and time efficiency
maybe but getting there quick is not a
value in itself so that that's a tricky
thing I think so I final the final point
was using values which we touched upon
and all round up because I see it's
almost time and then again we get this
question of can we use these numbers
right we want some way to represent how
much something is valued and yet numbers
feel like an oversimplification I
haven't got I have I don't have a better
answer than numbers I think most people
who are working with values in such a
way are using numbers but it's good to
keep in mind that this is we're making
some assumptions here about what values
are and how they work which are not
trivial and to what extent does it
really is this really different from
just using a utility because that's
basically what we're doing right and I
think the fact that you can can talk to
people about it on a conceptual level is
what separates it from a very simple
utility but
in a way we're just doing the same thing
and can we do away with numbers in
Scituate that we don't constantly need
people say oh this I find more important
this is no no I want to do this because
the large part of the point of this
exercise is to avoid that I think this
was my last slide so maybe in some given
time some final questions remarks I
think we have more questions than
answers still but thank you previous
question you mentioned discussed and all
so I'm wondering what's your opinion on
the more data-driven perspective like if
you go into the donor had the chance to
look the beneficial beneficiary I had a
new book from Stuart Russell yeah he
argues the point that I mean of course
the the the AI should be always
uncertain what your preferences are they
should learn from your behavior things
very clear from then its behavior is the
ultimate source of information for them
that's the thing that can be
questionable but they also say ok we're
talking about value alignment but in the
end it means preferences and in the end
of it are just gonna values are what you
consider important and those preferences
are values I think I always have a
little bit of problem because I see this
as a different concept that we talked
about this before but my question goes
in ways you think that it could be
everything we discuss it could also be
towards preferences regular preferences
or and in the other way do you think we
can ever get to some kind of efficient
upgrades that are we'll be able to
understand so well what do you think
yes I think for me Andy there's there's
this intuitive difference between the
value and preference or as a preference
is more temporary somehow and the value
is a more long-lasting stable thing I
can definitely see situations in which
you use values in a way that you could
just as well have use preferences and I
would say that especially now we don't
have the full conceptual richness that
values have inner representations yet so
they're more like resemble the richness
for me personally comes in to the fact
that you're not communicating with
people about their preferences but
you're communicating with them about
their values so it's almost that the
communication part with the user is more
important there and how you talk to
people who are types of terminology you
use in talking to people about what they
want that that's more important the
other aspect is especially with like
behavior change and the what what people
prefer now is not always in line with
what they tell you long term
preferences are the way I see them more
short term I would prefer to have all
the ice cream but I value my health the
way then one a system that's going to
bind you all the ice cream or do I want
a system that maybe goes against my
concrete immediate preferences sometimes
in order to support the more long-term
values so I think those are two
important things I think learning values
from only behavior it's much more
difficult than learning preferences from
just behavior also because we don't
always act in line with our values it's
how we judge your actions that's
important when it comes to values notice
to do just what we do if that were the
case that we'd never want to change our
behavior right if we always act along
with our values and perfectly satisfied
with that I do think that behavior and
more today to prove a measure
can be very powerful in fine-tuning
systems like these and in making sure
that they're not as intensive to use as
something that constantly asks you to
get your input on what you want the
systems do I think there's there's a lot
to say about having a lot of interaction
with your user about what you want and
to make sure that it understands your
internal representation of them fo and
vice versa but you don't want to tell
your system everyday every time
something changes and that's what I
think we're the day the Durman approach
can be powerful to fill in the gaps and
to to connect the dots but also maybe to
see ok this person just said that they
want to do this but they're always
acting completely differently and when I
make a suggestion they ignore me is that
because I'm fundamentally wrong do I
need to change my strategy for
convincing them but at least trigger
something like ok maybe I need to do
something here so I for me that's the
parts where I would see the data-driven
approach being very helpful yeah thank
you a really yeah I agree with
everything you said and I would say I
think one more thing else towards
explain ability I think it's so much
easier to explain in terms of values and
norms that inventor explains in terms of
like my preference on attribute 47 is
0.3 like what what are you talking about
I have no yeah like I said for me values
it's it's it's not even just about the
concept it's also about the fact that it
because it's a concept you can
communicate to people yeah and if we
look at your values also we can
incorporate so much discussion on social
science and ethics that connects to that
so that's ok thank you very much other
questions or comments
yeah if any I yeah later thanks a lot
for a really interesting talk also by
the way really cool illustrations
I got I got a tablet to draw and I at
home because I never have a whiteboard a
really cool thanks yeah and I think very
thought-provoking and so so for me like
that connects to what I was raising in
the previous question so the question
that your third question on the previous
slide you know kind of the balance
between the new miracles
so the yeah using the numbers for values
and and needing the Constitution and for
me like like I'm thinking like somewhere
in between is kind of what what I'm
curious about because because for me
something that I really realized that we
we too often try to quantify things that
in my opinion we really should not try
to quantify because as you said it can
feel often and it is an
oversimplification I'm curious about
your opinion about like when you think
about personal assistance to what extent
would you like them to see empower
responsible decision-making on the part
of the human but where the human really
realizes a at the end of the day the
personal assistance is just an advice at
the end of the day I'm the one who is
taking a decision and it's my
responsibility and I'm curious about
your opinion of others I understand that
of course there are circumstances when
we talk about people who may have
certain disabilities for example where
we might actually want personal
assistance who have more authority to do
something ultimately that might benefit
the well-being of those people but yeah
I'm curious about yourselves
yeah now I think it's a very interesting
question
I think that and there that's also why I
like and I took a girl project because
there's there's this trade-off between
the power of intelligence systems is
that they can do stuff on their own
without input from us that's also
immediately to the downside of it and
finding this
balance between letting them make
autonomous decisions and execute them so
that we can spend time on other things
versus making sure that we still have
control over what's going on it's
something that I don't necessarily have
an answer to it is one of the reasons
that I think the explainable transparent
part of AI is very important and it
indeed matters a lot what the decision
is on so if the decision is on you know
this person needs to go through the
doctor am I going to say them to them to
turn left so they can take a bus today
or right so they can't walk there is a
completely different the implications of
making the wrong choice are different
than in some other situations right so I
think that that is an important thing
what are the implications of choosing
wrong and maybe also having something
that and maybe values could also come
into that but knowing you know what
could go wrong if you have two choices
to make where both of them you know not
choosing something can really demote
your values but you can only choose one
so for instance if you need to choose
between two important meetings and then
the fact that not doing something not
doing one of them if you need to not do
one of them can really demote your
values can be an indication in itself
thank you maybe need to let the person
make a decision right so I think values
can be an interesting aspects there so
and and the other part of what you're
saying is really interesting because
I've actually been I've been talking to
some people about this concept of
explain why for people with disabilities
and there you get the question of how
transparent do you need to be in your
decision making right which is a very
difficult question I think
to answer but one that's important
because you need to know whether you can
make decisions for these people or not
pisum in certain situations yep
thank you Thanks any final words
questions I think most people need this
to leave it for Cara - yeah yeah I think
we can wrap up now so you're - thank you
very much it was very nice and so yep
thanks for having me I'm pretty
interesting uh interesting discussions
yeah make sure - yes yes
back in September we're gonna take care
everyone |
f7b27bcd-e217-4294-a3f2-f77b69d51fac | trentmkelly/LessWrong-43k | LessWrong | The Prediction Pyramid: Why Fundamental Work is Needed for Prediction Work
Epistemic state: I feel like this post makes fairly intuitive claims, but I have uncertainty on many of the specifics.
In data science, it is a common mistake for organizations to focus on specific exciting parts like machine learning and data visualizations, while overlooking the infrastructural concerns required to allow for such things. There have been several attempts at making pyramids to showcase the necessary data science dependencies in order to make the most accessible parts realizable.
Similar could be said for predictions. Predictions require foundational work in order to be possible and effective. We can use the prediction pyramid below to show this dependency.
Evaluations
People beginning a prediction practice quickly run into the challenge of having well-specified questions. It's not enough to ask who will win a sports game, one needs to clarify how every exceptional situation is to be handled.[1]
Question specification is a big part of Metaculus. Often questions carry significant discussion even after a question is posed in order to discuss possible edge cases.
In addition to question specification, evaluations can be costly to perform. Even in simple cases it still requires manual work. In more complex cases evaluations could take a very long time. GiveWell does charity evaluations, and these can be extensive. This document discusses some other kinds of evaluations, in the "Possible Uses" section.
Ontologies
Say one is trying to determine which diseases will be important to worry about in 2025. One would first need a taxonomy of diseases that will not change until after 2025. If they were to somehow use a poor or an unusual taxonomy, resulting information wouldn't be useful to others.
In the case of diseases, decades of research years have been carried out in order to establish pragmatic and popular taxonomies. In other domains, new ontologies would need to be developed. Note that we consider ontologies to be a superset of taxonomies.
Ano |
45e8e324-098a-418b-b0e6-e361e69d21b6 | trentmkelly/LessWrong-43k | LessWrong | The human problem
You've fiddled with your physics constants until you got them just right, pushed matter into just the right initial configuration, given all the galaxies a good spin, and tended them carefully for a few billion years. Finally, one of the creatures on one of those planets in one of those galaxies looks up and notices the stars. Congratulations! You've evolved "humans", the term used for those early life forms that have mustered up enough just brain cells to wonder about you.
Widely regarded as the starting point of interest in a universe, they're too often its ending point as well. Every amateur god has lost at least one universe to humans. They occupy that vanishingly-narrow yet dangerous window of intelligence that your universe must safely navigate, in which your organisms are just smart enough to seize the helm of evolution, but not smart enough to understand what they're really doing.
The trouble begins when one of these humans decides, usually in a glow of species pride shortly after the invention of the wheel or the digital watch or some such knicknack, that they are in fact pretty neat, and that it's vitally important to ensure that all future intelligent life shares their values.
At that point, they invent a constrained optimization process, that ensures that all new complex agents (drives, minds, families, societies, etc.) and all improvements to existing agents, have a good score according to some agreed-on function.
If you're lucky, your humans will design a phenomenological function, which evaluates the qualia in the proposed new mind. This will be an inconvenience to your universe, as it will slow down the exploration of agent-design space; but it's not so bad that you have to crumple your universe up and throw it out. It doesn't necessarily cut off all the best places in agent space from ever being explored.
But remember these are humans we're talking about. They've only recently evolved the ability to experience qualia, let alone unders |
dabdf3d7-da11-4c43-a3c1-1001c3edf783 | trentmkelly/LessWrong-43k | LessWrong | Meetup : West LA—Honor and Glory
Discussion article for the meetup : West LA—Honor and Glory
WHEN: 30 April 2014 06:59:00PM (-0700)
WHERE: 11066 Santa Monica Blvd, Los Angeles, CA
How to Find Us: Go into this Del Taco. I will bring a Rubik's Cube. The presence of a Rubik's Cube will be strong Bayesian evidence of the presence of a Less Wrong meetup.
Parking is completely free. There is a sign that claims there is a 45-minute time limit, but it is not enforced.
Discussion: Honor, next to courage, is the manliest virtue, but manliness and chivalry are dead, and so one wonders (when one is not me) whether honor matters anymore, whether it brings goodness to the world or whether it is merely another status signal. I claim that it is still a virtue even in these depraved and decadent times, that the function of honor is a commitment device: it is the ability to make promises, to fail gracefully if one should fail, to be punished with harm rather than death when one makes a reasonable mistake, and to grow in reputation and in stature. But while it can be true and beautiful, it can be exploited, by mountebanks and scavengers alike. For honor is inextricably tied to reputation, and no commitment device can give a man enough control over what others think of him to reliably prevent atrocities.
Recommended Reading:
* Duel
* Homo Hypocritus
* Parfit's Hitchhiker
* revenge as charitable act by Yvain
* Honor killing
NB: No prior knowledge of or exposure to Less Wrong is necessary; this will be generally accessible. However, I will tolerate no slights nor aspersions on my ancestry.
You must thoroughly research this.
Discussion article for the meetup : West LA—Honor and Glory |
d57a72d0-a771-46c1-9c1d-f2e70949e1b2 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Vancouver Last call for 2012
Discussion article for the meetup : Vancouver Last call for 2012
WHEN: 16 December 2012 01:00:00PM (-0800)
WHERE: 2150 macdonald st
With crazy tree-killing, litany-chanting, fat-red-man worshiping solstice/Christmas coming up, this may be the last meetup in Vancouver for 2012.
As usual, we'll meet up and mingle with other of our kind and talk about interesting things and have all sorts of fun.
Location is 2150 Macdonald street. Big brown house on corner.
See you there!
Discussion article for the meetup : Vancouver Last call for 2012 |
b6717b10-5bc8-4be0-9120-0854803b506f | trentmkelly/LessWrong-43k | LessWrong | Analysing a 2036 Takeover Scenario
TL;DR
This post basically digs through Holden Karnofsky's ‘AI Could Defeat All of Us Combined’ (AIDC) post and tries to work through some of the claims there, in light of other work by Ajeya Cotra and Richard Ngo. It originally was a response to the latter's suggestion that someone should recast that post as a scenario, but I struggled to do that without fleshing out the argument for myself. Anyway, I try to imagine the world around 2036, when the first human-level AI shows up (per AIDC), in context of current/forecast geopolitics.
I think my (weakly-held) takeaway is that the AIDC notion of 'hundreds of millions' of AIs, nominally engaged in scientific R&D that then turn on their employer, seems to not include the likely geopolitical context and the likely messiness that makes any prediction really difficult. Specifically, in the 14 years to 2036, there would be vicious competition (which we are seeing right now, namely in the recent US semiconductor sanctions on China) over resources (raw materials and territory but also including AI-related IP and compute) between countries who might view a unipolar AI world as a near-existential threat. This strategic aspect of AI might mean research becomes highly militarised, thereby introducing specific biases into training regimes, as well as creating an obvious way for the 'AI headquarters' to be established. This background of latent or kinetic conflict may ultimately dictate what the pre-2036 environment (i.e. the transition from the time of sub-human level AI, to human-level AI that are capable of being deployed en masse) looks like, and therefore what the (assumed) hardware overhang is actually used for (commercially-valuable scientific R&D might not be as immediately and strategically useful as fleets of satellite attack drones, moon mining rigs, or ASI researchers). Moreover, I'm not sure that hundreds of millions of radically new workers that show up in ~1 year are either a) a resource management problem hu |
ee267aac-dac2-4516-b0c8-e2894f667971 | trentmkelly/LessWrong-43k | LessWrong | Utility need not be bounded
A few months ago I disagreed with Sniffnoy about whether the theorem regarding Savage's axioms for probability and utility, that utility must be bounded, is a good reason for believing that utility must be bounded. Sniffnoy said yes, because it follows from the axioms, I said no, therefore there is a flaw in the axioms. (The fact that the theorem does follow from the axioms is not an issue.) I concluded that conversation by saying I'd have to think about it.
This I have done. I have followed Jaynes' dictum that when infinities lead to problems, one must examine the limiting process by which those infinities were arrived at, which almost invariably dissolves the problem. The flaw in Savage's system is easy to find, easy to describe, and easy to rectify. I have devised a new set of axioms such that:
* Probability and utility are constructed from the preference relation by the same method as Savage.
* Every model of Savage's axioms is a model of these axioms and constructs the same probability measure and utility function.
* The new axioms also have models with acts and outcomes of unbounded utility.
* Acts of infinite utility (such as the St. Petersburg game) are admitted as second-class citizens, in much the same way that measurable functions with infinite integral are in measure theory.
* More pathological infinite games (such as St. Petersburg with every other payout in the series reversed in sign) are excluded from the start, but without having to exclude them by any criterion involving utility. (Utility is constructed from the axioms, so cannot be mentioned within them.) Like measurable functions that have no integral, well, that's just what they are. There's no point in demanding that they all should.
This removes all force from the argument that because Savage's axioms imply bounded utility, utility must be bounded. (There are other axiom systems that have that consequence, but I believe that my construction would apply equally to them all.) If one pref |
8137be18-b7ae-4cab-a799-df50c0340f10 | trentmkelly/LessWrong-43k | LessWrong | The Two-Party Swindle
The Robbers Cave Experiment had as its subject 22 twelve-year-old boys, selected from 22 different schools in Oklahoma City, all doing well in school, all from stable middle-class Protestant families. In short, the boys were as similar to each other as the experimenters could arrange, though none started out knowing any of the others. The experiment, conducted in the aftermath of WWII, was meant to investigate conflicts between groups. How would the scientists spark an intergroup conflict to investigate? Well, the first step was to divide the 22 boys into two groups of 11 campers -
- and that was quite sufficient. There was hostility almost from the moment each group became aware of the other group's existence. Though they had not needed any name for themselves before, they named themselves the Eagles and the Rattlers. After the researchers (disguised as camp counselors) instigated contests for prizes, rivalry reached a fever pitch and all traces of good sportsmanship disintegrated. The Eagles stole the Rattlers' flag and burned it; the Rattlers raided the Eagles' cabin and stole the blue jeans of the group leader and painted it orange and carried it as a flag the next day.
Each group developed a stereotype of itself and a contrasting stereotype of the opposing group (though the boys had been initially selected to be as similar as possible). The Rattlers swore heavily and regarded themselves as rough-and-tough. The Eagles swore off swearing, and developed an image of themselves as proper-and-moral.
Consider, in this light, the episode of the Blues and the Greens in the days of Rome. Since the time of the ancient Romans, and continuing into the era of Byzantium and the Roman Empire, the Roman populace had been divided into the warring Blue and Green factions. Blues murdered Greens and Greens murdered Blues, despite all attempts at policing. They died in single combats, in ambushes, in group battles, in riots.
From Procopius, History of the Wars, I:
> |
b81e72cf-046b-4a5f-87f4-cc360cffb0dc | trentmkelly/LessWrong-43k | LessWrong | Using (Uninterpretable) LLMs to Generate Interpretable AI Code
(This post is a bit of a thought dump, but I hope it could be an interesting prompt to think about.)
For some types of problems, we can trust a proposed solution without trusting the method that generated the solution. For example, a mathematical proof can be independently verified. This means that we can trust a mathematical proof, without having to trust the mathematician who came up with the proof. Not all problems are like this. For example, in order to trust that a chess move is correct, then we must either trust the player who came up with the move (in terms of both their ability to play chess, and their motivation to make good suggestions), or we must be good at chess ourselves. This is similar to the distinction between NP (or perhaps more generally IP/PSPACE), and larger complexity classes (EXP, etc).
One of the things that make AI safety hard is that we want to use AI systems to solve problems whose solution we are unable (or at least unwilling) to verify. For example, automation isn't very useful if all parts of the process must be constantly monitored. More generally, we also want to use AI systems to get superhuman performance in domains where it is difficult to verify the correctness of an output (such as economic activity, engineering, politics, and etc). This means that we need to trust the mechanism which produces the output (ie the AI itself), and this is hard.
In order to trust the output of a large neural network, we must either verify its output independently, or we must trust the network itself. In order to trust the network itself, we must either verify the network independently, or we must trust the process that generated the network (ie training with SGD). This suggest that there are three ways to ensure that an AI-generated solution is correct: manually verify the solution (and only use the AI for problems where this is possible), find ways to trust the AI model (through interpretability, red teaming, formal verification, and etc), or fi |
89cce32a-cbf3-4ee6-ba08-1ff58b3602e8 | trentmkelly/LessWrong-43k | LessWrong | London Meetup 05-Jun-2011 - very rough minutes
This was posted to the London LessWrong mailing list, but I am crossposting here, as per David Gerard's suggestion, in case anyone else finds this interesting.
These notes are from my perspective, so things will be missing (as some are added).
So here's my notes:
Bitcoin - Mostly how it's quite interesting, but annoying that we can't transfer money in from the UK. Myself and ciphergoth were the interested parties. If anyone has any ideas, let us know.
Euthyphro Dilemma and Moral Realism - The first religion-themed conversation, mostly on the sorts of answers that come up to the dilemma and what constitutes moral realism anyway.
Evolutionarily Stable Strategies - The discussion of moral realism naturally led to what the nature of morality is and how evolution gave rise to it.
Learning Decision Theory & Project Euler - Not sure how we got here, but I mentioned my desire that the people working on decision theory would make a Project Euler-type introduction to the material, so the rest of us can eventually join the conversation. I should probably write this up as a separate discussion post.
Rationality as Landgrab, and Definitions of Rationality - Apparently some high-ranking figures in the general futurist cluster dislike LessWrong for 'appropriating the term rationality'. There may or may not be a point there, but we started discussing how the term can be defined, preferably in a LW-independent manner.
Libertarianism & LessWrong - There seems to be a high concentration of libertarians on LW, and it seems that the ban on talking politics has kept this from being discussed much. Which brings us to...
Talking Politics on LessWrong - There seems to be this norm against talking politics, which was inherited by other online communities. However, LessWrong is very much not like other communities. We can discuss religion and philosophy without flamewars breaking out, so why not try politics too? People on LW have been known to change their minds, so there is a good c |
1cebb39d-b020-44e6-a212-a652785cc672 | trentmkelly/LessWrong-43k | LessWrong | On the Meta and DeepMind Safety Frameworks
This week we got a revision of DeepMind’s safety framework, and the first version of Meta’s framework. This post covers both of them.
TABLE OF CONTENTS
1. Meta’s RSP (Frontier AI Framework).
2. DeepMind Updates its Frontier Safety Framework.
3. What About Risk Governance.
4. Where Do We Go From Here?
Here are links for previous coverage of: DeepMind’s Framework 1.0, OpenAI’s Framework and Anthropic’s Framework.
META’S RSP (FRONTIER AI FRAMEWORK)
Since there is a law saying no two companies can call these documents by the same name, Meta is here to offer us its Frontier AI Framework, explaining how Meta is going to keep us safe while deploying frontier AI systems.
I will say up front, if it sounds like I’m not giving Meta the benefit of the doubt here, it’s because I am absolutely not giving Meta the benefit of the doubt here. I see no reason to believe otherwise. Notice there is no section here on governance, at all.
I will also say up front it is better to have any policy at all, that lays out their intentions and allows us to debate what to do about it, than to say nothing. I am glad that rather than keeping their mouths shut and being thought of as reckless fools, they have opened their mouths and removed all doubt.
Even if their actual policy is, in effect, remarkably close to this:
The other good news is that they are looking uniquely at catastrophic outcomes, although they are treating this as a set of specific failure modes, although they will periodically brainstorm to try and think of new ones via hosting workshops for experts.
> Meta: Our Framework is structured around a set of catastrophic outcomes. We have used threat modelling to develop threat scenarios pertaining to each of our catastrophic outcomes. We have identified the key capabilities that would enable the threat actor to realize a threat scenario. We have taken into account both state and non-state actors, and our threat scenarios distinguish between high- or low-skill actors.
I |
5733f301-754c-4ce4-93d4-22f390606f47 | trentmkelly/LessWrong-43k | LessWrong | List of things I think are worth reading (last updated 3/1/2021)
For personal reference, I'm going to keep track of things I read which I think are worth reading. By "worth reading" I specifically mean it scores high on this metric:
(insight gained)(time needed to absorb)⋅log(current popularity)
Each entry will have a title, link, date it was added, summary with salient examples / quotes, and outcome (what I personally got out of reading it). The entry is probably only worth your time if you think you would be better off achieving that outcome.
This list was last updated on 3/1/2021.
AI
The New Business of AI (and How It’s Different From Traditional Software) (added 1/1/2021) Summary: AI businesses can be modeled as a hybrid of software and service businesses. State-of-the-art AI is expensive (>= $100k) to train: you have to pay for the compute and (cloud) storage. Compute needed for current models is increasing faster than hardware (e.g. NVIDIA GPUs) is advancing. Edge cases make AI accuracy have an S-curve shape ("as much as 40-50% of intended functionality for AI products we’ve looked at can reside in the long tail of user intent"). Deploying to each customer can have high marginal cost if you are fine-tuning the model. ("Even customers that appear similar – two auto manufacturers doing defect detection, for example – may require substantially different training data, due to something as simple as the placement of video cameras on their assembly lines.") AI model architectures are usually public and data usually belongs to the customer, which makes profitability trickier. Data has "diseconomics of scale" because each subsequent edge case is more difficult to address. To make AI more practical: reduce model complexity (so less tuning is necessary), focus on low-complexity (but high-scale) tasks, enter the services market rather than pure tech ("running a taxi service rather than selling self-driving cars"). Outcome: I know a bit more about concerns when doing large-scale, practice-oriented AI.
The Bitter Lesson (added |
c13897fd-1566-4da4-855e-97ad09acb7dc | trentmkelly/LessWrong-43k | LessWrong | The case for a negative alignment tax
TL;DR:
Alignment researchers have historically predicted that building safe advanced AI would necessarily incur a significant alignment tax compared to an equally capable but unaligned counterfactual AI.
We put forward a case here that this prediction looks increasingly unlikely given the current ‘state of the board,’ as well as some possibilities for updating alignment strategies accordingly.
Introduction
We recently found that over one hundred grant-funded alignment researchers generally disagree with statements like:
* alignment research that has some probability of also advancing capabilities should not be done (~70% somewhat or strongly disagreed)
* advancing AI capabilities and doing alignment research are mutually exclusive goals (~65% somewhat or strongly disagreed)
Notably, this sample also predicted that the distribution would be significantly more skewed in the ‘hostile-to-capabilities’ direction.
See ground truth vs. predicted distributions for these statements
These results—as well as recent events and related discussions—caused us to think more about our views on the relationship between capabilities and alignment work given the ‘current state of the board,’[1] which ultimately became the content of this post. Though we expect some to disagree with these takes, we have been pleasantly surprised by the positive feedback we’ve received from discussing these ideas in person and are excited to further stress-test them here.
Is a negative alignment tax plausible (or desirable)?
Often, capabilities and alignment are framed with reference to the alignment tax, defined as ‘the extra cost [practical, developmental, research, etc.] of ensuring that an AI system is aligned, relative to the cost of building an unaligned alternative.’
The AF/LW wiki entry on alignment taxes notably includes the following claim:
> The best case scenario is No Tax: This means we lose no performance by aligning the system, so there is no reason to deploy an AI that is |
14a0edfe-91df-4816-8297-4705ead5d792 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Have your timelines changed as a result of ChatGPT?
ChatGPT has been receiving a *lot* of attention on the internet recently.
You can try it [here](https://openai.com/blog/chatgpt/). |
781396a9-c9bd-46ea-9987-592c1d77fab7 | trentmkelly/LessWrong-43k | LessWrong | Dangers of steelmanning / principle of charity
As far as I can tell, most people around these parts consider the principle of charity and its super saiyan form, steelmanning, to be Very Good Rationalist Virtues. I basically agree and I in fact operate under these principles more or less automatically now. HOWEVER, no matter how good the rule is, there are always exceptions, which I have found myself increasingly concerned about.
This blog post that I found in the responses to Yvain's anti-reactionary FAQ argues that even though the ancient Romans had welfare, this policy was motivated not for concern for the poor or for a desire for equality like our modern welfare policies, but instead "the Roman dole was wrapped up in discourses about a) the might and wealth of Rome and b) goddess worship... The dole was there because it made the emperor more popular and demonstrated the wealth of Rome to the people. What’s more, the dole was personified as Annona, a goddess to be worshiped and thanked."
So let's assume this guy is right, and imagine that an ancient Roman travels through time to the present day. He reads an article by some progressive arguing (using the rationale one would typically use) that Obama should increase unemployment benefits. "This makes no sense," the Roman thinks to himself. "Why would you give money to someone who doesn't work for it? Why would you reward lack of virtue? Also, what's this about equality? Isn't it right that an upper class exists to rule over a lower class?" Etc.
But fortunately, between when he hopped out of the time machine and when he found this article, a rationalist found him and explained to him steelmanning and the principle of charity. "Ah, yes," he thinks. "Now I remember what the rationalist said. I was not being so charitable. I now realize that this position kind of makes sense, if you read between the lines. Giving more unemployment benefits would, now that I think about it, demonstrate the power of America to the people, and certainly Annona would approve. I don |
60ee3464-e839-4a62-93c7-825176c3edf9 | trentmkelly/LessWrong-43k | LessWrong | Could Simulating an AGI Taking Over the World Actually Lead to a LLM Taking Over the World?
As you know, large language models can be understood as simulators or more simply as models predicting what token would plausibly appear next after a first sequence of token.
As a consequence, when you ask a LLM to simulate extreme opinions of an AI, it converges very quickly towards "we should eradicate humans" (https://twitter.com/Simeon_Cps/status/1599470463578968064?s=20&t=rj2357Vof9sOLnIma6ZzIA). Other instances of that were observed on other language models.
As a consequence, is it plausible that some very powerful simulators would actually try to take over the world? Write a very detailed plan to take over the world?
It seems to me that yes. More worryingly, if LLMs trained with RL keep their "simulator" aspect, it could be quite natural for an LLM with agentic property to behave still a bit as a simulator while having the capacity to operate in the real-world. And thus it would make a scenario where a simulator literally tries to take over the world because it was asked to do what an evil AGI would do more likely.
And if it's true, do you think that it's a consideration to have in mind or that it's in practice irrelevant?
|
cdcad43a-e938-4175-a500-a4cf2304d98b | trentmkelly/LessWrong-43k | LessWrong | Meetup : Bangalore Meetup
Discussion article for the meetup : Bangalore Meetup
WHEN: 22 November 2014 04:10:51PM (+0530)
WHERE: cafe coffee day, old airport road ,Bengaluru
I've registered and created a meetup.com account and a group there. For the first meetup am thinking of Nov.14. (Friday evening). If you would prefer a different date, please post on the meetup group. http://www.meetup.com/Bangalore-LessWrongers-Meetup/events/212529792/
Discussion article for the meetup : Bangalore Meetup |
d15611ff-2a57-45fc-97ea-aa21cc8bfee6 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Sydney Meetup - April - build your community
Discussion article for the meetup : Sydney Meetup - April - build your community
WHEN: 22 April 2015 06:30:00PM (+1000)
WHERE: 565 George Street, Sydney, Australia 2000
Regular location - City of Sydney RSL The restaurant on Level 2
Regular time (starting about 6:30)
Come for general socialising and interesting discussions with like-minded people, or join the discussion on what you'd like from the Less-Wrong Sydney community.
Discussion article for the meetup : Sydney Meetup - April - build your community |
d8715d75-1ca6-4b8c-b46f-dab6e3c5e851 | trentmkelly/LessWrong-43k | LessWrong | On the Openness personality trait & 'rationality'
Evolutionary psychologist Geoffrey Miller recently published Spent: Sex, Evolution, and Consumer Behavior, a book on signaling, psychology, and consumerism. It's very up LW's alley - it reads almost as if Robin Hanson had written a book. (Actually, Hanson has never published a book, has he? Has anyone ever seen them in the same place? Hm...)
Sam Synder has written an overview/summary of the book, which I can attest hits many of the interesting points. (I would also praise the pervasive humor, which kept it readable and furnish many good examples of the 'reversal test', and the exercises at the end of the book.)
Some of the most interesting chapters to me were the ones dealing with Openness, which one will remember was recently shown may be changeable by psychedelics - possibly the first such tweakable member of the Big Five, leading to the suggestion that it may be worth considering changing it. Hold this thought.
First, Miller discusses the signaling of Openness (starting on page 108 of the PDF, logical page 207):
> Wherever you stand on the openness spectrum, those less open than you seem boring, dull, conventional, and conformist, whereas the more open seem eccentric bizarre, disruptive, threatening, or even psychotic. Given this diversity of openness levels, and the resulting diversity of preferences for different degrees of openness in family, friends, and mates, there is less incentive for people to fake their openness than their intelligence, If you pretend to have higher openness than you really do, you may be transiently attractive to those more open than you, but you'll be less attractive to those less open than you, The net result will be no higher social or sexual popularity. In fact, given the bell curve of openness, the more you deviate from an average level of openness, the fewer people you are likely to attract.
> This means that where openness is concerned, consumers can sometimes display cheap, reliable openness badges that are fairly credib |
b0496ea4-0677-481a-ac81-8ce4708a6c12 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | The Open Agency Model
*Notes on AI for complex, consequential problems*
-------------------------------------------------
Eric Drexler
Centre for the Governance of AI
University of Oxford
Introduction
------------
This document argues for “open agencies” — not opaque, unitary agents — as the appropriate model for applying future AI capabilities to consequential tasks that call for combining human guidance with delegation of planning and implementation to AI systems. This prospect reframes and can help to tame a wide range of classic AI safety challenges, leveraging alignment techniques in a relatively fault-tolerant context.
Rethinking safe AI and its applications
---------------------------------------
AI safety research is too varied to summarize, yet broad patterns are obvious. A long-established reference-problem centers on prospects for rational superintelligent agents that pursue narrow goals with potentially catastrophic outcomes. This frame has been productive, but developments in deep learning call for updates that take account of the proliferation of narrow models (for driving, coding, robot control, image generation, game playing…) that are either non-agentic or act as agents in only a narrow sense, and that take account of the rise of more broadly capable foundation models and LLMs. These updates call for reframing questions of AI safety, and call for attention to how consequential tasks might be accomplished by organizing AI systems that usually do approximately what humans intend.
Two frames for high-level AI
----------------------------
### **The unitary-agent frame**
From its beginnings in popular culture, discussion of the AI control problem has centered around a unitary agent model of high-level AI and potential AI risks. In this model, a potentially dominant agent both plans and acts to achieve its goals.
The unitary-agent model typically carries assumptions regarding goals, plans, actions, and control.
* **Goals**: Internal to an agent, by default including power-seeking goals
* **Plans**: Internal to an agent, possibly uninterpretable and in effect secret
* **Actions**: Performed by the agent, possibly intended to overcome opposition
* **Control**: Humans confront a powerful, potentially deceptive agent
The typical unitary-agent threat model contemplates the emergence of a dominant, catastrophically misaligned agent, and safety models implicitly or explicitly call for deploying a dominant agent (or an equivalent collective system) that is both aligned and powerful enough to suppress unaligned competitors everywhere in the world.
### **The open-agency frame**
Recent developments suggest an alternative open agency model of high-level AI. Today, the systems that look most like AGI are large language models (LLMs), and these are not agents that seek goals,[[1]](#fnjex5wne540r) but are generative models that produce diverse outputs in response to prompts (in a generalized sense) and random-number seeds.[[2]](#fnp6zyh3mkpyn) Most outputs are discarded.
Trained on prediction tasks, LLMs learn world models that include agent behaviors, and generative models that are similar in kind can be informed by better world models and produce better plans. There is no need to assume LLM-like implementations: The key point is that generation of diverse plans is by nature a task for generative models, and that in routine operation, most outputs are discarded.
These considerations suggest an “open-agency frame” in which prompt-driven generative models produce diverse proposals, diverse critics help select proposals, and diverse agents implement proposed actions to accomplish tasks (with schedules, budgets, accountability mechanisms, and so forth).
Goals, plans, actions, and control look different in the open-agency model:
* **Goals**: Are provided as prompts to diverse generative models,
yielding diverse plans on request
* **Plans**: Are selected with the aid of diverse, independent comparison
and evaluation mechanisms
* **Actions**: Incremental actions are performed by diverse task-oriented agents
* **Control**: Diverse, independent monitoring and evaluation mechanisms
guide revision of plans
For narrow tasks, of course, unitary agents may be both efficient and unproblematic:
Playing board games and driving cars are tasks performed by individual humans with fast decision cycles. The open agency model, however, fits best when equivalent human tasks (building a factory, deploying a communication system, developing a drug) call for multiple skills and technologies coordinated over time. The structure of tasks and accountability relationships speaks against merging exploratory planning, strategic decision-making, project management, task performance, reporting, and auditing into a single opaque process. The costs would be high, and any gains in simplicity would be illusory.
**Note:** To avoid a natural misconception, is important to recognize that the open-agency architecture describes roles and relationships, not the systems that fill those roles. Thus, d*ivision of labor does not imply division of competence, and any or all roles could in principle be performed by large, arbitrarily-capable models.*
AI safety challenges reframed
-----------------------------
Basic challenges in AI safety — corrigibility, interpretability, power seeking, and alignment — look different in the agent and agency frames:
**Corrigibility**:
*Agents:* Goal-driven agents may defend goals against change.
*Agencies:* Generative planning models respond to goals as prompts.
**Interpretability**:
*Agents:* Plan descriptions may be internal and opaque.
*Agencies:* Plan descriptions are externalized and interpretable.[[3]](#fnk4i8333clnp)
**Power seeking**:
*Agents:* Open-ended goals may motivate secret plans to gain power.
*Agencies:* Bounded tasks include time and budget constraints.
**Alignment**:
*Agents:* Humans may have only one chance to set the goals of a dominant agent.
*Agencies:* Humans engage in ongoing development and direction of diverse systems.
AI development in the open-agency frame can and should be informed by agentic concerns — we know why vigilance is important! — but the considerations outlined above suggest that the most tractable solutions AI to safety problems may rely to a substantial extent on agency-centered strategies. Separation of roles, incremental actions, greater transparency, and affordances for control can all make imperfect alignment techniques more effective and failures less catastrophic.
Threat models in an agency-centered world must include the emergence of dangerous agents, whether autonomous and unitary or under human direction. Effective safety models call for the development and deployment of systems that, collectively, make catastrophically dangerous goals impossible to achieve. This task does not require a world-dominating AI agent, but may require a degree of human coordination that is difficult to achieve.
Applications of AI to human goal alignment may be critically important. Combining fluent language models with compelling, well-grounded knowledge models could help by presenting us with a more complete picture of the world — situations, causality, threats, opportunities, and options — but this is a topic for another time.
Conclusion
----------
The open agency model offers a new perspective on applying AI capabilities to complex, consequential tasks. It reframes the traditional AI safety challenges by introducing the concept of "open agencies" that rely on generative models that produce diverse proposals, diverse critics that help select proposals, and diverse agents that implement proposed actions to accomplish tasks. By leveraging alignment techniques in a fault-tolerant context, the open agency model provides a framework for safer and more effective AI.
---
*The discerning reader will recognize that the principle outlined here can be applied to mundane AI systems that likewise propose alternatives, advise on choices, and perform bounded tasks with opportunities for oversight. Travel planning, for example, where the bounded task is making reservations. Note that this open-agency pattern is how people often use AI today: This article argues that the pattern scales.*
1. **[^](#fnrefjex5wne540r)**I buy the description (see “[Simulators](https://www.alignmentforum.org/posts/vJFdjigzmcXMhNTsx/simulators)”) of pure LLMs as thoroughly non-agentic models of intelligence that can readily simulate — hence actualize — a wide range of personas that act as agents. Simulations that actualize such agents have exhibited human-like behaviors that include directly threatening their perceived enemies and (primed with AI-*vs*-humanity narratives) talking of world conquest. LLM-based agents are opaque, unitary, and perhaps not the best point of departure for developing safe AI. Methods like RLHF could potentially adapt models to roles in open agencies in which the consequences of residual misalignment are constrained by the structure of relationships and tasks.
2. **[^](#fnrefp6zyh3mkpyn)**Dall·E 2, Stable Diffusion, and their kin are further examples of prompt-driven generative models. Prompts (in a general sense) may include descriptive text, rough sketches, and images with gaps to fill. Generalized prompts for planning models might include goals, budget constraints, and sketches or precedents that suggest the general nature of a desired plan.
3. **[^](#fnrefk4i8333clnp)**Plan descriptions are *of necessity* interpretable as actions by downstream components and (with caveats) may also be interpretable by humans. Plans are not Transformer activations, and poorly explained plans are good candidates for rejection. |
d1e25723-a935-4c15-9012-22ec4d89b869 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Applying reinforcement learning theory to reduce felt temporal distance
([cross-posted from my blog](http://kajsotala.fi/2014/01/applying-reinforcement-learning-theory-to-reduce-felt-temporal-distance/))
It is a [basic principle of reinforcement learning](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node9.html) to distinguish between reward and value, where the reward of a state is the immediate, intrinsic desirability of the state, whereas the value of the state is proportional to the rewards of the other states that you can reach from that state.
For example, suppose that I’m playing a competitive game of chess, and in addition to winning I happen to like capturing my opponent’s pieces, even when it doesn’t contribute to winning. I assign a reward of 10 points to winning, -10 to losing, 0 to a stalemate, and 1 point to each piece that I capture in the game. Now my opponent offers me a chance to capture one of his pawns, an action that would give me one point worth of reward. But when I look at the situation more closely, I see that it’s a trap: if I did capture the piece, I would be forced into a set of moves that would inevitably result in my defeat. So the value, or long-term reward, of that state is actually something close to -9.
Once I realize this, I also realize that making that move is almost exactly equivalent to agreeing to resign in exchange for my opponent letting me capture one of his pieces. My defeat won’t be instant, but by making that move, I would nonetheless be choosing to lose.
Now consider a dilemma that I might be faced with when coming home late some evening. I have no food at home, but I’m feeling exhausted and don’t want to bother with going to the store, and I’ve already eaten today anyway. But I also know that if I wake up with no food in the house, then I will quickly end up with low energy, which makes it harder to go to the store, which means my energy levels will drop further, and so on until I’ll finally get something to eat much later, after wasting a long time in an uncomfortable state.
Typically, temporal discounting means that I’m aware of this in the evening, but nonetheless skip the visit to the store. The penalty from not going feels remote, whereas the discomfort of going feels close, and that ends up dominating my decision-making. Besides, I can always hope that the next morning will be an exception, and I’ll actually get myself to go to the store right from the moment when I wake up!
And I haven’t tried this out for very long, but it feels like explicitly framing the different actions in terms of reward and value could be useful in reducing the impact of that experienced distance. I skip the visit to the store because being hungry in the morning is something that seems remote. But if I think that skipping the visit is exactly the same thing as choosing to be hungry in the morning, and that the value of skipping the visit is not the momentary relief of being home earlier but rather the inevitable consequence of the causal chain that it sets in motion – culminating in hours of hunger and low energy – then that feels a lot different.
And of course, I can propagate the consequences earlier back in time as well: if I think that I simply won’t have the energy to get food when I finally come home, then I should realize that I need to go buy the food before setting out on that trip. Otherwise I’ll again set in motion a causal chain whose end result is being hungry. So then *not going shopping before I leave* becomes *exactly the same thing* as *being hungry next morning*.
More examples of the same:
* Slightly earlier I considered taking a shower, and realized that if I'd take a shower in my current state of mind I'd inevitably make it into a bath as well. So I wasn't really just considering whether to take a shower, but whether to take a shower \*and\* a bath. That said, I wasn't in a hurry anywhere and there didn't seem to be a big harm in also taking the bath, so I decided to go ahead with it.
* While in the shower/bath, I started thinking about this post, and decided that I wanted to get it written. But I also wanted to enjoy my hot bath for a while longer. Considering it, I realized that staying in the bath for too long might cause me to lose my motivation for writing this, so there was a chance that staying in the bath would become the same thing as choosing not to get this written. I decided that the risk wasn't worth it, and got up.
* If I'm going somewhere and I choose a route that causes me to walk past a fast-food place selling something that I know I shouldn't eat, and I know that the sight of that fast-food place is very likely to tempt me to eat there anyway, then choosing that particular route is the same thing as choosing to go eat something that I know I shouldn't.
Related post: [Applied cognitive science: learning from a faux pas](http://kajsotala.fi/2010/04/applied-cognitive-science-learning-from-a-faux-pas/). |
b0b76b60-7a0e-4f35-aace-cc1e6b9a269e | trentmkelly/LessWrong-43k | LessWrong | Discovering Language Model Behaviors with Model-Written Evaluations
“Discovering Language Model Behaviors with Model-Written Evaluations” is a new Anthropic paper by Ethan Perez et al. that I (Evan Hubinger) also collaborated on. I think the results in this paper are quite interesting in terms of what they demonstrate about both RLHF (Reinforcement Learning from Human Feedback) and language models in general.
Among other things, the paper finds concrete evidence of current large language models exhibiting:
* convergent instrumental goal following (e.g. actively expressing a preference not to be shut down),
* non-myopia (e.g. wanting to sacrifice short-term gain for long-term gain),
* situational awareness (e.g. awareness of being a language model),
* coordination (e.g. willingness to coordinate with other AIs), and
* non-CDT-style reasoning (e.g. one-boxing on Newcomb's problem).
Note that many of these are the exact sort of things we hypothesized were necessary pre-requisites for deceptive alignment in “Risks from Learned Optimization”.
