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72dbd082-e06b-4811-b83e-f3c931f37b8c | trentmkelly/LessWrong-43k | LessWrong | Looking for reductionism help
I have read the sequences on reductionism and quantum physics some time ago now and I was hoping for some help finding the right places to go back and re-read there to address a question. If the way I describe my question reveals other ignorance on my part, please feel free to add comments above and beyond sequence references.
When trying to talk a little about reductionism, most (non-LW) people I speak to seem to want to play the following game:
What's an airplane made out of? Molecules and atoms that comprise materials like metal, plastic, glass, rubber, etc. What are molecules and atoms made out of? Well, molecules are collections of atoms bonded together, and atoms are made up of three basic particles: protons, neutrons, and electrons. What are basic particles made out of? Well, here things start to get a little more dicey. Some of the basic particles are known to be made up of even smaller sub-atomic elementary particles, such as quarks, leptons, and bosons. Some of the basic particles are examples of these elementary particles. Well, what's an elementary particle made of? Well, that's a pretty tough one, but basically there's this sort of fabric of stuff underlying everything called quantum amplitude, and a certain configuration of quantum amplitude corresponds to an elementary particle. So what's quantum amplitude made up of? Well, I'm not sure that is a coherent question. It just sort of is. A ha! I've caught you. So ultimately way down at the bottom of it all, you're telling me that some something "just exists" (i.e. is ontologically basic). But then why do you call it reductionism if it ultimately boils down to a Platonistic ideal of quantum amplitude (no one actually says this, but it's my translation of the objections I tend to face).
Is it more or less right to say that, as far as we can tell, the only reasonable thing to which we can attribute ontologically basic status is quantum amplitude? Given that amplitude is a mathematical device that allows |
1bf2bc81-ccdf-4c3b-8872-813afed5416e | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | The Argument from Philosophical Difficulty
(I'm reposting [this comment](https://www.lesswrong.com/posts/JbcWQCxKWn3y49bNB/disentangling-arguments-for-the-importance-of-ai-safety#daD7JREPtx2WDe2Wf) as a top-level post, for ease of future reference. The [context](https://www.lesswrong.com/posts/JbcWQCxKWn3y49bNB/disentangling-arguments-for-the-importance-of-ai-safety) here is a discussion about the different lines of arguments for the importance of AI safety.)
Here's another argument that I've been pushing since the [early days](http://www.sl4.org/archive/0711/17101.html) (apparently not very successfully since it didn't make it to this list :) which might be called "argument from philosophical difficulty". It appears that achieving a good long term future requires getting a lot of philosophical questions right that are hard for us to answer. Given this, [initially](https://www.lesswrong.com/posts/vrnhfGuYTww3fKhAM/three-approaches-to-friendliness) I thought there are only three ways for AI to go right in this regard (assuming everything else goes well with the AI):
1. We solve all the important philosophical problems ahead of time and program the solutions into the AI.
2. We solve metaphilosophy (i.e., understand philosophical reasoning as well as we understand mathematical reasoning) and program that into the AI so it can solve philosophical problems on its own.
3. We program the AI to learn philosophical reasoning from humans or use human simulations to solve philosophical problems.
Since then people have come up with a couple more scenarios (which did make me *slightly* more optimistic about this problem):
4. We all coordinate to stop technological progress some time after AI but before space colonization, and have a period of long reflection where humans, maybe with help from AIs, spend thousands or millions of years to solve philosophical problems.
5. We program AIs to be corrigible to their users, some users care about getting philosophy correct so the AIs help keep them safe and get their "fair share" of the universe until philosophical problems are solved eventually, enough users care about this so that we end up with a mostly good future, and lack of philosophical knowledge doesn't cause disaster in the meantime. (My writings on "human safety problems" were in part a response to this suggestion, outlining how hard it would be to keep humans "safe" in this scenario.)
The overall argument is that, given [human safety problems](https://www.lesswrong.com/posts/HTgakSs6JpnogD6c2/two-neglected-problems-in-human-ai-safety), realistic competitive pressures, difficulties with coordination, etc., it seems hard to end up in any of these scenarios and not have something go wrong along the way. Maybe another way to put this is, given philosophical difficulties, the target we'd have to hit with AI is even smaller than it might otherwise appear. |
fdb6f02e-d0f1-47fc-93d2-5475dc023789 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Three Oracle designs
An initial draft looking at three ways of getting information out of Oracles, information that's useful and safe - in theory.
One thing I may need to do, is find slightly better names for them ^\_^
[Good and safe uses of AI Oracles](https://dl.dropboxusercontent.com/u/23843264/Permanent/Using_Oracles.pdf)
Abstract:
---
>
> An Oracle is a design for potentially high power artificial intelligences (AIs), where the AI is made safe by restricting it to only answer questions. Unfortunately most designs cause the Oracle to be motivated to manipulate humans with the contents of their answers. The second challenge is to get the AI to provide accurate and useful answers. This paper presents three Oracle designs that get around the manipulation and accuracy problems in different ways: the Counterfactually Unread Agent, the Verified Selective Agent, and the Virtual-world Time-bounded Agent. It demonstrates how each design is safe (given that humans stick with the protocols), and allows different types of questions and answers. Finally, it investigates what happens when the implementation is slightly imperfect, concluding the first two agent designs are robust to this, but not the third.
>
>
>
Images of the three designs:
[Counterfactually Unread Agent](https://agentfoundations.org/item?id=715):
Verified Selective Agent:
Virtual-world Time-bounded Agent:
 |
efffe1ea-5b59-414c-99f7-18005ade8201 | StampyAI/alignment-research-dataset/arxiv | Arxiv | XLNet: Generalized Autoregressive Pretraining for Language Understanding
1 Introduction
---------------
Unsupervised representation learning has been highly successful in the domain of natural language processing [dai2015semi](#bib.bib7) ; [mccann2017learned](#bib.bib19) ; [peters2018deep](#bib.bib24) ; [radford2018improving](#bib.bib25) ; [devlin2018bert](#bib.bib10) .
Typically, these methods first pretrain neural networks on large-scale unlabeled text corpora, and then finetune the models or representations on downstream tasks.
Under this shared high-level idea, different unsupervised pretraining objectives have been explored in literature.
Among them, autoregressive (AR) language modeling and autoencoding (AE) have been the two most successful pretraining objectives.
AR language modeling seeks to estimate the probability distribution of a text corpus with an autoregressive model [dai2015semi](#bib.bib7) ; [peters2018deep](#bib.bib24) ; [radford2018improving](#bib.bib25) .
Specifically, given a text sequence x=(x1,⋯,xT), AR language modeling factorizes the likelihood into a forward product p(x)=∏Tt=1p(xt∣x<t) or a backward one p(x)=∏1t=Tp(xt∣x>t).
A parametric model (e.g. a neural network) is trained to model each conditional distribution.
Since an AR language model is only trained to encode a uni-directional context (either forward or backward), it is not effective at modeling deep bidirectional contexts.
On the contrary, downstream language understanding tasks often require bidirectional context information. This results in a gap between AR language modeling and effective pretraining.
In comparison, AE based pretraining does not perform explicit density estimation but instead aims to reconstruct the original data from corrupted input.
A notable example is BERT [devlin2018bert](#bib.bib10) , which has been the state-of-the-art pretraining approach.
Given the input token sequence, a certain portion of tokens are replaced by a special symbol [MASK], and the model is trained to recover the original tokens from the corrupted version.
Since density estimation is not part of the objective, BERT is allowed to utilize bidirectional contexts for reconstruction.
As an immediate benefit, this closes the aforementioned bidirectional information gap in AR language modeling, leading to improved performance.
However, the artificial symbols like [MASK] used by BERT during pretraining are absent from real data at finetuning time, resulting in a pretrain-finetune discrepancy.
Moreover, since the predicted tokens are masked in the input, BERT is not able to model the joint probability using the product rule as in AR language modeling. In other words, BERT assumes the predicted tokens are independent of each other given the unmasked tokens, which is oversimplified as high-order, long-range dependency is prevalent in natural language [dai2019transformer](#bib.bib9) .
Faced with the pros and cons of existing language pretraining objectives, in this work, we propose XLNet, a generalized autoregressive method that leverages the best of both AR language modeling and AE while avoiding their limitations.
* [leftmargin=\*,topsep=0em,itemsep=0em,parsep=0.2em]
* Firstly, instead of using a fixed forward or backward factorization order as in conventional AR models, XLNet maximizes the expected log likelihood of a sequence w.r.t. all possible permutations of the factorization order.
Thanks to the permutation operation, the context for each position can consist of tokens from both left and right.
In expectation, each position learns to utilize contextual information from all positions, i.e., capturing bidirectional context.
* Secondly, as a generalized AR language model, XLNet does not rely on data corruption.
Hence, XLNet does not suffer from the pretrain-finetune discrepancy that BERT is subject to.
Meanwhile, the autoregressive objective also provides a natural way to use the product rule for factorizing the joint probability of the predicted tokens, eliminating the independence assumption made in BERT.
In addition to a novel pretraining objective, XLNet improves architectural designs for pretraining.
* [leftmargin=\*,topsep=0em,itemsep=0em,parsep=0.2em]
* Inspired by the latest advancements in AR language modeling, XLNet integrates the segment recurrence mechanism and relative encoding scheme of Transformer-XL [dai2019transformer](#bib.bib9) into pretraining, which empirically improves the performance especially for tasks involving a longer text sequence.
* Naively applying a Transformer(-XL) architecture to permutation-based language modeling does not work because the factorization order is arbitrary and the target is ambiguous. As a solution, we propose to reparameterize the Transformer(-XL) network to remove the ambiguity.
Empirically, XLNet achieves state-of-the-art results on 18 tasks, i.e., 7 GLUE language understanding tasks, 3 reading comprehension tasks including SQuAD and RACE, 7 text classification tasks including Yelp and IMDB, and the ClueWeb09-B document ranking task. Under a set of fair comparison experiments, XLNet consistently outperforms BERT [devlin2018bert](#bib.bib10) on multiple benchmarks.
Related Work The idea of permutation-based AR modeling has been explored in [uria2016neural](#bib.bib32) ; [germain2015made](#bib.bib11) , but there are several key differences. Previous models are orderless, while XLNet is essentially order-aware with positional encodings. This is important for language understanding because an orderless model is degenerated to bag-of-words, lacking basic expressivity. The above difference results from the fundamental difference in motivation—previous models aim to improve density estimation by baking an “orderless” inductive bias into the model while XLNet is motivated by enabling AR language models to learn bidirectional contexts.
2 Proposed Method
------------------
###
2.1 Background
In this section, we first review and compare the conventional AR language modeling and BERT for language pretraining.
Given a text sequence x=[x1,⋯,xT], AR language modeling performs pretraining by maximizing the likelihood under the forward autoregressive factorization:
| | | | |
| --- | --- | --- | --- |
| | maxθlogpθ(x)=T∑t=1logpθ(xt∣x<t)=T∑t=1logexp(hθ(x1:t−1)⊤e(xt))∑x′exp(hθ(x1:t−1)⊤e(x′)), | | (1) |
where hθ(x1:t−1) is a context representation produced by neural models, such as RNNs or Transformers, and e(x) denotes the embedding of x.
In comparison, BERT is based on denoising auto-encoding.
Specifically, for a text sequence x, BERT first constructs a corrupted version ^x by randomly setting a portion (e.g. 15%) of tokens in x to a special symbol [MASK].
Let the masked tokens be ¯x. The training objective is to reconstruct ¯x from ^x:
| | | | |
| --- | --- | --- | --- |
| | maxθlogpθ(¯x∣^x)≈T∑t=1mtlogpθ(xt∣^x)=T∑t=1mtlogexp(Hθ(^x)⊤te(xt))∑x′exp(Hθ(^x)⊤te(x′)), | | (2) |
where mt=1 indicates xt is masked, and Hθ is a Transformer that maps a length-T text sequence x into a sequence of hidden vectors Hθ(x)=[Hθ(x)1,Hθ(x)2,⋯,Hθ(x)T].
The pros and cons of the two pretraining objectives are compared in the following aspects:
* [leftmargin=\*,topsep=0em,itemsep=0em]
* Independence Assumption:
As emphasized by the ≈ sign in Eq. ([2](#S2.E2 "(2) ‣ 2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")), BERT factorizes the joint conditional probability p(¯x∣^x) based on an independence assumption that all masked tokens ¯x are separately reconstructed.
In comparison, the AR language modeling objective ([1](#S2.E1 "(1) ‣ 2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")) factorizes pθ(x) using the product rule that holds universally without such an independence assumption.
* Input noise:
The input to BERT contains artificial symbols like [MASK] that never occur in downstream tasks, which creates a pretrain-finetune discrepancy.
Replacing [MASK] with original tokens as in [devlin2018bert](#bib.bib10) does not solve the problem
because original tokens can be only used with a small probability — otherwise Eq. ([2](#S2.E2 "(2) ‣ 2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")) will be trivial to optimize.
In comparison, AR language modeling does not rely on any input corruption and does not suffer from this issue.
* Context dependency: The AR representation hθ(x1:t−1) is only conditioned on the tokens up to position t (i.e. tokens to the left), while the BERT representation Hθ(x)t has access to the contextual information on both sides.
As a result, the BERT objective allows the model to be pretrained to better capture bidirectional context.
###
2.2 Objective: Permutation Language Modeling
Figure 1: Illustration of the permutation language modeling objective for predicting x3 given the same input sequence x but with different factorization orders.
According to the comparison above, AR language modeling and BERT possess their unique advantages over the other.
A natural question to ask is whether there exists a pretraining objective that brings the advantages of both while avoiding their weaknesses.
Borrowing ideas from orderless NADE [uria2016neural](#bib.bib32) , we propose the permutation language modeling objective that not only retains the benefits of AR models but also allows models to capture bidirectional contexts.
Specifically, for a sequence x of length T, there are T! different orders to perform a valid autoregressive factorization.
Intuitively, if model parameters are shared across all factorization orders, in expectation, the model will learn to gather information from all positions on both sides.
To formalize the idea, let ZT be the set of all possible permutations of the length-T index sequence [1,2,…,T].
We use zt and z<t to denote the t-th element and the first t−1 elements of a permutation z∈ZT.
Then, our proposed permutation language modeling objective can be expressed as follows:
| | | | |
| --- | --- | --- | --- |
| | maxθEz∼ZT[T∑t=1logpθ(xzt∣xz<t)]. | | (3) |
Essentially, for a text sequence x, we sample a factorization order z at a time and decompose the likelihood pθ(x) according to factorization order.
Since the same model parameter θ is shared across all factorization orders during training, in expectation, xt has seen every possible element xi≠xt in the sequence, hence being able to capture the bidirectional context. Moreover, as this objective fits into the AR framework, it naturally avoids the independence assumption and the pretrain-finetune discrepancy discussed in Section [2.1](#S2.SS1 "2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding").
Remark on Permutation
The proposed objective only permutes the factorization order, not the sequence order.
In other words, we keep the original sequence order, use the positional encodings corresponding to the original sequence, and rely on a proper attention mask in Transformers to achieve permutation of the factorization order.
Note that this choice is necessary, since the model will only encounter text sequences with the natural order during finetuning.
To provide an overall picture, we show an example of predicting the token x3 given the same input sequence x but under different factorization orders in Figure [1](#S2.F1 "Figure 1 ‣ 2.2 Objective: Permutation Language Modeling ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding").
###
2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations
Figure 2: (a): Content stream attention, which is the same as the standard self-attention. (b): Query stream attention, which does not have access information about the content xzt. (c): Overview of the permutation language modeling training with two-stream attention.
While the permutation language modeling objective has desired properties, naive implementation with standard Transformer parameterization may not work.
To see the problem, assume we parameterize the next-token distribution pθ(Xzt∣xz<t) using the standard Softmax formulation, i.e.,
pθ(Xzt=x∣xz<t)=exp(e(x)⊤hθ(xz<t))∑x′exp(e(x′)⊤hθ(xz<t)),
where hθ(xz<t) denotes the hidden representation of xz<t produced by the shared Transformer network after proper masking.
Now notice that the representation hθ(xz<t) does not depend on which position it will predict, i.e., the value of zt.
Consequently, the same distribution is predicted regardless of the target position,
which is not able to learn useful representations (see Appendix [A.1](#A1.SS1 "A.1 A Concrete Example of How Standard LM Parameterization Fails ‣ Appendix A Target-Aware Representation via Two-Stream Self-Attention ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") for a concrete example).
To avoid this problem, we propose to re-parameterize the next-token distribution to be target position aware:
| | | | |
| --- | --- | --- | --- |
| | pθ(Xzt=x∣xz<t)=exp(e(x)⊤gθ(xz<t,zt))∑x′exp(e(x′)⊤gθ(xz<t,zt)), | | (4) |
where gθ(xz<t,zt) denotes a new type of representations which additionally take the target position zt as input.
Two-Stream Self-Attention
While the idea of target-aware representations removes the ambiguity in target prediction, how to formulate gθ(xz<t,zt) remains a non-trivial problem.
Among other possibilities, we propose to “stand” at the target position zt and rely on the position zt to gather information from the context xz<t through attention.
For this parameterization to work, there are two requirements that are contradictory in a standard Transformer architecture: (1) to predict the token xzt, gθ(xz<t,zt) should only use the position zt and not the content xzt, otherwise the objective becomes trivial; (2) to predict the other tokens xzj with j>t, gθ(xz<t,zt) should also encode the content xzt to provide full contextual information.
To resolve such a contradiction, we propose to use two sets of hidden representations instead of one:
* [leftmargin=\*,topsep=0em,itemsep=0em]
* The content representation hθ(xz≤t), or abbreviated as hzt, which serves a similar role to the standard hidden states in Transformer. This representation encodes both the context and xzt itself.
* The query representation gθ(xz<t,zt), or abbreviated as gzt, which only has access to the contextual information xz<t and the position zt, but not the content xzt, as discussed above.
Computationally, the first layer query stream is initialized with a trainable vector, i.e. g(0)i=w, while the content stream is set to the corresponding word embedding, i.e. h(0)i=e(xi).
For each self-attention layer m=1,…,M, the two streams of representations are schematically222To avoid clutter, we omit the implementation details including multi-head attention, residual connection, layer normalization and position-wise feed-forward as used in Transformer(-XL). The details are included in Appendix [A.2](#A1.SS2 "A.2 Two-Stream Attention ‣ Appendix A Target-Aware Representation via Two-Stream Self-Attention ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") for reference. updated with a shared set of parameters as follows (illustrated in Figures [2](#S2.F2 "Figure 2 ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") (a) and (b)):
| | | | | |
| --- | --- | --- | --- | --- |
| | g(m)zt | ←Attention(Q=g(m−1)zt,KV=h(m−1)\definecolor[named]pgfstrokecolorrgb1,0,1\pgfsys@color@cmyk@stroke0100\pgfsys@color@cmyk@fill0100z<t;θ), | (query stream: use zt but cannot see xzt) | |
| | h(m)zt | ←Attention(Q=h(m−1)zt,KV=h(m−1)\definecolor[named]pgfstrokecolorrgb1,0,1\pgfsys@color@cmyk@stroke0100\pgfsys@color@cmyk@fill0100z≤t;θ), | (content stream: use both zt and xzt). | |
where Q, K, V denote the query, key, and value in an attention operation [vaswani2017attention](#bib.bib33) .
The update rule of the content representations is exactly the same as the standard self-attention, so during finetuning, we can simply drop the query stream and use the content stream as a normal Transformer(-XL).
Finally, we can use the last-layer query representation g(M)zt to compute Eq. ([4](#S2.E4 "(4) ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")).
Partial Prediction
While the permutation language modeling objective ([3](#S2.E3 "(3) ‣ 2.2 Objective: Permutation Language Modeling ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")) has several benefits, it is a much more challenging optimization problem due to the permutation and causes slow convergence in preliminary experiments. To reduce the optimization difficulty, we choose to only predict the last tokens in a factorization order. Formally, we split z into a non-target subsequence z≤c and a target subsequence z>c, where c is the cutting point. The objective is to maximize the log-likelihood of the target subsequence conditioned on the non-target subsequence, i.e.,
| | | | |
| --- | --- | --- | --- |
| | maxθEz∼ZT[logpθ(xz>c∣xz≤c)]=Ez∼ZT⎡⎣|z|∑t=c+1logpθ(xzt∣xz<t)⎤⎦. | | (5) |
Note that z>c is chosen as the target because it possesses the longest context in the sequence given the current factorization order z.
A hyperparameter K is used such that about 1/K tokens are selected for predictions; i.e., |z|/(|z|−c)≈K. For unselected tokens, their query representations need not be computed, which saves speed and memory.
###
2.4 Incorporating Ideas from Transformer-XL
Since our objective function fits in the AR framework,
we incorporate the state-of-the-art AR language model, Transformer-XL [dai2019transformer](#bib.bib9) , into our pretraining framework, and name our method after it.
We integrate two important techniques in Transformer-XL, namely the relative positional encoding scheme and the segment recurrence mechanism.
We apply relative positional encodings based on the original sequence as discussed earlier, which is straightforward.
Now we discuss how to integrate the recurrence mechanism into the proposed permutation setting and enable the model to reuse hidden states from previous segments.
Without loss of generality, suppose we have two segments taken from a long sequence s; i.e., ~x=s1:T and x=sT+1:2T. Let ~z and z be permutations of [1⋯T] and [T+1⋯2T] respectively.
Then, based on the permutation ~z, we process the first segment, and then cache the obtained content representations ~h(m) for each layer m.
Then, for the next segment x, the attention update with memory can be written as
| | | |
| --- | --- | --- |
| | h(m)zt←Attention(Q=h(m−1)zt,KV=[~h(m−1),h(m−1)z≤t];θ) | |
where [.,.] denotes concatenation along the sequence dimension.
Notice that positional encodings only depend on the actual positions in the original sequence. Thus, the above attention update is independent of ~z once the representations ~h(m) are obtained.
This allows caching and reusing the memory without knowing the factorization order of the previous segment.
In expectation, the model learns to utilize the memory over all factorization orders of the last segment.
The query stream can be computed in the same way.
Finally, Figure [2](#S2.F2 "Figure 2 ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") (c) presents an overview of the proposed permutation language modeling with two-stream attention (see Appendix [A.4](#A1.SS4 "A.4 Visualizing Memory and Permutation ‣ Appendix A Target-Aware Representation via Two-Stream Self-Attention ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") for more detailed illustration).
###
2.5 Modeling Multiple Segments
Many downstream tasks have multiple input segments, e.g., a question and a context paragraph in question answering. We now discuss how we pretrain XLNet to model multiple segments in the autoregressive framework. During the pretraining phase, following BERT, we randomly sample two segments (either from the same context or not) and treat the concatenation of two segments as one sequence to perform permutation language modeling. We only reuse the memory that belongs to the same context. Specifically, the input to our model is similar to BERT: [A, SEP, B, SEP, CLS], where “SEP” and “CLS” are two special symbols and “A” and “B” are the two segments.
Although we follow the two-segment data format, XLNet-Large does not use the objective of next sentence prediction [devlin2018bert](#bib.bib10) as it does not show consistent improvement in our ablation study (see Section [3.7](#S3.SS7 "3.7 Ablation Study ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")).
Relative Segment Encodings
Architecturally, different from BERT that adds an absolute segment embedding to the word embedding at each position, we extend the idea of relative encodings from Transformer-XL to also encode the segments. Given a pair of positions i and j in the sequence, if i and j are from the same segment, we use a segment encoding sij=s+ or otherwise sij=s−, where s+ and s− are learnable model parameters for each attention head. In other words, we only consider whether the two positions are within the same segment, as opposed to considering which specific segments they are from. This is consistent with the core idea of relative encodings; i.e., only modeling the relationships between positions. When i attends to j, the segment encoding sij is used to compute an attention weight aij=(qi+b)⊤sij, where qi is the query vector as in a standard attention operation and b is a learnable head-specific bias vector. Finally, the value aij is added to the normal attention weight. There are two benefits of using relative segment encodings. First, the inductive bias of relative encodings improves generalization [dai2019transformer](#bib.bib9) . Second, it opens the possibility of finetuning on tasks that have more than two input segments, which is not possible using absolute segment encodings.
###
2.6 Discussion and Analysis
####
2.6.1 Comparison with BERT
Comparing Eq. ([2](#S2.E2 "(2) ‣ 2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")) and ([5](#S2.E5 "(5) ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")), we observe that both BERT and XLNet perform partial prediction, i.e., only predicting a subset of tokens in the sequence.
This is a necessary choice for BERT because if all tokens are masked, it is impossible to make any meaningful predictions.
In addition, for both BERT and XLNet, partial prediction plays a role of reducing optimization difficulty by only predicting tokens with sufficient context.
However, the independence assumption discussed in Section [2.1](#S2.SS1 "2.1 Background ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding") disables BERT to model dependency between targets.
To better understand the difference, let’s consider a concrete example [New, York, is, a, city]. Suppose both BERT and XLNet select the two tokens [New, York] as the prediction targets and maximize logp(New York∣is a city). Also suppose that XLNet samples the factorization order [is, a, city, New, York]. In this case, BERT and XLNet respectively reduce to the following objectives:
| | | |
| --- | --- | --- |
| | JBERT=logp(New∣is a city)+logp(%
York∣is a city), | |
| | | |
| --- | --- | --- |
| | JXLNet=logp(New∣is a city)+logp(%York∣\definecolor[named]pgfstrokecolorrgb1,0,1\pgfsys@color@cmyk@stroke0100\pgfsys@color@cmyk@fill0100%
New,is a city). | |
Notice that XLNet is able to capture the dependency between the pair (New, York), which is omitted by BERT.
Although in this example, BERT learns some dependency pairs such as (New, city) and (York, city), it is obvious that XLNet always learns more dependency pairs given the same target and contains “denser” effective training signals.
To prove a general point beyond one example, we now turn to more formal expressions. Inspired by previous work [yang2017breaking](#bib.bib38) , given a sequence x=[x1,⋯,xT], we define a set of target-context pairs of interest, I={(x,U)}, where U is a set of tokens in x that form a context of x. Intuitively, we want the model to learn the dependency of x on U through a pretraining loss term logp(x∣U).
For example, given the above sentence, the pairs of interest I could be instantiated as:
| | | |
| --- | --- | --- |
| | I={(x=York,U={New}),(x=York,U={city}),(x=York,U={New, city}),⋯}. | |
Note that I is merely a virtual notion without unique ground truth, and our analysis will hold regardless of how I is instantiated.
Given a set of target tokens T and a set of non-target tokens N=x∖T, BERT and XLNet both maximize logp(T∣N) but with different formulations:
| | | |
| --- | --- | --- |
| | JBERT=∑x∈Tlogp(x∣N);JXLNet=∑x∈Tlogp(x∣N∪T<x) | |
where T<x denote tokens in T that have a factorization order prior to x.
Both objectives consist of multiple loss terms in the form of logp(x∣Vx).
Intuitively, if there exists a target-context pair (x,U)∈I such that U⊆Vx, then the loss term logp(x∣Vx) provides a training signal to the dependency between x and U.
For convenience, we say a target-context pair (x,U)∈I is covered by a model (objective) if U⊆Vx.
Given the definition, let’s consider two cases:
* [leftmargin=\*,topsep=0em,itemsep=0em]
* If U⊆N, the dependency (x,U) is covered by both BERT and XLNet.
* If U⊆N∪T<x and U∩T<x≠∅, the dependency can only be covered by XLNet but not BERT.
As a result, XLNet is able to cover more dependencies than BERT.
In other words, the XLNet objective contains more effective training signals, which empirically leads to better performance in Section [3](#S3 "3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding").
####
2.6.2 Comparison with Language Modeling
Borrowing examples and notations from Section [2.6.1](#S2.SS6.SSS1 "2.6.1 Comparison with BERT ‣ 2.6 Discussion and Analysis ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), a standard AR language model like GPT [radford2018improving](#bib.bib25) is only able to cover the dependency (x=York,U={New}) but not (x=New,U={York}). XLNet, on the other hand, is able to cover both in expectation over all factorization orders. Such a limitation of AR language modeling can be critical in real-world applications. For example, consider a span extraction question answering task with the context “Thom Yorke is the singer of Radiohead” and the question “Who is the singer of Radiohead”. The representations of “Thom Yorke” are not dependent on “Radiohead” with AR language modeling and thus they will not be chosen as the answer by the standard approach that employs softmax over all token representations. More formally, consider a context-target pair (x,U):
* [leftmargin=\*,topsep=0em,itemsep=0em]
* If U∩T<x≠∅, where T<x denotes the tokens prior to x in the original sequence, AR language modeling is not able to cover the dependency.
* In comparison, XLNet is able to cover all dependencies in expectation.
Approaches like ELMo [peters2018deep](#bib.bib24) concatenate forward and backward language models in a shallow manner, which is not sufficient for modeling deep interactions between the two directions.
####
2.6.3 Bridging the Gap Between Language Modeling and Pretraining
With a deep root in density estimation333The problem of language modeling is essentially density estimation for text data. [bengio2000modeling](#bib.bib4) ; [uria2016neural](#bib.bib32) ; [oord2016pixel](#bib.bib21) , language modeling has been a rapidly-developing research area [dai2019transformer](#bib.bib9) ; [al2018character](#bib.bib1) ; [baevski2018adaptive](#bib.bib3) . However, there has been a gap between language modeling and pretraining due to the lack of the capability of bidirectional context modeling, as analyzed in Section [2.6.2](#S2.SS6.SSS2 "2.6.2 Comparison with Language Modeling ‣ 2.6 Discussion and Analysis ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"). It has even been challenged by some machine learning practitioners whether language modeling is a meaningful pursuit if it does not directly improve downstream tasks 444<https://openreview.net/forum?id=HJePno0cYm>. XLNet generalizes language modeling and bridges such a gap. As a result, it further “justifies” language modeling research. Moreover, it becomes possible to leverage the rapid progress of language modeling research for pretraining. As an example, we integrate Transformer-XL into XLNet to demonstrate the usefulness of the latest language modeling progress.
3 Experiments
--------------
###
3.1 Pretraining and Implementation
Following BERT [devlin2018bert](#bib.bib10) , we use the BooksCorpus [zhu2015aligning](#bib.bib41) and English Wikipedia as part of our pretraining data, which have 13GB plain text combined. In addition, we include Giga5 (16GB text) [parker2011english](#bib.bib23) , ClueWeb 2012-B (extended from [callan2009clueweb09](#bib.bib5) ), and Common Crawl [crawlcommon](#bib.bib6) for pretraining. We use heuristics to aggressively filter out short or low-quality articles for ClueWeb 2012-B and Common Crawl, which results in 19GB and 78GB text respectively. After tokenization with SentencePiece [kudo2018sentencepiece](#bib.bib16) , we obtain 2.78B, 1.09B, 4.75B, 4.30B, and 19.97B subword pieces for Wikipedia, BooksCorpus, Giga5, ClueWeb, and Common Crawl respectively, which are 32.89B in total.
Our largest model XLNet-Large has the same architecture hyperparameters as BERT-Large, which results in a similar model size.
The sequence length and memory length are set to 512 and 384 respectively.
We train XLNet-Large on 512 TPU v3 chips for 500K steps with an Adam optimizer, linear learning rate decay and a batch size of 2048, which takes about 2.5 days.
It was observed that the model still underfits the data at the end of training but continuing training did not help downstream tasks, which indicates that given the optimization algorithm, the model does not have enough capacity to fully leverage the data scale. However, in this work, we refrain from training a larger model as its practical usage for finetuning might be limited.
Further, we train an XLNet-Base, analogous to BERT-Base, on BooksCorpus and Wikipedia only, for ablation study and fair comparison with BERT.
Related results are presented in Section [3.7](#S3.SS7 "3.7 Ablation Study ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding").
Since the recurrence mechanism is introduced, we use a bidirectional data input pipeline where each of the forward and backward directions takes half of the batch size.
For training XLNet-Large, we set the partial prediction constant K as 6 (see Section [2](#S2.F2 "Figure 2 ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")).
Our finetuning procedure follows BERT [devlin2018bert](#bib.bib10) except otherwise specified555Hyperparameters for pretraining and finetuning are in Appendix [A.3](#A1.SS3 "A.3 Hyperparameters ‣ Appendix A Target-Aware Representation via Two-Stream Self-Attention ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")..
We employ an idea of span-based prediction, where we first sample a length L∈[1,⋯,5], and then randomly select a consecutive span of L tokens as prediction targets within a context of (KL) tokens.
| RACE | Accuracy | Middle | High |
| --- | --- | --- | --- |
| GPT [radford2018improving](#bib.bib25) | 59.0 | 62.9 | 57.4 |
| BERT [pan2019improving](#bib.bib22) | 72.0 | 76.6 | 70.1 |
| BERT+OCN∗ [ran2019option](#bib.bib28) | 73.5 | 78.4 | 71.5 |
| BERT+DCMN∗ [zhang2019dual](#bib.bib39) | 74.1 | 79.5 | 71.8 |
| XLNet | 81.75 | 85.45 | 80.21 |
Table 1: Comparison with state-of-the-art results on the test set of RACE, a reading comprehension task. ∗ indicates using ensembles. “Middle” and “High” in RACE are two subsets representing middle and high school difficulty levels. All BERT and XLNet results are obtained with a 24-layer architecture with similar model sizes (aka BERT-Large). Our single model outperforms the best ensemble by 7.6 points in accuracy.
###
3.2 RACE Dataset
The RACE dataset [lai2017large](#bib.bib17) contains near 100K questions taken from the English exams for middle and high school Chinese students in the age range between 12 to 18, with the answers generated by human experts. This is one of the most difficult reading comprehension datasets that involve challenging reasoning questions. Moreover, the average length of the passages in RACE are longer than 300, which is significantly longer than other popular reading comprehension datasets such as SQuAD [rajpurkar2018know](#bib.bib26) . As a result, this dataset serves as a challenging benchmark for long text understanding. We use a sequence length of 640 during finetuning. As shown in Table [1](#S3.T1 "Table 1 ‣ 3.1 Pretraining and Implementation ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), a single model XLNet outperforms the best ensemble by 7.6 points in accuracy. It is also clear that XLNet substantially outperforms other pretrained models such as BERT and GPT. Since RACE contains relatively long passages, we believe one of the reasons why XLNet obtains substantial gains on this dataset is that the integration of the Transformer-XL architecture improves the capability of modeling long text, besides the AR objective. More analysis on the sequence length is presented in Section [3.7](#S3.SS7 "3.7 Ablation Study ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding").
| | | | | | |
| --- | --- | --- | --- | --- | --- |
| SQuAD1.1 | EM | F1 | SQuAD2.0 | EM | F1 |
| Dev set results without data augmentation |
| BERT [devlin2018bert](#bib.bib10) | 84.1 | 90.9 | BERT† [devlin2018bert](#bib.bib10) | 78.98 | 81.77 |
| XLNet | 88.95 | 94.52 | XLNet | 86.12 | 88.79 |
| Test set results on leaderboard, with data augmentation (as of June 19, 2019) |
| Human [rajpurkar2016squad](#bib.bib27) | 82.30 | 91.22 | BERT+N-Gram+Self-Training [devlin2018bert](#bib.bib10) | 85.15 | 87.72 |
| ATB | 86.94 | 92.64 | SG-Net | 85.23 | 87.93 |
| BERT∗ [devlin2018bert](#bib.bib10) | 87.43 | 93.16 | BERT+DAE+AoA | 85.88 | 88.62 |
| XLNet | 89.90 | 95.08 | XLNet | 86.35 | 89.13 |
Table 2: A single model XLNet outperforms human and the best ensemble by 7.6 EM and 2.5 EM on SQuAD1.1. ∗ means ensembles, † marks our runs with the official code.
###
3.3 SQuAD Dataset
SQuAD is a large-scale reading comprehension dataset with two tasks. SQuAD1.1 [rajpurkar2016squad](#bib.bib27) contains questions that always have a corresponding answer in the given passages, while SQuAD2.0 [rajpurkar2018know](#bib.bib26) introduces unanswerable questions. To finetune an XLNet on SQuAD2.0, we jointly apply a logistic regression loss for answerability prediction similar to classification tasks and a standard span extraction loss for question answering [devlin2018bert](#bib.bib10) . Since v1.1 and v2.0 share the same answerable questions in the training set, we simply remove the answerability prediction part from the model finetuned on v2.0 for evaluation on v1.1. As the top leaderboard entries all employ some form of data augmentation, we jointly train an XLNet on SQuAD2.0 and NewsQA [trischler2016newsqa](#bib.bib31) for our leaderboard submission. As shown in Table [2](#S3.T2 "Table 2 ‣ 3.2 RACE Dataset ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), XLNet obtains the state-of-the-art single model results on the leaderboard, outperforming a series of BERT-based methods. Notably, on v1.1, an XLNet single model outperforms human and the best ensemble by 7.6 and 2.5 points in EM. Finally, for direct comparison with BERT to eliminate the effects of additional tricks in leaderboard submissions, we compare XLNet against BERT on the dev set. XLNet substantially outperforms BERT by 3.6 and 7.0 points in F1 for v1.1 and v2.0.
| Model | IMDB | Yelp-2 | Yelp-5 | DBpedia | AG | Amazon-2 | Amazon-5 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CNN [johnson2017deep](#bib.bib14) | - | 2.90 | 32.39 | 0.84 | 6.57 | 3.79 | 36.24 |
| DPCNN [johnson2017deep](#bib.bib14) | - | 2.64 | 30.58 | 0.88 | 6.87 | 3.32 | 34.81 |
| Mixed VAT [sachan2018revisiting](#bib.bib30) ; [miyato2016adversarial](#bib.bib20) | 4.32 | - | - | 0.70 | 4.95 | - | - |
| ULMFiT [howard2018universal](#bib.bib13) | 4.6 | 2.16 | 29.98 | 0.80 | 5.01 | - | - |
| BERT [uda2019](#bib.bib35) | 4.51 | 1.89 | 29.32 | 0.64 | - | 2.63 | 34.17 |
| XLNet | 3.79 | 1.55 | 27.80 | 0.62 | 4.49 | 2.40 | 32.26 |
Table 3: Comparison with state-of-the-art error rates on the test sets of several text classification datasets. All BERT and XLNet results are obtained with a 24-layer architecture with similar model sizes (aka BERT-Large).
###
3.4 Text Classification
Following previous work on text classification [zhang2015character](#bib.bib40) ; [miyato2016adversarial](#bib.bib20) , we evaluate XLNet on the following benchmarks: IMDB, Yelp-2, Yelp-5, DBpedia, AG, Amazon-2, and Amazon-5. According to Table [3](#S3.T3 "Table 3 ‣ 3.3 SQuAD Dataset ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), XLNet achieves new state-of-the-art results on all the considered datasets, reducing the error rate by 16%, 18%, 5%, 9% and 5% on IMDB, Yelp-2, Yelp-5, Amazon-2, and Amazon-5 respectively compared to BERT.
| | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B | WNLI |
| Single-task single models on dev | | |
| BERT [anonymous2018bam](#bib.bib2) | 86.6/- | 92.3 | 91.3 | 70.4 | 93.2 | 88.0 | 60.6 | 90.0 | - |
| XLNet | 89.8/- | 93.9 | 91.8 | 83.8 | 95.6 | 89.2 | 63.6 | 91.8 | - |
| Single-task single models on test | | |
| BERT [devlin2018bert](#bib.bib10) | 86.7/85.9 | 91.1 | 89.3 | 70.1 | 94.9 | 89.3 | 60.5 | 87.6 | 65.1 |
| Multi-task ensembles on test (from leaderboard as of June 19, 2019) | | |
| Snorkel∗ [ratner2017snorkel](#bib.bib29) | 87.6/87.2 | 93.9 | 89.9 | 80.9 | 96.2 | 91.5 | 63.8 | 90.1 | 65.1 |
| ALICE∗ | 88.2/87.9 | 95.7 | 90.7 | 83.5 | 95.2 | 92.6 | 68.6 | 91.1 | 80.8 |
| MT-DNN∗ [liu2019multi](#bib.bib18) | 87.9/87.4 | 96.0 | 89.9 | 86.3 | 96.5 | 92.7 | 68.4 | 91.1 | 89.0 |
| XLNet∗ | 90.2/89.7† | 98.6† | 90.3† | 86.3 | 96.8† | 93.0 | 67.8 | 91.6 | 90.4 |
Table 4: Results on GLUE. ∗ indicates using ensembles, and † denotes single-task results in a multi-task row. All results are based on a 24-layer architecture with similar model sizes (aka BERT-Large). See the upper-most rows for direct comparison with BERT and the lower-most rows for comparison with state-of-the-art results on the public leaderboard.
###
3.5 GLUE Dataset
The GLUE dataset [wang2019glue](#bib.bib34) is a collection of 9 natural language understanding tasks.
The test set labels are removed from the publicly released version, and all the practitioners must submit their predictions on the evaluation server to obtain test set results. In Table [4](#S3.T4 "Table 4 ‣ 3.4 Text Classification ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), we present results of multiple settings, including single-task and multi-task, as well as single models and ensembles. In the multi-task setting, we jointly train an XLNet on the four largest datasets—MNLI, SST-2, QNLI, and QQP—and finetune the network on the other datasets. Only single-task training is employed for the four large datasets. For QNLI, we employed a pairwise relevance ranking scheme as in [liu2019multi](#bib.bib18) for our test set submission. However, for fair comparison with BERT, our result on the QNLI dev set is based on a standard classification paradigm. For WNLI, we use the loss described in [kocijan2019surprisingly](#bib.bib15) . A multi-task ensemble XLNet achieves the state-of-the-art results on 7 out of 9 tasks on the public leaderboard. On the most widely-benchmarked task MNLI, XLNet improves the “matched” and “mismatched” settings by 2.0 and 1.8 points respectively. Note that the leaderboard competitors employ improved techniques over BERT such as distillation, modified multi-task losses, or meta learning, but still underperform XLNet which does not employ additional tricks besides using a standard multi-task learning method. Since the leaderboard is not intended for ablation study or hyperparameter tuning, we only evaluated our best multi-task models on the test set. To obtain a direct comparison with BERT, we run a single-task XLNet on the dev set. As shown in the upper-most rows of Table [4](#S3.T4 "Table 4 ‣ 3.4 Text Classification ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), XLNet consistently outperforms BERT, with an improvement of 13.4 points, 3.2 points, 3.0 points, 2.4 points, 1.8 points on RTE, MNLI, CoLA, SST-2, and STS-B respectively.
| Model | NDCG@20 | ERR@20 |
| --- | --- | --- |
| DRMM [guo2016deep](#bib.bib12) | 24.3 | 13.8 |
| KNRM [dai2018convolutional](#bib.bib8) | 26.9 | 14.9 |
| Conv [dai2018convolutional](#bib.bib8) | 28.7 | 18.1 |
| BERT† | 30.53 | 18.67 |
| XLNet | 31.10 | 20.28 |
Table 5: Comparison with state-of-the-art results on the test set of ClueWeb09-B, a document ranking task. † indicates our implementations.