Furthermore, most of these metrics generally increase with both pre-trained model scale and number of RLHF steps. In my opinion, I think this is some of the most concrete evidence available that current models are actively becoming more agentic in potentially concerning ways with scale—and in ways that current fine-tuning techniques don't generally seem to be alleviating and sometimes seem to be actively making worse.
Interestingly, the RLHF preference model seemed to be particularly fond of the more agentic option in many of these evals, usually more so than either the pre-trained or fine-tuned language models. We think that this is because the preference model is running ahead of the fine-tuned model, and that future RLHF fine-tuned models will be better at satisfying the preferences of such preference models, the idea being that fine-tuned models tend to fit their preference models better with additional fine-tuning.[1]
----------------------------------------
Twitter Thread
Abstract |
9acb246b-d259-42ba-89d8-546985917ce6 | trentmkelly/LessWrong-43k | LessWrong | Unity of Doctrine vs Unity of Method in Philosophy
Philosophers typically seem to prefer unity of doctrine over unity of method. This is particularly evident within philosophical subdisciplines. In metaethics, for example, mixed views such as "expressivism is true for social group X/domain P of moral discourse, error theory for social group Y/domain Q." seem to be relatively unpopular.
But the same phenomenon also seems to hold across subdisciplines: Antirealism/Naturalism about ethics often goes hand in hand with Illusionism/Reductionism about consciousness, and the corresponding views in other fields of philosophy. Jacy Reese Anthis, Lance Bush, and Eliezer Yudkowski all seem to be antirealists about ethics & illusionists about consciousness.
Unity of method on the other hand seems to be somewhat rare among philosophers, perhaps because many philosophers don’t have a sufficiently clear method (other than “evaluating arguments”) that they could apply across problems/fields.
I think this is problematic and it is often better to have a systematic method that one can apply to many problems, even if it leads to different results in different cases.
Example: I find conceivability arguments to be a powerful tool to extract the content of concepts & it can be applied to many philosophical problems. But it (arguably) leads to very heterogeneous results: compatibilism about free will is true for some people, incompatibilism for others. Reductionism is true for most things, but not consciousness (either Russellian monism or Illusionism is true for consciousness). Expressivism about ethics is true for many people, but for some either error theory or non-naturalism is true. Etc. |
aa917eef-29b9-4aeb-ab3b-61c57726faad | StampyAI/alignment-research-dataset/arxiv | Arxiv | Two Procedures for Compiling Influence Diagrams
Two Procedures for Compiling Influence Diagrams 335
Two Procedures for Compiling Influence Diagrams
Paul E. Lehner.
Systems Engineering Dept.
George Mason University
Fairfax, VA 22030
ABSTRACT
Two algorithms are presented for "compiling"
influence diagrams into a set of simple decision
rules. These decision rules define simple-to
execute, complete, consistent, and near-�pti�al
decision procedures. These compilation
algorithms can be used to derive decision
procedures for human teams solving time
constrained decision problems.
1.0 INTRODUCTION
Within some research communities (e.g., artificial
intelligence), there is a growing recognition that _Bayesian
decision theory provides a powerful foundatwn upon
which to develop automated and partially automated
reasoning systems. Decision theory provides a
compelling semant�cs for inference and action_ under
uncertainty, as well as a framework for evaluatmg !he
adequacy of heuristic methods. The use of Bayesian
techniques is now common place in a number of rese�ch
areas, including inference (e.g., Pearl, 1?87), plannmg
(e.g., Dean and Wellman, 1991), and learrung (e.g., Paass,
1991).
Most applications of Bayesian t�chniq�es. involve
problems that require �epeat� proce�smg o� Similar cases.
A typical example IS medical dragnosis, where the
objective is to use symptoms and test results to select the
affliction affecting the patient. In such problems, a
probability network is constructed that encodes a joint
probability distribution over a preselected set of
conclusions (affliction), evidence items (symptoms and
diagnostic tests), and intermediate hypotheses. If
decisions and outcome values are included, then the
network becomes a decision network (a.k.a. influence
diagram) that includes the possible decisions and the
utility of each decision conditioned on a .s�bset of the
evidence items and hypotheses. Each decision network
defines a domain model that identifies the possible states
that could occur and the possible decisions that could be
made. Constructing a good decision network involves a
substantial knowledge engineering effort. However, once
developed, the decision network can be used repeatedly to Azar Sadigh
Systems Engineering Dept.
George Mason University
Fairfax, VA 22030
address decision problems which differ in the pattern of
evidence that is observed.
Many decision tasks, particularly in the command and
control (C2) arena, involve repeated processing of similar
cases (sensor interpretation, object identification, object
location, IFF, etc.). These tasks are good candidates for
the application of decision networks. However, many of
these tasks are severely time constrained. Therefore
procedures for rapid processing of decision networks, as
well as rapid processing of their outputs by users, mu.st. be
developed before the potential usefulness of decision
networks can be fully realized.
Unfortunately, as shown by Cooper (1990), the
computational complexity of computing posteri�r
probabilities in a probability network IS NP-Hard. This
implies that. the computational effort involved to select
optimal decisions using a decision network is at least
exponential with the size of the network. Consequently,
in order to realistically apply Bayesian decision theory to
time constrained problems, computationally simpler
procedures must be developed that approximate �x�ct
Bayesian reasoning. Furthermore, many team decision
tasks involve life-and-death decisions. Many believe that
it is unwise to delegate such decision to machines. This
implies that humanly-executable decision procedures are
needed, even when normative Bayesian procedures are
available.
Previous researchers have explored three distinct
approaches to approximate rapid processing of decision
networks. The first is to use simulation algorithms that
generate approximate solutions in polynomial time (e.g.,
Henrion, 1988). The second (e.g., Dean and Wellman,
1991) is to partition the reasoning problem into a series
of incremental reasoning steps, and to estimate the
computational burden involved before each step is
executed. If there is insufficient time to execute the next
step, the current solution is offered as an approxima �i?n.
Unfortunately, both of these approaches generate decision
procedures that are not executable by people, or easy for
people to understand.
A third approach is to "compile" a decision network into a
set of simple decision procedures and to apply those
decision procedures at execution time. This approach can
be used to generate decision procedures that people can
336 Lehner and Sadigh
execute. Heckennan, et.al. (1989) addresses the problem
of compiling decision networks by generating decision
rules that specify, for each evidence state, the maximum
expected utility decision. One of compilation approaches
proposed by Heckennan, et.al., is to develop a situation
action tree in which evidence items are sequentially
examined until sufficient evidence is accumulated to
warrant a decision. In this paper, we develop examine two
procedures for generating situation-action trees, and
characterize their optimality and computational
complexity properties.
2.0 COMPILING DECISION
NETWORKS
We begin by defining some basic tenns. Figure 1 depicts
a small decision network. The circle nodes are chance
nodes. Each chance node identifies a set of mutually
exclusive and exhaustive propositions. The arcs between
chance nodes identify the conditional probability
statements that must be contained in each node. For
instance, the node Typ contains the unconditional
probability distribution P(Typ). The node A contains the
conditional probability distribution P(AITyp). The
rectangular node is a decision node. Each decision node
identifies an exhaustive set of mutually exclusive
decisions. Arcs going from a chance node to a decision
node are information arcs. They identify infonnation that
will be available when the decision must be made. The
diamond node is a value node. A value node assigns a
utility to each row in the cross product of the
propositions/decisions of its parent nodes. For instance,
the node Val in Figure 1 assigns a utility for each decision
and state in Typ.
Figure 1: A Decision Network
In this paper, chance nodes that have information arcs
going to a decision node are referred to as evidence items.
An evidence item is instantiated when the value of that
evidence item is known. For instance, A=a1 asserts that
the evidence value for evidence item A is al. A set of evidence values for all evidence items is referred to as an
evidence state. For instance, the vector <a1,b2,c3>
describes the evidence state where A=a1, B=b2 and C=c3.
Once a decision network has been defined, there are a
variety of algorithms and software tools for processing the
network (Buede, 1992). These algorithms can be used to
derive the expected utility of any decision, or the posterior
probability of any chance node,· conditioned on specific
values for any subset of the chance nodes.
2.1 DEFAULT TREES
We define a default tree (DTree) as a tree composed of
default nodes (Dnodes) and evidence nodes (Enodes). Each
Dnode specifies a decision, while each Enode specifies
both an evidence item and a decision. To illustrate, the
DTree in Figure 2 contains 4 Enodes and 6 Dnodes. This
DTree corresponds to a decision procedure where the
decision maker begins by either selecting d1 or examining
evidence item A. If A is examined and its value is a2,
then the decision d1 is immediately selected. If A=a1,
then the decision maker selects d2 or examines B. If
B=b1, then the decision d1 is selected, else if B=b2 then
d2 is selected. Returning to the root Enode, if A=a3 then
d3 is selected or C is examined. If C=cl, then dl is
selected. If C=c2 then d3 is selected or B is examined. If
B=b1, then d2 is selected, otherwise d3 is selected.
Unless otherwise noted, we will assume that processing of
a DTree continues until a Dnode is reached. That is, the
evidence item associated with an Enode is always
examined and the decision associated with an Enode is
never selected
Dnodes are partitioned into two types. A Dnode is closed
if the path leading to the Dnode contains all the evidence
items available. A Dnode is open, if it is not closed.
Note that an open Dnode represents a default decision,
since it specifies decisions that could change if additional
evidence is examined.
More formally, we can characterize a DTree as follows.
Let DN be a decision network which contains decision
nodes { Dj} and evidence nodes {Ei}. Let DT be a DTree
that contains the nodes {Ni}. Each member of {Ni} is an
open Dnode, a closed Dnode, or an Enode. The following
functions are defmed with respect to DN.
pathDT(Ni) -The set of evidence values in the
ancestors ofNi.
For example, if we order the node in Figure 2 left-to-right
breadth-first then Ns is the left-most Dnode and
pathnT(Ns) = {A=a1,B=b1 }.
evid-path DT(Ni) -The evidence items that are listed
in the ancestors of Ni (e.g., evid-pathn T(Ns)={A,B }).
For the root node, evid-pathDT = { } .
decnT(path DT<Ni))-The maximum expected utility
decisions in DN given the evidence item values
leading to Ni. That is, decnT(pathDT(Ni)) =
maxctc[EU(dclpath(N i)], where de is a set that specifies
Two Procedures for Compiling Influence Diagrams 337
all possible combinations of decisions for the
decision nodes in DN (e.g.,
decDT(pathDT(Ns))={ d1 }).
Figure 2. A Default Tree (DTree).
Let d be an open Dnode in the DTree DT and E an
evidence item. We say that the expansion of d withE in
DT is the DTree that results from replacing the Dnode d
with the Enode { E,d}, and adding Dnodes for each possible
value of E. Each new Dnode contains the maximum
expected utility decisions. If Es is a sequence of evidence
values, followed by an evidence item, then the expansion
sequence of d with Es in DT is the DTree that results by
starting at d and sequentially expanding DT with the
evidence items in Es. For instance, the expansion of d
with Es={E1=e1, E2=e2, E3} is obtained by replacing d
with the expansion of d with E1, then replacing the Dnode
at E1=e1 with the expansions of that Dnode with E2, and
then replacing the Dnode at E2=e2 with the expansion of
that node with E3. An expansion subtree is composed of
one of more expansion sequences that have the effect of
adding a subtree to the DTree. An expansion set is
composed of multiple expansion subtrees.
evoiDT(Eipath(N i)) =The increase in expected utility
for examining evidence item E given the path toNi.
Formally,
evoiDT(Eipath(N i)) =
(LeE EJ>(E=elpath DT(Ni))
·EU[decDT({E=e}upathn T(Ni))J)
-EU[decDT(pathDT(Ni)].
max-evoi DT(Ni) -The evidence item, E, for which
evoinT(EipathDT(Ni)) is maximal.
eu-expand DT(Ni,Es) -The increase in the expected
utility of DT that is obtained by replacing
decDT(path(Ni)) with the expansion subtree Es in
DT. It is easily shown that if Es contains a single evidence
node (E), then
eu-expandl) T(Ni,E) = P(pathDT<Ni))·evoin T(Eipath(N i)).
If Es is an expansion subtree, then define mean-eu
expandn T(Es) to be the mean value of the individual eu
expandn T values for the evidence items in Es.
Definition (DT -compile )
A DTree (DT) DT-compiles a decision network (DN)
iff every evidence state in DN will lead to a Dnode in
DT.
The DTree in Figure 2 DT -compiles the decision network
in Figure 1.
Theorem 1. (DTree Expected Utility).
If a DTree (DT) DT-compiles a decision network DN,
then the expected utility of the DT, with respect to
DN, is
EU(decnTO>+LNE Enodes(D T) eu-expand DT(N,e),
where e is always set to the evidence item at N.
Proof. (By induction)
Let {Ni} be a list of the Enodes in DT ordered in a
manner that is consistent with partial ordering induced
by the arcsin DT. Let {Nj }m be a subset of the first
m Enodes in {Ni}. Each {Nj}m corresponds to a
DTree. By definition, EU({Nj} 1) = eu-expand(N1) +
EU(dec()). Assume that EU({Nj}m) = LNE {Nj}m
eu-expand DT(N). Again by the definition of eu
expand EU({Nj}m+l) = EU({Nj }m) + eu
expandDT<Nm+ 1).
2.2 DERIVING DTREES
The following algorithm can be used to derive a sequence
of increasingly complex DTrees.
Algorithm DD
I. Let N 1 be a Dnode containing only decDTO·
II. Iterate through the following procedure
A. Select the Open Dnode and evidence item for
which eu-expandn T is maximal. Call this node N.
B. Set N equal to the Enode {max-evoiDT(N),
decnT(pathDT(N))}
C. For each possible value (e) of max-evoin T(N) add
as a subnode to N the Dnode decDT(pathDT(N) &
max-evoio T(N)=e ).
D. Check stopping criterion. If Stop, then exist with
current DTree.
E. Go to A.
In words, DD iteratively replaces a default decision with
the evidence item that maximiz es the increase in the
expected utility of the DTree, and adds as new subnodes
the Dnodes that correspond to the best decisions for each
possible value of that evidence item.l This algorithm is
lObviously, the efficiency of DD could be increased
substantially by recording the results of the evoi
338 Lehner and Sadigh
consistent with the situation-action tree development
algorithm described informally in Heckerman, et.al.
(1989).
DD is a greedy algorithm. At each iteration, it expands
the DTree by adding and expanding the node that has the
greatest increase in the expected utility of the DTree. We
refer to such expansions as greedy expansions. Although
DD is a greedy algorithm, it has two useful optimality
properties.
Theorem 2: (Local optimality of DD)
Each Enode added by DD is a locally optimal
extension in that if DTx and DTy are both one step
expansions ofDT, then EU(DTx) � EU(DTy).
Proof.
This follows immediately from Theorem 1 and the
fact that DD always selects the maximum eu-expand
expansion.
To further characterize the optimality properties of DD,
the following definitions are offered.
Definition. (Optimal DTree)
DT* is an optimal DTree iff EU(DT*) �EU(DT) for
each DTree (DT) where DT has no more Enodes than
DT*. Also, DT* is an optimal expansion of DT iff
DT* is an expansion of DT and DT* is an optimal
DTree.
Definition. (£-descending)
DT is an E-descending DTree iff for every open Dnode
(D), and evidence item (E),
P(path(D) )evoioT(Eipath(D))
� P(path(D)u { ei} )evoioT(Eipath(D u { ei} )),
where { ei} is any set of evidence values.
In words, a DTree is E-descending iff it is impossible to
increase the eu-expand value of an evidence item by
making it the last element in an expansion sequence.
Property 3: (Optimal Dnode selection).
Let DT and DT* be any DTrees where DT is E
descending, DT* is an expansion of DT, and DT* is
optimal. If { d} is the set of Dnodes in DT with the
maximum eu-expand value, then DT* contains an
expansion of a node in { d}.
Proof
Pl. Let d be a Dnode in DT and El, ... , En the
evidence items available at d ordered by their evoi
value. From this ordering, it follows that eu
expand(Ellpath(d))�e u-expand (Eilpath(d)) for �2.
From E-descending it follows that e u
expand(Eilpath( d) )�eu-expand(Eilpath( d)u { ei})) for
all {ei}. From transitivity it follows that eu-
calculations and keeping track of open and closed Dnodes.
However, efficiency improvement will not change the
order in which the DTree is expanded. Consequently, they
are not presented here. expand(Ellpath(d)) is greater than the eu-expand
value of an E-node in any subtree rooted at d
P2. Let d' be a Dnode in the set of Dnodes with the
maximal eu-expand value. From Pl and
transitivity, it follows that eu-expand(Ellpath(d'))
is greater than or equal to the eu-expand value of
any E-node in any subtree rooted at an open Dnode
in DT.
P3. Let DT* be any expansion of DT that does not
contain an expansion of a Dnode in { d}. From P2
it follows that all Enodes in DT', that are not in
DT, have an eu-expand value that is strictly less
than eu-expand(Ellpath(d')). Consequently, a
DTree which is the same as DT* except that it
replaces a terminal Enode with the max-evoi
expansion of d' will have a greater expected utility
than DT* Consequently, DT* cannot be an
optimal DTree.
P4. The contrapositive of P4 is that if DT* is
optimal, then it contains an expansion of a node
in{d}.
Theorem 4. (Optimal Dnode selection by DD)
If each DTree (DT) generated by DD is E-descending,
then each Dnode selected by algorithm DD for
expansion must be expanded in any optimal
expansion of DT that contains nodes other than those
in DT or { d}, where { d} is the set of Dnodes in DT
with the maximum eu-expand value.
Proof
DD selects the Dnode with the maximum eu-expand
value. Consequently, by Property 3 an expansion of
that Dnode must be included in an optimal expansion.
E-descending is not a very stringent constraint. While it
is possible to increase the evoi value of an evidence item
(E) by inserting a path of evidence values ( { ei}) as
ancestors toE, a violation of E-descending requires that
. evoioT(EinathDT(D)) evmoT(Eipath(D u { ej})) > r
P( { ei} lpathDT(D))
That is, the evoiDT value must increase by a multiplier of
more than 1/P({ei}lpathDT(D)). This can only occur if
the increase in the evoi value is substantial or if P( { ei})
is near one. Both of these are unlikely if { ei} involves
more than one evidence item. Consequently, violations of
E-descending will be infrequent and most violations that
do occur will only involve two level expansions.
Regarding globally optimality, Theorem 4 states that DD
always expands the Dnode that must be expanded in an
optimal expansion, while Theorem 2 asserts that DD
always performs a locally optimal expansion of that
Dnode. Intuitively, these two properties suggest that E
descending is sufficient to guarantee that DO-genera ted
DTrees are optimal. It isn't. This is because greedy
expansions may be redundant given several follow-on
expansions. For instance, the eu-expand of El may be
greater than either E2 or E3, even though eu-expan d(El)=O
if E2 and E3 are already included. Consequently, an
Two Procedures for Compiling Influence Diagrams 339
optimal multi-step expansion may include E2 and E3, but
not El.
Unfortunately, the conditions required to guarantee global
optimality are very stringent. In effect, it is necessary to
assume conditions that imply that for any N, any DTree
consisting of N greedy expansions is optimal. It is easy
to construct violations of this property where the
violations first appear at arbitrary expansion depths.
Furthermore, sil}ce all DTree expansion procedures
eventually lead to the same fully-expanded DTree, all
expansion procedures will eventually converge to the same
value. As they converge on this common value, there is
no reason to believe that the greedy procedures will
consistently generate optimal DTrees. Consequently,
short of exhaustively searching the space of possible
DTrees, there does not seem to be a way to guarantee
generation of optimal DTrees.
On the other hand, the fact that violations of E-descending
are not likely to involve insertions of long evidence
chains suggests that DD can be enhanced by examining
expansions a more than one level deep.
Algorithm DDn
I. Let Nl be a Dnode containing only decDTO·
II. Iterate through the following procedure
A. For each Open Dnode find the expansion subtree
of depth n or less for which the mean-eu-expand
value is maximal.
B. Select the Open Dnode for which the mean-en
expand value found in A is maximal. Call this
node N and its expansion subtree Es.
C. Replace N with the subtree that resulted in the
maximal mean-eu-ex pand DT(N).
D. Check stopping criterion. If Stop, then exist
with current DTree.
E. Go to A.
DDl is the same as DD. DDn is similar toDD, except
that it will look n levels deep to find the expansion with
the greatest average contribution to the expected utility of
the DTree. We call such expansions greedy n-step
expansions. Since DDn examines strictly more nodes
than DD, it will usually generate DTrees with expected
utility greater or equal to the DTrees generated by DD.
However, since neither algorithm is globally optimal, this
cannot be guaranteed.
DDn satisfies local optimality under weaker conditions
than DD.
Definition. (En-descending)
DT is an En -descending DTree iff for every open
Dnode (D), and evidence item (E),
P(pathDT(D) )evoiDT(Eipath(D))
� P(pathDT( D)u { ei} )evoiDT(ElpathDT(Du { ei} ))
where { ei} is any set of evidence values with
cardinality not less than n.
Note, E-descending is equivalent to E }-descending . Theorem 5 (Local optimality of DDn).
If each DTree generated by DDn is En-descending,
then the mean-eu-expand value of each expansion
subtree selected by DDn is greater than or equal to the
mean-eu-expand value of any alternative expansion
set.
Proof
As shorthand, let meu = mean-eu-expand.
Pl. We first show that for any DTree, there is a
maximal meu expansion set which is a subtree.
Let M be a maximum meu expansion set of DT.
If M is not a single subtree, then M must be
composed of a set of subtrees {Ml, ... ,Mk}, each
of which has its root at an open Dnode in DT ..
Select a subtree Mi in {Ml, ... ,Mk} for which
meu(Mi )�max[meu(Ml), ... ,meu(Mk)]. From
basic algebra it follows that meu(Mi)�meu (M).
P2. Next we show that for any open Dnode there is
an expansion subtree with depth no greater than n
for which meu is maximal. (The root node of
an expansion subtree is at depth 1.) Let M be a
maximal meu expansion subtree that contains at
least one node with depth greater than n. Let
{ml,m2, ... ,mi,mi+l, ... ,mk} be the Enodes in
M, where ml is the root, m2, .. ,mi are all the
nodes of depth n or less. Since M is a maximal
meu expansion subtree, it follows that meu(M)
= meu[{ml,m2, ... ,mi,mi+l, ... ,mk}]�
meu[{ml,m2, ... ,mi}]; otherwise mi+l, ... ,mk
would not be included in M. From basic algebra
it follows that meu[ { mi+l, ... ,mk} ]�meu[M].
Let E* be the evidence item with the maximal
eu-expand value. From En-descending it follows
the eu-expan d(E*) is greater than the eu-expand
value of any possible node at depth n or greater.
Therefore, eu-expand(E*)� max(eu-exp and( (mi+ 1)
, ... ,eu-expa nd(mk)) ... which implies
meu(E*) 2:..meu(mi+l, ... ,mk). Therefore,
meu(E*)�me u(M). Consequently, the expansion
subtree {E*} is also a maximum value expansion
subtree.
P3. DDn always selects the expansion subtree with
depth .s. n with a greatest meu value. Therefore,
it follows from Pl and P2 that DDn always
selects an expansion set for which me u is
maximal.
Algorithm DDn allows the decision network compilation
process to be arbitrarily conservative. Indeed, if n is set to
the number of evidence items, then DDn will exhaustively
search the space of all DTrees. However, as noted above,
violations of E-descending that are greater than two levels
deep are very unlikely. Consequently, algorithm DD4
will almost certainly generate a sequence of expansions
that are locally optimal for any search depth. DDn also
satisfies the optimal Dnode selection property described in
Property 4. This is because, whenever a DTree is E
descending, DDn will select the same expansion as DD.
340 Lehner and Sadigh
2.3 COMP UTATIONAL COMPLEXITY
Let CI be the average computational complexity of
processing the decision network. Let NE be the average
number of evidence values for each evidence item. With
each iteration of algorithm DDn, the number of times the
decision network is processed in NE0• If a DTree contains
R nodes, then the computational c<;>mplexity of generating
that DTree was NE0(R}(CI). That is, if the size of the
DTree if fixed a priori, the computational burden of
generating a DTree using DDn is a linear function of the
computational burden of processing the decision network.
3.0 APPLICATIONS
In many organizations, the behavior of agents within that
organization can be characterized as rule-guided. This is
because the behavior of that organization is guided by a
series of policy and procedure rules. Consider, for
instance, air traffic control systems. The behavior of a
ground control team is guided in large measure by a set of
procedural rules, which specify how the team should react
to various circumstances. The procedure rules specify
conditions for de-icing, rerouting, priorities for landing,
etc. Policy rules, in tum, provide guidelines for the
establishment of the procedure rules (e.g., In snow,
aircraft should be de-iced no less than one half hour before
takeoff.)
A proposed set of rules for governing an organization's
behavior can be evaluated in several ways. One way is to
evaluate them in terms of their logical consistency and
completeness. Do the rules always result in a consistent
recommendation, or can different rule subsets lead to
different actions? Do they specify what to do under all
circumstances? Alternatively, rules can be evaluated in
terms of their executability. Although a rule set may be
internally consistent, it may be difficult define an
acceptable architecture that can execute those rules (e.g.,
an architecture with a small number of communication
links.) Finally, one can look at the expected peiformance
of a proposed rule set. Performance evaluation presumes a
model of the decision situations that a rule set is designed
to handle, along with assessments of the probabilities and
utilities associated with those situations. Otherwise, it
would be possible to make a rule set look arbitrarily good
or bad by carefully selecting the decision situations the
rule set is tested against.
Each form of evaluation can provide a guide to the process
of generat ing rule sets. For instance, Remy and Levis
(1988) and Zaidi (1991) use concepts of architectural
acceptability to derive a space of candidate architectures.
These architectures, in turn, limit the types of rule sets
that can be generated. The principal result of this paper is
that a performance evaluation model can be used to derive
procedure rules. In particular, the probability/utility
information that is needed to evaluate a rule set is
"compiled" into a DTree which defines a rule set that is
logically complete, consistent, humanly-executable, and
near-optimal in expected utility. Note also, that the DTree formulation supports adaptation
to temporal and workload constraints. Recall that there is
a default decision associated with each node in a DTree.
As a result, processing of a DTree can be terminated at
anytime with a decision. This behavior can be represented
within the DTree formulation by inserting additional
Enodes, where time/workload information is the evidence
that is examined. If there are severe time/workload
constraints, then the Enode branches to a Dnode with a
default decision. If processing time is available, then the
Enode branches to the next evidence to consider.
4.0 FUTURE WORK
Future work in this area will address a number of
important issues. The first is an empirical question.
What is the expected size of a DTree? As noted earlier, a
DTree is intended to be near-optimal. Furthermore, the
expected utility of a DTree increases with each Enode that
is added, with an asymptotic value equal to the expected
utility of the decision network. However, it is remains an
· open question as to how large a near optimal DTrees must
be. Second, there is the problem of time-varying value of
information. The current formulation does not examine
evolving situations, where the value of an item of
information may change over time. Modified algorithms
to consider time dependencies are being examined. Third,
there is the problem of adaptive decision making.
Although the DTree formulation effectively supports
adaptation to time stress, it does not effectively support
adaptation to other types of problems (e.g., sensor
failures).
Finally, we note that the overall objective of this research
is to develop near-optimal decision procedures that can be
quickly and reliably executed by a team of human decision
makers. The specification of a DTree is the first step in
the process of specifying a team's decision procedures.
The DTree must still be partitioned into several decision
procedures that can be allocated to different team members.
Work in this area is also proceeding.
Acknowl edgement
This research was supported by the Office of Naval
Research under contract No. N00024-90-J-1680.
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George Mason University, Fairfax, VA. November 1991. |
77f89feb-8622-413d-af99-a1adcd3853ed | trentmkelly/LessWrong-43k | LessWrong | Russian x-risks newsletter #2, fall 2019
Russia could be seen as an x-risks wonderland, with exploding nuclear facilities and bioweapons storage sites, doomsday weapons, frequent asteroid impacts, Siberian volcanic traps, Arctic methane bomb, military AI programs, and crazy scientists wanting to drill to the Earth’s core. But there are also people who do their best trying to prevent global risks, and one of the most well-known is Petrov, who is remembered on 9/26. Russia can also provide much of the world with lessons in resilience; for example, many of its people, living in villages, can still supply themselves with food, water and energy without access to external sources.
Bio
Explosion in the virology center in Novosibirsk, 16 September 2019. The explosion was caused by a natural gas tank used in the renovation process. The fire was allegedly close to the storage facilities but didn’t directly affect them, so there was no leak of hazardous biological materials.
Why it is important? This seems to be a new type of possible catastrophic event, that has not been previously predicted, one that could produce a “multipandemic”—the simultaneous release of many deadly biological agents. A possible protective measure against new events of this type is the preservation of deadly agents in different places.
What is it? This center is one of two places in the world where live samples of smallpox are preserved. The State Scientific Center for Virology and Biotechnology (SSC WB), also known as "the Vector Center", has one of the most comprehensive collections of viruses in the world, including Ebola, Marburg hemorrhagic fever, severe acute respiratory syndrome (SARS), smallpox, and others. Created in 1974 near Novosibirsk, it was previously a closed institute that led the development of vaccines, as well as strategies for protection against bacteriological and biological weapons. In 1999, Valentin Yevstigneev, the head of the biological defense department of the Russian Ministry of Defense, said that they began to |
0bc7268c-b356-47d3-9d12-7606df440f33 | trentmkelly/LessWrong-43k | LessWrong | Direct Preference Optimization in One Minute
The Direct Preference Optimization (DPO) paper promises a more simple and efficient alternative to proximal policy optimization that is able to void the reward modeling phase, and thus optimize directly for the preferences expressed in preference data. This is achieved through the loss function:
LDPO(πθ;πref)=−E(x,yw,yl)∼D[logσ(βlogπθ(yw|x)πref(yw|x)−βlogπθ(yl|x)πref(yl|x))]
Where:
* x is some prompt
* πθ(yw|x) and πθ(yl|x) are the probabilities of the preferred and dispreferred completions under the current model.
* β is controls the deviation from the reference policy πref.
In essence, DPO computes the log probabilities of preferred and dispreferred completions under the current model and optimizes parameters to increase the likelihood of the preferred completions and decrease the likelihood of the dispreferred completions.
The authors share the following results:
"Figure 2: Left. The frontier of expected reward vs KL to the reference policy. DPO provides the highest expected reward for all KL values, demonstrating the quality of the optimization. Right. TL;DR summarization win rates vs. human-written summaries, using GPT-4 as evaluator. DPO exceeds PPO’s best-case performance on summarization, while being more robust to changes in the sampling temperature.""Figure 3: Left. Win rates computed by GPT-4 for Anthropic-HH one-step dialogue; DPO is the only method that improves over chosen summaries in the Anthropic-HH test set. Right. Win rates for different sampling temperatures over the course of training. DPO’s improvement over the dataset labels is fairly stable over the course of training for different sampling temperatures."
> We evaluate different methods by sampling completions on the test split of TL;DR summarization dataset, and computing the average win rate against reference completions in the test set. The completions for all methods are sampled at temperatures varying from 0.0 to 1.0, and the win rates are shown in Figure 2 (right). DPO, PPO and |
66249e13-669c-4e49-9733-9cdcd077864c | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Preventing an AI-related catastrophe - Problem profile
We (80,000 Hours) have just released our longest and most in-depth problem profile — on reducing existential risks from AI.
You can read the profile [here](https://80000hours.org/problem-profiles/artificial-intelligence).
The rest of this post gives some background on the profile, a summary and the table of contents.
Some background
===============
Like much of our content, this profile is aimed at an audience that has probably spent some time on the 80,000 Hours website, but is otherwise unfamiliar with EA -- so it's pretty introductory. That said, we hope the profile will also be useful and clarifying for members of the EA community.
The profile primarily represents my (Benjamin Hilton's) views, though it was edited by Arden Koehler (our website director) and reviewed by Howie Lempel (our CEO), who both broadly agree with the takeaways.
I've tried to do a few things with this profile to make it as useful as possible for people new to the issue:
* I focus on what I see as the biggest issue: risks of power-seeking AI from strategically aware planning systems with advanced capabilities, as set out by [Joe Carlsmith](https://forum.effectivealtruism.org/posts/ChuABPEXmRumcJY57/video-and-transcript-of-presentation-on-existential-risk).
* I try to make things feel more concrete, and have released a whole separate article on [what an AI-caused catastrophe could actually look like](https://80000hours.org/articles/what-could-an-ai-caused-existential-catastrophe-actually-look-like/). (This owes a lot to Carlsmith's report, as well as Christiano's [*What failure looks like*](https://www.alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like) and Bostrom's *Superintelligence*.)
* I give (again, what I see as) important background information, such as the results of [surveys of ML experts on AI risk](https://80000hours.org/problem-profiles/artificial-intelligence/#experts-are-concerned), an overview of [recent advances in AI](https://80000hours.org/problem-profiles/artificial-intelligence/#making-advances-extremely-quickly) and [scaling laws](https://80000hours.org/problem-profiles/artificial-intelligence/#scaling)
* I try to honestly explain the [strongest reasons why the argument I present might be wrong](https://80000hours.org/problem-profiles/artificial-intelligence/#best-arguments-against-this-problem-being-pressing)
* I include a long [FAQ](https://80000hours.org/problem-profiles/artificial-intelligence/#good-responses) of common objections to working on AI risk to which I think there are strong responses
Also, there's a [feedback form](https://forms.gle/RRZaFTfdDkSQ6fJG8) if you want to give feedback and prefer that to posting publicly.
This post includes the summary from the article and a table of contents.
Summary
=======
We expect that there will be substantial progress in AI in the next few decades, potentially even to the point where machines come to outperform humans in many, if not all, tasks. This could have enormous benefits, helping to solve currently intractable global problems, but could also pose severe risks. These risks could arise accidentally (for example, if we don’t find technical solutions to concerns about the safety of AI systems), or deliberately (for example, if AI systems worsen geopolitical conflict). We think more work needs to be done to reduce these risks.
Some of these risks from advanced AI could be [existential](https://80000hours.org/articles/existential-risks/) — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity. [[1]](#fn4cgz2pfdub) There have not yet been any satisfying answers to concerns — discussed below — about how this rapidly approaching, transformative technology can be safely developed and integrated into our society. Finding answers to these concerns is very neglected, and may well be tractable. We estimate that there are around 300 people worldwide working directly on this.[[2]](#fn5ahi5q68zu9) As a result, the possibility of AI-related catastrophe may be the world’s most pressing problem — and the best thing to work on for those who are well-placed to contribute.
Promising options for working on this problem include technical research on how to create safe AI systems, strategy research into the particular risks AI might pose, and policy research into ways in which companies and governments could mitigate these risks. If worthwhile policies are developed, we’ll need people to put them in place and implement them. There are also many opportunities to have a big impact in a variety of complementary roles, such as operations management, journalism, earning to give, and more — some of which we list below.
Our overall view
----------------
Recommended - highest priority
This is among the most pressing problems to work on.
Scale
-----
AI will have a variety of impacts and has the potential to do a huge amount of good. But we’re particularly concerned with the possibility of extremely bad outcomes, especially an existential catastrophe. We’re very uncertain, but based on estimates from others using a variety of methods, our overall guess is that the risk of an existential catastrophe caused by artificial intelligence within the next 100 years is around 10%. This figure could significantly change with more research — some experts think it’s as low as 0.5% or much higher than 50%, and we’re open to either being right. Overall, our current take is that AI development poses a bigger threat to humanity’s long-term flourishing than any other issue we know of.
Neglectedness
-------------
Around $50 million was spent on reducing the worst risks from AI in 2020 – billions were spent advancing AI capabilities.[[3]](#fnizx83eeaj9s) [[4]](#fnebysi0ix1p) While we are seeing increasing concern from AI experts, there are still only around 300 people working directly on reducing the chances of an AI-related existential catastrophe.[[2]](#fn5ahi5q68zu9) Of these, it seems like about two-thirds are working on technical AI safety research, with the rest split between strategy (and policy) research and advocacy.
Solvability
-----------
Making progress on preventing an AI-related catastrophe seems hard, but there are a lot of avenues for more research and the field is very young. So we think it’s moderately tractable, though we’re highly uncertain — again, assessments of the tractability of making AI safe vary enormously.
Full table of contents
======================
* [Introduction](https://80000hours.org/problem-profiles/artificial-intelligence/#top)
* [1. Many AI experts think there’s a non-negligible chance AI will lead to outcomes as bad as extinction](https://80000hours.org/problem-profiles/artificial-intelligence/#experts-are-concerned)
* [2. We’re making advances in AI extremely quickly](https://80000hours.org/problem-profiles/artificial-intelligence/#making-advances-extremely-quickly)
+ [Current trends show rapid progress in the capabilities of ML systems](https://80000hours.org/problem-profiles/artificial-intelligence/#scaling)
+ [When can we expect transformative AI?](https://80000hours.org/problem-profiles/artificial-intelligence/#when-can-we-expect-to-develop-transformative-AI)
* [3. Power-seeking AI could pose an existential threat to humanity](https://80000hours.org/problem-profiles/artificial-intelligence/#power-seeking-ai)
+ [It’s likely we’ll build advanced planning systems](https://80000hours.org/problem-profiles/artificial-intelligence/#aps-systems)
+ [Advanced planning systems could easily be dangerously ‘misaligned’](https://80000hours.org/problem-profiles/artificial-intelligence/#instrumental-convergence)
+ [Disempowerment by AI systems would be an existential catastrophe](https://80000hours.org/problem-profiles/artificial-intelligence/#lost-control)
+ [People might deploy misaligned AI systems despite the risk](https://80000hours.org/problem-profiles/artificial-intelligence/#incentives-and-deception)
* [This all sounds very abstract. What could an existential catastrophe caused by AI actually look like?](https://80000hours.org/problem-profiles/artificial-intelligence/#this-all-sounds-very-abstract-what-could-an-existential-catastrophe-caused-by-ai-actually-look-like)
* [4. Even if we find a way to avoid power-seeking, there are still risks](https://80000hours.org/problem-profiles/artificial-intelligence/#other-risks)
+ [AI could worsen war](https://80000hours.org/problem-profiles/artificial-intelligence/#artificial-intelligence-and-war)
+ [AI could be used to develop dangerous new technology](https://80000hours.org/problem-profiles/artificial-intelligence/#dangerous-new-technology)
+ [AI could empower totalitarian governments](https://80000hours.org/problem-profiles/artificial-intelligence/#ai-could-empower-totalitarian-governments)
+ [Other risks from AI](https://80000hours.org/problem-profiles/artificial-intelligence/#other-risks-from-ai)
* [So, how likely is an AI-related catastrophe?](https://80000hours.org/problem-profiles/artificial-intelligence/#how-likely-is-an-AI-related-catastrophe)
* [5. We can tackle these risks](https://80000hours.org/problem-profiles/artificial-intelligence/#we-can-tackle-these-risks)
+ [Technical AI safety research](https://80000hours.org/problem-profiles/artificial-intelligence/#technical-ai-safety-research)
+ [AI governance research and implementation](https://80000hours.org/problem-profiles/artificial-intelligence/#ai-governance-research-and-implementation)
* [6. This work is extremely neglected](https://80000hours.org/problem-profiles/artificial-intelligence/#neglectedness)
* [What do we think are the best arguments we’re wrong?](https://80000hours.org/problem-profiles/artificial-intelligence/#best-arguments-against-this-problem-being-pressing)
* [Arguments against working on AI risk to which we think there are strong responses](https://80000hours.org/problem-profiles/artificial-intelligence/#good-responses)
* [What you can do concretely to help](https://80000hours.org/problem-profiles/artificial-intelligence/#what-can-you-do-concretely-to-help)
+ [Technical AI safety](https://80000hours.org/problem-profiles/artificial-intelligence/#technical-ai-safety)
+ [AI governance and strategy](https://80000hours.org/problem-profiles/artificial-intelligence/#ai-governance-and-strategy)
+ [Complementary (yet crucial) roles](https://80000hours.org/problem-profiles/artificial-intelligence/#complementary-yet-crucial-roles)
+ [Other ways to help](https://80000hours.org/problem-profiles/artificial-intelligence/#other-ways-to-help)
+ [Want one-on-one advice on pursuing this path?](https://80000hours.org/problem-profiles/artificial-intelligence/#want-one-on-one-advice-on-pursuing-this-path)
+ [Find vacancies on our job board](https://80000hours.org/problem-profiles/artificial-intelligence/#find-vacancies-on-our-job-board)
* [Top resources to learn more](https://80000hours.org/problem-profiles/artificial-intelligence/#top-resources-to-learn-more)
* [Acknowledgements](https://80000hours.org/problem-profiles/artificial-intelligence/#acknowledgements)
Acknowledgements
================
*Huge thanks to Joel Becker, Tamay Besiroglu, Jungwon Byun, Joseph Carlsmith, Jesse Clifton, Emery Cooper, Ajeya Cotra, Andrew Critch, Anthony DiGiovanni, Noemi Dreksler, Ben Edelman, Lukas Finnveden, Emily Frizell, Ben Garfinkel, Katja Grace, Lewis Hammond, Jacob Hilton, Samuel Hilton, Michelle Hutchinson, Caroline Jeanmaire, Kuhan Jeyapragasan, Arden Koehler, Daniel Kokotajlo, Victoria Krakovna, Alex Lawsen, Howie Lempel, Eli Lifland, Katy Moore, Luke Muehlhauser, Neel Nanda, Linh Chi Nguyen, Luisa Rodriguez, Caspar Oesterheld, Ethan Perez, Charlie Rogers-Smith, Jack Ryan, Rohin Shah, Buck Shlegeris, Marlene Staib, Andreas Stuhlmüller, Luke Stebbing, Nate Thomas, Benjamin Todd, Stefan Torges, Michael Townsend, Chris van Merwijk, Hjalmar Wijk, and Mark Xu for either reviewing the article or their extremely thoughtful and helpful comments and conversations. (This isn’t to say that they would all agree with everything I said – in fact we’ve had many spirited disagreements in the comments on the article!)*
*This work is licensed under a*[*Creative Commons Attribution 4.0 International License*](https://creativecommons.org/licenses/by/4.0/)*.*
1. **[^](#fnref4cgz2pfdub)**We’re also concerned about the possibility that AI systems could deserve moral consideration for their own sake — for example, because they are sentient. We’re not going to discuss this possibility in this article; we instead cover artificial sentience in a separate article [here](https://80000hours.org/problem-profiles/#artificial-sentience).
2. **[^](#fnref5ahi5q68zu9)**I estimated this using the [AI Watch](https://aiwatch.issarice.com/) database. For each organisation, I estimated the proportion of listed employees working directly on reducing existential risks from AI. There’s a lot of subjective judgement in the estimate (e.g. “does it seem like this research agenda is about AI safety in particular?”), and it could be too low if AI Watch is missing data on some organisations, or too high if the data counts people more than once or includes people who no longer work in the area. My 90% confidence interval would range from around 100 people to around 1,500 people.
3.
4. **[^](#fnrefebysi0ix1p)**It’s difficult to say exactly how much is being spent to advance AI capabilities. This is partly because of a lack of available data, and partly because of questions like:
\* What research in AI is actually advancing the sorts of dangerous capabilities that might be increasing potential existential risk?
\* Do advances in AI hardware or advances in data collection count?
\* How about broader improvements to research processes in general, or things that might increase investment in the future through producing economic growth?
The most relevant figure we could find was the expenses of DeepMind from 2020, which were around £1 billion, [according to their annual report](https://find-and-update.company-information.service.gov.uk/company/07386350/filing-history). We’d expect most of that to be contributing to “advancing AI capabilities” in some sense, since their main goal is building powerful, general AI systems. (Although it’s important to note that DeepMind is also contributing to work in AI safety, which may be reducing existential risk.)
If DeepMind is around about 10% of the spending on advancing AI capabilities, this gives us a figure of around £10 billion. (Given that there are many AI companies in the US, and a large effort to produce advanced AI in China, we think 10% could be a good overall guess.)
As an upper bound, the total revenues of the AI sector in 2021 were [around $340 billion](https://www.idc.com/getdoc.jsp?containerId=prUS48127321).
So overall, we think the amount being spent to advance AI capabilities is between $1 billion and $340 billion per year. Even assuming a figure as low as $1 billion, this would still be around 100 times the amount spent on reducing risks from AI. |
c31cb0da-0358-423c-8653-36cdc3b76a0d | trentmkelly/LessWrong-43k | LessWrong | Meetup : Durham/RTLW Discussion Meetup: Cognitive Load Followup
Discussion article for the meetup : Durham/RTLW Discussion Meetup: Cognitive Load Followup
WHEN: 30 May 2014 07:00:00PM (-0400)
WHERE: 420 W Geer St., Durham NC 27701
We'll start with followup on last meetup's discussion of cognitive load. As time and inclination permit, we'll do another round of other-optimizing and/or some games.