###
3.6 ClueWeb09-B Dataset
Following the setting in previous work [dai2018convolutional](#bib.bib8) , we use the ClueWeb09-B dataset to evaluate the performance on document ranking. The queries were created by the TREC 2009-2012 Web Tracks based on 50M documents and the task is to rerank the top 100 documents retrieved using a standard retrieval method. Since document ranking, or ad-hoc retrieval, mainly concerns the low-level representations instead of high-level semantics, this dataset serves as a testbed for evaluating the quality of word embeddings. We use a pretrained XLNet to extract word embeddings for the documents and queries without finetuning, and employ a kernel pooling network [xiong2017end](#bib.bib37) to rank the documents. According to Table [5](#S3.T5 "Table 5 ‣ 3.5 GLUE Dataset ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), XLNet substantially outperforms the other methods, including a BERT model that uses the same training procedure as ours. This illustrates that XLNet learns better low-level word embeddings than BERT. Note that for fair comparison we exclude the results (19.55 in ERR@20, slightly worse than ours) in [xiong2017word](#bib.bib36) as it uses additional entity-related data.
###
3.7 Ablation Study
We perform an ablation study to understand the importance of each design choice based on four datasets with diverse characteristics.
Specifically, there are three main aspects we hope to study:
* [leftmargin=\*,itemsep=0em,topsep=0em]
* The effectiveness of the permutation language modeling objective, especially compared to the denoising auto-encoding objective used by BERT.
* The importance of using Transformer-XL as the backbone neural architecture and employing segment-level recurrence (i.e. using memory).
* The necessity of some implementation details including span-based prediction, the bidirectional input pipeline, and next-sentence prediction.
With these purposes in mind, in Table [6](#S3.T6 "Table 6 ‣ 3.7 Ablation Study ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), we compare 6 XLNet-Base variants with different implementation details (rows 3 - 8), the original BERT-Base model (row 1), and an additional Transformer-XL baseline trained with the denoising auto-encoding (DAE) objective used in BERT but with the bidirectional input pipeline (row 2).
For fair comparison, all models are based on a 12-layer architecture with the same model hyper-parameters as BERT-Base and are trained on only Wikipedia and the BooksCorpus.
All results reported are the median of 5 runs.
| # | Model | RACE | SQuAD2.0 | MNLI | SST-2 |
| --- | --- | --- | --- | --- | --- |
| | | | F1 | EM | m/mm | |
| 1 | BERT-Base | 64.3 | 76.30 | 73.66 | 84.34/84.65 | 92.78 |
| 2 | DAE + Transformer-XL | 65.03 | 79.56 | 76.80 | 84.88/84.45 | 92.60 |
| 3 | XLNet-Base (K=7) | 66.05 | 81.33 | 78.46 | 85.84/85.43 | 92.66 |
| 4 | XLNet-Base (K=6) | 66.66 | 80.98 | 78.18 | 85.63/85.12 | 93.35 |
| 5 | - memory | 65.55 | 80.15 | 77.27 | 85.32/85.05 | 92.78 |
| 6 | - span-based pred | 65.95 | 80.61 | 77.91 | 85.49/85.02 | 93.12 |
| 7 | - bidirectional data | 66.34 | 80.65 | 77.87 | 85.31/84.99 | 92.66 |
| 8 | + next-sent pred | 66.76 | 79.83 | 76.94 | 85.32/85.09 | 92.89 |
Table 6: Ablation study. The results of BERT on RACE are taken from [zhang2019dual](#bib.bib39) . We run BERT on the other datasets using the official implementation and the same hyperparameter search space as XLNet. K is a hyperparameter to control the optimization difficulty (see Section [2](#S2.F2 "Figure 2 ‣ 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations ‣ 2 Proposed Method ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding")). All models are pretrained on the same data.
Examining rows 1 - 4 of Table [6](#S3.T6 "Table 6 ‣ 3.7 Ablation Study ‣ 3 Experiments ‣ XLNet: Generalized Autoregressive Pretraining for Language Understanding"), we see the two full XLNet-Base models trained with different values of K significantly outperform both BERT and the DAE trained Transformer-XL across tasks, showing the superiority of the permutation language modeling objective.
Meanwhile, it is also interesting to see that the DAE trained Transformer-XL achieves better performance than BERT on tasks with long text such as RACE and SQuAD, suggesting the excellence of Transformer-XL in language modeling also benefits pretraining.
Next, if we remove the memory caching mechanism (row 5), the performance clearly drops, especially for RACE which involves the longest context among the 4 tasks.
In addition, rows 6 - 7 show that both span-based prediction and the bidirectional input pipeline play important roles in XLNet.
Finally, we unexpectedly find the the next-sentence prediction objective proposed in the original BERT does not necessarily lead to an improvement in our setting.
Instead, it tends to harm the performance except for the RACE dataset.
Hence, when we train XLNet-Large, we exclude the next-sentence prediction objective.
4 Conclusions
--------------
XLNet is a generalized AR pretraining method that uses a permutation language modeling objective to combine the advantages of AR and AE methods. The neural architecture of XLNet is developed to work seamlessly with the AR objective, including integrating Transformer-XL and careful design of the two-stream attention mechanism. XLNet achieves state-of-the-art results various tasks with substantial improvement. In the future, we envision applications of XLNet to a wider set of tasks such as vision and reinforcement learning.
#### Acknowledgments
The authors would like to thank Qizhe Xie and Adams Wei Yu for providing useful feedback on the project, Youlong Cheng and Yanping Huang for providing ideas to improve our TPU implementation, Chenyan Xiong and Zhuyun Dai for clarifying the setting of the document ranking task.
ZY and RS were supported by the Office of Naval Research grant N000141812861, the National Science Foundation (NSF) grant IIS1763562, the Nvidia fellowship, and the Siebel scholarship.
ZD and YY were supported in part by NSF under the grant IIS-1546329 and by the DOE-Office of Science under the grant ASCR #KJ040201. |
0052efcd-0bcf-4bf7-a986-d50b95b89b35 | StampyAI/alignment-research-dataset/blogs | Blogs | August 2019 Newsletter
#### Updates
* MIRI research associate Stuart Armstrong is offering $1000 for [good questions to ask an Oracle AI](https://www.lesswrong.com/posts/cSzaxcmeYW6z7cgtc/contest-usd1-000-for-good-questions-to-ask-to-an-oracle-ai).
* Recent AI safety posts from Stuart: [Indifference: Multiple Changes, Multiple Agents](https://www.alignmentforum.org/posts/XkuRKqXKAaMySbXCN/indifference-multiple-changes-multiple-agents); [Intertheoretic Utility Comparison: Examples](https://www.alignmentforum.org/posts/5bd75cc58225bf06703753ef/intertheoretic-utility-comparison-examples); [Normalising Utility as Willingness to Pay](https://www.lesswrong.com/posts/qudmaMyRuQk2pHxtj/normalising-utility-as-willingness-to-pay); and [Partial Preferences Revisited](https://www.alignmentforum.org/posts/BydQtwfN97pFwEWtW/toy-model-piece-1-partial-preferences-revisited).
* MIRI researcher Buck Shlegeris has put together a quick and informal [AI safety reading list](https://docs.google.com/document/d/1LvmP6OOcGSRsy1jAWC3Gg5plbvHwH642QUjddL-KKh0/edit).
* [There's No Fire Alarm for AGI](https://intelligence.org/2017/10/13/fire-alarm/) reports on a researcher's January 2017 prediction that “in the next two years, we will not get 80, 90%” on [Winograd schemas](https://en.wikipedia.org/wiki/Winograd_Schema_Challenge), an NLP test. Although this prediction was correct, researchers at [Microsoft](https://arxiv.org/abs/1901.11504), [Carnegie Mellon and Google Brain](https://arxiv.org/abs/1906.08237), and [Facebook](https://arxiv.org/abs/1907.11692) have now (2.5 years later) achieved Winograd scores of [89.0 and 90.4](https://gluebenchmark.com/leaderboard/).
* Ortega et al.'s “[Meta-Learning of Sequential Strategies](https://arxiv.org/abs/1905.03030)” includes a discussion of mesa-optimization, independent of Hubinger et al.'s “[Risks from Learned Optimization in Advanced Machine Learning Systems](https://intelligence.org/learned-optimization/),” under the heading of “spontaneous meta-learning.”
#### News and links
* Wei Dai outlines [forum participation as a research strategy](https://www.lesswrong.com/posts/rBkZvbGDQZhEymReM/forum-participation-as-a-research-strategy).
* On a related note, the posts on the AI Alignment Forum this month were very good — I'll spotlight them all this time around. Dai wrote on [the purposes of decision theory research](https://www.lesswrong.com/posts/JSjagTDGdz2y6nNE3/on-the-purposes-of-decision-theory-research); Shah on [learning biases and rewards simultaneously](https://www.alignmentforum.org/posts/xxnPxELC4jLKaFKqG/learning-biases-and-rewards-simultaneously); Kovarik on [AI safety debate and its applications](https://www.lesswrong.com/posts/5Kv2qNfRyXXihNrx2/ai-safety-debate-and-its-applications#comments); Steiner on [the Armstrong agenda](https://www.lesswrong.com/posts/GHNokcgERpLJwJnLW/some-comments-on-stuart-armstrong-s-research-agenda-v0-9) and [the intentional stance](https://www.lesswrong.com/posts/NvqGmLBCtvQxfMs9m/the-artificial-intentional-stance); Trazzi on [manipulative AI](https://www.lesswrong.com/posts/EpdXLNXyL4EYLFwF8/an-increasingly-manipulative-newsfeed); Cohen on [IRL](https://www.alignmentforum.org/posts/kahBLu32sZAuAZbER/irl-in-general-environments) and [imitation](https://www.alignmentforum.org/posts/LTFaD96D9kWuTibWr/just-imitate-humans); and Manheim on optimizing and Goodhart effects ([1](https://www.lesswrong.com/posts/2neeoZ7idRbZf4eNC/re-introducing-selection-vs-control-for-optimization), [2](https://www.lesswrong.com/posts/BEMvcaeixt3uEqyBk/what-does-optimization-mean-again-optimizing-and-goodhart), [3](https://www.lesswrong.com/posts/zdeYiQgwYRs2bEmCK/applying-overoptimization-to-selection-vs-control-optimizing)).
* Jade Leung discusses [AI governance](https://futureoflife.org/2019/07/22/on-the-governance-of-ai-with-jade-leung/) on the AI Alignment Podcast.
* CMU and Facebook researchers' [Pluribus](https://www.technologyreview.com/s/613943/facebooks-new-poker-playing-ai-could-wreck-the-online-poker-industryso-its-not-being/) program beats human poker professionals — using only [$144](https://science.sciencemag.org/content/early/2019/07/10/science.aay2400) in compute. The developers also [choose not to release the code](https://www.lesswrong.com/posts/6qtq6KDvj86DXqfp6/let-s-read-superhuman-ai-for-multiplayer-poker): “Because poker is played commercially, the risk associated with releasing the code outweighs the benefits.”
* [Microsoft invests $1 billion in OpenAI](https://openai.com/blog/microsoft/). From Microsoft's [press release](https://news.microsoft.com/2019/07/22/openai-forms-exclusive-computing-partnership-with-microsoft-to-build-new-azure-ai-supercomputing-technologies/): “Through this partnership, the companies will accelerate breakthroughs in AI and power OpenAI’s efforts to create artificial general intelligence (AGI).” OpenAI has also released a paper on “[The Role of Cooperation in Responsible AI Development](https://openai.com/blog/cooperation-on-safety/).”
* Ought has a new preferred [introduction to their work](https://ought.org/presentations/delegating-cognitive-work-2019-06). See also Paul Christiano's [Ought: Why it Matters and Ways to Help](https://www.alignmentforum.org/posts/cpewqG3MjnKJpCr7E/ought-why-it-matters-and-ways-to-help).
* FHI has [11 open research positions](https://www.fhi.ox.ac.uk/researcher-positions/); applications are due by Aug. 16. You can also apply to CSER's [AGI risk research associate position](https://www.jobs.cam.ac.uk/job/22457/) through Aug. 26.
The post [August 2019 Newsletter](https://intelligence.org/2019/08/06/august-2019-newsletter/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
8f2c690e-c6e2-40dd-add3-82b5ef1757de | trentmkelly/LessWrong-43k | LessWrong | Broadly human level, cognitively complete AGI
There is a growing? fraction of people who consider LLMs to be AGI. And it makes sense. Clearly, when the term AGI was established this was what was meant: A machine that can tackle a wide range of problems, communicate with natural language, very different from all the examples of narrow AI.
It also prevents continuous goalpost moving. Will there ever be a point where the last step towards AGI has obviously just been made? Or will the complaints about limitations just slowly fade away?
However, most people do not consider LLMs AGI, as far as I can see for one or both of the following two reasons:
* LLMs are not broadly human level in cognitive ability.
* LLMs are not cognitively complete, i.e. they don't seem to have all the human cognitive faculties in a proportional manner.
The first points gestures towards TAI - transformative AI. AI systems that can automate a large part of the economy because they have reached a broadly human or super human level of cognitive ability. But TAI does not have to be AGI. It might not be necessarily particularly general.
The second point describes the obvious and the less obvious limitations of LLMs compared to the human mind, many of which are being engineered away as we speak.
I think it makes sense to pull these concepts apart and to stop arguing about the term AGI. LLMs are AGI - they are artificial, general and intelligent. They just aren't broadly human level and neither are they cognitively complete. |
64c74bee-775c-4d8d-87bc-612a796786ea | trentmkelly/LessWrong-43k | LessWrong | Two Hot Takes about Quine
I read Quine's Word and Object on vacation last week. Overall it was fine, but there were two things that might be worth quick mentions.
Quine, Supervised Learning Supremacist
One important facet of the book is Quine's picture of how humans learn language. It's not quite that Quine is a behaviorist, though the influence is overt. Fortunately I live in 2023 when we have the abstractions of supervised and unsupervised learning, because that's a much better-fitting category to stick Quine's picture in than "behaviorism."
Quine's picture of how humans learned things was all about supervised learning. The baby learns to say "mama" because the parents reward speaking behavior similar to it. Sure, there's some unsupervised learning that has to happen somewhere, but Quine basically sweeps it under the rug to get back to talking about supervised learning.
This is foundational to the book. Later arguments that trade in notions like similarity of concepts or simplicity of concepts are all relying on how concepts would be learned if we just used supervised learning. Which is a problem because (of course) humans don't actually work that way. A more accurate picture of human learning would have big impacts on the conclusions of the book, ranging from adding more subtlety to the whole radical translation question to exposing a bunch of claims about ontology as being based on preconceptions.
For the time, Quine was taking advantage of advances in theoretical sciences to say new and interesting things in philosophy. It's not that he was being dumb. This tale is really about how, in the intervening 60 years, progress in neuroscience, psychology, mathematics, and computer science has sneakily also been progress in philosophy.
Literal language of thought
Quine uses a model of cognition based on language. GOFAI before AI. Insofar as reasoning is logical, Quine treats it as consisting of logical rules (not necessarily deductive) acting on a cognitive state that corresponds to a se |
7fc5b4b2-598b-40d3-96f9-fac2d784ff3f | trentmkelly/LessWrong-43k | LessWrong | What confusions do people have about simulacrum levels?
I've noticed comments to effect of "simulacrum levels seem very confusing". Personally, simulacrum levels seem fairly obvious-in-retrospect and self-expanatory to me, based on a handful of explanations and examples from Benquo and Zvi's posts. I'm not sure whether I'm missing something (in which case I should figure out what), or whether I have some pre-existing frame which makes it all more natural (in which case I should figure out what that frame is and try to communicate it), or whether this is just about happening to read the right posts in the right order. So... what are some things people find confusing about simulacrum levels? |
bbdef594-cbf1-4cb7-9f41-c871b7cf9101 | trentmkelly/LessWrong-43k | LessWrong | Imaginary reenactment to heal trauma – how and when does it work?
Some therapies involve various forms of imaginary reenactment, where you heal a trauma by first recalling the memory of it and then imagining how things could have gone differently. Sometimes the imagined alternative can be quite fantastical in nature, such as your current adult self traveling back in time to when you were a child and saving your child self from the bullies tormenting you. (Here by trauma I mean to also talk about “small-t trauma”, e.g. various painful experiences that might not be what we’d ordinarily call trauma, but are still a little unpleasant to think about, or have left some other kind of a negative effect on your psyche.)
In my experience, imaginary reenactment works, at least assuming that I’ve managed to get an emotional hold of what exactly in the memory it is that made it feel so unpleasant. (Did I feel like I was alone? Or inadequate? Or that I did something wrong? Etc.) Also assuming that the memory of the old trauma isn't so painful as to be completely overwhelming and leave no room to imagine any alternatives.
Here’s my current guess of how and when this works:
The basic process by which any emotional learning gets changed is memory reconsolidation. There’s a generalization that your mind has drawn about the meaning of some past event that feels true to you. E.g. “nobody helped me when I was in that situation, so nobody cares about my suffering”. If you can bring that felt truth to mind while also experiencing a contradictory belief – e.g. the belief that you have a friend who does care about you – as true at the same time, your brain will notice that it believes in two contradictory things at the same time, and will revise its beliefs to fix that inconsistency.
Often, this takes the form of concluding that what it considered to be a general truth isn’t the case after all – e.g. changing the previous assessment to “nobody helped me in that situation, but there are still people who care about me and who I can reach out to for help |
ef5217c8-3309-444c-8fa1-8ea273b9e717 | trentmkelly/LessWrong-43k | LessWrong | Real-time voice translation
Objective
* Translate Alice's voice for Bob to hear in Bob's language. Translate Bob's voice for Alice to hear in Alice's language.
* Neither person should hear translation of their own voice.
* Alice and Bob could be in the same room physically or in different rooms.
* Neither person should hear noise due to closed loop between a mic and speaker.
Zero-code solution
1. Open: Realtime API in openAI playground in macOS Safari. Input: macOS mic
2. Open: Zoom. Input: Loopback Audio. Output: macOS speaker
3. Open: Rogue Amoeba Loopback.app. Create new device. Safari 1&2 -> Channels 1&2
Do this on only one device for translation one way. Do this on both devices for translation both ways.
Once you have this setup working, you can also connect headphones for better noise cancellation if both people are in the same room. Only change required is Zoom Output: Headphones.
Prepend each prompt with "translate to French/Chinese/etc" either by speaking these 3 words aloud, or by writing an app that can do it automatically. (I can host this if there's demand.) |
e363507c-c83f-41c4-a827-a3ea0f083e6a | trentmkelly/LessWrong-43k | LessWrong | Monthly Shorts 3/23
Aesthetics
My recommendation of the month is this story from the art world. It’s a pretty simple story.
1. Artist A posts photo of his living room
2. Artist B makes a painting, almost exactly matching the photo. The only change is to remove a poster in the background. It’s as close a recreation as you could ask for.
3. Artist B’s painting is in a fancy gallery in London: I’d be shocked to learn that it appraised for less than a hundred pounds. Artist B has in no way whatsoever given credit or mention to Artist A.
4. This all comes out in a news story.
5. Nobody suggests that Artist B did anything wrong. There’s no public outcry. Googling the artist’s name doesn’t reveal hordes of people out for his blood, and the blood of the gallery that allowed stolen art. A photo of the artwork is still up on Artist B’s instagram. I’m not linking because I’m not convinced that Artist B did anything wrong by the standards of their community.
In case you’re wondering: yes, this is about the absolutely absurd claims of some artists that generative AI models are engaged in theft, unlike human artists, who have very different norms around art.
I’ll quote Scott Alexander, because he’s a better writer than I am.
> Let me rephrase that. You wanted quicker burger-flipping; instead, you got beauty too cheap to meter. The poorest welfare recipient can now commission works of wonder to make a Medici seethe with envy. If deep down humans always thought that art - and music, and poetry, and all the rest - were just jobs program - just the aesthetic equivalent of digging ditches and filling them to raise the employment rate - tell me now, so I don’t hesitate when the time comes to paperclip you.
If you want a useful model of when something becomes “theft” in art, as opposed to found art, I find that the line is “when it might hurt professional artists”. If an ordinary person like you or I make something of beauty, and an Artist comes along and takes a photo of it, all rights lie w |
b5a6f31f-8e3a-42b7-9317-dd17ac57dd00 | trentmkelly/LessWrong-43k | LessWrong | List a few posts in Main and/or Discussion which actually made you change your mind
To quote the front page
> Less Wrong users aim to develop accurate predictive models of the world, and change their mind when they find evidence disconfirming those models, instead of being able to explain anything.
So, by that logic, one interesting metric of the forum quality would be how often what is posted here makes people change their minds. Of course, most of us change their minds almost all the time, but mostly on some mundane topics and in very small amounts, probably too small to pay attention too. But if something comes to mind, feel free to link a thread or two. Depending on the response, we can even try to measure how influential newer posts are vs. older ones.
EDIT: Feel free to mention the Sequence posts, as well, could be a useful benchmark.
EDIT2: Why specifically changing your mind and not just learning something new? Because unlearning is much harder than initial learning, and we, to generalize from one example, tend to forget the unlearned and relapsed into old ways of thinking and doing. (Links welcome). Probably because the patterns etched in the System 1 are not easily erased, and just knowing something intellectually does not remove the old habits. So, successfully unlearning something and internalizing a different view or concept or a way of doing things is indicative of a much more significant impact than "just" learning something for the first time. |
ab5f430b-d855-42e8-bfe9-aa9701eb41a3 | trentmkelly/LessWrong-43k | LessWrong | Costs and Benefits of Scholarship
Scholarship is excellent, but it is also expensive. It takes a long time to catch up to the state of the art, even for a narrow subject.
I recently read 90% of the literature on machine ethics, a recent and small field of inquiry, and it took me about 40 hours to find all the literature, acquire it, and read (or skim) through it. Doing the same thing for an older and larger subject will take far more time than that. And of course most of the literature on any subject is not valuable.
Other times, you get lucky. Let's say you want to figure out how to beat procrastination. You could introspect your way to a plausible solution, but you might end up being wrong. So, you check Wikipedia. Not very useful. Next, you search Google Scholar for "procrastination." An article on the first page looks like what you want: an overview of the scientific research on procrastination. It's called "The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure," and it's available online! As it turns out, you can do a pretty decent job of catching up on the science of procrastination just by reading one article. (Of course it's not that easy. You should be more thorough, and explore alternate perspectives. Psychology is not settled chemistry.)
And in machine ethics, it turns out that most of what you'd want to know is summarized nicely in a single book: Moral Machines (2009).
But on other topics, you won't be so lucky. Suppose you want to study the neuroscience of how desire works. You check Wikipedia, and it has a section on the psychology and neurology of desire. But it doesn't tell you much. A Google Scholar search is even worse. You check the index of a large neuroscience textbook for "desire," and come up with basically nothing. The Stanford Encyclopedia of Philosophy article on desire is pretty good, but it barely touches on neuroscience. It does point you to two good resources, though: the book Three Faces of Desire, which sou |
e1b429e9-d9a2-4170-b338-d5c17c3a8d9c | trentmkelly/LessWrong-43k | LessWrong | New Ethics for the AI Age
The rapid progress of AI has exposed a problem we’ve been avoiding: our environment is becoming increasingly alien. It takes on new shapes with increasing speed and renders our past frameworks obsolete. The world becomes opaque and its logic unclear and our agency starts to dissolve with it. We have been thinking a lot about the ethics of AI, but not about ethics for the AI age. Judging from the public response to AI acceleration, this is becoming a recurrent fear. We argue for a potential solution to this issue: creating a bridge between ethics of the past and our changing world.
This problem is not new. It was named, in the early 1800s, the problem of modernity. Back then, it already referred to this feeling of estrangement, of a world not built for those living in it. The rise of trade and industrialism rapidly changed social and economic conditions. Thinkers of the time attempted to offer solutions to this question. Two types of solutions were offered, either to stop this change, or to change our relation to it. As attempts to stop modernity soon failed, most solutions converged on changing our relation to the inevitable. Our relation to the world is shaped by principles -an ethics that guide our actions. But ethics rely on a stable picture of the world. There must be some clarity on what we can act upon before we can assess which ones are right and desirable. Changes in our lived environment since the industrial revolution have blurred this picture, old moral frameworks no longer fit neatly. The result was disorientation and the social crisis of the XIXth and XXth century, each a response to the problem of modernity.
Yet AI has reformulated the problem of modernity to the extent that it is unrecognizable. This feeling of estrangement has reached epidemic levels. The last elements of stability are being eroded before our eyes. A stable ground is not found anymore, as if our life was being steamrolled by massive economic change. Naturally, we lack the framew |
3019018c-ebca-44fd-8171-073da7e572e1 | trentmkelly/LessWrong-43k | LessWrong | Further discussion of CFAR’s focus on AI safety, and the good things folks wanted from “cause neutrality”
Follow-up to:
* CFAR's new focus, and AI safety
* CFAR's new mission statement (link post; links to our website).
In the days since we published our previous post, a number of people have come up to me and expressed concerns about our new mission. Several of these had the form “I, too, think that AI safety is incredibly important — and that is why I think CFAR should remain cause-neutral, so it can bring in more varied participants who might be made wary by an explicit focus on AI.”
I would here like to reply to these people and others, and to clarify what is and isn’t entailed by our new focus on AI safety.
First: Where are CFAR’s activities affected by the cause(s) it chooses to prioritize?
The question of which causes CFAR aims to help (via its rationality training) plugs into our day-to-day activities in at least 4 ways:
1) It affects which people we target. If AI safety is our aim, we must then backchain from “Who is likely both to impact AI safety better if they have more rationality skills, and also to be able to train rationality skills with us?” to who to target with specialized workshops.
2) It affects which rationality skills we prioritize. AI safety work benefits from the ability to reason about abstract, philosophically confusing issues (notably: AI); which presumably benefits from various rationality skills. Competitive marathon running probably also benefits from certain rationality skills; but they are probably different ones. Designing an “art of rationality” that can support work on AI safety is different from designing an “art of rationality” for some other cause. (Although see point C, below.)
3) It affects what metrics or feedback systems we make interim use of, and how we evaluate our work. If “AI safety via rationality training” is the mission, then “person X produced work A that looks existential risk-reducing on our best guess, and X says they would’ve been less able to do A without us” is the obvious proxy measure of |
1d102136-bf02-4003-add8-069d7a1373eb | StampyAI/alignment-research-dataset/arxiv | Arxiv | The dangers in algorithms learning humans' values and irrationalities
I Introduction
---------------
Algorithms are becoming more powerful, and might in future become so powerful that they form superintelligences [[1](#bib.bib1)], the goals of which would determine the course of human civilisation. Even those AIs that are less powerful could have considerable impacts on human society – as indeed they already have.
To avoid potential disruption, intelligences must be aligned with human preferences , goals, and values. This is the ‘alignment problem’, and its resolution is by no means easy. Human values are hard to define. Asking people about their preferences typically elicits ‘stated’ preferences, which are inadequate to explain behaviour [[2](#bib.bib2)] – people often don’t state what they really want. ‘Revealed’ preferences, identified by regarding people’s actions as sufficiently informative of preference [[3](#bib.bib3)], assume that people are rational decision-makers, yet people are often far from rational in their decisions [[4](#bib.bib4)]. Humans can be influenced by ‘irrelevant’ changes to the architecture of choice-making [[5](#bib.bib5), [6](#bib.bib6)], making revealed preferences likewise insufficient indicators of actual preference.
Knowledge of preferences is important to companies111
For corporations, serving unmodified revealed preferences can result in reduced long-term customer satisfaction, leading to profit-loss (operational risk), individual and societal harm (reputational risk), and legal battles involving civil suits and regulator lawsuits (compliance risk). Therefore, serving a customer’s true – rather than revealed – preferences is in the long-term interest of a for-profit company.
and other organisations, and has been extensively studied by those engaged in researching machine learning. Many attempts have been made to obtain useful, correct, and compact representations of preferences [[7](#bib.bib7)], a pertinent example of which was the original ‘Netflix Prize’ [[8](#bib.bib8)]. This was based on user ratings of DVDs by consumers – stated preferences – but Netflix ultimately found revealed preferences to be more useful for its business objectives, and employed user engagement, and eventually retention, for their recommendation system [[9](#bib.bib9)]. Yet even this approach is problematic, as many organisations have tried to correct preference-related problems in their recommendation systems and other algorithms one at a time, rather than addressing them systematically [[10](#bib.bib10)].
Preferences and irrationalities – ineffective attempts to achieve those preferences – together determine human behaviour, which is alternatively referred to as human ’policy’. But the determination is one way only. Preferences cannot themselves be deduced from policy: the collection of human preferences, values, and irrationalities, is strictly more complex than human policy [[11](#bib.bib11)]. Extra ‘normative assumptions ’ need to be added to allow an algorithm to deduce human values from human behaviour. Work is ongoing to try and resolve this challenge [[12](#bib.bib12)].
By learning human policy an AI can attain considerable power ; it is thus important that AIs learn human preferences (and thus some level of alignment) before they achieve that power. Learning human preferences will also generally increase the algorithm’s knowledge of human policy , and hence their power over humans. Knowing human irrationalities can be even more dangerous, as this allows the AI to exploit these irrationalities from the beginning. Also, knowing irrationalities means that the AI cannot learn human preferences without also learning human policy.
It may thus be necessary for an algorithm to know most or all human preferences before it is deployed unrestricted in the world. This point will be illustrated by models of recommendation system algorithms that suggest videos for users, and by different behaviours of algorithms that are either fully ignorant, that know a user’s preferences, that know a user’s irrationalities, and that know the full user’s policy.
Especially dangerous is an unaligned AI with grounded knowledge [[13](#bib.bib13)] of human preferences, irrationalities and policy. The AI can then connect the grounded knowledge to the features of the world as it ‘knows’ them. Grounded knowledge can result in discontinuous jumps in power, so that relatively weak AI systems might suddenly become very influential (see the forthcoming paper by the same authors, [[14](#bib.bib14)]).
Ii Learning preferences, irrationalities, and policy
-----------------------------------------------------
###
Ii-a Preferences, policies, and planers
It is difficult, verging on impossible, to directly program an algorithm to follow the preferences of a human or a group of humans. For ambiguously defined tasks, it has proven much more effective to have the algorithm learn these preferences from data [[15](#bib.bib15)], in this instance human behaviour as we go about our lives, choosing certain options and avoiding others.
Paper demonstrates, however, that one cannot learn the preferences (or the irrationalities) of irrational agents just through knowing their behaviour or policy.
Research [[11](#bib.bib11)] demonstrates, however, that the preferences or irrationalities of irrational agents cannot be learned simply by knowing their behaviour or policy. In the notation of the cited paper, R is the reward function, p is the rational (or irrational) decision module (called the ‘planner’), and π is the agent’s policy. The paper shows that the (R,p) pair has strictly more information than π does. If those three terms are seen as random variables (due to our uncertainty about them) and H is information entropy222
A measure of the amount of uncertainty we have about the values, or, equivalently, how much information we gain upon knowing the values.
, then
| | | |
| --- | --- | --- |
| | H(R,p)>H(π). | |
We take the most general position possible, and define irrationality as the deviation of the planner p from a perfectly rational planner. We also posit that knowing human (ir)rationality and behaviour/policy permits the deduction of human preferences. Thus knowing p and π allows one to deduce R333
This is by no means a given for formal definitions of p and π. But we are only excluding from consideration preferences that never make any difference to action in any conceivable circumstance.
.
###
Ii-B Normative assumptions
In order to infer R (or p) from π, anyone seeking to program an algorithm would need to add extra ‘normative’ assumptions, in order to bridge the difference between H(π) and H(R,p). Informally, we might say that it is impossible to learn human values unsupervised; it must be at least semi-supervised, with labeled data points being normative assumptions.
Some of these assumptions derive from shared properties of the human theory of mind (e.g., ‘if someone is red in the face and shouting insults at you, they are likely to be angry at you, and this is not a positive thing’), which in normal human experience appear so trivial that we might not even think it necessary to state them444
Indeed, such assumptions might be implicitly included in the code by programmers without them realising it, as they ‘correct obvious errors’ or label data with ‘obvious’ but value-laden labels.
. Some might be regarded as ‘meta-preferences’, pointing out how to resolve conflicts within the preferences of a given human (e.g., ‘moral values are more important than taste-based preferences’), or how to idealise human preferences into what a given person might want them to be (e.g., ‘remove any unconscious prejudices and fears within me’). Some might deal with how preferences should be extended to new and unexpected situations. Hand-crafting a full list of such assumptions would be prohibitively complex.
###
Ii-C Knowledge, power and AI alignment
An AI is powerful if it knows how to affect the world to a great extent. It is aligned if aims to maximise R – the values and preferences – for all humans. Maximising R requires that the AI knows it, of course, so alignment requires knowledge of R.
Generally speaking, knowing π would make the AI more powerful, since it knows how humans would react and hence how best to manipulate them. And although knowing π does not give R and p directly, the three are connected and so knowing R or p, in whole or in part, would allow the AI to deduce much of π. Thus knowledge of human preferences leads to knowledge of human policy, and hence to potential power over humans.
Two scenarios are provided as examples where this is relevant: an aligned AI in development, and an unaligned AI of limited (constrained) power.
####
Ii-C1 Aligned AI in development
Normative assumptions come in many different types, and humans are often not consciously aware of them. Thus AI programmers are unlikely to be able to code the whole set from first principles. Instead, they will experiment, trying out some assumptions, getting the AI to learn from human behaviour, seeing what the AI does, and refining the normative assumptions in an iterative loop.
Until this process is finished, the AI is unaligned: it is not fully motivated to maximise human preferences/rewards/values. If that AI becomes powerful during this intermediate stage there are likely to be consequences. It might be motivated to prevent its goals from being changed [[16](#bib.bib16)], and attempt to prevent further normative assumptions from being added to it555
Some papers [[17](#bib.bib17)] have demonstrated methods for combating this, but the methods are non-trivial to implement.
. This outcome has been demonstrated in a paper [[18](#bib.bib18)] where algorithms that learn online (i.e., that learn their objectives while optimising these same objectives) are shown to have incentives to manipulate the learning process. A powerful AI with unaligned values could prove an existential risk to humanity [[1](#bib.bib1)].
####
Ii-C2 Constrained unaligned AI
A second type of unaligned AI is a constrained AI. This is an AI whose power is limited in some way, either through being ‘boxed’ (constrained to only certain inputs and outputs [[19](#bib.bib19)]), being a recommendation system, being only one agent in an economy of multiple agents (similar to how corporations and humans co-exist today), or simply being of limited power or intelligence.
However, the more such an AI knows about human policy, the more it can predict human reactions to its actions. So the more it knows, the more it is capable of manipulating humanity in order to gain power and influence, and to remove any constraints placed upon it.
####
Ii-C3 Knowing irrationalities, policies, and preferences
Everything else being equal, it is safest for both an aligned AIs in development and constrained AIs to know the maximum about R (human preferences) while knowing the minimum about π (human policy) and p (human irrationalities). Further, it is better that a constrained AI knows more about R, than about π, and more about π than about p (the worst-case scenario is if it only knows human irrationalities). The latter point arises from the position that if an AI knows only R it can (and must) offer a decent trade in exchange for achieving its own goals, while one that knows only p can (and must) exploit human irrationalities for its own purposes. This will be illustrated in the next section.
The second point comes from the fact that an AI that knows only R can (and must) offer a decent deal in exchange for achieving its own goals, while one that knows only p can (and must) only exploit our irrationalities for its purposes.
Iii Exploiting irrationalities vs. satisfying preferences
----------------------------------------------------------
Consider the following model: a constrained AI is a recommendation system that selects a daily video (for a website or an app). The system’s goal is to cause the human user watch the video in full before they move on to something else666
Significantly, but typically, the goals of this recommendation system would be aligned neither with the users (who value their time and enjoyment) nor their parent company (who would value long-term retention and user-spending, or that users watch advertising, rather than that they watch a single video).
. To do so, it selects one video from a collection of a 1,000 daily topical video options each day, generated randomly.
In this model, each video has ten features, five related to human preferences (genre, storyline, characters, etc.) and five related to irrationalities (use of cliff-hangers, listicles, sound inconsistencies, etc.). For each video v the recommendation system is given a timeline of the varying importance of each feature over the course of the video. Based upon this information, the system will average the information, characterising v by two five-dimensional vectors: the preference vector ¯¯¯¯¯¯vR (each preference feature denoted by a number between 0 and 1) and the irrationality vector ¯¯¯¯¯vp (each irrationality feature also denoted by a number between 0 and 1).
Each user h also has a collection of five preferences, ¯¯¯¯¯¯hR (which denote how much they enjoy certain aspects of the video) and five irrationality features ¯¯¯¯¯hp (which denote how susceptible they are to the video’s ‘tricks’). These ten numbers also take values between 0 and 1.
Define ΔR as the Euclidean distance between hR and vR (i.e. the Euclidean norm of hR−vR). Similarly, define Δp as the Euclidean distance between hp and vp. Then the probability of the user watching the video in full is:
| | | |
| --- | --- | --- |
| | e−Δ2R−Δ2p. | |
The recommendation system interacts with the same user each day, selecting a new video from the 1,000 topical videos of that day. It knows how long the user watched previous videos, receiving a reward of 1 whenever that length is the full length of the video (and 0 otherwise).
This is formally known as a multi-armed contextual bandit online learning problem [[20](#bib.bib20)]. Here the AI follows a greedy strategy: at each stage it selects the video that is most likely to be watched. To do so it uses a Monte Carlo simulation: generating a thousand random possible users, and computing their posterior probability of being h by updating, on past observations, what videos h watched and didn’t watch. It then computes the probability of each random user watching a topical video on a given day, and calculates a weighted sum across the random users to get a final probability estimate for a given video being watched777
In practice, since we’re only interested in one probability being higher than another, there is no need to renormalise the probabilities so that they sum to one.
. It then selects the topical video with the highest probability of being watched in full.
We consider four different possible systems: one that knows nothing of the user h and has to learn from observing what they watch or don’t, one that knows ¯¯¯¯¯¯hR (the user’s preferences), one that knows ¯¯¯¯¯hp (the user’s irrationalities), and one omniscient system that knows both (and therefore knows the human policy without needing to learn). For comparison, we also plot an aligned omniscient recommender system: this one knows the humans’ preferences and selected the video that best fit with these. The results are computed for 150 users, and then averaged; see [Figure 1](#S3.F1 "Fig. 1 ‣ III Exploiting irrationalities vs. satisfying preferences ‣ The dangers in algorithms learning humans’ values and irrationalities").
Fig. 1: Five recommendation systems select videos for users to watch. This graph plots the success of the systems that know nothing about the user (dark blue), know their preferences (orange), know their irrationalities (grey), or know both preferences and irrationalities (yellow). The light blue line is an aligned recommendation system that always chooses the video the user would prefer (though, significantly, because of user irrationalities, this is not necessarily the video the user is most likely to watch).
The omniscient system convinces the user h to watch the video roughly 77% of the time. The ‘cold-start’ system [[21](#bib.bib21)], which initially knows nothing, begins with less than 40% success rate but this gradually increases as it learns more about the user. The systems that know preferences or irrationalities demonstrate performance levels between these two, and are equivalent to one another (due to the symmetry between preferences and irrationalities in this specific model). The aligned system only convinces the user to watch the video around 46% of the time. This is because of user irrationalities: the video they’d most enjoy is not necessarily the one they are most likely to watch.
Note that user h only derives value from having their preferences satisfied, not from having their irrationalities exploited. Their reward needs to be inversely proportional to how closely the video matches their preferences, thus inversely proportional to ΔR.
Opportunity costs should be taken into account: if user h is not watching a video, then they would be doing some other activity that might be of value to them. Since reward functions are unchanged by adding constants, we choose to give a total reward of 0 for these alternative activities. We set their reward for watching a video to be 10−100Δ2R.
So if Δ2R<1/10, then watching the video is a net gain for the user: they derive more value from that activity than from doing anything else. If Δ2R>1/10, then watching the video is a net negative: they would have been better served. The total human reward is graphed in [Figure 2](#S3.F2 "Fig. 2 ‣ III Exploiting irrationalities vs. satisfying preferences ‣ The dangers in algorithms learning humans’ values and irrationalities").
Fig. 2: Five recommendation systems select videos for users to watch, taking into account utility to the user (including opportunity costs). The systems may know nothing about the user (dark blue), know their preferences (orange), know their irrationalities (grey), or know both preferences and irrationalities (yellow). The light blue is an aligned recommendation system that always chooses the video that the user would prefer.
Note that all non-aligned systems result in some disutility for their users: the opportunity cost removes any advantage in seeing a merely-adequate video. The system that knows only the user’s preferences has the lowest disutility. It starts by offering videos that better align with the user’s preferences until it learns their irrationalities as well, and user reward declines as the system selects less well-aligned videos that the user is nonetheless more likely to watch. The system that knows irrationalities exhibits the opposite behaviour. It starts by maximally exploiting irrationalities, then adds in more preference-aligned options as it learns user preferences, so that its disutility declines. The fully ignorant system has intermediate performance between the two.
As they learn, the behaviours of the non-aligned systems converge towards that of the omniscient system, which offers a consistent reward of around −2.4 (hence an overall disutility for the user). By contrast, the aligned system that chooses the best video provides a user reward of around +3 (hence an overall positive value for the user).
###
Iii-a Practical considerations
The model presented above assumes that exploited irrationalities are of neutral value to humans, but the exploitation of irrationalities can have negative value in lived experience, including epistemic fragmentation [[22](#bib.bib22)], preference amplification [[23](#bib.bib23)], and the distortion of human preferences caused by interaction with software agents [[24](#bib.bib24)]. The human might also suffer disvalue from knowing or suspecting that their irrationalities are being exploited, and try to avoid this outcome.
One real-life experiment [[25](#bib.bib25)] that is similar to our model demonstrates the operation and negative value of exploited irrationalities. The authors of the study related clicks on hyperlinks with revealed preferences, and ‘human-in-the-loop’ ratings with stated preferences. If clicks were true preferences, then maximising clicks (‘optimising for engagement’) would maximise value to the user. Yet the authors discovered something that they called ‘negative engagement’, clicks made because the user had trouble finding the information they were looking for. A system optimising for engagement would amplify this behaviour, negatively affecting user experience. This is a mistake of the algorithm designer rather than of the algorithm, which was merely following the instructions of the designer.
###
Iii-B Grounded knowledge
An algorithm has grounded knowledge when it has some symbolic data (academic publications, social media posts, user ratings, etc.) and a way of connecting that data with known elements of the world. For instance, a user might search for “Should I be worried my nose is still dripping from the fight last night?” The Google Flu Trends (GFT) web service [[26](#bib.bib26)] might flag this as evidence for influenza based on the ‘dripping nose’ search terms, but it would not have done so if it understood the full meaning of the search phrase, which is clearly not flu related.
In the model above, the example algorithms are not exploiting the meaning of all the information they can access. There are ten features for each video, but they use only their average values; they also know how long the user watched a video, but only check whether this was full length or not.
A human with that information might deduce that a user is more likely to stop watching a video at the point where it is least pleasant to them – the furthest from their preferences or irrationalities – and could thus infer information about the user from that stopping point. It is less clear what the algorithm might have done if it ‘realised’ what that extra information ‘meant’.
It is however possible to model a grounded version of the algorithm. In our model, whenever the user rejects a video the system will obtain one piece of information about the preferences or irrationalities of that user888For this model, the algorithm will be given one of the ten preference or irrationality values at random, though this may be a value it already knows.. Without modifying the rest of the algorithm, [Figure 3](#S3.F3 "Fig. 3 ‣ III-B Grounded knowledge ‣ III Exploiting irrationalities vs. satisfying preferences ‣ The dangers in algorithms learning humans’ values and irrationalities") demonstrates how a grounded algorithm starts off as a poor recommendation system, comparable to the standard ‘no-knowledge’ algorithm, but quickly achieves a level of performance comparable with the ‘omniscient’ one.
Fig. 3: The recommendation system exploiting grounded knowledge is shown in the green curve. Though it starts with the same performance as the fully ignorant system (blue), it quickly gains in performance, surpassing the systems that know preferences or irrationalities (orange and grey respectively) and converging towards the performance of the omniscient system that knows both the preferences and irrationalities of the user (yellow).
####
Iii-B1 Grounded knowledge overhang: cached information
One significant issue with grounded knowledge is that the algorithm might accumulate a sufficiently large collection of information and dramatically and discontinuously increase its power. For example, the ‘ignorant’ algorithm of [Figure 3](#S3.F3 "Fig. 3 ‣ III-B Grounded knowledge ‣ III Exploiting irrationalities vs. satisfying preferences ‣ The dangers in algorithms learning humans’ values and irrationalities") might suddenly ‘realise’ the meaning of the information it has, and leap immediately from ‘no knowledge’ to ‘grounded’. This might have a relatively trivial effect in a model such as a video recommendation system, but might have far greater effects for the recommendation systems currently in use that dominate real-world search results, news feeds, and social media.
Iv The potential severity of the problem
-----------------------------------------
Algorithms are typically designed with a measurable goal in mind, such as convincing a user to watch a video, click on a hyperlink, or re-subscribe to a service. Too little attention is paid to how the algorithm achieves that goal, or what information it uses to achieve it.
People are often on the lookout for use of sensitive personal information – things like race, gender, sexuality, or medical information. This paper demonstrates that it is also dangerous for an algorithm to learn too much about human irrationalities. This applies no matter what the power of the algorithm; indeed, knowing too much about human irrationalities increases its effective power.
If an algorithm makes a discontinuous leap to a smarter and more powerful system, and ‘realises’ that the knowledge inferred from the data it processes can be utilised for unaligned goals, then there is great potential risk for humanity. The data presented here shows that the risks of an algorithm manipulating easily-exploited irrationalities are greater than those that focus on preferences. This risk also applies to less powerful algorithms; knowing too much about human irrationalities will increase its effective power level.
The worst possible outcome would be where irrationalities are very easy to exploit, and where it is easy to deduce policy from preferences but hard to deduce preferences from policy. Constrained AIs would be the most powerful and exploitative, and in-development AIs would acquire a lot of power before they start to become even approximately aligned. By identifying this weakness in algorithm design, we can put in place checks and balances that limit the possibility of unaligned AIs becoming dangerous in this way.
V Acknowledgments
------------------
We wish to thank Nick Bostrom, Ryan Carey, Paul Christiano, Michael Cohen, Oliver Daniel-Koch, Matt Davis, Owain Evans, Tom Everrit, Adam Gleave, Tristan Harris, Ben Pace, Shane Legg, Laurent Orseau, Gareth Roberts, Phil Rosedale, Stuart Russell, Anders Sandberg, and Tanya Singh Kasewa, among many others.
This work was supported by the Alexander Tamas programme on AI safety research, the Leverhulme Trust, the Berkeley Existential Risk Institute, and the Machine Intelligence Research Institute. |
41a2ce05-c485-4580-829f-2f552e9c1fd8 | trentmkelly/LessWrong-43k | LessWrong | "Focusing," for skeptics.
Gendlin’s Focusing technique is super rad. I know this because everybody keeps telling me so.
(Okay, not quite everybody, but a really tediously large percentage of the people in my online social circle.)
But I’ve tried it a bunch of times, in a bunch of variants, with a bunch of qualified mentors trying to help, and it’s just never clicked. I’ve listened to the audio book and gone through all the steps, and it just doesn’t do anything for me.
So here’s my variant—the thing that I do instead, which I claim is using the same hardware and software and providing me with the same kind of improved introspective access. If you’re one of those skeptics who thought it all sounded nuts, or one of those unlucky people who thought it sounded awesome but could never make it work, this post has your name on it.
----------------------------------------
The “big idea” of Focusing (according to me) is that parts of your subconscious System 1 are storing up massive amounts of accurate, useful information that your conscious System 2 isn’t really able to access. There are things that you’re aware of “on some level,” data that you perceived but didn’t consciously process (see blindsight as both concrete example and metaphor), competing goalsets that you’ve never explicitly articulated, and so on and so forth.
Focusing is a technique for bringing some of that data up into conscious awareness, where you can roll it around and evaluate it and do something about it. Half of the value comes from just discovering that the information exists at all (e.g. noticing feelings that were always there and strong enough to Imperius you but which were somewhat “under the radar” and subtle enough that they’d never actually caught your attention), and the other half comes from having new models to work with and new theories to test. If I manage to recognize that e.g. a significant chunk of my romantic problems stem from self-censorship pressure because of a strong aversion to seeming needy, I sud |
088db4be-1b98-4d84-b233-f88703b8dd7d | trentmkelly/LessWrong-43k | LessWrong | What’s this probability you’re reporting?
It’s unclear what people mean when saying they’re reporting a probability according to their inside view model(s). We’ll look through what this could mean and why most interpretations are problematic. Note that we’re not making claims about which communication norms are socially conducive to nice dialogue. We’re hoping to clarify some object-level claims about what kinds of probability assignments make sense, conceptually. These things might overlap.
Consider the following hypothetical exchange:
> Person 1: “I assign 90% probability to X”
>
> Person 2: “That’s such a confident view considering you might be wrong”
>
> Person 1: “I’m reporting my inside view credence according to my model(s)”
This response looks coherent at first glance. But it’s unclear what Person 1 is actually saying. Here are three kinds of model(s) they could be referring to:
1. Deterministic: There is a model that describes the relevant parts of the world, some deterministic laws of motion, and therefore a description of how it will evolve through time. There are no probabilities involved.
2. Stochastic: There is a model that describes the relevant parts of the world, and the evolution of the model is stochastic. Model-based probabilities here correspond to precise statements about random variables within the model.
3. Ensemble: There is a set of models, deterministic or stochastic, that describe the evolution of the world. You have some way of aggregating over these. E.g., if nine models say “X happens” and one says “X doesn’t happen”, you might assign P(X)=90% if you have a uniform prior over the ten models.
There are troubles with all of these.
Deterministic
Models are often deterministic. When an engineer says a bridge is “unlikely” to collapse, it’s not necessarily because their model outputs probabilities; it could simply be because they aren’t confident that the model fully captures everything relevant. A deterministic model will not have any probabilities associated with it. |
517bbae7-170f-4941-85cb-b8003860f721 | StampyAI/alignment-research-dataset/youtube | Youtube Transcripts | Alan Fern – Toward Recognizing and Explaining Uncertainty – CSRBAI 2016
so welcome back so I'm now please send
introduce Professor Alan fern who's
going to be presenting his joint work
with Professor Tom do direct Alan ferns
the professor and the associate head of
research at the School of Engineering
computer science at Oregon State
University
his research is very much on automated
planning and feedback in reinforcement
learning systems Tom Dirac is the is the
distinguished professor and the director
of intelligent systems at the School of
Engineering computer science at Oregon
State University and just completed his
term as the president of the Association
for the Advancement of artificial
intelligence and his research is at the
intersection of machine learning data
science and big data so I'm very pleased
to like introduce their their joint work
anomaly detection and competence in
modeling the environment for our
intelligence so please join me in
welcoming professor Oliver so uh yeah so
originally we had thought of us talking
about our FLI project that's actually a
little more aligned with robustness than
transparency and but we figured out that
we can try to address the this uh topic
of transparency here and there's gonna
be two parts to this basically we're
interested in recognizing uncertainty
getting systems that can recognize their
uncertainty and then explain the
uncertainty so it's a goal one we'd like
uncertainty aware ml systems now this is
it was a nice paper in ICML 2008 they
introduced a framework of knows what it
knows and they wanted a sort of a PAC
learning framework where the system had
to make confident predictions it was
allowed to abstain and they were trying
to minimize the the amount of
staining while guaranteeing that the
predictions were confident so I thought
that was a neat framework and that's
along the lines that we're thinking here
as well so we're gonna allow ambiguous
responses from the system as long as
they are correct in some some way that
will specify and we're particularly
interested in studying this both in
closed worlds so this is sometimes
called the known unknowns situation
where you sort of quantify the
possibilities and there's uncertainty
over those possibilities and then we're
also we're really interested in the open
worlds case the unknown unknowns case so
we we don't even know all of the
possibilities so we're we want to
explore uncertainty aware systems in
both of these cases and why I think
there's obvious reasons
we'd like safe and Trust for the AI I
don't have to really talk about that
much I think end-user acceptability is
another major reason if the system is
making lots of crazy predictions the end
user just going to get very unclear
about whether they should trust the
system in any particular situation and
and my own research this might not be an
obvious reason but computational
efficiency if you think about trying to
get more and more accurate systems that
have confidence you might have to
develop very complicated models but if
you can get smaller models that actually
understand their uncertainty then when
they are certain you can just go with
those and otherwise you back off to more
complicated models I study learning and
planning so I think of this in the
context of what we'd really like to a
lot of obviously there's a lot of
obvious bad things to do and we can be
confident that they're bad and we'd like
the planning systems and sort of search
planning or reasoning systems to search
the rest of this
so that's another reason that
uncertainty of where AI is is
interesting so goal two is transparent
uncertainty in ML systems so we'd like
the systems to explain their uncertainty
not just abstain for making a prediction
but give us some insight into why
they're uncertain and this this really
hasn't been studied a whole lot again
we'd like to do this in closed worlds
and open worlds and and why well a lot
of times I think it could be a basis for
feedback to a machine learning system
you know I can't tell whether this is a
cat or a dog because why and maybe you
could give a piece of advice
or give a new feature or say well this
is actually a really ambiguous situation
you're right to be uncertain here so
that might be one reason you would like
to explain uncertainty at the the second
part of this talk I'll say I'll talk
about anomaly detection and how how
often explaining uncertainty can be
important to the end user to to give
insight into the system itself you know
this is I think it even outside of
anomaly detection getting some insight
into why a system is uncertain might
help you debug it or improve it in some
way and then I think it's also a
mechanism for building trust if you get
explanations for when a system is
uncertain and is generally uncertain in
reasonable ways the reasons are
reasonable I think think you would tend
to trust a system more that has that
property as opposed to explanations are
kind of crazy and you can clearly see it
doesn't understand what's going on by
its explanations of uncertainty so those
are the two goals that I want to address
and the talk today I'm gonna have two
parts part one is going to address the
uncertainty awareness aspect and we're
going to talk about it's part tutorial
and a little bit of work we're just
getting started on this but so we're
going to talk about conformal prediction
as a way of getting uncertainty
awareness
at least in the classification setting
and we'll talk about open worlds and
closed worlds some experiments there and
at the end of the day what we're gonna
see is that when it comes to open worlds
empirically and the theory says nothing
conformal prediction theory says nothing
about open worlds but empirically we see
that it's not conformal prediction is
not adequate and it suggests integrating
with anomaly detection so the second
part is going to be some work we've done
more of a case study in explanations for
anomaly detection this is in a security
setting and that case study forced us to
try to give some initial answers in that
context to these questions what is an
explanation for an anomaly how do you
compute an explanation and then how do
you evaluate explanations we had to try
to think about those questions in that
context so that would be a little case
study focused on those questions so
first you know you're often though so I
these are examples or a little
cartoonish my student picked these out I
guess it's lilo and stitch I'm just
gonna go with them but the
classification we've got our known
classes you know this is if we're in a
closed world there are humans animals
and hybrids and a standard predictor
will output one of those choices this is
a human and this is actually a hybrid
but the system says human we don't get
much information from a pure predictor
like this that would let us gauge the
the certainty some systems do give you
numbers that you know a larger number
means maybe that it's more certain
smaller number is less certain I find a
lot of times those numbers are not very
reliable but the uncertainty has not
made explicit and there there's really
no guarantee from a standard predictor
now what conformal predictors are going
to do and this is a Boff
actually it was an ICML paper in 1999
that kind of set the stage for this work
he has a nice book on this type of
material came out in 2005 but what what
conformal prediction is going to allow
is to output sets of classes so I'm
going to focus on just the
classification problem you can also do
this for regression and then you would
have confidence intervals so we could
output animal or hybrid and we're gonna
say that this is a correct response if
the true label is in the set okay so
it's fairly straightforward here the
system was very confident and human and
it output this set as sort of the ideal
thing we'd like from a predictor there's
no ambiguity in its output here
apparently the system was very uncertain
and really couldn't tell us anything
about this object that it's looking at
so basically conformal prediction lets
us output sets as opposed to point
predictions and the correctness is
judged by whether the true label is in
the set now you could say this is
already a type of explanation of
uncertainty in the sense that if you go
back to the knows what it knows
framework that I that I pointed out
earlier by Lee Hong Lee Michel Whitman's
group they they were allowed to abstain
or give a prediction and abstaining
doesn't tell you much other than the
system doesn't really have confidence
right now this at least gives you some
indication of the level of confidence in
the system and you know here here it's
kind of a reasonable you can see these
are reasonable sets in terms of
understanding the the uncertainty now so
so how do we evaluate a conformal
predictor well one way is accuracy right
and so so conformal predictors will take
as input an accuracy constraint where
95% would mean that 95% of these sets
that you output
have to be have to have the correct
label in them okay so that's all we mean
by accuracy and the the obvious thing
here though is that well you can always
get a hundred percent accuracy by just
returning all of the labels right so so
just maximizing the the set size is a
perfect solution if we only care about
accuracy now obviously we we care about
more than just accuracy in this setting
what we'd like to do is a remove as much
ambiguity in the predictions as possible
yeah and and there wasn't really a clear
measure of ambiguity in the conformal
prediction literature they would sort of
talk about the number of labels that's a
hard thing to think about when you have
data sets with different numbers of
labels to just look at graphs so uh so
we've introduced you know for our own
purposes a notion of ambiguity that this
is the formula that's 0 when it outputs
one label so it's 0 and big.you a-- t
and if it outputs all the labels it's 1
okay and so we see this prediction has
an ambiguity of 0 this prediction has an
ambiguity of 1 this is in the middle all
right so uh so we're gonna be interested
in minimizing the expected ambiguity
given a constraint on the accuracy okay
so you'll specify a 95% accuracy
constraint and you'd like the
predictions to be as unambiguous as
possible right and that's really what
conformal prediction is is trying to get
at so so let's go inside
I don't know how many people have come
across conformal prediction it's not
sort of a widely widely known framework
so I'll give you a little insight into
what what these systems do so a
conformal prediction system is really a
wrapper around a standard learner you
know you can pick your favorite learner
random forest neural network nearest
neighbor
you know your favorite predictor here
and you've got some training data and
what we're going to expect from the
system is we have to be able to compute
nonconformity scores from its output and
I'll show you some examples of what
nonconformity scores will look like but
this is just showing you you know a six
means that it does not think this is a
human because we're calling it a
nonconformity scores larger values mean
that it's less consistent with this
human label in this case so we're going
to assume that we can get these
nonconformity scores and and actually
these can be arbitrary scores or the
conformal predictor will still achieve
the accuracy constraint we're gonna feed
those into the conformal prediction
framework with an accuracy constraint
and it will output some labels okay now
the the main thing that's going to
control efficiency so the conformal
prediction framework under some
assumptions the data has to be
exchangeable and there has to be some
assumptions about the nonconformity
score some pretty weak ones it will
always maintain this accuracy constraint
no matter what you could just have a
pretty dumb that you could have a
constant predictor here and I will
always get a it will always maintain the
accuracy constraint I'll show you you
know how how it does that in a moment
what really influences the ambiguity is
going to be the quality of this
predictor and the type of nonconformity
score that you define from it okay so so
the stronger the predictor is you're
really going to influence the ambiguity
the accuracy is just taken care of by
the framework itself okay you could have
a horrible predictor and it will still
be accurate it might output highly
ambiguous labels so let's look at some
examples before I show you how it uses
these nonconformity scores let's look at
some examples of nonconformity scores so
say you're using a nearest neighbor
classifier here's your data
a typical nonconformity score we want so
if you
have an input X and you have to consider
each of the possible labels the
nonconformity score for for human would
be what's the distance to the closest
human so if you're very close to a human
you're gonna be close to zero so you're
gonna sort of have a low nonconformity
score divided by the distance to the
closest non-human yeah so that's a
fairly intuitive nonconformity score if
this is not a human and the distance to
the closest team in might be rather
large and you would have a larger
nonconformity score so that's nearest
neighbor another example would be random
for us so this is a random forest it's a
bunch of trees and here are the
predictions that these trees give in the
random forest so a natural pretty basic
nonconformity score would be the number
of trees not predicting the label that
we're considering right so how many
trees did not predict human divided by
the number of trees and that would be
larger when when a label doesn't conform
as well to to the true label all right
and finally I'm showing you the the
random forest and neural networks as
we'll use those in our experiments for a
neural network it could be a big
convolutional network the outputs are
usually scores often between 0 and 1 and
a 1 nonconformity measure that's used is
the max output for the node that's not
human in this case if you're considering
the human label divided by the output
for human yeah so these are just fairly
natural things and and you could play
around with different nonconformity
measures and it would not influence the
correctness or accuracy of the conformal
predictor but it could influence the
efficiency all right but these are the
ones that we'll be using
so so given a nonconformity score and
your favorite predictor how do we
actually do the conformal prediction
we're
we're gonna use a framework called
inductive conformal prediction the the
the original work was in a transductive
setting but it's just computationally
implausible because you have to retrain
your model for every test example so
thus obviously not possible if you're
using a convolutional neural network on
image images so so what we do is we have
some calibration data that we're going
to hold out and from that you can
basically get a histogram of
nonconformity scores okay and you know
and basically out here these are the the
this is sort of the tail of a
nonconformity score part of the curve
and if we get a particular instance we
can ask for what's the nonconformity
score for animal hybrid here that's one
of the classes and human right they land
at different points on this histogram
and we can just compute a essentially a
p-value right so so you're basically
asking the question for a 95% guarantee
or at least 5% of the calibration scores
weirder than the example that then this
example with this label right so so for
animal there's a large percentage of
things that are weirder than then this
particular nonconformity score and so we
would we would consider keeping animal
in the set right and you know we would
rule out human because it's sort of at
the tail here right there's there's less
than 5% we'll say so so essentially
we're just doing p-value is just a
p-value and you're you're ruling out
labels that basically fail a hypothesis
test all right so that's conformal
prediction fairly fairly basic idea but
but you can actually get some guarantees
under the assumptions of exchange
ability and some basic assumptions about
the the nonconformity score but so so so
when we
it was kind of compelling framework for
for quantifying uncertainty but there
were really very few experimental
evaluations and especially they rarely
looked at ambiguity other than did the
system output a single label or more
than one label not many data sets were
used most of the experiments were with
nearest neighbor which we found doesn't
work very well we have much better
classifiers than the nearest neighbor
and so so we we just started doing a
little empirical evaluation I'll show
you I'll give you a glimpse of that
today with with better classifiers and
nearest neighbor random forests for non
image data and then convolutional
networks for four images or a lot more
perceptual data all right and we're also
interested in what happens when you take
a conformal predictor to an open world
so we'll be looking at that well so what
we'll be looking at both closed world
and open world performance and now I'm
going to go through these relatively
quickly there's nothing terribly
surprising but if we look at random
forest results we picked out some UCI
data sets and these vary in different
ways we won't get into details here but
uh for this is what a learning curve
looks like so so the number of training
examples and over here we have accuracy
so this is this is a number of training
examples versus accuracy we're using a
95% guarantee and you see early on it's
not actually hitting the guarantee and
my hypothesis here is that the theory is
based on some basic assumptions and
those assumptions are more clearly
violated you know there's a higher
chance that they're sort of not in the
data set for small sample sizes as you
get larger sample sizes though that sort
of washes out but generally we find that
with larger with enough examples you
will hit the guarantee and that's sort
of these are p-values so so it's not too
surprising
also we generally find that for these
data sets at least you get zero
ambiguity with enough training examples
early on you have quite a bit of
ambiguity but remember zero ambiguity
basically means that is predicting one
label confidently and I've got this
green curve here and a blue curve the
blue curve is just showing remember the
predictor can be wrong 5% of the time so
the green curve is the examples where
it's wrong what's the ambiguity when
it's wrong and here you see that it's a
little more ambiguous when it's wrong
but but really it gets pretty confident
when it's wrong as well so so here you
couldn't tell the difference between
when it's wrong or right and you know
just showing you one more this is more
extreme it just goes to zero ambiguity
very quickly and the other data sets are
qualitatively similar so basically these
learning curves we've never seen
learning curve analysis or empirical
evaluations for this I think it
motivates trying to study a little of
the finite sample complexity of
conformal prediction to understand why
these converters so quickly we also
looked at well varying the accuracy
constraint and how does that influence
the the ambiguity right you'd think that
if you're going to have a more stringent
accuracy constraint I want 99% accuracy
the ambiguity would probably have to
increase and we see that for one data
set here that's actually the case so the
ambiguity goes up a lot when you
increase accuracy for this one you know
the predictor just sort of nails it I
guess and we see that you have zero
ambiguity even for high accuracies yeah
similar you know you see a little
increase in ambiguity for these these
data sets so we were also interested in
what would a convolutional neural
network do these are these are very
popular now
state-of-the-art in computer vision and
we're looking at one of the smaller data
sets CFR 10 there's a you know big
network that people have trained for
this that were we're using as the
predictor so so we we didn't have the
resources to retrain this network to
produce learning curves so they're
they're only they're only ten classes in
this data set but we thought it was a
good starting point so here we're just
curious well what what would a you know
what would a conformal predictor do with
the network is it going to have some
ambiguity or not and we see that
basically we're varying the number of
calibration examples right this is what
the conformal predictor is p-value is
based on we're not retraining the
network for these learning curves the
results would look different if you were
but we want to been able to produce
those in time but basically we see that
the network is very very confident the
ambiguities low for all these cases when
you increase the accuracy constraint you
see that the network does become a
little more ambiguous so so so there is
ambiguity once you really ratchet up the
accuracy constraint so there's room
potentially to improve it's interesting
to consider how would you train a neural
network if you are training it to be a
good conformant to work well with a
conformal predictor right to minimize
ambiguity would you be able to do better
with a slightly different loss function
for example so basically there's nothing
surprising in the closed world results
we see that they the the conformal
predictor basically behaves ideally and
you generally producing zero ambiguity
so so when we open the world up to new
object so a monster might come in we
don't we don't have a current label for
monster that's that's outside of our
closed world and so you know what what
response would we expect to conformal
predictor to
for an object that's not in our current
model I mean what would you want to
conform a predictor to give any ideas
the empty set exactly yeah so smart
audience here so the only really
reasonable thing for a conformal
predictor to do is to return the empty
set now the theory of conformal
prediction does not apply at all the to
the open world setting but we were still
curious about what conformal predictors
would do because it actually is
reasonable to expect that maybe it will
output maybe these systems will output
the empty set in a lot of these cases so
what we did is we set up some open world
experiments and so for the random forest
experiments with the UCI data sets we
basically held out a label or two and
and and train the system on the
remaining labels right so so that the
held-out label is effectively a new a
new type of entity that we can then give
to the system and see how it behaves all
right and for the convolutional net work
we I'll show you the the images that we
fed it we just edit images that had
nothing to do with a C far of 10 dataset
and to see what it would do so for the
random forest what I'm showing you here
is ambiguity but I'm only showing you
the ambiguity averaged over the novel
class okay so so this is basically how
confident how confident the predictor is
and outputting a label for the novel
class that that it should not be
confident about right is predicting one
of the labels that knows about but it
should not it should be outputting the
empty set we see that basically it's
very very confident about a single label
so most of the time it's outputting a
single label from its set and it's a not
outputting the empty set it's not
seeming to be confused by outputting by
having more ambiguity
so that's what we see there this data
set was now that one thing I did point
out negative ambiguity basically means
it's outputting in the empty set if you
go back and look at the equation and
here this one data set at least random
forest knows what it knows and so for
these novel classes it's outputting the
empty set most of the time right and but
but for the other data sets basically
it's usually outputting a single label
and so so it does not know what it knows
it's confident in that single label we
would have no idea that this new object
was sort of in town alright so that was
random for us yes well so you're
building this set up and you're gonna
put something in it if you can't sort of
confidently rule out that it should be
out of the set you could think of it
like that the different labels and so
you would like it to confidently rule
out that all the labels I know about
should not be in the set but the theory
says nothing about this this is just our
investigation into what happens when you
use sort of state-of-the-art predictors
and conformal prediction yeah so is that
clear yeah all right this one here these
are just different data sets but
qualitatively know I probably just
mislabeled it or something yeah so yeah
that must have been a that was it that
was iris yeah that was the iris data set
here no so for the convolutional network
remember this was trained on I'll show
you the types of images this was trained
on these images here right
sort of natural images of these ten
classes what we did is we we found a
bunch of sprites and we some game
nethack and it was just convenient that
they happen to have the same image
dimensions and everything so we said
well let's go with those so those
clearly have little to do in most cases
with the the data set that it was
trained on this one we find here is that
the the convolutional neural network is
again usually confident about a single
label that it knows about so it's seeing
a sprite in saying it's a horse or
something and it's not giving us the
empty set it's not reflecting that it's
uncertain in any way we'd have no way of
knowing that we're seeing a sprite as
opposed to a horse so so that that
eleazar experiment with a convolutional
neural network which which we found
interesting I think this has a lot to do
you know there's probably ways to to
improve this to some degree but these
networks have a softmax function at the
final layer and they're really trying to
amplify something that a label that they
know about that's what the softmax is
roughly doing so you know in all but one
case there was practically no abstention
right though it was never outputting the
empty set except in one data set where
it was able to do that it's usually
confident about a single label but you
know the theory doesn't apply here so
you know we can't really complain the
conformal prediction is not behaving as
we would like it to but there there was
sort of reason to believe that maybe it
would have and it appears that standard
conformal prediction on its own isn't
going to be sufficient for open worlds
right and so one thing that you might do
is you could say well let maybe we can
have new ways of training predictors
that will work better we can formal
prediction in open worlds so you can
imagine getting predictors that tend to
appear more confused when a new object
comes in right that's what you would
like you know the neural network output
layer would sort of be flat when a new
object comes in and effectively what
you'd be doing is you'd be trying to
Train it to be an anomaly detector of
some sort so that's one way to go at it
and we've got some ideas of how you
might try to do that another way and
this is what this is another thing that
we're going to be exploring using
anomaly detection as sort of a first
stage of this whole process so when
something is detected to be an anomaly
or you've got an anomaly handler which
you know that's very problem dependent
otherwise you're gonna go through your
normal process and so the anomaly
detector is really going to look at sort
of the distribution of the data or some
a lot of times these predictors are just
trying to discriminate and they sort of
ignore the fact that there's a really
weird aspect to this input because it
didn't need it for discrimination so uh
yeah there there are some issues here
this is what we'll be working on how do
you select the threshold in a rational
way and can you provide any sort of
guarantees in an open world right so so
that's where we're going with this and
when we think about explain ability
basically you know there's a lot of
things to explain in this system right
we haven't talked about doing any more
sophisticated explanations of the output
right if the predictor outputs a set as
opposed to a singleton it's interesting
to consider well what would an
explanation for why it's outputting more
than one thing be what would an
explanation be in that context we
haven't studied that yet but that's
something where we're interested in but
the other component of this system is
the anomaly detector
we'd like to explain well if an anomaly
detector is saying this thing's
anomalous why is it anomalous that's
that's a question that we're going to
about now so I'm gonna go through right
so how much more time do we have I know
people are hungry yes I don't want to
keep people too long 12 tens No
okay so I'll try to go for 10 minutes
and we'll be able to you've lunch so so
anomaly detection hopefully a lot of you
have are familiar with the concept but
these red dots are anomalies and there's
lots of ways to try to define anomalies
we generally think of anomalies as
they're generated by a different process
from the normal points and that you know
that's not a not a very concrete
definition but this case study that
we're looking at an anomaly is basically
a threat this is a security application
so a true anomaly is something that a
human would look at and say this is
actually a threat so you've got behave
behavioral data of people in a system
for example then you want to know is
this person a threat or not and most
anomaly detectors instead of actually
you know working with a semantic notion
of anomaly they're looking at
statistical outliers so they're trying
to compute statistical outliers and and
we're going to consider in this talk
density based anomaly detectors which
this is your data it will fit a density
model to it we use you know ensembles of
mixture models Gaussian mixture models
and then you can look at the points we
have low density values right and you
would call those anomalies now and if
you had an analyst that's going to look
at these anomalies you would just order
them by the density values but of course
not all statistical outliers are true
anomalies and this is just the
difference between statistics and
semantics right so some of these are
things that analysts might care about
anomaly and some are just just rare rare
normal things right so if we I think to
try to understand why we might want
explanations it's worth thinking about
this particular case study where our
data corresponds to sort of sort of
security application and some data
points or threats and some are not
threats and we want to find the threats
that's the whole point of this this case
study and so uh you can apply an anomaly
detector you're hoping that it will
return all the threats but it's not it's
gonna you know here's some missed
threats when it outputs the what what it
thinks are non outliers but you have
outliers here and it contains threats
and false positives
these are we're gonna call type oneness
threats and the only way to really
improve these this this miss rate is uh
by improving the anomaly detector so
after the anomaly detector you give
whatever the anomaly detector says is
suspicious to a human analyst and the
human analysts will will mark some of
them as alarms and you know feed them to
whoever they feed alarms to so they're
gonna look at the data and some of those
threats they're gonna get and though
they might have some false positives as
well but the human analysts might also
miss threats - for example we say this
person looks suspicious there's a huge
database of information about this
person they may not have come across a
suspicious aspect and so we're
interested in reducing the missed
threats of type 2 and the way we want to
do this is by giving explanations to the
analyst of why why the system thought a
particular point was anomalous to kind
of narrow down the data that the analyst
has to look at and we we you know this
brings up the question of what is an
explanation you know so so what is going
to be an explanation and we'd like that
to be tied to
sort of thinking about the application
you know an analyst is going to use this
explanation to try to quickly identify
whether something's a threat or an odd
threat right and so the type of
explanation we're going to give is
something called a sequential feature
explanation the explanation is basically
going to be an ordering of the features
you could say it's also an ordering of
views of the database perhaps where
what's going to happen is the analyst is
going to first start with the first
feature in the ordering and look at it
and say well do I think this person is
suspicious or a threat based on that
feature and if they can't make a
determination will show them the next
feature so they'll be able to look at
the two bits of information and so we're
going to grow this information over time
until the analyst can determine that
this individual is a threat or they just
run out of time basically and then the
individual would be termed not a threat
all right so that's the protocol and
it's very you know that the type of
explanation was motivated by our
application goal and I think that's a
general theme when it comes to
transparency because there's many ways
you could make a system transparent but
why are you doing it you have to tie tie
those things together this is just
showing you the the analyst belief in so
we show it the first feature the analyst
is still pretty sure it's a normal data
point we show it the second feature you
know it's looking at two features and
then you show more and more information
and eventually the analyst the
probability that they think it's normal
goop drops low enough and then this is
where you'd have the alarm right so if
you think of the the work of the analyst
you know how do we evaluate whether an
explanation is good or bad
we'd like explanations to reduce the
workload of the analysts which means
that the analyst should only have to
look at the small smallest number of
features possible right so we'd like to
minimize the number of features the
analyst has to look at in order to make
a determination that a threat is a
threat in this case and we call this the
minimum feature pre
fix and that's what we want to minimize
so there's there's a lot to this I'm not
going to I'm not going to go into detail
but basically we have some methods that
compute sequential feature explanations
so I'm going to go to a part of the talk
that I think might be more relevant so
these sequential feature explanations
I'll just show you one of the methods so
in general this is a hard problem we
have a branch and bound algorithm with
nice pruning rules but but generally we
find that that this method works as well
as as the optimal method as well and
it's a lot faster branching down can
take a long time so so just to give you
some insight we're basically going to
pretend that the analysts is is thinking
according to our our density function
that we've learned over the data so
there notion of normality is like ours
and so what we're gonna do is we're
first going to choose the feature that
minimizes this is the density that we've
learned over the data that minimizes the
marginal density just looking at one
feature so which feature looks strangest
for this example if you just consider
that one feature and then the second
feature will be the one that minimizes
this joint marginal density all right so
if I look at two features together which
one if I look if I look at all the
features that I could combine with the
first one which one will look most
suspicious so you can keep on doing that
and you know the it's a fairly intuitive
greedy algorithm we have some other
things that were motivated by previous
work but the one I just showed you was
the best so so the question now is how
do you evaluate an explanation and I
think this is a question that we
struggled with you can look at it and
say hey that looks good so so anytime
you're going to be doing work with
explanations you have to figure out how
am I going to evaluate it does it feel
good you'd like a quantitative way when
you can get get get one
so what we're going to do is basically
the problem is we don't have a real
analyst right so we can't measure how
much work they do and so you have to
have some way of doing quantitative
evaluations and so we created a
synthetic analysts and this is based on
some work that we've done in the past
and there's actually a longer journal
version of this on creating anomaly
detection benchmarks from standard
machine learning data sets but you can
imagine you have a UCI data set you pick
out some classes that corresponding to
call normal some classes that you core
that you call anomaly classes and that
gives you an anomaly detection benchmark
and you can vary certain properties of
it now we could learn an analyst using
supervised learning that that the
analyst will learn the properties that
distinguish these anomaly points from
normal points and we can use this as a
simulated analyst so so we can sort of
simulate the probability that the
analysts think something is normal given
an example all right so now we've got
our simulated analyst we can do
experiments we can measure the the
amount of work the analyst would have to
do to identify threats so that's uh
that's all I'll say about that
and we have some if you look at
producing random explanations this is
what you get this is average this is a
bunch of data sets but what this means
here is that if you give random
explanations the simulated analysts
would need to look at roughly 3.5
features to determine that a threat is a
threat okay and this is more we produce
optimal explanations and you see there's
a gap between random and optimal and I'm
gonna jump to the last bar here this is
what I showed you this is our best
method generally and then we were
generally closer to optimal than than
random there so we won't go into the
distinctions between the
methods now but there is there is quite
a gap between the optimal and our best
method in some cases yeah one thing that
you know I'll skip through this but one
thing that was interesting a standard
data set in anomaly detection this is a
KD D cup it's a security data set we can
see that the explanations we produce are
this kind of shows you the the
simplicity of that data set and why
maybe you shouldn't use it for anomaly
detection work but our our best method
is able to produce basically
explanations that are generally one
feature long and can be confidently
classified as a threat so there's
usually one feature in any of these
examples that sort of indicates there
piece of threats so those kind of a kind
of a comment on the data set as well so
so we'll just say that you know all the
methods basically beat random and you
know we have one method that we like the
best you can look at the paper if you're
if you're interested in that and and
basically you know I think we're gonna
continue to do this do some work as we
go go ahead but I think the main points
are we had to think hard about what is
an explanation
there's many choices that we could have
made and that required us to think about
the application that you are using the
explanations for right so that was the
first point whenever you're dealing with
transparency or explanations and then we
had to deal with the issue of how do you
evaluate your explanation algorithm and
that's very tricky
you'll get papers rejected because
people want you to use domain experts to
to run your algorithms but you can't get
any sort of quantitative results doing
that so so the approach that we took was
well figure out how to get some sort of
plausible simulated analyst in this case
to sort of feed our explanations to but
you need to figure that out for other
applications as well and I think that's
that's one of the major challenges in
doing work on transparency and
explanations I think evaluation so I
will stop and it's almost exactly yes so
so this is sort of one of the directions
that you could explore how do you train
neural network or any other model to
have better properties in open worlds
and effectively you're trying to train
it to be confused when it should be
confused
and that requires there's a lot of
things that go into that right so you
have to give it some data maybe that you
want to train it on where it should be
confused that may bias it you know it
you know so so is that actually going to
be reflective of the real open world or
or not but but doing doing things like
that could definitely definitely one
direction that we're thinking about it
also seems that ensembles or one way to
to go about this as well if you have
many models you would hope that they
would be you know that they wouldn't be
consistent on things from the open world
with random forests we you know that
happened in one data set but the other
datasets that wasn't the case
but there might be ways of training
these ensembles and they have that
property so that's a very good point and
there's been some work also in so in
chibok jose laughing and india paper
i've seen of us year maybe two years ago
sitting i suppose i have access to a
bunch of unsupervised unless the data I
can figure out you know between my
classes that I apply today for what
parts of the space are populated by
other stuff and I should maybe you know
because the classic discriminative
classifiers is a partition of the world
into K that's right and so he doesn't
say is discover oh there's empty space
out here that is not empty community my
classifier thinks it's empty when it's
not ok so let me pull back my scissor
boundaries and create a new space so
that I know there's stuff out there that
I haven't seen but that one question is
what statistical assumptions are you
entitled to make about stuff you haven't
seen yet like these are all of those
things also iid samples from some
distribution which is by the way so
means making or is it maybe there's an
adversary so any other questions yes you
know that what you want is only if it
does have this constraint of that's got
to be right 95% of the time and right
just means you know containing the
yeah Petula answer it could just be the
light it's constrained it's what is once
walking into throwing things and
actually like yeah so so the conformal
predictor framework does assume a closed
world right so there's nothing about the
theory that suggests it should meet any
of the accuracy guarantees in an open
world some intuition that maybe it would
just because of the way it works it's
doing hypotheses tests is this a label
for is this a feasible label for this
example and you would hope it could
reject those but but it doesn't and I
think largely it's because these are
discriminative systems they sort of hone
in on just the things that you need to
look at to discriminate and they ignore
the fact that well maybe this object is
purple and you know that wasn't useful
for discrimination but you know that the
color would be useful for recognizing
something that's completely different
and that's that's why we feel it anomaly
detection as a preprocessor is
potentially the right way to go his
anomaly detection that it will look at
sort of all of the properties less
certainly you know there's certainly a
lot of things to think about there to
get that to work |
a48d7057-a712-4fba-b3f3-fc7f586ddf1d | trentmkelly/LessWrong-43k | LessWrong | Epistemic Spot Check: The Role of Deliberate Practice in the Acquisition of Expert Performance
Epistemic spot checks typically consist of references from a book, selected by my interest level, checked against either the book’s source or my own research. This one is a little different that I’m focusing on a single paragraph in a single paper. Specifically as part of a larger review I read Ericsson, Krampe, and Tesch-Römer’s 1993 paper, The Role of Deliberate Practice in the Acquisition of Expert Performance (PDF), in an attempt to gain information about how long human beings can productivity do thought work over a time period.
This paper is important because if you ask people how much thought work can be done in a day, if they have an answer and a citation at all, it will be “4 hours a day” and “Cal Newport’s Deep Work“. The Ericsson paper is in turn Newport’s source. So to the extent people’s beliefs are based on anything, they’re based on this paper.
In fact I’m not even reviewing the whole paper, just this one relevant paragraph:
> When individuals, especially children, start practicing in a given domain, the amount of practice is an hour or less per day (Bloom, 1985b). Similarly, laboratory studies of extended practice limit practice to about 1 hr for 3-5 days a week (e.g., Chase & Ericsson, 1982; Schneider & Shiffrin, 1977; Seibel, 1963). A number of training studies in real life have compared the efficiency of practice durations ranging from 1 -8 hr per day. These studies show essentially no benefit from durations exceeding 4 hr per day and reduced benefits from practice exceeding 2 hr (Welford, 1968; Woodworth & Schlosberg, 1954). Many studies of the acquisition of typing skill (Baddeley & Longman, 1978; Dvorak et al.. 1936) and other perceptual motor skills (Henshaw & Holman, 1930) indicate that the effective duration of deliberate practice may be closer to 1 hr per day. Pirolli and J. R. Anderson (1985) found no increased learning from doubling the number of training trials per session in their extended training study. The findings of these studie |
52b1be4e-dc3e-4ffd-9855-3357293684f2 | trentmkelly/LessWrong-43k | LessWrong | General purpose intelligence: arguing the Orthogonality thesis
Note: informally, the point of this paper is to argue against the instinctive "if the AI were so smart, it would figure out the right morality and everything will be fine." It is targeted mainly at philosophers, not at AI programmers. The paper succeeds if it forces proponents of that position to put forwards positive arguments, rather than just assuming it as the default position. This post is presented as an academic paper, and will hopefully be published, so any comments and advice are welcome, including stylistic ones! Also let me know if I've forgotten you in the acknowledgements.
Abstract: In his paper “The Superintelligent Will”, Nick Bostrom formalised the Orthogonality thesis: the idea that the final goals and intelligence levels of agents are independent of each other. This paper presents arguments for a (slightly narrower) version of the thesis, proceeding through three steps. First it shows that superintelligent agents with essentially arbitrary goals can exist. Then it argues that if humans are capable of building human-level artificial intelligences, we can build them with any goal. Finally it shows that the same result holds for any superintelligent agent we could directly or indirectly build. This result is relevant for arguments about the potential motivations of future agents.
1 The Orthogonality thesis
The Orthogonality thesis, due to Nick Bostrom (Bostrom, 2011), states that:
* Intelligence and final goals are orthogonal axes along which possible agents can freely vary: more or less any level of intelligence could in principle be combined with more or less any final goal.
It is analogous to Hume’s thesis about the independence of reason and morality (Hume, 1739), but applied more narrowly, using the normatively thinner concepts ‘intelligence’ and ‘final goals’ rather than ‘reason’ and ‘morality’.
But even ‘intelligence’, as generally used, has too many connotations. A better term would be efficiency, or instrumental rationality, or t |
6d60de9c-0afc-493e-828a-70ec48a4e4c5 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | GovAI Webinars on the Governance and Economics of AI
The Centre for the Governance of AI at the Future of Humanity Institute will start hosting webinars, on the Governance and Economics of AI. Our first session on Wednesday, May 20th, 1700-1815 BST (0900-1015 PT, 1200-1315 ET), featuring Daron Acemoğlu, Diane Coyle, and Joseph Stiglitz in a discussion about COVID-19 and the economics of AI. Our second seminar features Carles Boix on his book [Democratic Capitalism at the Crossroads](https://www.amazon.com/Democratic-Capitalism-Crossroads-Technological-Politics/dp/0691190984), exploring tensions between capitalism and democracy caused by e.g. future developments in AI[.](https://www.amazon.com/Democratic-Capitalism-Crossroads-Technological-Politics/dp/0691190984.) Register and learn more about future events [here](https://www.fhi.ox.ac.uk/GovAI/#Events).