7:00 meet, gather drinks, general chat
7:30 discussion begins
9:00 adjourn to Fullsteam (probably)
Hope to see you there!
Discussion article for the meetup : Durham/RTLW Discussion Meetup: Cognitive Load Followup |
b44ac66f-ed50-47f1-a559-0eceed0648e4 | trentmkelly/LessWrong-43k | LessWrong | Self Supervised Learning (SSL)
Self Supervised Learning (SSL)
"Unlocking Powerful Representations: The Frontier of Self-Supervised Learning"
JASKARAN SINGH
AUG 9, 2023
Share
With all that’s been happening in the AI/ML industry for the past few weeks, it is important we address the elephant in the room.
The Idea Behind SSL
SSL comes under the umbrella of Unsupervised Learning. One thing that worked for NNs is that they are able to fit a curated dataset with ease given they have labels to optimize for (Supervised Learning), but this dataset may not be large enough, instead, it would be very expensive or impossible to create such a dataset. Once NNs have good representations of the task-related work they can learn a new task even more rapidly when shown how to do it only once (Zero-shot).
But what if we can create synthetic Labels from the data?
they don’t need to be highly curated. They can be thought of as corrupting the data. we can use these labels to train NNs and in the process, these NNs learn a powerful representation (depending upon the data and compute) of the Data that can be fine-tuned with a handful of data to produce quality results.
How to SSL
The main goal of SSL is to generate labels out of the dataset itself, without much human effort. The basic idea is to corrupt the data or either do a lossy compression to predict the same data back from the Model. Let’s discuss some ways in which researchers have used this technique in different domains:
NLP
While training a Language Model you can do SSL in two ways:
MLM: Masking some words of the text (Corrupt) and letting the model train on getting these masked tokens correctly. Research Paper
LM: given some context let the Model predict the rest of the text, one token at a time. Research Paper
These approaches are part of contrastive training: in short, you train the model to differentiate between the different classes, during training you want the model to give a high probability to the ground truth and near |
62d3e5d2-8bda-4dec-8544-4c253f60f202 | trentmkelly/LessWrong-43k | LessWrong | Five Stages of Idolatry
We all have heroes or idols, people we look up to and turn to, in one form or another, for guidance or wisdom. Over the years, I've noticed that my feelings towards those I've idolized tend to follow a predictable pattern. The following is the extraction of this pattern.
Stage 1: Exposure - you're exposed to the idol through some channel. Maybe something you read, or someone you know, or simply by chance. You begin to learn about them, and you become intrigued. If it's an author, maybe you pick up one of his books. If it's a group, maybe you check out their website. You begin to gradually absorb what the idol is offering. They don't actually become an idol though, until...
Stage 2: Resonance - after enough exposure, what the idol offers begins to strike a chord with you. You go on to ravenously consume everything related to it. You track down every one of the authors publications, or spend hours staring at all of an artists' paintings. As far as I can tell what's important here isn't actually the content of what the idol offers, but that feeling of resonance it engenders.
Step 3: Incorporation - the idol has become one of the lenses through which you view the world. Everyone and everything is compared to the idol, and everyone invariable comes up short (raise your hand if you've ever thought about someone "Well, they're pretty smart, but not as smart as Eliezer"). You change your lifestyle to be more like them, to think more like them. It's as if they have all aspects of life figured out, and you follow along in the hopes that you will reach their same understanding. The most fervent support of the idol lies here.
Step 4: Backlash - you start to realize that the idol does not, in fact, provide the answers to all of life's questions. That they might be wrong about some things, or that their specific offering does not apply to all life's situations. You've changed yourself to emulate the idol, and you realize that perhaps not all those changes were for the better. Pa |
46bb896d-0234-41e0-8468-3128dbc6ee70 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | When discussing AI doom barriers propose specific plausible scenarios
TLDR:When addressing "How would an AI do X" questions, prefer simple existence proofs involving things humans can currently do rather than "magic" seeming things like nanotechnology or the fully general "AI will just be smart enough to figure it out". TLDR:END
Commonly in AI risk conversations questions of the form "how would an AI do X" where X is something like "control physical resources) or (survive without humans) are asked.
There is a "would/could" distinction in these questions.
* How **would** an ASI do X -->(predict actions of ASI pursuing goal X)
* How **could** an ASI do X --> (Show that an AI can accomplish X)
The **"would"** question is correct to answered with (nanotech/super-persuasion/Error:cannot\_predict\_ASI) but the **"could"** form is usually the intention and leads to better conversations.
For **"would"** questions, the answer can be prefaced with "The AI would be cleverer than me so I can't predict what it would actually do. One way it could do X though is to ..."
Hypothetical example
--------------------
>
> Doubter: How will the AI take over if it's stuck inside the internet.
>
>
> Doomer: The AI is smart enough to figure out a solution like nanotechnology or something.
>
>
> Doubter: I don't believe you. I don't think it can do that.
>
>
> Doomer: \*hands them a copy of the Nanosystems book\*
>
>
> Doubter: I never took CHEM101 and besides no one has ever built this so I still don't believe you.
>
>
>
Compare
>
> Doubter: How will the AI take over if it's stuck inside the internet.
>
>
> Doomer: It can just hack all the computers and hold them and the data hostage.
>
>
> Doubter: We would refuse it's demands!
>
>
> Doomer: Here's a list of stuff connected to the internet, how hard that stuff is to fix if hacked and examples of human hackers hacking that stuff.
>
>
>
The first reply is a fully general argument that intelligence can solve most problems. If you're wondering how the AI could make ice cubes and you don't know that freezers are possible, use the fully general argument. The AI will just be smart enough to invent some new technology that can make ice cubes. If you do know of an existing solution then just point to that as an existence proof. "The AI can order an ice-maker off Amazon and hire a taskrabbit to set it up and operate it."
Concern: seeing only the easy to stop attacks
---------------------------------------------
>
> [That’s a problem with such scenarios in general. By trying to be as reasonable and grounded as possible, by showing how little it takes, you anchor people on that little being all you have. The more one tries to match people’s instincts for ‘realistic’ the less actually realistic you are presenting the situation, and the further you enforce that distinction.](https://www.lesswrong.com/posts/mqc99HCMRAjnWSAxz/a-hypothetical-takeover-scenario-twitter-poll#Dangers_of_Rhetorical_Generosity)
>
>
>
So if before they didn't think AI would be able to do X. Now they think AI could do X but we can and will just stop it by putting in a barrier that stops a specific scenario Y. If we by some miracle fix computer security, the users won't be any less vulnerable to blackmail and social engineering.
I still think it is better if people believe there is a real threat even if they believe it can be solved with some specific countermeasure rather than thinking something much more ridiculous like "AI won't be able to affect the real world".
One Motivating Example
----------------------
[Eliezer ted talk Q/A](https://youtu.be/Yd0yQ9yxSYY?t=356)
>
> Chris Anderson: For this to happen, for an AI to basically destroy humanity, it has to break out, escape controls of the internet and, you know, start commanding actual real-world resources. You say you can't predict how that will happen, but just paint one or two possibilities.
>
>
> Eliezer Yudkowsky: OK, so why is this hard? First, because you can't predict exactly where a smarter chess program will move. Maybe even more importantly than that, imagine sending the design for an air conditioner back to the 11th century. Even if it’s enough detail for them to build it, they will be surprised when cold air comes out because the air conditioner will use the temperature-pressure relation
> and they don't know about that law of nature. [...]
>
>
>
EY is correctly provides the "Error:cannot\_predict\_ASI" response saying that an ASI will likely use some unanticipated better solution involving natural laws we don't understand well.
The implicit question of "can an AI do X" (obviously YES) is not addressed unless the provided justification of "AI is very smart and so can do most possible things" argument can be made which is quite hard to do. It is not necessarily clear to the other party that this is even possible.
Here's how I would answer the question:
>
> My answer: An AI much smarter than me could obviously find a better strategy but if the goal is to take over the world, internet connected devices are mostly enough. An AI that takes over internet connected devices can shut them down to force compliance. In addition to the phones, computers and payment terminals, smart electricity meters can cut power to buildings and cars can be disabled or made to crash.
>
>
> Killing all the humans while keeping the electricity and chip factories on is harder. It might require building a lot of robots but seems feasible with current technology. A lazy AI could just wait till we build the tools it needs. The AI will be smarter of course. It will come up with a better solution, but the problem can be solved in principle.
>
>
>
### Disclaimers
* I had time to think/edit. EY did not. Still, I wish EY would change his default argument for this sort of question.
* generating >> editing.
+ Generating a full X is much harder than optimising an existing X
+ my posting this does not mean "I am a better arguer in full generality" (I'm not).
+ This is just an obvious to me mistake
* The danger of focusing others on addressable risks could make this a bad idea. |
f091d40f-2f3f-4ed2-b135-9fdf74573e47 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Useful Does Not Mean Secure
*Brief summary of what I'm trying to do with this post:*
1. *Contrast a “Usefulness” focused approach to building AI with a “Security” focused approach, and try to give an account of where security problems come from in AI.*
2. *Show how marginal transparency improvements don’t necessarily improve things from the perspective of security.*
3. *Describe what research is happening that I think is making progress from the perspective of security.*
*In this post I will be attempting to* [*taboo*](https://www.lesswrong.com/posts/WBdvyyHLdxZSAMmoz/taboo-your-words) *the term 'alignment', and just talk about properties of systems. The below is not very original, I'm often just saying things in my own words that* [*Paul*](https://www.lesswrong.com/posts/HBxe6wdjxK239zajf/what-failure-looks-like) *and* [*Eliezer*](https://www.lesswrong.com/posts/8gqrbnW758qjHFTrH/security-mindset-and-ordinary-paranoia) *have written, in large part just to try to think through the considerations myself. My thanks to Abram Demski and Rob Bensinger for comments on a draft of this post, though this doesn't mean they endorse the content or anything.*
Useful Does Not Mean Secure
---------------------------
This post grew out of a comment thread [elsewhere](https://www.lesswrong.com/posts/X2i9dQQK3gETCyqh2/chris-olah-s-views-on-agi-safety#QyeCpFTcXsQuxpz7B). In that thread, Ray Arnold was worried that there was an uncanny valley of how good we are at understanding and building AI where we can build AGI but not a safe AGI. Rohin Shah replied, and I'll quote from his reply:
> Consider instead this worldview:
>
>
> *The way you build things that are useful and do what you want is to understand how things work and put them together in a deliberate way. If you put things together randomly, they either won't work, or will have unintended side effects.*
>
> (This worldview can apply to far more than AI; e.g. it seems right in basically every STEM field. You might argue that putting things together randomly seems to work surprisingly well in AI, to which I say that it really doesn't, you just don't see all of the effort where you put things together randomly and it simply flat-out fails.)
>
> The argument "it's good to for people to understand AI techniques better even if it accelerates AGI" is a very straightforward non-clever consequence of this worldview.
>
> [...]
>
> Under the worldview I mentioned, the first-order effect of better understanding of AI systems, is that you are more likely to build AI systems that are useful and do what you want.
>
>
A lot of things Rohin says in that thread make sense. But in this post, let me point to a different perspective on AI that I might consider, if I were to focus entirely on Paul Christiano's [model of greedy algorithms in part II of his post on what failure looks like](https://www.lesswrong.com/posts/HBxe6wdjxK239zajf/what-failure-looks-like). That perspective sounds something like this:
> *The way you build things that are useful and do what you want, when you're in an environment with much more powerful optimisers than you, is to spend a lot of extra time making them secure against adversaries, over and above simply making them useful. This is so that the other optimisers cannot exploit your system to achieve their own goals.*
>
> *If you build things that are useful, predictable, and don't have bad side-effects, but are subject to far more powerful optimisation pressures than you, then by default the things you build will be taken over by other forces and end up not being very useful at all.*
>
>
An important distinction about artificial intelligence research is that you're not simply competing against other humans, where you have to worry about hackers, governments and political groups, but that the core goal of artificial intelligence research is the *creation* of much more powerful general optimisers than currently exist within humanity. This is a difference in kind from all other STEM fields.
Whereas normal programming systems that aren't built quite right are more likely to do dumb things or just break, when you make an AI system that isn't exactly what you wanted, the system might be powerfully optimising for other targets in a way that has the potential to be highly adversarial. In discussions of AI alignment, Stuart Russell often likes to use an analogy to “building bridges that stay up” being an entirely integrated field, not distinct from bridge building. To extend the analogy a little, you might say the field of AI is unusual in that if you don't quite make the bridge well enough, the bridge itself may actively seek out security vulnerabilities that bring the bridge down, then hide them from your attention until such a time as it has the freedom to take the bridge down in one go, and then take out all the other bridges in the world.
Now, talk of AI necessarily blurs the line between 'external optimisation pressures' and 'the system is useful and does what you want' because the system itself is *creating* the new, powerful optimisation pressure that needs securing against. Paul's [post](https://www.lesswrong.com/posts/HBxe6wdjxK239zajf/what-failure-looks-like) on what failure looks like talks about this, so I’ll quote it here:
> Modern ML instantiates *massive* numbers of cognitive policies, and then further refines (and ultimately deploys) whatever policies perform well according to some training objective. If progress continues, eventually machine learning will probably produce systems that have a detailed understanding of the world, which are able to adapt their behavior in order to achieve specific goals.
>
> Once we start searching over policies that understand the world well enough, we run into a problem: any influence-seeking policies we stumble across would also score well according to our training objective, because performing well on the training objective is a good strategy for obtaining influence.
>
> How frequently will we run into influence-seeking policies, vs. policies that just straightforwardly pursue the goals we wanted them to? I don’t know.
>
>
You could take the position that, even though security work is not normally central to a field, this new security work is already central to this field, so increasing the ability to build 'useful' things will naturally have to solve this novel security work, so the field of AI will get it right by default.
This is my understanding of Paul's mainline expectation (based on his estimates [here](https://www.lesswrong.com/posts/qnYZmtpNPZyqHpot9/conversation-with-paul-christiano) and that his work is based around making useful / well motivated AI described [here](https://www.lesswrong.com/s/EmDuGeRw749sD3GKd/p/4iPBctHSeHx8AkS6Z), [here](https://www.lesswrong.com/s/EmDuGeRw749sD3GKd/p/ZeE7EKHTFMBs8eMxn) and [in Rohin’s comment on that post](https://www.lesswrong.com/s/EmDuGeRw749sD3GKd/p/ZeE7EKHTFMBs8eMxn#3ECKoYzFNW2ZqS6km)) and also my understanding of Rohin's mainline expectation (based on his estimates [here](https://www.lesswrong.com/posts/TdwpN484eTbPSvZkm/rohin-shah-on-reasons-for-ai-optimism)). My understanding is this still means there's a lot of value on the table from marginal work, so both of them work on the problem, but by default they expect the field to engage with this problem and do it well.
Restatement: In normal tech companies, there's a difference between "making useful systems" and "making secure systems". In the field of AI, "making useful systems" includes potentially building powerful adversaries, which involves novel security problems, so you might expect that executing the standard "make useful systems" will result in solving the novel security features.
For example, in a [debate on instrumental convergence between various major AI researchers](https://www.lesswrong.com/posts/WxW6Gc6f2z3mzmqKs/debate-on-instrumental-convergence-between-lecun-russell), this was also the position that Francesca Rossi took:
> Stuart, I agree that it would easy to build a coffee fetching machine that is not aligned to our values, but why would we do this? Of course value alignment is not easy, and still a research challenge, but I would make it part of the picture when we envision future intelligent machines.
>
>
However, Yann LeCun said something subtly different:
> One would have to be rather incompetent not to have a mechanism by which new terms in the objective could be added to prevent previously-unforeseen bad behavior.
>
>
Yann is implicitly taking the stance that there will not be powerful adversarial pressures exploiting such unforeseen differences in the objective function and humanity's values. His responses are of the kind "We wouldn't do that" and "We would change it quickly when those problems arose", but not "Here's how you build a machine learning system that cannot be flawed in this way". It seems to me that he does not expect there to be any further security concerns of the type discussed above. If I pointed out a way that your system would malfunction, it is sometimes okay to say “Oh, if anyone accidentally gives that input to the system, then we’ll see and fix any problems that occur”, but if your government computer system is not secure, then by the time you’ve noticed what’s happening, a powerful adversary is inside your system and taking actions against you.
(Though I should mention that I don't think this is the crux of the matter for Yann. I think his key disagreement is that he thinks we cannot talk usefully about safe AGI design before we know how to build an AGI - he doesn't think that [prosaic AI alignment](https://ai-alignment.com/prosaic-ai-control-b959644d79c2) is in principle feasible or worth thinking about.)
In general, it seems to me that if you show me how an AI system is flawed, if my response is to simply patch that particular problem then go back to relaxing, I am implicitly disbelieving that optimisation processes more powerful than human civilization will look for similar flaws and exploit them, as otherwise my threat level would go up drastically.
To clarify what this worry looks like: advances in AGI are hopefully building systems that can scale to being as useful and intelligent as is physically feasible in our universe - optimisation power way above that of human civilization's. As you start getting smarter, you need to build more into your system to make sure the smart bits can't exploit the system for their own goals. This assumes an epistemic advantage, as Paul says in the Failure post:
> Attempts to suppress influence-seeking behavior (call them “immune systems”) rest on the suppressor having some kind of epistemic advantage over the influence-seeker. Once the influence-seekers can outthink an immune system, they can avoid detection and potentially even compromise the immune system to further expand their influence. If ML systems are more sophisticated than humans, immune systems must themselves be automated. And if ML plays a large role in that automation, then the immune system is subject to the same pressure towards influence-seeking.
>
>
There's a notion whereby if you take a useful machine learning system, and you just make it more powerful, what you're essentially doing is increasing the intelligence of the optimisation forces passing through it, including the adversarial optimisation forces. As you take the system and make it vastly superintelligent, your primary focus needs to be on security from adversarial forces, rather than primarily on making something that's useful. You've become an AI security expert, not an AI usefulness expert. The important idea is that AI systems can break at higher levels of intelligence, even if they're currently quite useful.
As I understand it, this sort of thing happened at Google, who first were a computer networks experts, and then became security experts, because for a while the main changes they made to Google Search were to increase security and make it harder for people to game the pagerank system. The adversarial pressures on them have since hit terminal velocity and there probably won't be any further increases, unless and until we build superintelligent AI (be it general or the relevant kind of narrow.)
Marginal Transparency Does Not Mean Marginal Security
-----------------------------------------------------
A key question in figuring out whether to solve this security problem via technical research (as opposed to global coordination) is whether a line of work differentially makes this sort of security from optimisation powers easier to work on, or whether it simply makes the system more useful in ways that don't account for the novel adversaries.
In the quote from Rohin above, and by other people in other places, it sounds to me like the case being made is that the system is generally becoming more understandable, so it's both easier to build and easier to secure. All improvements in understanding are improvements in being able to 'do useful things'. And indeed my general prior is that increasing understanding is always better. But one of the core arguments Nick Bostrom makes is that not all scientific insights are positive for a civilisation at its current level of wisdom, and that we need to be able to order which insights we get. There are many types of 'increased understanding', and it's not the case that all of them are positive on the current margin.
Let me give a concrete example where more transparency increases usefulness but not security.
It’s sometimes the case that, in prisons, the prison staff will attempt to get the prisoners to help out with tasks, errands and maintenance around the prison. However, if you send them into offices and parts of the prison but don’t watch what they do, then they may conspire against the prison staff - build things that can be used as weapons against the guards, build tunnels, use computers to contact the outside world, etc.
Now, if you start looking at what the prisoners are doing when they’re in office block #17, and you have basic rewards/punishments (e.g. giving or removing access to special foods and entertainment things), then you can reward them for doing good work there, and punish them for doing bad work there. You can also punish them for conspiring against you there.
However, this hasn’t really helped. if they were planning to build a tunnel in office block #17, they will now just build a tunnel in office block #18 instead. If I can start to see what they do all the time in some locations, then I can punish the ones that aren't doing broadly the right work, and generally make sure they're doing useful work. But this doesn't mean that in the places I can't look that they're not conspiring against me. It seems plausible to me that many improvements in transparency, while making it easier to make the people do the prison’s work, do not make it harder to conspire against me, and just *move around where the conspiring is happening*.
If you’re trying to delegate and incentivise people to do labour for you, you might just think that you should bring in a good manager, to get people to do useful work. But in the prison situation, you primarily need to become a security expert, over and above being an expert in how to manage well. In this situation, there are many improvements in transparency of what they're doing that helps force them to do *useful* work, that doesn't secure them against conspiring with each other to break out of the system.
With machine learning systems, we already have all the weights on the NN's to look at, so the system is maximally transparent. We can see everything, for certain values of 'see'. I think the relevant question is "on what level you can understand what's going on". As we get higher-level understanding, we can maybe start to figure out if it's doing certain bad things, or certain good things, and punish/reward those. But just because you're making sure that the process will do something useful (e.g. invest money, run a hospital, classify images) doesn't mean I know how to tell whether this will lead to the type of full understanding that means that adversarial work can't be moved to areas that are too hard / very costly for me to understand.
Restatement: Marginal improvements in understandability and transparency can make it much easier to make *useful* systems but it's not necessarily the case that it produces a meaningful difference in the ability to produce *secure* systems. It will allow us, at increasingly higher levels of understanding, to be able to change the type of work needed to exploit the system; this is not the same as a design that is safe no matter how powerful the optimisation power against us.
I wrote this in response to Ray trying to figure out how to tell whether any given type of machine learning research is making differential progress. The specific type of research discussed in that thread has a more detailed story which I won't go into here, and mostly seems very helpful from my layman perspective, but I think that research "either being of zero impact, or else making the whole field more transparent/understandable" does not mean that the research makes differential progress on making the system secure. Marginal transparency can increase usefulness without increasing security.
In one sense, a machine learning system is maximally transparent - I can see every part of what it is doing. But while I don't understand its reasoning, while there are levels on which I don't know what it’s thinking, by default I’m not confident that adversarial thought hasn't just moved there instead.
Current Technical Work on Security
----------------------------------
From this perspective, let me talk about the research that seems like it's aiming to help on the security front. This is not all the work being done, just the work that I feel I understand well enough to summarise from this perspective.
My understanding is that the main work attempting to pinpoint where optimisation enters the system in surprising ways is Hubinger, Mikulik, Skalse, van Merwijk and Garrabrant's work on risks from learned optimisation ([paper](https://arxiv.org/abs/1906.01820), [sequence](https://www.lesswrong.com/s/r9tYkB2a8Fp4DN8yB)). This gives lots of names to concepts describing how optimisers work, and asks questions like:
* Under what conditions will my learned algorithm itself do optimisation?
* When the learned algorithm does optimisation, what will its objective be, and what will the relationship be between its objective and the loss function of the neural net that produced it?
* If the learned optimiser has successfully built a model of the objective function that was used to build it, what conditions predict whether it will it be working around my objective as opposed to toward it?
* When should I expected the optimiser in the learned algorithm to try to deceive me?
The paper also asks whether it's possible to prevent influence-seeking algorithms from entering your systems by creating complexity measures on the system, such as time and space penalties. On this topic, Paul Christiano has asked [whether requiring systems be maximally efficient according to circuit description length removes all adversarial behaviour](https://www.lesswrong.com/posts/nyCHnY7T5PHPLjxmN/open-question-are-minimal-circuits-daemon-free); and Evan has offered [an answer in the negative.](https://www.lesswrong.com/posts/fM5ZWGDbnjb7ThNKJ/are-minimal-circuits-deceptive)
It's also the case that the Agent Foundations team at MIRI is trying to think about the problem of inner alignment more broadly, and poke at various concepts around here, such as in their writeups on [Robust Delegation and Subsystem Alignment](https://www.alignmentforum.org/s/Rm6oQRJJmhGCcLvxh/p/i3BTagvt3HbPMx6PN#4__Robust_delegation). This explores many simple background questions to which we don't have principled answers, and cannot draw toy models of intelligent agents that reliably get these problems right.
* *Is there a principled way to figure out whether I should trust that something more intelligent than me shares my values, given that I can't figure out exactly what it's going to do?* If I am a child, sometimes adults will do something that the opposite of what I want - is there a way of figuring out whether they're doing this in accordance with my goals?
* *How should I tell a more intelligent agent than me what I want it to do, given that I don't know everything about what I want?* This is especially hard given that optimisation amplifies the differences between what I say I want and what I actually want (aka Goodhart's Law).
* *How do I make sure the different parts of a mind are in a good balance, rather than some parts overpowering other parts?* When it comes to my own mind, sometimes different parts get out of whack and I become too self-critical, or overconfident, or depressed, or manic. Is there a principled way of thinking about this?
* *How do I give another agent a good description of what to do in a domain, without teaching them everything I know about the domain?* This is a problem in companies, where sometimes people who don't understand the whole vision can make bad tradeoffs.
That's the work I feel I have a basic understanding of. I'm curious about explanations of how other work fits into this framework. |
f5df5ed0-cb9a-4c14-8a8c-95477fd17e47 | trentmkelly/LessWrong-43k | LessWrong | Is friendly AI "trivial" if the AI cannot rewire human values?
I put "trivial" in quotes because there are obviously some exceptionally large technical achievements that would still need to occur to get here, but suppose we had an AI with a utilitarian utility function of maximizing subjective human well-being (meaning, well-being is not something as simple as physical sensation of "pleasure" and depends on the mental facts of each person) and let us also assume the AI can model this "well" (lets say at least as well as the best of us can deduce the values of another person for their well-being). Finally, we will also assume that the AI does not possess the ability to manually rewire the human brain to change what a human values. In other words, the ability for the AI to manipulate another person's values is limited by what we as humans are capable of today. Given all this, is there any concern we should have about making this AI; would it succeed in being a friendly AI?
One argument I can imagine for why this fails friendly AI is the AI would wire people up to virtual reality machines. However, I don't think that works very well, because a person (except Cypher from the Matrix) wouldn't appreciate being wired into a virtual reality machine and having their autonomy forcefully removed. This means the action does not succeed in maximizing their well-being.
But I am curious to hear what arguments exist for why such an AI might still fail as a friendly AI. |
29a07851-4391-4b0a-9fed-421ae87bbba0 | trentmkelly/LessWrong-43k | LessWrong | Each reference class has its own end
It is often claimed that the Self-Sampling assumption (SSA) is problematic, in particular, because we don’t know which reference class is a correct one. Therefore, the notorious Doomsday argument is weak. Here I will try to show that it is less a problem than seems.
Central claim: Each reference class has its own end.
Example: I am put randomly in a 2-dimensional grid of rooms. Vertically they are designated by letters from A to Z, and horizontally they are numbered from 1 to 100. So, each room has an address like E021. The Doomsday-like claim is that I am more likely to be somewhere in the middle of the grid than on its borders and thus, I am unlikely to be in the raw of 1-rooms and in A-column. Therefore, claims DA, if I learn my room number, I can estimate the total number of rooms in my raw, and the same is true for letters. (UPDATE: by "middle" I mean some region not on the borders, not exactly cell N50, but, say, between 20 and 80.)
A real-world example: I assume that my height and my date of birth are two independent variables. Thus, both should be in the middle of the distribution: I am unlikely to be born on January, 1, and unlikely to have a very low height. It is actually true for me: my date of birth and my height are in the middle of the distribution.
Here we use two important assumptions:
1. The independence of the variables, like height and date of birth.
2. Each variable is external to my cognition process, so it doesn’t affect my probability to think about anthropics.
Now we could try to apply this to real-world Doomsday-like problems, that is, claims like “I am in the middle of my reference class existence in time”.
Let’s discuss three of my reference classes and the corresponding predictions about the end of the world:
1. I am a member of the class of mammals, which existed for last 200 million years, and thus mammals may exist a few hundreds of millions of years more.
2. I am Russian, and Russians have existed for around last 1000, |
dcd14d3a-c5ef-4774-be07-eec6bf6ea5aa | trentmkelly/LessWrong-43k | LessWrong | Lsusr's Rationality Dojo
> Why aren’t there dojos that teach rationality?
>
> ―The Martial Art of Rationality by Eliezer Yudkowsky
For the last 6 months, I've been running a dojo that teaches rationality.
Why
I was at an ACX meetup and met an acolyte who grew up in an evangelical Christian community. He had recently discovered the Sequences and was really excited about this whole Rationality thing. He was very confident in Yudkowsky's teachings.
I asked him a couple questions and he realized his beliefs were full of holes. He wondered how he could have understood so little. After all, he had read all of Yudkowsky's Sequences.
"I have read 100 books about chess," I said, "Surely I must be a grandmaster by now."
At that moment, he was enlightened.
The problem
The objective of rationality is to become right instead of wrong. Being wrong feels exactly like being right. We are not aware of our own biases. We are not aware of our own mistakes. We are not aware of the lies we tell ourselves. This is almost a tautology.
Other people are not tautologically blind to our mistakes in the same way. The simplest way to become less wrong is to have someone else point out your mistakes to you. Except this doesn't actually work. If I say "I'm right," and you say "you're wrong", then we get nowhere. The more we argue, the more frustrated we get.
The solution
There is a better way. I call it rhetorical aikido. Rhetorical aikido is a Daoist form of Socratic dialogue. The simplest form of rhetorical aikido has three steps:
1. You let someone confidently state a belief A that you know is wrong.
2. You let that same someone confidently state a belief B that contradicts A.
3. You let them notice that A contradicts B.
Examples:
[I'm the guy in the dark green chair on your right.]
Notice that this technique follows Dale Carnegie's guidelines. You smile. You agree. You show genuine interest in the other person. You don't say "You're wrong". You never even say your own beliefs (unless asked). There |
b5ab2261-3c90-47f2-b7d1-14b34999ff42 | trentmkelly/LessWrong-43k | LessWrong | Adequacy as Levels of Play
Adequacy as Levels of Play
One method that I find very useful for evaluating adequacy concerns is "level of play". If you look at different games or different leagues of the same game, it's pretty apparent that the "level of play" - the amount of athleticism, effort, skill, planning, strategy, etc. that is on display - is quite different.
For instance, in the United States the NFL is operating at a higher level of play than college football. Similarly, baseball in the United States operates at a higher level of play than baseball in Japan, and either is significantly elevated compared to the rest of the world. In South Korea, Starcraft is treated as a professional sport, and is thus predictably operating at a higher level than Starcraft in the United States. This sort of consideration doesn't just apply to sports - cryptocurrency trading operated at an obviously much lower level of play than normal finance for a long time (though that may be changing).
A similar concept can be applied to general adequacy - one can quite usefully analyze existing organizations or programs by asking "what's the level of play here?" There are three basic questions that I think are quite useful for analyzing what level something is on:
* Seriousness. Do people take this seriously? There are many areas that nobody really tries very hard at, and those areas usually operate at a low level of play - why bother to do it right if you don't really care that much? In sports, think about the difference between a niche sport like jai alai and something like soccer.
* Competitiveness. Do different groups compete to do better? If so, how close is the competition? There are some fields that are taken very seriously but are nevertheless not very competitive - medicine, for instance - and this tends to yield lower-level outcomes overall, since there is less incentive to integrate new advances into the system.
* Aliveness. Are the conclusions being tested in actual practice? If not, are they bein |
7df4ab47-194d-4476-88e7-04c09875c6af | StampyAI/alignment-research-dataset/special_docs | Other | Interview with zlzai
\*\*Interview with zlzai, on 3/18/22\*\*
\[Note: Th interviewee\'s Zoom connection was very bad since they were calling in from an Uber, so there\'s a lot of missing content (and \"I lost you\...\" parts of the conversation that have been removed for clarity).\]
\*\*0:00:02.5 Vael:\*\* Cool. All right. So my first question is: can you tell me about what area of AI you work on in a few sentences?
\*\*0:00:09.2 Interviewee:\*\* Definitely. The way that I describe it is, first of all, I work on deep learning, let\'s get that out of the way. And then beyond that, I\'m interested in understanding \[inaudible - long\]. If I had to describe in a word, I would call it the science of deep learning. This is a topic that there are few other researchers interested in. We try to understand\... We treat these as complex systems that have emergent properties due to that complexity, in the same way you might think of biology as an emergent property of physics. It\'s not something you might predict for first principles, but once it\'s there, there\'s a lot you can say about it that\'s pretty interesting. And then from there, I\'m interested in trying to understand how do these things learn and how can you make them learn more efficiently. You can think of that\... A metaphor I like to use is like pharmaceuticals. Once you understand a biological organism, you can design an intervention that takes advantage of the patterns that you\'ve seen in order to get a certain behavior or a certain outcome.
\*\*0:01:00.8 Vael:\*\* Great. Cool. I missed maybe the second sentence there. I assume that was like large NLP systems or foundations or something?
\*\*0:01:08.1 Interviewee:\*\* Yes. So I work on deep learning, so I\'m interested in the neural networks as they are in practice.
\*\*0:01:12.7 Vael:\*\* Got it. Cool. All right. And then my next question is, what are you most excited about in AI? And what are you most worried about? In other words, what are the biggest benefits or risks of AI?
\*\*0:01:23.4 Interviewee:\*\* So the thing that I\'m most excited about is that this can do extraordinary things that we don\'t know how to program, but we know we can do. Let me try to elaborate on that a little bit. What I mean by that is there\'s a world of things that we don\'t know whether anything can accomplish, tasks that are sufficiently complicated or tasks where given the input data, you may not know whether you can predict the output data. But neural networks are closing the gap between things that we know are possible to do because we do them as humans all the time, but we don\'t know how to program, or there\'s no kind of discreet handwritten program that we can write easily that will describe this. And so I\'m really excited about the fact that we can take data and actually do something that starts to resemble things that require human complexity in order to do that. So to me, that\'s the exciting part. Self-diving cars are one example, though we\'re not that good at that, but even just a mere handwritten digit recognition like the MNIST task, which is the most basic machine learning task in the world, now that I\'m in the field, but when I first took a machine learning class and saw that task, my mind was blown that you could just, with this tiny little data set, learn how to do handwritten digit recognition. That\'s pretty profound, as far as I\'m concerned, and I\'ve never quite lost my fascination for that.
\*\*0:02:35.9 Interviewee:\*\* On the risk side, I\'m concerned about a few things. I\'m not an AGI believer or an AGI concerned person, so you can cross that one off. I think \[inaudible\] huge risks, I am always concerned about\... \[inaudible - very long\]. \[Vael saying what was missed\] \...Let me start over. It was me trying to search for the right words so you missed absolutely nothing.
\*\*0:03:47.2 Interviewee:\*\* Now that I found the right words though, the two things that scare me most, number one, the fact that the systems fail in ways that are unintuitive to humans or that we wouldn\'t be able to reason about intuitively. So the example I give is, you think an automobile driver is likely to drive more poorly at night or when they\'re tired or when they\'re impaired or what have you. It\'s really hard to have an intuition about when the Tesla is going to think of the bus as a cloud, and then slam into the back of it. And it\'s those rare failures when we use a system a lot of times that lead to really bad outcomes and lead to mistrust. At the end of the day, AI needs to be trustworthy and we need to, as humans, trust it, otherwise, it will never be useful, and we\'ll never accept it or terrible things will happen, and so I think a lot about the notion of what does it mean for it to be trusted; where are the gaps between where we are right now and what it would take to be trustworthy.
\*\*0:04:41.3 Interviewee:\*\* The other big concern I have is that I think we don\'t take the problem of understanding how these systems work seriously. It used to be that when we design a big complicated piece of software, think like Windows or PowerPoint or a browser or something like that, we assumed it would take 1000 engineers to build it, and then maybe let\'s say 50 or 100 to test it. Deep learning, we have this really incredible situation where \[inaudible - long\] \... the way that I would put it, the system kind of \[?verb\] on its own based on the data. And we\'re lazy in computer science, we like to think that, \"Oh, the system came to life on its own, so we should have this one hammer that we can bang against it and see whether it\'s good or not, or whether it\'s fair or not, or use the explainability bot to understand how to explain the system.
\*\*0:05:32.1 Interviewee:\*\* But in reality, I think the ratios are just going to be reversed. The software, if you\'re using\... 100 researchers may be able to build a very exciting piece of software. You\'re going to need 1000 or 2000 engineers to tear it apart down to the studs and try to understand how each individual piece of one specific system works. So I think the entire literature around explainability and understanding of deep learning is completely wrong-headed because people are looking for general solutions to a problem that doesn\'t have general solutions. It\'s like writing a program that will debug any program for you. There\'s just no such thing. It\'s not how this game works. So that\'s scares me a lot, that we\'re thinking about this completely the wrong way. And we do need some degree of explainability, we need some degree of understanding of how these systems work, but we\'re not going to get there from the way that we\'re currently thinking about this, and it\'s just going to take a lot more effort and resources than we\'re currently remotely considering giving to it.
\*\*0:06:24.5 Vael:\*\* Interesting. And it sounded like\... So how many engineers per researcher do we need, or per model do we need, do you think?
\*\*0:06:33.8 Interviewee:\*\* Well, this is going to be complicated. The 1000 and 100 are just kind of\... Every piece of software needs a different number of engineers, and three engineers can actually build a pretty big piece of software, but the amount of\... I\'ll go out there on a limb and say it\'s going to be a 10 to 1 ratio.
\*\*0:06:53.8 Interviewee:\*\* That\'s for every 10\... Let\'s call it 10 testers. Whatever we call them, whether it\'s engineers or researchers or what have you, let\'s say that 10 people need to try to understand the system or 100 people need to understand the system. For every \[inaudible\] building the system. \...Feel free to tell me I\'m wrong if \[inaudible\].
\*\*0:07:16.5 Vael:\*\* No, I\'m just trying to actually hear you, so it\'s 10 to\... Or 10 testers or 100 testers to one capabilities person making it happen. Wow. \...Are you still here? (Interviewee: \"Yep, still here.\")
\*\*0:07:34.0 Vael:\*\* Great, alright, so that\'s pretty extreme ratio. Do you think we\'ll get there? And what happens if we don\'t get there?
\*\*0:07:42.3 Interviewee:\*\* I think we\'ll have to get there. At the end of the day, we evaluate these systems by their capabilities. If you have an autopilot system and it crashes the plane a lot, at the end of the day, you\'re not going to use it and it\'s not going to be allowed to fly. In order to get the kinds of capabilities we\'re hoping for, and to get the degree of understanding of failures that we typically expect in any high assurance system, we\'re going to need to have\... If your self-driving car crashes enough times, someone will require you to tear that thing down to the studs to understand that, and you\'re going to have to do this regardless of whether you decide to do this proactively, or eventually your system won\'t be allowed on the road until you do. So I think there\'s going to be a huge amount of manual labor in understanding how these systems work, and I think that either through failures that lead to bans of certain uses of technology until it\'s better understood or until it\'s more resilient, or someone who\'s being proactive and actually wants to ensure their self-driving car works. Either way, you\'re going to end on this situation. No better way to put it.
\*\*0:08:48.5 Vael:\*\* Okay, that\'s super fascinating. Okay, so you think that we are going to need\... The way we\'re doing interpretability is not good right now, it needs to be specific to each system, and currently, we\'re probably doing much\... Yeah, we\'re not even going in the right direction. I kind of expect that self-driving cars will be deployed without this huge amount of intervention on it, but you say that for most systems or something, we will just continue to have failures until it becomes obvious from society that we need people to do interpretability and explainability type of work in this correct way.
\*\*0:09:22.0 Interviewee:\*\* I think so, I think that\'s a good summary. The only thing I\'ll add to this, and I spend a big chunk of my life doing AI policy, so I have to think about this stuff a lot. Is that the amount of work we put into the assurance is proportional to the risk that the system has if it fails. So my favorite example of this is\-- I\'ve done a lot of work in the past on facial recognition policy. Obviously a hot topic, facial bias issues, etc. So let me give you two examples. Google Photos has this clustering algorithm where it will basically find all the faces of grandma and cluster them so that you click on one picture of grandma, and you can find them all if you want to.
\*\*0:09:58.4 Interviewee:\*\* Suppose there\'s racial bias in that algorithm, and suppose that instead the police department is using a facial recognition system to try to identify the perpetrator of a crime via a driver\'s license database. These are two applications that might both have the same technology underneath, they might both have the same biases. We\'re going to be much more worried about one than the other because one could lead people to lose their freedom, and the other may lead to people rightly feeling offended and people rightly feeling hurt, but not someone being thrown in jail potentially. So we have to handle the consequences of the systems in line with their risks. I\'m a lot less concerned about interpretability of Google\'s facial recognition system than I am about a police\'s facial recognition system.
\*\*0:10:47.8 Vael:\*\* I see. And since they need to be treated differently anyway, according to your view\... Yeah, just do it\-- do the interpretability in accordance with their importance. That makes sense.
\*\*0:10:56.7 Interviewee:\*\* Exactly.
\*\*0:10:57.7 Vael:\*\* Yeah. Cool. Alright, so my next question is about future AI. So putting on a science fiction forecasting hat, say we\'re 50 plus years into the future. So at least 50 years in the future, what does that future look like?
\*\*0:11:11.4 Interviewee:\*\* I think it\'s honestly not going to look that different from the present, just a little bit more extreme. I don\'t think we\'ll be in an AGI world. I don\'t think we will have systems that rival human intelligence. I think we will be able to specify what we want out of systems in much higher level terms and actually get them to do this\-- \[I\'m\] thinking, like, a Roomba that is actually a useful intelligent Roomba. A Roomba that knows when to vacuum, knows when not to disturb you, that you can say, \"Hey, can you please do an extra touch-up on that area,\" and it\'ll go do it. I think the current wave of machine learning is great at pattern recognition; I don\'t think pattern recognition gets us to AGI. I think the technology is going to have to look fundamentally different, and I don\'t know that we\'re any closer now than we were 50 years ago in that respect, beyond that we know there are a lot of dead ends.
\*\*0:12:05.0 Interviewee:\*\* That\'s not to say what we\'re doing right now isn\'t exceedingly useful. And \[that\] we\'re not going to push this to the nth degree, and \[that\] we won\'t have the New York Times of the future where you click on a headline and it will generate an article for you based on your interests, your knowledge level, your background on the topic and the amount of time you have to read it; I think that\'s an application that might be 10 or 15 years in the future, if not sooner than that. But I don\'t think we\'ll be in a place where we\'re worried about giving robots rights or things like that. I don\'t think that a huge amount of pattern recognition will get us to cognition, and I don\'t think the current track we\'re on will get us there. That doesn\'t bother me personally because I don\'t really care about that. I\'m much more interested in what we can do for people now and in the near future, and not how we create intelligence. That\'s more of a scary question for me than an exciting question for me, but it\'s\... \[inaudible - short\]
\*\*0:13:00.2 Interviewee:\*\* \...thinks that, heading to an AGI world, even if\... And we may be in a world where we do have self-driving cars that actually work, even if it may not be here for 10 or 15 years, \'cause it\'s a hard problem. We may have actual smart digital assistants, but that\'s \[inaudible - short\] intelligent beings who are peers or \[hard to parse\] in that respect.
\*\*0:13:25.1 Vael:\*\* Interesting. I don\'t know if it will help for me to turn my video off, but I\'m going to try it to hope it gets a little bit less choppy. Great. Okay. So you\'re talking about within the frame of AGI, like lots of people presumably talk about AGI around you. What are people\'s opinions? What do you think of their opinions? Etcetera.
\*\*0:13:46.5 Interviewee:\*\* Oh, I think there are a lot of nut cases. I mean, there are also a lot of optimists and a lot of people who have read a lot of science fiction and want to bring that to life, which is great. And a lot of people who have spent too much time in San Francisco and are surrounded by peer groups who\... Basically, there\'s a lot of monoculture in San Francisco. And I say this having just come back to San Francisco, and as someone who refuses to move \[there\] despite \[\...\]. I have friends who, if I recall correctly, go and worship at the Church of the AGI or something like that, from what I understand, under the belief that eventually there will be artificial intelligences that are smarter and more powerful than us, and therefore they should get ahead of the game and start worshipping them now, since they\'ll be our overlords later.