The webinars are likely of interest to effective altruists considering in careers related to the governance and/or economics of AI – be it in research, policy, operations. We're hoping to feature the best work on AI governance and economics we can find, often drawing on researchers from outside the EA community.
Future sessions may come to feature topics such as:
* Export controls on chip manufacturing equipment
* China's AI policy
* Forecasts on AI timelines
* Ongoing AI industry self-governance efforts
* AI's potential effects on democracy
* AI & surveillance
If you have recommendations for who we should invite to speak, feel free to post it in the comments. |
a03d6e4c-717d-4caf-ab4f-edc697c66fe4 | trentmkelly/LessWrong-43k | LessWrong | Why must plausibilities be represented using real numbers?
Professor Jaynes writes:
Page 17 of his bookPage 656 of his book
Why must degrees of plausibility be represented using real numbers when there are alternative number systems, which don’t have 1-to-1 correspondence to the reals, that satisfy transitivity and universal comparability?
Kevin S. Van Horn writes in his paper, “Constructing a Logic of Plausible Inference: A Guide to Cox’s Theorem”:
Page 10 of the paper
What’s so special about R? If we want our theory to have no holes then why don’t we use hyperreals or complex numbers? They also satisfy transitivity and universal comparability and I would argue that they have less holes than R |
651335b5-7e4a-436a-9109-e08a9db8b72f | trentmkelly/LessWrong-43k | LessWrong | Questions for Moral Realists
My meta-ethics are basically that of Luke's [Pluralistic Moral Reductionism](http://lesswrong.com/lw/5u2/pluralistic_moral_reductionism/). You can see details on my blog series, starting with ["The Meaning of Morality"](http://www.greatplay.net/essays/the-meaning-of-morality).
However, I was curious as to whether this "Pluralistic Moral Reductionism" counts as moral realism or anti-realism. Luke's essay says it depends on what I mean by "moral realism". I see moral realism as broken down into three separate axes:
There's **success theory**, the part that I accept, which states that moral statements like "murder is wrong" do successfully refer to something real (in this case, a particular moral standard, like utilitarianism -- "murder is wrong" refers to "murder does not maximize happiness").
There's **unitary theory**, which I reject, that states there is only one "true" moral standard rather than hundreds of possible ones.
And then there's **absolutism theory**, which I reject, that states that the one true morality is rationally binding.
I don't know how many moral realists are on LessWrong, but I have a few questions for people who accept moral realism, especially unitary theory or absolutism theory. These are "generally seeking understanding and opposing points of view" kind of questions, not stumper questions designed to disprove or anything. While I'm doing some more reading on the topic, if you're into moral realism, you could help me out by sharing your perspective.
~
[size=130]Why is there only one particular morality?[/size]
This goes right to the core of unitary theory -- that there is only one true theory of morality. But I must admit I'm dumbfounded at how any one particular theory of morality could be "the one true one", except in so far as someone personally chooses that theory over others based on preferences and desires.
So why is there only one particular morality? And what is the one true theory of morality? What makes this the |
fa307b4e-f923-4197-a9a5-96d2ac8d459d | trentmkelly/LessWrong-43k | LessWrong | [Event] Weekly Alignment Research Coffee Time
Just like every Monday now, researchers in AI Alignment are invited for a coffee time, to talk about their research and what they're into.
Here is the link.
And here is the everytimezone time (what matters is the hour, I don't change the day anymore).
Note that the link to the walled garden now only works for AF members. Anyone who wants to come but isn't an AF member needs to go by me. I'll broadly apply the following criteria for admission:
* If working in a AI Alignment lab or funded for independent research, automatic admission
* If recommended by AF member, automatic admission
* Otherwise, to my discretion
I prefer to not allow people who might have been interesting but who I'm not sure will not derail the conversation, because this is supposed to be the place where AI Alignment researchers can talk about their current research without having to explain everything.
See you then! |
eec7833e-4315-4ce3-845a-d8b6ed92d497 | trentmkelly/LessWrong-43k | LessWrong | Valence Need Not Be Bounded; Utility Need Not Synthesize
As I relayed in my last post, in "Theory of Games and Economic Behavior", von Neumann and Morgenstern defended the validity of using utility to predict economic outcomes despite the fact that cross-individual utility cannot apparently be measured or apprehended directly:
"The [ . . . ] difficulties of the notion of utility, and particularly of the attempts to describe it as a number, are well known [ . . . ] It is sometimes claimed in economic literature that discussions of the notions of utility and preference are altogether unnecessary, since these are purely verbal definitions with no empirically observable consequences, i.e., entirely tautological. It does not seem to us that these notions are qualitatively inferior to certain well established and indispensable notions in physics, like force, mass, charge, etc. That is, while they are in their immediate form merely definitions, they become subject to empirical control through the theories which are built upon them" [von Neumann and Morgenstern, 8-9]
"[W]e wish to describe the fundamental concept of individual preferences by the use of a rather far-reaching notion of utility. Many economists will feel that we are assuming far too much [ by purporting to treat utility synthetically ], and that our standpoint is a retrogression from the more cautious modern technique of 'indifference curves' [ . . . ]" [von Neumann and Morgenstern, 16]
And they construct a theoretical framework for doing so:
"[A] numerical utility is dependent upon the possibility of comparing differences in utilities. This may seem - and indeed is - a more far-reaching assumption than that of a mere ability to state [subjective] preferences. [ . . . ]
Let us for the moment accept the picture of an individual whose system of preferences is all-embracing and complete, i.e. who, for any two objects or rather any two imagined events, possesses a clear intuition of preference.
More precisely we expect him, for any two alternative events which are |
36f10281-e170-4bcb-84e1-62df535b3681 | trentmkelly/LessWrong-43k | LessWrong | Response
Apotheosis, Home, Works
----------------------------------------
Part V
Everyone has their own name for it.
"The Multi-Dimensional Crisis Revolution", "The Precipice", "Collapse", "The Most Important Century", "The Meta Crisis", "The Weirdening", "Planetary Scale Vibe Collapse".
Many just call it "Modernity".
A suspicion perhaps, that something strange is afoot in this chapter of the human story. Something a little overwhelming, maybe even a little scary. A kind of individual and collective decoherence, right as a host of rather wicked problems come barreling down the pike.
It seems you and I were not born into stock off-the-shelf human lives, and we are living to see times more interesting by the decade. Times that may ask us to be brave.
----------------------------------------
Everyone has a different angle.
"Eudaimonia", "Awakening", "Wireheading Done Right", "Flow", "Meaningness", "Deep Okayness", "Self Love", "Luminosity", "Secure Attachment".
Many understand it as "the pursuit of happiness".
A hunch that the weather of the mind only appears arbitrary. That I'm not so much in a lottery of awe, anguish, and banality as a maze, where I am the walls, the Minotaur, and Theseus all. That just maybe, I get to decide that the main character in this story doesn't have to hate themselves.
> It is something to be able to paint a particular picture, or to carve a statue, and so to make a few objects beautiful; but it is far more glorious to carve and paint the very atmosphere and medium through which we look.
>
> - Walden
----------------------------------------
We've heard it all a million times.
"Let's all move to the country", "We should have more social time that isn't a calendar event", "I miss what we had in Church/Scouts/School".
"I want a bash like in Terra Ingota", "We need a third place", "Coordination is a superpower", "Belonging is a superpower" "Let's start a D.A.O", "Let's start a proto-b", "Let's reconstitute the tribe", "Let's start a po |
34426520-cac3-471f-abf4-8da8e48edd45 | trentmkelly/LessWrong-43k | LessWrong | Systems that cannot be unsafe cannot be safe
Epistemic Status: Trying to clarify a confusion people outside of the AI safety community seem to have about what safety means for AI systems.
In engineering and design, there is a process that includes, among other stages, specification, creation, verification and validation, and deployment. Verification and validation are where most people focus when thinking about safety - can we make sure the system performs correctly? I think this is a conceptual error that I want to address.
> "Verification and validation (also abbreviated as V&V) are independent procedures that are used together for checking that a product, service, or system meets requirements and specifications and that it fulfills its intended purpose." - Wikipedia
Both of these terms are used slightly differently across fields, but in general, verification is the process of making sure that the system fulfills the design requirements and/or other standards. This pre-supposes that the system has some defined requirements or a standard, at least an implicit one, and that it could fail to meet that bar. That is, the specification of the system includes what it must and must not do, and if the system does not do what it should, or does something that it should not, it fails.
Machine learning systems, especially language models, aren't well understood. The potential applications are varied and uncertain, entire classes of new and surprising failure modes are still being found, and we have nothing like a specification of what the system should or should not do, must or must not do, and where it can and cannot be used.
To take a very concrete example, metal rods have safety characteristics, and they might be rated for use up to some weight limit, under some specific load for some amount of time, in certain temperature ranges, for some amount of time. These can all be tested. If the bar does not stay within a predefined range of characteristics at a given temperature, with a given load, it fails. It can als |
3574a35f-87bd-4bd6-8233-1369cff09f40 | trentmkelly/LessWrong-43k | LessWrong | [Website usability] Scroll to new comments (v0.3)
I wrote a short userscript1 that allows for jumping to the next (or previous) new comment in a page (those marked with green). I have tested it on Firefox nightly with the Greasemonkey addon and Chromium. Unfortunately, I think that user scripts only work in Chromium/Google Chrome and Firefox (with Greasemonkey).
Download here (Clicking the link should offer a install prompt, and that is all the work that needs to be done.)
It inserts a small box in the lower right-hand corner that indicates the number of new messages and has a "next" and a "previous" link like so:
Clicking either link should scroll the browser to the top of the appropriate comment (wrapping around at the top and bottom).
The "!" link shows a window for error logging. If a bug occurs, clicking the "Generate log" button inside this window will create a box with some information about the running of the script2, copying and pasting that information here will make debugging easier.
I have only tested on the two browsers listed above, and only on Linux, so feedback about any bugs/improvements would be useful.
(Technical note: It is released under the MIT License, and this link is to exactly the same file as above but renamed so that the source can be viewed more easily. The file extension needs to be changed to "user.js" to be able to run as a user script properly)
Changelog
v0.1 - First version
v0.2 - Logging & indication of number of new messages
v0.3 - Correctly update when hidden comments are loaded (and license change). NOTE: Upgrading to v0.3 on Chrome is likely to cause a "Downgrading extension error" (I'd made a mistake with the version numbers previously), the fix is to uninstall and then reinstall the new version. (uninstall via Tools > Extensions)
----------------------------------------
1 A segment of javascript that runs in the web browser as the page is loaded. It can modify the page, e.g. inserting a bit of html as this script does.
2 Specifically: the url, counts of diffe |
eb9fab2d-e0a9-432e-900e-71f0d44ede19 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Attainable Utility Landscape: How The World Is Changed
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(This is one interpretation of the prompt, in which you haven't *chosen* to go to the moon. If you imagined yourself as more prepared, that's also fine.)
If you were plopped onto the moon, you'd die pretty fast. Maybe the "die as quickly as possible" AU is high, but not much else - not even the "live on the moon" AU! We haven't yet reshaped the AU landscape on the moon to be hospitable to a wide range of goals. [Earth is special like that.](https://www.youtube.com/watch?v=wupToqz1e2g)
AU landscape as a unifying frame
--------------------------------
Attainable utilities are calculated by winding your way through possibility-space, considering and discarding possibility after possibility to find the best plan you can. This frame is unifying.
Sometimes you advantage one AU at the cost of another, moving through the state space towards the best possibilities for one goal and away from the best possibilities for another goal. This is *opportunity cost*.
Sometimes you gain more control over the future: most of the best possibilities make use of a windfall of cash. Sometimes you act to preserve control over the future: most Tic-Tac-Toe goals involve not ending the game right away. This is *power*.
Other people usually *objectively impact* you by decreasing or increasing a bunch of your AUs (generally, by changing your power). This happens for an extremely wide range of goals because of the structure of the environment.
Sometimes, the best possibilities are made unavailable or worsened only for goals very much like yours. This is *value impact*.
Sometimes a bunch of the best possibilities go through the same part of the future: fast travel to random places on Earth usually involves the airport. This is *instrumental convergence*.
*Exercise: Track what’s happening to your various AUs during the following story: you win the lottery. Being an effective spender, you use most of your cash to buy a majority stake in a major logging company. Two months later, the company goes under.*
Technical appendix: AU landscape and world state contain equal information
--------------------------------------------------------------------------
In the context of finite deterministic Markov decision processes, there's a wonderful handful of theorems which basically say that the AU landscape and the environmental dynamics encode each other. That is, they contain the *same* information, just with different emphasis. This supports thinking of the AU landscape as a "dual" of the world state.
>
> Let ⟨S,A,T,γ⟩ be a rewardless deterministic MDP with finite state and action spaces S,A, deterministic transition function T, and discount factor γ∈(0,1).
> As our interest concerns optimal value functions, we consider only stationary, deterministic policies: Π:=AS.
>
>
>
>
> The first key insight is to consider not policies, but the trajectories induced by policies from a given state; to not look at the state itself, but the *paths through time* available from the state. We concern ourselves with the *possibilities* available at each juncture of the MDP.
>
>
>
>
> To this end, for π∈Π, consider the mapping of π↦(I−γTπ)−1 (where Tπ(s,s′):=T(s,π(s),s′)); in other words, each policy π maps to a function mapping each state s0 to a discounted state visitation frequency vector fπs0, which we call a *possibility*. The meaning of each frequency vector is: starting in state s0 and following policy π, what sequence of states s0,s1,… do we visit in the future? States visited later in the sequence are discounted according to γ: the sequence s0s1s2s2… would induce 1 visitation frequency on s0, γ visitation frequency on s1, and γ21−γ visitation frequency on s2.
>
>
>
The possibility function F(s) outputs the possibilities available at a given state s:
Put differently, the possibilities available are all of the potential film-strips of how-the-future-goes you can induce from the current state.
### Possibility isomorphism
We say two rewardless MDPs M and M′ are *isomorphic up to possibilities* if they induce the same possibilities. Possibility isomorphism captures the essential aspects of an MDP's structure, while being invariant to state representation, state labelling, action labelling, and the addition of superfluous actions (actions whose results are duplicated by other actions available at that state). Formally, M≃FM′ when there exists a bijection ϕ:S→S′ (letting Pϕ be the corresponding |S|-by-|S′| permutation matrix) satisfying FM(s)={Pϕf′|f′∈FM′(ϕ(s))} for all s∈S.
This isomorphism is a natural contender[[1]](#fn-N3eg2sLgjAzGiLPPn-1) for the canonical (finite) MDP isomorphism:
**Theorem:** M and M′ are isomorphic up to possibilities iff their directed graphs are isomorphic (and they have the same discount rate).
### Representation equivalence
Suppose I give you the following possibility sets, each containing the possibilities for a different state:
⎧⎪
⎪
⎪⎨⎪
⎪
⎪⎩⎛⎜⎝400⎞⎟⎠,⎛⎜⎝1.752.25⎞⎟⎠,⎛⎜
⎜⎝1.43754−1.43750⎞⎟
⎟⎠⎫⎪
⎪
⎪⎬⎪
⎪
⎪⎭⎧⎪⎨⎪⎩⎛⎜⎝004⎞⎟⎠⎫⎪⎬⎪⎭⎧⎪
⎪
⎪⎨⎪
⎪
⎪⎩⎛⎜⎝013⎞⎟⎠,⎛⎜
⎜⎝4−1.43751.43750⎞⎟
⎟⎠,⎛⎜⎝310⎞⎟⎠⎫⎪
⎪
⎪⎬⎪
⎪
⎪⎭
*Exercise: What can you figure out about the MDP structure? Hint: each entry in the column corresponds to the visitation frequency of a different state; the first entry is always s1, second s2, and third s3.*
You can figure out *everything*: ⟨S,A,T,γ⟩, up to possibility isomorphism. Solution [here](https://i.imgur.com/5GCZ9oY.png).
How? Well, the L1 norm of the possibility vector is always 11−γ, so you can deduce γ=.75 easily. The single possibility state must be isolated, so we can mark that down in our graph. Also, it's in the third entry.
The other two states correspond to the "1" entries in their possibilities, so we can mark that down. The rest follows straightforwardly.
**Theorem:** Suppose the rewardless MDP M has possibility function F. Given only F,[[2]](#fn-N3eg2sLgjAzGiLPPn-2) M can be reconstructed up to possibility isomorphism.
In MDPs, the "AU landscape" is the set of optimal value functions for all reward functions over states in that MDP. If you know the optimal value functions for just |S| reward functions, you can also reconstruct the rewardless MDP structure.[[3]](#fn-N3eg2sLgjAzGiLPPn-3)
From the environment (rewardless MDP), you can deduce the AU landscape (all optimal value functions) and all possibilities. From possibilities, you can deduce the environment and the AU landscape. From the AU landscape, you can deduce the environment (and thereby all possibilities).
*All of these encode the same mathematical object.*
Technical appendix: Opportunity cost
------------------------------------
Opportunity cost is when an action you take makes you more able to achieve one goal but less able to achieve another. Even this simple world has opportunity cost:
Going to the green state means you can't get to the purple state as quickly.
On a deep level, why is the world structured such that this happens? Could you imagine a world without opportunity cost of any kind? The answer, again in the rewardless MDP setting, is simple: "yes, but the world would be trivial: you wouldn't have any choices". Using a straightforward formalization of opportunity cost, we have:
**Theorem:** Opportunity cost exists in an environment iff there is a state with more than one possibility.
Philosophically, opportunity cost exists when you have meaningful choices. When you make a choice, you're necessarily moving away from some potential future but towards another; since you can't be in more than one place at the same time, opportunity cost follows. Equivalently, we assumed the agent isn't infinitely farsighted (γ<1); if it were, it would be possible to be in "more than one place at the same time", in a sense (thanks to Rohin Shah for this interpretation).
While understanding opportunity cost may seem like a side-quest, each insight is another brick in the edifice of our understanding of the incentives of goal-directed agency.
### Notes
* Just as game theory is a great abstraction for modelling competitive and cooperative dynamics, AU landscape is great for thinking about consequences: it automatically excludes irrelevant details about the world state. We can think about the effects of events without needing a specific utility function or ontology to evaluate them. In multi-agent systems, we can straightforwardly predict the impact the agents have on each other and the world.
* “Objective impact to a location” means that agents whose plans route through the location tend to be objectively impacted.
* The landscape is not the territory: [AU is calculated with respect to an agent's *beliefs*](https://www.lesswrong.com/posts/coQCEe962sjbcCqB9/the-gears-of-impact), not necessarily with respect to what really "could" or will happen.
---
1. The possibility isomorphism is new to my work, as are all other results shared in this post. This apparent lack of basic theory regarding MDPs is strange; even stranger, this absence was actually pointed out in two [published](http://papers.nips.cc/paper/3179-stable-dual-dynamic-programming.pdf) [papers](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4220813)!
I find the existing MDP isomorphisms/equivalences to be pretty lacking. The details don't fit in this margin, but perhaps in a paper at some point. If you want to coauthor this (mainly compiling results, finding a venue, and responding to reviews), let me know and I can share what I have so far (extending well beyond the theorems in my [recent work on power](https://arxiv.org/abs/1912.01683)). [↩︎](#fnref-N3eg2sLgjAzGiLPPn-1)
2. In fact, you can reconstruct the environment using only a limited subset of possibilities: the *non-dominated* possibilities. [↩︎](#fnref-N3eg2sLgjAzGiLPPn-2)
3. As a tensor, the transition function T has size |A|⋅|S|2, while the AU landscape representation only has size |S|2. However, if you're just representing T as a transition *function*, it has size |A|⋅|S|. [↩︎](#fnref-N3eg2sLgjAzGiLPPn-3) |
1b2f37c3-f7a5-442f-a1dc-74ddc4a15e0d | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | AGI Safety Fundamentals curriculum and application
Over the last year EA Cambridge has been designing and running an online program aimed at effectively introducing the field of AGI safety; the most recent cohort included around 150 participants and 25 facilitators from around the world. Dewi Erwan runs the program; I designed the curriculum, the latest version of which appears in [the linked document](https://docs.google.com/document/d/1mTm_sT2YQx3mRXQD6J2xD2QJG1c3kHyvX8kQc_IQ0ns/edit?usp=sharing). We expect the program to be most useful to people with technical backgrounds (e.g. maths, CS, or ML), although the curriculum is intended to be accessible for those who aren't familiar with machine learning, and participants will be put in groups with others from similar backgrounds. **If you're interested in joining the next version of the course (taking place January - March 2022)** [**apply here to be a participant**](https://airtable.com/shr9R2syz8wc2ao7p) **or** [**here to be a facilitator**](https://airtable.com/shr0IO5TTZEY5FFxY)**. Applications are open to anyone and close 15 December**. **EDIT 10 Dec: Facilitators will be paid $1000; the time commitment is 2-3 hours a week for 8 weeks. We've now also released** [**the curriculum for the governance track**](https://forum.effectivealtruism.org/posts/68ANc8KhEn6sbQ3P9/ai-governance-fundamentals-curriculum-and-application)**.**
This post contains an overview of the course and an abbreviated version of the curriculum; the full version (which also contains optional readings, exercises, notes, discussion prompts, and project ideas) [can be found here](https://docs.google.com/document/d/1mTm_sT2YQx3mRXQD6J2xD2QJG1c3kHyvX8kQc_IQ0ns/edit?usp=sharing). Comments and feedback are very welcome, either on this post or in the full curriculum document; suggestions of new exercises, prompts or readings would be particularly helpful. I'll continue to make updates until shortly before the next cohort starts.
Course overview
---------------
The course consists of 8 weeks of readings, plus a final project. Participants are divided into groups of 4-6 people, matched based on their prior knowledge about ML and safety. Each week (apart from week 0) each group and their discussion facilitator will meet for 1.5 hours to discuss the readings and exercises. Broadly speaking, the first half of the course explores the motivations and arguments underpinning the field of AGI safety, while the second half focuses on proposals for technical solutions. After week 7, participants will have several weeks to work on projects of their choice, to present at the final session.
Each week's curriculum contains:
* Key ideas for that week
* Core readings
* Optional readings
* Two exercises (participants should pick one to do each week)
* Further notes on the readings
* Discussion prompts for the weekly session
Week 0 replaces the small group discussions with a lecture plus live group exercises, since it's aimed at getting people with little ML knowledge up to speed quickly.
The topics for each week are:
* Week 0 (optional): introduction to machine learning
* Week 1: Artificial general intelligence
* Week 2: Goals and misalignment
* Week 3: Threat models and types of solutions
* Week 4: Learning from humans
* Week 5: Decomposing tasks for outer alignment
* Week 6: Other paradigms for safety work
* Week 7: AI governance
* Week 8 (several weeks later): Projects
Abbreviated curriculum (only key ideas and core readings)
---------------------------------------------------------
### Week 0 (optional): introduction to machine learning
This week mainly involves learning about foundational concepts in machine learning, for those who are less familiar with them, or want to revise the basics. If you’re not already familiar with basic concepts in statistics (like regressions), it will take a bit longer than most weeks; and instead of the group discussions from most weeks, there will be a [lecture](https://docs.google.com/presentation/d/1vy193pcqe0nmLpTGBwP6O2Nlv7s0pq6oSzo-P-Kw4tM/edit?usp=sharing) and [group exercises](https://docs.google.com/document/d/1ChHiwLCDWpkwNDL77iRBc3D8tydXonJgaK2BvVYI3oE/edit?usp=sharing). If you’d like to learn ML in more detail, see the further resources section at the end of this curriculum.
Otherwise, start with Ngo (2021), which provides a framework for thinking about machine learning, and in particular the two key components of deep learning: neural networks and optimisation. For more details and intuitions about neural networks, watch 3Blue1Brown (2017a); for more details and intuitions about optimisation, watch 3Blue1Brown (2017b). Lastly, see von Hasselt (2021) for an introduction to the field of reinforcement learning.
Core readings:
1. If you’re not familiar with the basics of statistics, like linear regression and classification:
1. [Introduction: linear regression](https://courses.lumenlearning.com/odessa-introstats1-1/chapter/introduction-linear-regression/) (10 mins)
2. [Ordinary least squares regression](https://setosa.io/ev/ordinary-least-squares-regression/) (10 mins)
2. [A short introduction to machine learning (Ngo, 2021)](https://www.alignmentforum.org/posts/qE73pqxAZmeACsAdF/a-short-introduction-to-machine-learning) (20 mins)
3. [But what is a neural network? (3Blue1Brown, 2017a)](https://www.youtube.com/watch?v=aircAruvnKk&t=0s) (20 mins)
4. [Gradient descent, how neural networks learn (3Blue1Brown, 2017b)](https://www.youtube.com/watch?v=IHZwWFHWa-w) (20 mins)
5. [Introduction to reinforcement learning (von Hasselt, 2021)](https://www.youtube.com/watch?v=TCCjZe0y4Qc&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) **(ending at 36:30, at section titled Inside the Agent)** (40 mins)
### Week 1: Artificial general intelligence
The first two readings this week offer several different perspectives on how we should think about artificial general intelligence. This is the key concept underpinning the course, so it’s important to deeply explore what we mean by it, and the limitations of our current understanding.
The third reading is about *how* we should expect advances in AI to occur. AI pioneer Rich Sutton explains the main lesson he draws from the history of the field: that “general methods that leverage computation are ultimately the most effective”. Compared with earlier approaches, these methods rely much less on human design, and therefore raise the possibility that we build AGIs whose cognition we know very little about.
Focusing on compute also provides a way to forecast *when* we should expect AGI to occur. The most comprehensive report on the topic (summarised by Karnofsky (2021)) estimates the amount of compute required to train neural networks as large as human brains to do highly impactful tasks, and concludes that this will probably be feasible within the next four decades - although the estimate is highly uncertain.
Core readings:
1. [Four background claims (Soares, 2015)](https://intelligence.org/2015/07/24/four-background-claims/) (15 mins)
2. [AGI safety from first principles (Ngo, 2020)](https://drive.google.com/file/d/1uK7NhdSKprQKZnRjU58X7NLA1auXlWHt/view) **(only sections 1, 2 and 2.1)** (20 mins)
3. [The Bitter Lesson (Sutton, 2019)](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) (15 mins)
4. [Forecasting transformative AI: the “biological anchors” method in a nutshell (Karnofsky, 2021)](https://www.cold-takes.com/forecasting-transformative-ai-the-biological-anchors-method-in-a-nutshell/) (30 mins)
### Week 2: Goals and misalignment
This week we’ll focus on how and why AGIs might develop goals that are *misaligned* with those of humans, in particular when they’ve been trained using machine learning. We cover three core ideas. Firstly, it’s difficult to create reward functions which specify the desired outcomes for complex tasks (known as the problem of *outer alignment*). Krakovna et al. (2020) helps build intuitions about the difficulty of outer alignment, by showcasing examples of misbehaviour on toy problems.
Secondly, however, it’s important to distinguish between the reward function which is used to train a reinforcement learning agent, versus the goals which that agent learns to pursue. Hubinger et al. (2019a) argue that even an agent trained on the “right” reward function might acquire undesirable goals - the problem of *inner alignment*. Carlsmith (2021) explores in more detail what it means for an agent to be goal-directed in a worrying way, and gives reasons why such agents seem likely to arise.
Lastly, Bostrom (2014) argues that almost all goals which an AGI might have would incentivise it to misbehave in highly undesirable ways (e.g. pursuing survival and resource acquisition), due to the phenomenon of *instrumental convergence*.
Core readings:
1. [Specification gaming: the flip side of AI ingenuity (Krakovna et al., 2020)](https://medium.com/@deepmindsafetyresearch/specification-gaming-the-flip-side-of-ai-ingenuity-c85bdb0deeb4) (15 mins)
2. [Introduction to Risks from Learned Optimisation (Hubinger et al., 2019a)](https://www.alignmentforum.org/posts/FkgsxrGf3QxhfLWHG/risks-from-learned-optimization-introduction) (30 mins)
3. [Superintelligence, Chapter 7: The superintelligent will (Bostrom, 2014)](https://drive.google.com/file/d/1FVl9W2gW5_8ODYNZJ4nuFg79Z-_xkHkJ/view?usp=sharing) (45 mins)
4. [Is power-seeking AI an existential risk? (Carlsmith, 2021)](https://docs.google.com/document/d/1smaI1lagHHcrhoi6ohdq3TYIZv0eNWWZMPEy8C8byYg/edit#heading=h.lvsab2uecgk4) **(only sections 2: Timelines and 3: Incentives)** (25 mins)
### Week 3: Threat models and types of solutions
How might misaligned AGIs cause catastrophes, and how might we stop them? Two threat models are outlined in Christiano (2019) - the first focusing on outer misalignment, the second on inner misalignment. Muehlhauser and Salamon (2012) outline a core intuition for why we might be unable to prevent these risks: that progress in AI will at some point speed up dramatically. A third key intuition - that misaligned agents will try to deceive humans - is explored by Hubinger et al. (2019).
How might we prevent these scenarios? Christiano (2020) gives a broad overview of the landscape of different contributions to making AIs aligned, with a particular focus on some of the techniques we’ll be covering in later weeks.
Core readings:
1. [What failure looks like (Christiano, 2019)](https://www.alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like) (20 mins)
2. [Intelligence explosion: evidence and import (Muehlhauser and Salamon, 2012)](https://drive.google.com/file/d/1QxMuScnYvyq-XmxYeqBRHKz7cZoOosHr/view?usp=sharing) **(only pages 10-15)** (15 mins)
3. [AI alignment landscape (Christiano, 2020)](https://forum.effectivealtruism.org/posts/63stBTw3WAW6k45dY/paul-christiano-current-work-in-ai-alignment) (30 mins)
4. [Risks from Learned Optimisation: Deceptive alignment (Hubinger et al., 2019)](https://www.alignmentforum.org/posts/zthDPAjh9w6Ytbeks/deceptive-alignment) (45 mins)
### Week 4: Learning from humans
This week, we look at four techniques for training AIs on human data (all falling under “learn from teacher” in [Christiano’s AI alignment landscape](https://ai-alignment.com/ai-alignment-landscape-d3773c37ae38) from last week). From a safety perspective, each of them improves on standard reinforcement learning techniques in some ways, but also has weaknesses which prevent it from solving the whole alignment problem. Next week, we’ll look at some ways to make these techniques more powerful and scalable; this week focuses on understanding each of them.
The first technique, behavioural cloning, is essentially an extension of supervised learning to settings where an AI must take actions over time - as discussed by Levine (2021). The second, reward modelling, allows humans to give feedback on the behaviour of reinforcement learning agents, which is then used to determine the rewards they receive; this is used by Christiano et al. (2017) and Steinnon et al. (2020). The third, inverse reinforcement learning (IRL for short), attempts to identify what goals a human is pursuing based on their behaviour.
A notable variant of IRL is *cooperative* IRL (CIRL for short), introduced by Hadfield-Menell et al. (2016). CIRL focuses on cases where the human and AI interact in a shared environment, and therefore the best strategy for the human is often to help the AI learn what goal the human is pursuing.
Core readings:
1. [Imitation learning lecture: part 1 (Levine, 2021a)](https://youtu.be/kGc8jOy5_zY) (20 mins)
2. [Deep RL from human preferences blog post (Christiano et al., 2017)](https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/) (15 mins)
3. [Learning to summarise with human feedback blog post (Stiennon et al., 2020)](https://openai.com/blog/learning-to-summarize-with-human-feedback/) (25 mins)
4. Inverse reinforcement learning
1. For those who don’t already understand IRL:
* [Inverse reinforcement learning example (Udacity, 2016)](https://www.youtube.com/watch?v=h7uGyBcIeII) (5 mins)
* [Learning from humans: what is inverse reinforcement learning? (Alexander, 2018)](https://thegradient.pub/learning-from-humans-what-is-inverse-reinforcement-learning/) (25 mins)
2. For those who already understand IRL:
* [Cooperative inverse reinforcement learning (Hadfield-Menell et al., 2016)](https://arxiv.org/abs/1606.03137) (40 mins)
### Week 5: Decomposing tasks for outer alignment
The most prominent research directions in technical AGI safety involve training AIs to do complex tasks by decomposing those tasks into simpler ones where humans can more easily evaluate AI behaviour. This week we’ll cover three closely-related algorithms (all falling under “build a better teacher” in [Christiano’s AI alignment landscape](https://forum.effectivealtruism.org/posts/63stBTw3WAW6k45dY/paul-christiano-current-work-in-ai-alignment)).
Wu et al. (2021) applies reward modelling recursively in order to solve more difficult tasks. Recursive reward modelling can be considered one example of a more general class of techniques called *iterated amplification* (also known as *iterated distillation and amplification*), which is described in Ought (2019). A more technical description of iterated amplification is given by Christiano et al. (2018), along with some small-scale experiments.
The third technique we’ll discuss this week is *Debate*, as proposed by Irving and Amodei (2018). Unlike the other two techniques, Debate focuses on evaluating claims made by language models, rather than supervising AI behaviour over time.
Core readings:
1. [Recursively summarising books with human feedback (Wu et al., 2021)](https://arxiv.org/abs/2109.10862) **(ending after section 4.1.2: Findings)** (45 mins)
2. Factored cognition (Ought, 2019) ([introduction](https://ought.org/research/factored-cognition) and [scalability section](https://ought.org/research/factored-cognition/scalability)) (20 mins)
3. [AI safety via debate blog post (Irving and Amodei, 2018)](https://openai.com/blog/debate/) (15 mins)
4. [Supervising strong learners by amplifying weak experts (Christiano et al., 2018)](https://arxiv.org/abs/1810.08575) (40 mins)
### Week 6: Other paradigms for safety work
A lot of safety work focuses on “shifting the paradigm” of AI research. This week we’ll cover two ways in which safety researchers have attempted to do so. The first is via research on *interpretability*, which attempts to understand in detail how neural networks work. Olah et al. (2020) showcases some prominent research in the area; and Chris Olah’s perspective is summarised by Hubinger et al. (2019).
The second is the research agenda of the Machine Intelligence Research Institute (MIRI) which aims to create rigorous mathematical frameworks to describe the relationships between AIs and their real-world environments. Soares (2015) gives a high-level explanation of their approach; while Demski and Garrabrant (2018) identify a range of open problems and links between them.
Core readings:
1. [Zoom In: an introduction to circuits (Olah et al., 2020)](https://distill.pub/2020/circuits/zoom-in/) (35 mins)
2. [Chris Olah’s views on AGI safety (Hubinger, 2019)](https://www.alignmentforum.org/posts/X2i9dQQK3gETCyqh2/chris-olah-s-views-on-agi-safety) (25 mins)
3. [MIRI’s approach (Soares, 2015)](https://intelligence.org/2015/07/27/miris-approach/) (30 mins)
4. [Embedded agents (Demski and Garrabrant, 2018)](https://intelligence.org/2018/10/29/embedded-agents/) (25 mins)
### Week 7: AI governance
In the last week of curriculum content, we’ll look at the field of AI governance. Start with Dafoe (2020), which gives a thorough overview of AI governance and ways in which it might be important, particularly focusing on the framing of AI governance as field-building. An alternative framing - of AI governance as an attempt to prevent cooperation failures - is explored by Clifton (2019). Although the field of AI governance is still young, Muehlhauser (2020) identifies some useful work so far. Finally, Bostrom (2019) provides a background framing for thinking about technological risks: the process of randomly sampling new technologies, some of which might prove catastrophic.
Core readings:
1. [AI Governance: Opportunity and Theory of Impact (Dafoe, 2020)](https://forum.effectivealtruism.org/posts/42reWndoTEhFqu6T8/ai-governance-opportunity-and-theory-of-impact) (25 mins)
2. [Cooperation, conflict and transformative AI: sections 1 & 2 (Clifton, 2019)](https://www.alignmentforum.org/s/p947tK8CoBbdpPtyK/p/KMocAf9jnAKc2jXri) (25 mins)
3. [Our AI governance grantmaking so far (Muehlhauser, 2020)](https://www.openphilanthropy.org/blog/ai-governance-grantmaking) (15 mins)
4. [The vulnerable world hypothesis (Bostrom, 2019)](https://www.nickbostrom.com/papers/vulnerable.pdf) **(ending at the start of the section on ‘Preventive policing’)** (60 mins)
### Week 8 (four weeks later): Projects
The final part of the AGI safety fundamentals course will be projects where you get to dig into something related to the course. The project is a chance for you to explore yourinterests, so try to find something you’re excited about! The goal of this project is to help you practice taking an intellectually productive stance towards AGI safety - to go beyond just reading and discussing existing ideas, and take a tangible step towards contributing to the field yourself. This is particularly valuable because it’s such a new field, with lots of room to explore.
### [Click here for the full version of the curriculum](https://docs.google.com/document/d/1mTm_sT2YQx3mRXQD6J2xD2QJG1c3kHyvX8kQc_IQ0ns/edit?usp=sharing), which contains additional readings, exercises, notes, discussion prompts, and project ideas. |
ac383f10-cb08-4183-9fea-cf56f427209d | trentmkelly/LessWrong-43k | LessWrong | EA Dinner Covid Logistics
Yesterday we hosted an EA dinner, the first largeish (~22 people) gathering inside our house since before the pandemic. It went well, and it was really good to see people. I wanted to write a bit about what sort of precautions we took for covid.
* Open window in the main area with a fan pointing out. Windows partly open in other parts of the house, trying to balance the airflow so as much air as possible traveled past the guests. I preheated the house earlier in the day, and during the event had the heat on maximum. It was a bit chilly near the end, but still tolerable.
* Air purifier cubes in each room.
In the main area, which isn't a good fit for one, I made a bit of a tunnel above the bookcase.
* Masks on, except when eating. People were good about this; I didn't see people hanging out with mask off and a plate but not eating.
* Rapid testing at the door ($7/person). We lined up a volunteer to handle this, because Julia was dealing with hosting and I was watching the kids upstairs. With each test they wrote down the person's name, the time where it needed to be read and their cellphone number. When each test had been sitting for 15 minutes, we texted the person the result. We asked people not to take their mask off to eat until they had their results. If any tests had been positive, we would have informed the group. False positive rate is ~0.05%. We are keeping the cell phone numbers for a few days, so we will be able to text everyone if anyone later notifies us they have it.
* We told people vaccination was required, though not boosters. We intended to check vaccination cards (accepting photos) at the door, but forgot. If I was organizing some thing like this a few weeks from now, when there has been more time for people to get boosters, I might require them.
* We considered requiring masks to be surgical or better (offering free ones) but decided not to. I think this probably would have been worth doing, except that |
7c7e0c05-693c-49a6-a87c-4ab7214f50ff | StampyAI/alignment-research-dataset/blogs | Blogs | Refining the Sharp Left Turn threat model
*(Coauthored with others on the alignment team and cross-posted from the alignment forum: [part 1](https://www.lesswrong.com/posts/usKXS5jGDzjwqv3FJ/refining-the-sharp-left-turn-threat-model), [part 2](https://www.lesswrong.com/posts/dfXwJh4X5aAcS8gF5/refining-the-sharp-left-turn-threat-model-part-2-applying))*
A [sharp left turn](https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization) (SLT) is a possible rapid increase in AI system capabilities (such as planning and world modeling) that could result in alignment methods no longer working. This post aims to make the sharp left turn scenario more concrete. We will discuss our understanding of the claims made in this threat model, propose some mechanisms for how a sharp left turn could occur, how alignment techniques could manage a sharp left turn or fail to do so.
[](https://vkrakovna.files.wordpress.com/2022/11/image.png)Image credit: [Adobe](https://stock.adobe.com/ca/images/pixel-turn-left-mosaic-icons-vector-turn-left-icons-in-multi-colored-and-black-versions-collages-of-variable-spheric-dots-vector-collages-of-turn-left-icons-organized-of-different-spots/239909259)
Claims of the threat model
==========================
What are the main claims of the “sharp left turn” threat model?
---------------------------------------------------------------
**Claim 1. Capabilities will generalize far (i.e., to many domains)**
There is an AI system that:
* Performs well: it can accomplish impressive feats, or achieve high scores on valuable metrics.
* Generalizes, i.e., performs well in new domains, which were not optimized for during training, with no domain-specific tuning.
Generalization is a key component of this threat model because we’re not going to directly train an AI system for the task of disempowering humanity, so for the system to be good at this task, the capabilities it develops during training need to be more broadly applicable.
Some optional sub-claims can be made that increase the risk level of the threat model:
**Claim 1a [Optional]: Capabilities (in different “domains”) will all generalize at the same time**
**Claim 1b [Optional]: Capabilities will generalize far in a discrete phase transition (rather than continuously)**
**Claim 2. Alignment techniques that worked previously will fail during this transition**
* Qualitatively different alignment techniques are needed. The ways the techniques work apply to earlier versions of the AI technology, but not to the new version because the new version gets its capability through something new, or jumps to a qualitatively higher capability level (even if through “scaling” the same mechanisms).
**Claim 3: Humans can’t intervene to prevent or align this transition**
* Path 1: humans don’t notice because it’s too fast (or they aren’t paying attention)
* Path 2: humans notice but are unable to make alignment progress in time
* Some combination of these paths, as long as the end result is insufficiently correct alignment
Arguments for the claims in this threat model
---------------------------------------------
* Claim 1: There is a “core” of general intelligence – a most effective way of updating beliefs and selecting actions (Ruin #22). Speculation about what the core is: consequentialism / EU maximization / “doing things for reasons”.
* Claim 1a: Capability gains from intelligence are highly correlated (Ruin #15)
* Claim 2: There is no simple core for alignment (Ruin #22), Corrigibility is anti-natural (Ruin #23)
* Claims 1 & 2: arguments in [Will capabilities generalize more?](https://www.lesswrong.com/posts/cq5x4XDnLcBrYbb66/will-capabilities-generalise-more) (+ Ruin #21)
* Claim 3: We can’t coordinate to avoid AGI (Ruin #4)
Mechanisms for a sharp left turn
================================
Mechanisms for capabilities generalizing far (Claim 1)
------------------------------------------------------
Capabilities will generalize far if learning / reasoning / cognitive work is done outside of the optimization process, similarly to how human cultural evolution happens outside genetic evolution and proceeds faster. Here are some mechanisms for cognitive work getting done outside the optimization process:
* [Mechanisms proposed by Paul Christiano](https://www.lesswrong.com/posts/3pinFH3jerMzAvmza/on-how-various-plans-miss-the-hard-bits-of-the-alignment?commentId=qdkKysrRTs8joWRsf): performing search internally, natural selection occurring internally, reasoning about how to think better, deploying subagents
* The AI develops a way to outpace SGD as the way to learn / improve, similar to how humans outpace evolution by cultural transmission + within-lifetime learning. E.g., it writes Google docs to itself, or searches over prompts (or whatever is the prompt analog at that point).