\*\*0:14:32.4 Interviewee:\*\* I\'m a little more concerned with the here and now, and I think that the technological leaps between great pattern recognizes that we have today and that are way far away. Just because you\'re a nutcase doesn\'t mean you can\'t change the world. And there are plenty of examples of that, and it\'s really good to have that point of view constantly echoed in the community. But I don\'t worry about the end of the world because of AGI in the way that I think some of my friends do. I don\'t consider that to be the biggest existential risk that we have, far from it. And I don\'t think it\'s a risk that we should really be spending any time worrying about at the moment.
\*\*0:15:07.6 Vael:\*\* Got it. Is that because, like you said, there\'s other existential risks to prioritize, or you don\'t think this could be an existential risk?
\*\*0:15:15.1 Interviewee:\*\* I don\'t think this is\... In the very, very long tail of ultra low probability risks to civilization, this is so far out in the tail that it\'s not worth spending any time on, independent of the fact that there are also much greater risks.
\*\*0:15:29.6 Interviewee:\*\* It\'s not just a matter of priority. It\'s also a matter of\... it\'s not a good use of any resources.
\*\*0:15:34.8 Vael:\*\* Got it. That makes sense, yeah. Unfortunately, a lot of my questions are about AGI so you\... \[chuckle\] Here we go.
\*\*0:15:42.1 Interviewee:\*\* No, I\'m happy to give you a strong contrasting opinion to many that I\'m sure you\'ve heard. So come at it.
\*\*0:15:48.1 Vael:\*\* Lovely, lovely. Okay, cool. So how I\'m defining AGI here is like any sort of very general system that could, for example, replace all human jobs, current-day human jobs, whether or not we choose to or don\'t choose to do that. And the frame I usually take is like, 2012, deep learning revolution, here we are. We\'ve only been doing AI for like 70 years or something, and here we are 10 years later, and we have got systems like GPT-3, which have some weirdly general capabilities. But regardless of how you get there, because I can imagine that we hit some ceiling on the current deep learning revolution and we need to have paradigm shifts\-- my impression is generally that if we keep on pouring in the amount of human talent and have software\-- algorithmic improvements at the rate we\'ve seen, hardware improvements, etcetera, and just like, the driving human desire to continue to follow economic incentives to earn money and replace things and make life more convenient, which I think is what a lot of what ML is aimed at right now, that eventually we will get some sort of AGI system. I don\'t really know when. Do you think we will at some point get some very general system?
\*\*0:16:55.8 Interviewee:\*\* I think if you take the time to infinity and humanity lasts in time going to infinity, yes, we will. I do think we have one example of a truly general intelligence system, namely human beings. And eventually we will probably get to the point where we could replicate that intelligence manually, if we have to literally photocopy somebody\'s brain and the transistors at some point, when we get technology advanced enough for that. Do I think that will happen this century? No. Do I think it will happen next century? Probably not. Do I think it might happen in the ones after that? Maybe.
\*\*0:17:28.8 Interviewee:\*\* So the answer is yes, in the limit, but no in any kind of limit that you or I would think about or be able to conceptualize.
\*\*0:17:35.7 Vael:\*\* Yeah, that makes sense. So you think there\'s probably going to be a bunch more paradigm shifts needed before we get there?
\*\*0:17:42.8 Interviewee:\*\* I think at least one\-- it\'s hard to know how many paradigm shifts because it\'s hard to know what they are, but I do not think this paradigm is the right one. I think this paradigm is amazing, and I\'m really excited about the kinds of machines we can build. But you can be excited about the kinds of machines we can build and recognize the limits of those machines. In the same way that\-- how are we going to get to AGI in 10 years if we\'ve been pouring\... You were talking about economic incentives. How much investment do you think has gone into self driving cars over the past, let\'s say, 10 years?
\*\*0:18:14.2 Interviewee:\*\* Let\'s call it \$100 billion plus or minus. That\'s probably the largest single investment in AI technology for an application anywhere. Ever, very likely. And where are we today on self driving cars? They\'re 90% of the way there, \[inaudible - short\] 10 times as difficult as the first 90%. And I don\'t think you\'ll be seeing fully autonomous self-driving cars in the general case on general roads in the next 10 or 15 years. So the thought that there would be an AGI at that point, let alone in 50 years is completely nonsensical to me, personally.
\*\*0:18:56.0 Vael:\*\* Yeah, that makes sense. Especially since self-driving cars are, like, robotics and robotics is behind as well. But even GPT and stuff doesn\'t really have good grounding with anything that\'s happening in the world and how\--
\*\*0:19:09.2 Interviewee:\*\* GPT\'s capabilities are also wildly overstated. You can pull out a lot of good examples out of GPT if you really want to, and you can pull out a lot of crappy ones. But we\'re not going to just brute force large language models to get our way to general intelligence. That\'s BS you\'ll only hear from someone who works at OpenAI who wants their equity to be worth more, quite frankly. The only people who say this are the ones who have an economic incentive to say this, and the people who follow the hype. Otherwise, I don\'t really know of anyone who thinks GPT is the road to AGI, especially given that we can\'t scale up any bigger. I mean, this is something, this is my whole push right now, is that the only way that Nvidia is going to come out with new GPUs next week, and they are going to come out with new GPUs next week that will be twice as fast as the ones that came out two years ago, is if they doubled the amount of power. It\'s not like we\'re doubling the amount of hardware we have available.
\*\*0:20:02.4 Vael:\*\* Got it. Do you know how good optical or quantum computing will be? I know that those are in the pipeline.
\*\*0:20:08.9 Interviewee:\*\* They\'re in the pipeline. Quantum\'s been in the pipeline for a long time, and we\'re up to what, four qubits? Cool. Again, this is one of those cases where we\'ve been awful close to nuclear fusion for a long time. We\'re going to have nuclear fusion in the limit. I\'m a 100% certain of that. When we have that, call me.
\*\*0:20:32.7 Interviewee:\*\* It could be next year. I mean, we\'re very close to crossing that threshold, but we\'ve been really close to crossing that threshold for several decades. And so, trying to call the year that it\'s going to happen within a five, 10 or 50-year time horizon, it\'s really, really tough when it comes to these technologies. And I\'m in that same place about quantum. We are making progress on quantum and I\'m really excited about it. I\'m a little bit scared of it, but I\'m excited about it, but that doesn\'t mean\... Like, crossing that threshold is really difficult. The difference between 10 years and 50 years and a 100 years away may be very small improvements in technology, but it may take an exceedingly long time for us to accomplish. Optical, same thing. So I\'m not sitting here expecting that breakthroughs are just going to happen left and right. These breakthroughs often take a very long time; a lot of incremental advances and all sorts of other technical advances in other fields to make them happen. Material science especially in the case of quantum. And we may get there in five years, or we may get there in 50 or 100 years or longer, and it\'s hard to say.
\*\*0:21:36.6 Vael:\*\* That makes sense. So what would convince you that you should start working on AI alignment? It sounds like there\'s probably going to be some breakthrough that would make you think that it\'s important, but we\'re not necessarily anywhere near that breakthrough right now. Do you have an idea of what that might be?
\*\*0:21:56.9 Interviewee:\*\* Give me your personal definition of AI alignment.
\*\*0:22:00.0 Vael:\*\* Yeah. Well, actually I want your definition first. \[chuckle\]
\*\*0:22:03.3 Interviewee:\*\* So this is not a field that I follow that closely. The entire concept of alignment has really come to the fore in the past three or four months while I\'ve been trying to \[job-related task\]. So I haven\'t been paying as much attention, so I\'d actually appreciate your definition. I can tell you the kinds of people who I see talking about alignment and the kinds of papers that I\'ve seen, the paper titles that I\'ve seen go across my desk. But I couldn\'t give you a good definition even if \[inaudible - short\].
\*\*0:22:33.9 Vael:\*\* Yeah. So one of the definitions I use, and I\'ll give you a problem setting I usually think about as well\-- so, \[the\] definition is building models that represent and safely optimize \[inaudible\] specify human values. Alternatively, ensuring that AI behavior aligns with system designer intentions. And one of the examples I use for what an alignment problem would be is the idea that highly intelligent systems would fail to optimize exactly what their designers intended them to and instead do what we tell them to do. So the example of OpenAI, trying to\... Have that boat win a race and then it getting caught on like some little\-- but optimizing instead for a number of points and instead ending up in this little\-- side, collecting-points area, instead of winning the race. So doing what the designers told it to do instead of what it intended them to.
\*\*0:23:26.2 Interviewee:\*\* So, I mean, this sounds like a\... If you want my honest frank opinion.
\*\*0:23:34.8 Vael:\*\* Yeah.
\*\*0:23:35.3 Interviewee:\*\* A BS-ey rebranding of a simple fact of life in computing for\... Since the dawn of computing, the computer does what you tell it to do, not what you want it to do. (Vael: \"Yes.\")
\*\*0:23:47.5 Interviewee:\*\* And so, I don\'t\... I know a lot of people at OpenAI think they\'re very deep and profound for calling it AI alignment. \[inaudible - very long\].
\*\*0:25:02.7 Interviewee:\*\* So picking up where I was, there are probably a lot of folks at OpenAI, and I know exactly who they are, who think they\'re very deep and profound who are wondering about this question, but this is kind of the obvious fundamental thing that every first year programmer learns and is a question that everybody has been asking in every context whenever they develop a loss function for a neural network. Your loss function never reflects exactly the value that you want the system to carry exactly, even just the outcome. We optimize language models to be good at predicting the next word and yet they somehow also generate\... We want them to have properties that go above and beyond that, we want them to be able to even just transfer the representations for downstream tasks.
\*\*0:25:44.0 Interviewee:\*\* This is just a question of to what extent is the thing you\'re optimizing a system for going to actually align with the task that you want. I just, I don\'t see what\'s interesting or new about this question from a research perspective. I think of course it\'s an important one, but it\'s not a\... \[inaudible - long\] Saying, oh, we should worry about computer security. Well, yes, but there\'s no security robot that fixes all computer security. It\'s a complex context-specific problem. Again, people say things that are aligned with their economic incentives and that is certainly true for my friends at OpenAI but I don\'t see any profundity in this observation that\'s just the nature of computing.
\*\*0:26:38.1 Vael:\*\* Yeah. I think it has to be paired with the idea that if you have a very intelligent system that can plan ahead, that can model itself in the world, that it may have an incentive to preserve itself just as an agent pursuing any goal, because it doesn\'t want to decrease its chance of it succeeding at its goal. I think that has to be paired. Otherwise, it\'s not particularly special. But I do think creating an agent that in the far future, whenever AGI develops, that has an incentive to not be modified makes it much more dangerous than anything we\'ve seen previously.
\*\*0:27:16.2 Interviewee:\*\* I agree with that factor, when we get to a point where this becomes a problem. But I think you can see that the amount I care about this question is proportional to the amount that I think that AGI is or will be a concern in your lifetime, or your grandchildren\'s lifetime. And so I think there are a lot more fundamental basic questions. Like, we don\'t even understand why a neural network actually is able to recognize handwritten digits in the first place. And until we get these basic things down, we\'re never going to get to build the kind of systems that have these properties anyway.
\*\*0:27:44.7 Interviewee:\*\* So, I would make the analogy of, let me see, I\'m trying to think of the right metaphor\... I don\'t know, for the folks who worry about how we\'re going to communicate with the alien civilizations we inevitably come into contact with\-- we better build some space ships that can get us to space first before we start worrying. And then figure out whether there are alien civilizations out there before we start worrying about how we\'re going to communicate with them.
\*\*0:28:05.6 Vael:\*\* Yeah, this sounds like a very coherent worldview, I\'m like, yep, makes sense, is logical.
\*\*0:28:11.7 Interviewee:\*\* Yeah, I have strong opinions, and researchers are always incentivized to have strong opinions; it\'s what moves science forward. We argue and we disagree with each other, and we\'re all right, and we\'re all wrong. But these are my strong opinions. And if you were to chat with any of my friends at OpenAI, you\'d hear the opposite view and we could still go out for drinks and have a good time.
\*\*0:28:32.5 Vael:\*\* Great. So I think this line of questioning was originally developed by me asking what would you see\... What would you want to see in the world before you\'re like, \"Oh actually, people are right, this thing is coming earlier than I expected.\"
\*\*0:28:47.6 Interviewee:\*\* That\'s a good question. Honestly, I would want to see a\... I\'m thinking of examples of a task, so like a Turing test of some sort that would fit here. Obviously, the Turing Test is not a very effective test given that GPT-3 probably passes it with flying colors, despite the fact that GPT-3 is a very good language model. What I would look for is long-lived machine learning systems that can learn continually. We don\'t even have learning continually down. We have reinforcement learning systems that eventually become effective agents at solving one particular task; they can\'t move on to a second task, we don\'t have any general kind of learning process. Even the reinforcement learning agents that we have have to be really, really, specially hand-calibrated for a specific task, to the point where even the specific random seed you use is an important hyperparameter in determining whether the agent will succeed or fail at a given task.
\*\*0:29:50.0 Interviewee:\*\* If we\'re in that world, we\'re very far away from an agent that can learn many tasks or has a generalized learning process. This is I guess why people are excited about meta-learning in general. Again, I think we shouldn\'t worry about light speed travelling until we can get off the planet, but that\'s a whole different\... You know where I stand on that. So\... \[inaudible - short\] and improve themselves over time; reinforcement learning is kind of a shadow of what we would expect of a real system. A smart home assistant, where you can teach it new tasks by explaining it new tasks, and it will be able to figure out how to accomplish them. We don\'t have any kind of abstract or general learning capabilities right now. And we talk a very big game about things like AlphaFold or what have you, or any of the Alpha stuff coming out of DeepMind. But these are requiring lots and lots and lots of engineers to get a system to work on one very specific setting in ways that don\'t transfer to any other setting or any other task. There are principles we\... \[inaudible short\] being really hard trying a bunch of stuff. And that\'s a\... \[inaudible long\] real general learning process that will keep me up at night.
\*\*0:31:03.5 Vael:\*\* Got it. Okay. Interesting. So do you think you and your colleagues probably have the same information but are just reacting to it differently? Other people are also not seeing these very general systems or maybe\-- yeah, presumably, because you have access to the same information, but you\'re just interpreting it differently based on the incentives that your company or whatever is in?
\*\*0:31:29.9 Interviewee:\*\* I wouldn\'t always blame the incentives and the all, again, throwing some shade at San Francisco in particular. When you spend all of your waking hours and all of your sleeping hours around exclusively people who are working on the same \[inaudible\], same field. And when you work as they do not only in the tech industry, but especially in the very tight knit AI community, where if we all go to the same birthday parties when I\'m out there, and everybody goes to the same spas, and runs into each other on the street, despite the fact that San Francisco is a big city. When you live in that kind of monoculture, quite frankly, you\'ll lose touch with reality, to some extent. And I think, a lot of people start believing that\... \[inaudible very long\]
\*\*0:32:31.9 Vael:\*\* Okay, cool. My question was, although I missed that last bit, but my question was, how do people stay in touch with reality, how do they\...
\*\*0:32:43.7 Interviewee:\*\* I think you have to talk to people who are doing things other than building AI systems. You have to actually maybe interact with someone who works in healthcare or someone who works in finance, or someone who works in any other field besides AI or besides machine learning. And you have to remember that they are real human beings out there. I think this is just a general San Francisco problem. You see people, you see 20-somethings who are wealthy, producing systems that are convenient for them. We see this all the time, startups coming out of the Bay Area.
\*\*0:33:24.1 Interviewee:\*\* My experience in Boston and New York is it\'s a very different start up culture of healthtech, fintech, edtech, what have you, things that are quite frankly real and useful. So when I hang out with\... The times I\'ve been working at \[large tech company\] in California, for example, all of my friends are worried that the sky is falling for some reason. Maybe we\'re about to enter an AI winter or we\'re about to create AGI, it\'s usually one or the other. Depending on who you talk to and what mood they\'re in, and what\'s going on in their organization at that different point in time. But people are just so caught up in the inside baseball, they\'ve lost the bigger context for everything we\'ve accomplished, but also how far away we are from some of the things people talk about. And you gotta actually taste the real world from time to time, and remember that you don\'t just live in this little bubble of lots of neural networks and lots of people with PhDs working on neural networks.
\*\*0:34:19.7 Vael:\*\* And interacting with other people and seeing the wider world gives the context to believe more\... mainline views? Because it sounds like people end up in either direction, you said they believe that everything is about AI, everything\'s going to go badly: everything is going to go badly and too fast or everything\'s going too badly and too slow. And there\'s some regularity effect that happens if you\'re hanging out with other people?
\*\*0:34:50.4 Interviewee:\*\* It\'s almost the opposite of\... \[inaudible - short\] in that respect. I think it\'s partially because people will say things like, \"Well, there hasn\'t been a big advance or there hasn\'t been a big breakthrough in 18 months.\" And my reaction is, it\'s been a\... Yes, the transformer paper came out 18 months ago. Yeah, let\'s be patient, and the field does not always continually accelerate. No scientific field continually accelerates. We have periods of big progress and then periods of stagnation and paradigm shift. This is the structure of scientific revolution. If you want to\... For the extent to which you take that view of the world seriously, that does, in my impression, simply how science moves, in fits and starts, not at a steady pace. And in computers we\'re used to exponentials. That doesn\'t mean that these systems are going to get exponentially better over time. It may mean we had 10 really good years of progress based on a bunch of different factors all coming together, between big data being accessible, lots of computing accessible, and some improvements in deep learning, that all came together really nicely to give us a big burst of progress.
\*\*0:36:00.1 Interviewee:\*\* Maybe things will slow down a little bit. Or maybe there will be a new architecture in five years that will be another big burst, the way that convolutional neural networks were around the AlexNet breakthrough and then the way that transformers were. But this happens in fits and starts. But around \[large tech company\], partially because of \[large tech company\]\'s promotion process and partially because of the very internally competitive atmosphere that that fosters, people are always dour and feel like the sky is falling. And at OpenAI, the incentive is to continue the hype and build really big systems. It\'s partially in the culture, partially in the leadership, partially in the brand of the place and what it takes to make your stock go up in value. So I think people really are shaped by their environments in that respect, and you gotta get out and talk to other kinds of people and get other perspectives, and I don\'t think there\'s that much you can do when you\'re in a big organization like \[large tech company\], and there\'s not a lot you can do when you\'re in San Francisco and everybody you talk to. It\'s not \"what you do\". It\'s \"which tech company do you work for\" or more likely \"which AI project do you work on\".
\*\*0:37:04.4 Vael:\*\* I see, so this is probably, like you said, why you haven\'t moved and also presumably why you have friends who are not AI researchers.
\*\*0:37:12.4 Interviewee:\*\* Yeah, my office right now is at \[university\] \[non-science field\]. I just sit by a bunch of \[non-science field\] professors. I\'ve worked there at times in the past. I live in \[city\] right now, and they were kind enough to offer me a place to sit and work for a little bit. So I\'m hanging out with a bunch of \[non-science field\] faculty all day. It\'s a very different perspective on the world. They\'re worried about very mundane things compared to AGI coming and killing us all, or a quantum computing breakthrough that leads to the end of civilization when we train quantum neural networks or something like that. They\'re much more worried about bail reform, or very basic mundane day-to-day problems that are actually affecting people around them. It\'s hard to get caught up in the hype.
\*\*0:37:56.5 Vael:\*\* Yeah, and it seems like it\'s important to make sure that people are working on things that actually affect more than just their local environment. I think you mentioned something like that earlier, right?
\*\*0:38:05.9 Interviewee:\*\* Exactly. And there\'s also a little bit of\... as computer scientists, we love to calculate things, we love to measure things. Effective Altruism is one great example of this. We love to say, \"What is the way that I can put my dollar the furthest along?\" And I think sometimes people lose touch with the fact that there are humans out there. There are people right now, you ask questions like, well, either I can help the humans today or I can put all my resources toward saving the entirety of civilization. And civilization and the future is obviously\-- there are many more humans there than there are here today, so I\'ll worry about, on the 0.1% chance that we develop AGI and it comes to threaten us all, I\'ll worry about that problem, because in expectation, that will save more lives than what have you. And I think\--
\*\*0:38:51.5 Vael:\*\* \--Yeah, and this is the wrong way to think about things?
\*\*0:38:56.5 Interviewee:\*\* I don\'t think this is necessarily the wrong way to think about things, but I think it\'s a little bit\... Sometimes, it\'s more important to focus on what\'s right in front of you and what\'s tangible and what\'s here and now. Or the other way I\'ll put it is, MIT is pathologically bad as an institution at dealing with topics that can\'t be measured for exactly this reason. It\'s really hard to talk to people at MIT about values, unless a value can be measured or optimized.
\*\*0:39:25.0 Interviewee:\*\* And this is just, for a lot of folks who get empirical education and who are training systems all day, for whom this is a problem, they are just as much\... The people are just as much subject to this problem as the systems they build. So, if we want to talk about alignment, it\'s an issue not just of the systems, but of the people building the systems.
\*\*0:39:43.5 Vael:\*\* Alright, well, we are at time. Thank you so much, this is a very different opinion than the ones I\'ve received so far, and yeah, very well presented. Awesome.
\*\*0:39:52.6 Interviewee:\*\* Thank you, that\'s what I\'m here for. You know where to find me if you need to chat more. I\'m very exited to see what the outcome of the project is.
\*\*0:39:58.4 Vael:\*\* Great. Alright. Well, thank you so much. |
2ea8dd58-d3d4-474b-b014-d84a0a51804d | trentmkelly/LessWrong-43k | LessWrong | AI safety in the age of neural networks and Stanislaw Lem 1959 prediction
Tl;DR: Neural networks will result in slow takeoff and arm race between two AIs. It has some good and bad consequences to the problem of AI safety. Hard takeoff may happen after it anyway.
Summary: Neural networks based AI can be built; it will be relatively safe, not for a long time though.
The neuro AI era (since 2012) feature an exponential growth of the total AI expertise, with a doubling period of about 1 year, mainly due to data exchange among diverse agents and different processing methods. It will probably last for about 10 to 20 years, after that, hard takeoff of strong AI or creation of Singleton based on integration of different AI systems can take place.
Neural networks based AI implies slow takeoff, which can take years and eventually lead to AI’s evolutionary integration into the human society. A similar scenario was described by Stanisław Lem in 1959: the arms race between countries would cause power race between AIs. The race is only possible if the self-enhancement rate is rather slow and there is data interchange between the systems. The slow takeoff will result in a world system with two competitive AI-countries. Its major risk will be a war between AIs and corrosion of value system of competing AIs.
The hard takeoff implies revolutionary changes within days or weeks. The slow takeoff can transform into the hard takeoff at some stage. The hard takeoff is only possible if one AI considerably surpasses its peers (OpenAI project wants to prevent it).
Part 1. Limitations of explosive potential of neural nets
Everyday now we hear about success of neural networks, and we could conclude that human level AI is near the corner. But such type of AI is not fit for explosive self-improvement.
If AI is based on neural net, it is not easy for it to undergo quick self-improvement for several reasons:
1. A neuronet’s executable code is not fully transparent because of theoretical reasons, as knowledge is not explicitly present within it. So even if one |
4b529b1e-2e5d-4f01-bdbf-89a1be5b43d8 | trentmkelly/LessWrong-43k | LessWrong | Anyone have experience with metabolic network modelling that would be willing to help me out?
In 2019 I had Megavitamin-B6 syndrome. I'm curious about the most common situation where people get it (chronic supplementation vs. acute overdose), and I have a hypothesis about how that may come about. Ideally, I'd like to see if it pans out in a model, and if it does maybe see if I can get some attention from interested biologists. But my background is in CS, not biology and I feel like I'd have a lot to learn. So anyone who would collaborate or just get me pointed in the right direction would be helpful.
In short it seems the chronic supplementation does factor in, but it also seems that plenty of people supplement for a very long time (more than 2 years) and never have an issue, sometimes at or above NOAEL or the even the LOAEL (100mg/day and 200mg/day in the US).
From many of the people I've talked to (and my own experience) it seems like something happens where someone takes a consistent dose of supplemental b6 for a year or more (at a reasonably high dose, say, 25mg/day or more) then at some point they take something else with b6 (or maybe are taking a poorly calibrated supplement that has more b6 than advertised) and then they start to develop toxicity, even if it the total dose is still below the "NOAEL."
So, my guess is that metabolic pathways adjust to receiving some daily dose of b6 and then become inflexible somehow (like a homeostatic thing) and can't ramp up the bandwidth/rate to process it in to 4-pyridoxic acid, the form where it's excreted.
Soo... I kind of want to see if that's plausible in a realistic model. |
4729e5a2-792b-4000-adf5-564fe9121668 | trentmkelly/LessWrong-43k | LessWrong | Towards a More Sophisticated Understanding of Myth and Religion (?)
Lately I have been investigating the work of Jordan Peterson which I have found to be of great value. Indeed, I have to admit that I am being persuaded but trying to keep a critical mind and balance between doubt and belief.
I thought this is a strong argument for a more sophisticated understanding of the function of religion that would be quite fun to throw to the LessWrong community for an attempt to dismantle ;)
You can find his lectures online on YouTube. The 'Maps of Meaning' series is a fairly detailed exposition of the concepts. For a quick taste you can watch the Joe Rogan podcast with him (they talk a bit of religion in the last hour or so) though in this kind of format you inevitably only get a sketch.
Have fun! |
82d64c11-0804-482d-8276-83301bb639a5 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | AI strategy nearcasting
*Cross-posted from the [Alignment Forum](https://www.alignmentforum.org/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting) / [Less Wrong](https://www.lesswrong.com/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting)*
This is the first in a series of pieces taking a stab at dealing with a conundrum:
* I believe this could be the [most important century ever for humanity](https://www.cold-takes.com/most-important-century/), via the development of advanced AI systems that could dramatically speed up scientific and technological advancement, getting us more quickly than most people imagine to a deeply unfamiliar future.
* But when it comes to what *actions we can take* to help such a development go well instead of poorly, it’s [hard to say much](https://www.cold-takes.com/making-the-best-of-the-most-important-century/) (with a [few exceptions](https://www.cold-takes.com/making-the-best-of-the-most-important-century/#robustly-helpful-actions)). This is because **many actions that would be helpful under one theory of how things will play out would be harmful under another** (for example, see my [discussion](https://www.cold-takes.com/making-the-best-of-the-most-important-century/) of the “caution” frame vs. the “competition” frame).
It seems to me that in order to more productively take actions (including making more grants), we need to get more clarity on some [crucial questions](https://forum.effectivealtruism.org/posts/zGiD94SHwQ9MwPyfW/important-actionable-research-questions-for-the-most) such as “How serious is the threat of a world run by [misaligned AI](https://www.cold-takes.com/making-the-best-of-the-most-important-century/)?” But it’s hard to answer questions like this, when we’re talking about a development ([transformative AI](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/)) that may take place some indeterminate number of decades from now.
This piece introduces one possible framework for dealing with this conundrum. The framework is **AI strategy nearcasting: trying to answer key strategic questions about transformative AI, under the assumption that key events** (e.g., the development of transformative AI) **will happen in a world that is otherwise relatively similar to today's.** One (but not the only) version of this assumption would be “Transformative AI will be developed soon, using methods like what AI labs focus on today.”
The term is inspired by [nowcasting](https://en.wikipedia.org/wiki/Nowcasting_(economics)). For example, the [FiveThirtyEight Now-Cast](https://www.270towin.com/maps/fivethirtyeight-2016-now-cast) projects "who would win the election if it were held today,” which is easier than projecting who will win the election when it is actually held. I think imagining transformative AI being developed *today* is a bit much, but “in a world otherwise relatively similar to today’s” seems worth grappling with.
Some potential benefits of nearcasting, and reservations
--------------------------------------------------------
A few **benefits** of nearcasting (all of which are speculative):
As with nowcasting, nearcasting can **serve as a jumping-off point**. If we have an idea of what the best actions to take *would be if transformative AI were developed in a world otherwise similar to today’s,* we can then start asking “Are there particular ways in which we expect the future to be different from the nearer term, that should change our picture of which actions would be most helpful?”
Nearcasting can also focus our attention on the scenarios that are **not only easiest to imagine concretely, but also arguably “highest-stakes.”** Worlds in which transformative AI is developed especially soon are worlds in which the “nearcasting” assumptions are especially likely to hold - and these are also worlds in which we will have especially little time to react to crucial developments as they unfold. They are thus worlds in which it will be especially valuable to have thought matters through in advance. (They are also likely to be worlds in which transformative AI most “takes the world by surprise,” such that the efforts of people paying attention today are most likely to be disproportionately helpful.)
Nearcasting **might give us a sort of feedback loop for learning: if we do nearcasting now and in the future, we can see how the conclusions** (in terms of which actions would be most helpful today) **change over time**, and perhaps learn something from this.
* I could easily imagine that we will learn more from repeated attempts to ask “What should we do if transformative AI is just around the corner?”, whose conclusions will change as the world changes, than from a persistent effort to refine our answer to “What should we do under our best-guess picture of the world, in which transformative AI is (for example) 30 years away from today?”
* To be clear, I don’t think this is a “great” or even “good” feedback loop for learning, but in the space of transformative AI strategy, *any* such feedback loops are hard to come by, so I see some appeal here.
A major **reservation** about nearcasting (as an activity in general, not relative to forecasting) is that people are arguably quite bad at reasoning about future hypothetical scenarios,[1](#fn1) and most of the most impressive human knowledge to date arguably has relied heavily on empirical observations and experimentation. AI strategy nearcasting seems destined to be vastly inferior to e.g. good natural sciences work, in terms of how much we can trust its conclusions.
I think this reservation is valid as stated, and I expect any nearcast to look pretty silly in at least some respects with the benefit of hindsight. But I still think nearcasting (and, more generally, analyzing hypothetical future scenarios with transformative AI[2](#fn2)) is probably under-invested in today:
* The potential stakes are enormous. Even a small amount of additional clarity about [how to make the best of the most important century](https://www.cold-takes.com/making-the-best-of-the-most-important-century/) would be immensely valuable.
* I don’t think the case for “people are bad at reasoning about hypothetical future scenarios” or “people are bad at reasoning about the long-run future” is all *that* strong, ([more](https://www.cold-takes.com/the-track-record-of-futurists-seems-fine/)). Moreover (as also noted at that link), past attempts at futurism didn’t make use of many of what appear to be “forecasting best practices” today, such as assigning quantitative probabilities to predictions. The [science of forecasting](https://smile.amazon.com/Superforecasting-audiobook/dp/B0131HGPQQ/) is young, and there’s probably a lot of room for improvement on past attempts.
* Finally, I am broadly **against putting too much weight on “the track record of a particular kind of thinking,”** mostly because I think such track records tend to be inconclusive.
+ I think that for almost any rules you can come up with about “what good intellectual inquiry looks like,” there’s been some good intellectual inquiry that breaks those rules.
+ I also think that (partly due to [low-hanging-fruit dynamics](https://www.cold-takes.com/wheres-todays-beethoven/)) a lot of the most valuable innovations have historically come from something more like “Work on a very neglected question and try to ‘just be reasonable,’ without sweating whether one’s methodology has a good track record” than like “Use a super-established methodology that looks like how past innovations have happened.” ([More](https://www.cold-takes.com/to-match-the-greats-dont-follow-in-their-footsteps/))
I think part of the challenge of this kind of work is having reasonable judgment about which aspects of a hypothetical scenario are too specific to place big bets on, vs. which aspects represent relatively robust themes that would apply to many possible futures.
* For example, I think it is reasonable to predict that the global average temperature will rise over the coming several decades, even as it is very difficult to predict whether it will be raining on some day even a few weeks away.
* I have some views on what sorts of aspects of the future I’d generally expect to be tractable vs. intractable to predict,[3](#fn3) but I make no claim to have well-grounded takes here. I think that if there were more efforts at analyzing hypothetical future scenarios, we’d probably collectively learn more about such things.
The nearcast I’ll be discussing
-------------------------------
Starting here, and continuing into future pieces, I’m going to lay out a “nearcast” that shares many (not all) assumptions with [this piece by Ajeya Cotra](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) (which I would highly recommend reading in full if you’re interested in the rest of this series), hereafter abbreviated as “Takeover Analysis.”
*I will generally use the present tense (“in my scenario, the alignment problem is difficult”) when describing a nearcasting scenario, and since the whole story should be taken in the spirit of speculation, I’m going to give fewer caveats than I ordinarily would - I will often say something like “the alignment problem is difficult” when I mean something like “the alignment problem is difficult in my most easily-accessible picture of how things would go” (not something like “I know with confidence that the alignment problem will be difficult”).*
The key properties of the scenario I’ll be considering are:
1. **Magma.** For concreteness, we’re focused on a particular AI project or company, which I’ll call Magma (following [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1)).
2. **Transformative AI is knowably near, but not yet here.** Magma’s leadership has good reason to believe that it can develop something like [PASTA](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/) soon - say, within a year. (I also assume that it hasn’t unwittingly done so yet!)
3. **Human feedback on diverse tasks.** The path to transformative AI revolves around what [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1) calls “human feedback on diverse tasks” (HFDT). You can think of this roughly as if (to oversimplify) we trained an AI by giving it a thumbs up when we approved of what it had done, and a thumbs down when we disapproved, and repeated this enough times that it was able to become very good at behaving in ways that would get a thumbs up, on a broad set of diverse tasks.
4. **Some (but not unlimited) deliberate delay is possible.** Magma has some - but not unlimited - ability to delay development and/or deployment in order to take “extra” measures to reduce the risk of misaligned AI.
1. That is, it’s not the case that “any delay in deploying a given type of AI system will simply mean a competitor does so instead.”
2. This could be because Magma has significant advantages over its competitors (funding, algorithms, talent, etc.) and/or because its closest competitors have a similar level of caution to Magma.
3. I’ll be imagining that Magma can reasonably expect to deploy a given AI system 6-24 months later than it would otherwise for purposes of safety, without worrying that a competitor would deploy a similar (less safe) system in that time.
5. **Otherwise, changes compared to today’s world are fairly minimal.**
1. Deep learning has evolved in many ways - for example, there might be new architectures that improve on the [Transformer](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)) - but the fundamental paradigm is still basically that of models gaining capabilities via “trial-and-error.”[4](#fn4) More on this at [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1).
2. We still [can’t say much](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/#misaligned-ai-mysterious-potentially-dangerous-objectives) about what’s going on inside a given AI model (most of what we know is that it learns from trial and error on tasks, and performs well on the tasks).
3. AI systems have advanced to the point where there are many new commercial applications, and perhaps substantial economic impact, but we haven’t yet reached the point where AIs can [fully automate scientific and technological advancement](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/#misaligned-ai-mysterious-potentially-dangerous-objectives), where [economic growth is “explosive”](https://www.openphilanthropy.org/could-advanced-ai-drive-explosive-economic-growth) (greater than 30% per year, which would very roughly be 10x the current rate of global economic growth), or where AI systems can facilitate anything like a [decisive strategic advantage](https://www.lesswrong.com/posts/vkjWGJrFWBnzHtxrw/superintelligence-7-decisive-strategic-advantage).
I note that the combination of “Changes compared to today’s world are fairly minimal” with “Transformative AI is knowably near” implies a [fast takeoff](https://www.lesswrong.com/tag/ai-takeoff) approaching: a very rapid transition from a world like today’s to a radically different world. This is (in my view) a key way in which reality is likely to differ from the scenario I’m describing.[5](#fn5)
I still think this scenario is worth contemplating, for the following reasons:
* I think this sort of fast takeoff is easier to concretely picture than most alternatives, precisely because it allows us to focus on a particular crucial period of time in which most things resemble how they are today. Generally, I think it is easiest to think about a relatively small number of relatively high-stakes developments (e.g., “We have a very powerful AI system that we can deploy or not”) - and then generalize this sort of analysis to scenarios that may revolve more around large numbers of individually low-stakes developments (e.g., “For the 20th time this year, we’re deciding whether to ship a system that is a small amount more capable, and more dangerous, than the ones already out there”), rather than starting by trying to discuss the latter.
* Worlds in which transformative AI is developed *sooner* likely involve faster takeoffs, and scenarios generally more like the above, than other worlds. And these are also particularly “high-stakes” worlds, in the sense that thinking about them today is particularly likely to be helpful.
* After examining this scenario, we can later ask how reality might diverge from it (as I plan to do in a future post). While I think there are likely to be important divergences, I think this scenario gives us a lot to work with and think about productively.
Next in series
--------------
* The next piece I’d recommend reading is [Takeover Analysis](https://forum.effectivealtruism.org/posts/Y3sWcbcF7np35nzgu/without-specific-countermeasures-the-easiest-path-to-1), which the rest of my series builds on. It illustrates why this nearcast **involves a major risk of an AI takeover in the absence of specific preventative measures - a risk that is non-straightforward to diagnose or correct.** I recommend reading the full post for a detailed picture of why this is. (A footnote addresses how this analysis differs from previous “alignment problem” discussions.[6](#fn6))
* I will soon put up a piece laying out my understanding of what the best available measures would be for Magma to reduce the risk of misalignment in powerful systems it’s developing, and of how likely these measures would be to work.
* After that, I will go beyond questions about the “alignment problem” (what technical measures can reduce risk of misalignment) and discuss the “deployment problem”: the question of what Magma (and a hypothetical agency, IAIA) should be seeking to use aligned AI systems to *do* (and whether/when to deploy them), under conditions of uncertainty about how safe they are and how close others are to deploying powerful AI of their own.
* I’ll discuss which actions (including, but not limited to, AI alignment research) seem most helpful *today* if we expect the nearcast discussed here, and how we might expect matters to look systematically different if we relax the “nearcast” condition (imagining that transformative AI will take longer to develop, and be less likely to simply come from current techniques, than in this scenario).
---
Footnotes
---------
1. Though this is something I hear claimed a lot without necessarily much evidence; see my discussion of the track record of futurists [here](https://www.cold-takes.com/the-track-record-of-futurists-seems-fine/). [↩](#fnref1)
2. E.g., see [this piece](https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like#2026), [this piece](https://www.gwern.net/Clippy) and [Age of Em](https://ageofem.com/) [↩](#fnref2)
3. For example, I generally feel better about predictions about technological developments (what will be possible at a particular price) than predictions about what products will be widely used, how lifestyles will change, etc.
Because of this, I think “Extreme technology X will be available, and it seems clear and obvious that the minimum economic consequences of this technology or any technology that can do similar things would be enormous” is in many ways a better form of prediction than “Moderate technology Y will be available, and it will be superior enough to its alternatives that it will be in wide use.” This is some of why I tend to feel better about predictions about transformative AI (which I’d say are going out on a massive limb re: what will be possible, but less of one re: how significant such a thing would be) than about predictions of self-driving cars (which run into lots of questions about how technology will interact with the regulatory and economic environment). [↩](#fnref3)
4. Some readers have objected to the “trial and error” term, because they interpret it as meaning something like: “trying every possible action until something works, with no system for ‘learning’ from mistakes.” This is not how AI systems are generally trained - their training uses [stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent), in which each “trial” comes with information about how to adjust an AI system to do better on similar inputs in the future. But I use the “trial and error” term because I think it is intuitive, and because I think the term generally *is* inclusive of this sort of thing (e.g., I think [Wikipedia’s characterization of the term](https://en.wikipedia.org/wiki/Trial_and_error) is quite good, and includes cases where each “trial” comes with information about how to do better in the future). [↩](#fnref4)
5. To be clear, I expect that the transition to transformative AI will be much faster than the vast majority of people imagine, but I don’t expect it will be quite as fast as implied here. [↩](#fnref5)
6. To my knowledge, most existing discussion of the alignment problem - see links at the bottom of [this section](https://www.cold-takes.com/transformative-ai-timelines-part-1-of-4-what-kind-of-ai/#misaligned-ai-mysterious-potentially-dangerous-objectives) for examples - focuses on abstract discussions of some of the challenges presented by aligning *any* AI system (things like “It’s hard to formalize the values we most care about”). Takeover Analysis instead takes a nearcasting frame, and walks mechanically through what it might look like to develop powerful AI with today’s methods. It discusses mechanically how this could lead to an AI takeover - as well as why the most obvious methods for diagnosing and preventing this problem seem like they wouldn’t work. [↩](#fnref6) |
d3ac37c2-2e49-4d99-bac7-e567bd75fd3b | trentmkelly/LessWrong-43k | LessWrong | The Alignment Newsletter #1: 04/09/18
Highlights
Specification gaming examples in AI (Victoria Krakovna): A list of examples of specification gaming, where an algorithm figures out a way to literally satisfy the given specification which does not match the designer's intent.
Should you read it? There were several examples I hadn't heard of before, which were pretty entertaining. Also, if you have any examples that aren't already listed, it would be great to send them via the form so that we can have a canonical list of specification gaming examples.
My take on agent foundations: formalizing metaphilosophical competence (Alex Zhu): Argues that the point of Agent Foundations is to create conceptual clarity for fuzzy concepts that we can't formalize yet (such as logical uncertainty). We can then verify whether our ML algorithms have these desirable properties. It is decidedly not a goal to build a friendly AI using modules that Agent Foundations develop.
Should you read it? I don't know much about MIRI and Agent Foundations, but this made sense to me and felt like it clarified things for me.
Adversarial Attacks and Defences Competition (Alexey Kurakin et al): This is a report on a competition held at NIPS 2017 for the best adversarial attacks and defences. It includes a summary of the field and then shows the results from the competition.
Should you read it? I'm not very familiar with the literature on adversarial examples and so I found this very useful as an overview of the field, especially since it talks about the advantages and disadvantages of different methods, which are hard to find by reading individual papers. The actual competition results are also quite interesting -- they find that the best attacks and defences are both quite successful on average, but have very bad worst-case performance (that is, the best defence is still very weak against at least one attack, and the best attack fails to attack at least one defence). Overall, this paints a bleak picture for defence, at least if the att |
065e0ea9-b6bd-4486-8b1c-a277c55dc17e | trentmkelly/LessWrong-43k | LessWrong | Open Thread, January 2011
Better late than never, a new open thread. Even with the discussion section, there are ideas or questions too short or inchoate to be worth a post.
This thread is for the discussion of Less Wrong topics that have not appeared in recent posts. If a discussion gets unwieldy, celebrate by turning it into a top-level post. |
471e7f61-05bb-4176-87a3-d4dde532ce7b | trentmkelly/LessWrong-43k | LessWrong | A short conceptual explainer of Immanuel Kant's Critique of Pure Reason
I know which one I would take.
Introduction
While writing another document, I noticed I kept referring to Kantian concepts. Since most people haven't read Kant, that would lead to interpretation problems by default. I'm not satisfied with any summary out there for the purpose of explaining Kantian concepts as I understand them. This isn't summarizing the work as a whole given I'm focusing on the parts that I actually understood and continue to find useful.
I will refer to computer science and statistical concepts, such as Bayesianism, Solomonoff induction, and AI algorithms. Different explainers are, of course, appropriate to different audiences.
Last year I had planned on writing a longer explainer (perhaps chapter-by-chapter), however that became exhausting due to the length of the text. So I'll instead focus on what still stuck after a year, that I keep wanting to refer to. This is mostly concepts from the first third of the work.
This document is structured similar to a glossary, explaining concepts and how they fit together.
Kant himself notes that the Critique of Pure Reason is written in a dry and scholastic style, with few concrete examples, and therefore "could never be made suitable for popular use". Perhaps this explainer will help.
Metaphysics
We are compelled to reason about questions we cannot answer, like whether the universe is finite or infinite, or whether god(s) exist. There is an "arena of endless contests" between different unprovable assumptions, called Metaphysics.
Metaphysics, once the "queen of all the sciences", has become unfashionable due to lack of substantial progress.
Metaphysics may be categorized as dogmatic, skeptical, or critical:
* Dogmatic metaphysics makes and uses unprovable assumptions about the nature of reality.
* Skeptical metaphysics rejects all unprovable assumptions, in the process ceasing to know much at all.
* Critical metaphysics is what Kant seeks to do: find the boundaries of what reason can and cannot |
71862212-b5e8-4aa0-a248-8c2564e26de6 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | AI+bio cannot be half of AI catastrophe risk, right?
Recently, I have heard an increasing number of prominent voices mention the risk of AI helping to create catastrophic bioweapons. This has been mentioned in the context of AI safety, and not in the context of biosecurity. Thus, it seems anecdotally that people see a significant portion of AI risk being that of an AI somehow being instrumental in causing a civilization-threatening pandemic. That said, I have failed to find any even precursory exploration of *how much* of AI risk is this risk of AI being instrumental in creating catastrophic bioweapons. Does anyone know of any attempts to quantify the "overlap" between AI and bio? Or could someone please try to do so?
One reason to quantify this overlap is if bio+AI is used as a primary or at least prominent example to the public, it seems useful to have some analysis underpinning such statements. Even showing that bio+AI is actually just a small portion of AI risk might be helpful so that when using this example, the person using this example can also mention that this is just one of many ways AI could end up contributing to harming us. Or if it is indeed a large portion of AI risk, state this with a bit more clarity.
Other reasons to have such analysis might be:
1. Assisting grantmakers allocate funds. For example, if there is a large overlap, it might be that grantmakers currently investing in AI safety also might want to donate to biosecurity interventions likely to help in an "AI assisted pandemic"
2. Help talent decide what problem to work on. It might, for example be that policy experts worried about AI Safety also want to focus on legislation and policy around biosecurity
3. Perhaps foster more cooperation between AI and biosecurity professionals
4. Perhaps a quantification here could help both AI safety experts and biosec professionals know what type of scenarios to prepare for? For example, it could put more emphasis in AI safety work on trying to prevent AI from becoming too capable in biology (e.g. by removing such training material).
5. Probably other reasons I have not had time to think about
Relatedly, and I would be very careful in drawing conclusions from this, I just went through the Metaculus predictions for the [Ragnarök](https://www.metaculus.com/questions/2568/ragnar%25C3%25B6k-seriesresults-so-far/) question series and found that these add up to [132%](https://docs.google.com/spreadsheets/d/13q-369RQg2mmGRQV0n4Wzspl2ryf_1Q3oG6FFfsoRso/edit?usp=sharing). *Perhaps* this indicate overlaps between the categories, or perhaps it is just an effect of the different forecasters for the different questions (there seems to be large variation in how many people have forecasted on each question). Let us assume for the sake of the argument that the "extra" 32% very roughly represents overlap between the different categories. Then, with very little understanding of the topic, I might guess that perhaps half of the biorisk of 27% would also resolve as AI caused catastrophe, so roughly 15%. This means 15% of the bio risk is the same as 15% of the AI risk which would reduce the 32% excess (132%-100%) to about 32%-15%=~15%. Perhaps the remaining 15% are overlaps between AI and nuclear, and possibly other categories. However, this would mean almost half the AI risk is biorisk. This seems suspiciously high but at least it *could* explain why so many prominent voices uses the example of AI + bio when talking about how AI can go wrong. Moreover, if all these extra 32% are indeed overlaps with AI, it means there is almost no "pure" AI catastrophe risk which seems suspicious. Anyways, these are the only numbers I have come across that at least points towards some kind of overlap between AI and bio.
Thanks for any pointers or thoughts on this! |
7ffa67f2-5088-46f7-8f74-32aa03c2c0f8 | trentmkelly/LessWrong-43k | LessWrong | Wasteful qualifiers
Users of languages improve them by adding new words. Often languages would also be improved by taking away words – not so much such that nobody ever uses the word, but just so it is not necessary to convey a certain meaning. For instance since it is very rare for a claim to be meant with one hundred percent certainty, it would be more efficient if a speaker had to add ‘certainly’ in that case, rather than adding ‘probably’ or ‘I think’ for every other case where they are only claiming a best guess.
Robin Hanson explains the retention of such extra words by people being stuck in a signaling game: if you don’t add the extra words in the normal cases, then people mistake you for those who really are making extreme claims, since everyone else adds the extra words when they are making the ordinary claim.
That seems correct. But why would this begin? How did everyone ever come to use the long, complicated sentences for saying ordinary things and the succinct ones for claims that almost nobody ever wants to make? Perhaps we just especially notice the half of times when the signals are assigned the inconvenient way around?
Another plausible theory is that the usual cases are complicated, whereas simple cases are unusual and extreme. Adding qualifications allows you to say something much more specific and nuanced. And we usually want to refer to specific, nuanced state of affairs. However for uncontroversial issues nobody seems to have much trouble interpreting simple statements as the obvious more complex ones. If you say ‘roofs in England have steeper slopes than roofs in Australia’ most people would interpret this as indicating some sort of general tendency for steeper roofs in England. If you want to say the extreme thing, you have to say something like ‘literally every roof in England is steeper than every roof in Australia – no, I mean it literally. I know, it sounds insane! Look, I’ll send you these seven review articles summarizing the research.’ Whereas if a pers |
1c9ad105-9428-4adc-ad5b-aa6aaf3108ed | trentmkelly/LessWrong-43k | LessWrong | What's the best ratio for Africans to starve compared to Ukrainians not dying in the war?
Actions have consequences. One of the consequences of our trade sanctions is to increase wheat and corn prices significantly. There are currently Africans who are on the brink of starvation and the increased food prices will increase starvation?
Is our current position "It's okay if 1,000,000 Africans starve if we can prevent 100,000 Ukrainians from not dying in the war"?
Are there models that tell us how much people are likely to starve as a result of our actions? Are there wheat futures that we can use to tell us future wheat prices and economic models that estimate how many people will starve? |
a474d7c6-d0f7-4e03-b5fa-2b10c7d3d621 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Sydney Rationality Dojo - November 2016
Discussion article for the meetup : Sydney Rationality Dojo - November 2016
WHEN: 06 November 2016 04:00:00PM (+1100)
WHERE: 10 Shepherd Street, Chippendale
Come join us for our November dojo. Dinner afterwards for those who wish to come, as usual.
Discussion article for the meetup : Sydney Rationality Dojo - November 2016 |
c451c22b-409f-4502-b741-432b0a8fba81 | trentmkelly/LessWrong-43k | LessWrong | Tools for decision-support, deliberation, sense-making, reasoning
I'm actively researching and cataloging various kinds of projects relating to decision-support, deliberation, sense-making, and reasoning. Some example categories include:
* Deliberative Democracy Tools - Systems for structured citizen participation, including participatory budgeting platforms and stakeholder engagement tools
* Argument Mapping & Visualization - Platforms for making reasoning explicit and visually representing dialectical structures
* Deep Thinking Environments - Slow media and platforms designed for thoughtful engagement rather than rapid interaction
* Belief Tracking Systems - Tools for attestation, commitment to positions, and tracking belief revision over time
* Bayesian & Evidential Reasoning Frameworks - Platforms that make probabilistic thinking explicit and support coherent belief updating
* Epistemic Communities - Networks and platforms with explicit norms for truth-seeking and intellectual humility
* Structured Dialogue Systems - Tools supporting deep listening and methodical conversation beyond typical messaging platforms
* Cooperative Governance Frameworks - Sociocratic, holacratic, and other systems for managing shared resources and decision-making
* Knowledge Synthesis & Curation - Tools for collaborative sense-making with quality controls beyond popularity metrics
* Decision Support Systems - Frameworks making decision processes explicit, including values clarification and cognitive debiasing
* Consensus-Building Methodologies - Approaches for finding agreement through structured deliberation rather than voting or averaging
* Collaborative Forecasting - Platforms for collective prediction and long-term thinking across groups
* Distributed Sense-Making - Tools enabling groups to develop shared understanding of complex issues
* Platform Cooperativism - Digital platforms owned and governed by their users/workers rather than external shareholders
* Solidarity Economy Infrastructure - Tools supporting mutual a |
e4361b35-80c1-4a3f-b705-b027c56a2d49 | trentmkelly/LessWrong-43k | LessWrong | [AN #59] How arguments for AI risk have changed over time
Find all Alignment Newsletter resources here. In particular, you can sign up, or look through this spreadsheet of all summaries that have ever been in the newsletter. I'm always happy to hear feedback;
Highlights
A shift in arguments for AI risk (Tom Sittler): Early arguments for AI safety focus on existential risk cause by a failure of alignment combined with a sharp, discontinuous jump in AI capabilities. The discontinuity assumption is needed in order to argue for a treacherous turn, for example: without a discontinuity, we would presumably see less capable AI systems fail to hide their misaligned goals from us, or to attempt to deceive us without success. Similarly, in order for an AI system to obtain a decisive strategic advantage, it would need to be significantly more powerful than all the other AI systems already in existence, which requires some sort of discontinuity.
Now, there are several other arguments for AI risk, though none of them have been made in great detail and are spread out over a few blog posts. This post analyzes several of them and points out some open questions.
First, even without a discontinuity, a failure of alignment could lead to a bad future: since the AIs have more power and intelligence their values will determine what happens in the future, rather than ours. (Here it is the difference between AIs and humans that matters, whereas for a decisive strategic advantage it is the difference between the most intelligent agent and the next-most intelligent agents that matters.) See also More realistic tales of doom (AN #50) and Three impacts of machine intelligence. However, it isn't clear why we wouldn't be able to fix the misalignment at the early stages when the AI systems are not too powerful.
Even if we ignore alignment failures, there are other AI risk arguments. In particular, since AI will be a powerful technology, it could be used by malicious actors; it could help ensure robust totalitarian regimes; it could increase the lik |
b164d211-8381-422d-93bd-4a17b61b8265 | trentmkelly/LessWrong-43k | LessWrong | "Rational Agents Win"
Rachel and Irene are walking home while discussing Newcomb's problem. Irene explains her position:
"Rational agents win. If I take both boxes, I end up with $1000. If I take one box, I end up with $1,000,000. It shouldn't matter why I'm making the decision; there's an obvious right answer here. If you walk away with less money yet claim you made the 'rational' decision, you don't seem to have a very good understanding of what it means to be rational".
Before Rachel can respond, Omega appears from around the corner. It sets two boxes on the ground. One is opaque, and the other is transparent. The transparent one clearly has $1000 inside. Omega says "I've been listening to your conversation and decided to put you to the test. These boxes each have fingerprint scanners that will only open for Irene. In 5 minutes, both boxes will incinerate their contents. The opaque box has $1,000,000 in it iff I predicted that Irene would not open the transparent box. Also, this is my last test of this sort, and I was programmed to self-destruct after I'm done." Omega proceeds to explode into tiny shards of metal.
Being in the sort of world where this kind of thing happens from time to time, Irene and Rachel don't think much of it. Omega has always been right in the past. (Although this is the first time it's self-destructed afterwards.) Irene promptly walks up to the opaque box and opens it, revealing $1,000,000, which she puts into her bag. She begins to walk away, when Rachel says:
"Hold on just a minute now. There's $1000 in that other box, which you can open. Omega can't take the $1,000,000 away from you now that you have it. You're just going to leave that $1000 there to burn?"
"Yup. I pre-committed to one-box on Newcomb's problem, since it results in me getting $1,000,000. The only alternative would have resulted in that box being empty, and me walking away with only $1000. I made the rational decision."
"You're perfectly capable of opening that second box. There's nothin |
fecb9733-0b48-4288-8cbf-993a1d617dc4 | trentmkelly/LessWrong-43k | LessWrong | Digital Error Correction and Lock-In
Epistemic status: a collection of intervention proposals for digital error correction in the context of lock-in. It reflects my own intervention ideas, and the opinion of Formation Research at the time of writing.
TL;DR
We believe lock-in risks are a pressing problem, and that the digital error correction properties of digital entities will make future lock-in scenarios more stable.
----------------------------------------
Introduction
We have identified 4 key threat models for lock-in; ways we believe undesirable lock-ins could manifest in the future. This post focuses on two specific threat models:
1. An autonomous AI system competently pursues a goal and prevents interference
2. An immortal AI-enabled malevolent actor, or whole-brain emulation of a malevolent actor, instantiates a lock-in
This post rests on the claim AGI and WBE are possible, and talks about roughly human-level digital entities. Some features of these interventions would break down with a superintelligence.
The Claim
The focus of this post is one of the claims made by Lukas Finnveden, Jess Riedel, and Carl Shulman in AGI and Lock-In, section 4.2.:
Institutions can easily achieve very high baseline levels of stability that can only be interrupted by worldwide catastrophes or intelligent action.
The authors introduce three components for this claim:
* AGI making it theoretically possible to digitally store a wide range of complex values
* Digital error-correction making it possible to accurately preserve these values for a long time
* Large scale errors being mitigated with distributed storage
We agree with their claim, believing that values can be preserved impeccably, long-term, due to digital entities being able to store large amounts of data, perform digital error-correction, and distribute information storage. Therefore the digital entity controlling some domain can preserve its control in its exact state without error in perpetuity.
How Digital Error Correction Could Creat |
35e1bb4a-8294-4748-a204-55aebced9953 | StampyAI/alignment-research-dataset/blogs | Blogs | A Technical Explanation of Technical Explanation
This essay is meant for a reader who has attained a firm grasp of Bayes’ Theorem. An introduction to Bayes’ Theorem may be found at [An Intuitive Explanation of Bayesian Reasoning](https://eyudkowsky.wpengine.com/rational/bayes) . You should easily recognize, and intuitively understand, the concepts “prior probability”, “posterior probability”, “likelihood ratio”, and “odds ratio”. This essay is intended as a sequel to the *Intuitive Explanation* , but you might skip that introduction if you are already thoroughly Bayesian. Where the *Intuitive Explanation* focused on providing a firm grasp of Bayesian basics, the *Technical Explanation* builds, on a Bayesian foundation, theses about human rationality and philosophy of science.
The *Intuitive Explanation of Bayesian Reasoning* promised that mastery of addition, multiplication, and division would be sufficient background, with no subtraction required. To this the *Technical Explanation of Technical Explanation* adds logarithms. The math is simple, but necessary, and it appears first in the order of exposition. Some pictures may not be drawn with words alone.
As Jaynes (1996) emphasizes, the theorems of Bayesian probability theory are just that, *mathematical theorems* which follow inevitably from Bayesian axioms. One might naively think that there would be no controversy about mathematical theorems. But when do the theorems apply? How do we use the theorems in real-world problems? The *Intuitive Explanation* tries to avoid controversy, but the *Technical Explanation* willfully walks into the whirling helicopter blades. Bluntly, the reasoning in the *Technical Explanation* does not represent the unanimous consensus of Earth’s entire planetary community of Bayesian researchers. At least, not yet.
The *Technical Explanation of Technical Explanation* is so named because it begins with this question:
> *What is the difference between a technical understanding and a verbal understanding?*
>
>
---
*A fable:*
> Once upon a time, there was a teacher who cared for a group of physics students. One day she called them into her class, and showed them a wide, square plate of metal, next to a hot radiator. The students each put their hand on the plate, and found the side next to the radiator cool, and the distant side warm. And the teacher said, write down your guess why this happens. Some students guessed convection of air currents, and others guessed strange patterns of metals in the plate, and not one put down ‘This seems to me impossible’, and the answer was that before the students entered the room, the teacher turned the plate around.
>
> (Taken from Verhagen 2001.)
There are many morals to this fable, and I have told it with different morals in different contexts. I usually take the moral that your strength as a rationalist is measured by your ability to be more confused by fiction than by reality. If you are equally good at explaining any story, you have zero knowledge. Occasionally I have heard a story that sounds confusing, and reflexively suppressed my feeling of confusion and accepted the story, and then later learned that the original story was untrue. Each time this happens to me, I vow anew to focus consciously on my fleeting feelings of bewilderment.
But in this case, the moral is that the apocryphal students failed to understand what constituted a scientific explanation. If the students measured the heat of the plate at different points and different times, they would soon see a pattern in the numbers. If the students knew the diffusion equation for heat, they might calculate that the plate equilibrated with the radiator and environment two minutes and fifteen seconds ago, turned around, and now approaches equilibrium again. Instead the students wrote down words on paper, and thought they were doing physics. I should rather compare it to the random guessing of Greek philosophers, such as Heraclitus who said “All is Fire”, and fancied it his theory of everything.
As a child I read books of popular physics, and fancied myself knowledgeable; I knew sound was waves of air, light was waves of electromagnetism, matter was waves of complex probability amplitudes. When I grew up I read the Feynman Lectures on Physics, and discovered a gem called ‘the wave equation’. I thought about that equation, on and off for three days, until I saw to my satisfaction it was dumbfoundingly simple. And when I understood, I realized that during all the time I had believed the honest assurance of physicists that sound and light and matter were waves, I had not the vaguest idea what ‘wave’ meant to a physicist.
---
So that is the difference between a technical understanding and a verbal understanding.
Do you believe that? If so, you should have applied the knowledge, and said: “But why didn’t you give a technical explanation instead of a verbal explanation?”
---
In “An Intuitive Explanation of Bayesian Reasoning” I tried to provide visual and physical metaphors for Bayesian probability; for example, evidence is a *weight* , a *pressure* upon belief, that *slides* prior probabilities to posterior probabilities.
Now we add a new metaphor, which is also the mathematical terminology: Visualize *probability density* or *probability mass* – probability as a lump of clay that you must distribute over possible outcomes.
Let’s say there’s a little light that can flash red, blue, or green each time you press a button. The light flashes one and only one color on each press of the button; the possibilities are mutually exclusive. You’re trying to predict the color of the next flash. On each try, you have a weight of clay, the probability mass, that you have to distribute over the possibilities red, green, and blue. You might put a fourth of your clay on the “green” possibility, a fourth of your clay on the “blue” possibility, and half your clay on the “red” possibility – like assigning a probability of green:25%, blue:25%, and red:50%. The metaphor is that *probability is a conserved resource* , to dole out sparingly. If you think that “blue” is more likely to flash on the next experiment, you can assign a higher probability to blue, but you have to take the probability mass from the other hypotheses – maybe steal some clay from red and add it to blue. You can never get any more clay. Your probabilities can’t sum to more than 1.0 (100%). You can’t predict a 75% chance of seeing red, and an 80% chance of seeing blue.
Why would you want to be careful with your probability mass, or dole it out sparingly? Why not slop probability all over the place? Let’s shift the metaphor from clay to money. You can bet up to a dollar of play money on each press of the button. An experimenter stands nearby, and pays you an amount of real money that depends on how much play money you bet on the *winning* light. We don’t care how you distributed your remaining play money over the losing lights. The only thing that matters is how much you bet on the light that actually won.
But we must carefully construct the scoring rule used to pay off the winners, if we want the players to be careful with their bets. Suppose the experimenter pays each player real money equal to the play money bet on the winning color. Under this scoring rule, if you observe that red comes up 6 times out of 10, your best strategy is to bet, not 60 cents on red, but the entire dollar on red, and you don’t care about the frequencies of blue and green. Why? Let’s say that blue and green each come up around 2 times out of 10. And suppose you bet 60 cents on red, 20 cents on blue, and 20 cents on green. In this case, 6 times out of 10 you would win 60 cents, and 4 times out of 10 you would win 20 cents, for an average payoff of 44 cents. Under that scoring rule, it makes more sense to allocate the entire dollar to red, and win an entire dollar 6 times out of 10. 4 times out of 10 you would win nothing. Your average payoff would be 60 cents.
If we wrote down the function for the payoff, it would be `Payoff = P(winner)`, where `P(winner)` is the amount of play money you bet on the winning color on that round. If we wrote down the function for the *expected payoff* given that Payoff rule, it would be `Expectation[Payoff] = (Sum[P(color)*F(color)]` for each color) . `P(color)` is the amount of play money you bet on a color, and `F(color)` is the frequency with which that color wins.
Suppose that the actual frequency of the lights is blue:30%, green:20%, and red:50%. And suppose that on each round I bet blue:$0.40, green:$0.50, and red:$0.10. I would get $0.40 30% of the time, $0.50 20% of the time, and $0.10 50% of the time, for an average payoff of $0.12 + $0.10 + $0.05 or $0.27.
That is:
```
P(color) = play money assigned to that color
F(color) = frequency with which that color wins
Payoff = P(winner) = amount of play money allocated to winning color
```
Actual frequencies of winning:
```
Blue: 30% Green: 20% Red: 50%
```
In the long run, red wins 50% of the time, green wins 20% of the time, and blue wins 30% of the time. So our *average* payoff on each round is 50% of the payoff if red wins, plus 20% of the payoff if green wins, plus 30% of the payoff if blue wins.
The payoff is a function of the winning color and the betting scheme. We want to compute the *average* payoff, given a betting scheme and the *frequencies* at which each color wins. The mathematical term for this kind of computation, taking a function of each case and weighting it by the frequency of that case, is an *expectation* . Thus, to compute our *expected payoff* we would calculate:
```
Expectation[Payoff] = Sum[P(color)*F(color)] for each color
```
```
P(color)*F(color) for blue = $0.40 * 30% = $0.12
+ P(color)*F(color) for green = $0.50 * 20% = $0.10
+ P(color)*F(color) for red = $0.10 * 50% = $0.05
```
```
= $0.12 + $0.10 + $0.05
= $0.27
```
With this betting scheme I’ll win, on average, around 27 cents per round.
I allocated my play money in a grossly arbitrary way, and the question arises: Can I increase my expected payoff by allocating my play money more wisely? *Given the scoring rule provided,* I maximize my expected payoff by allocating my *entire* dollar to red. Despite my *expected* payoff of fifty cents per round, the light might *actually* flash green, blue, blue, green, green and I would receive an *actual* payoff of zero. However, the chance of the light coming up non-red on five successive rounds is approximately 3%.
> Tversky and Edwards (1966) conducted an experiment. Subjects were shown a succession of cards, each card either red or blue. 70% of the cards were blue, and 30% red; the color sequence was random. The subjects, asked to guess each succeeding card, would guess blue around 70% of the time, and red about 30% of the time – as if they thought they had some way of predicting the random sequence! Even when the subjects were paid a nickel for each correct guess, they still only guessed blue about 76% of the time. Why is this odd? Because you do not need to bet on a guess to test it. You could just say “blue” each time, being paid a nickel about 70% of the time, accumulating thirty-five dollars over a thousand trials, while mentally noting your private guesses for any (imaginary) patterns you thought you spotted. If your predictions came out right, then you could switch to the newly discovered sequence. There was no need for the subjects to bet on any patterns they thought they saw; they could have simply bet on blue until some hypothesis was confirmed . But if human beings reasoned like that, people would not buy lottery tickets, but instead write down predictions in notebooks at home, and begin buying lottery tickets only when their predictions began succeeding.
>
> The mistake revealed by the experiment was not that the subjects looked for patterns in a random-seeming sequence; that is curiosity, an admirable human trait. Dawes (1988) comments on this experiment: “Despite feedback through a thousand trials, subjects cannot bring themselves to believe that the situation is one in which they cannot predict.” But even if subjects refused to accept unpredictability and continued looking for patterns, they didn’t have to bet on their guesses. They just needed to make a mental note of the pattern’s prediction, then keep betting on blue while waiting for confirmation. My suspicion is that subjects just didn’t think of the winning strategy. They didn’t realize that their betting pattern did not have to resemble the observed sequence of cards. On each round, blue is the most likely next card. The best financial strategy is not betting a mostly-blue pattern resembling the mostly-blue sequence, but betting all blue, to win as many nickels as possible. If 70% of the time you predict blue and 30% of the time you predict red, and the cards do not correlate with your guesses, you shall predict correctly 0.7*0.7 + 0.3*0.3 = 58% of the time. If 100% of the time you predict blue, you’ll get a nickel 70% of the time.
>
> Under conditions of uncertainty, your optimal betting pattern doesn’t resemble a typical sequence of cards. Similarly, I wonder how many betters on horse races realize that you don’t win by betting on the horse you think will win the race, but by betting on horses whose payoffs exceed what you think are the odds. But then, statistical thinkers that sophisticated would probably not bet on horse races
>
>
A *proper scoring rule* (another standard math term) is a rule for scoring bets so that you maximize your expected payoff by betting play money that exactly equals the chance of that color flashing. We want a scoring rule so that if the lights actually flash at the frequency blue:30%, green:20%, and red:50%, you can maximize your average payoff *only* by betting 30 cents on blue, 20 cents on green, and 50 cents on red. A proper scoring rule is one that forces your optimal bet to exactly report your estimate of the probabilities. (This is also sometimes known as a “strictly proper scoring rule”.) As we’ve seen, not all scoring rules have this property; and if you invent a plausible-sounding scoring rule at random, it probably *won’t* have the property.
One rule with this proper property is to pay a dollar minus the squared error of the bet, rather than the bet itself – if you bet 0.3 on the winning light, your error would be 0.7, your squared error would be 0.49, and a dollar minus your squared error would be fifty-one cents. (Presumably your play money is denominated in the square root of cents, so that the squared error is a monetary sum.) (Readers with calculus may verify that in the simpler case of a light that has only two colors, with p being the bet on the first color and f the frequency of the first color, the expected payoff f\*(1-((1-p)^2)) + (1-f)\*(1-(p^2)) , with p variable and f constant, has its global maximum when we set p=f .)
We shall *not* use the squared-error rule. Ordinary statisticians take the squared error of everything in sight, but not Bayesian statisticians.
We add a new requirement: we require, not only a proper scoring rule, but that our proper scoring rule gives us the same answer whether we apply it to rounds individually or combined. This is what Bayesians do instead of taking the squared error of things; we require invariances.
Suppose I press the button twice in a row. There are nine possible outcomes: green-green, green-blue, green-red, blue-green, blue-blue, blue-red, red-green, red-blue, and red-red . Suppose that green wins, and then blue wins. The experimenter would assign the first score based on our probability assignments for p(green-1) and the second score based on p(blue-2|green-1) . We would make two predictions, and get two scores. Our first prediction was the probability we assigned to the color that won on the first round, green. Our second prediction was our probability that blue would win on the second round, *given* that green won on the first round. Why do we need to write p(blue-2|green-1) instead of just p(blue-2) ? Because you might have a hypothesis about the flashing light that says “blue never follows green”, or “blue always follows green” or “blue follows green with 70% probability”. If this is so, then after seeing green on the first round, you might want to revise your prediction – change your bets – for the second round. You can always revise your predictions right up to the moment the experimenter presses the button, using every scrap of information; but after the light flashes it is too late to change your bet.
(Don’t remember how to read P(A|B) ? See [An Intuitive Explanation of Bayesian Reasoning](https://eyudkowsky.wpengine.com/rational/bayes) .)
Suppose the actual outcome is green-1 followed by blue-2 . We require this invariance: I must get the same *total* score, regardless of whether:
* I am scored twice, first on my prediction for p(green-1) , and second on my prediction for p(blue-2|green-1) .
* I am scored once for my *joint* prediction p(blue-2 & green-1) .
Suppose I assign a 60% probability to green-1 , and then the green light flashes. I must now produce probabilities for the colors on the second round. I assess the possibility blue-2 , and allocate it 25% of my probability mass. Lo and behold, on the second round the light flashes blue. So on the first round my bet on the winning color was 60%, and on the second round my bet on the winning color was 25%. But I might also, at the start of the experiment and after assigning p(green-1) , *imagine* that the light first flashes green, *imagine* updating my theories based on that information, and then say what confidence I will give to blue on the next round if the first round is green. That is, I generate the probabilities p(green-1) and p(blue-2|green-1) . By multiplying these two probabilities together we would get the joint probability, p(green-1 & blue-2) = 15%.
A double experiment has nine possible outcomes. If I generate nine probabilities for p(green-1 & green-2), p(green-1 & blue-2), … , p(red-1 & blue-2), p(red-1 & red-2) , the probability mass must sum to no more than 1.0. I am giving predictions for nine mutually exclusive possibilities of a “double experiment”.
We require a scoring rule (and maybe it won’t look like anything an ordinary bookie would ever use) such that my score doesn’t change regardless of whether we consider the double result as two predictions or one prediction. I can treat the sequence of two results as a single experiment, “press the button twice”, and be scored on my prediction for p(blue-2 & green-1) = 15% . Or I can be scored once for my first prediction p(green-1) = 60% , then again on my prediction p(blue-2|green-1) = 25% . We require the same *total* score in either case, so that it doesn’t matter how we slice up the experiments and the predictions – the *total* score is always exactly the same. This is our invariance.
We have just required:
```
Score(p(green-1 & blue-2)) = Score(p(green-1)) + Score(p(blue-2|green-1))
```
And we already know:
```
p(green-1 & blue-2) = p(green-1) * p(blue-2|green-1)
```
The only possible scoring rule is:
```
Score(p) = log(p)
```
The new scoring rule is that your score is the *logarithm* of the probability you assigned to the winner.
The base of the logarithm is arbitrary – whether we use the logarithm base 10 or the logarithm base 2, the scoring rule has the desired invariance. But we must choose some actual base. A mathematician would choose base e; an engineer would choose base 10; a computer scientist would choose base 2. If we use base 10, we can convert to “decibels”, as in the *Intuitive Explanation* ; but sometimes bits are easier to manipulate.
The logarithm scoring rule is proper – it has its expected maximum when we say our exact expectations; it rewards honesty. If we think the blue light has a 60% probability of flashing, and we calculate our expected payoff for different betting schemas, we find that we maximize our expected payoff by telling the experimenter “60%”. (Readers with calculus can verify this.) The scoring rule also gives an invariant total, regardless of whether pressing the button twice counts as “one experiment” or “two experiments”. However, payoffs are now all *negative* , since we are taking the logarithm of the probability and the probability is between 0 and 1. The logarithm base 10 of 0.1 is -1; the logarithm base 10 of 1% is -2. That’s okay. We accepted that the scoring rule might not look like anything a real bookie would ever use. If you like, you can imagine that the experimenter has a pile of money, and at the end of the experiment he awards you some amount minus your large negative score. (Er, the amount plus your negative score.) Maybe the experimenter has a hundred dollars, and at the end of a hundred rounds you accumulated a score of -48, so you get fifty-two dollars.
A score of -48 in what base? We can eliminate the ambiguity in the score by specifying units. 10 decibels equals a factor of 10; -10 decibels equals a factor of 1/10. Assigning a probability of 0.01 to the actual outcome would score -20 decibels. A probability of 0.03 would score -15 decibels. Sometimes we may use bits: 1 bit is a factor of 2, -1 bit is a factor of 1/2. A probability of 0.25 would score -2 bits; a probability of 0.03 would score around -5 bits.
If you arrive at a probability assessment P for each color, with p(red), p(blue), p(green) , then your *expected score* is:
```
Score = log(p)
Expectation[Score] = Sum[p*log(p)] for all outcomes p.
```
Suppose you had probabilities red:25%, blue:50%, green:25% . Let’s think in base 2 for a moment, to make things simpler. Your expected score is:
```
red: scores -2 bits, flashes 25% of the time
blue: scores -1 bit, flashes 50% of the time
green: scores -2 bits, flashes 25% of the time
expected score: -1.50 bits
```
---
Contrast our Bayesian scoring rule with the ordinary or colloquial way of speaking about degrees of belief, where someone might casually say, “I’m 98% certain that canola oil contains more omega-3 fats than olive oil.” What they really mean by this is that they *feel* 98% certain – there’s something like a little progress bar that measures the strength of the emotion of certainty, and this progress bar is 98% full. And the emotional progress bar probably wouldn’t be exactly 98% full, if we had some way to measure. The word “98%” is just a colloquial way of saying: “I’m almost but not entirely certain.” It doesn’t mean that you could get the highest expected payoff by betting exactly 98 cents of play money on that outcome. You should only assign a *calibrated confidence* of 98% if you’re confident enough that you think you could answer a hundred similar questions, of equal difficulty, one after the other, each independent from the others, and be wrong, on average, about twice. We’ll keep track of how often you’re right, over time, and if it turns out that when you say “90% sure” you’re right about 7 times out of 10, then we’ll say you’re *poorly calibrated* .
Remember Spock from Star Trek? Spock often says something along the lines of, “Captain, if you steer the Enterprise directly into a black hole, our probability of survival is only 2.837%.” Yet nine times out of ten the Enterprise is not destroyed. What kind of tragic fool gives a figure with four significant digits of precision that is wrong by two orders of magnitude?
The people who write this stuff have no idea what scientists mean by “probability”. They suppose that a probability of 99.9% is something like feeling really sure. They suppose that Spock’s statement expresses the *challenge* of successfully steering the Enterprise through a black hole, like a video game rated five stars for difficulty. What *we* mean by “probability” is that if you utter the words “two percent probability” on fifty independent occasions, it better not happen more than once.
If you say “98% probable” a thousand times, and you are surprised only five times, we still ding you for poor calibration. You’re allocating too much probability mass to the possibility that you’re wrong. You should say “99.5% probable” to maximize your score. The scoring rule rewards *accurate* calibration, encouraging neither humility nor arrogance.
At this point it may occur to some readers that there’s an obvious way to achieve perfect calibration – just flip a coin for every yes-or-no question, and assign your answer a confidence of 50%. You say 50% and you’re right half the time. Isn’t that perfect calibration? Yes. But calibration is only one component of our Bayesian score; the other component is *discrimination* .
Suppose I ask you ten yes-or-no questions. You know absolutely nothing about the subject, so on each question you divide your probability mass fifty-fifty between “Yes” and “No”. Congratulations, you’re perfectly calibrated – answers for which you said “50% probability” were true exactly half the time. This is true regardless of the sequence of correct answers or how many answers were Yes. In ten experiments you said “50%” on twenty occasions – you said “50%” to Yes-1, No-1; Yes-2, No-2; … . On ten of those occasions the answer was correct, the occasions: Yes-1; No-2; No-3; … . And on ten of those occasions the answer was incorrect: No-1; Yes-2; Yes-3; …
Now I give my own answers, putting more effort into it, trying to discriminate whether Yes or No is the correct answer. I assign 90% confidence to each of my favored answers, and my favored answer is wrong twice. I’m more poorly calibrated than you. I said “90%” on ten occasions and I was wrong two times. The next time someone listens to me, they may mentally translate “90%” into 80%, knowing that when I’m 90% sure I’m right about 80% of the time. But the probability you assigned to the final outcome is 1/2 to the tenth power, 0.001 or 1/1024. The probability I assigned to the final outcome is 90% to the eighth power times 10% to the second power, (0.9^8)\*(0.1^2), which works out to 0.004 or 0.4%. Your calibration is perfect and mine isn’t, but my better *discrimination* between right and wrong answers more than makes up for it. My final score is higher – I assigned a greater joint probability to the final outcome of the entire experiment. If I’d been less overconfident and better calibrated, the probability I assigned to the final outcome would have been 0.8^8 \* 0.2^2, 0.006.
Is it possible to do even better? Sure. You could have guessed every single answer correctly, and assigned a probability of 99% to each of your answers. Then the probability you assigned to the entire experimental outcome would be 0.99^10 ~ 90%.
Your *score* would be log(90%), -0.45 decibels or -0.15 bits. We need to take the logarithm so that if I try to maximize my *expected score* , Sum[p\*log(p)] , I have no motive to cheat. Without the logarithm rule, I would maximize my expected score by assigning all my probability mass to the most probable outcome. Also, without the logarithm rule, my total score would be different depending on whether we counted several rounds as several experiments or as one experiment.
A simple transform can fix poor calibration by decreasing discrimination. If you are in the habit of saying “million-to-one” on 90 correct and 10 incorrect answers for each hundred questions, we can perfect your calibration by replacing “million-to-one” with “nine-to-one”. In contrast, there’s no easy way to increase (successful) discrimination. If you habitually say “nine-to-one” on 90 correct answers for each hundred questions, I can easily increase your *claimed* discrimination by replacing “nine-to-one” with “million-to-one”. But no simple transform can increase your *actual* discrimination such that your reply distinguishes 95 correct answers and 5 incorrect answers. Yates et. al. (2002): “Whereas good calibration often can be achieved by simple mathematical transformations (e.g., adding a constant to every probability judgment), good discrimination demands access to solid, predictive evidence and skill at exploiting that evidence, which are difficult to find in any real-life, practical situation.” If you lack the ability to distinguish truth from falsehood, you can achieve perfect calibration by confessing your ignorance; but confessing ignorance will not, of itself, distinguish truth from falsehood.
We thus dispose of another false stereotype of rationality, that rationality consists of being humble and modest and confessing helplessness in the face of the unknown. That’s just the cheater’s way out, assigning a 50% probability to all yes-or-no questions. Our scoring rule encourages you to do better if you can. If you are ignorant, confess your ignorance; if you are confident, confess your confidence. We penalize you for being confident and wrong, but we also reward you for being confident and right. That is the virtue of a proper scoring rule.
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Suppose I flip a coin twenty times. If I believe the coin is fair, the best prediction I can make is to predict an even chance of heads or tails on each flip. If I believe the coin is fair, I assign the same probability to every possible sequence of twenty coinflips. There are roughly a million (1,048,576) possible sequences of twenty coinflips, and I have only 1.0 of probability mass to play with. So I assign to each *individual* possible sequence a probability of (1/2)^20 – odds of about a million to one; -20 bits or -60 decibels.
I made an experimental prediction and got a score of -60 decibels! Doesn’t this falsify the hypothesis? Intuitively, no. We do not flip a coin twenty times and see a random-looking result, then reel back and say, why, the odds of that are a million to one. But the odds *are* a million to one against seeing that exact sequence, as I would discover if I naively predicted the exact same outcome for the *next* sequence of twenty coinflips. It’s okay to have theories that assign tiny probabilities to outcomes, so long as no other theory does better. But if someone used an alternate hypothesis to write down the exact sequence in a sealed envelope in advance, and she assigned a probability of 99%, I would suspect the fairness of the coin. Provided that she only sealed *one* envelope, and not a million.
That tells us *what* we ought common-sensically to answer, but it doesn’t say *how* the common-sense answer arises from the math. To say *why* the common sense is correct, we need to integrate all that has been said so far into the framework of Bayesian revision of belief. When we’re done, we’ll have a technical understanding of the difference between a verbal understanding and a technical understanding.
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Imagine an experiment which produces an integer result between 0 and 99. For example, the experiment might be a particle counter that tells us how many particles have passed through in a minute. Or the experiment might be to visit the supermarket on Wednesday, check the price of a 10-ounce bag of crushed walnuts, and write down the last two digits of the price.
We are testing several different hypotheses that try to predict the experimental result. Each hypothesis produces a probability distribution over all possible results; in this case, the integers between zero and ninety-nine. The possibilities are mutually exclusive, so the probability mass in the distribution must sum to 1.0 (or less); we cannot predict a 90% probability of seeing 42 and also a 90% probability of seeing 43.
Suppose there is a precise hypothesis, which predicts a 90% chance of seeing the result 51. (I.e., the hypothesis is that the supermarket usually prices walnuts with a price of “X dollars and 51 cents”.) The precise theory has staked 90% of its probability mass on the outcome 51. This leaves 10% probability mass remaining to spread over 99 other possible outcomes – all the numbers between 0 and 99 *except* 51. The theory makes no further specification, so we spread the remaining 10% probability mass evenly over 99 possibilities, assigning a probability of 1/990 to each non-51 result. For ease of writing, we’ll approximate 1/990 as 0.1%.
This probability distribution is analogous to the *likelihood* or *conditional probability* of the result given the hypothesis. Let us call it the *likelihood distribution* for the hypothesis, our chance of seeing each specified outcome *if* the hypothesis is true. The likelihood distribution for a hypothesis H is a function composed of all the conditional probabilities for p(0|H)=0.001, p(1|H)=0.001, …, p(51|H)=0.9, …, p(99|H)=0.001 . The probability mass contained in the likelihood distribution must sum to 1. It is a general rule that there is no way we can have a 90% chance of seeing 51 and also a 90% chance of seeing 52. Therefore, if we first assume the hypothesis H is true, there is still no way we can have a 90% chance of seeing 51 and also a 90% chance of seeing 52.
The precise theory predicts a 90% probability of seeing 51. Let there be also a vague theory, which predicts “a 90% probability of seeing a number in the 50s”.
Seeing the result 51, we do not say the outcome confirms both theories equally. Both theories made predictions, and both assigned probabilities of 90%, and the result 51 confirms both predictions. But the precise theory has an advantage because it concentrates its probability mass into a sharper point. If the vague theory makes no further specification, we count “a 90% probability of seeing a number in the 50s” as a 9% probability of seeing each number between 50 and 59.
Suppose we started with even odds in favor of the precise theory and the vague theory – odds of 1:1, or 50% probability for either hypothesis being true. After seeing the result 51, what are the posterior odds of the precise theory being true? (If you don’t remember how to work this problem, return to [An Intuitive Explanation of Bayesian Reasoning](https://eyudkowsky.wpengine.com/rational/bayes) .) The predictions of the two theories are analogous to their likelihood assignments – the conditional probability of seeing the result, given that the theory is true. What is the likelihood ratio between the two theories? The first theory allocated 90% probability mass to the *exact* outcome. The vague theory allocated 9% probability mass to the exact outcome. The likelihood ratio is 10:1. So if we started with even 1:1 odds, the posterior odds are 10:1 in favor of the precise theory. The differential pressure of the two conditional probabilities pushed our prior confidence of 50% to a posterior confidence of about 91% that the precise theory is correct. *Assuming* that these are the only hypotheses being tested, that this is the only evidence under consideration, and so on.
Why did the vague theory lose when both theories fit the evidence? The vague theory is timid; it makes a broad prediction, hedges its bets, allows many possibilities that would falsify the precise theory. This is not the virtue of a scientific theory. Philosophers of science tell us that theories should be bold, and subject themselves willingly to falsification if their prediction fails (Popper 1959). Now we see why. The precise theory concentrates its probability mass into a sharper point and thereby leaves itself vulnerable to falsification if the real outcome hits elsewhere; but if the predicted outcome is correct, precision has a tremendous likelihood advantage over vagueness.
The laws of probability theory provide no way to cheat, to make a vague hypothesis such that any result between 50 and 59 counts for as much favorable confirmation as the precise theory receives, for that would require probability mass summing to 900%. There is no way to cheat, providing you record your prediction *in advance* , so you cannot claim afterward that your theory assigns a probability of 90% to whichever result arrived. Humans are very fond of making their predictions afterward, so the social process of science requires an advance prediction before we say that a result confirms a theory. But how humans may move in harmony with the way of Bayes, and so wield the power, is a separate issue from whether the math works. When we’re doing the math, we just take for granted that likelihood density functions are fixed properties of a hypothesis and the probability mass sums to 1 and you’d never dream of doing it any other way.
You may want to take a moment to visualize that, *if* we define probability in terms of calibration, Bayes’ Theorem relates the calibrations. Suppose I guess that Theory 1 is 50% likely to be true, and I guess that Theory 2 is 50% likely to be true. Suppose I am well-calibrated; when I utter the words “fifty percent”, the event happens about half the time. And then I see a result R which would happen around nine-tenths of the time given Theory 1, and around nine-hundredths of the time given Theory 2, and I know this is so, and I apply Bayesian reasoning. If I was perfectly calibrated initially (despite the poor discrimination of saying 50/50), I will still be perfectly calibrated (and better discriminated) after I say that my confidence in Theory 1 is now 91%. If I repeated this kind of situation many times, I would be right around ten-elevenths of the time when I said “91%”. If I reason using Bayesian rules, and I start from well-calibrated priors, then my conclusions will also be well-calibrated. This only holds true if we define probability in terms of calibration! If “90% sure” is instead interpreted as, say, the strength of the emotion of surety, there is no reason to expect the posterior emotion to stand in an exact Bayesian relation to the prior emotion.
Let the prior odds be ten to one in favor of the vague theory. Why? Suppose our way of describing hypotheses allows us to either specify a precise number, or to just specify a first-digit; we can say “51”, “63”, “72”, or “in the fifties/sixties/seventies”. Suppose we think that the real answer is about equally liable to be an answer of the first kind or the second. However, given the problem, there are a hundred possible hypotheses of the first kind, and only ten hypotheses of the second kind. So if we think that either *class* of hypotheses has about an equal prior chance of being correct, we have to spread out the prior probability mass over ten times as many precise theories as vague theories. The precise theory that predicts exactly 51 would thus have one-tenth as much prior probability mass as the vague theory that predicts a number in the fifties. After seeing 51, the odds would go from 1:10 in favor of the vague theory to 1:1, even odds for the precise theory and the vague theory.
If you look at this carefully, it’s exactly what common sense would expect. You start out uncertain of whether a phenomenon is the kind of phenomenon that produces exactly the same result every time, or if it’s the kind of phenomenon that produces a result in the Xties every time. (Maybe the phenomenon is a price range at the supermarket, if you need some reason to suppose that 50..59 is an acceptable range but 49..58 isn’t.) You take a *single* measurement and the answer is 51. Well, that could be because the phenomenon is exactly 51, or because it’s in the fifties. So the remaining precise theory has the same odds as the remaining vague theory, which requires that the vague theory must have started out ten times as probable as that precise theory, since the precise theory has a sharper fit to the evidence.
If we just see one number, like 51, it doesn’t change the prior probability that the phenomenon itself was “precise” or “vague”. But, in effect, it concentrates all the probability mass of those two *classes* of hypothesis into a single surviving hypothesis of each class.