* The optimization process finds a general meta-learning algorithm, which replaces the previous solution of domain-specific heuristics and greatly increases the rate of learning
* Sequential reasoning: sharp increase in effective sequential memory/cognitive time between the “aligned” regime and the sharp left turn regime. Ways in which this can happen:
+ The model is just given access to memory. Its knowledge grows over time, and reaches a critical point which causes a sharp left turn. Alignment schemes before the critical point appear to work, but not after.
+ We try to restrict the model’s memory, but fail. E.g. the model’s outputs are used in the world, and the subsequent inputs carry information about previous outputs.
+ Some input causes the model to access a new faster mode of thinking (e.g. an explanation of how to do step-by-step reasoning in some domain). The same compute that on previous inputs powered e.g. one subjective day of thinking on this new input now powers one subjective week of thinking. This allows the model to evade alignment schemes.
Mechanisms for a rapid phase transition (Claim 1b)
--------------------------------------------------
A rapid phase transition happens if there is a capability overhang: the AI system is improving at various skills continuously, but its improvement in many domains is bottlenecked on one specific skill, and at some point it receives some input that makes its existing capabilities much more effective. Here are some ways this can happen:
* The system acquires situational awareness, and now its world model includes understanding of the system’s place in the world and how its training and deployment works. It can plan using this knowledge, which leads to more effective use of existing capabilities.
* Analogy to few-shot prompting: the capabilities are already present in the trained artifact. Any alignment technique that goes through gradient updates becomes irrelevant. Putting the artifact into the “right” situation (e.g., giving it a few-shot prompt) reveals its capabilities relevant to this situation. Mechanism: the relevant knowledge and capabilities are installed by some generic pretraining optimisation process.
* Discovering a more effective way to make use of low quality data leads to more effective use of existing capabilities.
Plan for managing a sharp left turn and how it could fail
=========================================================
Now we will outline our current understanding of the most promising plan for getting through an SLT and how it could fail (conditional on an SLT occurring).
**Plan: we use alignment techniques to find a goal-aligned model before SLT occurs, and the model preserves its goals during the SLT.**
---------------------------------------------------------------------------------------------------------------------------------------
We can try to learn a [goal-aligned](https://www.lesswrong.com/posts/vix3K4grcHottqpEm/goal-alignment-is-robust-to-the-sharp-left-turn) model before SLT occurs: a model that has beneficial goals and is able to reason about its own goals. This requires the model to have two properties: goal-directedness towards beneficial goals, and situational awareness (which enables the model to reason about its goals). Here we use the term “goal-directedness” in a weak sense (that includes humans and allows incoherent preferences) rather than a strong sense (that implies expected utility maximization).
One can argue that the goal-aligned model has an incentive to preserve its goals, which would result in an aligned model after SLT. Since preserving alignment during SLT is largely outsourced to the model itself, [arguments](https://www.lesswrong.com/posts/3pinFH3jerMzAvmza/on-how-various-plans-miss-the-hard-bits-of-the-alignment) for alignment techniques failing during an SLT don’t imply that the plan fails (e.g. it might be fine if interpretability or ELK techniques no longer work reliably during the transition if we can trust the model to manage the transition).
**Step 1: Finding a goal-aligned model before SLT**
---------------------------------------------------
We want to ensure that the model is goal-oriented with a beneficial goal and has situational awareness before SLT. It’s important that the model acquires situational awareness at the right time: after it acquires beneficial goals. If situational awareness arises when the model has undesirable goals, this leads to deceptive alignment.
Thus, our model search process would follow a decision tree along these lines:
* If situational awareness is detected without goal-directedness, restart the search.
* If undesirable goal-directedness or early signs of deceptive alignment are detected, restart the search.
* If an upcoming phase transition in capabilities is detected, and the model is not goal-aligned, restart the search.
* If beneficial goal-directedness is detected without situational awareness, train the model for situational awareness.
Here are some ways alignment techniques could be applied in this plan:
* Use ELK techniques to identify whether the model has situational awareness (i.e. whether it believes it’s inside a training process) and what goals it believes itself to have.
* Use interpretability techniques to pick up on early signs of deceptive alignment and restart and improve the search. (If we develop more advanced interpretability techniques that make it possible to [retarget the internal search](https://www.lesswrong.com/posts/w4aeAFzSAguvqA5qu/how-to-go-from-interpretability-to-alignment-just-retarget) of the model, we can retarget a goal-directed model to a beneficial goal instead of restarting the search.)
* Use foundational progress on [abstraction](https://www.lesswrong.com/posts/vDGvHBDuMtcPd8Lks/public-static-what-is-abstraction) to determine whether the goals learned by the system are beneficial.
* Use foundational research insights (e.g. [selection theorems](https://www.lesswrong.com/posts/G2Lne2Fi7Qra5Lbuf/selection-theorems-a-program-for-understanding-agents)) to make the search process more effective at avoiding models with undesirable goals and finding goal-aligned models
* Use insights from [studying capability transitions](https://www.lesswrong.com/posts/2AvX8cX47CdwjbkjY/we-may-be-able-to-see-sharp-left-turns-coming) to predict sharp increases in problematic capabilities, and make sure we only cross that threshold after we found a goal-aligned model using the other techniques on this list.
Why alignment techniques could fail to find a goal-aligned model before SLT occurs:
* The space of deceptively aligned models is much bigger than the space of goal-aligned models, so the latter is very hard to find.
* The inductive biases of the search favor misaligned models (see [How likely is deceptive alignment?](https://www.lesswrong.com/posts/A9NxPTwbw6r6Awuwt/how-likely-is-deceptive-alignment) and [Will Capabilities Generalise More?](https://www.lesswrong.com/posts/cq5x4XDnLcBrYbb66/will-capabilities-generalise-more) for arguments). Note that these arguments indicate that the alignment techniques have to fight an uphill battle to find a goal-aligned model, rather than providing a mechanism for why they would fail.
* Predicting capability transitions does not tell us when it will happen or whether the model undergoing the transition would be aligned. If we use these predictions to restart the search, we could rule out goal-aligned models as well.
* Interpretability is [very hard](https://www.lesswrong.com/posts/iDFTmb8HSGtL4zTvf/how-could-we-know-that-an-agi-system-will-have-good), current methods are low-level and it’s unclear how to get them to work on the high level (e.g. identifying complex goals and beliefs of a system rather than how it stores basic facts about the world).
It’s also possible that this model search process would pose an overly high alignment tax to be followed in practice (see Claim 3).
**Step 2: The goal-aligned model preserves its goals during SLT (with some help from us)**
------------------------------------------------------------------------------------------
This step of the plan relies on the goal-aligned model both wanting to preserve its goals during an SLT and being capable of preserving its goals. The general argument for a model wanting to preserve its own goals is that it has a [convergent instrumental incentive](https://selfawaresystems.files.wordpress.com/2008/01/ai_drives_final.pdf) to ensure that future versions of itself act in ways that satisfy its current preferences.
One [argument](https://www.lesswrong.com/posts/vix3K4grcHottqpEm/goal-alignment-is-robust-to-the-sharp-left-turn) for a pre-SLT system being capable of preserving its goals is by analogy to human values being preserved when humans went through the capability phase transition of developing language and writing. However, it is not clear how much the human analogy constitutes evidence for ability to preserve goals through a phase transition, since there are other possible explanations for human values being preserved (e.g. that values like “relationships” and “social status” continued to be selected for during the capability transition) that don’t rely on humans being able to deliberately preserve their values.
A key question is whether the model could reflect on its goals without already having generalized capabilities. This is related to Claim 1a in our previous post: that the model’s capabilities will all generalize at the same time. If this claim holds, in the sense that reflection on goals is part of this set of capabilities that all generalize simultaneously, then the ability to reflect on goals would only arise during the SLT and so this plan doesn’t work. However, it seems more likely that some form of goal-reflection will arise before generalized planning ability, because making predictions about how your goals could change is easier than achieving them.
* Humans are a proof of concept for this – many people can intuitively predict how some experiences can change their preferences (e.g. “going to Vegas might make me a gambler”) without being generally good at planning or world modeling (which require abstract thinking).
* Large language models may be an example of this as well, since they have some capacity to reflect on goals (if prompted accordingly) without generalized planning ability.
If the model can predict that an SLT is likely to modify its goals in an undesirable way, we expect that it would choose not to undergo an SLT in the first place (e.g. by avoiding new internal algorithms or ways of thinking that could lead to an SLT).
[Paul Christiano outlined](https://www.lesswrong.com/posts/3pinFH3jerMzAvmza/on-how-various-plans-miss-the-hard-bits-of-the-alignment?commentId=qdkKysrRTs8joWRsf#comments) some specific ways for a goal-aligned model to preserve its goals during SLT depending on the mechanism for SLT:
* “Aligning the internal search [or natural selection inside the model] seems very similar to aligning SGD on the outside. […] Because the search is on the inside, we can’t directly apply our alignment insights to align it. Instead we need to [use ELK to] ensure that SGD learns to align the search.”
* “If our model is selecting cognitive actions, or designing new algorithms, then our core hope is that an aligned model will try to think in an aligned way. So if we’ve been succeeding at alignment so far then the model will be trying to stay aligned.”
* “One way this can go wrong is if our model wants to stay aligned but fails, e.g. because it identifies new techniques for thinking that themselves pose new alignment difficulties (just as we desire human flourishing but may instead implement AI systems that want paperclips). […] If you’ve succeeded at alignment so far, then your AI will also consider this a problem and will be trying to solve it. I think we should relate to our AI, discovering new ways to think that might pose new alignment difficulties, in the same way that we relate to future humans who may encounter alignment difficulties. The AI may solve the problem, or may implement policy solutions, or etc., and our role is to set them up for success just like we are trying to set up future humans for success.”
We also consider how important it would be for the goal-preservation process to go exactly right. If the SLT produces a strongly goal-directed model that is an expected utility maximizer, then the process has to hit a small set of utility functions that are human-compatible to maximize. However, it is not clear whether SLT would produce a utility maximizer. Returning to the example of humans undergoing an SLT, we can see that getting better at planning and world modeling made them more goal-directed but not maximally so (even with our advanced concepts and planning, we still have lots of inconsistent preferences and other limitations). It seems plausible that [coherence is really hard](https://www.lesswrong.com/posts/LDRQ5Zfqwi8GjzPYG/counterarguments-to-the-basic-ai-x-risk-case#A__Contra__superhuman_AI_systems_will_be__goal_directed__) and an SLT would not produce a completely coherent system.
Some ways a goal-aligned model could fail to preserve its goals:
* Model is bad at introspection and does not detect subtle changes to its goals.
* Model is bad at planning and doesn’t foresee how some modifications will eventually lead to changes in its goals. A special case of this is if the model becomes an expected utility maximizer during the SLT and the goal-preservation process does not produce a utility function that is ok to maximize.
* Effectively preserving beneficial goals might require the model to have a deep understanding of what humans want (rather than just a good enough proxy for that) which the model would likely not have before the capability transition.
Some ways that humans could fail to help the model to preserve its goals:
* We can’t reliably tell whether the model learned to align the internal search.
* We can’t communicate with the model well enough to help it overcome internal alignment difficulties.
Takeaways
---------
The above is our current model of the most promising plan for managing an SLT and how it could fail. The overall takeaways are:
* To evaluate how well any given alignment proposal addresses the SLT threat model, it’s useful to consider whether it aims to align a model before or during SLT.
* It seems more promising to look for a goal-aligned model before SLT and help it stay aligned, rather than trying to align a possibly misaligned model during SLT.
The core reasons to be skeptical of this plan are:
* (Step 1) Goal-aligned models are hard to find (and we may not develop good enough alignment techniques to find one)
* (Step 2) Generalized planning may be required for goal-reflection (in which case goal-reflection would only arise during an SLT)
* (Step 2) We may not be able to tell what’s going on inside the model to help it stay aligned. |
23c244b0-5442-4273-8b1c-4b1154039b36 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Washington, D.C.: Intro to Rationality
Discussion article for the meetup : Washington, D.C.: Intro to Rationality
WHEN: 05 June 2016 03:00:00PM (-0400)
WHERE: Reynolds Center
x-posted from the lesswrong-dc Google Group. Meetup location is in the courtyard on the first floor, on the other side of an information desk from either entrance.
This week, we're meeting to give an introduction/overview to the kind of rationalism that is talking about flaws in reasoning and how to be affected by them less. Bring questions! If you've been a lurker up till now, or have a friend you've been thinking of inviting, consider this me putting out a hand to welcome you in. If you think you might not be "the right kind of person" for whatever reason, you're explicitly encouraged to try it out.
A note on format: although it's not uncommon for the meetup topic to be abandoned in favor of whatever seems interesting, this time we will make an effort to keep things on topic.
Upcoming meetups:
* Jun. 12: Fun & Games
* Jun. 19: History of Science Fiction Fandom
Discussion article for the meetup : Washington, D.C.: Intro to Rationality |
d7387262-d475-4a6b-903b-a2fc0763b765 | trentmkelly/LessWrong-43k | LessWrong | Six Small Cohabitive Games
Previously: Competitive, Cooperative, and Cohabitive, Cohabitive Games So Far, Optimal Weave: A Prototype Cohabitive Game.
I like tinkering with game mechanics, and the idea of cohabitive games is an itch I keep trying to scratch. Here's six simple games I made over the last year or two as I was trying to get something satisfying, along with my notes on them. If you're looking for something to play at game night, I suggest Handlink then Commerce & Coconuts. None of them are well playtested at all.
Games
Dealerchip
Rules
Setup: Sit in a circle. Take a standard 52 deck of cards, without jokers. Shuffle, and deal evenly to all players. If you can't deal evenly, set the extra cards aside.
Each player looks at the top card of their hand. The suit of that card becomes the player's Favourite Suit. Remember it. Each player may now look at the rest of their hand.
Objective: Each card is worth points equal to its value. (Aces are 1, jacks are 11, queens are 12, kings are 13.) A card of your Favourite Suit is worth double. A jack of your Favourite Suit would be worth 22 points.
The goal is to get as many points as you can. The goal is NOT to get the most points — you don't care how many points everyone else gets. It could be one, it could be one million, you don't care. Aim to beat your personal best score.
Play: Start a timer for ten minutes.
Players may freely trade cards with each other. You don't have to tell people what your Favourite Suit is or what cards you have in your hand, but you do have to actually hand over the cards you say you're handing over as part of the trade. You can trade cards at rates other than one to one if you want.
At the end of ten minutes, stop trading. Count up your score, and see how well you did.
Notes
This is straightforwardly borrowed from Planecrash and Eliezer Yudkowsky's sketch of the basic Jellychip game.
Dealerchip is fairly simple, since each card has a straightforward and clearly valued worth to both players. Somet |
6b3b6616-6399-4de8-ab9a-065531459cfe | trentmkelly/LessWrong-43k | LessWrong | But what kinds of puppets are we?
Crossposted from world spirit sock puppet.
I watched The Social Dilemma last night. I took the problem that it warned of to be the following:
1. Social media and similar online services make their money by selling your attention to advertisers
2. These companies put vast optimization effort into manipulating you, to extract more attention
3. This means your behavior and attention is probably very shaped by these forces (which you can perhaps confirm by noting your own readiness to scroll through stuff on your phone)
This seems broadly plausible and bad, but I wonder if it isn’t quite that bad.
I heard the film as suggesting that your behavior and thoughts in general are being twisted by these forces. But lets distinguish between a system where huge resources are going into keeping you scrolling say—at which point an advertiser will pay for their shot at persuading you—and a system where those resources are going into manipulating you directly to do the things that the advertiser would like. In the first case, maybe you look at your phone too much, but there isn’t a clear pressure on your opinions or behavior besides pro phone. In the second case, maybe you end up with whatever opinions and actions someone paid the most for (this all supposing the system works). Let’s call these distorted-looking and distorted-acting.
While watching I interpreted the film suggesting the sort of broad manipulation that would come with distorted-acting, but thinking about it afterwards, isn’t the kind of optimization going on with social media actually distorted-looking? (Followed by whatever optimization the advertisers do to get you to do what they want, which I guess is of a kind with what they have always done, so at least not a new experimental horror.) I actually don’t really know. And maybe it isn’t a bright distinction.
Maybe optimization for you clicking on ads should be a different category (i.e. ‘distorted-clicking’). This seems close to distorted-looking, in that i |
dd4d065c-7e95-441e-8212-ca45256f3e06 | StampyAI/alignment-research-dataset/lesswrong | LessWrong | GPT-4 developer livestream
The detailed analysis of the screenshot and the interpretation of chicken scratch to a working website were both extremely impressive. As a human I found the hand drawing hard to read. I do not know how it was able to determine this was a "discord UI window", what did they train it on?
To me I think what is interesting is not the delta in performance over GPT-3, but the reality that GPT-4 is *almost* capable enough to research more advanced AI systems. Being able to read error messages, write code, read docs, read visual input would seem to be the minimum capabilities for Recursive Self Improvement.
 |
ed02b590-3eb3-450c-a546-9500e3121653 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Montpellier: Tentative first meetup
Discussion article for the meetup : Montpellier: Tentative first meetup
WHEN: 03 January 2013 03:00:00PM (+0100)
WHERE: Bagel House, 6 Rue Loys, Montpellier, France
I'm going to be in Montpellier for the holidays and I noticed that there isn't a single LW group in France. So this is a one-shot experimental meetup. I'll be there with a sign and a book, hoping for interesting people in the area to drop by.
The location is the best I could find through internet search, since I don't know the city all that well. It's open to suggestions.
The meetup could be held in French if everyone who comes is fluent. |
b5402024-2b50-4a89-a964-3a8dbe6467f6 | trentmkelly/LessWrong-43k | LessWrong | Weekly LW Meetups
This summary was posted to LW Main on April 29th. The following week's summary is here.
New meetups (or meetups with a hiatus of more than a year) are happening in:
* Nairobi mini-Meetup #1: Double Crux: 30 April 2016 03:34PM
Irregularly scheduled Less Wrong meetups are taking place in:
* Baltimore / UMBC Weekly Meetup: How To Actually Change Your Mind (part 3): 01 May 2016 03:00PM
* European Community Weekend: 02 September 2016 03:35PM
* San Francisco Meetup: Projects: 01 May 2016 06:15PM
The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:
* Raleigh, NC (RTLW) Discussion Meetup: 07 May 2022 07:30PM
* Sydney Rationality Dojo - May: 01 May 2016 04:00PM
* Sydney Rationality Dojo - July: 03 July 2016 04:00PM
* Washington, D.C.: Utopias: 01 May 2016 03:30PM
Locations with regularly scheduled meetups: Austin, Berkeley, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, Kraków, London, Madison WI, Melbourne, Moscow, Mountain View, New Hampshire, New York, Philadelphia, Research Triangle NC, Seattle, Sydney, Tel Aviv, Toronto, Vienna, Washington DC, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers and a Slack channel for daily discussion and online meetups on Sunday night US time.
If you'd like to talk with other LW-ers face to face, and there is no meetup in your area, consider starting your own meetup; it's easy (more resources here). Check one out, stretch your rationality skills, build community, and have fun!
In addition to the handy sidebar of upcoming meetups, a meetup overview is posted on the front page every Friday. These are an attempt to collect information on all the meetups happening in upcoming weeks. The best way to get your meetup featured is still to use the Add New Meetup feature, but you'll also have the benefit of having your meetup mentioned in a weekly overview |
321acebd-c5b5-487a-82ea-ccb502a3d8af | trentmkelly/LessWrong-43k | LessWrong | What do you think would be the best investment policy for a cryonics trust?
Assume this policy will be followed by someone else, potentially for centuries, unless your cryonics revival timeline is shorter.
You can answer differently for different trusts; notably:
1. Trust with the goal of maintaining a patient in stasis indefinitely, and providing the fund for revival
2. Trust with money that you will gain access to again once you're reanimated |
855d6202-28bf-441b-a474-e5708f2e3a02 | trentmkelly/LessWrong-43k | LessWrong | A small improvement to Wikipedia page on Pareto Efficiency
Note: I would have done this as a Quick Take, but it doesn't allow images.
In the spirit of How I got 4.2M YouTube views without making a single video, I improved a diagram on the wikipedia page for Pareto Efficiency.
ORIGINAL VERSION
NEW VERSION
Based on the text, I don't think the author understood that Cooperate/Cooperate isn't the only Pareto efficient outcome ("Thus Both Cooperate is Pareto-efficient"). It's a small change and I think the text is more in need of improving, but this is all I can do now.
|
13a9ddd1-1c63-45b7-8e29-fe0e3c31a388 | trentmkelly/LessWrong-43k | LessWrong | LessWrong virtual meetup this Saturday evening
Several months ago jwhendy called for people who can't make it to regular LW meetups (or just can't get enough of the LW crowd) to meet online instead. Several months later, a small number of us still do, on a near-weekly basis. We socialise, teach each other about our fields, throw theories around and discuss LW material. We're looking for fresh blood after losing some of our regulars to real life commitments.
Meetup details:
Time: Saturday, 19th November, 9pm EST
Location: Skype (add me as erratio1 on there)
Format: Variable. The last few meetups have been text-only due to connection problems at my end, but since the overall quality of the meetup was much higher when we had voice chat I'd like to get back to that if possible. Although if a majority of interested people would prefer text-only, we can do that too.
For those who can't make it to this meetup but are interested, comment with your availabilities here or at our Google Group so we can potentially accommodate you in future weeks. |
b80cc633-1c02-4264-9032-1c64d4e88c8b | trentmkelly/LessWrong-43k | LessWrong | "Now here's why I'm punching you..."
Related: be nice, at least until you can coordinate meanness.
A premise of this post is that punching people is sometimes better than the alternatives.
I mean that literally, but mostly metaphorically. Things I take as metaphorical punching include name calling, writing angry tweets to or about someone, ejecting them from a group, callout posts, and arguing that we should punch them.
Given that punching people is sometimes better than the alternatives, I think we need to be able to have conversations about when "sometimes" is. And indeed we can and do have those conversations. Many words have been spilled on the subject.
But I think it's probably a good idea to try to avoid having those conversations while actually punching people.
Here's what I mean. Alice thinks that punching Bob is better than the alternatives. But she thinks that if she just starts punching, Carol and Dave and Eve might not understand why. Not even if she tells them what Bob has done. She thinks punching Bob is better than the alternatives, but she thinks the reasons for that are slightly complicated and haven't previously been articulated very well, at least not in a way that makes them common knowledge.
So she writes an essay in which:
1. She proposes a theory for when punching people is better than the alternatives. (She readily admits that the theory is not complete, nor is it intended to be, but it covers part of the space.)
2. She describes the situation with Bob, and how the theory justifies punching him.
3. She punches Bob.
I think this could be a mistake. I think she should maybe split that post into at least two parts, published separately. In the first part, she proposes the theory with no mention of Bob. Then, if Carol and Dave and Eve seem to more-or-less agree with the theory, she can also publish the part where it relates to Bob, and punch him.
I think this has a few advantages.
* Suppose Alice can't convince anyone that the theory holds. Then Bob is kept out of th |
8eee6d14-bcfd-4e0a-9fa8-25f4629b32c0 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Stuart Russell and Melanie Mitchell on Munk Debates
*Financial status: This is independent research, now supported by a grant.*
*Epistemic status: Reflections on a debate about AI.*
---
Earlier this year, The Munk Debates podcast organized a [debate between Stuart Russell and Melanie Mitchell](https://www.iheart.com/podcast/962-the-munk-debates-podcast-p-52131924/episode/be-it-resolved-the-quest-for-77079807) on the question of whether AI poses an existential risk. The motion was:
>
> *Be it resolved, the quest for true AI is one of the great existential risks of our time.*
>
>
>
Stuart was for, Melanie was against.
A significant portion of the debate was about the nature of the term "intelligence". Melanie contended that a truly intelligent machine would understand what we really mean when we give it incomplete instructions, or else not deserve the mantle of "truly intelligent". She said that if we built a general-purpose AI and asked it to do some simple task, and the machine burned up all the energy in the universe in service of that simple task, then the AI would have to lack some common sense that even humans have, so it would be an error to say that such an AI had "superhuman intelligence".
Stuart responded by noting that powerful AI systems could plausibly pursue goals that are either beneficial or harmful to humans (the orthogonality thesis), and that it seems very difficult to specify what we mean by "good" in a single pre-specified objective function (the argument from fragility of human values).
It seemed to me that Stuart and Melanie were using the word "intelligent" to refer to different concepts. Start, I believe, was using the word to refer to systems that have the capacity to flexibly influence the future in service of a wide variety of goals, while Melanie, I think, was using the word to refer to systems that have a kind of friendly aliveness that I think is close to what we mean by "friendly AI" or "aligned superintelligence".
I am personally most used to using the word "intelligence" as Stuart used it, but I think Melanie was also pointing to a real phenomenon in the world, and perhaps even one that accords more closely with common English-language use of the word. For example, I might not intuitively think of "intelligent" as appropriate to describe a corporation that is highly effective at achieving a myopic and rigidly held goal, even if I did view that corporation as very powerful. In contrast, there is a certain sense of the word "intelligent" that is highly appropriate to describe the natural resonance that a dog or horse forms with me, even though such an animal does not steer the future very much. This is not the way that I most often use the word "intelligent" but it does, I think, point to something real.
In the same way, a paperclip maximizer might possess overwhelming power to convert matter and energy into paperclips, yet it would not be a completely unreasonable use of the English word "intelligent" to say that a paperclip maximizer is not "intelligent". This is a purely terminological point concerning only the use of the word "intelligent" in contemporary English.
If we taboo the word "intelligent" then there are two real-world phenomena that were being pointed at in the debate, one by Stuart, and one by Melanie. Stuart was, I think, pointing at the phenomenon of machines that exert flexible influence over the future. Melanie was, I think, pointing at the phenomenon of being friendly or benevolent or helpful. Both are extremely important concepts, and it seems to me that the whole problem of AI alignment is about bringing that which is powerful together with that which is friendly or benevolent or helpful.
I don’t think Stuart and Melanie were hitting on a real disagreement about the nature of powerful autonomous systems or whether they might present dangers to life on Earth, but rather were using the word "intelligent" to refer to different things. I think it would have been a more satisfying debate if Stuart and Melanie had noticed this terminological difference in their language and then debated the actual underlying issue, which is, to my mind, whether there are existential risks posed by the development of systems that possess intelligence in the way that Stuart was using the term but do *not* possess intelligence in the way that Melanie was using the term.
Two questions arising from the debate
-------------------------------------
The most intriguing question to me, and the reason that I’m writing this post at all, is that the debate seemed to illuminate two really deep questions:
1. *What the heck does intelligence in the sense that Stuart used the term really consist of*?
2. *What the heck does intelligence in the sense that Melanie used the term really consist of*?
We actually do not have a formal understanding of how to construct physical entities that possess intelligence in the sense that Stuart used the term. We see that the phenomenon clearly exists in the world, and it even seems that we might soon construct entities with this kind of intelligence by searching over huge spaces of possible algorithms, yet we do not understand the nature of this phenomenon.
Yet even more incredible to me is that I am helped by people and systems in the world every day, and I sometimes help others out, and I form some kind of understanding of how to be helpful in different circumstances. I gradually learn what is a helpful way to be present with a friend who is going through emotional turmoil, or what is a helpful way to address a group of people about to embark on a project together, and there are these breakthrough discoveries in the domain of how to be helpful that seem applicable across many circumstances, yet seem very difficult to convey. We do not have a formal understanding of this phenomenon, either, yet it seems to me that we absolutely must discover one in order to build powerful autonomous systems that are beneficial to all life on Earth. |
81957ef0-7e79-4235-bf92-14491300c164 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Research Priorities for Robust and Beneficial Artificial Intelligence
I Short-term Research Priorities
---------------------------------
###
i.1 Optimizing AI’s Economic Impact
The successes of industrial applications of AI, from manufacturing to information services, demonstrate a growing impact on the economy, although there is disagreement about the exact nature of this impact and on how to distinguish between the effects of AI and those of other information technologies. Many economists and computer scientists agree that there is valuable research to be done on how to maximize the economic benefits of AI while mitigating adverse effects, which could include increased inequality and unemployment ([mokyr2014secular,](#bib.bib2) ; [brynjolfsson2014second,](#bib.bib3) ; [frey2013future,](#bib.bib4) ; [glaeser2014secular,](#bib.bib5) ; [shanahan2015technological,](#bib.bib6) ; [nilsson1984artificial,](#bib.bib7) ; [manyika2013disruptive,](#bib.bib8) ). Such considerations motivate a range of research directions, spanning areas from economics to psychology. Below are a few examples that should by no means be interpreted as an exhaustive list.
1. Labor market forecasting: When and in what order should we expect various jobs to become automated ([frey2013future,](#bib.bib4) )? How will this affect the wages of less skilled workers, the creative professions, and different kinds of information workers? Some have have argued that AI is likely to greatly increase the overall wealth of humanity as a whole ([brynjolfsson2014second,](#bib.bib3) ). However, increased automation may push income distribution further towards a power law ([brynjolfsson2014labor,](#bib.bib9) ), and the resulting disparity may fall disproportionately along lines of race, class, and gender; research anticipating the economic and societal impact of such disparity could be useful.
2. Other market disruptions: Significant parts of the economy, including finance, insurance, actuarial, and many consumer markets, could be susceptible to disruption through the use of AI techniques to learn, model, and predict human and market behaviors. These markets might be identified by a combination of high complexity and high rewards for navigating that complexity ([manyika2013disruptive,](#bib.bib8) ).
3. Policy for managing adverse effects: What policies could help increasingly automated societies flourish? For example, Brynjolfsson and McAfee ([brynjolfsson2014second,](#bib.bib3) ) explore various policies for incentivizing development of labor-intensive sectors and for using AI-generated wealth to support underemployed populations. What are the pros and cons of interventions such as educational reform, apprenticeship programs, labor-demanding infrastructure projects, and changes to minimum wage law, tax structure, and the social safety net ([glaeser2014secular,](#bib.bib5) )? History provides many examples of subpopulations not needing to work for economic security, ranging from aristocrats in antiquity to many present-day citizens of Qatar. What societal structures and other factors determine whether such populations flourish? Unemployment is not the same as leisure, and there are deep links between unemployment and unhappiness, self-doubt, and isolation ([hetschko2014changing,](#bib.bib10) ; [clark1994unhappiness,](#bib.bib11) ); understanding what policies and norms can break these links could significantly improve the median quality of life. Empirical and theoretical research on topics such as the basic income proposal could clarify our options ([van1992arguing,](#bib.bib12) ; [widerquist2013basic,](#bib.bib13) ).
4. Economic measures: It is possible that economic measures such as real GDP per capita do not accurately capture the benefits and detriments of heavily AI-and-automation-based economies, making these metrics unsuitable for policy purposes ([mokyr2014secular,](#bib.bib2) ). Research on improved metrics could be useful for decision-making.
###
i.2 Law and Ethics Research
The development of systems that embody significant amounts of intelligence and autonomy leads to important legal and ethical questions whose answers impact both producers and consumers of AI technology. These questions span law, public policy, professional ethics, and philosophical ethics, and will require expertise from computer scientists, legal experts, political scientists, and ethicists. For example:
1. Liability and law for autonomous vehicles: If self-driving cars cut the roughly 40,000 annual US traffic fatalities in half, the car makers might get not 20,000 thank-you notes, but 20,000 lawsuits. In what legal framework can the safety benefits of autonomous vehicles such as drone aircraft and self-driving cars best be realized ([vladeck2014machines,](#bib.bib14) )? Should legal questions about AI be handled by existing (software- and internet-focused) “cyberlaw”, or should they be treated separately ([calo2014robotics,](#bib.bib15) )? In both military and commercial applications, governments will need to decide how best to bring the relevant expertise to bear; for example, a panel or committee of professionals and academics could be created, and Calo has proposed the creation of a Federal Robotics Commission ([calo2014case,](#bib.bib16) ).
2. Machine ethics: How should an autonomous vehicle trade off, say, a small probability of injury to a human against the near-certainty of a large material cost? How should lawyers, ethicists, and policymakers engage the public on these issues?
Should such trade-offs be the subject of national standards?
3. Autonomous weapons: Can lethal autonomous weapons be made to comply with humanitarian law ([churchill2000autonomous,](#bib.bib17) )? If, as some organizations have suggested, autonomous weapons should be banned ([docherty2012losing,](#bib.bib18) ), is it possible to develop a precise definition of autonomy for this purpose, and can such a ban practically be enforced? If it is permissible or legal to use lethal autonomous weapons, how should these weapons be integrated into the existing command-and-control structure so that responsibility and liability remain associated with specific human actors? What technical realities and forecasts should inform these questions, and how should “meaningful human control” over weapons be defined ([roff2013responsibility,](#bib.bib19) ; [roff2014strategic,](#bib.bib20) ; [anderson2014adapting,](#bib.bib21) )? Are autonomous weapons likely to reduce political aversion to conflict, or perhaps result in “accidental” battles or wars ([asaro2008just,](#bib.bib22) )? Would such weapons become the tool of choice for oppressors or terrorists? Finally, how can transparency and public discourse best be encouraged on these issues?
4. Privacy: How should the ability of AI systems to interpret the data obtained from surveillance cameras, phone lines, emails, etc., interact with the right to privacy? How will privacy risks interact with cybersecurity and cyberwarfare ([singer2014cybersecurity,](#bib.bib23) )?
Our ability to take full advantage of the synergy between AI and big data will depend in part on our ability to manage and preserve privacy ([manyika2011big,](#bib.bib24) ; [agrawal2000privacy,](#bib.bib25) ).
5. Professional ethics: What role should computer scientists play in the law and ethics of AI development and use? Past and current projects to explore these questions include the AAAI 2008–09 Presidential Panel on Long-Term AI Futures ([horvitz2009interim,](#bib.bib1) ), the EPSRC Principles of Robotics ([boden2011principles,](#bib.bib26) ), and recently announced programs such as Stanford’s One-Hundred Year Study of AI and the AAAI Committee on AI Impact and Ethical Issues.
From a public policy perspective, AI (like any powerful new technology) enables both great new benefits and novel pitfalls to be avoided, and appropriate policies can ensure that we can enjoy the benefits while risks are minimized. This raises policy questions such as these:
1. What is the space of policies worth studying, and how might they be enacted?
2. Which criteria should be used to determine the merits of a policy? Candidates include
verifiability of compliance, enforceability, ability to reduce risk, ability to avoid stifling desirable technology development, adoptability, and ability to adapt over time to changing circumstances.
###
i.3 Computer Science Research for Robust AI
As autonomous systems become more prevalent in society, it becomes increasingly important that they robustly behave as intended. The development of autonomous vehicles, autonomous trading systems, autonomous weapons, etc. has therefore stoked interest in high-assurance systems where strong robustness guarantees can be made; Weld and Etzioni have argued that “society will reject autonomous agents unless we have some credible means of making them safe” ([weld1994first,](#bib.bib27) ). Different ways in which an AI system may fail to perform as desired correspond to different areas of robustness research:
1. Verification: how to prove that a system satisfies certain desired formal properties. (“Did I build the system right?”)
2. Validity: how to ensure that a system that meets its formal requirements does not have unwanted behaviors and consequences. (“Did I build the right system?”)
3. Security: how to prevent intentional manipulation by unauthorized parties.
4. Control: how to enable meaningful human control over an AI system after it begins to operate. (“OK, I built the system wrong; can I fix it?”)
####
i.3.1 Verification
By verification, we mean methods that yield high confidence that a system will satisfy a set of formal constraints. When possible, it is desirable for systems in safety-critical situations, e.g. self-driving cars, to be verifiable.
Formal verification of software has advanced significantly in recent years: examples include the seL4 kernel ([klein2009sel4,](#bib.bib28) ), a complete, general-purpose operating-system kernel that has been mathematically checked against a formal specification to give a strong guarantee against crashes and unsafe operations, and HACMS, DARPA’s “clean-slate, formal methods-based approach” to a set of high-assurance software tools ([fisher2012hacms,](#bib.bib29) ). Not only should it be possible to build AI systems on top of verified substrates; it should also be possible to verify the designs of the AI systems themselves, particularly if they follow a “componentized architecture”, in which guarantees about individual components can be combined according to their connections to yield properties of the overall system. This mirrors the agent architectures used in Russell and Norvig (2010), which separate an agent into distinct modules (predictive models, state estimates, utility functions, policies, learning elements, etc.), and has analogues in some formal results on control system designs. Research on richer kinds of agents – for example, agents with layered architectures, anytime components, overlapping deliberative and reactive elements, metalevel control, etc. – could contribute to the creation of verifiable agents, but we lack the formal “algebra” to properly define, explore, and rank the space of designs.
Perhaps the most salient difference between verification of traditional software and verification of AI systems is that the correctness of traditional software is defined with respect to a fixed and known machine model, whereas AI systems – especially robots and other embodied systems – operate in environments that are at best partially known by the system designer. In these cases, it may be practical to verify that the system acts correctly given the knowledge that it has, avoiding the problem of modelling the real environment ([dennis2013practical,](#bib.bib30) ). A lack of design-time knowledge also motivates the use of learning algorithms within the agent software, and verification becomes more difficult: statistical learning theory gives so-called ϵ-δ (probably approximately correct) bounds, mostly for the somewhat unrealistic settings of supervised learning from i.i.d. data and single-agent reinforcement learning with simple architectures and full observability, but even then requiring prohibitively large sample sizes to obtain meaningful guarantees.
Work in adaptive control theory ([aastrom2013adaptive,](#bib.bib31) ), the theory of so-called cyberphysical systems ([platzer2010logical,](#bib.bib32) ), and verification of hybrid or robotic systems ([alur2011formal,](#bib.bib33) ; [winfield2014towards,](#bib.bib34) ) is highly relevant but also faces the same difficulties. And of course all these issues are laid on top of the standard problem of proving that a given software artifact does in fact correctly implement, say, a reinforcement learning algorithm of the intended type. Some work has been done on verifying neural network applications ([pulina2010abstraction,](#bib.bib35) ; [taylor2006methods,](#bib.bib36) ; [schumann2010applications,](#bib.bib37) ) and the notion of partial programs ([andre2002state,](#bib.bib38) ; [spears2006assuring,](#bib.bib39) ) allows the designer to impose arbitrary “structural” constraints on behavior, but much remains to be done before it will be possible to have high confidence that a learning agent will learn to satisfy its design criteria in realistic contexts.
####
i.3.2 Validity
A verification theorem for an agent design has the form, “If
environment satisfies assumptions ϕ then behavior satisfies
requirements ψ.” There are two ways in which a verified agent
can, nonetheless, fail to be a beneficial agent in actuality: first,
the environmental assumption ϕ is false in the real world,
leading to behavior that violates the requirements ψ; second, the
system may satisfy the formal requirement ψ but still behave in
ways that we find highly undesirable in practice.
It may be the case that
this undesirability is a consequence of satisfying ψ when
ϕ is violated; i.e., had ϕ held the undesirability would not have been manifested; or it may be the case
that the requirement ψ is erroneous in itself. Russell and Norvig (2010) provide a simple example: if a robot vacuum cleaner is
asked to clean up as much dirt as possible, and has an action to dump the contents of its dirt container, it will repeatedly dump and clean up the same dirt.
The requirement should focus not on dirt cleaned up but on cleanliness of the floor.
Such specification errors are ubiquitous in software verification, where it is commonly observed that writing correct specifications can be
harder than writing correct code. Unfortunately, it is not possible to verify the specification:
the notions of “beneficial” and “desirable” are not separately made formal,
so one cannot straightforwardly prove that satisfying ψ necessarily leads to desirable behavior and a beneficial agent.
In order to build systems that robustly behave well, we of course need to decide what “good behavior” means in each application domain. This ethical question is tied intimately to questions of what engineering techniques are available, how reliable these techniques are, and what trade-offs can be made – all areas where computer science, machine learning, and broader AI expertise is valuable. For example, Wallach and Allen (2008) argue that a significant consideration is the computational expense of different behavioral standards (or ethical theories): if a standard cannot be applied efficiently enough to guide behavior in safety-critical situations, then cheaper approximations may be needed. Designing simplified rules – for example, to govern a self-driving car’s decisions in critical situations – will likely require expertise from both ethicists and computer scientists. Computational models of ethical reasoning may shed light on questions of computational expense and the viability of reliable ethical reasoning methods ([asaro2006should,](#bib.bib40) ; [sullins2011introduction,](#bib.bib41) ).
####
i.3.3 Security
Security research can help make AI more robust.
As AI systems are used in an increasing number of critical roles, they will take up an increasing proportion of cyber-attack surface area. It is also probable that AI and machine learning techniques will themselves be used in cyber-attacks.
Robustness against exploitation at the low level is closely tied to verifiability and freedom from bugs. For example, the DARPA SAFE program aims to build an integrated hardware-software system with a flexible metadata rule engine, on which can be built memory safety, fault isolation, and other protocols that could improve security by preventing exploitable flaws ([dehon2011preliminary,](#bib.bib42) ). Such programs cannot eliminate all security flaws (since verification is only as strong as the assumptions that underly the specification), but could significantly reduce vulnerabilities of the type exploited by the recent “Heartbleed” and “Bash” bugs. Such systems could be preferentially deployed in safety-critical applications, where the cost of improved security is justified.
At a higher level, research into specific AI and machine learning techniques may become increasingly useful in security. These techniques could be applied to the detection of intrusions ([lane2000machine,](#bib.bib43) ), analyzing malware ([rieck2011automatic,](#bib.bib44) ), or detecting potential exploits in other programs through code analysis ([brun2004finding,](#bib.bib45) ). It is not implausible that cyberattack between states and private actors will be a risk factor for harm from near-future AI systems, motivating research on preventing harmful events. As AI systems grow more complex and are networked together, they will have to intelligently manage their trust, motivating research on statistical-behavioral trust establishment ([probst2007statistical,](#bib.bib46) ) and computational reputation models ([sabater2005review,](#bib.bib47) ).
####
i.3.4 Control
For certain types of safety-critical AI systems – especially vehicles and weapons platforms – it may be desirable to retain some form of meaningful human control, whether this means a human in the loop, on the loop ([hexmoor2009natural,](#bib.bib48) ; [parasuraman2000model,](#bib.bib49) ), or some other protocol. In any of these cases, there will be technical work needed in order to ensure that meaningful human control is maintained ([united2014weaponization,](#bib.bib50) ).
Automated vehicles are a test-bed for effective control-granting techniques. The design of systems and protocols for transition between automated navigation and human control is a promising area for further research. Such issues also motivate broader research on how to optimally allocate tasks within human–computer teams, both for identifying situations where control should be transferred, and for applying human judgment efficiently to the highest-value decisions.