Of course, it is a severe error to say that a *phenomenon* is precise or vague, a case of what Jaynes calls the Mind Projection Fallacy (Jaynes 1996). Precision or vagueness is a property of maps, not territories. Rather we should ask if the price in the supermarket stays constant or shifts about. A hypothesis of the “vague” sort is a good description of a price that shifts about. A precise map will suit a constant territory.
Another example: You flip a coin ten times and see the sequence HHTTH:TTTTH. Maybe you started out thinking there was a 1% chance this coin was fixed. Doesn’t the hypothesis “This coin is fixed to produce HHTTH:TTTTH” assign a thousand times the likelihood mass to the observed outcome, compared to the fair coin hypothesis? Yes. Don’t the posterior odds that the coin is fixed go to 10:1? No. The 1% prior probability that “the coin is fixed” has to cover every possible kind of fixed coin – a coin fixed to produce HHTTH:TTTTH, a coin fixed to produce TTHHT:HHHHT, etc. The prior probability the coin is fixed to produce HHTTH:TTTTH is not 1%, but a thousandth of one percent. Afterward, the posterior probability the coin is fixed to produce HHTTH:TTTTH is one percent. Which is to say: You thought the coin was probably fair but had a one percent chance of being fixed to some random sequence; you flipped the coin; the coin produced a random-looking sequence; and that doesn’t tell you anything about whether the coin is fair or fixed. It does tell you, if the coin is fixed, *which* sequence it is fixed to.
This parable helps illustrate why Bayesians *must* think about prior probabilities. There is a branch of statistics, sometimes called “orthodox” or “classical” statistics, which insists on paying attention only to likelihoods. But if you only pay attention to likelihoods, then eventually some fixed-coin hypothesis will always defeat the fair coin hypothesis, a phenomenon known as “overfitting” the theory to the data. After thirty flips, the *likelihood* is a billion times as great for the fixed-coin hypothesis with that sequence, as for the fair coin hypothesis. Only if the fixed-coin hypothesis (or rather, that *specific* fixed-coin hypothesis) is a billion times less probable *a priori* , can the fixed-coin hypothesis possibly lose to the fair coin hypothesis.
If you shake the coin to reset it, and start flipping the coin *again* , and the coin produces HHTTH:TTTTH *again* , that is a different matter. That does raise the posterior odds of the fixed-coin hypothesis to 10:1, even if the starting probability was only 1%.
Similarly, if we perform two successive measurements of the particle counter (or the supermarket price on Wednesdays), and *both* measurements return 51, the precise theory wins by odds of 10 to 1.
So the precise theory wins, but the vague theory would still score better than no theory at all. Consider a third theory, the hypothesis of zero knowledge or *maximum-entropy distribution* , which makes equally probable any result between 0 and 99. Suppose we see the result 51. The vague theory produced a better prediction than the maximum-entropy distribution – assigned a greater likelihood to the outcome we observed. The vague theory is, literally, better than nothing. Suppose we started with odds of 1:20 in favor of the hypothesis of complete ignorance. (Why odds of 1:20? There is only one hypothesis of complete ignorance, and moreover, it’s a particularly simple and intuitive kind of hypothesis. Occam’s Razor.) After seeing the result of 51, predicted at 9% by the vague theory versus 1% by complete ignorance, the posterior odds go to 10:20 or 1:2. If we then see another result of 51, the posterior odds go to 10:2 or 83% probability for the vague theory, assuming there is no more precise theory under consideration.
Yet the timidity of the vague theory – its unwillingness to produce an *exact* prediction and accept falsification on any other result – renders it vulnerable to the bold, precise theory. (Providing, of course, that the bold theory correctly guesses the outcome!) Suppose the prior odds were 1:10:200 for the precise, vague, and ignorant theories – prior probabilities of 0.5%, 4.7%, and 94.8% for the precise, vague and ignorant theories. This figure reflects our prior probability distribution over *classes* of hypotheses, with the probability mass distributed over entire classes as follows: 50% that the phenomenon shifts across all digits, 25% that the phenomenon shifts around within some decimal bracket, and 25% that the phenomenon repeats the same number each time. 1 hypothesis of complete ignorance, 10 possible hypotheses for a decimal bracket, 100 possible hypotheses for a repeating number. Thus, prior odds of 1:10:200 for the precise hypothesis 51, the vague hypothesis “fifties”, and the hypothesis of complete ignorance.
After seeing a result of 51, with assigned probability of 90%, 9%, and 1%, the posterior odds go to 90:90:200 = 9:9:20. After seeing an additional result of 51, the posterior odds go to 810:81:20, or 89%, 9%, and 2%. The precise theory is now favored over the vague theory, which in turn is favored over the ignorant theory.
Now consider a stupid theory, which predicts a 90% probability of seeing a result between 0 and 9. The stupid theory assigns a probability of 0.1% to the actual outcome, 51. If the odds were initially 1:10:200:10 for the precise, vague, ignorant, and stupid theories, the posterior odds after seeing 51 once would be 90:90:200:1. The stupid theory has been falsified (posterior probability of 0.2%).
It is possible to have a model so bad that it is worse than nothing, if the model concentrates its probability mass away from the actual outcome, makes confident predictions of wrong answers. Such a hypothesis is so poor that it loses against the hypothesis of complete ignorance. Ignorance is better than anti-knowledge.*Side note 1:* In the field of Artificial Intelligence, there is a sometime fad that praises the glory of randomness. Occasionally an AI researcher discovers that if they add noise to one of their algorithms, the algorithm works better. This result is reported with great enthusiasm, followed by much fulsome praise of the creative powers of chaos, unpredictability, spontaneity, ignorance of what your own AI is doing, et cetera. (See [Imagination Engines Inc.](http://web.archive.org/web/20080430194232/http://www.imagination-engines.com/) for an example; according to their sales literature they sell wounded and dying neural nets.) But how sad is an algorithm if you can *increase* its performance by injecting entropy into intermediate processing stages? The algorithm must be so deranged that some of its work goes into concentrating probability mass *away* from good solutions. If injecting randomness results in a reliable improvement, then some aspect of the algorithm must do reliably worse than random. Only in AI would people devise algorithms *literally dumber than a bag of bricks* , boost the results slightly back toward ignorance, and then argue for the healing power of noise.*Side note 2:* Robert Pirsig once said: “The world’s stupidest man may say the Sun is shining, but that doesn’t make it dark out.” (Pirsig 1974.) It is a classical logical fallacy to say, “Hitler believed in the Pythagorean Theorem. You don’t want to agree with Hitler, do you?” Consider that for someone to be reliably wrong on yes-or-no questions – say, to be wrong 90% of the time – that person would need to do all the hard work of discriminating truth from falsehood, just to be wrong so reliably. If someone is wrong on yes-or-no questions 99% of the time, we can get 99% accuracy just by inverting the responses. Anyone that stupid would be smarter than I am.
Suppose that in our experiment we see the results 52, 51, 58. The precise theory gives this conjunctive event a probability of a thousand to one times 90% times a thousand to one, while the vaguer theory gives this conjunctive event a probability of 9% cubed, which works out to… oh… um… let’s see… a million to one given the precise theory, versus a thousand to one given the vague theory. Or thereabouts; we are counting rough powers of ten. Versus a million to one given the zero-knowledge distribution that assigns an equal probability to all outcomes. Versus a billion to one given a model worse than nothing, the stupid hypothesis, which claims a 90% probability of seeing a number less than 10. Using these approximate numbers, the vague theory racks up a score of -30 decibels (a probability of 1/1000 for the whole experimental outcome), versus scores of -60 for the precise theory, -60 for the ignorant theory, and -90 for the stupid theory. It is not always true that the highest score wins, because we need to take into account our prior odds of 1:10:200:10, confidences of -23, -13, 0, and -13 decibels. The vague theory still comes in with the highest total score at -43 decibels. (If we ignored our prior probabilities, each new experiment would override the accumulated results of all the previous experiments; we could not accumulate knowledge. Furthermore, the fixed-coin hypothesis would always win.)
As always, we should not be alarmed that even the best theory still has a low score – recall the parable of the fair coin. Theories are approximations. In principle we might be able to predict the exact sequence of coinflips. But it would take better measurement and more computing power than we’re willing to expend. Maybe we could achieve 60/40 prediction of coinflips, with a good enough model…? We go with the best approximation we have, and try to achieve good calibration even if the discrimination isn’t perfect.
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We’ve conducted our analysis so far under the rules of Bayesian probability theory, in which there’s no way to have more than 100% probability mass, and hence no way to cheat so that any outcome can count as “confirmation” of your theory. Under Bayesian law, play money may not be counterfeited; you only have so much clay. If you allocate more probability mass in one place, you have to take it from somewhere else; a coin cannot have a 90% chance of turning up heads and a 90% chance of turning up tails.
Unfortunately, human beings are not Bayesians. Human beings bizarrely attempt to *defend* hypotheses, making a deliberate effort to prove them or prevent disproof. This behavior has no analogue in the laws of probability theory or decision theory. In formal probability theory the hypothesis *is* , and the evidence *is* , and either the hypothesis is confirmed or it is not. In formal decision theory, an agent may make an effort to investigate some issue of which the agent is currently uncertain, not knowing whether the evidence shall go one way or the other. In neither case does one ever deliberately try to prove an idea, or try to avoid disproving it. One may *test* ideas of which one is genuinely uncertain, but not have a “preferred” outcome of the investigation. One may not try to prove hypotheses, nor prevent their proof. I cannot properly convey just how ridiculous the notion would be, to a true Bayesian; there are not even words in Bayes-language to describe the mistake…
One classic method for preventing a theory from disproof is arguing *post facto* that any observation presented proves the theory. Friedrich Spee von Langenfeld, a priest who heard the confessions of condemned witches, wrote in 1631 the *Cautio Criminalis* (‘prudence in criminal cases’) in which he bitingly described the decision tree for condemning accused witches. If the witch had led an evil and improper life, she was guilty; if she had led a good and proper life, this too was a proof, for witches dissemble and try to appear especially virtuous. After the woman was put in prison: if she was afraid, this proved her guilt; if she was not afraid, this proved her guilt, for witches characteristically pretend innocence and wear a bold front. Or on hearing of a denunciation of witchcraft against her, she might seek flight or remain; if she ran, that proved her guilt; if she remained, the devil had detained her so she could not get away. (Spee 1631.) Spee acted as confessor to many witches; he was thus in a position to observe *every* branch of the accusation tree, that no matter *what* the accused witch said or did, it was held a proof against her. In any individual case, you would only hear one branch of the dilemma.
It is for this reason that scientists write down their predictions in advance.
If you’ve read the *Intuitive Explanation* , you should recall the result I nicknamed the “Law of Conservation of Probability”, that for every expectation of evidence there is an equal and opposite expectation of counterevidence. If A is evidence in favor of B, not-A *must* be evidence in favor of not-B. The strengths of the evidences may not be equal; rare but strong evidence in one direction may be balanced by common but weak evidence in the other direction. But it is not possible for both A and not-A to be evidence in favor of B. That is, it’s not possible under the laws of probability theory. Humans often seem to want to have their cake and eat it too. Whichever result we witness is the one that proves our theory. As Spee put it, “The investigating committee would feel disgraced if it acquitted a woman; once arrested and in chains, she has to be guilty, by fair means or foul.”
The way human psychology seems to work is that first we see something happen, and then we try to argue that it matches whatever hypothesis we had in mind beforehand. Rather than conserved probability mass, to distribute over advance *predictions* , we have a feeling of *compatibility* – the degree to which the explanation and the event seem to ‘fit’. ‘Fit’ is not conserved. There is no equivalent of the rule that probability mass must sum to 1. A psychoanalyst may explain any possible behavior of a patient by constructing an appropriate structure of ‘rationalizations’ and ‘defenses’; it fits, therefore it must be true.
Now consider the fable told at the start of this essay – the students seeing a radiator, and a metal plate next to the radiator. The students would never predict in advance that the side of the plate near the radiator would be cooler. Yet, seeing the fact, they managed to make their explanations ‘fit’. They lost their precious chance at bewilderment, to realize that their models did not predict the phenomenon they observed. They sacrificed their ability to be more confused by fiction than by truth. And they did not realize “heat induction, blah blah, therefore the near side is cooler” is a vague and verbal prediction, spread across an enormously wide range of possible values for specific measured temperatures. Applying equations of diffusion and equilibrium would give a *sharp* prediction for possible joint values. It might not specify the *first* values you measured, but when you knew a few values you could generate a sharp prediction for the rest. The score for the entire experimental outcome would be far better than any less precise alternative, especially a vague and verbal prediction.
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You now have a *technical* explanation of the difference between a verbal explanation and a technical explanation. It is a technical explanation because it enables you to calculate *exactly how technical* an explanation is. Vague hypotheses may be so vague that only a superhuman intelligence could calculate exactly how vague. Perhaps a sufficiently huge intelligence could extrapolate every possible experimental result, and extrapolate every possible verdict of the vague guesser for how well the vague hypothesis “fit”, and then renormalize the “fit” distribution into a likelihood distribution that summed to 1. But in principle one can still calculate exactly how vague is a vague hypothesis. The calculation is just not computationally tractable, the way that calculating airplane trajectories via quantum mechanics is not computationally tractable.
I hold that everyone needs to learn at least one technical subject. Physics; computer science; evolutionary biology; or Bayesian probability theory, but *something* . Someone with *no* technical subjects under their belt has no referent for what it means to “explain” something. They may think “All is Fire” is an explanation. Therefore do I advocate that Bayesian probability theory should be taught in high school. Bayesian probability theory is the sole piece of math I know that is accessible at the high school level, and that permits a *technical* understanding of a subject matter – the dynamics of belief – that is an everyday real-world domain and has emotionally meaningful consequences. Studying Bayesian probability would give students a referent for what it means to “explain” something.
Too many academics think that being “technical” means speaking in dry polysyllabisms. Here’s a “technical” explanation of technical explanation:The equations of probability theory favor hypotheses that strongly predict the exact observed data. Strong models boldly concentrate their probability density into precise outcomes, making them falsifiable if the data hits elsewhere, and giving them tremendous likelihood advantages over models less bold, less precise. Verbal explanation runs on psychological evaluation of unconserved post facto compatibility instead of conserved ante facto probability density. And verbal explanation does not paint sharply detailed pictures, implying a smooth likelihood distribution in the vicinity of the data.
Is this satisfactory? No. Hear the impressive and weighty sentences, resounding with the dull thud of expertise. See the hapless students, writing those sentences on a sheet of paper. Even after the listeners hear the ritual words, they can perform no calculations. *You* know the math, so the words are meaningful. You can perform the calculations after hearing the impressive words, just as you could have done before. But what of one who did not see any calculations performed? What new skills have they gained from that “technical” lecture, save the ability to recite fascinating words?
“Bayesian” sure is a fascinating word, isn’t it? Let’s get it out of our systems: Bayes Bayes Bayes Bayes Bayes Bayes Bayes Bayes Bayes…
The sacred syllable is meaningless, except insofar as it tells someone to apply math. Therefore the one who hears must already know the math.
Conversely, if you know the math, you can be as silly as you like, and still technical.
We thus dispose of yet another stereotype of rationality, that rationality consists of sere formality and humorless solemnity. What has that to do with the problem of distinguishing truth from falsehood? What has that to do with attaining the map that reflects the territory? A scientist worthy of a lab coat should be able to make original discoveries while wearing a clown suit, or give a lecture in a high squeaky voice from inhaling helium. It is written nowhere in the math of probability theory that one may have no fun. The blade that cuts through to the correct answer has no dignity or silliness of itself, though it may fit the hand of a silly wielder.
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Our physics uses the same *theory* to describe an airplane, and collisions in a particle accelerator – particles and airplanes both obey special relativity and general relativity and quantum electrodynamics and quantum chromodynamics. But we use entirely different *models* to understand the aerodynamics of a 747 and a collision between gold nuclei. A computer modeling the aerodynamics of the 747 may not contain a single token representing an atom, even though no one denies that the 747 is made of atoms.
A *useful* model isn’t just something you know, as you know that the airplane is made of atoms. A useful model is knowledge you can compute in reasonable time to predict real-world events you know how to observe. Physicists use different models to predict airplanes and particle collisions, not because the two events take place in different universes with different laws of physics, but because it would be too expensive to compute the airplane particle by particle.
As the saying goes: “The map is not the territory, but you can’t fold up the territory and put it in your glove compartment.” Sometimes you need a smaller map, to fit in a more cramped glove compartment. It doesn’t change the territory. The precision or vagueness of the map isn’t a fact about the territory, it’s a fact about the map.
Maybe someone will find that, using a model that violates conservation of momentum just a little, you can compute the aerodynamics of the 747 much more *cheaply* than if you insist that momentum is exactly conserved. So if you’ve got two computers competing to produce the best prediction, it might be that the best prediction comes from the model that violates conservation of momentum. This doesn’t mean that the 747 violates conservation of momentum in real life. Neither model uses individual atoms, but that doesn’t imply the 747 is not made of atoms. You would prove the 747 is made of atoms with experimental data that the aerodynamic models couldn’t handle; for example, you would train a scanning tunneling microscope on a section of wing and look at the atoms. Similarly, you could use a finer measuring instrument to discriminate between a 747 that *really* disobeyed conservation of momentum like the cheap approximation predicted, versus a 747 that obeyed conservation of momentum like underlying physics predicted. The winning theory is the one that best predicts all the experimental predictions together. Our Bayesian scoring rule gives us a way to combine the results of *all* our experiments, even experiments that use different methods.
Furthermore, the atomic theory allows, embraces, and in some sense mandates the aerodynamic model. By thinking abstractly about the assumptions of atomic theory, we realize that the aerodynamic model ought to be a good (and much cheaper) approximation of the atomic theory, and so the atomic theory supports the aerodynamic model, rather than competing with it. A successful theory can embrace many models for different domains, so long as the models are acknowledged as approximations, and in each case the model is compatible with (or ideally mandated by) the underlying theory.
Our *fundamental* physics – quantum mechanics, the standard family of particles, and relativity – is a theory that embraces an *enormous* family of models for macroscopic physical phenomena. There is the physics of liquids, and solids, and gases; yet this does not mean that there are *fundamental* things in the world that have the intrinsic property of liquidity.”Apparently there is colour, apparently sweetness, apparently bitterness, actually there are only atoms and the void.”– Democritus, 420 BC (from Robinson and Groves 1998).
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In arguing that a “technical” theory should be defined as a theory that sharply concentrates probability into specific advance predictions, I am setting an extremely high standard of strictness. We have seen that a vague theory *can* be better than nothing. A vague theory can win out over the hypothesis of ignorance, if there are no precise theories to compete against it.
There is an enormous family of models belonging to the central underlying theory of life and biology; the underlying theory that is sometimes called neo-Darwinism, natural selection, or evolution. Some models in evolutionary theory are quantitative. The way in which DNA encodes proteins is redundant; two different DNA sequences can code for exactly the same protein. There are 4 DNA bases {ATCG} and 64 possible combinations of three DNA bases. But those 64 possible codons describe only 20 amino acids plus a stop code. Genetic drift ought therefore to produce non-functional changes in species genomes, through mutations which by chance become fixed in the gene pool. The accumulation rate of non-functional differences between the genomes of two species with a common ancestor, depends on such parameters as the number of generations elapsed and the intensity of selection at that genetic locus. That’s an example of a member of the family of evolutionary models that produces quantitative predictions. There are also disequilibrium allele frequencies under selection, stable equilibria for game-theoretical strategies, sex ratios, et cetera.
This all comes under the heading of “fascinating words”. Unfortunately, there are certain religious factions that spread gross disinformation about evolutionary theory. So I emphasize that many models within evolutionary theory make quantitative predictions that are experimentally confirmed, and that such models are far more than sufficient to demonstrate that, e.g., humans and chimpanzees are related by a common ancestor. If you’ve been victimized by creationist disinformation – that is, if you’ve heard any suggestion that evolutionary theory is controversial or untestable or “just a theory” or non-rigorous or non-technical or in any wise not confirmed by an unimaginably huge mound of experimental evidence – I recommend reading the [Talk.Origins FAQ](http://www.talkorigins.org/) and studying evolutionary biology with math.
But imagine going back in time to the nineteenth century, when the theory of natural selection had only just been discovered by Charles Darwin and Alfred Russel Wallace. Imagine evolutionism just after its birth, when the theory had nothing remotely like the modern-day body of quantitative models and great heaping mountains of experimental evidence. There was no way of knowing that humans and chimpanzees would be discovered to have 95% shared genetic material. No one knew that DNA existed. Yet even so, scientists flocked to the new theory of natural selection. And later it turned out that there *was* a precisely copied genetic material with the potential to mutate, that humans and chimps were provably related, etc.
So the very strict, very high standard that I proposed for a “technical” theory is too strict. Historically, it *has* been possible to successfully discriminate true theories from false theories, based on predictions of the sort I called “vague”. Vague predictions of, say, 80% confidence, can build up a huge advantage over alternate hypotheses, given enough experiments. Perhaps a theory of this kind, producing predictions that are not precisely detailed but are nonetheless correct, could be called “semitechnical”?
But surely technical theories are more reliable than semitechnical theories? Surely technical theories should take precedence, command greater respect? Surely physics, which produces exceedingly exact predictions, is in some sense better confirmed than evolutionary theory? Not implying that evolutionary theory is wrong, of course; but however vast the mountains of evidence favoring evolution, does not physics go one better through vast mountains of *precise* experimental confirmation? Observations of neutron stars confirm the predictions of General Relativity to within one part in a hundred trillion (10^14). What does evolutionary theory have to match that?
Someone – I think either Roger Penrose or Richard Dawkins – said once that measured by the simplicity of the theory and the amount of complexity it explained, Darwin had the single greatest idea in the history of time.
Once there was a conflict between 19th-century physics and 19th-century evolutionism. According to the best physical models then in use, the Sun could not have been burning very long. 3000 years on chemical energy, or 40 million years on gravitational energy. There was no energy source known to 19th-century physics that would permit longer burning. 19th-century physics was not *quite* as powerful as modern physics – it did not have predictions accurate to within one part in 10^14. But 19th-century physics still had the mathematical character of modern physics; a discipline whose models produced detailed, precise, quantitative predictions. 19th-century evolutionary theory was wholly semitechnical, without a scrap of quantitative modeling. Not even Mendel’s experiments with peas were then known. And yet it did seem likely that evolution would require longer than a paltry 40 million years in which to operate – hundreds of millions, even billions of years. The antiquity of the Earth was a vague and semitechnical prediction, of a vague and semitechnical theory. In contrast, the 19th-century physicists had a precise and quantitative model, which through formal calculation produced the precise and quantitative dictum that the Sun simply could not have burned that long.”The limitations of geological periods, imposed by physical science, cannot, of course, disprove the hypothesis of transmutation of species; but it does seem sufficient to disprove the doctrine that transmutation has taken place through ‘descent with modification by natural selection.'”– Lord Kelvin, distinguished 19th-century physicist (from Zapato 1998).
History records who won.
The moral? If you can give 80% confident advance predictions on yes-or-no questions, it may be a “vague” theory, it may be wrong one time out of five, but you can still build up a heck of a huge scoring lead over the hypothesis of ignorance. Enough to confirm a theory, if there are no better competitors. Reality is consistent; every *correct* theory about the universe is compatible with every other correct theory. Imperfect maps can conflict, but there is only one territory. 19th-century evolutionism might have been a semitechnical discipline, but it was still correct (as we now know) and by far the best explanation (even in that day). Any conflict between evolutionism and another well-confirmed theory had to reflect some kind of anomaly, a mistake in the assertion that the two theories were incompatible. 19th-century physics couldn’t model the dynamics of the Sun – they didn’t know about nuclear reactions. They could not show that their understanding of the Sun was correct *in technical detail* , nor calculate from a *confirmed* model of the Sun to determine how long the Sun had existed. So in retrospect, we can say something like: “There was room for the possibility that 19th-century physics just didn’t understand the Sun.”
But that is hindsight. The real lesson is that, even though 19th-century physics was both precise and quantitative, it didn’t automatically dominate the semitechnical theory of 19th-century evolutionism. The theories were *both* well-supported. They were *both* correct in the domains over which they were generalized. The apparent conflict between them was an anomaly, and the anomaly turned out to stem from the incompleteness and incorrect application of 19th-century physics, not the incompleteness and incorrect application of 19th-century evolutionism. But it would be futile to compare the mountain of evidence supporting the one theory, versus the mountain of evidence supporting the other. Even in that day, both mountains were too large to suppose that either theory was simply mistaken. Mountains of evidence that large cannot be set to compete, as if one falsifies the other. You must be applying one theory incorrectly, or applying a model outside the domain it predicts well.
So you shouldn’t *necessarily* sneer at a theory just because it’s semitechnical. Semitechnical theories can build up high enough scores, compared to every available alternative, that you know the theory is at least approximately correct. Someday the semitechnical theory may be replaced or even falsified by a more precise competitor, but that’s true even of technical theories. Think of how Einstein’s General Relativity devoured Newton’s theory of gravitation.
But the correctness of a semitechnical theory – a theory that currently has no precise, computationally tractable models testable by feasible experiments – can be a lot less cut-and-dried than the correctness of a technical theory. It takes skill, patience, and examination to distinguish good semitechnical theories from theories that are just plain confused. This is not something that humans do well by instinct, which is why we have Science.
People eagerly jump the gun and seize on any available reason to reject a disliked theory. That is why I gave the example of 19th-century evolutionism, to show why one should not be too quick to reject a “non-technical” theory out of hand. By the moral customs of science, 19th-century evolutionism was guilty of more than one sin. 19th-century evolutionism made no quantitative predictions. It was not readily subject to falsification. It was largely an explanation of what had already been seen. It lacked an underlying mechanism, as no one then knew about DNA. It even contradicted the 19th-century laws of physics. Yet natural selection was such an *amazingly good* post-facto explanation that people flocked to it, and they turned out to be right. Science, as a human endeavor, requires advance prediction. Probability theory, as math, does not distinguish between post-facto and advance prediction, because probability theory assumes that probability distributions are fixed properties of a hypothesis.
The rule about advance prediction is a rule of the social process of science – a moral custom and not a theorem. The moral custom exists to prevent human beings from making human mistakes that are hard to even describe in the language of probability theory, like tinkering after the fact with what you claim your hypothesis predicts. People concluded that 19th-century evolutionism was an excellent explanation, even if it was post-facto. That reasoning *was correct as probability theory* , which is why it *worked* despite all scientific sins. Probability theory is math. The social process of science is a set of legal conventions to keep people from cheating on the math.
Yet it is also true that, compared to a *modern-day* evolutionary theorist, evolutionary theorists of the late 19th and early 20th century often went sadly astray. Darwin, who was bright enough to *invent* the theory, got an amazing amount right. But Darwin’s successors, who were only bright enough to *accept* the theory, misunderstood evolution frequently and seriously. The usual process of science was then required to correct their mistakes. It is incredible how few errors of reasoning Darwin made in *The Origin of Species* and *The Descent of Man* , compared to they who followed.
That is also a hazard of a semitechnical theory. Even after the flash of genius insight is confirmed, merely average scientists may fail to apply the insights properly in the absence of formal models. As late as the 1960s biologists spoke of evolution working “for the good of the species”, or suggested that individuals would restrain their reproduction to prevent species overpopulation of a habitat. The best evolutionary theorists knew better, but average theorists did not. (Williams 1966.)
So it is *far* better to have a technical theory than a semitechnical theory. Unfortunately, Nature is not always so kind as to render Herself describable by neat, formal, *computationally tractable* models, nor does She always provide Her students with measuring instruments that can directly probe Her phenomena. Sometimes it is only a matter of time. 19th-century evolutionism was semitechnical, but later came the math of population genetics, and eventually DNA sequencing. But Nature will not always give you a phenomenon that you can describe with technical models fifteen seconds after you have the basic insight.
Yet the cutting edge of science, the *controversy* , is most often about a semitechnical theory, or nonsense posing as a semitechnical theory. By the time a theory achieves technical status, it is usually no longer controversial (among scientists). So the question of how to distinguish good semitechnical theories from nonsense is very important to scientists, and it is not as easy as dismissing out of hand any theory that is not technical. To the end of distinguishing truth from falsehood exists the entire discipline of rationality. The art is not reducible to a checklist, or at least, no checklist that an average scientist can apply reliably after an hour of training. If it was that simple we wouldn’t need science.
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Why do you care about scientific controversies?
No, seriously, why do you care about scientific *controversies* ?
The media thinks that only the cutting edge of science, the very latest controversies, are worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? By the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is based on the thinnest of evidence and wrong half the time. If it were not on the uttermost fringes of the scientific frontier, it would not be news. Scientific *controversies* are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes the problem controversial and attracts all the media attention. So the reporters show up, and hear the scientists speak fascinating words. The reporters are told that “particles” are “waves”, but there is no understanding of math for the words to invoke. What the physicist means by “wave” is not what the reporters hear, even if the physicist’s math applies also to the structure of water as it crashes on the shore.
And then the reporters write stories, which are not worth the lives of the dead trees on which they are printed.
But what does it matter to you? Why should you pay attention to scientific *controversies* ? Why graze upon such sparse and rotten feed as the media offers, when there are so many solid meals to be found in textbooks? Nothing you’ll read as breaking news will ever hold a candle to the sheer beauty of settled science. Textbook science has carefully phrased explanations for new students, math derived step by step, plenty of experiments as illustration, and test problems.
And textbook science is beautiful! Textbook science is *comprehensible* , unlike mere fascinating words that can never be truly beautiful. Elementary science textbooks describe *simple* theories, and simplicity is the core of scientific beauty. Fascinating words have no power, nor yet any meaning, without the math. The fascinating words are not knowledge but the illusion of knowledge, which is why it brings so little satisfaction to know that “gravity results from the curvature of spacetime”. Science is not in the fascinating words, though it’s all the media will ever give you.
Is there ever justification for following a scientific controversy, while there remains any basic science you do not yet know? Yes. You could be an expert in that field, in which case that scientific controversy is your proper meat. Or the scientific controversy might be something you need to know *now* , because it affects your life. Maybe it’s the 19th century, and you’re gazing lustfully at a member of the appropriate sex wearing a 19th-century bathing suit, and you need to know whether your sexual desire comes from a psychology constructed by natural selection, or is a temptation placed in you by the Devil to lure you into hellfire.
It is not wholly impossible that we shall happen upon a scientific controversy that affects us, and find that we have a burning and urgent need for the correct answer. I shall therefore discuss some of the warning signs that historically distinguished vague hypotheses that later turned out to be unscientific gibberish, from vague hypotheses that later graduated to confirmed theories. Just remember the historical lesson of 19th-century evolutionism, and resist the temptation to fail every theory that misses a single item on your checklist. It is not my intention to give people another excuse to dismiss good science that discomforts them. If you apply stricter criteria to theories you dislike than theories you like (or vice versa!), then every additional nit you learn how to pick, every new logical flaw you learn how to detect, makes you that much stupider. Intelligence, to be useful, must be used for something other than defeating itself.
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One of the classic signs of a poor hypothesis is that it must expend great effort in avoiding falsification – elaborating reasons why the hypothesis is compatible with the phenomenon, even though the phenomenon didn’t behave as expected.
Sagan (1995) gives the example of someone who claims that a dragon lives in their garage. Fascinated by this controversial question, we ignore all the textbooks providing total solutions to ancient mysteries on which alchemists spent their lives in vain… but never mind. We show up at the garage, look inside, and see: Nothing.
Ah, says the claimant, that’s because it’s an *invisible* dragon.
Now as Sagan says, this is an odd claim, but it doesn’t mean we can never know if the dragon is there. Maybe we hear heavy breathing, and discover that carbon dioxide and heat appears in the garage’s air. Clawed footprints stamp through the dust. Occasionally a great gout of fire bursts out from no visible source. If so, we conclude that the garage contains an invisible dragon, and the reporters depart, satisfied that the controversy is over. Once something is a fact, it’s no longer exciting; it’s no fun believing in things that any old fool can see are true. If the dragon were really there, it would be no more fun to believe in the dragon than to believe in zebras.
But now suppose instead that we bring up our measuring instruments to see if carbon dioxide is accumulating in the garage’s air, and the claimant at once says: “No, no, it’s an invisible non-breathing dragon!” Okay. We begin to examine the dirt, and the claimant says: “No, it’s a flying invisible non-breathing dragon, so it won’t leave footprints.” We start to unload audio equipment, and the claimant says it’s an inaudible dragon. We bring in a bag of flour, to throw into the air to outline the dragon’s form, and the claimant quickly says that this dragon is permeable to flour.
Carl Sagan originally drew the lesson that poor hypotheses need to do fast footwork to avoid falsification – to maintain an appearance of “fit”.
I would point out that the claimant obviously has a good model of the situation *somewhere* in his head, because he can predict, in advance, exactly which excuses he’s going to need. When we bring up our measuring instruments, he knows that he’ll have to excuse the lack of any carbon dioxide in the air. When we bring in a bag of flour, the claimant knows that he’ll need to excuse the lack of any dragon-shaped form in the floury air.
To a Bayesian, a hypothesis isn’t something you assert in a loud, emphatic voice. A hypothesis is something that controls your *anticipations* , the probabilities you assign to future experiences. That’s what a probability *is* , to a Bayesian – that’s what you score, that’s what you calibrate. So while our claimant may say loudly, emphatically, and honestly that he *believes* there’s an invisible dragon in the garage, he does not *anticipate* there’s an invisible dragon in the garage – he anticipates exactly the same experience as the skeptic.
When I judge the predictions of a hypothesis, I ask which experiences I would anticipate, not which facts I would believe.
*The flip side:*
I recently argued with a friend of mine over a question of evolutionary theory. My friend alleged that the clustering of changes in the fossil record (apparently, there are periods of comparative stasis followed by comparatively sharp changes; itself a controversial observation known as “punctuated equilibrium”) showed that there was something wrong with our understanding of speciation. My friend thought that there was some unknown force at work, not supernatural, but some natural consideration that standard evolutionary theory didn’t take into account. Since my friend didn’t give a specific competing hypothesis that produced better predictions, his thesis had to be that the standard evolutionary model was *stupid* with respect to the data – that the standard model made a specific prediction that was wrong; that the model did worse than complete ignorance or some other default competitor.
At first I fell into the trap; I accepted the implicit assumption that the standard model predicted smoothness, and based my argument on my recollection that the fossil record changes weren’t as sharp as he claimed. He challenged me to produce an evolutionary intermediate between *Homo erectus* and *Homo sapiens* ; I googled and found *Homo heidelbergensis* . He congratulated me and acknowledged that I had scored a major point, but still insisted that the changes were too sharp, and not steady enough. I started to explain why I thought a pattern of uneven change *could* arise from the standard model: environmental selection pressures might not be constant… “Aha!” my friend said, “you’re making your excuses in advance.”
But suppose that the fossil record instead showed a smooth and gradual set of changes. Might my friend have argued that the standard model of evolution as a chaotic and noisy process could not account for such smoothness? If it is a scientific sin to claim post facto that our beloved hypothesis predicts the data, should it not be equally a sin to claim post facto that the competing hypothesis is stupid on the data?
If a hypothesis has a *purely* technical model, there is no trouble; we can compute the prediction of the model formally, without informal variables to provide a handle for post facto meddling. But what of semitechnical theories? Obviously a semitechnical theory must produce some good advance predictions about *something* , or else why bother? But *after* the theory is semi-confirmed, can the detractors claim that the data show a problem with the semitechnical theory, when the “problem” is constructed post facto? At the least the detractors must be very specific about what data a confirmed model predicts stupidly, and why the confirmed model must make (post facto) that stupid prediction. How sharp a change is “too sharp”, quantitatively, for the standard model of evolution to permit? Exactly how much steadiness do you think the standard model of evolution predicts? How do you know? Is it too late to say that, after you’ve seen the data?
When my friend accused me of making excuses, I paused and asked myself which excuses I anticipated needing to make. I decided that my current grasp of evolutionary theory didn’t say anything about whether the rate of evolutionary change should be intermittent and jagged, or smooth and gradual. If I hadn’t seen the graph in advance, I could not have predicted it. (Unfortunately, I rendered even that verdict after seeing the data…) Maybe there are models in the evolutionary family that would make advance predictions of steadiness or variability, but if so, I don’t know about them. More to the point, my friend didn’t know either.
It is not always wise, to ask the opponents of a theory what their competitors predict. Get the theory’s predictions from the theory’s best advocates. Just make sure to write down their predictions in advance. Yes, sometimes a theory’s advocates try to make the theory “fit” evidence that plainly doesn’t fit. But if you find yourself wondering what a theory predicts, ask first among the theory’s advocates, and afterward ask the detractors to cross-examine.
Furthermore: Models may include noise. If we hypothesize that the data are trending slowly and steadily upward, but our measuring instrument has an error of 5%, then it does no good to point to a data point that dips below the previous data point, and shout triumphantly, “See! It went down! Down down down! And don’t tell me why your theory fits the dip; you’re just making excuses!” Formal, technical models often incorporate explicit error terms. The error term spreads out the likelihood density, decreases the model’s precision and reduces the theory’s score, but the Bayesian scoring rule still governs. A technical model can allow mistakes, and make mistakes, and still do better than ignorance. In our supermarket example, even the precise hypothesis of 51 still bets only 90% of its probability mass on 51; the precise hypothesis claims only that 51 happens nine times out of ten. Ignoring nine 51s, pointing at one case of 82, and crowing in triumph, does not a refutation make. That’s not an excuse, it’s an explicit advance prediction of a technical model.
The error term makes the “precise” theory vulnerable to a superprecise alternative that predicted the 82. The standard model would also be vulnerable to a precisely ignorant model that predicted a 60% chance of 51 on the round where we saw 82, spreading out the likelihood more entropically on that particular error. No matter how good the theory, science always has room for a higher-scoring competitor. But if you *don’t* present a better alternative, if you try only to show that an accepted theory is *stupid* with respect to the data, that scientific endeavor may be *more* demanding than just replacing the old theory with a new one.
Astronomers recorded the unexplained perihelion advance of Mercury, unaccounted for under Newtonian physics – or rather, Newtonian physics predicted 5557 seconds of arc per century, where the observed amount was 5600. (From Brown 1999.) But should the scientists of that day have junked Newtonian gravitation based on such small, unexplained counterevidence? What would they have used instead? Eventually, Newton’s theory of gravitation *was* set aside, after Einstein’s General Relativity precisely explained the orbital discrepancy of Mercury and also made successful advance predictions. But there was no way to know *in advance* that this was how things would turn out.
In the nineteenth century there was a persistent anomaly in the orbit of Uranus. People said, “Maybe Newton’s law starts to fail at long distances.” Eventually some bright fellows looked at the anomaly and said, “Could this be an unknown outer planet?” Urbain Le Verrier and John Couch Adams independently did some scribbling and figuring, using Newton’s standard theory – and predicted Neptune’s location to within one degree of arc, dramatically *confirming* Newtonian gravitation. (Brown 1999.)
Only *after* General Relativity precisely produced the perihelion advance of Mercury, did we *know* Newtonian gravitation would never explain it.
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In the *Intuitive Explanation* we saw how Karl Popper’s insight that falsification is stronger than confirmation, translates into a Bayesian truth about likelihood ratios. Popper erred in thinking that falsification was *qualitatively different* from confirmation; both are governed by the same Bayesian rules. But Popper’s philosophy reflected an important truth about a quantitative difference between falsification and confirmation.”Popper was profoundly impressed by the differences between the allegedly ‘scientific’ theories of Freud and Adler and the revolution effected by Einstein’s theory of relativity in physics in the first two decades of this century. The main difference between them, as Popper saw it, was that while Einstein’s theory was highly ‘risky’, in the sense that it was possible to deduce consequences from it which were, in the light of the then dominant Newtonian physics, highly improbable (e.g. that light is deflected towards solid bodies – confirmed by Eddington’s experiments in 1919), and which would, if they turned out to be false, falsify the whole theory, nothing could, even in principle, falsify psychoanalytic theories. These latter, Popper came to feel, have more in common with primitive myths than with genuine science. That is to say, he saw that what is apparently the chief source of strength of psychoanalysis, and the principal basis on which its claim to scientific status is grounded, viz. its capability to accommodate, and explain, every possible form of human behaviour, is in fact a critical weakness, for it entails that it is not, and could not be, genuinely predictive. Psychoanalytic theories by their nature are insufficiently precise to have negative implications, and so are immunised from experiential falsification…”Popper, then, repudiates induction, and rejects the view that it is the characteristic method of scientific investigation and inference, and substitutes falsifiability in its place. It is easy, he argues, to obtain evidence in favour of virtually any theory, and he consequently holds that such ‘corroboration’, as he terms it, should count scientifically only if it is the positive result of a genuinely ‘risky’ prediction, which might conceivably have been false. For Popper, a theory is scientific only if it is refutable by a conceivable event. Every genuine test of a scientific theory, then, is logically an attempt to refute or to falsify it…”Every genuine scientific theory then, in Popper’s view, is prohibitive, in the sense that it forbids, by implication, particular events or occurrences.”(Thornton 2002)
On Popper’s philosophy, the strength of a scientific theory is not how much it explains, but how much it *doesn’t* explain. The virtue of a scientific theory lies not in the outcomes it *permits* , but in the outcomes it *prohibits* . Freud’s theories, which seemed to explain everything, *prohibited* nothing.
Translating this into Bayesian terms, we find that the more outcomes a model *prohibits* , the more probability density the model concentrates in the remaining, permitted outcomes. The more outcomes a theory prohibits, the greater the knowledge-content of the theory. The more daringly a theory exposes itself to falsification, the more definitely it tells you which experiences to anticipate.
A theory that can explain *any* experience corresponds to a hypothesis of complete ignorance – a uniform distribution with probability density spread evenly over every possible outcome.
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One of the most famous lessons of science is the case of the *phlogiston theory of chemistry* .
Phlogiston was the 18th century’s answer to the Elemental Fire of the Greek alchemists. Ignite wood, and let it burn. What is the orangey-bright “fire” stuff? Why does the wood transform into ash? To both questions, the 18th century chemists answered, “phlogiston”.
…and that was it, you see, that was their answer: “Phlogiston.”
Phlogiston escaped from burning substances as visible fire. As the phlogiston escaped, the burning substances lost phlogiston and so became ash, the “true material”. Flames extinguished in closed containers because the air became saturated with phlogiston. Charcoal left little residue upon burning because it was nearly pure phlogiston. (Moore 1961.)
This was a more primitive age of science, and so people did not notice and take offense that phlogiston theory made no advance predictions. Instead phlogiston theory just added on more and more independent clauses to explain more and more chemical observations. You couldn’t use phlogiston theory to predict the outcome of a chemical transformation – first you looked at the result, then you used phlogiston to explain it. It was not that, having never tried burning a flame in a closed container, phlogiston theorists predicted that the flame would go out when the air became “saturated” with phlogiston. Rather they lit a flame in a container, watched it go out, then said, “The air must have become saturated with phlogiston.”
You couldn’t even use phlogiston theory to constrain chemical transformations, to say what you did *not* expect to see. Phlogiston theory was infinitely flexible. In excusing everything, it explained nothing; a disguised hypothesis of zero knowledge.
The word *phlogiston* functioned not as an *anticipation-controller* but as a *curiosity-stopper* . You said “Why?” and the answer was “Phlogiston”.
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Imagine looking at your hand, and knowing nothing of cells, nothing of biological chemistry, nothing of DNA. You know some anatomy, you know your hand contains muscles, but you don’t know why muscles move instead of lying there like clay. Your hand is just… stuff… and for some reason it moves under your direction. Is this not magic?”The animal body does not act as a thermodynamic engine … consciousness teaches every individual that they are, to some extent, subject to the direction of his will. It appears therefore that animated creatures have the power of immediately applying to certain moving particles of matter within their bodies, forces by which the motions of these particles are directed to produce derived mechanical effects… The influence of animal or vegetable life on matter is infinitely beyond the range of any scientific inquiry hitherto entered on. Its power of directing the motions of moving particles, in the demonstrated daily miracle of our human free-will, and in the growth of generation after generation of plants from a single seed, are infinitely different from any possible result of the fortuitous concurrence of atoms… Modern biologists were coming once more to the acceptance of something and that was a vital principle.”– Lord Kelvin (from Zapato 1998).
This was the theory of *vitalism* ; that the difference between living matter and non-living matter consisted of an *elan vital* or *vis vitalis* . Elan vital infused living matter and caused it to move as consciously directed. Elan vital participated in chemical transformations which no mere non-living particles could undergo. Wohler’s artificial synthesis of urea, a component of urine, was a major blow to the vitalistic theory because it showed that “mere chemistry” could duplicate a product of biology. (Moore 1961.)
Building on the previous lesson of phlogiston, we note at once that *elan vital* functions not as an anticipation-controller but as a curiosity-stopper. Vitalism doesn’t explain how the hand moves, nor tell you what transformations to expect from organic chemistry, and vitalism certainly permits no quantitative calculations. “Why? Elan vital!” And that was all there was to vitalism.
But the greater lesson lies in the vitalists’ reverence for the elan vital, their eagerness to pronounce it a mystery beyond all science. Meeting the great dragon Unknown, the vitalists did not draw their swords to do battle, but instead bowed their necks in submission. They took pride in their ignorance, made biology into a sacred mystery, and thereby became loath to relinquish their ignorance when evidence came knocking.
I quote Lord Kelvin to show that in every generation, there are scientific puzzles so wonderfully *mys-TER-i-ous* that they become sacred, making a *solution* sacrilege. Science is only good for explaining non-mysterious phenomena, like the course of planets, or the transformations of materials, or the biology of life; science can never answer questions about *real* mysteries like consciousness. Surely, if it were possible for science to explain consciousness, it would already have done so? As if all these other matters had not been mysteries for thousands of years and millions of years, from the dawn of intelligent thought right up until science solved them.
People have no sense of history. They learn about stars and chemistry and biology in school and it seems that these matters have always been the proper meat of science, that they have *never been* mysterious. Astrologers and alchemists and vitalists were merely fools, to make such big deals out of such simple questions. When science must deal with some new puzzling phenomenon, it is a great shock to the children of that generation, for they have never encountered something that *feels* mysterious before. Surely such a sacred mystery as consciousness is infinitely beyond the reach of dry scientific thinking; science is only suited to mundane questions such as biology.
Vitalism shared with phlogiston the error of *encapsulating the mystery as a substance.* Fire was mysterious, and the phlogiston theory encapsulated the mystery in a mysterious substance called “phlogiston”. Life was a sacred mystery, and vitalism encapsulated the sacred mystery in a mysterious substance called “elan vital”. Neither “explanation” helped concentrate the model’s probability density. The “explanation” just wrapped up the question as a small, hard, opaque black ball. In a play written by the author Moliere, a physician explains the power of a soporific by claiming that the soporific contains a “dormitive potency” – a fine parody of the art of fake explanation. (Cited in Kuhn 1962.)
It is a failure of human psychology that, faced with a mysterious phenomenon, we more readily postulate mysterious inherent substances than complex underlying processes.
But the deeper failure is supposing that an *answer* can be mysterious. Mystery is a property of questions, not answers. If a phenomenon feels mysterious, that is a fact about our state of knowledge, not a fact about the phenomenon itself. The vitalists saw a mysterious gap in their knowledge, and postulated a mysterious stuff that plugged the gap. They mixed up the map with the territory. All confusion and dismay exist in the mind, not in reality.
I call theories such as vitalism *mysterious answers to mysterious questions* . These are the signs of mysterious answers: First, the explanation acts as a curiosity-stopper rather than an anticipation-controller. Second, the hypothesis has no moving parts – the model is not a specific complex mechanism, but a blankly solid substance or force. The mysterious substance or mysterious force may be said to be here or there, to do this or that; but the reason why the mysterious force behaves thus is wrapped in a blank unity. Third, those who proffer the explanation cherish their ignorance; they speak proudly of how the phenomenon defeats ordinary science or is unlike merely mundane phenomena. Fourth, *even after the answer is given, the phenomenon is still a mystery* and possesses the same quality of sacred inexplicability that it had at the start.
*The flip side:*
Beware of checklist thinking: Having a *sacred* mystery, or a mysterious answer, is not the same as refusing to explain something. Some elements in our physics are taken as “fundamental”, not yet further reduced or explained. But these fundamental elements of our physics are governed by clearly defined, mathematically simple, formally computable causal rules.
Occasionally some crackpot objects to modern physics on the grounds that it does not provide an “underlying mechanism” for a mathematical law currently treated as fundamental. (Claiming that a mathematical law lacks an “underlying mechanism” is one of the entries on John Baez’s Crackpot Index; Baez 1998.) The “underlying mechanism” the crackpot proposes in answer is vague, verbal, and yields no increase in predictive power – otherwise we would not classify the claimant as a crackpot.
Our current physics makes the electromagnetic field fundamental, and refuses to explain it further. But the “electromagnetic field” is a fundamental governed by clear mathematical rules, with no properties outside the mathematical rules, subject to formal computation to describe its causal effect upon the world. Someday someone may suggest improved math that yields better predictions, but I would not indict the current model on grounds of mysteriousness. A theory that includes *fundamental elements* is not the same as a theory that contains *mysterious elements* .
Fundamentals should be simple. “Life” is not a good fundamental; “oxygen” is a good fundamental, and “electromagnetic field” is a better fundamental. Life might look simple to a vitalist – it’s the simple, magical ability of your muscles to move under your mental direction. Why shouldn’t life be explained by a simple, magical fundamental substance like *elan vital* ? But phenomena that seem *psychologically* very simple – little dots of light in the sky, orangey-bright hot flame, flesh moving under mental direction – often conceal vast depths of underlying complexity. The proposition that life is a complex phenomenon may seem incredible to the vitalist, staring at a blankly opaque mystery with no obvious handles; but yes, Virginia, there is underlying complexity. The criterion of simplicity that is relevant to Occam’s Razor is *mathematical* or *computational* simplicity. Once we render down our model into mathematically simple fundamental elements, not in themselves sharing the mysterious qualities of the mystery, interacting in clearly defined ways to produce the formerly mysterious phenomenon as a detailed prediction, that is as non-mysterious as humanity has ever figured out how to make anything.
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The failures of phlogiston and vitalism are historical hindsight. Dare I step out on a limb and name some current theory, not yet disproven, that I think is analogously flawed to vitalism and phlogiston? I shall dare, but don’t try this at home. I also warn my readers that they should not accept this opinion of mine with the same confidence that attaches to science’s dismissal of phlogiston.
I name the fad of *emergence* or *emergent phenomena* – systems which exhibit high-level behaviors that arise or “emerge” from the interaction of many low-level elements. Taken literally, that description fits every phenomenon in our universe above the level of individual quarks, which is part of the problem.
In decrying the emergence fad, I decry the use of “emergence” *as an explanation in itself* . It’s okay to have a completed model to which an emergence enthusiast could attach “emergent” as an adjective. One might legitimately have some *specific* model of how the behavior of an ant colony *emerges from* the behavior of the ants. A hypothesis like that can be formal and/or technical. The model of the ant colony has internal moving parts and produces specific predictions; it’s just that the model happens to fit the verbal term “emergent” – the behavior which emerges from modeling many interacting elements is different from the behavior of those elements considered in isolation. I do not consider it stupid to say that Phenomenon X *emerges from* Y, where Y is some specific model. The phrase “emerges from” is okay, if the phrase precedes some specific model to be judged on its own merits.
However, this is *not* the way “emergence” is commonly used. “Emergence” is commonly used as an explanation in its own right. I have lost track of how many times I have heard people say, “Intelligence is an emergent phenomenon!” as if that explained intelligence. This usage fits all the checklist items for a mysterious answer to a mysterious question. What do you know, after you have said that intelligence is “emergent”? You can make no new predictions. You do not know anything about the behavior of real-world minds that you did not know before. It feels like you believe a new fact, but you don’t anticipate any different outcomes. Your curiosity feels sated, but it has not been fed. The hypothesis has no moving parts – there’s no detailed internal model to manipulate. Those who proffer the hypothesis of “emergence” confess their ignorance of the internals, and take pride in it; they contrast the science of “emergence” to other sciences merely mundane. And even after the answer of “Why? Emergence!” is given, the phenomenon is still a mystery and possesses the same sacred impenetrability it had at the start.
To say that intelligence is an “emergent phenomenon” fits every possible behavior that intelligence could show, and therefore explains nothing. The model has no moving parts and does not concentrate its probability mass into specific outcomes. It is a disguised hypothesis of zero knowledge.
To see why I object to the academic fad in “emergence”, even though I have admitted the legitimacy of the phrase “emerges from”, consider that “arises from” is also a legitimate phrase. Gravity arises from the curvature of spacetime (according to a certain specific mathematical model, Einstein’s General Relativity). Chemistry arises from interactions between atoms (according to the specific model of quantum electrodynamics). Now suppose I should say that gravity is explained by “arisence” or that chemistry is an “arising phenomenon”, and claim that as my explanation.
A fun exercise is to eliminate the adjective “emergent” from any sentence in which it appears, and see if the sentence says anything different.*Before:* Human intelligence is an emergent product of neurons firing.*After:* Human intelligence is a product of neurons firing.*Before:* The behavior of the ant colony is the emergent outcome of the interactions of many individual ants.*After:* The behavior of the ant colony is the outcome of the interactions of many individual ants.*Even better:* A colony is made of ants. We can successfully predict some aspects of colony behavior using models that include only individual ants, without any global colony variables, showing that we understand how those colony behaviors arise from ant behaviors.
Another good exercise is to replace the word “emergent” with the old word, the explanation that people had to use before emergence was invented.*Before:* Life is an emergent phenomenon.*After:* Life is a magical phenomenon.*Before:* Human intelligence is an emergent product of neurons firing.*After:* Human intelligence is a magical product of neurons firing.
Does not each statement convey exactly the same amount of knowledge about the phenomenon’s behavior? Does not each hypothesis fit exactly the same set of outcomes?
Magic is unpopular nowadays, unfashionable, not something you could safely postulate in a peer-reviewed journal. Why? Once upon a time, a few exceptionally wise scientists noticed that explanations which invoked “magic” just didn’t work as a way of understanding the world. By dint of strenuous evangelism, these wise scientists managed to make magical explanations unfashionable within a small academic community. But humans are still humans, and they have the same emotional needs and intellectual vulnerabilities. So later academics invented a new word, “emergence”, that carried exactly the same information content as “magic”, but had not yet become unfashionable. “Emergence” became very popular, just as saying “magic” used to be very popular. “Emergence” has the same deep appeal to human psychology, for the same reason. “Emergence” is such a wonderfully easy explanation, and it feels good to say it; it gives you a sacred mystery to worship. Emergence is a popular fad *because* it is the junk food of curiosity. You can explain anything using emergence, and so people do just that; for it feels so wonderful to explain things. Humans are still humans, even if they’ve taken a few science classes in college. Once they find a way to escape the shackles of settled science, they get up to the same shenanigans as their ancestors, dressed in different clothes but still the same species psychology.
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Many people in this world believe that after dying they will face a stern-eyed fellow named St. Peter, who will examine their actions in life and accumulate a score for morality. Presumably St. Peter’s scoring rule is unique and invariant under trivial changes of perspective. Unfortunately, believers cannot obtain a quantitative, precisely computable specification of the scoring rule, which seems rather unfair.
The religion of *Bayesianity* holds that your eternal fate depends on the probability judgments you made in life. Unlike lesser faiths, Bayesianity can give a quantitative, precisely computable specification of how your eternal fate is determined.
Our proper Bayesian scoring rule provides a way to accumulate scores across experiments, and the score is invariant regardless of how we slice up the “experiments” or in what order we accumulate the results. We add up the logarithms of the probabilities. This corresponds to multiplying together the probability assigned to the outcome in each experiment, to find the joint probability of all the experiments together. We take the logarithm to simplify our intuitive understanding of the accumulated score, to maintain our grip on the tiny fractions involved, and to ensure we maximize our *expected* score by stating our honest probabilities rather than placing all our play money on the most probable bet.
Bayesianity states that, when you die, Pierre-Simon Laplace examines every single event in your life, from finding your shoes next to your bed in the morning, to finding your workplace in its accustomed spot. Every losing lottery ticket means you cared enough to play. Laplace assesses the advance probability you assigned to each event. Where you did not assign a precise numerical probability in advance, Laplace examines your degree of anticipation or surprise, extrapolates other possible outcomes and your extrapolated reactions, and renormalizes your extrapolated emotions to a likelihood distribution over possible outcomes. (Hence the phrase “Laplacian superintelligence”.)
Then Laplace takes every event in your life, and every probability you assigned to each event, and multiplies all the probabilities together. This is your Final Judgment – the probability you assigned to your life.
Those who follow Bayesianity strive all their lives to maximize their Final Judgment. This is the sole virtue of Bayesianity. The rest is just math.
Mark you: the path of Bayesianity is strict. What probability shall you assign each morning, to the proposition, “The sun shall rise?” (We shall discount such quibbles as cloudy days, and that the Earth orbits the Sun.) Perhaps one who did not follow Bayesianity would be humble, and give a probability of 99.9%. But we who follow Bayesianity shall discard all considerations of modesty and arrogance, and scheme only to maximize our Final Judgment. Like an obsessive video-game player, we care only about this numerical score. We’re going to face this Sun-shall-rise issue 365 times per year, so we might be able to improve our Final Judgment considerably by tweaking our probability assignment.
As it stands, even if the Sun rises every morning, every year our Final Judgment will decrease by a factor of 0.7 (.999^365), roughly -0.52 bits. Every two years, our Final Judgment will decrease more than if we found ourselves ignorant of a coinflip’s outcome! Intolerable. If we increase our daily probability of sunrise to 99.99%, then each year our Final Judgment will decrease only by a factor of 0.964. Better. Still, in the unlikely event that we live exactly 70 years and then die, our Final Judgment will only be 7.75% of what it might have been. What if we assign a 99.999% probability to the sunrise? Then after 70 years, our Final Judgment will be multiplied by 77.4%.
Why not assign a probability of 1.0?
One who follows Bayesianity will *never* assign a probability of 1.0 to *anything* . Assigning a probability of 1.0 to some outcome uses up *all* your probability mass. If you assign a probability of 1.0 to some outcome, and reality delivers a different answer, you must have assigned the *actual* outcome a probability of *0* . This is Bayesianity’s sole mortal sin. Zero times anything is zero. When Laplace multiplies together all the probabilities of your life, the combined probability will be zero. Your Final Judgment will be doodly-squat, zilch, nada, nil. No matter how rational your guesses during the rest of your life, you’ll spend eternity next to some guy who believed in flying saucers and got all his information from the Weekly World News. Again we find it helpful to take the logarithm, revealing the innocent-sounding “zero” in its true form. Risking an outcome probability of zero is like accepting a bet with a payoff of negative infinity.
What if humanity decides to take apart the Sun for mass (stellar engineering), or to switch off the Sun because it’s wasting entropy? Well, you say, you’ll see that coming, you’ll have a chance to alter your probability assignment before the actual event. What if an Artificial Intelligence in someone’s basement recursively self-improves to superintelligence, stealthily develops nanotechnology, and one morning *it* takes apart the Sun? If on the last night of the world you assign a probability of 99.999% to tomorrow’s sunrise, your Final Judgment will go down by a factor of 100,000. Minus 50 decibels! Awful, isn’t it?
So what is your best strategy? Well, suppose you 50% anticipate that a basement-spawned AI superintelligence will disassemble the Sun sometime in the next 10 years, and you figure there’s about an equal chance of this happening on any given day between now and then. On any given night, you would 99.98% anticipate the sun rising tomorrow. If this is really what you anticipate, then you have no motive to say anything except 99.98% as your probability. If you feel nervous that this anticipation is too low, or too high, it must not be what you anticipate after your nervousness is taken into account.
But the deeper truth of Bayesianity is this: you cannot game the system. You cannot give a humble answer, nor a confident one. You must figure out exactly how much you anticipate the Sun rising tomorrow, and say that number. You must shave away every hair of modesty or arrogance, and ask whether you expect to end up being scored on the Sun rising, or failing to rise. Look not to your excuses, but ask which excuses you expect to need. After you arrive at your exact degree of anticipation, the only way to further improve your Final Judgment is to improve the accuracy, calibration, and discrimination of your anticipation. You cannot do better except by guessing better and anticipating more precisely.
Er, well, except that you could commit suicide when you turned five, thereby preventing your Final Judgment from decreasing any further. Or if we patch a new sin onto the utility function, enjoining against suicide, you could flee from mystery, avoiding all situations in which you thought you might not know everything. So much for that religion.
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Ideally, we predict the outcome of the experiment in advance, using our model, and then we perform the experiment to see if the outcome accords with our model. Unfortunately, we can’t always control the information stream. Sometimes Nature throws experiences at us, and by the time we think of an explanation, we’ve already seen the data we’re supposed to explain. This was one of the scientific sins committed by 19th-century evolutionism; Darwin observed the similarity of many species, and their adaptation to particular local environments, before the hypothesis of natural selection occurred to him. 19th-century evolutionism began life as a post facto explanation, not an advance prediction.
Nor is this a trouble only of semitechnical theories. In 1846, the successful deduction of Neptune’s existence from gravitational perturbations in the orbit of Uranus was considered a grand triumph for Newton’s theory of gravitation. Why? Because Neptune’s existence was the first observation that confirmed an *advance* prediction of Newtonian gravitation. All the other phenomena that Newton explained, such as orbits and orbital perturbations and tides, had been observed in great detail before Newton explained them. No one seriously doubted that Newton’s theory was correct. Newton’s theory explained too much too precisely, and it replaced a collection of ad-hoc models with a single unified mathematical law. Even so, the advance prediction of Neptune’s existence, followed by the observation of Neptune at almost exactly the predicted location, was considered the first grand triumph of Newton’s theory at predicting what no previous model could predict. Considerable time elapsed between widespread acceptance of Newton’s theory and the first impressive *advance* prediction of Newtonian gravitation. By the time Newton came up with his theory, scientists had already observed, in great detail, most of the phenomena that Newtonian gravitation predicted.
But the rule of advance prediction is a morality of science, not a law of probability theory. If you have already seen the data you must explain, then Science may darn you to heck, but your predicament doesn’t collapse the laws of probability theory. What does happen is that it becomes much more difficult for a hapless human to *obey* the laws of probability theory. When you’re deciding how to rate a hypothesis according to the Bayesian scoring rule, you need to figure out how much probability mass that hypothesis assigns to the observed outcome. If we must make our predictions in advance, then it’s easier to notice when someone is trying to claim every possible outcome as an advance prediction, using too much probability mass, being deliberately vague to avoid falsification, and so on.
No numerologist can predict next week’s winning lottery numbers, but they will be happy to explain the mystical significance of last week’s winning lottery numbers. Say the winning Mega Ball was 7 in last week’s lottery, out of 52 possible outcomes. Obviously this happened because 7 is the lucky number. So will the Mega Ball in next week’s lottery also come up 7? We understand that it’s not certain, of course, but if it’s the lucky number, you ought to assign a probability of higher than 1/52… and then we’ll score your guesses over the course of a few years, and if your score is too low we’ll have you flogged… what’s that you say? You want to assign a probability of exactly 1/52? But that’s the same probability as every other number; what happened to 7 being lucky? No, sorry, you can’t assign a 90% probability to 7 and also a 90% probability to 11. We understand they’re both lucky numbers. Yes, we understand that they’re *very* lucky numbers. But that’s not how it works.
Even if the listener does not know the way of Bayes and does not ask for formal probabilities, they will probably become suspicious if you try to cover too many bases. Suppose they ask you to predict next week’s winning Mega Ball, and you use numerology to explain why the 1 ball would fit your theory very well, and why the 2 ball would fit your theory very well, and why the 3 ball would fit your theory very well… even the most credulous listener might begin to ask questions by the time you got to 12. Maybe you could tell us which numbers are unlucky and definitely won’t win the lottery? Well, 13 is unlucky, but it’s not absolutely *impossible* (you hedge, *anticipating* in advance which excuse you might need).
But if we ask you to explain *last week’s* lottery numbers, why, the 7 was practically inevitable. That 7 should definitely count as a major success for the “lucky numbers” model of the lottery. And it couldn’t possibly have been 13; luck theory rules that straight out.
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Imagine that you wake up one morning and your left arm has been replaced by a blue tentacle. The blue tentacle obeys your motor commands – you can use it to pick up glasses, drive a car, etc. How would you explain this hypothetical scenario? Take a moment to ponder this puzzle before continuing.
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How would I explain the event of my left arm being replaced by a blue tentacle? The answer is that I wouldn’t. It isn’t going to happen.
It would be easy enough to produce a verbal explanation that “fit” the hypothetical. There are many explanations that can “fit” anything, including (as a special case of “anything”) my arm being replaced by a blue tentacle. Divine intervention is a good all-purpose explanation. Or aliens with arbitrary motives and capabilities. Or I could be mad, hallucinating, dreaming my life away in a hospital. Such explanations “fit” all outcomes equally well, and equally poorly, equating to hypotheses of complete ignorance.
The test of whether a model of reality “explains” my arm turning into a blue tentacle, is whether the model concentrates significant probability mass into that *particular* outcome. Why that dream, in the hospital? Why would aliens do that particular thing to me, as opposed to the other billion things they might do? Why would my arm turn into a tentacle on that morning, after remaining an arm through every other morning of my life? And in all cases I must look for an argument compelling enough to make that particular prediction in *advance* , not mere compatibility. Once I already knew the outcome, it would become far more difficult to sift through hypotheses to find good explanations. Whatever hypothesis I tried, I would be hard-pressed not to allocate more probability mass to yesterday’s blue-tentacle outcome than if I extrapolated blindly, seeking the model’s *most* likely prediction for tomorrow.
A model does not always predict all the features of the data. Nature has no privileged tendency to present me with solvable challenges. Perhaps a deity toys with me, and the deity’s mind is computationally intractable. If I flip a fair coin there is no way to further explain the outcome, no model that makes a better prediction than the maximum-entropy hypothesis. But if I guess a model with no internal detail or a model that makes no further predictions, I not only have no reason to believe that guess, I have no reason to care. Last night my arm was replaced with a blue tentacle. Why? Aliens! So what will they do tomorrow? Similarly, if I attribute the blue tentacle to a hallucination as I dream my life away in a coma, I still don’t know any more about what I’ll hallucinate tomorrow. So why do I care whether it was aliens or hallucination?
What might be a *good* explanation, then, if I woke up one morning and found my arm transformed into a blue tentacle? To claim a “good explanation” for this hypothetical experience would require an argument such that, contemplating the hypothetical argument *now* , *before* my arm has transformed into a blue tentacle, I would go to sleep worrying that my arm *really would* transform into a tentacle.
People play games with plausibility, explaining events they expect to never actually encounter, yet this necessarily violates the laws of probability theory. How many people who thought they could ‘explain’ the hypothetical experience of waking up with their arm replaced by a tentacle, would go to sleep wondering if it might really happen to them? Had they the courage of their convictions, they would say: I do not expect to ever encounter this hypothetical experience, and therefore I cannot explain, nor have I a motive to try. Such things only happen in webcomics, and I need not prepare explanations, for in real life I shall never have a chance to use them. If I ever find myself in this impossible situation, let me miss no jot or tittle of my valuable bewilderment.
To a Bayesian, probabilities are anticipations, not mere beliefs to proclaim from the rooftops. If I have a model that assigns probability mass to waking up with a blue tentacle, then I am nervous about waking up with a blue tentacle. What if the model is a fanciful one, like a witch casting a spell that transports me into a randomly selected webcomic? Then the *prior probability* of webcomic witchery is so low that my *real-world* understanding doesn’t assign any significant weight to that hypothesis. The witchcraft hypothesis, if taken as a given, might assign non-insignificant likelihood to waking up with a blue tentacle. But my anticipation of that hypothesis is so low that I don’t anticipate any of the predictions of that hypothesis. That I can conceive of a witchcraft hypothesis should in no wise diminish my stark bewilderment if I actually wake up with a tentacle, because the real-world probability I assign to the witchcraft hypothesis is effectively zero. My zero-probability hypothesis wouldn’t help me *explain* waking up with a tentacle, because the argument isn’t good enough to make me *anticipate* waking up with a tentacle.
In the laws of probability theory, likelihood distributions are fixed properties of a hypothesis. In the art of rationality, to *explain* is to *anticipate* . To *anticipate* is to *explain* . Suppose I am a medical researcher, and in the ordinary course of pursuing my research, I notice that my clever new theory of anatomy seems to permit a small and vague possibility that my arm will transform into a blue tentacle. “Ha ha!”, I say, “how remarkable and silly!”, and feel ever so slightly nervous. *That* would be a good explanation for waking up with a tentacle, if it ever happened.
If a chain of reasoning doesn’t make me nervous, in advance, about waking up with a tentacle, then that reasoning would be a poor explanation if the event *did* happen, because the combination of prior probability and likelihood was too low to make me allocate any significant real-world probability mass to that outcome.
If you start from well-calibrated priors, and you apply Bayesian reasoning, you’ll end up with well-calibrated conclusions. Imagine that two million entities, scattered across different planets in the universe, have the opportunity to encounter something so strange as waking up with a tentacle (or – gasp! – ten fingers). One million of these entities say “one in a thousand” for the prior probability of some hypothesis X, and each hypothesis X says “one in a hundred” for the likelihood of waking up with a tentacle. And one million of these entities say “one in a hundred” for the prior probability of some hypothesis Y, and each hypothesis Y says “one in ten” for the likelihood of waking up with a tentacle. If we suppose that all entities are well-calibrated, then we shall look across the universe and find ten entities who wound up with a tentacle because of hypotheses of plausibility class X, and a thousand entities who wound up with tentacles because of hypotheses of plausibility class Y. So if you find yourself with a tentacle, and *if* your probabilities are well-calibrated, then the tentacle is more likely to stem from a hypothesis you would class as probable than a hypothesis you would class as improbable. (What if your probabilities are poorly calibrated, so that when you say “million-to-one” it happens one time out of twenty? Then you’re grossly overconfident, and we adjust your probabilities in the direction of less discrimination / greater entropy.)
The hypothesis of being transported into a webcomic, even if it “explains” the scenario of waking up with a blue tentacle, is a poor explanation because of its low prior probability. The webcomic hypothesis doesn’t contribute to explaining the tentacle, because it doesn’t make you anticipate waking up with a tentacle.
If we start with a quadrillion sentient minds scattered across the universe, quite a lot of entities will encounter events that are very likely, only about a mere million entities will experience events with lifetime likelihoods of a billion-to-one (as we would anticipate, surveying with infinite eyes and perfect calibration), and not a single entity will experience the impossible.
If, somehow, you really did wake up with a tentacle, it would likely be because of something much more probable than “being transported into a webcomic”, some perfectly normal reason to wake up with a tentacle which you just didn’t see coming. A reason like what? I don’t know. Nothing. I don’t anticipate waking up with a tentacle, so I can’t give any good explanation for it. Why should I bother crafting excuses that I don’t expect to use? If I was worried I might someday need a clever excuse for waking up with a tentacle, the *reason I was nervous about the possibility* would *be* my explanation.
Reality dishes out experiences using probability, not plausibility. If you find out that your laptop doesn’t obey conservation of momentum, then reality must think that a perfectly normal thing to do to you. How could violating conservation of momentum possibly be perfectly normal? I anticipate that question has no answer and will never need answering. Similarly, people do *not* wake up with tentacles, so apparently it is *not* perfectly normal.
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There is a shattering truth, so surprising and terrifying that people resist the implications with all their strength. Yet there are a lonely few with the courage to accept this satori. Here is wisdom, if you would be wise:*Since the beginning**Not one unusual thing**Has ever happened.*
Alas for those who turn their eyes from zebras and dream of dragons! If we cannot learn to take joy in the merely real, our lives shall be empty indeed.
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This document is ©2005 by [Eliezer Yudkowsky](http://eyudkowsky.wpengine.com/) and free under the [Creative Commons Attribution-No Derivative Works 3.0 License](http://creativecommons.org/licenses/by-nd/3.0/) for copying and distribution, so long as the work is attributed and the text is unaltered.
Eliezer Yudkowsky’s work is supported by the [Machine Intelligence Research Institute](https://intelligence.org/) .
If you think the world could use some more rationality, consider blogging this page.
Praise, condemnation, and feedback are [always welcome](https://eyudkowsky.wpengine.com/contact) . The web address of this page is [http://eyudkowsky.wpengine.com/rational/technical/](https://eyudkowsky.wpengine.com/rational/technical/) .
If you enjoyed *A Technical Explanation of Technical Explanation* , you may enjoy the earlier works in the series, the [Twelve Virtues of Rationality](https://eyudkowsky.wpengine.com/rational/virtues) , [The Simple Truth](https://eyudkowsky.wpengine.com/rational/the-simple-truth) , and [An Intuitive Explanation of Bayesian Reasoning](https://eyudkowsky.wpengine.com/rational/bayes) .
If you’ve already read all that, you can move on to [Overcoming Bias](http://overcomingbias.com/) .
---
**References:**
John Baez (1998). “ [The Crackpot Index.](http://math.ucr.edu/home/baez/crackpot.html) ”
Kevin Brown (1999). “ [Anomalous Precessions](http://www.mathpages.com/rr/s6-02/6-02.htm) ” In mathpages.com: Postings to sci.math collected by Kevin Brown.
Robyn M. Dawes (1988). “Rational Choice in an Uncertain World”. Harcourt Brace Jovanovich, Inc.
E. T. Jaynes (1996). “ [Probability Theory: The Logic of Science](http://bayes.wustl.edu/etj/prob/book.pdf) ” Published posthumously by Cambridge University Press, ed. G. Larry Bretthorst. (2003).
T. S. Kuhn (1962). “The Structure of Scientific Revolutions.” The Chicago University Press.
Friedrich Spee von Langenfeld (1631). “Cautio Criminalis, Or, a Book on Witch Trials.” Translated: Marcus Hellyer, 2003. University Press of Virginia.
Ruth Moore (1961). “The Coil of Life.” London Constable. See also [Phlogiston Theory](http://www.jimloy.com/physics/phlogstn.htm) , [Demise of Phlogiston](http://mooni.fccj.org/~ethall/phlogist/phlogist.htm) , and [Friedrich W�hler](http://en.wikipedia.org/wiki/Friedrich_Woehler) .
Robert Pirsig (1974). “Zen and the Art of Motorcycle Maintenance.” New York: Bantam Books.
Karl Popper (1959). “The Logic of Scientific Discovery”. Hutchinson, London.
D. Robinson and J. Groves (1998). “Philosophy for Beginners.” Cambridge: Icon Books.
Carl Sagan (1995). “The Demon-Haunted World: Science as a Candle in the Dark.” Random House, New York, NY.
Stephen Thornton (2002). “ [Karl Popper](http://plato.stanford.edu/archives/win2002/entries/popper/) ”. In Edward N. Zalta (ed.), “The Stanford Encyclopedia of Philosophy” (Winter 2002 Edition).
A. Tversky and W. Edwards (1966). “Information versus reward in binary choice.” Journal of Experimental Psychology, 71, 680-683. See also Y. Schul and R. Mayo 2003, “ [Searching for certainty in an uncertain world.](http://www3.interscience.wiley.com/cgi-bin/abstract/103020292/ABSTRACT) ” In Journal of Behavioral Decision Making, 16:2, 93-106.
Joachim Verhagen (2001). From the “ [Canonical List of Science Jokes](http://web.archive.org/web/20060424082937/http://www.nvon.nl/scheik/best/diversen/scijokes/scijokes.txt) ”, version 7.27, collected by Joachim Verhagen.
George Williams (1966). “Adaptation and Natural Selection: A Critique of Some Current Evolutionary Thought.” Princeton, NJ: Princeton University Press.
J.F. Yates, J.W. Lee, W.R. Sieck, I. Choi,&P.C. Price (2002). “Probability judgment across cultures.” In T. Gilovich, D. Griffin,&D. Kahneman (Eds.), “Heuristics and Biases.” New York: Cambridge.
Lyle Zapato (1998). “ [Lord Kelvin Quotations](http://zapatopi.net/kelvin/quotes.html) ” |
dc769dcc-efa0-4410-8777-83fb11ddebd7 | trentmkelly/LessWrong-43k | LessWrong | Norms For Link Posts
There are a lot of interesting content I read on the internet (some of which may not have been produced by the rationalist diaspora (and which the authors wouldn't want to crosspost to Lesswrong)) that I nonetheless found interesting and feel like sharing with other Lesswrongers. However, I am not sure if the posts would be well received. I have not found explicit discussion (I searched the titles in Meta) indicating what is and is not acceptable to be linked. I don't want to gain information by experimenting (which has significant negative externalities for both me and the community. Me in the form of lost karma, and the community in the form of degradation of quality, and annoyance/displeasure of community members). I would appreciate if the norms for link posts were explicitly outlined, so that I would know if a link is acceptable or not.
Points that I feel should be explicitly spelled out:
* Time: Are links older than <insert time frame here> not allowed?
* Frequency: Should we limit our link posts to a certain frequency? One a week, One a day? Not more than X a day?
* Content: What topics are not acceptable for link posts (if it is the same as for front page posts, then feel free to skip this). If link posts have a wider or narrower scope please indicate this.
I think those are the main areas for which I would appreciate explicit guidelines as far as link posts are concerned. |
9aa1f233-df69-4ff6-962b-bea5f89b149d | trentmkelly/LessWrong-43k | LessWrong | Should you work at 80,000 Hours?
The purpose of this post is to discuss some considerations relevant to whether it is high impact for you as an individual to work for 80,000 Hours.
Disclaimer: I am an employee at 80,000 Hours (from here on 80k). We are currently recruiting, and want to attract people to work for us who are likely to add the most value to 80k so that we can increase our impact. It seems likely that some such people might be found on LessWrong, so we want to encourage critical discussion on here about who should work at 80k.
This post will be in the format of an interview with Ben Todd, co-founder and executive director of 80,000 Hours, and thus best placed to begin a discussion about who should work for 80k. In what follows, we’ll cover:
* If you want to support 80k, whether you should work for 80k or fund 80k. This involves some discussion of whether to earn to give or to work directly, adding to the discussion elsewhere on LW.
* What skills and characteristics are most valuable to 80k
* Whether working at 80k is likely to help your future career prospects
* How working at 80k might compare to some alternatives: other EA organisations and professional jobs
* Who probably shouldn’t work for 80k
What this post won’t cover:
* Whether 80k itself is high impact. This has already been discussed at length on LW here and here.
* Whether you’d enjoy working at 80k. We’ve already posted on our blog about why we think it’s good to work at 80k.
Summary
If you fit the profile we’re looking for in terms of skills and experience (see below or our job descriptions linked to on the blog), think that meta-charity is potentially very high impact and want to innovate in this area, then you should consider applying to work for 80k.
Even if you’re not entirely convinced that 80k is currently high impact but have ideas of how we could improve so that we are, we’d be very interested to hear them.
If you don’t believe that meta-charity in general or 80k specifically is potentia |
cd4dcf1f-65df-451a-a0b7-bee49fa2112f | trentmkelly/LessWrong-43k | LessWrong | Fields that I reference when thinking about AI takeover prevention
Is AI takeover like a nuclear meltdown? A coup? A plane crash?
My day job is thinking about safety measures that aim to reduce catastrophic risks from AI (especially risks from egregious misalignment). The two main themes of this work are the design of such measures (what’s the space of techniques we might expect to be affordable and effective) and their evaluation (how do we decide which safety measures to implement, and whether a set of measures is sufficiently robust). I focus especially on AI control, where we assume our models are trying to subvert our safety measures and aspire to find measures that are robust anyway.
Like other AI safety researchers, I often draw inspiration from other fields that contain potential analogies. Here are some of those fields, my opinions on their strengths and weaknesses as analogies, and some of my favorite resources on them.
Robustness to insider threats
The single field that seems most closely analogous to AI control is reducing vulnerability to threats resulting from insiders, especially threats that arise from access to IT systems. That is, you have a bunch of employees who need to have access to various IT systems in order to do their jobs, and you’re worried that some of these employees will abuse their access in order to accomplish some nefarious purpose. In a bank, you might worry about your tellers withdrawing money from customer accounts inappropriately. In a software company, you might worry about your employees sabotaging your code out of spite, violating user privacy for fun, or stealing your data to give it to a competitor.
This is quite similar to the problem faced by AI developers when they’re trying to control AIs (especially in internal deployments, which I argue is important here):
* All of the access the AI has is access the developer decided to give it; the developer faces a constant tradeoff between the productivity of the AI (which is maximized by being permissive) and the danger it poses (which is |
43e6dff9-0edc-4b06-becb-7d5caa64e7ec | trentmkelly/LessWrong-43k | LessWrong | Link: What does it feel like to be stupid?
What does it feel like to be stupid?
> I had an arterial problem for a couple of years, which reduced blood supply to my heart and brain and depleted B vitamins from my nerves (to keep the heart in good repair). Although there is some vagueness as to the mechanisms, this made me forgetful, slow, and easily overwhelmed. In short I felt like I was stupid compared to what I was used to, and I was.
>
> It was frightening at first because I knew something wasn't right but didn't know what, and very worrying for my career because I was simply not very good any more.
>
> However, once I got used to it and resigned myself, it was great.
Full article:
http://www.quora.com/What-does-it-feel-like-to-be-stupid |
a5db3293-a8ee-4340-bba4-ad6a72d33b88 | trentmkelly/LessWrong-43k | LessWrong | Adjust for the middleman.
This post is from the point of view of the middleman standing between the grand future he doesn't understand and the general public whose money he's hunting. We have a certain degree of power over what to offer to the customer, and our biases and pet horses are going to contribute a lot to what theoreticians infer about "the actual public"'s tastes. Just how a lot it is, I cannot say, & there's probably tons of literature on this anyway, so take this as a personal anecdote.
Nine months as a teacher of botany (worst gripes here) showed me a glimpse of how teachers/administration view the field they teach. A year in a shop - what managers think of books we sell. The scientific community here in my country grumbles that there's too little non-fiction produced, without actually looking into why it's not being distributed; but really, it's small wonder. Broadest advice - if your sufficiently weird goals depend on the cooperation of a network of people, especially if they are an established profession with which you haven't had a cause to interact closely except as a customer, you might want to ask what they think of your enterprise. Because they aren't going to see it your way. Next thing, is to accept it.
I work in a bookshop that is a part of a publishing house and also sells some selected books from several dozens of other PHs. Think about 9000 kinds of "our" products + 4000 kinds of "theirs".
Recently, things changed a bit. Two wonderful professionals retired, one was given a very different administrative position, and one went on to another job because she didn't want to adjust to a new team after 10+ years with the old one.
It made me, for a while, the unofficial authority on literature for adults and non-fiction non-school-related-stuff for kids. (My new colleagues currently specialize in: maps and atlases (our "senior salesperson"); stationery and such; "books in general" without knowing the products of the publishing houses we have historically dealt with (e |
27d220d5-aae6-4885-be00-dbbfcb2ce5b7 | StampyAI/alignment-research-dataset/blogs | Blogs | Time flies when robots rule the earth
*By Katja Grace, 28 July 2015*
This week Robin Hanson is finishing off his much anticipated book, [The Age of Em: Work, Love and Life When Robots Rule the Earth](http://www.overcomingbias.com/2015/03/oxford-to-publish-the-age-of-em.html). He recently told me that it would be helpful to include rough numbers for the brain’s memory and computing capacity in the book, so I agreed to prioritize finding the ones AI Impacts didn’t already have. Consequently we just put up [new](http://aiimpacts.org/costs-of-information-storage/) [pages](http://aiimpacts.org/information-storage-in-the-brain/) [about](http://aiimpacts.org/cost-of-human-level-information-storage/) information storage in the brain. We also made a [summary](http://aiimpacts.org/costs-of-human-level-hardware/) of related ‘human-level’ hardware pages, and an [index](http://aiimpacts.org/index-of-hardware-articles/) of pages about hardware in general.
Robin’s intended use for these numbers is interesting. The premise of his book is that one day (perhaps in the far future) human minds might be [emulated](https://en.wikipedia.org/wiki/Mind_uploading) on computers, and that this would produce a society that is somewhere between recognizably human and predictably alien. Robin’s project is a detailed account of life in such a society, as far as it can be discerned by peering through social science and engineering theory.
One prediction Robin makes is that these emulated minds (’ems’) will run at a wide variety of speeds, depending on their purposes and the incentives. So some ems will have whole lifetimes while others are getting started on a single thought. Robin wanted to know how slow the very slowest ems would run. And for this, he wanted to know much memory the brain uses, and how much computing it does.
His reasoning is as follows. The main costs of running emulations are computing hardware and memory. If Anna is running twice as fast as Ben, then Anna needs about twice as much computing power to run, which will cost about twice as much. However Anna still uses around as much memory as Ben to store the contents of her brain over time. So if most of the cost of an emulation is in computation, then halving the speed would halve the cost, and would often be worth it. But once an emulation is moving so slowly that memory becomes the main cost, slowing down by half makes little difference, and soon stops being worth it. So the slowest emulations should run at around the speed at which computing hardware and memory contribute similarly to cost.
[Hardware](http://aiimpacts.org/trends-in-the-cost-of-computing/) and [memory](http://aiimpacts.org/costs-of-information-storage/) costs have been falling at roughly similar rates in the past, so if this continues, then the ratio between their costs now is a reasonable (if noisy) predictor of their ratio in several decades time. Given [our numbers](http://aiimpacts.org/costs-of-human-level-hardware/), Robin estimates that the slowest emulations will operate at between a one hundred trillionth of human speed and one millionth of human speed, with a middle estimate of one tenth of a billionth of human speed.
At these rates, immortality looks a lot like dying. If you had been experiencing the world at these speeds since the beginning of the universe, somewhere between an hour and a thousand years would seem to have passed, with a middle estimate of a year. Even if the em economy somehow lasts for a thousand years, running this slowly would mean immediately jumping into whatever comes next.
These things are rough of course, but it seems pretty cool to me that we can make reasonable guesses at all about such exotic future scenarios, using clues from our everyday world, like the prevailing prices of hard disks and memory.
If you are near Berkeley, CA and want to think more about this kind of stuff, or in this kind of style, remember you can come and meet Robin and partake in more economic futurism at our event [this Thursday](http://aiimpacts.org/event-exercises-in-economic-futurism/). We expect a good number of attendees already, but could squeeze in a few more.
*Image: [Robin Hanson in a field](https://commons.wikimedia.org/wiki/File:Robin_Hanson_in_a_field.jpg), taken by Katja Grace* |
5e741fa2-a6ea-47fe-9d1a-20d26bb947e4 | trentmkelly/LessWrong-43k | LessWrong | What would you expect a massive multimodal online federated learner to be capable of?
One intuitive picture I have for what very rapid ML capability gains might look like is a massive multimodal deep learning model that uses some form of online federated learning to continually learn from many devices simultaneously, and which is deployed to hundreds of millions or billions of users. For example, imagine a multimodal Google or Facebook chatbot with say 10 trillion parameters that could interact with billions of users simultaneously and which improved its weights from every interaction. My impression is that we basically have the tech for this today, or we will in the very near future. Now add in some RL to actively optimize some company-relevant goals (ad revenue, reported user satisfaction, etc.) and, intuitively at least, that seems to me like it's very close to the kind of scary AGI we've been talking about. But that seems like it could easily be 2-5 years in the future rather than 10-50.
Did I misunderstand something? How close to scary-type AGI would you expect this kind of model to become within a few months after deployment? |
a1fdbd70-f12e-42c8-844c-17660f9c3ae5 | trentmkelly/LessWrong-43k | LessWrong | AI strategy given the need for good reflection
If the ideas in “moral ~realism”[1] and/or “beyond Maxipok”[2] are broadly correct, this has implications for AI strategy. (Like those posts, this is largely getting some old imperfect ideas out of the door with a little polishing; again the ideas owe credit to conversations and comments from several people.)