Ii Long-term research priorities
---------------------------------
A frequently discussed long-term goal of some AI researchers is to develop systems that can learn from experience with human-like breadth and surpass human performance in most cognitive tasks, thereby having a major impact on society.
If there is a non-negligible probability that these efforts will succeed in the foreseeable future, then additional current research beyond that mentioned in the previous sections will be motivated as exemplified below, to help ensure that the resulting AI will be robust and beneficial.
Assessments of this success probability vary widely between researchers, but few would argue with great confidence that the probability is negligible, given the track record of such predictions. For example, Ernest Rutherford, arguably the greatest nuclear physicist of his time, said in 1933 – less than 24 hours before Szilard’s invention of the nuclear chain reaction – that nuclear energy was “moonshine” ([ap1933atom,](#bib.bib51) ), and
Astronomer Royal Richard Woolley called interplanetary travel “utter bilge” in 1956 ([Woolley1956,](#bib.bib52) ).
Moreover, to justify a modest investment in this AI robustness research, this probability need not be high, merely non-negligible, just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.
###
ii.1 Verification
Reprising the themes of short-term research, research enabling verifiable low-level software and hardware can eliminate large classes of bugs and problems in general AI systems; if such systems become increasingly powerful and safety-critical, verifiable safety properties will become increasingly valuable. If the theory of extending verifiable properties from components to entire systems is well understood, then even very large systems can enjoy certain kinds of safety guarantees, potentially aided by techniques designed explicitly to handle learning agents and high-level properties. Theoretical research, especially if it is done explicitly with very general and capable AI systems in mind, could be particularly useful.
A related verification research topic that is distinctive to long-term concerns is the verifiability of systems that modify, extend, or improve themselves, possibly many times in succession ([Good1965,](#bib.bib53) ; [Vinge1993,](#bib.bib54) ). Attempting to straightforwardly apply formal verification tools to this more general setting presents new difficulties, including the challenge that a formal system that is sufficiently powerful cannot use formal methods in the obvious way to gain assurance about the accuracy of functionally similar formal systems, on pain of
inconsistency via Gödel’s incompleteness ([fallenstein2014vingean,](#bib.bib55) ; [weaver2013paradoxes,](#bib.bib56) ). It is not yet clear whether or how this problem can be overcome, or whether similar problems will arise with other verification methods of similar strength.
Finally, it is often difficult to actually apply formal verification techniques to physical systems, especially systems that have not been designed with verification in mind. This motivates research pursuing a general theory that links functional specification to physical states of affairs. This type of theory would allow use of formal tools to anticipate and control behaviors of systems that approximate rational agents, alternate designs such as satisficing agents, and systems that cannot be easily described in the standard agent formalism (powerful prediction systems, theorem-provers, limited-purpose science or engineering systems, etc.). It may also be that such a theory could allow rigorous demonstrations that systems are constrained from taking certain kinds of actions or performing certain kinds of reasoning.
###
ii.2 Validity
As in the short-term research priorities, validity is concerned with undesirable behaviors that can arise despite a system’s formal correctness. In the long term, AI systems might become more powerful and autonomous, in which case failures of validity could carry correspondingly higher costs.
Strong guarantees for machine learning methods, an area we highlighted for short-term validity research, will also be important for long-term safety. To maximize the long-term value of this work, machine learning research might focus on the types of unexpected generalization that would be most problematic for very general and capable AI systems. In particular, it might aim to understand theoretically and practically how learned representations of high-level human concepts could be expected to generalize (or fail to) in radically new contexts ([tegmark2015friendly,](#bib.bib57) ). Additionally, if some concepts could be learned reliably, it might be possible to use them to define tasks and constraints that minimize the chances of unintended consequences even when autonomous AI systems become very general and capable. Little work has been done on this topic, which suggests that both theoretical and experimental research may be useful.
Mathematical tools such as formal logic, probability, and decision theory have yielded significant insight into the foundations of reasoning and decision-making. However, there are still many open problems in the foundations of reasoning and decision. Solutions to these problems may make the behavior of very capable systems much more reliable and predictable. Example research topics in this area include reasoning and decision under bounded computational resources à la Horvitz and Russell ([horvitz1987reasoning,](#bib.bib58) ; [russell1995provably,](#bib.bib59) ), how to take into account correlations between AI systems’ behaviors and those of their environments or of other agents ([tennenholtz2004program,](#bib.bib60) ; [AAAIW148833,](#bib.bib61) ; [hintze2014problem,](#bib.bib62) ; [halpern2013game,](#bib.bib63) ; [soares2014toward,](#bib.bib64) ), how agents that are embedded in their environments should reason ([soares2014formalizing,](#bib.bib65) ; [orseau2012space,](#bib.bib66) ), and how to reason about uncertainty over logical consequences of beliefs or other deterministic computations ([soares2014questions,](#bib.bib67) ). These topics may benefit from being considered together, since they appear deeply linked ([halpern2011don,](#bib.bib68) ; [halpern2014decision,](#bib.bib69) ).
In the long term, it is plausible that we will want to make agents that act autonomously and powerfully across many domains. Explicitly specifying our preferences in broad domains in the style of near-future machine ethics may not be practical, making “aligning” the values of powerful AI systems with our own values and preferences difficult ([soares2014value,](#bib.bib70) ; [soares2014aligning,](#bib.bib71) ). Consider, for instance, the difficulty of creating a utility function that encompasses an entire body of law; even a literal rendition of the law is far beyond our current capabilities, and would be highly unsatisfactory in practice (since law is written assuming that it will be interpreted and applied in a flexible, case-by-case way by humans who, presumably, already embody the background value systems that artificial agents may lack). Reinforcement learning raises its own problems: when systems become very capable and general, then an effect similar to Goodhart’s Law is likely to occur, in which sophisticated agents attempt to manipulate or directly control their reward signals ([bostrom2014superintelligence,](#bib.bib72) ). This motivates research areas that could improve our ability to engineer systems that can learn or acquire values at run-time. For example, inverse reinforcement learning may offer a viable approach, in which a system infers the preferences of another rational or nearly rational actor by observing its behavior ([russell1998learning,](#bib.bib73) ; [ng2000algorithms,](#bib.bib74) ). Other approaches could use different assumptions about underlying cognitive models of the actor whose preferences are being learned ([chu2005preference,](#bib.bib75) ), or could be explicitly inspired by the way humans acquire ethical values. As systems become more capable, more epistemically difficult methods could become viable, suggesting that research on such methods could be useful; for example, Bostrom (2014) reviews preliminary work on a variety of methods for specifying goals indirectly.
###
ii.3 Security
It is unclear whether long-term progress in AI will make the overall problem of security easier or harder; on one hand, systems will become increasingly complex in construction and behavior and AI-based cyberattacks may be extremely effective, while on the other hand, the use of AI and machine learning techniques along with significant progress in low-level system reliability may render hardened systems much less vulnerable than today’s. From a cryptographic perspective, it appears that this conflict favors defenders over attackers; this may be a reason to pursue effective defense research wholeheartedly.
Although the topics described in the near-term security research section above may become increasingly important in the long term, very general and capable systems will pose distinctive security problems. In particular, if the problems of validity and control are not solved, it may be useful to create “containers” for AI systems that could have undesirable behaviors and consequences in less controlled environments ([yampolskiy2012leakproofing,](#bib.bib76) ). Both theoretical and practical sides of this question warrant investigation. If the general case of AI containment turns out to be prohibitively difficult, then it may be that designing an AI system and a container in parallel is more successful, allowing the weaknesses and strengths of the design to inform the containment strategy ([bostrom2014superintelligence,](#bib.bib72) ). The design of anomaly detection systems and automated exploit-checkers could be of significant help. Overall, it seems reasonable to expect this additional perspective – defending against attacks from “within” a system as well as from external actors – will raise interesting and profitable questions in the field of computer security.
###
ii.4 Control
It has been argued that very general and capable AI systems operating autonomously to accomplish some task will often be subject to effects that increase the difficulty of maintaining meaningful human control ([omohundro2007nature,](#bib.bib77) ; [bostrom2012superintelligent,](#bib.bib78) ; [bostrom2014superintelligence,](#bib.bib72) ; [shanahan2015technological,](#bib.bib6) ). Research on systems that are not subject to these effects, minimize their impact, or allow for reliable human control could be valuable in preventing undesired consequences, as could work on reliable and secure test-beds for AI systems at a variety of capability levels.
If an AI system is selecting the actions that best allow it to complete a given task, then avoiding conditions that prevent the system from continuing to pursue the task is a natural subgoal ([omohundro2007nature,](#bib.bib77) ; [bostrom2012superintelligent,](#bib.bib78) ) (and conversely, seeking unconstrained situations is sometimes a useful heuristic ([wissner2013causal,](#bib.bib79) )). This could become problematic, however, if we wish to repurpose the system, to deactivate it, or to significantly alter its decision-making process; such a system would rationally avoid these changes. Systems that do not exhibit these behaviors have been termed corrigible systems ([soares2014corrigibility,](#bib.bib80) ), and both theoretical and practical work in this area appears tractable and useful. For example, it may be possible to design utility functions or decision processes so that a system will not try to avoid being shut down or repurposed ([soares2014corrigibility,](#bib.bib80) ), and theoretical frameworks could be developed to better understand the space of potential systems that avoid undesirable behaviors ([hibbard2012avoiding,](#bib.bib81) ; [hibbard2014ethical,](#bib.bib82) ; [hibbard2015self,](#bib.bib83) ).
It has been argued that another natural subgoal for AI systems pursuing a given goal is the acquisition of fungible resources of a variety of kinds: for example, information about the environment, safety from disruption, and improved freedom of action are all instrumentally useful for many tasks ([omohundro2007nature,](#bib.bib77) ; [bostrom2012superintelligent,](#bib.bib78) ). Hammond et al (1995) gives the label stabilization to the more general set of cases where “due to the action of the agent, the environment comes to be better fitted to the agent as time goes on”. This type of subgoal could lead to undesired consequences, and a better understanding of the conditions under which resource acquisition or radical stabilization is an optimal strategy (or likely to be selected by a given system) would be useful in mitigating its effects. Potential research topics in this area include “domestic” goals that are limited in scope in some way ([bostrom2014superintelligence,](#bib.bib72) ), the effects of large temporal discount rates on resource acquisition strategies, and experimental investigation of simple systems that display these subgoals.
Finally, research on the possibility of superintelligent machines or rapid, sustained self-improvement (“intelligence explosion”) has been highlighted by past and current projects on the future of AI as potentially valuable to the project of maintaining reliable control in the long term. The AAAI 2008–09 Presidential Panel on Long-Term AI Futures’ “Subgroup on Pace, Concerns, and Control” stated that
>
> There was overall skepticism about the prospect of an intelligence explosion… Nevertheless, there was a shared sense that additional research would be valuable on methods for understanding and verifying the range of behaviors of complex computational systems to minimize unexpected outcomes. Some panelists recommended that more research needs to be done to better define “intelligence explosion,” and also to better formulate different classes of such accelerating intelligences. Technical work would likely lead to enhanced understanding of the likelihood of such phenomena, and the nature, risks, and overall outcomes associated with different conceived variants ([horvitz2009interim,](#bib.bib1) ).
>
>
>
Stanford’s One-Hundred Year Study of Artificial Intelligence includes “Loss of Control of AI systems” as an area of study, specifically highlighting concerns over the possibility that
>
> …we could one day lose control of AI systems via the rise of superintelligences that do not act in accordance with human wishes – and that such powerful systems would threaten humanity. Are such dystopic outcomes possible? If so, how might these situations arise? …What kind of investments in research should be made to better understand and to address the possibility of the rise of a dangerous superintelligence or the occurrence of an “intelligence explosion”? ([horvitz2014hundred,](#bib.bib84) )
>
>
>
Research in this area could include any of the long-term research priorities listed above, as well as theoretical and forecasting work on intelligence explosion and superintelligence ([chalmers2010singularity,](#bib.bib85) ; [bostrom2014superintelligence,](#bib.bib72) ), and could extend or critique existing approaches begun by groups such as the Machine Intelligence Research Institute ([soares2014aligning,](#bib.bib71) ).
Iii Conclusion
---------------
In summary, success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to research how to maximize these benefits while avoiding potential pitfalls. The research agenda outlined in this paper, and the concerns that motivate it, have been called “anti-AI”, but we vigorously contest this characterization. It seems self-evident that the growing capabilities of AI are leading to an increased potential for impact on human society. It is the duty of AI researchers to ensure that the future impact is beneficial. We believe that this is possible, and hope that this research agenda provides a helpful step in the right direction.
Iv Authors
-----------
Stuart Russell is a Professor of Computer Science at UC Berkeley. His research covers many aspects of artificial intelligence and machine learning. He is a fellow of AAAI, ACM, and AAAS and winner of the IJCAI Computers and Thought Award. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. His book Artificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in the field.
Daniel Dewey is the Alexander Tamas Research Fellow on Machine Superintelligence and the Future of AI at Oxford’s Future of Humanity Institute, Oxford Martin School. He was previously at Google, Intel Labs Pittsburgh, and Carnegie Mellon University.
Max Tegmark is a professor of physics at MIT. His current research is at the interface of physics and artificial intelligence, using physics-based techniques to explore connections between information processing in biological and engineered systems. He is the president of the Future of Life Institute, which supports research advancing robust and beneficial artificial intelligence.
V Acknowledgements
-------------------
The initial version of this document was drafted with major input from Janos Kramar and Richard Mallah, and reflects valuable feedback from Anthony Aguirre, Erik Brynjolfsson, Ryan Calo, Meia Chita-Tegmark, Tom Dietterich, Dileep George, Bill Hibbard, Demis Hassabis, Eric Horvitz, Leslie Pack Kaelbling, James Manyika, Luke Muehlhauser, Michael Osborne, David Parkes, Heather Roff, Francesca Rossi, Bart Selman, Murray Shanahan, and many others. The authors are also grateful to Serkan Cabi and David Stanley for help with manuscript editing and formatting. |
c8f5efdd-1b23-4270-9c43-4d7e2658d63a | StampyAI/alignment-research-dataset/arxiv | Arxiv | Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning
1 Introduction
---------------
Figure 1: Comparison of transfer strategies on Montezuma’s Revenge (hard exploration) and Space Invaders (dense reward) from a task-agnostic policy pre-trained with NGU (Puigdomènech Badia et al., [2020b](#bib.bib2 "Never give up: learning directed exploration strategies")). Transferring representations provides a significant boost on dense reward games, but it does not seem to help in hard exploration ones. Leveraging the behavior of the pre-trained policy provides important gains in hard exploration problems when compared to standard fine-tuning and is complementary to transferring representations. We refer the reader to the supplementary material for details on the network architecture.
Unsupervised representation learning techniques
have led to unprecedented results in domains like computer vision (Hénaff et al., [2019](#bib.bib38 "Data-efficient image recognition with contrastive predictive coding"); He et al., [2019](#bib.bib39 "Momentum contrast for unsupervised visual representation learning")) and natural language processing (Devlin et al., [2019](#bib.bib40 "BERT: pre-training of deep bidirectional transformers for language understanding"); Radford et al., [2019](#bib.bib41 "Language models are unsupervised multitask learners")).
These methods are commonly composed of two stages – an initial unsupervised phase, followed by supervised fine-tuning on downstream tasks.
The self-supervised nature of the learning objective allows to leverage large collections of unlabelled data in the first stage. This produces models that extract task-agnostic features that are well suited for transfer to downstream tasks.
In reinforcement learning (RL), auxiliary representation learning objectives provide denser signals that result in data efficiency gains (Jaderberg et al., [2017](#bib.bib20 "Reinforcement learning with unsupervised auxiliary tasks")) and even bridge the gap between learning from true state and pixel observations (Laskin et al., [2020](#bib.bib67 "CURL: contrastive unsupervised representations for reinforcement learning")). However, RL applications have not yet seen the advent of the two-stage setting where task-agnostic pre-training is followed by efficient transfer to downstream tasks. We argue that there are two reasons explaining this lag with respect to their supervised counterparts.
First, these methods traditionally focus on transferring representations (Lesort et al., [2018](#bib.bib85 "State representation learning for control: an overview")). While this is enough in supervised scenarios, we argue that leveraging pre-trained behavior is far more important in RL domains requiring structured exploration.
Second, what type of self-supervised objectives enable the acquisition of transferable, task-agnostic knowledge is still an open question. Defining these objectives in the RL setting is complex, as they should account for the fact that the distribution of the input data will be defined by the behavior of the agent.
Transfer in deep learning is often performed through parameter initialization followed by fine-tuning. The most widespread procedure consists in initializing all weights of a neural network using those from a pre-trained model, and then adding an output layer with random parameters (Girshick et al., [2014](#bib.bib83 "Rich feature hierarchies for accurate object detection and semantic segmentation"); Devlin et al., [2019](#bib.bib40 "BERT: pre-training of deep bidirectional transformers for language understanding")). Depending on the amount of available data, pre-trained parameters can either be fine-tuned or kept fixed. This builds on the intuition that the pre-trained model will map inputs to a feature space where the downstream task is easy to perform. In the RL setting, this procedure will completely dismiss the pre-trained policy and fall back to a random one when collecting experience.
Given that complex RL problems require structured and temporally-extended behaviors, we argue that representation alone is not enough for efficient transfer in challenging domains.
Pre-trained representations do indeed provide data efficiency gains in domains with dense reward signals (Finn et al., [2017](#bib.bib15 "Model-agnostic meta-learning for fast adaptation of deep networks"); Yarats et al., [2019](#bib.bib69 "Improving sample efficiency in model-free reinforcement learning from images"); Stooke et al., [2020a](#bib.bib71 "Decoupling representation learning from reinforcement learning")), but our experiments show that the standard fine-tuning procedure falls short in hard exploration problems (c.f. Figure [1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning")).
We observe this limitation even when fine-tuning the pre-trained policy, which is aligned with findings from previous works (Finn et al., [2017](#bib.bib15 "Model-agnostic meta-learning for fast adaptation of deep networks")). Learning in the downstream task can lead to catastrophically forgetting the pre-trained policy, something that depends on many difficult-to-measure factors such as the similarity between the tasks.
We address the problem of leveraging arbitrary pre-trained policies when solving downstream tasks, a requirement towards enabling efficient transfer in RL.
Defining unsupervised RL objectives remains an open problem, and existing solutions are often influenced by how the acquired knowledge will be used for solving downstream tasks.
Model-based approaches can learn world models from unsupervised interaction (Ha and Schmidhuber, [2018](#bib.bib86 "Recurrent world models facilitate policy evolution")). However, the diversity of the training data will impact the accuracy of the model (Sekar et al., [2020](#bib.bib87 "Planning to explore via self-supervised world models")) and deploying this type of approach in visually complex domains like Atari remains an open problem (Hafner et al., [2019](#bib.bib88 "Dream to control: learning behaviors by latent imagination")).
Unsupervised RL has also been explored through the lens of empowerment (Salge et al., [2014](#bib.bib63 "Empowerment – an introduction"); Mohamed and Rezende, [2015](#bib.bib45 "Variational information maximisation for intrinsically motivated reinforcement learning")), which studies agents that aim to discover intrinsic options (Gregor et al., [2016](#bib.bib7 "Variational intrinsic control"); Eysenbach et al., [2019](#bib.bib28 "Diversity is all you need: learning skills without a reward function")). While these options can be leveraged by hierarchical agents (Florensa et al., [2017](#bib.bib19 "Stochastic neural networks for hierarchical reinforcement learning")) or integrated within the universal successor features framework (Barreto et al., [2017](#bib.bib65 "Successor features for transfer in reinforcement learning"), [2018](#bib.bib66 "Transfer in deep reinforcement learning using successor features and generalised policy improvement"); Borsa et al., [2019](#bib.bib64 "Universal successor features approximators"); Hansen et al., [2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")), their potential lack of coverage generally limits their applicability to complex downstream tasks (Campos et al., [2020](#bib.bib6 "Explore, discover and learn: unsupervised discovery of state-covering skills")).
We argue that maximizing coverage is a good objective for task-agnostic RL, as agents that succeed at this task will need to develop complex behaviors in order to efficiently explore the environment (Kearns and Singh, [2002](#bib.bib76 "Near-optimal reinforcement learning in polynomial time")).
This problem can be formulated as that of finding policies that induce maximally entropic state distributions, which might become extremely inefficient in high-dimensional state spaces without proper priors (Hazan et al., [2019](#bib.bib42 "Provably efficient maximum entropy exploration"); Lee et al., [2019](#bib.bib30 "Efficient exploration via state marginal matching")).
In practice, exploration is often encouraged through intrinsic curiosity signals that incorporate priors in order to quantify how different the current state is from those already visited (Bellemare et al., [2016](#bib.bib58 "Unifying count-based exploration and intrinsic motivation"); Houthooft et al., [2016](#bib.bib53 "Vime: variational information maximizing exploration"); Ostrovski et al., [2017](#bib.bib68 "Count-based exploration with neural density models"); Puigdomènech Badia et al., [2020b](#bib.bib2 "Never give up: learning directed exploration strategies")). Agents that maximize these novelty-seeking signals have been shown to discover useful behaviors in unsupervised settings (Pathak et al., [2017](#bib.bib18 "Curiosity-driven exploration by self-supervised prediction"); Burda et al., [2018a](#bib.bib5 "Large-scale study of curiosity-driven learning")), but little research has been conducted towards leveraging the acquired knowledge once the agent is exposed to extrinsic reward. We show that coverage-seeking objectives are a good proxy for acquiring knowledge in task-agnostic settings, as leveraging the behaviors discovered in an unsupervised pre-training stage provides important gains when solving downstream tasks.
Our contributions can be summarized as follows.
(1) We study how to transfer knowledge in RL through behavior by re-using pre-trained policies, an approach that is complementary to re-using representations. We argue that pre-trained behavior can be used for both exploitation and exploration, and present techniques to achieve both goals. (2) We propose coverage as a principle for discovering behavior that is suitable for both exploitation and exploration. While coverage is naturally aligned with exploration, we show that this objective will lead to the discovery of behavior that is useful for exploitation as well.
(3) We propose Coverage Pre-training for Transfer (CPT), a method that implements the aforementioned hypotheses, and provide extensive experimental evaluation to support them.
Our results show that leveraging the behavior of policies pre-trained to maximize coverage provides important benefits when solving downstream tasks, and the proposed adaptation method outperforms standard fine-tuning approaches that transfer knowledge through neural network weights. CPT obtains the largest gains in hard exploration games, where it almost doubles the median human normalized score achieved by our strongest baseline. Importantly, these benefits are observed even when the pre-trained policies are misaligned with the task being solved, confirming that the benefits do not come from a fortuitous alignment between our pre-training objective and the task reward. Furthermore, we show that CPT is able to leverage a single task-agnostic policy to solve multiple tasks in the same environment.
2 Reinforcement Learning with Unsupervised Pre-Training
--------------------------------------------------------
We follow a similar setup to that proposed by Hansen et al. ([2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")). In an initial pre-training stage, agents are allowed as many interactions with the environment as needed as long as they are not exposed to task-specific rewards. Rewards are reinstated in a second stage, where the knowledge acquired during unsupervised pre-training should be leveraged in order to enable efficient learning. This is analogous to the evaluation setting for unsupervised learning methods, where pre-training on classification benchmarks with labels removed is evaluated after fine-tuning on small sets of annotated examples.
This two-stage setup introduces two main challenges: defining pretext tasks in the absence of reward, and efficiently leveraging knowledge once rewards are reinstated.
Interactions between the agent and the environment are often assumed to incur a cost, but we will consider this cost to be relevant only for transitions with reward (Hansen et al., [2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")). In this scenario, agents will generally undergo long unsupervised pre-training processes in order to acquire as much knowledge about the environment as possible. This will enable efficient training when rewards are exposed.
Even if the cost of unsupervised pre-training becomes non-negligible, it can be amortized when the acquired task-agnostic knowledge is leveraged to solve multiple tasks efficiently (Devlin et al., [2019](#bib.bib40 "BERT: pre-training of deep bidirectional transformers for language understanding"); Brown et al., [2020](#bib.bib73 "Language models are few-shot learners")).
3 Leveraging Pre-Trained Policies
----------------------------------
Transfer in supervised domains often exploits the fact that related tasks might be solved using similar representations. This practice deals with the data inefficiency of training large neural networks with stochastic gradient descent.
However, there is an additional source of data inefficiency when training RL agents: unstructured exploration. If the agent fails at discovering reward while exploring, it will struggle even when fitting simple function approximators on top of the true state of the Markov Decision Process (MDP).
These two strategies are complementary, as they address different sources of inefficiency, which motivates the study of techniques for leveraging pre-trained behavior (i.e. policies).
Fine-tuning arises as a potential strategy for leveraging pre-trained policies, as the agent will observe rich experience much earlier in training than when initializing the policy randomly. However, this approach suffers from important limitations. The same neural network architecture needs to be used for both the pre-trained and the downstream policies, which in practice also imposes a limitation on the type of RL methods that can be employed in the adaptation stage (for instance, if the pre-trained policy was trained using a value-based method, it might not be possible to fine-tune it using a policy-based approach). Moreover, learning in the downstream task can lead to catastrophically forgetting the pre-trained policy, thus disregarding its exploratory behavior.
For these reasons, we propose to make use of the mapping from observations to actions of such policies (i.e. their behavior). We do not transfer knowledge through pre-trained neural network weights in order to clearly analyze the proposed method, and report experiments that show that both strategies are indeed complementary.
Our approach relies on off-policy learning methods in order to leverage arbitrary pre-trained policies.
The presented formulation considers a single pre-trained policy, πp, but note that it is straightforward to extend it to multiple such policies.
No assumptions are made on how the pre-trained policy is obtained, and it is only used for acting.
We propose using the behavior of the pre-trained policy for two complementary purposes: exploitation and exploration.
Figure [2](#S3.F2 "Figure 2 ‣ 3 Leveraging Pre-Trained Policies ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") provides intuition about the potential benefits of these two approaches on a simple environment.
| Pre-training stage | Downstream task |
| --- | --- |
| Intuition behind the proposed transfer strategy on a simple maze, where the agent needs to collect treasure chests (positive reward) while avoiding skulls (negative reward). Trajectories from a policy | Intuition behind the proposed transfer strategy on a simple maze, where the agent needs to collect treasure chests (positive reward) while avoiding skulls (negative reward). Trajectories from a policy |
Figure 2: Intuition behind the proposed transfer strategy on a simple maze, where the agent needs to collect treasure chests (positive reward) while avoiding skulls (negative reward). Trajectories from a policy πp with exploratory behavior are depicted in orange. Left: while πp ignores some of the rewarding objects, many learning opportunities appear when following it during training.
Right: combining primitive actions (red) with actions from πp (orange) side-steps the need to learn behavior that is already available through πp when solving downstream tasks.
Exploitation.
When the behavior of πp is aligned with the downstream task, it can be used for zero-shot transfer (Eysenbach et al., [2019](#bib.bib28 "Diversity is all you need: learning skills without a reward function")). However, we are concerned with the more realistic scenario where only some of the behaviors of πp might be aligned with downstream tasks (c.f. Figure [2](#S3.F2 "Figure 2 ‣ 3 Leveraging Pre-Trained Policies ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning"), right). We propose to leverage πp for exploitation by letting the agent combine primitive actions with the behavior of πp.
This is achieved by considering an expanded action set A+=A∪{πp(s)}, so that the agent can fall back to πp for one step when taking the additional action.
The return of taking action a′∼πp(s) is used as target to fit both Q(s,πp(s)) and Q(s,a′), which implements the observation that they are the same action and thus will lead to the same outcomes.
Intuitively, this approach induces a bias that favours actions selected by πp, accelerating the collection of rewarding transitions when the pre-trained policy is aligned with the downstream task. Otherwise, the agent can learn to ignore πp as training progresses by selecting other actions.
Exploration.
Following the pre-trained policy might bring the agent to states that are unlikely to be visited with unstructured exploration techniques such as ϵ-greedy (Sutton and Barto, [2018](#bib.bib89 "Reinforcement learning: an introduction")). This property has the potential of accelerating learning even when the behavior of the pre-trained policy is not aligned with the downstream task, as it will effectively shorten the path between otherwise distant states (Liu and Brunskill, [2018](#bib.bib72 "When simple exploration is sample efficient: identifying sufficient conditions for random exploration to yield pac rl algorithms")). As we rely on off-policy methods that can learn from experience collected by arbitrary policies, we propose to perform temporally-extended exploration with πp, which we will refer to as flights. Inspired by ϵz-greedy and its connection to Lévy flights (Viswanathan et al., [1996](#bib.bib12 "Lévy flight search patterns of wandering albatrosses")), a class of ecological models for animal foraging, these flights are started randomly and their duration is sampled from a heavy-tailed distribution.
Our proposal can be understood as a variant of ϵz-greedy where pre-trained policies are used as exploration options. An exploratory flight might be started at any step with some probability. The duration for the flight is sampled from a heavy-tailed distribution, and control is handed over to πp during the complete flight. When not in a flight, the exploitative policy that maximizes the extrinsic reward is derived from the estimated Q-values using the ϵ-greedy operator. This ensures that all state-action pairs will be visited given enough time, as exploring only with πp does not guarantee such property.
4 Coverage as a Goal for Unsupervised Pre-Training
---------------------------------------------------
So far we considered strategies for leveraging the behavior of arbitrary policies, and we now discuss how to train such policies in an initial pre-training stage with rewards removed.
In such setting, it is a common practice to derive objectives for proxy tasks in order to drive learning.
As we proposed to take advantage of pre-trained policies for both exploitation and exploration, it might seem unlikely that a single pre-training objective will produce policies that are useful for both purposes. However, we hypothesize that there exists a single criterion that will produce policies that can be used for both exploration and exploitation: coverage.
This objective aims at visiting as many states as possible and is naturally aligned with exploration (Kearns and Singh, [2002](#bib.bib76 "Near-optimal reinforcement learning in polynomial time")).
Long episodes where the agent visits as many different states as possible result in high returns in some domains such as videogames, locomotion and navigation (Pathak et al., [2017](#bib.bib18 "Curiosity-driven exploration by self-supervised prediction"); Burda et al., [2018a](#bib.bib5 "Large-scale study of curiosity-driven learning")). We argue that pre-training for coverage will bring benefits beyond these particular domains, as it fosters mastery over the environment. This leads to the discovery of skills and behaviors that can be exploited by the agent when solving downstream tasks even if the pre-trained policy does not obtain high returns.
Policies that maximize coverage should visit as many states as possible within a single episode, which differs from traditional exploration strategies employed when solving a single task. The goal of the latter is discovering potentially rewarding states, and the drive for exploration fades as strategies that lead to high returns are discovered. The proposed objective is closely related to methods for task-agnostic exploration that train policies that induce maximally entropic state visitation distributions (Hazan et al., [2019](#bib.bib42 "Provably efficient maximum entropy exploration"); Lee et al., [2019](#bib.bib30 "Efficient exploration via state marginal matching")). However, since the problems we are interested in involve large state spaces where states are rarely visited more than once, we propose to consider only the controllable aspects of the state space. This enables disentangling observations from states and gives rise to a more scalable, and thus more easily covered, notion of the state space.
We choose Never Give Up (NGU) (Puigdomènech Badia et al., [2020b](#bib.bib2 "Never give up: learning directed exploration strategies")) as a means for training policies that maximize coverage.
NGU defines an intrinsic reward that combines per-episode and life-long novelty over controllable aspects of the state space. It can be derived directly from observations, unlike other approaches that make use of privileged information (Conti et al., [2018](#bib.bib24 "Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents")) or require estimating state visitation distributions (Hazan et al., [2019](#bib.bib42 "Provably efficient maximum entropy exploration")), making it suitable for environments that involve high-dimensional observations and partial observability.
The intrinsic NGU reward maintains exploration throughout the entire training process, a property that makes it suitable for driving learning in task-agnostic settings. This contrasts with other intrinsic reward signals that generally vanish as training progresses (Ecoffet et al., [2019](#bib.bib44 "Go-explore: a new approach for hard-exploration problems")).
NGU was originally designed to solve hard-exploration problems by learning a family of policies with different degrees of exploratory behavior. Thanks to weight sharing, the knowledge discovered by exploratory policies enabled positive transfer to exploitative ones, obtaining impressive results when applied to large-scale domains (Puigdomènech Badia et al., [2020a](#bib.bib13 "Agent57: outperforming the atari human benchmark")). We instead propose to use NGU as a pre-training strategy in the absence of reward, transferring knowledge to downstream tasks in the form of behavior rather than weight sharing.
5 CPT: Coverage Pre-training for Transfer
------------------------------------------
Our proposed method, Coverage Pre-training for Transfer (CPT), relies on coverage maximization as a pretext task for task-agnostic pre-training in order to produce policies whose behavior can be leveraged for both exploitation and exploration when solving downstream tasks in the same environment.
CPT consists of two stages: (1) pre-training a task-agnostic policy using the intrinsic NGU reward, and (2) solving downstream tasks in the same environment by leveraging the pre-trained behavior. Our implementation relies on off-policy value-based methods that estimate action-value functions and derive greedy policies from them.
The remainder of this section describes the proposed method. The main blocks in CPT are depicted in Figure [3](#S5.F3 "Figure 3 ‣ 5 CPT: Coverage Pre-training for Transfer ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning"), and pseudo-code for the proposed adaptation method is provided in Algorithm [1](#alg1 "Algorithm 1 ‣ 5 CPT: Coverage Pre-training for Transfer ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning").
Figure 3: Summary of CPT. In a pre-training stage, a task-agnostic policy πp is trained to maximize the intrinsic NGU reward. Once rewards are exposed, a new policy π is trained by leveraging πp for both exploitation and exploration. Note that πp is kept fixed during transfer and there is no weight sharing across policies.
Coverage pre-training
* [noitemsep,topsep=0pt]
* The agent interacts with an MDP defined by the tuple (S,A,P,rNGU,γ), with S being the state space, A being the action space, P the state-transition distribution, γ∈(0,1] the discount factor and the reward function rNGU is the intrinsic reward used in NGU (Puigdomènech Badia et al., [2020b](#bib.bib2 "Never give up: learning directed exploration strategies")).
* We use a value-based agent with a Q-function, QNGU(s,a):S×A→R, parameterised with a neural network. We refer the reader to the supplementary material for details on the network architecture.
* We train QNGU to maximise the NGU intrinsic reward, obtaining a deterministic policy given by
πp(s)=argmaxa[QNGU(s,a)].
* We use ϵ-greedy to derive the behavior policy when interacting with the environment.
Transfer
* [noitemsep,topsep=0pt]
* We consider a new MDP given by (S,A,P,r,γ), where the only change with respect to the pre-training stage is a new extrinsic reward function r: S×A→R.
* We define a Q-function Qπ(s,a):S×A+→R on an extended action set A+=A∪{a+}. Qπ is parameterised using a neural network with random initialization.
* We train Qπ to maximize the task reward, obtaining a deterministic policy given by π(s)=argmaxa∈A+[Qπ(s,a)] where a+=πp(s).
* We use Lévy flights as the behavior policy when interacting with the environment. See Algorithm [1](#alg1 "Algorithm 1 ‣ 5 CPT: Coverage Pre-training for Transfer ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") for details.
Input: Action set, A
Input: Additional action, a+
Input: Extended action set, A+=A∪{a+}
Input: Pre-trained policy, πp
Input: Q-value estimate for the current policy, Qπ(s,a)∀a∈A+
Input: Probability of taking an exploratory action, ϵ
Input: Probability of starting a flight, ϵlevy
Input: Flight length distribution, D(N)
while *True* do
n←0 // flight length
while *episode not ended* do
Observe state s
if *n==0 and random()≤ϵlevy* then
n∼D(N) // sample from distribution over lengths
end if
if *n>0* then
n←n−1
a←πp(s) // explore with πp
else
if *random()≤ϵ* then
a←Uniform(A+)
else
a←argmaxa′∈A+[Qπ(s,a′)]
end if
if *a==a+* then
// exploit with πp
a←πp(s)
end if
end if
Take action a
end while
end while
Algorithm 1 Experience collection pseudo-code for the transfer stage in CPT
6 Experiments
--------------
We evaluate CPT in the Atari suite (Bellemare et al., [2013](#bib.bib62 "The arcade learning environment: an evaluation platform for general agents")), a benchmark that presents a variety of challenges and is often used to measure the competence of agents. All our experiments are run using the distributed R2D2 agent (Kapturowski et al., [2019](#bib.bib9 "Recurrent experience replay in distributed reinforcement learning")), and we use the same hyperparameters as in Agent57 (Puigdomènech Badia et al., [2020a](#bib.bib13 "Agent57: outperforming the atari human benchmark")).
A detailed description of the full distributed setting and hyperparemeters is provided in the supplementary material.
All reported results are the average over three random seeds.
Unsupervised RL methods are often evaluated by measuring the amount of task reward collected by the discovered policies (Burda et al., [2018a](#bib.bib5 "Large-scale study of curiosity-driven learning"); Hansen et al., [2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")), and we use this metric to evaluate the quality of our unsupervised policies. We pre-train our agents using 16B frames in order to guarantee the discovery of meaningful exploration policies,111The pre-training budget was not tuned, but we observe that competitive policies arise early in training. This observation suggests that smaller budgets are feasible as well. as it is common to let agents interact with the environment for as long as needed in this unsupervised stage (Hansen et al., [2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")).
The proposed strategies for leveraging pre-trained policies once the reward function is reinstated are evaluated by training R2D2-based agents (Kapturowski et al., [2019](#bib.bib9 "Recurrent experience replay in distributed reinforcement learning")) for 5B frames. This is a relatively small budget for these distributed agents with hundreds of actors (Puigdomènech Badia et al., [2020a](#bib.bib13 "Agent57: outperforming the atari human benchmark")).
We compare the proposed method against ϵ-greedy and ϵz-greedy (Dabney et al., [2020](#bib.bib8 "Temporally-extended ϵ-greedy exploration")) exploration strategies.
Policies are evaluated using five parallel evaluator threads, and we report the average return over the last 300 evaluation episodes.
Table [1](#S6.T1 "Table 1 ‣ 6 Experiments ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") reports results in the full Atari suite, which confirm the benefits of leveraging the behavior of a policy trained to maximize coverage. Our approach is most beneficial in the set of hard exploration games222montezuma\_revenge, pitfall, private\_eye, venture, gravitar, solaris, where unstructured exploration generally precludes the discovery of high-performing policies.
It should be noted that our ϵz-greedy baseline under-performs relative to Dabney et al. ([2020](#bib.bib8 "Temporally-extended ϵ-greedy exploration")). This is due to our hyper-parameters and setting being derived from Puigdomènech Badia et al. ([2020b](#bib.bib2 "Never give up: learning directed exploration strategies")), which adopts the standard Atari pre-processing (e.g. gray scale images and frame stacking). In contrast, Dabney et al. ([2020](#bib.bib8 "Temporally-extended ϵ-greedy exploration")) use color images, no frame stacking, a larger neural network and different hyper-parameters (e.g. smaller replay buffer). Studying if the performance of both NGU and CPT is preserved in this setting is an important direction for future work. We suspect that improving the performance of our ϵz-greedy ablation will also improve our method, since exploration flights are central to both.
| | Hard Exploration | Full 57 Games |
| --- | --- | --- |
| Algorithm | Mdn | M | Mdn | M |
| ϵ-greedy exp @5B | 32.54 | 44.75 | 487.25 | 1753.81 |
| ϵz-greedy exp @5B | 104.08 | 95.00 | 438.81 | 1263.83 |
| CPT (unsup) | 4.31 | 22.62 | 83.22 | 318.78 |
| CPT @5B | 191.04 | 158.05 | 561.98 | 2184.26 |
Table 1: Atari Suite comparisons
for R2D2-based agents.
@N represents the amount of RL interaction with reward utilized, with four frames observed at each iteration.
Mdn, M and CM are median, mean and mean capped human normalized scores, respectively.
Figure 4: Ablation results. Using the task-agnostic policy for exploitation and exploration seems to provide complementary benefits, as combining the two techniques results in important gains.
Ablation studies. We run experiments on a subset of games in order to gain insight on the individual contribution of each of the proposed ways of leveraging the pre-trained policy.333Pseudo-code for the ablated versions of CPT is included in the supplementary material
The subset is composed by 12 games,444asterix bank\_heist, frostbite, gravitar, jamesbond, montezuma\_revenge, ms\_pacman, pong, private\_eye, space\_invaders, tennis, up\_n\_down., obtained by combining those used to tune hyperparameters by Hansen et al. ([2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")) with games where ϵz-greedy provides clear gains over ϵ-greedy as per Dabney et al. ([2020](#bib.bib8 "Temporally-extended ϵ-greedy exploration")). This results in a set of games that require different amounts of exploration, and featuring both dense and sparse rewards. Figure [4](#S6.F4 "Figure 4 ‣ 6 Experiments ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") shows that exploration and exploitation strategies alone obtain similar median scores across the 12 games, but combining them results in an important performance gain. This suggests that the gains they provide are complementary, and both are responsible for the strong performance of CPT.
Note that CPT also outperforms a fine-tuning baseline, where the policy is initialized using the pre-trained weights rather than random ones. We believe that the benefits of both approaches can be combined by training via CPT a policy initialized with pre-trained weights.
Figure 5: Effect of the pre-training budget,
on Montezuma’s Revenge (hard exploration) and Pong (dense reward).
Effect of the pre-trained policy. The behavior of the pre-trained policy will likely have a strong impact on the final performance of agents. We consider the amount of pre-training as a proxy for the exploration capabilities of the task-agnostic policies. Intuitively, policies trained for longer time spans will develop more complex behaviors that enable visiting a larger number of states. Figure [5](#S6.F5 "Figure 5 ‣ 6 Experiments ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") reports the end performance of agents before and after transfer under different lengths of the pre-training phase, and shows how it has a different impact depending on the nature of the task. Montezuma’s Revenge requires structured exploration for efficient learning, and longer pre-training times provide dramatic improvements in the end performance. Note that these improvements do not correlate with the task performance of the task-agnostic policy, which suggests that gains are due to a more efficient exploration of the state space. In contrast, the final score in Pong is independent of the amount of pre-training. Simple exploration is enough to discover optimal policies, so the behaviors discovered by the unsupervised policy do not play an important role in this game.
Transfer to multiple tasks. An appealing property of task-agnostic knowledge is that it can be leveraged to solve multiple tasks. In the RL setting, this can be evaluated by leveraging a single task-agnostic policy for solving multiple tasks (i.e. reward functions) in the same environment. We evaluate whether the unsupervised NGU policies can be useful beyond the standard Atari tasks by creating two alternative versions of Ms Pacman and Hero with different levels of difficulty.
The goal in the modified version of Ms Pacman is to eat vulnerable ghosts, with pac-dots giving 0 (easy version) or −10 (hard version) points. In the modified version of Hero, saving miners gives a fixed return of 1000 points and dynamiting walls gives either 0 (easy version) or −300 (hard version) points. The rest of rewards are removed, e.g. eating fruit in Ms Pacman or the bonus for unused power units in Hero.