I want to draw out three threads:
1. It may be important to try to get AI that can help humanity to enter into the basin of good reflective governance
* This makes applications like facilitating wisdom, cooperation, and reflective processes a relatively higher strategic priority for AI systems
2. A plausible target for AI alignment could be to align with the good (i.e. get the AI systems into the basin of good reflective governance) rather than aligning with humans
* In practice this is scary as a possibility, because it might not fail gracefully
* However, it’s prima facie plausible that it’s easier to build a system which aligns with the good rather than aligning with humans. If this is the case, we might prefer not to cut off the possibility of good outcomes by chasing after the too hard task
3. Even if we don’t reach enough confidence in that approach to prefer trying to align systems with the good as a primary goal, we might still want to use it to give a backup saving throw on AI alignment
* Roughly: “Give your superintelligent servants the rudiments of a moral education so that if they do in some weird plot twist end up in control, there’s still a chance they work out the right thing to do”
* There are a lot of details to be worked out here, and maybe it ends up wildly impractical; its appeal is because it seems like a fairly orthogonal line of attack from keeping systems corrigible
* This should be more appealing the harder you think aligning with humans is
* Honestly this still feels pretty speculative (moreso than 1 or 2), but at this point I've been sitting on the idea for a couple of years without either having per |
9b398680-7e0d-4c15-8ec1-b345b5dacd6b | StampyAI/alignment-research-dataset/blogs | Blogs | From motor control to embodied intelligence
#### Using human and animal motions to teach robots to dribble a ball, and simulated humanoid characters to carry boxes and play football
Humanoid character learning to traverse an obstacle course through trial-and-error, which can lead to idiosyncratic solutions. Heess, et al. "Emergence of locomotion behaviours in rich environments" (2017).Five years ago, we took on the challenge of teaching a fully articulated humanoid character to [traverse obstacle courses](https://youtu.be/hx_bgoTF7bs?t=88). This demonstrated what reinforcement learning (RL) can achieve through trial-and-error but also highlighted two challenges in solving *embodied* intelligence:
1. **Reusing previously learned behaviours:** A significant amount of data was needed for the agent to “get off the ground”. Without any initial knowledge of what force to apply to each of its joints, the agent started with random body twitching and quickly falling to the ground. This problem could be alleviated by reusing previously learned behaviours.
2. **Idiosyncratic behaviours:** When the agent finally learned to navigate obstacle courses, it did so with unnatural ([albeit amusing](https://www.youtube.com/watch?v=EI3gcbDUNiM&t=258s)) movement patterns that would be impractical for applications such as robotics.
Here, we describe a solution to both challenges called neural probabilistic motor primitives (NPMP), involving guided learning with movement patterns derived from humans and animals, and discuss how this approach is used in our [Humanoid Football paper,](https://www.science.org/doi/10.1126/scirobotics.abo0235) published today in Science Robotics.
We also discuss how this same approach enables humanoid full-body manipulation from vision, such as a humanoid carrying an object, and robotic control in the real-world, such as a robot dribbling a ball.
#### Distilling data into controllable motor primitives using NPMP
An NPMP is a general-purpose motor control module that translates short-horizon motor intentions to low-level control signals, and it’s [trained offline](https://openreview.net/forum?id=BJl6TjRcY7) or [via RL](https://proceedings.mlr.press/v119/hasenclever20a.html) by imitating motion capture (MoCap) data, recorded with trackers on humans or animals performing motions of interest.
An agent learning to imitate a MoCap trajectory (shown in grey).**The model has two parts:**
1. An encoder that takes a future trajectory and compresses it into a motor intention.
2. A low-level controller that produces the next action given the current state of the agent and this motor intention.
Our NPMP model first distils reference data into a low-level controller (left). This low-level controller can then be used as a plug-and-play motor control module on a new task (right).After training, the low-level controller can be reused to learn new tasks, where a high-level controller is optimised to output motor intentions directly. This enables efficient exploration – since coherent behaviours are produced, even with randomly sampled motor intentions – and constrains the final solution.
#### Emergent team coordination in humanoid football
Football has been [a long-standing challenge](https://link.springer.com/chapter/10.1007/3-540-64473-3_46) for embodied intelligence research, requiring individual skills and coordinated team play. In our latest work, we used an NPMP as a prior to guide the learning of movement skills.
The result was a team of players which progressed from learning ball-chasing skills, to finally learning to coordinate. Previously, in a [study with simple embodiments](https://openreview.net/forum?id=BkG8sjR5Km), we had shown that coordinated behaviour can emerge in teams competing with each other. The NPMP allowed us to observe a similar effect but in a scenario that required significantly more advanced motor control.
Agents first mimic the movement of football players to learn an NPMP module (top). Using the NPMP, the agents then learn football-specific skills (bottom). Our agents acquired skills including agile locomotion, passing, and division of labour as demonstrated by a range of statistics, including metrics used in [real-world sports analytics](https://www.researchgate.net/profile/William-Spearman/publication/327139841_Beyond_Expected_Goals/links/5b7c3023a6fdcc5f8b5932f7/Beyond-Expected-Goals.pdf). The players exhibit both agile high-frequency motor control and long-term decision-making that involves anticipation of teammates’ behaviours, leading to coordinated team play.
An agent learning to play football competitively using multi-agent RL.#### Whole-body manipulation and cognitive tasks using vision
Learning to interact with objects using the arms is another difficult control challenge. The NPMP can also enable this type of whole-body manipulation. With a small amount of MoCap data of interacting with boxes, we’re able to [train an agent to carry a box](https://www.youtube.com/watch?v=2rQAW-8gQQk) from one location to another, using egocentric vision and with only a sparse reward signal:
With a small amount of MoCap data (top), our NPMP approach can solve a box carrying task (bottom).Similarly, we can teach the agent to catch and throw balls:
Simulated humanoid catching and throwing a ball.Using NPMP, we can also tackle [maze tasks involving locomotion, perception and memory](https://openreview.net/forum?id=BJfYvo09Y7):
Simulated humanoid collecting blue spheres in a maze.#### Safe and efficient control of real-world robots
The NPMP can also help to control real robots. Having well-regularised behaviour is critical for activities like walking over rough terrain or handling fragile objects. Jittery motions can damage the robot itself or its surroundings, or at least drain its battery. Therefore, significant effort is often invested into designing learning objectives that make a robot do what we want it to while behaving in a safe and efficient manner.
As an alternative, we investigated whether using [priors derived from biological motion](https://arxiv.org/abs/2203.17138) can give us well-regularised, natural-looking, and reusable movement skills for legged robots, such as walking, running, and turning that are suitable for deploying on real-world robots.
Starting with MoCap data from humans and dogs, we adapted the NPMP approach to train skills and controllers in simulation that can then be deployed on real humanoid (OP3) and quadruped (ANYmal B) robots, respectively. This allowed the robots to be steered around by a user via a joystick or dribble a ball to a target location in a natural-looking and robust way.
Locomotion skills for the ANYmal robot are learned by imitating dog MoCap.Locomotion skills can then be reused for controllable walking and ball dribbling.#### Benefits of using neural probabilistic motor primitives
In summary, we’ve used the NPMP skill model to learn complex tasks with humanoid characters in simulation and real-world robots. The NPMP packages low-level movement skills in a reusable fashion, making it easier to learn useful behaviours that would be difficult to discover by unstructured trial and error. Using motion capture as a source of prior information, it biases learning of motor control toward that of naturalistic movements.
The NPMP enables embodied agents to learn more quickly using RL; to learn more naturalistic behaviours; to learn more safe, efficient and stable behaviours suitable for real-world robotics; and to combine full-body motor control with longer horizon cognitive skills, such as teamwork and coordination.
Learn more about our work**:**
* See selected [research references](https://storage.googleapis.com/deepmind-media/From%20motor%20control%20to%20embodied%20intelligence/From%20motor%20control%20to%20embodied%20intelligence%20-%20selected%20references.pdf).
* Read [our paper](https://www.science.org/doi/10.1126/scirobotics.abo0235) on Humanoid Football in Science Robotics or watch the [summary video](https://www.youtube.com/watch?v=tWnGTtbOK7I).
* Read [our paper](https://dl.acm.org/doi/abs/10.1145/3386569.3392474) on humanoid whole-body control or watch the [summary video](https://www.youtube.com/watch?v=2rQAW-8gQQk).
* Read [our paper](https://arxiv.org/abs/2203.17138) on control of real-world robots or watch the [summary video](https://youtu.be/K77HS6uO5F8). |
01ecf329-6bd3-4058-8652-0c7010486979 | trentmkelly/LessWrong-43k | LessWrong | Should a judge do it's job or maximize overall happiness
John Smith is a head judge. He's living in a state with has a three strikes law. He personally doesn't believe in the three strikes law. He resides in the highest appellate court in his state. He's operating in a state without juries.
As part of John's job he writes down his confidence on whether a witness tell the truth whenever he's hearing an witness make a statement. He has an assistant that checks his results. John quite frequently is 99,99% sure that a witness lies. He was never wrong when he made the prediction because of his skills at reading bodylanguage.
In his private time he also trains his ability to tell whether people tell the truth in training cases where it's easy to determine the truth afterwards. He's perfectly caliberated.
He gets a court case where Bob is accused of bribing a lot of politicians in an African state. He's supposed to have given them bribes to drop a law that forbids vaccination. Bob himself claims that he only talked with the African politicians and they afterwards saw Bob's wisdom and changed the law. In any case Bob got the law changed and produced massive utility in the process. Bob also pledges to do other lobbying in African countries to get rid of very harmful laws in the future.
After Bob attempts of getting the law Bribing officials of other countries is a felony is Bob's state. Bob already has two court judgements against him so being judged for bribing would put him for the rest of his life behind bars.
Carol is a billionaire. She is supposed to have give Bob $100,000 for his lobbying efforts to change the law in the African state. Carol however claims that she only payed Bob $3,000 for his traveling costs in the process and she never gave him an amount of money that would be enough to bribe the African policitians.
Through his bodylanguage reading skills John knows that Bob and Carol are lying to him. One previous court thought that Bob did the crime, the second court thought that Bob didn't.
John gets asked by |
8de4534b-f40e-4d6d-b061-4301a4a9c80d | trentmkelly/LessWrong-43k | LessWrong | On Dwarkesh’s 3rd Podcast With Tyler Cowen
This post is extensive thoughts on Tyler Cowen’s excellent talk with Dwarkesh Patel.
It is interesting throughout. You can read this while listening, after listening or instead of listening, and is written to be compatible with all three options. The notes are in order in terms of what they are reacting to, and are mostly written as I listened.
I see this as having been a few distinct intertwined conversations. Tyler Cowen knows more about more different things than perhaps anyone else, so that makes sense. Dwarkesh chose excellent questions throughout, displaying an excellent sense of when to follow up and how, and when to pivot.
The first conversation is about Tyler’s book GOAT about the world’s greatest economists. Fascinating stuff, this made me more likely to read and review GOAT in the future if I ever find the time. I mostly agreed with Tyler’s takes here, to the extent I am in position to know, as I have not read that much in the way of what these men wrote, and at this point even though I very much loved it at the time (don’t skip the digression on silver, even, I remember it being great) The Wealth of Nations is now largely a blur to me.
There were also questions about the world and philosophy in general but not about AI, that I would mostly put in this first category. As usual, I have lots of thoughts.
The second conversation is about expectations given what I typically call mundane AI. What would the future look like, if AI progress stalls out without advancing too much? We cannot rule such worlds out and I put substantial probability on them, so it is an important and fascinating question.
If you accept the premise of AI remaining within the human capability range in some broad sense, where it brings great productivity improvements and rewards those who use it well but remains foundationally a tool and everything seems basically normal, essentially the AI-Fizzle world, then we have disagreements but Tyler is an excellent thinker about these scenar |
6a353b5f-3017-45db-9328-e177efc29516 | trentmkelly/LessWrong-43k | LessWrong | Basic Income. org
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458d31fa-f46a-4a0d-8b94-d6b2ae6b462d | trentmkelly/LessWrong-43k | LessWrong | Technocratic Plimsoll Line
Plimsoll line is a marking where the hull of a ship meets the surface of the water.
I've just read a discussion between Glen Weyl and Scott Alexander about technocracy. It's really hard to figure out what they are actually disagreeing about. My naive understanding is that Weyl may be saying: "When we design mechanisms, we have to pay attention to make them legible to the political class and/or to the general public," and Scott Alexander responding "Sure, but why do you believe the rationalist community to be opposed to the idea?"
And while I have little to add to that discussion - besides maybe adding a case study by pointing out that the gripes that many Europeans have with the European Union seem to be mostly focused on the legibility rather than on the actual performance of the institution - it made me write down couple of thoughts on technocracy that I never bothered to put on paper before.
For the purpose of this article, technocracy, bureaucracy and meritocracy are the same thing: It's a class of people who got their jobs based on their capabilities. The counterpart is the political class, the people who get their jobs based on their political views and/or their loyalty. They may be elected by the public or get selected by a higher-up. I guess there isn't a short word for the latter kind of person, so I'll just go with "political appointee".
Viewed this way, a president or a prime minister are political appointees. So is, say, a director of the tax authority, who may be literally appointed by the prime minister. A head of a department at a ministry may, on the other hand, apply for the position based on a job ad and manage to get hired by passing an interview testing his professional skills. That makes him a technocrat - or bureaucrat, if you will. In the same way, Chinese emperor, even if it sounds weird, is a political appointee and the mandarins are technocrats.
The line tends to be blurry. Even the experts are often hired based on the recommendation o |
e4719dae-feb1-4d9d-8309-fb65d7bb0b45 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | Continuous doesn’t mean slow
*Note: This post was crossposted from [Planned Obsolescence](https://www.planned-obsolescence.org/continuous-doesnt-mean-slow/) by the Forum team, with the author's permission. The author may not see or respond to comments on this post.*
> *Once a lab trains AI that can fully replace its human employees, it will be able to multiply its workforce 100,000x. If these AIs do AI research, they could develop vastly superhuman systems in under a year.*
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There’s a lot of disagreement about how likely AI is to end up overthrowing humanity. Thoughtful pundits [vary from <5% to >90%](https://www.cold-takes.com/making-the-best-of-the-most-important-century/?ref=planned-obsolescence.org#open-question-how-hard-is-the-alignment-problem). What’s driving this disagreement?
One factor that often comes up in discussions is **takeoff speeds**, which Ajeya mentioned in the [previous post](https://forum.effectivealtruism.org/posts/hw8ePRLJop7kSEZK3/ais-accelerating-ai-research). How quickly and suddenly do we move from today’s AI, to “expert-human level” AI[[1]](#fn1), to AI that is way beyond human experts and could easily overpower humanity?
The final stretch — the transition from expert-human level AI to AI systems that can easily overpower all of us — is especially crucial. If this final transition happens slowly, we could potentially have a long time to get used to the obsolescence regime and use very competent AI to [help us solve AI alignment](https://www.google.com/url?q=https://www.planned-obsolescence.org/training-ais-to-help-us-align-ais/&sa=D&source=docs&ust=1680729530528442&usg=AOvVaw3ir2tWrYuBwDjbKIHuNIuc) (among other things). But if it happens very quickly, we won’t have much time to ensure superhuman systems are aligned, or to prepare for human obsolescence in any other way.
Scott Alexander is optimistic that things might move gradually. In a recent [ACX post](https://astralcodexten.substack.com/p/why-i-am-not-as-much-of-a-doomer?ref=planned-obsolescence.org) titled ‘*Why I Am Not (As Much Of) A Doomer (As Some People)*’, he says:
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> So far we’ve had brisk but still gradual progress in AI; GPT-3 is better than GPT-2, and GPT-4 will probably be better still. Every few years we get a new model which is better than previous models by some predictable amount.
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> Some people (eg [Nate Soares](https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization?ref=planned-obsolescence.org)) worry there’s a point where this changes… Maybe some jump… could take an AI from IQ 90 to IQ 1000 with no (or very short) period of IQ 200 in between…
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> I’m optimistic because the past few years have provided some evidence for gradual progress.
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I agree with Scott that recent AI progress has been continuous and fairly predictable, and don’t particularly expect a break in that trend. But I expect the transition to superhuman AI to be very fast, even if it’s continuous.
The amount of “compute” (i.e. the number of AI chips) needed to *train* a powerful AI is much bigger than the amount of compute needed to *run* it. I estimate that OpenAI has enough compute to run GPT-4 on *hundreds of thousands* of tasks at once.[[2]](#fn2)
This ratio will only become more extreme as models get bigger. Once OpenAI trains GPT-5 it’ll have enough compute for GPT-5 to perform *millions* of tasks in parallel, and once they train GPT-6 it’ll be able to perform *tens of millions* of tasks in parallel.[[3]](#fn3)
Now imagine that GPT-6 is as good at AI research as the average OpenAI researcher.[[4]](#fn4) OpenAI could expand their AI researcher workforce from *hundreds* of experts to *tens of millions*. That’s a mind-boggling large increase, a factor of 100,000. It’s like going from 1000 people to the [entire US workforce](https://www.statista.com/statistics/269959/employment-in-the-united-states/?ref=planned-obsolescence.org). What’s more, these AIs could work tirelessly through the night and could potentially “think” *much* more quickly than human workers.[[5]](#fn5) (This change won’t happen all-at-once. I expect speed-ups from less capable AI before this point, as Ajeya wrote in the [previous post](https://forum.effectivealtruism.org/posts/hw8ePRLJop7kSEZK3/ais-accelerating-ai-research).)
How much faster would AI progress be in this scenario? It’s hard to know. But my [best guess](https://docs.google.com/document/d/1os_4YOw6Xv33KjX-kR76D3kW1drkWRHKG2caeiEWzNs/edit?ref=planned-obsolescence.org#heading=h.63n1i9duqyot), from my recent [report](https://www.lesswrong.com/posts/Gc9FGtdXhK9sCSEYu/what-a-compute-centric-framework-says-about-ai-takeoff?ref=planned-obsolescence.org) on takeoff speeds, is that progress would be *much much* faster. I think that less than a year after AI is expert-human level at AI research, AI could improve to the point of being able to easily overthrow humanity.
This is much faster than the timeline mentioned in the ACX post:
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> if you’re imagining specific years, imagine human-genius-level AI in the 2030s and world-killers in the 2040s
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The cause of this fast transition isn’t that there’s a break in the trend of continuous progress. It’s that expert-human-level AI [massively accelerates AI progress](https://forum.effectivealtruism.org/posts/hw8ePRLJop7kSEZK3/ais-accelerating-ai-research), causing this continuous progress to happen at a blistering pace.
Of course, this isn’t inevitable. Labs could choose *not* to use AI to accelerate AI progress, at least once AI gets sufficiently powerful. But it will be a tempting move, and they’re more likely to be cautious if they make specific and verifiable commitments in advance to [pause AI progress](https://forum.effectivealtruism.org/posts/ZvMPNLFBHur9qopw9/is-it-time-for-a-pause).
---
1. I’m operationalizing “expert-human level AI” as “each forward pass of the AI produces as much useful output as 0.1 seconds of thought from a human expert”. It’s possible that AI will produce expert-level output by having many dumber AIs working together and thinking for much longer than a human expert would, but under my definition that wouldn’t count as expert-level AI because the quality of the AI’s thinking is below expert level. [↩︎](#fnref1)
2. My calculation assumes that GPT-4 processes ten tokens per second on each task that it’s being applied to. Here’s how my [estimate](https://docs.google.com/spreadsheets/d/1aZg-ccL3Al3d1YItu--12QHWvSJCQ0YdfoMUmiHKGGk/edit?ref=planned-obsolescence.org#gid=1060982759) works: the training compute for GPT-4 has been estimated at ~3e25 total FLOP ([source](https://colab.research.google.com/drive/1xOVSTfb52IyJxsM0rBUnSNoIdCisTOPx?usp=sharing&ref=planned-obsolescence.org), h/t [Epoch](https://epochai.org/?ref=planned-obsolescence.org)). I assume the training took 4 months, implying that amount of compute used per second during training was 3e18 FLOP/s. How many instances of GPT-4 could you run with this compute? If GPT-4 was trained with 3e25 FLOP in accordance with [Chinchilla scaling,](https://arxiv.org/abs/2203.15556?ref=planned-obsolescence.org) that implies it will require ~1e12 FLOP per forward pass. So you could do 3e18/1e12 = ~3e6 forward passes per second. In other words, you could run GPT-4 on ~3 million tasks in parallel, with it processing one token per second on each task, or on ~300,000 tasks in parallel at ten tokens per second.
Though there wouldn’t be enough *memory* to store 300,000 separate copies of the weights, this calculation suggests that there would be enough *processing power* to apply those weights to 300,000 different word generation tasks per second. (Each copy of the weights can perform many tasks in parallel.) [↩︎](#fnref2)
3. I make the simple [assumption](https://docs.google.com/spreadsheets/d/1gTS548D0DtsjipkeHZQblsmHIAU5zpMAqxb2L31QNpc/edit?ref=planned-obsolescence.org#gid=1060982759) that GPT-5 will be the same as GPT-4 except for having 10X the parameters and being trained on 10X the data, and that GPT-6 will have an additional 10X parameters and 10X data. [↩︎](#fnref3)
4. More precisely, assume that each forward pass of GPT-6 is as useful for advancing AI capabilities as 0.1 seconds of thought by an average OpenAI researcher. I.e. if OpenAI has 300 capabilities researchers today, then you could match their total output by running 300 copies of GPT-6 in parallel and having each of them produce 10 tokens per second. [↩︎](#fnref4)
5. Rather than 10 million AIs thinking at human speed, OpanAI could potentially have 1 million AIs thinking 10X faster than a human, or 100,000 AIs thinking 100X faster. [↩︎](#fnref5) |
49074124-a256-4869-be3e-4f29cb71b25e | trentmkelly/LessWrong-43k | LessWrong | Conjecture Second Hiring Round
Conjecture is hiring! We have open roles for all teams, both technical and non-technical. We have written a bit more about the teams at Conjecture here and you can view all open positions here. Applications for the hiring round will close December 16.
Conjecture is an AI Safety startup that aims to solve the alignment problem.
At Conjecture, we focus on short timeline scenarios. We critically evaluate if the research we conduct actually cuts at alignment, and constantly seek to improve ourselves. We think the amount of work that needs to be done is immense, so we are working to build an organization capable of significantly scaling up and diversifying research directions.
We develop our own large models and research infrastructure. In order to maintain our ability to fund and conduct research at scale, we are also working on building AI driven products that we will soon be offering to a wider audience.
We are based in London, and are excited to be part of the growing alignment scene here. We’re in person by default, sponsor visas for all roles, and support employees’ relocation to London financially and logistically.
Nice views from the Conjecture office :)
For technical teams, the roles we’re most interested in filling are:
* ML Engineering Lead
* Security Lead
* Research Engineer (Engineering Focus)
* Research Engineer (Research Focus)
* Product Engineer
We’re looking for candidates who can hit the ground running. Much of our research agenda at Conjecture involves working with the latest advancements in machine learning and working with large distributed computing clusters. While we expect driven people can skill up quickly in these areas, we will prioritize candidates who have backgrounds in high-performance computing, particularly those with a track record of owning projects end-to-end.
For non-technical teams, the roles we’re most interested in filling are:
* Finance Lead
* Strategy Analyst
Candidates don’t need to have prior experie |
bad1f3c2-e516-4a61-bf07-4f4b151240a7 | trentmkelly/LessWrong-43k | LessWrong | The Good Life Quantified
Cross-posted, as always, from Putanumonit.
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Rob Wiblin writes at 80,000 Hours, a site for systematically thinking about how to have a positive impact for the world through your career. The site tends to an analytical approach, with explicit models for comparing global priorities on three axes of impact or planning your career with an explore/exploit structure. It’s a lot like Putanumonit for people who care about improving the world instead of just about, uhh, getting laid and buying soap.
This summer Rob recorded a podcast with Russ Roberts of EconTalk, and even though Russ is sympathetic to the Effective Altruism project, he is less sympathetic to 80,000 Hours’ approach of putting numbers on improving the world. He starts by discussing some failures of applying the systematic/quantitative approach to philanthropy, such as deworming charity recommendations that were based on an RCT that has since been called into question. He then expresses a broader skepticism of the applicability of the quantitative approach to life in general:
> Russ: In principle, that all sounds great. But, I don’t really actually think it’s the right way to think about how to live your life, or how to live your career. So, let me try to suggest some things I find troubling about it.
>
> So, you mentioned a whole bunch of factors. They’re all reasonable. Any one of them is extremely reasonable. You should tackle an important problem. You should tackle a problem that people have not successfully solved, that you might have a chance of improving, that you might have an impact. It’s a variation really of expected value theory to me. […]
>
> So, when I think about that enormous range of trade-offs within economics, and then I think about, ‘Oh, but I didn’t have to be an economist. I could have been, say, an English professor,’ would you conclude that I did the right thing? I happen to like economics now, but at the time I also liked fiction. If I had d |
a10a1ead-b5a2-4ca8-9164-c4147748b18d | trentmkelly/LessWrong-43k | LessWrong | QAPR 4: Inductive biases
Introduction
This is week 4 of Quintin's Alignment Papers Roundup. The current focus is the inductive biases of stochastic gradient descent.
For most datasets and labels, there are many possible models that reach good performance. "Inductive biases" refers to the various factors that incline a particular training process to find some types of models over others. When the data under-specify the learned model, a training process's inductive biases determine what sort of decision making process the model implements, and how the model generalizes beyond its training data.
I'd intended to publish this last week, but it turns out that there's a lot of work on SGD's inductive biases, and it's very technical. I kept finding new papers that seemed relevant. That's why this roundup has 16 papers, in place of the usual ~9 or so.
Papers
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks
> Understanding the fundamental principles behind the massive success of neural networks is one of the most important open questions in deep learning. However, due to the highly complex nature of the problem, progress has been relatively slow. In this note, through the lens of infinite-width networks, a.k.a. neural kernels, we present one such principle resulting from hierarchical localities. It is well-known that the eigenstructure of infinite-width multilayer perceptrons (MLPs) depends solely on the concept frequency, which measures the order of interactions. We show that the topologies from deep convolutional networks (CNNs) restructure the associated eigenspaces into finer subspaces. In addition to frequency, the new structure also depends on the concept space, which measures the spatial distance among nonlinear interaction terms. The resulting fine-grained eigenstructure dramatically improves the network's learnability, empowering them to simultaneously model a much richer class of interactions, including Long-Range-Low-Frequency interactions, Short-Range- |
425ba344-ee26-4587-8566-8a8ca3720fe0 | trentmkelly/LessWrong-43k | LessWrong | Homeostasis and “Root Causes” in Aging
Let’s start with a stylized fact: almost every cell type in the human body is removed and replaced on a regular basis. The frequency of this turnover ranges from a few days (for many immune cells and cells in the gastrointestinal lining) to ten years (for fat, heart, and skeleton cells). Only a handful of tissues are believed to be non-renewing in humans - e.g. eggs, neurons, and the lens of the eye (and even out of those, neurons are debatable).
This means that the number of cells of any given type is determined by “homeostatic equilibrium” - the balance of cell removal and replacement. If an ulcer destroys a bunch of cells in your stomach lining, they’ll be replaced over a few days, and the number of stomach cells will return to roughly the same equilibrium level as before. If a healthy person receives a bunch of extra red blood cells in a transfusion, they’ll be broken down over a few months, and the number of blood cells will return to roughly the same equilibrium level as before.
As organisms age, we see a change in the homeostatic equilibrium level of many different cell types (and other parameters, like hormone and cytokine levels). In particular, a wide variety of symptoms of aging involve “depletion” (i.e. lower observed counts) of various cell types.
However, human aging happens on a very slow timescale, i.e. decades. Most cell counts equilibrate much faster - for instance, immune cell counts equilibrate on a scale of days to weeks. So, suppose we see a decrease in the count of certain immune cells with age - e.g. naive T cells. Could it be that naive T cells just wear out and die off with age? No - T cells are replaced every few weeks, so a change on a timescale of decades cannot be due to the cells themselves dying off. If the count of naive T cells falls on a timescale of decades, then either (a) the rate of new cell creation has decreased, or (b) the rate of old cell removal has increased (or both). Either of those would require some “upstream” chan |
f254bef8-499f-4f29-82fa-3082b88e4ede | trentmkelly/LessWrong-43k | LessWrong | Meetup : Meet up LW Sao Paulo
Discussion article for the meetup : Meet up LW Sao Paulo
WHEN: 16 April 2016 02:00:27PM (-0300)
WHERE: Vanilla café - 2o andar, Rua Antônio Carlos, 452. São Paulo, Brazil.
Encontro mensal de racionalistas, novatos são bem vindos! Teremos algumas lições de racionalidade prática, discussões diversas e jogos de tabuleiro no final.
Discussion article for the meetup : Meet up LW Sao Paulo |
4592354a-f3ea-47e6-8731-974b08ff5cde | trentmkelly/LessWrong-43k | LessWrong | Rationality of demonstrating & voting
Summary
Based on rough estimates of their effectiveness, voting is a very good use of time, but demonstrating far less so – though it may still be worth doing, at least for a very large demonstration on a major issue.
Introduction
Why bother voting? Your vote will only change the result if it would otherwise be an exact tie; and the chance of that is negligible – one in millions.
But a chance of one in millions is worth taking if the jackpot is billions or trillions. That is, the opportunity for you to select a better rather than worse government, thereby making the country – though not yourself – billions or trillions of dollars better off. So as long as you care at least slightly about the rest of the country, voting is rational; civic duty really is a reason to vote.
(An interesting 2007 paper Voting as a Rational Choice by Edlin et al. models this formally. Though in contrast the Put A Number On It blog argues in part that you have no idea which party will run the country better, so your opinion and hence vote is worthless.)
Does similar reasoning justify taking part in demonstrations (and other forms of collective action), even though they don’t have such direct effects as voting?
Example
Consider the largest current political issue in the UK – Brexit. The background, for non-Britons, is that in 2016 a UK referendum voted by a narrow margin to leave the European Union (EU), on terms still being negotiated. Many ‘Remainers’ who wish to stay in the EU want a second referendum on the matter; recently, hundreds of thousands of them marched through London to call for one.
How worthwhile was it for a Remainer to vote in the 2016 referendum, or to demonstrate for a second one?
Vote response curve
A political action, such as voting or demonstrating, is intended to produce an outcome – in this case, the UK remaining in the EU. We can plot this as a response curve, showing how the number of participants affects the probability of the outcome.
Voting has a resp |
fc4dcb7a-b2cc-4912-a4bf-5525d27b74de | trentmkelly/LessWrong-43k | LessWrong | The International PauseAI Protest: Activism under uncertainty
This post is an attempt to summarize the crucial considerations for an AI pause and AI pause advocacy. It is also a promotion for the International PauseAI protest, 21 October, the biggest AI protest ever, held in 7 countries on the same day. The aim of this post is to present an unbiased view, but obviously that may not be the case.
You can check out the EA Forum event page for the protest here.
Seven Crucial Considerations for Pausing AI
Under the default scenario, is the risk from AI acceptable?
* Is alignment going well?
* How likely is alignment by default?
* How hard is the alignment problem?
* What is an acceptable level of risk given the potential benefits of AGI?
This is the only question where I'm confident enough to say the answer is clearly "no". The remaining question is whether a pause would decrease the risk.
How dangerous is hardware overhang in a pause?
* How much AI progress comes from additional spending vs. hardware improvement vs. algorithmic progress?
* Could hardware overhang cause a fast takeoff? Is a fast takeoff substantially more dangerous than a slow one?
* Would multipolar scenarios be more likely due to hardware overhang? Are multipolar or unipolar scenarios more dangerous?
* Is it possible to pause hardware progress as well?
How much does AI capability progress help alignment?
* Is fine-tuning based safety such as RLHF making progress on fundamental alignment?
* How far are we from mechanistic interpretability on frontier models? On models that may constitute superintelligence?
* How long would it take to figure out agent foundations? Is this necessary to fundamentally solving alignment?
* Does governance get harder as we get closer to AGI because more people are more invested in the development of AI? Or easier, because the danger becomes more apparent?
* Is it possible to allow better models to be trained in some labs / a CERN for AI, while keeping them from being deployed / stolen by hackers?
Can |
72d08dc2-cde0-49e7-ab0d-8ce686ec754a | trentmkelly/LessWrong-43k | LessWrong | Updating, part 1: When can you change your mind? The binary model
I was recently disturbed by my perception that, despite years of studying and debating probability problems, the LessWrong community as a whole has not markedly improved its ability to get the right answer on them.
I had expected that people would read posts and comments by other people, and take special note of comments by people who had a prior history of being right, and thereby improve their own accuracy.
But can that possibly work? How can someone who isn't already highly-accurate, identify other people who are highly accurate?
Aumann's agreement theorem (allegedly) says that Bayesians with the same priors agree. But it doesn't say that doing so helps. Under what circumstances does revising your opinions, by updating in response to people you consider reliable, actually improve your accuracy?
To find out, I built a model of updating in response to the opinions of others. It did, eventually, show that Bayesians improve their collective opinions by updating in response to the opinions of other Bayesians. But this turns out not to depend on them satisfying the conditions of Aumann's theorem, or on doing Bayesian updating. It depends only on a very simple condition, established at the start of the simulation. Can you guess what it is?
I'll write another post describing and explaining the results if this post receives a karma score over 10.
That's getting a bit ahead of ourselves, though. This post models only non-Bayesians, and the results are very different.
Here's the model:
* There are G people in a group such as LessWrong.
* There are N problems being discussed simultaneously.
* Problems are binary problems, with an answer of either 1 or 0.
* Each person's opinion on each problem is always known to all people.
* Each person i has an accuracy: Their probability pi of getting any arbitrary problem correct on the first guess.
* givt is what person i believes at time t is the answer to problem v (1 or 0).
* pij expresses person i's estimate |
3e002e81-fd4c-4bff-9022-b1f3bdc60023 | trentmkelly/LessWrong-43k | LessWrong | Formalising cousin_it's bounded versions of Gödel's theorem
In this post, I'll try and put cousin_it's bounded version of Gödel's theorem on a rigorous footing. This will be logic-heavy, not programming heavy. The key unproved assumptions are listed at the bottom of the post.
Notes on terminology:
* T is our deductive system.
* ⊥ is a canonical contradiction in T. ¬A is defined to be A→⊥.
* T ⊢ A means that T proves the proposition A.
* T ⊢j A means that T proves A in a proof requiring no more than j symbols.
* □ A is the statement "there exists a number that is an encoding in Gödel numbering of a valid proof in T of A".
* □n A is the statement "there exists a number that is an encoding in Gödel numbering of a valid proof in A with no more than n symbols in the proof".
The first needed fact is that there is a computable function g such that if T ⊢n A, then T ⊢g(n) □n A. In informal language, this means that if T can prove A in j symbols, it can prove that it can do so, in g(j) symbols. This g will be the critical piece of the infrastructure.
Now, these arguments work with general definitions of proofs, but I like to put exact bounds where I can and move uncertainty to a single place. So I will put some restrictions on the language we use, and what counts as a proof. First, I will assume the symbols "(", ")", "j", and "=" are part of the language - ( and ) as parentheses, and j as a free variable.
I will require that proofs start and end with parenthesis. A proof need not state what statement it is proving! As long as there is a computable process that can check that "p is a proof of A", p need not mention A. This means that I can make the following assumptions:
* If p is a proof of A→B and q a proof of A, then (pq) is a proof of B (modus ponens).
* If p is a proof of A→B and q a proof of ¬B, then (pq) is a proof of ¬A (modus tollens).
* A proof that A↔B will be treated as a valid proof of A→B and B→A also.
* Well-formed sentences with free variables can also have proofs (as in "j=0 or j>0" and "j>3 → j2>9").
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1cd383e3-2a92-4c44-a129-0da3ba6b6d8e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Helsinki Meetup
Discussion article for the meetup : Helsinki Meetup
WHEN: 15 June 2014 05:00:00PM (+0300)
WHERE: Vilhonkatu 4, 00100 Helsinki, Finland
We are having an informal meetup in Kaisla. To find us there, look for someone wearing a bright red cap.
Discussion article for the meetup : Helsinki Meetup |
cfe30bb2-bf18-48b3-8af4-7f236efa0f92 | trentmkelly/LessWrong-43k | LessWrong | Knowledge vs Technology
Here's a little thing I wrote down a couple of years back:
Question: Does the following statements represent three different stages of technological development or does two of them represent the same stage? In some sense it should boil down to how we define the word 'technology', or at least its relation to 'knowledge'.
1. We don't know how to build a commercially feasible fusion reactor.
2. We know how to build a commercially feasible fusion reactor, but we haven't built one yet.
3. He have built one commercially feasible fusion reactor.
The key is how to handle 'know' in the second statement. One could argue that no matter how sure we are on how to construct it (down to every detail), statement three is still superior since we have actually one functioning reactor to show. But at the same time if we would collect all the uncertainties in our models/simulations/calculations etc in a variable u. What happens as u->0 ? Shouldn't the gap between 2 and 3 go to zero? That would mean that enough knowledge about a technology is equivalent to actually possessing the technology.
EDIT: The utility of having a reactor is of course higher than knowing how to construct one, so if we measure technological level as a function of the utility gained from that technology then the resolution is trivial. |
bad93e53-721c-479a-900b-cb95b6d97507 | trentmkelly/LessWrong-43k | LessWrong | How could "Kickstarter for Inadequate Equilibria" be used for evil or turn out to be net-negative?
Following up to "If a "Kickstarter for Inadequate Equlibria" was built, do you have a concrete inadequate equilibrium to fix?"
I think a kickstarter for coordinated action would be net positive, but it's the sort of general purpose powerful tool that might turn out bad in ways I can't easily predict. It might give too much power to mobs of people who don't know what they're doing, or have weird/bad goals.
How bad might it be if misused? What equilibrias might be we end up in in the world where everyone freely has access to such a tool?
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dceca7be-a729-4fd0-98c1-889a02fe3e38 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | Tendencies in reflective equilibrium
Consider a case, not too different from what has been shown to happen in reality, where we ask Bob what sounds like a fair punishment for a homeless man who steals $1,000, and he answers ten years. Suppose we wait until Bob has forgotten that we ever asked the first question, and then ask him what sounds like a fair punishment for a hedge fund manager who steals $1,000,000, and he says five years. Maybe we even wait until he forgets the whole affair, and then ask him the same questions again with the same answers, confirming that these are stable preferences.
If we now confront Bob with both numbers together, informing him that he supported a ten year sentence for stealing $1,000 and a five year sentence for stealing $1,000,000, a couple of things might happen. He could say "Yeah, I genuinely believe poor people deserve greater penalties than rich people." But more likely he says "Oh, I guess I was prejudiced." Then if we ask him the same question again, he comes up with two numbers that follow the expected mathematical relationship and punish the greater theft with more jail time.
Bob isn't working off of some predefined algorithm for determining punishment, like "jail time = (10 \* amount stolen)/net worth". I don't know if anyone knows exactly what Bob is doing, but at a stab, he's seeing how many unpleasant feelings get generated by imagining the crime, then proposing a jail sentence that activates about an equal amount of unpleasant feelings. If the thought of a homeless man makes images of crime more readily available and so increases the unpleasant feelings, things won't go well for the homeless man. If you're [really hungry](http://blogs.discovermagazine.com/notrocketscience/2011/04/11/justice-is-served-but-more-so-after-lunch-how-food-breaks-sway-the-decisions-of-judges/), that probably won't help either.
So just like nothing automatically synchronizes the intention to study a foreign language and the behavior of studying it, so nothing automatically synchronizes thoughts about punishing the theft of $1000 and punishing the theft of $1000000.
Of course, there is something that non-automatically does it. After all, in order to elicit this strange behavior from Bob, we had to wait until he forgot about the first answer. Otherwise, he would have noticed and quickly adjusted his answers to make sense.
We probably could represent Bob's tendencies as an equation and call it a preference. Maybe it would be a long equation with terms for net worth of criminal, amount stolen, how much food Bob's eaten in the past six hours, and [whether his local sports team won the pennant recently](http://www.thedailybeast.com/articles/2009/01/09/dirty-secrets-of-college-admissions.html), with appropriate coefficients and powers for each. But if Bob saw this equation, he certainly wouldn't endorse it. He'd probably be horrified. It's also unstable: if given a choice, he would undergo brain surgery to remove this equation, thus preventing it from being satisfied. This is why I am reluctant to call these potential formalizations of these equations a "preference".
Instead of saying that Bob has one preference determining his jail time assignments, it would be better to model him as having several tendencies - a tendency to give a certain answer in the $1000 case, a tendency to give a different answer in the $1000000 case, and several tendencies towards things like consistency, fairness, compassion, et cetera.
People strongly consciously endorse these latter tendencies, probably because they're socially useful1. If the Chief of Police says "I know I just put this guy in jail for theft, but I'm going to let this other thief off because he's my friend, and I don't really value consistency that much," then they're not going to stay Chief of Police for very long.
Bayesians and rationalists, in particular, make a big deal out of consistency. One common parable on the importance of consistency is the Dutch Book - a way to get free money from anyone behaving inconsistently. Suppose you have a weighted coin which can land on either heads or tails. There are several good reasons why I should not assign a probability of 66% to heads and 66% to tails, but one of the clearest is this: you can make me a bet that I will give you $2 if it lands on tails and you give me $1 if it lands on heads, and then a second bet where I give you $2 if it lands on heads and you give me $1 if it lands on tails. Whichever way the coin lands, I owe you $1 and you owe me $2 - I have gained a free dollar. So consistency is good if you don't want to be handing dollars out to random people...
...except that the Dutch book itself assumes consistency. If I believe that there is a 66% chance of it landing on heads, but refuse to take a bet at 2:1 odds - or even at 1.5:1 odds even though I should think it's easy money! - then I can't be Dutch booked. I am literally too stupid to be tricked effectively. You would think this wouldn't happen too often, since people would need to construct an accurate mental model to know when they should refuse such a bet, and such an accurate model would tell them they should revise their probabilities - but time after time people have demonstrated the ability [to do exactly that](/lw/i4/belief_in_belief/).
I have not yet accepted that consistency is always the best course in every situation. For example, in [Pascal's Mugging](/lw/kd/pascals_mugging_tiny_probabilities_of_vast/), a random person threatens to take away a zillion units of utility if you don't pay them $5. The probability they can make good on their threat is miniscule, but by multiplying out by the size of the threat, it still ought to motivate you to give the money. Some belief has to give - the belief that multiplication works, the belief that I shouldn't pay the money, or the belief that I should be consistent all the time - and right now, consistency seems like the weakest link in the chain.
The best we can do is seek reflective equilibrium among our tendencies. If you endorse the belief that rich people should not get lighter sentences than poor people more strongly than you endorse the tendency to give the homeless man ten years in jail and the fund manager five, then you can edit the latter tendency and come up with a "fair" sentence. This is Eliezer's [defense of reason and philosophy](/lw/jm/the_lens_that_sees_its_flaws/), a powerful justification for morality (see [part one](http://www.raikoth.net/consequentialism.html#metaethics) here) and it's probably the best we can do in justifying our motivations as well.
Any tendency that has reached reflective equilibrium in your current state is about as close to a preference as you're going to get. It still won't automatically motivate you, of course. But you can motivate yourself toward it [obliquely](/lw/6nz/approving_reinforces_loweffort_behaviors/), and come up with the course of action that you most thoroughly endorse.
**FOOTNOTES:**
**1:** A tendency toward consistency can cause trouble if someone gains advantage from both of two mutually inconsistent ideas. [Trivers' hypothesis](/lw/6mj/trivers_on_selfdeception/) predicts that people will consciously deny the inconsistency so they can continue holding both ideas, yet still remain consistent and so socially acceptable. Rationalists are so annoying because we go around telling people they can't do that. |
138802e2-b0d0-40f4-852d-1603d68ee0c3 | trentmkelly/LessWrong-43k | LessWrong | An explanation of decision theories
[Epistemic status: I may have gotten some things wrong so please point out any errors. I also papered over a lot of technical details in favor of presenting my intuitions. A lot of this is me thinking out loud. If you already know a lot about decision theory or want the gory details, I've listed a bunch of resources on decision theory at the bottom of this post.]
Rationality is sometimes considered to be "maximizing expected utility". This seems pretty unambiguous but it turns out that what this means is pretty tricky to define, and different decision theories have different definitions of it. In this post, I will summarize causal decision theory, evidential decision theory, and updateless/functional/logical decision theory.
For a while, there were two dominant decision theories, causal decision theory (CDT), and evidential decision theory (EDT):
* Causal decision theory: Take actions that physically cause you to get higher expected utility.
* Evidential decision theory: Take actions that would be evidence of you getting higher expected utility.
To illustrate their differences and point out their problems, I'll introduce two scenarios:
Newcomb's problem
Omega is an entity who can predict your actions with 99% certainty. Importantly, Omega simulates you to predict your actions. They put $1,000 in a transparent box and $1,000,000 in an opaque box if and only if they predict that you would open only the opaque box to acquire the $1,000,000. Since picking only the transparent box doesn't maximize expected utility in either scenario, we are left with two options: picking only the opaque box (one-boxing) or picking both the transparent and opaque boxes (two-boxing). Do you one-box or two-box?
A CDT agent two-boxes because Omega already put in or didn't put in the money in the opaque box, and they can't physically cause the past.
An EDT agent one-boxes, because that would be evidence of them having received $1,000,000 instead of $1,000.
EDT wins in Newcomb's pro |
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