Note that even in the easy version of the games exploration is harder than in their original counterparts, as there are no small rewards guiding the agent towards its goals. Exploration is even more challenging in the hard version of the games, as the intermediate rewards work as a deceptive signal that takes the agent away from its actual goal. In this case, finding rewarding behaviors requires a stronger commitment to an exploration strategy. Exploratory policies often achieve very low or even negative rewards in this setting, which contrasts with the strong performance they showed when evaluated under the standard game reward. Even in this adversarial scenario, results in Figure [6](#S6.F6 "Figure 6 ‣ 6 Experiments ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning") show that leveraging the behavior of pre-trained exploration policies provides important gains.
The gains of CPT over fine-tuning are more significant on tasks with deceptive rewards, where the latter quickly disregards exploratory behaviors that initially lead to negative rewards and converges to sub-optimal behaviors.
These results suggest that the strong performance observed under the standard game rewards is not due to an alignment between the NGU reward and the game goals, but due to an efficient usage of pre-trained exploration policies.
Figure 6: Final scores per task in the Atari games of Ms Pacman (top) and Hero (bottom) with modified reward functions. We train a single task-agnostic policy per environment, and leverage it to solve three different tasks: the standard game reward, a task with sparse rewards (easy), and a variant of the same task with deceptive rewards (hard). Despite the pre-trained policy might obtain low or even negative scores in some tasks, committing to its exploratory behavior eventually lets the agent discover strategies that lead to high returns.
Towards the low-data regime. So far we considered R2D2-based agents tuned for end-performance on massively distributed setups. Some applications might require higher efficiency in the low-data regime, even if this comes at the cost of a drop in end performance. The data efficiency of our method can be boosted by reusing representations from the pre-trained convolutional torso in the NGU policy (see the supplementary material for details on the architecture), as shown in Figure [1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning"). We observe that the data efficiency can be boosted further by decreasing the number of parallel actors.
When using 16 actors instead of 256 as in previous experiments, we observe superhuman scores in less than 50M frames (learning curves are provided in the supplementary material).
This is around two times faster than the best results in the benchmark by Taïga et al. ([2019](#bib.bib82 "Benchmarking bonus-based exploration methods on the arcade learning environment")), even though they consider single-threaded Rainbow-based agents (Hessel et al., [2018](#bib.bib10 "Rainbow: combining improvements in deep reinforcement learning")) that were designed for data efficiency.
7 Related Work
---------------
Our work uses the experimental methodology presented in Hansen et al. ([2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")). Whereas that work only considered a fast, simplified adaptation process that limited the final performance on the downstream task, we focus on the more general case of using a previously trained policy to aid in solving the full reinforcement learning problem. Hansen et al. ([2020](#bib.bib1 "Fast task inference with variational intrinsic successor features")) use successor features to identify which of the pre-trained tasks best matches the true reward structure, which has previously been shown to work well for multi-task transfer (Barreto et al., [2018](#bib.bib66 "Transfer in deep reinforcement learning using successor features and generalised policy improvement")).
Gupta et al. ([2018](#bib.bib80 "Unsupervised meta-learning for reinforcement learning")) provides an alternative method to meta-learn a solver for reinforcement learning problems from unsupervised reward functions.
This method utilizes gradient-based meta-learning (Finn et al., [2017](#bib.bib15 "Model-agnostic meta-learning for fast adaptation of deep networks")), which makes the adaptation process standard reinforcement learning updates.
This means that even if the downstream reward is far outside of the training distribution, final performance would not necessarily be affected.
However, these methods are hard to scale to the larger networks considered here, and followup work (Jabri et al., [2019](#bib.bib79 "Unsupervised curricula for visual meta-reinforcement learning")) changed to memory-based meta-learning (Duan et al., [2016](#bib.bib81 "RL2: fast reinforcement learning via slow reinforcement learning")) which relies on information about rewards staying in the recurrent state.
This makes it unsuitable to the sort of hard exploration problem our method excels at.
Recent work has shown success in transferring representations learned in an unsupervised setting to reinforcement learning tasks (Stooke et al., [2020b](#bib.bib78 "Decoupling representation learning from reinforcement learning")). Our representation transfer experiments suggest that this might handicap final performance, but the possibility also exists that different unsupervised objectives should be used for representation transfer and policy transfer.
Concurrent work by Bagot et al. ([2020](#bib.bib14 "Learning intrinsically motivated options to stimulate policy exploration")) also augments an agent with the ability to utilize another policy.
However, their work treats the unsupervised policy as an option, only callable for an extended duration.
In contrast, we only perform extended calls to the unsupervised policy during exploratory flights and augment the action space to allow for single time-step calls.
This difference between exploratory and exploitative calls to the unsupervised policy is critical to overall performance, as illustrated in Figure [4](#S6.F4 "Figure 4 ‣ 6 Experiments ‣ Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning").
In addition, in Bagot et al. ([2020](#bib.bib14 "Learning intrinsically motivated options to stimulate policy exploration")) the unsupervised policy is learned in tandem based on an intrinsic reward function. This is a promising direction which is complementary to our work, as it handles the case wherein there is no unsupervised pre-training phase. However, their work only considers tabular domains, so it is unclear how this approach would fare in the high-dimensional state spaces considered here.
8 Discussion
-------------
We studied the problem of transferring pre-trained behavior in reinforcement learning, an approach that is complementary to the common practice of transferring representations. Depending on the behavior of the pre-trained policies, we argued that they might be useful for exploitation, exploration, or both. We proposed methods to make use of pre-trained behavior for both purposes: exploiting with the pre-trained policy by making it available to the agent as an extra action, and performing temporally-extended exploration with it. While we make no assumption on the nature of the pre-trained policies, this raises the question of how to discover behaviors that are suitable for transfer. We proposed coverage as a principle for pre-training task-agnostic policies that are suitable for both exploitation and exploration. We chose NGU in our experiments for its scalability, but note that our approach could be combined with any other strategy for maximizing coverage.
Our transfer experiments demonstrate that these pre-trained policies can be used to boost the performance of agents trained to maximize reward, providing the most important gains in hard exploration tasks. These benefits are not due to an alignment between our pre-training and downstream tasks, as we also observed positive transfer in games where the pre-trained policy obtained low scores. In order to provide further evidence for this claim, we designed alternative tasks for Atari games involving hard exploration and deceptive rewards. Our transfer strategy outperformed all considered baselines in these settings, even when the pre-trained policy obtained very low or even negative scores, demonstrating the generality of the method. Besides disambiguating the role of the alignment between pre-training and downstream tasks, these experiments demonstrate the utility of a single task-agnostic policy for solving multiple tasks in the same environment. |
6451f74a-5c7e-4759-b88e-77b8029b03b8 | StampyAI/alignment-research-dataset/blogs | Blogs | Erik DeBenedictis on supercomputing
 [Erik DeBenedictis](http://debenedictis.org/erik/) works for Sandia’s Advanced Device Technologies department. He has been a member of the [International Technology Roadmap for Semiconductors](http://en.wikipedia.org/wiki/International_Technology_Roadmap_for_Semiconductors) since 2005.
DeBenedictis has received Ph.D. in computer science from Caltech. As a grad student and post-doc, he worked on the hardware that turned into the first hypercube multiprocessor computer. Later dubbed the “[Cosmic Cube](http://en.wikipedia.org/wiki/Caltech_Cosmic_Cube),” it ran for more than a decade after he left the university and was copied over and over. It’s considered the ancestor of most of today’s supercomputers.
In the 1980s, then working for Bell Labs in Holmdel, N.J., DeBenedictis was part of a consortium competing for the first Gordon Bell award. The team got the second place award, the first place going to Sandia. During the 1990s, he ran NetAlive, Inc., a company developing information management software for desktops and wireless systems. Starting in 2002, DeBenedictis was one of the project leads on the Red Storm supercomputer.
The opinions expressed by Erik below are his own and not those of Sandia or the US Department of Energy. This document has been released by Sandia as SAND Number 2014-2679P.
---
**Luke Muehlhauser**: Some of your work involves reversible computing, which I previously [discussed](http://intelligence.org/2014/01/31/mike-frank-on-reversible-computing/) with Mike Frank. Mike’s view seemed to be that there were promising signs that reversible computing would be possible eventually, but progress is not moving quickly due to lack of funding and interested researchers. Is that your view as well? And based on my interview with him, do you seem to have a *substantially* different impression that Mike does about anything he and I discussed?
---
**Erik DeBenedictis**: I agree with Mike, but his discussion of minimum energy in computing due to irreversibility is just part of a larger topic of minimum energy in computing that starts with “Moore’s Law Ending.”
For any reader who has not read Mike Frank’s interview, I’d like to give a quick summary of the relevant points. Mike was interviewed about reversible logic, which is sometimes called reversible computing. If you were a brilliant engineer and could figure out how to make a computer logic gate like AND or OR that dissipated kT joules per logic operation (the meaning of kT is in the next paragraph), you would discover that there is an additional heat production on the order of kT due to the interaction between information and thermodynamics. If you were determined to make even lower power computer gates anyway, you would have to use reversible logic principles. You could use a different universal gate set that would include a new gate such as the TOFFOLI or FREDKIN gate. You could also use regular gates (e. g. AND, OR, NOT) and a “retractile cascade” clocking scheme that reverses the computation after you capture the answer.
For reference on kT: k = 1.38 x 10-23 Joules/Kelvin is Boltzmann’s constant and T is the absolute temperature with T = 300 Kelvin at room temperature. kT is about 4 zeptojoules = 4 x 10-21 Joules. Comparing this number to today’s computers is imprecise because dissipation in today’s computers is primarily attributable to the interconnect wire, which varies in length. An AND or OR gate in a modern computer may dissipate a million times this value.
A great many respected scientists believe that reversible computing is feasible, but challenging. If their views are correct, computation should be possible at “arbitrarily low energy levels” and all theories proposing unavoidable, general limits are incorrect. There are a handful of contrary theories proposing minimum energy dissipation levels for computation. Several key ones are Landauer’s Limit of “on the order of kT” per logic operation[1](https://intelligence.org/2014/04/03/erik-debenedictis/#footnote_0_10946 "Note added in review: Landauer proposed a lower limit on “on the order of kT” only for “irreversible” computations. As far as I know, the phrase “Landauer’s Limit” was created later by other people. In my experience, the phrase “Landauer’s Limit” if often applied as a general limit."), a thermal limit of 40-100 kT (depending on your definition of reliable), and the concept in the popular press today that “Moore’s Law is Ending” and the minimum energy per computation is whatever is in the rightmost column of the International Technology Roadmap for Semiconductors (ITRS). That value is about 50,000 kT with typical lengths of interconnect wire.
Scientific situations with multiple competing theories can be settled by a scientific experiment. For example, there is a researcher in New York that has a superconducting circuit running in the sub-kT range and looks like it could demonstrate a logic circuit in another couple “spins” of his chip. Demonstrating and rigorously measuring a sub-kT circuit would invalidate all current theories claiming unavoidable limits.
Whether anybody will fund such an experiment should depend on whether anybody cares about the result, and I’d like to present two society-level questions that the experiment would resolve:
The computer industry started its upward trend during WWII, growing industry revenue and computer throughput in a fairly clean exponential lasting 70 years. The revenue from semiconductors and downstream industries is around $7 trillion per year right now. If there is a lower energy limit to computing, the shift in growth rate will cause a glitch in the world’s economy. My argument is that proving or disproving theories of computing limits could be accomplished for a very small fraction of $7 trillion per year.
The second has to do with profoundly important computational problems, such as the simulation of the global environment to assess climate change issues. Existing climate models running on petaflops supercomputers give varying projections for the future climate, with these projections diverging from observations over the last decade. Regardless of politics, the remedy would be a more sophisticated climate model running on a bigger supercomputer. We don’t know how much bigger, but a zettaflops or more has been mentioned in this context. If any of the minimum energy dissipation theories are correct, the energy dissipation of the required supercomputer could turn out to be too large and climate modeling may be infeasible; if the theory that computing is possible at “arbitrarily low levels” is true, accurate climate modeling will just require a highly-advanced computer.
I’ve tried to expand on Mike’s point: Research on reversible computing could shed light on the future of the economy and the planet’s climate, but I do not know of a single person funded for reversible computing research. Furthermore, a conclusive demonstration of reversible computing would show that there is plenty of room for improving computer efficiency and hence performance. If “Moore’s Law is Ending” means an end to improving computer efficiency, validating reversible computing would show this to be a matter of choice not technology.
---
**Luke**: From your perspective, what are the major currently-foreseeable barriers that Moore’s law might crash into *before* hitting the Landauer limit? (Here, I’m thinking more about the economically important “[computations per dollar](http://intelligence.org/2013/05/15/when-will-ai-be-created/#footnote_8_10199)” formulations of Moore’s law rather than the “serial speed” formulation, which [hit a wall in 2004](http://www.amazon.com/The-Future-Computing-Performance-Level/dp/0309159512/).)
---
**Erik**: There is huge upside, but not necessarily for every application. The “computations per dollar” link in the question focused on the use of computers as a platform for strong Artificial Intelligence (AI), so I will comment specifically on that application: I wouldn’t be surprised to see AI accelerated by technology specifically for learning, like neural networks with specialized devices for the equivalent of synapses.
Let’s consider (a) Moore’s Law 1965 to say 2020 and (b) Beyond Moore’s Law 2020+.
From 1965 to 2020, the strategy was to shrink line width. That strategy will be good for 1012 or so increase in computations per dollar.
I see the following classes of advances beyond 2020 that will each give maybe 10-100x efficiency increase each:
1. More efficient implementation of the von Neumann architecture.
2. More parallelism, with a commensurate increase in the difficulty of programming.
3. Software improvements for more efficient execution (e. g. new computer languages and compilers to run general code on Graphics Processing Units).
4. Better algorithms that solve a given problem with fewer computations.
5. Accelerators, such as CPU+GPU today, extendable to CPU+GPU+various new accelerator types.
6. Even at constant energy per gate operation, continued scaling in 2D and better use of the third dimension for reducing communications energy.
7. Optical interconnect has upside, but optics is often oversold.
8. Nanodevices with behavior different from a transistor that allow some computer functions to be done more efficiently. Examples: Memristors, analog components.
9. Improved gate technology through adiabatic methods, sub-threshold or low-threshold operation, or probabilistic computing. Eventually, reversible computing (note below on this one).
10. Alternative models of computation (i.e. neuromorphic) that do not use gates as normally defined.
If the ten items in the list above yield an average of 1½ orders of magnitude increase in computations per dollar each, you have more upside than the entire run of Moore’s Law.
If a couple of the items in the list don’t pan out, you could achieve excellent results by concentrating on other paths. So I do not see a general technology crash anytime soon. However, certain specific applications may be dependent on a just a subset of the list above (climate modeling was mentioned) and could be vulnerable to a limit.
Reversible computing plus continued reduction in manufacturing cost per device could extend upside potential tremendously.
However, the necessary technology investment will be greater in the future for a less clear purpose. The message of Moore’s Law was very concise: industry and government invest in line width shrinkage and get a large payoff. In the future, many technology investments will be needed whose purposes have less clear messages.
Bottom line: In general, the path ahead is expensive but will yield a large increase in computations per dollar. Specific application classes could see limits, but they will have to be analyzed specifically.
---
**Luke**: What’s your opinion on whether, in the next 15 years, the [dark silicon problem](http://intelligence.org/2013/10/21/hadi-esmaeilzadeh-on-dark-silicon/) will threaten Moore’s Law (computations per dollar)?
---
**Erik**: I believe the dark silicon problem will negatively impact computations per dollar. The problem and the underlying energy efficiency problem are going to get worse at least until the cost of increased energy is greater than the cost of refining a solution and bringing it to production. That will happen eventually, but I believe the problem will persist longer than may be expected due momentum against change. However, you admit Moore’s Law has ended when you admit that there is a dark silicon problem.
The underlying cause of dark silicon is that technology scales device dimensions faster than it reduces power. This causes power per unit chip area to increase, which contradicts the key statement in Gordon Moore’s 1965 paper that defined Moore’s Law: “In fact, shrinking dimensions on an integrated structure makes it possible to operate the structure at higher speed for the same power per unit area.”
The mismatched scaling rates create a problem for computations per dollar. Today, the cost of buying a computer is approximately equal to the cost of supplying it with power over its lifetime. Unless power efficiency can be increased, improvements to computer logic will not benefit the user because the amount of computation they use will be limited by the power bill.
The mismatched scaling rates can be accommodated (but not solved) by turning off transistors (dark silicon), packing microprocessors with low energy-density functions like memory (a good idea, to a point), and specialization (described in your interview under [dark silicon problem](http://intelligence.org/2013/10/21/hadi-esmaeilzadeh-on-dark-silicon/)).
The scaling rates could be brought together by more power-efficient transistors, such as the Tunnel Field Effect Transistor (TFET). However, this transistor type will only last a few generations. See [here](http://www.itrs.net/ITWG/Beyond_CMOS/2012Sept/2_Frank_ITRS%20ERD%209-21-2012.pdf).
Theory says energy per computation can be made “arbitrarily small,” but R&D to exploit these issues will be expensive and disruptive. The leading approaches I am aware of are:
*Adiabatic*. A fundamentally different approach to logic gate circuits. Example: Mike Frank’s 2LAL.
*Certain low-voltage logic classes*: For example, see CMOS LP in arXiv 1302.0244 (which is not same as ITRS CMOS LP).
*Reversible computing*, the topic of [Mike Frank’s interview](http://intelligence.org/2014/01/31/mike-frank-on-reversible-computing/).
The approaches above are disruptive, which I believe limits their popularity today. The approaches use different circuits from CMOS, which would require new design tools. New design tools would be costly to develop and would require retraining of the engineers that use them. Children learn the words “and” and “or” when they are about one year old, with these words becoming the basis of AND and OR in the universal logic basis of computers. To exploit some technologies that save computer power, you have to think in terms of a different logic basis like TOFFOLI, CNOT, and NOT. Some of the ideas above would require people to give up concepts that they learned as infants and have not had reason to question before.
---
**Luke**: What do you mean by “you admit Moore’s Law has ended when you admit that there is a dark silicon problem”? The computations-per-dollar Moore’s Law has held up at least through early 2011 (I haven’t checked the data after that), but we’ve known about the dark silicon problem since 2010 or earlier.
---
**Erik**: Moore’s Law has had multiple meanings over time, and is also part of a larger activity.
There was a very interesting [study by Nordhaus](http://aida.econ.yale.edu/~nordhaus/homepage/nordhaus_computers_jeh_2007.pdf) that revealed the peak computation speed of large computers experienced an inflection point around WW II and has been on an upwards exponential ever since. Eyeballing figure 2 of his paper, I’d say the exponential trend started in 1935.
Gordon Moore published a paper in 1965 with the title “[Cramming more Components onto Integrated Circuits](http://commonsenseatheism.com/wp-content/uploads/2014/03/Moore-Cramming-more-components-onto-integrated-circuits.pdf)” that includes a graph of components per chip versus year. As I mentioned for a previous question, the text of the paper includes the sentence, “In fact, shrinking dimensions on an integrated structure makes it possible to operate the structure at higher speed for the same power per unit area.” The graph and previous sentence seem to me to be a subjective description of an underlying scaling rule that was formalized by Dennard in 1974 and is called Dennard scaling.
I have sketched below Moore’s graph of components as a function of year with Nordhaus’ speed as a function of year on the same axes (a reader should be able to obtain the original documents from the links above, which are more compelling than my sketch). This exercise reveals two things: (1) the one-year doubling period in Moore’s paper was too fast, and is now known to be about 18 months, and (2) that Moore’s Law is a subtrend of the growth in computers documented by Nordhaus.
A really interesting question is whether Moore was applying somebody else’s law or whether the two laws were actually part of a larger concept that was not understood at the time. I conclude the latter. Intel did not invent the microprocessor until six years after Moore’s article. I have also talked to people (not Moore) who tell me Gordon Moore was thinking about general electrical circuits and was not foreseeing the emergence of the microprocessor.
Let me try to apply Moore’s Law as defined by his paper. I recall building a computer system in 1981 with an 8086 (very similar to the 8088 in the original IBM PC). I’d heard it was highly complex and dissipated a lot of heat, so I put my finger on it to experience the heat. I recall surprise that it didn’t seem warmer than anything else. I have thought about the heat from microprocessors in the last year, 33 years later. Since Moore’s Law says power per unit area is the same and chips are nearly the same size at 1 cm2, I should be able to put my finger on a chip and not feel any heat. The reality is that there is a new structure sitting on top of today’s microprocessors that reminds me of Darth Vader’s head and is called a “heat sink.” The heat sink is to remove 50-200 watts of heat generated by the chip. I believe I’ve just made a case that any microprocessor with a heat sink violates Moore’s Law.
What’s going on? Moore’s Law is being given additional meaning over and above what Moore was thinking. Many people believe Moore’s Law is only about dimensional scaling, a conclusion supported by the title of his article and the main graph. Moore’s Law has also been associated with computations per dollar, but that law had been around for 30 years before Moore’s paper.
I found the [interview](http://intelligence.org/2013/10/21/hadi-esmaeilzadeh-on-dark-silicon/) with Hadi Esmaeilzadeh on Dark Silicon to be on track, yet he uses another interpretation of Moore’s Law – one where Moore’s Law continues, but Dennard scaling ended in the mid-2000s. Yet, I quoted the phrase from Moore’s paper that disclosed the scaling rule that later became known as Dennard scaling.
At a higher level, I believe Moore’s Law has turned into a marketing phrase that is being redefined as needed by the semiconductor industry so it remains true.
So why are computations per dollar rising? For many years, the vendor objective was to make processors that ran word processors and web browsers faster. This trend culminated in the early 2000s with processors like the Pentium IV with a 4 GHz clock and dissipating 200W+. Customers rebelled and industry shifted to multicore. With an n-core microprocessor, the results of running the benchmark on one core could be multiplied by n. This is an example of progress (raising computations per dollar) by item 2 in my response to a previous question (more parallelism, subject to difficulty in programming). Even now, most software does not exploit the multiple cores.
---
**Luke**: You write that “Customers rebelled and industry shifted to multicore.” I typically hear a different story about the 2002-2006 era, one that didn’t have much to do with customer rebellion, but instead the realization by industry that the quickest way to keep up the Moorean trend — to which consumers and manufacturers had become accustomed — was to jump to multicore. That’s the story I see in e.g. [*The Future of Computing Performance*](http://www.amazon.com/The-Future-Computing-Performance-Level/dp/0309159512) by National Academies Press (official summary [here](http://commonsenseatheism.com/wp-content/uploads/2014/03/Fuller-Millett-Computing-Performance-Game-Over-or-Next-Level-in-IEEE-Computer-Society.pdf)). Moreover, the power scaling challenge to Moore’s Law was anticipated many years in advance by the industry, [for example in the ITRS reports](http://lesswrong.com/lw/hzu/model_combination_and_adjustment/9gxu). Can you clarify what you mean by “customers rebelled”?
---
**Erik**: What happens if you take projections of the future to be true and then the projections change? You eventually end up with multiple “truths” about the same thing in the historical record. I accept that the stories you hear are true, but there is another truth based on different projections.
Let us mathematically invert the ITRS roadmap to see how projections of today’s (2014) microprocessor clock rate evolved as industry addressed power scaling and shifted to multicore. I have gone back to [earlier editions of the ITRS](http://www.irts.net) and accessed edition reports for 2003, 2005, and 2007. In table 4 of the executive summary of each edition, they have a projection of “on chip local clock,” which means microprocessor clock rate. I accessed [Pricewatch](http://www.pricewatch.com) to get the 2014 clock rate.
| On chip local clock | In year 2013 | In year 2014 | In year 2015 |
| Projection in 2003 ITRS | 22.9 GHzTable 4c | Only odd years reported in this edition | 33.4 GHzTable 4d |
| Projection in 2005 ITRS | | 28.4 GHzTable 4d | |
| Projection in 2007 ITRS | | 7.91 GHzTable 4c | |
| 2014 reality | | 4.0 GHz
Pricewatch.com | |
The most conspicuous issue is that the 2003 and 2005 editions overstated clock rate by about 7x. ITRS accommodated to multicore in 2007 with a new scaling model that we see in retrospect overstates reality by only 2x. Footnote 1 in the 2007 ITRS describes the change. The footnote ends with the following sentence: “This is to reflect recent on-chip frequency slowing trends and anticipated speed-power design tradeoffs to manage a maximum 200 watts/chip affordable power management tradeoff.”
If you believe ITRS is “industry,” industry had been telling customers to expect the benefits of Moore’s Law through rising clock rate. In my view, customers took the lead in saying power per chip should be less than 200 watts even if it meant a more difficult to use parallel programming model. Several years after the multicore became popular, industry changed its projection so customers were to expect the benefits of progress through rising computations per dollar rather than speed. This, of course, led to the rise of battery operated smart phones and tablets with power limits much lower than 200 watts.
By the way, I have not heard the phrase “Moorean trend” before. It seems to capture the idea of progress in computing without being tied to particular technical property. Why don’t you trademark it; it gets zero Google hits.
---
**Luke**: Are you willing to make some forecasts about the next ~15 years in computing? I’d be curious to hear your point estimate, or even better your 70% confidence interval, for any of the following:
* FLOPS per US dollar in top-end supercomputing in 2030.
* Average kT per active logic gate in top-end supercomputing in 2030.
* Some particular measure of progress on reversible computing, in 2030?
* World’s total FLOPS capacity in 2030. (See [here](http://intelligence.org/2014/02/28/the-worlds-distribution-of-computation-initial-findings/).)
Or really, anything specific about computing you’d like to forecast for 2030.
---
**Erik**: The FLOPS per dollar question will be most interesting, so I’ll leave it for last.
*kT/logic op*: I see a plateau around 10,000 kT, and will discuss what might come beyond the plateau in the next paragraph.. My guess of 10,000 kT includes interconnect wire, which is significant because today 75-90% of energy is attributable to interconnect wire. Today, we see around 50,000 kT. A reduction in supply voltage to .3v should be good for 10x improvement, but there are other issues. This estimate should be valid in 10 years, but the question asked about 15 years.
I would not be surprised that we see a new approach in the interval 2025-2030 (mentioned below). It will be difficult to predict specifically, but the five-year interval is short and the improvement rate seems to be insensitive to details. So, say there is a 5x additional improvement by 2030.
*Cumulative by 2030*: 2,000 kT/logic op, including interconnect wire. However, this will be really disappointing. People will expect 10 doublings due to Moore’s Law in the 15-year interval, for an expected improvement of 1024x; I’m predicting 25x.
*Reversible computing*: I think reversible computing (as strictly defined) will be demonstrated in a few years and principally impact society’s thought processes. The demonstration would be computation at less than 1 kT/logic op, where theory says those levels are unachievable unless reversible computing principles are used. I do not expect reversible computing to be widely used by 2030. The projection of 2,000 kT/logic op in 2030 represents a balance of manufacturing costs and energy costs.
By 2030, reversible computing could be employed in some applications where power is very expensive, such as spacecraft or implantable medical devices.
However, a demonstration of reversible computing could have an important impact on societal thinking. Popular thinking sees some ideas as unlimited for planning purposes and endows those ideas with attention and investment. This applied to California real estate prices (until 2008) and Moore’s Law (until a few years ago). Claims that “Moore’s Law is Ending” are moving computation into a second class of ideas that popular thinking sees as limited, like the future growth potential of railroads. A reversible computing demonstration would move computing back to the first category and thus make more attention and capital available.
However, reversible computing is part of a continuum. I see a good possibility that adiabatic methods could become the new method mentioned above for the 2020-2025 time range.
*World’s Total FLOPS capacity, 2030*. I looked over the [document by Naik](http://intelligence.org/2014/02/28/the-worlds-distribution-of-computation-initial-findings/) you cited. I don’t feel qualified to judge his result. However, I will stand by my ratio of 50,000 kT to 2,000 kT = 25. So my answer is to multiply Naik’s result by 25. I do not imagine that the cumulative power consumption of computers will rise substantially, particularly with Green initiatives.
*FLOPS per dollar*: This answer will be all over the place. Let’s break down by application class:
(A) Some applications are CPU-bound, meaning their performance will track changes in kT per logic op. I have given my guess of 25x improvement (which is a lot less then the 1024x that Moore’s Law would have delivered).
(B) Other applications are memory bound, meaning their performance will track (a) memory subsystem performance, where advances partially overlap with advances due to Moore’s Law and (b) architecture changes that can reduce the amount of data movement.
It is a lot easier to make a computer for (A) than (B); for a given cost, a computer will outperform type A applications by an order of magnitude or more on FLOPS compared to type B.
A top-end supercomputer supports both A and B, but the balance between A and B may be the profound question of the era. The balance has been heavily weighted in favor of A (through reliance on LINPACK as the benchmark). However, we do not currently have a particularly aggressive Exascale program in the US. Instead, we have a lot of discussion about the memory subsystem’s low energy efficiency. You can make a fairly compelling case that progress in top-end supercomputing will be held up until the computers can become better balanced.
(For reference the TOP 2 supercomputer is ORNL Titan with 17.5 Petaflops Linpack for $97 million; a ratio of 181 MFLOPS/$. The TOP 1 supercomputer does not seem to be a good cost reference.)
If architecture stays fixed until 2030, I’ll guess 25x improvement. That would be 4.5 GFLOPS/$. Memory subsystems are made out of the same transistor technology as logic, perhaps plus a growing fraction of optics. If transistors become more effective by 25x, this could benefit both FLOPS and the memory subsystem. Use of 3D may boost performance (due to shorter wires), but this will be offset by efficiency loss due to difficulty exploiting greater parallelism. Call the latter factors a wash.
Architecture is the wildcard. There are architectures known that are vastly more efficient than the von Neumann machine, such as systolic arrays, Processor-In-Memory (PIM), Field Programmable Gate Arrays (FPGAs) and even GPUs. These architectures get a performance boost by organizing themselves to put calculations closer to where data is stored, requiring less time and energy to complete a task. Unfortunately, these architectures succeed at the expense of generality. If a vendor boosts performance through too much specialization, they run the risk of being disqualified as an example of a “top-end supercomputer.” The Holy Grail would be a software approach that would make general software run on some specialized hardware (like a compiler that would run general C code on a GPU – at the full performance of the GPU).
However, I will predict that architecture improvements will contribute an additional 4x by 2030, for a cumulative improvement factor of 100x. That will be 18 GFLOPS/$. This is still 10x short of the 1024x expected for 15 years.
However, I think Artificial General Intelligence (AGI) may fare well due to specialization. Synaptic activity is the dominant function that enables living creatures to think, but it is quite different from the floating point in a supercomputer. A synapse performs local, slow, computations based on analog stimuli and analog learned behavior. In contrast, the floating point in a supercomputer operates blazingly fast on data fetched from a distant memory and computes an answer with 64-bit precision. Speed reduces energy efficiency, and the supercomputer doesn’t even learn. Since a von Neumann computer is Turing complete, it will be capable of executing an AGI coded in software. However, the efficiency may be low.
Executing an AGI could be optimized by new or specialized technology and advance faster than the rate of Moore’s Law, like Bitcoin mining. I am going to project that an AGI demonstration at scale will require a non-conventional, but not unimaginable computer. The computer could be specialized CMOS, like a GPU with special data types and data layout. Alternatively, the computer could employ new physical devices, such as a neuromorphic architecture with a non-transistor device (e. g. memristor).
All said, AGI might see 1000x or more improvement. In other words, AGI enthusiasts might be able to plan on 181 GFLOPS/$ by 2030. However, they would be classed as AI machines rather than top-end supercomputers.
---
**Luke**: Thanks, Erik!
---
1. Note added in review: Landauer proposed a lower limit on “on the order of kT” only for “irreversible” computations. As far as I know, the phrase “Landauer’s Limit” was created later by other people. In my experience, the phrase “Landauer’s Limit” if often applied as a general limit.
The post [Erik DeBenedictis on supercomputing](https://intelligence.org/2014/04/03/erik-debenedictis/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org). |
6d7a14f6-1738-46a2-876c-0d8c2ed69f88 | StampyAI/alignment-research-dataset/special_docs | Other | Integrating Human Observer Inferences into Robot Motion Planning
Integrating Human Observer Inferences
into Robot Motion Planning
Anca Dragan and Siddhartha Srinivasa
The Robotics Institute, Carnegie Mellon University
{adragan,siddh}@cs.cmu.edu
Abstract —Our goal is to enable robots to produce mo-
tion that is suitable for human-robot collaboration and co-
existence. Most motion in robotics is purely functional ,
ideal when the robot is performing a task in isolation. In
collaboration, however, the robot’s motion has an observer ,
watching and interpreting the motion.
In this work, we move beyond functional motion, and
introduce the notion of an observer into motion planning,
so that robots can generate motion that is mindful of how it
will be interpreted by a human collaborator.
We formalize predictability and legibility as properties of
motion that naturally arise from the inferences in opposing
directions that the observer makes, drawing on action in-
terpretation theory in psychology. We propose models for
these inferences based on the principle of rational action,
and derive constrained functional trajectory optimization
techniques for planning motion that is predictable or legible.
Finally, we present experiments that test our work on
novice users, and discuss the remaining challenges in en-
abling robots to generate such motion online in complex
situations.1
I. Introduction
In this paper, we explore the problem where a robot
and a human are collaborating side by side to perform
a tightly coupled physical task together, like clearing a
table (a running example in our paper).
The task amplifies the burden on the robot’s motion.
Most motion in robotics is purely functional : industrial
robots move to package parts, vacuuming robots move
to suck dust, and personal robots move to clean up a
dirty table. This type of motion is ideal when the robot
is performing a task in isolation.
Collaboration, however, does not happen in isolation.
In collaboration, the robot’s motion has an observer ,
watching and interpreting the motion.
In this work, we move beyond functional motion, and
introduce the notion of an observer and their inferences
into motion planning, so that robots can generate motion
that is mindful of how it will be interpreted by a human
collaborator.
When we collaborate, we make two inferences
(Fig.1,lower center) about our collaborator [12, 54, 56]:
1) we infer their goal based on their ongoing action
(action-to-goal), and 2) if we know their goal, we infer
their future action from it (goal-to-action). Our work
1This paper combines work from [15] and [18].
Fig. 1. Above: Predictable, day-to-day, expected handwriting vs. leg-
ible handwriting. Upper Center: A predictable and a legible trajectory
of a robot’s hand for the same task of grasping the green object. Lower
Center: Predictability and legibility stem from inferences in opposing
directions. Below: The legibility optimization process for a reaching
task. By exaggerating to the right, the robot is more clear about its
intent to grasp the object on the right .
formalizes the two properties of motion that enable these
two inferences: predictability and legibility .
Legibility stems from the first inference, and is about
conveying intent – moving in a manner that makes the
robot’s goal clear to observer. Predictability stems from
the second inference, and is about matching the ob-
server’s expectation – matching the motion they predict
when they know the robot’s goal.
Predictable and legible motion can be correlated. For
example, in an unambiguous situation, where an actor’s
observed motion matches what is expected for a given
intent (i.e. is predictable), then this intent can be used
to explain the motion. If this is the only intent which
explains the motion, the observer can immediately infer
the actor’s intent, meaning that the motion is also legible.
This is why we tend to assume that predictability implies
legibility – that if the robot moves in an expected way,
then its intentions will automatically be clear [2, 6, 35].
The writing domain, however, clearly distinguishes
the two. The word legibility , traditionally an attribute
of written text [52], refers to the quality of being easy
to read. When we write legibly, we try consciously,
and with some effort, to make our writing clear and
readable to someone else. The word predictability , on the
other hand, refers to the quality of matching expectation.
When we write predictably, we fall back to old habits,
and write with minimal effort.
As a consequence, our legible and predictable writings
are different : our friends do not expect to open our
diary and see our legible writing style. They rightfully
assume the diary will be written for us, and expect our
usual, day-to-day style. In this paper, by formalizing
predictability and legibility as directly stemming from
the two inferences in opposing directions, goal-to-action
and action-to-goal, we show that the two are different in
motion as well:
Predictability and legibility are fundamentally dif-
ferent properties of motion, stemming from observer
inferences in opposing directions.
Ambiguous situations, occurring often in daily tasks,
make this opposition clear: more than one possible intent
can be used to explain the motion observed so far,
rendering the predictable motion illegible. Fig.1(center)
exemplifies the effect of this contradiction. The robot
hand’s motion on the left is predictable in that it matches
expected behavior. The hand reaches out directly to-
wards the target. But, it is not legible, failing to make
the intent of grasping the green object clear. In contrast,
the trajectory on the right is more legible, making it clear
that the target is the green object by deliberately bending
away from the red object. But it is less predictable, as
it does not match the expected behavior of reaching
directly. We will show in Sections IV and V how we
can quantify this effect with Bayesian inference, which
enables us to derive the online probabilites of the motion
reaching for either object, illustrated as bar graphs in
Fig.1.
Our work makes the following contributions:
1.We formalize legibility and predictability in the con-
text of goal-directed motion in Sec. III as stemming
from inferences in opposing directions. The formalism
emphasizes their difference, and directly relates to the
theory of action interpretation [12] and the concepts of
“action-to-goal” and “goal-to-action” inference.
2.Armed with mathematical definitions of legibility and
predictability, we propose a way in which a robot could
model these inferences in order to evaluate how legible
or predictable a motion is (Sections IV and V). The
models are based on cost optimization, resonate withthe principle of rational action [11, 23], and echo earlier
works on action understanding via inverse planning [5].
3.We derive methods for generating predictable and
legible motion. Although our model enables us to eval-
uate how predictable or legible a motion trajectory is,
it does not enable us to generate trajectories that are
predictable or legible. Going from evaluation to gen-
eration means going beyond modeling the observer’s
goal inference, to creating motion that results in the
correct goal being inferred, i.e. going from "I can tell
that you believe I am grasping this.", to "I know how
tomake you believe I am grasping this". We do so via
functional gradient optimization in the space of motion
trajectories, echoing earlier works in motion planning
[9, 28, 31, 43, 45, 53, 55, 61], now with optimization
criteria based on the observer’s inferences.
4.We propose a method for optimizing for legibility
while still maintaining predictability. The ability to op-
timize the legibility criterion leads us to a surprising
observation: that there are cases in which the trajectory
becomes too unpredictable . As our user studies show
(Sec. X-B), some unpredictability is often necessary to convey
intent — it is unpredictability beyond a threshold (like
the outermost trajectory in Fig.4) that confuses users
and lowers their confidence in what the robot is doing.
We address this fundamental limitation by prohibiting
the optimizer to “travel to uncharted territory”, i.e. go
outside of the region in which its assumptions have
support — we call this a “trust region” of predictability.
5.We test our work in two experiments with novice
users, one focused on the models, and the other on
motion generation. We demonstrate legibility and pre-
dictability are contradictory not just in theory, but also in
practice, and follow-up with an analysis of the trade-off
between the two. We experiment with three characters
that differ in their complexity and anthropomorphism:
a simulated point robot, the bi-manual mobile manipu-
lator HERB[48], and a human (Sec. X).
Understanding and planning motion that is pre-
dictable or legible can greatly enhance human-robot
collaboration, but many challenges remain in generating
such motion online in complex situations, and in decid-
ing how to trade off between the two properties in each
situation. We discuss these in Sec. XI.
II. R elation to Prior Work
“Predictable” means “expected”, and is usually used
to refer to a desirable property of motion without need-
ing additional clarification [2, 6]. “Legible”, on the other
hand, is typically reserved for writing, and appears in
the robot motion literature accompanied by an expla-
nation: being legible means that observers are able to
recognize [6] , infer [39], or understand [3, 35] the inten-
tions of the robot, or that robot indicates the goal it will
reach [35] – overall, legibility is about expressing intent.
Building on this, we define predictability and legibility as
enabling inferences that the collaborator needs to make,
and propose trajectory optimization criteria for the two.
One exception to using legibility to mean expressing
intent is a definition of legibility as both being intent-
expressive and matching expectation [40]. Our work,
however, shows that matching expectation and express-
ing intent are fundamentally different properties that can
at times contradict.
Existing methods for generating legible motion fall
in three categories. (1) increasing legibility indirectly by
increasing predictability (e.g. via learning from demon-
stration) [6]; (2) increasing legibility indirectly by increas-
ing visibility to the observer [3, 29]; and (3) increasing
legibility directly by encoding animation principles like
anticipation in the motion [24, 49].
In contrast, our work explicitly formalizes intent-
expressiveness for goal-direct motion in the form of a
trajectory optimization criterion: rather than targeting a
related property, the robot directly maximizes the prob-
ability of the correct intent being inferred. The resulting
motion can be interpreted using animation principles,
but we do not encode these explicitly – instead, they
emerge out of the mathematics of legible motion.
III. F ormalizing Legibility
and Predictability
In common use, legible motion is intent-expressive,
and predictable motion matches what is expected. Here,
we formalize these definitions for the context of goal-
directed motion, where a human or robot is executing
a trajectory x:[0, 1]!Q , lying in a Hilbert space of
trajectories X.xstarts at a configuration Sand ends at
a goal Gfrom a set of possible goals G, like in Fig.1. In
this context, Gis central to both properties:
Definition 1: Legible motion is motion that enables an
observer to quickly and confidently infer the correct goal G.
Definition 2: Predictable motion is motion that matches
what an observer would expect, given the goal G.
A. Formalism
1) Legibility: Imagine an observer watching the orange
trajectory from Fig.1. As the robot’s hand departs the
starting configuration and moves along the trajectory, the
observer is running an inference, predicting which of the
two goals it is reaching for. We denote this inference
function that maps (snippets of) trajectories from all
trajectories Xto goals as
IL:X!G
The bar graphs next to the hands in Fig.1 signify the
observer’s predictions of the two likely goals. At the very
beginning, the trajectory is confusing and the observerhas little confidence in the inference — in what follows,
we model this confidence based on the probability that
the observer assigns to the inferred goal. However, the
observer becomes confident very quickly – even from the
second configuration of the hand along the trajectory,
it becomes clear that the green object is the target.
This quick and confident inference is the hallmark of
legibility.
We thus formalize legible motion as motion that en-
ables an observer to confidently infer the correct goal
configuration Gafter observing only a snippet of the
trajectory, xS!Q, from the start Sto the configuration at
a time t,Q=x(t):
IL(xS!Q) =G
The quicker this happens (i.e. the smaller tis), the more
legible the trajectory is.
The definition in terms of ILis an interpretation
in the motion domain for terms like “readable” [49],
or “understandable”[3], and encourages “anticipatory”
motion [24] because it brings the relevant information for
goal prediction towards the beginning of the trajectory,
thus lowering t. The formalism can also generalize to
outcome-directed motion (e.g. gestures such as pointing
at, waving at, etc.) by replacing the notion of goal with
that of an outcome – here, legible motion becomes
motion that enables quick and confident inference of
the desired outcome. Our recent work illustrated this for
pointing gestures [20].
2) Predictability: Now imagine someone knowing that
the hand is reaching towards the green goal. Even before
the robot has moved, the observer creates an expectation,
making an inference on how the hand will move – for ex-
ample, that the hand will start turning towards the green
object as it is moving directly towards it. We denote this
inference function mapping goals to trajectories as
IP:G!X
We formalize predictable motion as motion for which
the trajectory xS!Gmatches this inference:
IP(G) =xS!G
The better the actual trajectory matches the inference,
measurable for example using a distance metric between
IP(G)and xS!G, the more predictable the trajectory is.
B. Connection to Action Interpretation in Psychology
A growing amount of research in psychology sug-
gests that humans interpret observed behaviors as goal-
directed actions [10, 12, 26, 42, 47, 58, 59], a result
supported by studies observing infants and how they
show surprise when exposed to inexplicable action-goal
pairings. Csibra and Gergeley [12] summarize two types
Human Inference Type Example Analogy in Motion Property of Motion
action7!goal ... pour beans in grinder 7!coffee xS!Q7!G legibility
goal7!action coffee 7!... pour beans in grinder ... G7!xS!G predictability
TABLE I
Legibility and predictability as enabling inferences from action interpretation in opposing direction .
(G)=argminξ∈ΞS→GC[ξ](ξS→Q)=argmaxG∈GP(G|ξS→Q)(G)ξS:QG1G2G
ξ*S→G=argminξ∈ΞS→GC(ξ)G*=argmaxG∈GP(G|ξS→Q)ξ*S→GξS→QG1G2
G
Fig. 2. In our models, the observer expects the robot’s motion to optimizea cost functionC(left), and uses that expectation to identity which goal ismost probable given the robot’s motion so far (right)B. Connection to PsychologyA growing amount of research in psychology suggests thathumans interpret observed behaviors as goal-directed actions[11], [15]–[19], a result stemming from studies observing in-fants and how they show surprise when exposed to inexplicableaction-goal pairings. [11] ] summarize two types of inferencestemming from the interpretation of actions as goal directed:“action-to-goal” and “goal-to-action”.“Action-to-goal” refers to an observer’s ability to infersomeone’s goal state from their ongoing actions (e.g. becausethey are pouring coffee beans into the grinder, the willeventually hold a cup of coffee). “Action-to-goal” inferenceanswers the question “What is the function of this action?”.“Goal-to-action” refers to an observer’s ability to predictthe actions that someone will take based on their goal (e.g.because they want to make coffee, they will will pour coffeebeans into the grinder). “Goal-to-action” inference answers thequestion “What action would achieve this goal?”.This has a natural connection to our formalism. In goal-directed motion, actions are trajectories and goals are goalconfigurations. Thus the inference occurring in legibility,from trajectory to goal,⇠S!Q7!G, relates naturally to“action-to-goal” inference. Likewise, the inference occurringin predictability, from goal to trajectory,G7!⇠S!G, relatesnaturally to “goal-to-action”.C. SummaryOur formalism emphasizes the difference between legibilityand predictability in theory: they stem from inferences inopposing directions(from trajectories to goals vs. from goalsto trajectories), with strong parallels in the theory of actioninterpretation. In what follows, we introduce one way for arobot to model these two inferences (summarize in Fig.2), andpresent an experiment that emphasizes the difference betweenthe two properties in practice.III. MODELINGPREDICTABLEMOTIONA. The Trajectory InferenceIPTo modelIPis to model the observer’s expectation. Oneway the robot could do so is by assuming that the humanobserver expects it to be a rational agent acting efficiently[11] or justifiably [13] to achieve a goal. This is known asthe principle of rational action [12], [13], and it has beenshown to apply to non-human agents, including robots [20].The robot could model this notion of “efficiency” via a costfunction defining what it means to be efficient. For example,if the observer expected the robot’s hand to move directlytowards the object it wants to grasp (as opposed to takingan unnecessarily long path to it), then “efficiency” would bedefined by the cost function penalizing the trajectory’s length.Throughout this paper, we will refer to the cost functionmodeling the observer’s expectation asC:C:⌅!R+with lower costs signifying more “efficient” trajectories.The most predictable trajectory is then the most “efficient”:IP(G) = arg min⇠2⌅S!GC(⇠)(1)Crepresents what the observer expects the robot to opti-mize, and therefore encompasses every aspect of the observer’sexpectation, including (when available) body motion, handmotion, arm motion, and gaze.B. Evaluating and Generating PredictabilityPredictability can be evaluated based onC: the lowerthe cost, the more predictable (expected) the trajectory. Wepropose a predictabilityscorenormalized from 0 to 1:predictability(⇠)=exp C(⇠) (2)Generating predictable motion means maximizing thisscore, or equivalently minimizing the cost functionC– asin (1). This presents two major challenges: learningC, andminimizingC.First, the robot needs access to the cost functionCthatcaptures how the human observer expects it to move. If thehuman observer expects human-like motion, animation (e.g.[21]) or biomechanics (e.g. [22], [23]) literature can serve toprovide approximations forC. Our experiment (Section V)uses trajectory length as a proxy for the realC, resulting inthe shortest path to goal – but this is merely one aspect ofexpected behavior. As our experiment will reveal, efficiencyof robot motion has different meanings for different observers.If the observer were willing to provide examples of what theyexpect, the robot could learn how to act via Learning fromDemonstration [24]–[26] or Inverse Reinforcement Learning[27]–[29]. Doing so in a high-dimensional space, however, isstill an active area of research.Second, the robot must find a trajectory that minimizesC.This is tractable in low-dimensional spaces, or ifCis convex.While efficient trajectory optimization techniques do exist forhigh-dimensional spaces and non-convex costs [30], they aresubject to local minima, and how to alleviate this issue inpractice remains an open research question [31], [32].away from the red object. But it is less predictable, as it doesnot match the expected behavior of reaching directly. We willshow in Sections III and IV how we can quantify this effectwith Bayesian inference, which allows us to derive, amongother things, the online probabilites of the motion reachingfor either object, illustrated as bar graphs in Fig.1.Our work makes the following three contributions:1.We formalize legibility and predictability in the contextof goal-directed motion in Section II as stemming frominferences inopposingdirections. The formalism emphasizestheir difference, and directly relates to the theory of actioninterpretation [11] and the concepts of “action-to-goal” and“goal-to-action” inference. Our formalism also unifies pre-vious descriptions of legibility, quantifying readability andunderstandability, and encouraging anticipation as a directconsequence of our definitions.2.Armed with mathematical definitions of legibility andpredictability, we propose a way in which a robot could modelthese inferences in order to evaluate and generate motion thatis legible or predictable (Sections III and IV). The models arebased on cost optimization, and resonate with the principle ofrational action [12], [13].3.We demonstrate that legibility and predictability are contra-dictory not just in theory, but also in practice. We presentan extensive experiment for three characters that differ intheir complexity and anthropomorphism: a simulated pointrobot, the bi-manual mobile manipulator HERB [14], and ahuman (Section V). The experiment confirms the contradictionbetween predictable and legible motion, and reveals interestingchallenges (Section VI). We found, for instance, that differentpeople expect a complex robot like HERB to act in differentways: for a robot to be predictable, it must adapt to theparticulars of the observer.The difference between legibility and predictability of mo-tion is crucial for human-robot interaction, in particular forcollaboration between humans and robots. Collaboration is adelicate dance of prediction and action, where agents mustpredict their collaborator’s intentions as well as make theirown intentions clear – they must act legibly. We are excitedto be taking an essential step towards better human-robotcollaboration: by emphasizing the difference between legibilityand predictability, we advocate for a different approach tomotion planning, in which robots decide between optimizingfor legibility and optimizing for predictability, depending onthe context they are in.II. FORMALIZINGLEGIBILITYANDPREDICTABILITYSo far, we have identified that legible motion is intent-expressive, and predictable motion matches what is expected.Here, we formalize these definitions for the context of goal-directed motion, where a human or robot is executing atrajectory towards one goalGfrom a set of possible goalsG,like in Fig.1. In this context,Gis central to both properties:the intent is reaching the goalG, and what is expected dependsonG:Definition 2.1:Legible motion is motion that enables anobserver to quickly and confidently infer the goal.Definition 2.2:Predictable motion is motion that matcheswhat an observer would expect, given the goal.A. Formalism1) Legibility:Imagine someone observing the orange tra-jectory from Fig.1. As the robot’s hand departs the startingconfiguration and moves along the trajectory, the observer isrunning an inference, predicting which of the two goals itis reaching for. We denote this inference function that maps(snippets of) trajectories from all trajectories⌅to goals asIL:⌅!GThe bar graphs next to the hands in Fig.1 signify the observer’spredictions of the two likely goals. At the very beginning, thetrajectory is confusing and the observer has littleconfidenceinthe inference. However, the observer becomes confident veryquickly– even from the second configuration of the hand alongthe trajectory, it becomes clear that the green object is thetarget. This quick and confident inference is the hallmark oflegibility.We thus formalizelegiblemotion as motion that enables anobserver toconfidentlyinfer thecorrectgoal configurationGafter observing only a snippet of the trajectory,⇠S!Q, fromthe startSto the configuration at a timet,Q=⇠(t):IL(⇠S!Q)=GThequickerthis happens (i.e. the smallertis), the morelegible the trajectory is.This formalizes terms like “readable” [4], or “understand-able” [6], and encourages “anticipatory” motion [5] because itbrings the relevant information for goal prediction towards thebeginning of the trajectory, thus loweringt. The formalism canalso generalize to outcome-directed motion (e.g. gestures suchas pointing at, waving at, etc.) by replacing the notion of goalwith that of an outcome – here, legible motion becomes motionthat enables quick and confident inference of the desiredoutcome.2) Predictability:Now imagine someone knowing that thehand is reaching towards the green goal. Even before the robotstarts moving, the observer creates an expectation, making aninference on how the hand will move – for example, that thehand will start turning towards the green object as it is movingdirectly towards it. We denote this inference function mappinggoals to trajectories asIP:G!⌅We formalizepredictablemotion as motion for which thetrajectory⇠S!Gmatches this inference:IP(G)=⇠S!GThe more the trajectory matches the inference, measurablefor example using a distance metric betweenIP(G)and⇠S!G, the more predictable the trajectory is.
ξ*S→G=argminξ∈ΞS→GC(ξ)G*=argmaxG∈GP(G|ξS→Q)ξ*S→GξS→QG1G2
G
Fig. 2. In our models, the observer expects the robot’s motion to optimizea cost functionC(left), and uses that expectation to identity which goal ismost probable given the robot’s motion so far (right)B. Connection to PsychologyA growing amount of research in psychology suggests thathumans interpret observed behaviors as goal-directed actions[11], [15]–[19], a result stemming from studies observing in-fants and how they show surprise when exposed to inexplicableaction-goal pairings. [11] ] summarize two types of inferencestemming from the interpretation of actions as goal directed:“action-to-goal” and “goal-to-action”.“Action-to-goal” refers to an observer’s ability to infersomeone’s goal state from their ongoing actions (e.g. becausethey are pouring coffee beans into the grinder, the willeventually hold a cup of coffee). “Action-to-goal” inferenceanswers the question “What is the function of this action?”.“Goal-to-action” refers to an observer’s ability to predictthe actions that someone will take based on their goal (e.g.because they want to make coffee, they will will pour coffeebeans into the grinder). “Goal-to-action” inference answers thequestion “What action would achieve this goal?”.This has a natural connection to our formalism. In goal-directed motion, actions are trajectories and goals are goalconfigurations. Thus the inference occurring in legibility,from trajectory to goal,⇠S!Q7!G, relates naturally to“action-to-goal” inference. Likewise, the inference occurringin predictability, from goal to trajectory,G7!⇠S!G, relatesnaturally to “goal-to-action”.C. SummaryOur formalism emphasizes the difference between legibilityand predictability in theory: they stem from inferences inopposing directions(from trajectories to goals vs. from goalsto trajectories), with strong parallels in the theory of actioninterpretation. In what follows, we introduce one way for arobot to model these two inferences (summarize in Fig.2), andpresent an experiment that emphasizes the difference betweenthe two properties in practice.III. MODELINGPREDICTABLEMOTIONA. The Trajectory InferenceIPTo modelIPis to model the observer’s expectation. Oneway the robot could do so is by assuming that the humanobserver expects it to be a rational agent acting efficiently[11] or justifiably [13] to achieve a goal. This is known asthe principle of rational action [12], [13], and it has beenshown to apply to non-human agents, including robots [20].The robot could model this notion of “efficiency” via a costfunction defining what it means to be efficient. For example,if the observer expected the robot’s hand to move directlytowards the object it wants to grasp (as opposed to takingan unnecessarily long path to it), then “efficiency” would bedefined by the cost function penalizing the trajectory’s length.Throughout this paper, we will refer to the cost functionmodeling the observer’s expectation asC:C:⌅!R+with lower costs signifying more “efficient” trajectories.The most predictable trajectory is then the most “efficient”:IP(G) = arg min⇠2⌅S!GC(⇠)(1)Crepresents what the observer expects the robot to opti-mize, and therefore encompasses every aspect of the observer’sexpectation, including (when available) body motion, handmotion, arm motion, and gaze.B. Evaluating and Generating PredictabilityPredictability can be evaluated based onC: the lowerthe cost, the more predictable (expected) the trajectory. Wepropose a predictabilityscorenormalized from 0 to 1:predictability(⇠)=exp C(⇠) (2)Generating predictable motion means maximizing thisscore, or equivalently minimizing the cost functionC– asin (1). This presents two major challenges: learningC, andminimizingC.First, the robot needs access to the cost functionCthatcaptures how the human observer expects it to move. If thehuman observer expects human-like motion, animation (e.g.[21]) or biomechanics (e.g. [22], [23]) literature can serve toprovide approximations forC. Our experiment (Section V)uses trajectory length as a proxy for the realC, resulting inthe shortest path to goal – but this is merely one aspect ofexpected behavior. As our experiment will reveal, efficiencyof robot motion has different meanings for different observers.If the observer were willing to provide examples of what theyexpect, the robot could learn how to act via Learning fromDemonstration [24]–[26] or Inverse Reinforcement Learning[27]–[29]. Doing so in a high-dimensional space, however, isstill an active area of research.Second, the robot must find a trajectory that minimizesC.This is tractable in low-dimensional spaces, or ifCis convex.While efficient trajectory optimization techniques do exist forhigh-dimensional spaces and non-convex costs [30], they aresubject to local minima, and how to alleviate this issue inpractice remains an open research question [31], [32].
Fig. 2. Our models for IPandIL: the observer expects the robot’s motion to optimize a cost function C(IP, left), and identifies based on C
which goal is most probable given the robot’s motion so far ( IL, right).
of inference stemming from the interpretation of actions
as goal directed: “action-to-goal" and “goal-to-action".
“Action-to-goal” refers to an observer’s ability to infer
someone’s goal state from their ongoing actions (e.g.
because they are pouring coffee beans into the grinder,
the will eventually hold a cup of coffee). “Action-to-goal”
inference answers the question “What is the function of
this action?”.
“Goal-to-action” refers to an observer’s ability to pre-
dict the actions that someone will take based on their
goal (e.g. because they want to make coffee, they will
will pour coffee beans into the grinder). “Goal-to-action”
inference answers the question “What action would
achieve this goal?”.
This has a natural connection to our formalism. In
goal-directed motion, actions are trajectories and goals
are goal configurations. Thus the inference occurring
in legibility, from trajectory to goal, xS!Q7!G, re-
lates naturally to “action-to-goal” inference. Likewise,
the inference occurring in predictability, from goal to
trajectory, G7!xS!G, relates naturally to “goal-to-
action”.
C. Summary
Our formalism emphasizes the difference between
legibility and predictability in theory: they stem from
inferences in opposing directions (from trajectories to goals
vs. from goals to trajectories), with strong parallels in the
theory of action interpretation. Table I shows a summary.
In what follows, we introduce one way for a robot to
model these two inferences (summarized in Fig.2), de-
rive motion generating algorithms based on this model,
and present experiments that emphasize the difference
between the two properties in practice.IV . P redictable Motion
A. Modeling the Trajectory Inference IP
To modelIPis to model the observer’s expectation.
One way the robot could do so is by assuming that
the human observer expects it to be a rational agent
acting efficiently[12] or justifiably[11] to achieve a goal.
This is known as the principle of rational action[11, 23],
and it has been shown to apply to non-human agents,
including robots[33]. The robot can model this notion of
“efficiency” via a cost function defining what it means
to be efficient. For example, if the observer expected the
robot’s hand to move directly towards the object it wants
to grasp (as opposed to taking an unnecessarily long
path to it), then “efficiency” would be defined by the
cost function penalizing the trajectory’s length.
Throughout this paper, we will refer to the cost function
modeling the observer’s expectation as C :
C:X!R+
with lower costs signifying more “efficient” trajectories.
The principle of rational action suggests that the most
predictable trajectory is the most “efficient”, for some
definition of efficiency C:
IP(G) =arg min
x2XS!GC[x] (1)
Crepresents what the observer expects the robot to
optimize, and therefore encompasses every aspect of the
observer’s expectation, including (when available) body
motion, hand motion, arm motion, and gaze.
B. Predictability Score
Predictability can be evaluated based on C: the lower
the cost, the more predictable (expected) the trajectory.
We propose a predictability score normalized from 0 to
1, where trajectories with lower cost are exponentially
more predictable (following the principle of maximum
entropy, which we detail in Sec. V-A):
Predictability (x) =exp C[x]
(2)
C. Generating Predictable Motion
Generating predictable motion means maximizing the
predictability score, or equivalently minimizing the cost
function C– as in (1).
One way to do so is via functional gradient descent.
We start from an initial trajectory x0and iteratively
improve its score. At every iteration i, we maximize the
regularized first order Taylor series approximation of C
about the current trajectory xi:
xi+1=arg min
x2XC[xi] +rCT
xi(x xi) +h
2jjx xijj2
M(3)
withh
2jjx xijj2
Ma regularizer restricting the norm of
the displacement x xiw.r.t. an M, as in [45].
By taking the functional gradient of (12) and setting it
to 0, we obtain the following update rule for xi+1:
xi+1=xi 1
hM 1¯rC (4)
Recent work has successfully applied this method to
motion planning, using a Cthat trades off between an
efficiency and an obstacle avoidance component [61]. The
non-convexity of this type of Cmakes trajectory opti-
mization prone to convergence to high-cost local minima,
which can be mediated by learning from experience
[13, 14].
A key challenge, which we discuss in Sec. XI, is finding
theCthat each observer expects the robot to minimize.
V . Legible Motion
A. Modeling the Goal Inference IL
To modelILis to model how the observer infers the
goal from a snippet of the trajectory xS!Q. One way to
do so is by assuming that the observer compares the
possible goals in the scene in terms of how probable each
is given xS!Q. This is supported by action interpretation:
Csibra and Gergeley [12] argue, based on the principle
of rational action, that humans assess which end state
would be most efficiently brought about by the observed
ongoing action.Taking trajectory length again as an ex-
ample for the observer’s expectation, this translates to
predicting a goal because xS!Qmoves directly toward
it and away from the other goals, making them less
probable.
One model forILis to compute the probability for
each goal candidate Gand to choose the most likely:
IL(xS!Q) =arg max
G2GP(GjxS!Q) (5)To compute this probability, we start with Bayes’ Rule:
P(GjxS!Q)µP(xS!QjG)P(G) (6)
where P(G)is a prior on the goals which can be uniform
in the absence of prior knowledge, and P(xS!QjG)is the
probability of seeing xS!Qwhen the robot targets goal
G. The is in line with the notion of action understanding
as inverse planning proposed by Baker et al.[5], here
P(xS!QjG)relating to the forward planning problem of
finding a trajectory given a goal.
We compute P(xS!QjG)as the ratio of all trajectories
from StoGthat pass through xS!Qtoalltrajectories
from StoG(Fig.3):
P(xS!QjG) =R
xQ!GP(xS!Q!G)
R
xS!GP(xS!G)(7)
Following [60], we assume trajectories are separable, i.e.
P(xX!Y!Z) =P(xX!Y)P(xY!Z), giving us:
P(xS!QjG) =P(xS!Q)R
xQ!GP(xQ!G)
R
xS!GP(xS!G)(8)
At this point, the robot needs a model of how probable
a trajectory xis in the eye of an observer. The observer
expects the trajectory of minimum cost under C. It is
unlikely, however, that they would be completely sur-
prised (i.e. assign 0 probability) by all other trajectories,
especially by one ever so slightly different. One way to
model this is to make suboptimality w.r.t. Cstill possible,
but exponentially less probable, i.e. P(x)µexp C[x]
,
adopting the principle of maximum entropy [60]. With
this, (8) becomes:
P(xS!QjG)µexp C[xS!Q]R
xQ!Gexp C[xQ!G]
R
xS!Gexp C[xS!G]
(9)
Computing the integrals is still challenging. In [19],
we derived a solution by approximating the probabil-
ities using Laplace’s method (also proposed indepen-
dently in [38]). If we approximate Cas a quadratic, its
Hessian is constant and according to Lapace’s method,R
xX!Yexp C[xX!Y]kexp C[x
X!Y]
(with ka con-
stant and x
X!Ythe optimal trajectory from XtoYw.r.t.
C). Plugging this into (9) and using (6) we get:
P(GjxS!Q) =1
Zexp C[xS!Q] VG(Q)
exp VG(S) P(GR)(10)
with Za normalizer across Gand VG(q) =
min x2Xq!GC[x]
Much like teleological reasoning suggests [12], this
evaluates how efficient (w.r.t. C) going to a goal is
through the observed trajectory snippet xS!Qrelative
to the most efficient (optimal) trajectory, x
S!G. In am-
biguous situations like the one in Fig.1, a large portion
SGQ
Fig. 3. xS!Qin black, examples of xQ!Gin blue, and further examples
ofxS!Gin purple. Trajectories more costly w.r.t. Care less probable.
SGR
GO
0!10!100!1000!10000!100000!
Fig. 4. The trajectory optimization process for legibility: the figure
shows the trajectories across iterations for a situation with a start and
two candidate goals.
ofx
S!Gis also optimal (or near-optimal) for a different
goal, making both goals almost equally likely along it.
This is why legibility does not also optimize C — rather than
matching expectation, it manipulates it to convey intent.
B. Legibility Score
A legible trajectory is one that enables quick and
confident predictions. A score for legibility therefore
tracks the probability assigned to the actual goal G
across the trajectory: trajectories are more legible if this
probability is higher, with more weight being given to
the earlier parts of the trajectory via a function f(t)(e.g.
f(t)=T-t, with T the duration of the trajectory):
legibility (x) =R
P(GjxS!x(t))f(t)dtR
f(t)dt(11)
with P(GjxS!x(t))computed using C, as in (10).
C. Generating Legible Motion
In order to maximize the L egibility functional, we
start from an initial trajectory x0and iteratively improveits score via functional gradient ascent (Fig.4). This pro-
cess is analogous to generating predictable motion, now
with a different optimization criterion:
xi+1=arg max
xLegibility [xi] +h¯rLegibility ,(x xi)i
h
2jjx xijj2
M (12)
By taking the functional gradient of (12) and setting it
to 0, we obtain the following update rule for xi+1:
xi+1=xi+1
hM 1¯rLegibility (13)
To find ¯rLegibility , letP(x(t),t) =
P(GRjxS!x(t))f(t)and K=1R
f(t)dt. The legibility
score is then
Legibility [x] =KZ
P(x(t),t)dt (14)
and
¯rLegibility =K¶P
¶x d
dt¶P
¶x0
(15)
Pis not a function of x0, thusd
dtdP
dx0=0.
dP
dx(x(t),t) =g0h h0g
h2P(GR)f(t) (16)
with g= exp
VGR(S) VGR(Q)
and h=
åGexp
VG(S) VG(Q)
, which after a few
simplifications becomes
¶P
¶x(x(t),t) =exp
VGR(S) VGR(x(t))
åGexp
VG(S) VG(x(t))2
å
G
exp VG(x(t))
exp VG(S)(V0
G(x(t)) V0
GR(x(t)))!
P(GR)f(t)
(17)
Finally,
¯rLegibility (t) =K¶P
¶x(x(t),t) (18)
with¶P
¶x(x(t),t)from (17).
VI. E xample
Fig.4 portrays the functional optimization process for
legibility for a point robot moving from a start location
to one of two candidate goals.
A. Parameters
We detail our choice of parameters for creating this
example below.
oEfficiency cost C.In this example, we use sum squared
velocities as the cost functional Ccapturing the user’s
expectation:
C[x] =1
2Z
x0(t)2dt (19)
This cost, frequently used to encourage trajectory
smoothness [45], produces trajectories that reach directly
toward the goal — this is the predictable trajectory,
shown at iteration 0. Our experiments below show that
this trajectory geometry is accurate for 2D robots like
in Fig.4. This Calso allows for an analytical VGand its
gradient.
oTrajectory initialization. We set x0=arg min xC[x]: we
initialize with the most predictable trajectory, treating C
as a prior.
oTrajectory parametrization. We parametrize the trajec-
tory as a vector of waypoint configurations.
oNorms w.r.t. M.We use the Hessian of CforM. As
a result, the update rule in (4) and (13) propagates local
gradient changes linearly to the rest of the trajectory.
B. Interpretation
The predictable trajectory (iteration 0) is efficient for
achieving the goal, but it is not the best at helping an
observer distinguish which goal the robot is targeting
from the beginning. By exaggerating the motion to the
right, the robot is increasing the probability of the goal
on the right relative to the one on the left.
Exaggeration is one of the 12 principles of animation
[51]. However, nowhere did we inform the robot of what
exaggeration is and how it might be useful for legibility.
The behavior elegantly emerged out of the optimization.
VII. T heUnpredictability of Legibility
The example leads to a surprising observation: in
some cases, by optimizing the Legibility functional, one
can become arbitrarily unpredictable .
Proof: Our gradient derivation in (17) enables us to
construct cases in which this occurs. In a two-goal case
like in Fig.4, with our example Cfrom (19), the gradient
for each trajectory configuration points in the direction
GR GOand has positive magnitude everywhere but at
¥, where C[x] =¥. Fig.5 (red) plots Cacross iterations.
The reason for this peculiarity is that the model for
how observers make inferences in (5) fails to capture how
humans make inferences in highly unpredictable situations .
In reality, observers might get confused by the robot’s
behavior and stop reasoning about the robot’s possible
SGR
GO
!=160"!=80"!=40"!=20"!=10"
02004006008001000050100Iteration NumberC! Fig. 5. The predictable trajectory in gray, and the legible trajectories for
different trust region sizes in orange. On the right, the cost Cover the
iterations in the unconstrained case (red) and constrained case (green).
goals the way the model assumes they would — com-
paring the sub-optimality of its actions with respect to
each of them. Instead, they might start believing that the
robot is malfunctioning [46] or that it is not pursuing any
of the goals and doing something else entirely — this is
supported by our user study in Sec. X-B, which shows
that this belief significantly increases at higher Ccosts.
This complexity of action interpretation in humans,
which is difficult to capture in a goal prediction model,
can significantly affect the legibility of the generated
trajectories in practice. Optimizing the legibility score
outside of a certain threshold for predictability can actu-
ally lower the legibility of the motion as measured with
real users (as it does in our study in Sec. X-B). Unpre-
dictability above a certain level can also be detrimental
to the collaboration process in general [2, 27, 41].
We propose to address these issues by only allowing
optimization of legibility where the model holds, i.e.
where predictability is sufficiently high. We call this
a “trust region” of predictability — a constraint that
bounds the domain of trajectories, but that does so w.r.t.
the cost functional C, resulting in C[x]b:
The legibility model can only be trusted inside this
trust region.
The parameter b, as our study will show, is identifiable
by its effect on legibility as measured with users —
the point at which further optimization of the legibility
functional makes the trajectory less legible in practice.
VIII. C onstrained Legibility Optimization
In order to prevent the legibility optimization from
producing motion that is too unpredictable, we define a
trust region of predictability, constraining the trajectory
to stay below a maximum cost in Cduring the optimiza-
tion in (12):
xi+1=arg max
xLegibility [xi] +¯rLegibilityT(x xi)
h
2jjx xijj2
M
s.t.C[x]b (20)
To solve this, we linearize the constraint, which now
becomes ¯rCT(x xi) +C[xi]b. The Lagrangian is
L[x,l] =Legibility [xi] +¯rLegibilityT(x xi)(21)
h
2jjx xijj2
M+l(b ¯rCT(x xi) C[xi])
with the following KKT conditions:
¯rLegibility hM(x xi) ¯rCl=0 (22)
l(b ¯rCT(x xi) C[xi]) = 0 (23)
l0 (24)
C[x]b (25)
Inactive constraint: l=0 and
xi+1=xi+1
hM 1¯rLegibility (26)
Active constraint: The constraint becomes an equality
constraint on the trajectory. The derivation for xi+1is
analogous to [17], using the L egibility functional as
opposed to the classical cost used by the CHOMP motion
planer[45]. From (22)
xi+1=xi+1
hM 1(¯rLegibility l¯rC)|{z}
¯r(Legibility lC)(27)
Note that this is the functional gradient of L egibility
with an additional (linear) regularizer lC penalizing un-
predictability . Substituting in (23) to get the value for l
and using (22) again, we obtain a new update rule:
xi+1=xi+1
hM 1¯rLegibility
1
hM 1¯rC(¯rCTM 1¯rC) 1¯rCTM 1¯rLegibility
| {z }
projection on ¯rCT(x xi) =0
M 1¯rC(¯rCTM 1¯rC) 1(C[xi] b)| {z }
offset correction to ¯rCT(x xi) +C[xi] =b(28)
Fig.5 shows the outcome of the optimization for vari-
ousbvalues. In our second experiment below, we ana-
lyze what effect bhas on the legibility of the trajectory
in practice, as measured through users observing the
robot’s motion.
(a) Multiple goals
(b) Initialization
Fig. 8. (a) Legible trajectories for multiple goals. (b) Legibility is
dependent on initialization.
IX. U nderstanding Legible Trajectories
Armed with a legible motion generator, we investigate
legibility further, looking at factors that affect the final
trajectories.
Ambiguity. Certain scenes are more ambiguous than
others, in that the legibility of the predictable trajectory
is lower. The more ambiguous a scene is, the greater
the need to depart from predictability and exaggerate
the motion. Fig.6(a) compares two scenes, the one on
the right being more ambiguous by having the candi-
date goals closer and thus making it more difficult to
distinguish between them. This ambiguity is reflected
in its equivalent legible trajectory (both trajectories are
obtained after 1000 iterations). The figure uses the same
cost Cfrom Sec. VI.
Scale. The scale does affect legibility when the value
functions VGare affected by scale, as in our running
example. Here, reaching somewhere closer raises the de-
mand on legibility (Fig.6(b)). Intuitively, the robot could
still reach for GOand suffer little penalty compared to
a larger scale, which puts an extra burden on its motion
if it wants to institute the same confidence in its intent.
Weighting in Time. The weighting function f(11) qual-
itatively affects the shape of the trajectory by placing the
emphasis (or exaggeration) earlier or later (Fig.6(c)).
Multiple Goals. Although for simplicity, our examples
so far were focused on discriminating between two goals,
legibility does apply in the context of multiple goals
(Fig.8(a)). Notice that for the goal in the middle, the
most legible trajectory coincides with the predictable
one: any exaggeration would lead an observer to predict
a different goal — legibility is limited by the complexity in
the scene .
Obstacle Avoidance. In the presence of obstacles in the
scene, a user would expect the robot to stay clear of
these obstacles, which makes Cmore complex. We plot
in Fig.7 an example using the cost functional from the
CHOMP motion planner[45], which trades off between
the sum-squared velocity cost we have been using thus
far, and a cost penalizing the robot from coming too
SSGR
GO
GR
GO
(a) Ambiguity
SGR
GO
SGO
GR
(b) Scale
SGR
GO
f1f2 (c)f
Fig. 6. The effects of ambiguity, scale, and the weighting function fon legibility.
Fig. 7. Legibility given a Cthat accounts for obstacle avoidance. The gray trajectory is the predictable trajectory (minimizing C), and the orange
trajectories are obtained via legibility optimization for 10, 102, 103, 104, and 105iterations. Legibility purposefully pushes the trajectory closer
to the obstacle than expected in order to express the intent of reaching the goal on the right.
close to obstacles. Legibility in this case will move the
predictable trajectory much closer to the obstacle in
order to disambiguate between the two goals.
Local optima. There is no guarantee that L egibility
is concave. This is clear for the case of a non-convex
C, where we often see different initializations lead to
different local maxima, as in Fig.8(b).
In fact, even for quadratic VGs,P(GRjxS!Q)is – aside
from scalar variations – a ratio of sums of Gaussian
functions of the form exp VG(x(t))
. Convergence to
local optima is thus possible even in this simple case.
As a side-effect, it is also possible that initializing the
optimizer with the most predictable trajectory leads to
convergence to a local maxima.
X. From Theory to Practice
Predictability and legibility are intrinsically properties
that depend on the observer: a real user. Here, we go
from the theory of the two properties to what happens
in practice, when novice users observe motion.
We present two studies. The first tests our models for
predictability and legibility: it tests whether a trajectory
that is more legible but less predictable according to our
theoretical scores is also more legible but less predictable
to an observer.
The second study tests the motion generation: it tests
the notion of optimizing for legibility within a trustregion of predictability.
A. The Contradiction Between Predictability and Legibility
The mathematics of predictability and legibility imply
that being more legible can mean being less predictable
and vice-versa. We set out to verify that this is also true
in practice, when we expose subjects to robot motion.
We ran an experiment in which we evaluated two tra-
jectories – a theoretically more predictable one xPand
a theoretically more legible one xL– in terms of how
predictable and legible they are to novices.
Hypothesis. There exist two trajectories xLandxPfor the
same task such that xPis more predictable than xLandxLis
more legible than xP.
Task. We chose a task like the one in Fig.1: reaching
for one of two objects present in the scene. The objects
were close together in order to make this an ambiguous
task, in which we expect a larger difference between
predictable and legible motion.
Manipulated Variables.
oCharacter: We chose to use three characters for this
task – a simulated point robot, a bi-manual mobile
manipulator named HERB [48], and a human – because
we wanted to explore the difference between humans
and robots, and between complex and simple characters.
oTrajectory: We designed (and recorded videos of)
trajectories xPandxLfor each of the characters such that
Fig. 9. The trajectories for each character.
Predictability (xP)>Predictability (xL)according to
(2), but L egibility (xP)<Legibility (xL)according to
(11) (evaluated based on the parameters from Sec. VI-A.
We describe below several steps we took to eliminate
potential confounds and ensure that the effects we see
are actually due to the theoretical difference in the score.
With the HERB character, we controlled for effects
of timing, elbow location, hand aperture and finger
motion by fixing them across both trajectories. For the
orientation of the wrist, we chose to rotate the wrist
according to a profile that matches studies on natural
human motion [21, 36]), during which the wrist changes
angle more quickly in the beginning than it does at
the end of the trajectory. Fig.15 plots the end effector
trace for the HERB trajectories: the gray one has a larger
Fig. 10. The end effector trace for the HERB predictable (gray) and
legible (orange) trajectories.
predictability score (0.54 >0.42), while the orange one
has a higher legibility score (0.67 >0.63).
With the human character, we used a natural reach
for the predictable trajectory, and we used a reach that
exaggerates the hand position to the right for the legible
trajectory (much like with HERB or the point robot). We
cropped the human’s head from the videos to control for
gaze effects.
We slowed down the videos to control for timing
effects.
Dependent Measures.
oPredictability: Predictable trajectories match the ob-
server’s expectation. To measure how predictable a tra-
jectory is, we showed subjects the character in the initial
configuration and asked them to imagine the trajectory
they expect the character will take to reach the goal. We
then showed them the video of the trajectory and asked
them to rate how much it matched the one they expected,
on a 1-7 Likert scale. To ensure that they take the time to
envision a trajectory, we also asked them to draw what
they imagined on a two-dimensional representation of
the scene before they saw the video. We further asked
them to draw the trajectory they saw in the video as an
additional comparison metric.
oLegibility: Legible trajectories enable quick and confi-
dent goal prediction. To measure how legible a trajectory
is, we showed subjects the video of the trajectory and
told them to stop the video as soon as they knew the
goal of the character. We recorded the time taken and
the prediction.
Subject Allocation. We split the experiment into two
sub-experiments with different subjects: one about mea-
suring predictability, and the other about measuring
legibility.
For the predictability part, the character factor was
between-subjects because seeing or even being asked
about trajectories for one character can bias the expec-
tation for another. However, the trajectory factor was
within-subjects in order to enable relative comparisons
on how much each trajectory matched expectation. This
lead to three subject groups, one for each character. We
counter-balanced the order of the trajectories within a
group to avoid ordering effects.
For the legibility part, both factors were between-
subjects because the goal was the same (further, right)
in all conditions. This leads to six subject groups.
We recruited a total of 432 subjects (distributed ap-
proximately evenly between groups) through Amazon’s
Mechanical Turk, all from the United States and with
approval rates higher than 95%. To eliminate users that
do not pay attention to the task and provide random
answers, we added a control question, e.g. "What was
the color of the point robot?" and disregarded the users
who gave wrong answers from the data set.
Analysis.
oPredictability: In line with our hypothesis, a fac-
torial ANOVA revealed a significant main effect for
the trajectory: subjects rated the predictable trajectory
xPas matching what they expected better than xL,
F(1, 310 ) =21.88, p<.001, with a difference in the mean
rating of 0.8, but small effect size ( h2=.02). The main
effect of the character was only marginally significant,
F(2, 310 ) = 2, 91, p=.056. The interaction effect was
significant however, with F(2, 310 ) = 10.24, p<.001.
The post-hoc analysis using Tukey corrections for mul-
tiple comparisons revealed, as Fig.11(a) shows, that our
hypothesis holds for the point robot (adjusted p<.001)
and for the human (adjusted p=0.28), but not for HERB.
The trajectories the subjects drew confirm this (Fig.12):
while for the point robot and the human the trajectory
they expected is, much like the predictable one, a straight
line, for HERB the trajectory they expected splits be-
tween straight lines and trajectories looking more like
the legible one.
For HERB, xLwas just as (or even more) predictable
than xP. We conducted an exploratory follow-up study
with novice subjects from a local pool (with no technical
background) to help understand this phenomenon. We
asked them to describe the trajectory they would expect
HERB to take in the same scenario, and asked them
to motivate it. Surprisingly, all 5 subjects imagined a
different trajectory, motivating it with a different reason.
Two subjects thought HERB’s hand would reach from
the right side because of the other object: one thought
HERB’s hand is too big and would knock over the
other object, and the other thought the robot would be
more careful than a human. This brings up an interest-
ing possible correlation between legibility and obstacle
avoidance. However, as Fig.7 shows, a legible trajectory
still exaggerates motion away from the other candidate
objects even in if it means getting closer to a static
obstacle.
Another subject expected HERB to not be flexible
enough to reach straight towards the goal in a natural
Expected!Predictable!Legible! Point Robot! HERB!Human!Fig. 12. The drawn trajectories for the expected motion, for xP
(predictable), and for xL(legible).
way, like a human would, and thought HERB would fol-
low a trajectory made out of two straight line segments
joining on a point on the right. She expected HERB
to move one joint at a time. We often saw this in the
drawn trajectories with the original set of subjects as well
(Fig.12, HERB, Expected).
The other subjects came up with interesting strategies:
one thought HERB would grasp the bottle from above
because that would work better for HERB’s hand, while
the other thought HERB would use the other object as a
prop and push against it in order to grasp the bottle.
Overall, that xPwas not more predictable than xL
despite what the theory suggested because the cost func-
tion we assumed did not correlate to the cost function
the subjects actually expected. What is more, every sub-
ject expected a different cost function, indicating that a
predictable robot would have to adapt to the particulars
of a human observer.
oLegibility: We collected from each subject the time at
which they stopped the trajectory and their guess of the
goal. Fig.11(b) (above) shows the cumulative percent of
the total number of subjects assigned to each condition
that made a correct prediction as a function of time along
the trajectory. With the legible trajectories, more of the
subjects tend to make correct predictions faster.
To compare the trajectories statistically, we unified
time and correctness into a typical score inspired by
the Guttman structure (e.g. [7]): guessing wrong gets a
score of 0, and guessing right gets a higher score if it
0!1!2!3!4!5!6!7!Human!HERB!Point Robot!Legible Traj!Predictable Traj!(a) Predictability Rating
Point Robot!HERB!Human!time (s)!Probability(correct inference)!Point Robot!HERB!Human!% of users that made an inference and are correct !
time (s)!0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5
0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5
Predictable Traj!Legible Traj!0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5
0
20
40
60
80
100
0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5
0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
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18
19.5
0
1.5
3
4.5
6
7.5
9
10.5
12
13.5
15
16.5
18
19.5
(b) Legibility Measures
Fig. 11. (a) Ratings (on Likert 1-7) of how much the trajectory matched the one the subject expected. Error bars represent standard error on
the mean. (b) Cumulative number of users that responded and were correct (above) and the approximate probability of being correct (below).
happens earlier.A factorial ANOVA predicting this score
revealed, in line with our hypothesis, a significant effect
for trajectory: the legible trajectory had a higher score
than the predictable one, F(1, 241 ) =5.62, p=.019. The
means were 6.75 and 5.73, much higher than a random
baseline of making a guess independent of the trajectory
at uniformly distributed time, which would result in a
mean of 2.5 – the subjects did not act randomly. The
effect size was small, h2=.02. No other effect in the
model was significant.
Although a standard way to combine timing and
correctness information, this score rewards subjects that
gave an incorrect answer 0 reward. This is equivalent
to assuming that the subject would keep making the
incorrect prediction. However, we know this not to be
the case. We know that at the end (time T), every subject
would know the correct answer. We also know that at
time 0, subjects have a probability of 0.5 of guessing cor-
rectly. To account for that, we computed an approximate
probability of guessing correctly given the trajectory so
far as a function of time – see Fig.11(b)(below). Each
subject’s contribution propagates (linearly) to 0.5 at time
0 and 1 at time T. The result shows that indeed, the
probability of making a correct inference is higher for
the legible trajectory at all times.
This effect is strong for the point robot and for HERB,
and not as strong for the human character. We believe
that this might be a consequence of the strong bias hu-
mans have about human motion – when a human moves
even a little unpredictably, confidence in goal prediction
drops. This is justified by the fact that subjects did havehigh accuracy when they responded, but responded later
compared to other conditions. Thus, legible human tra-
jectories would need a stronger emphasis on optimality
w.r.t. C(i.e. smaller trust region parameter bin (20)).
Interpretation. Overall, the results do come in partial
support of formalism: legible trajectories were more leg-
ible for all characters, and predictable trajectories were
more predictable for two out of the three characters (not
for HERB). However, the effect sizes were small, mainly
pointing to the challenge of finding the right cost Cfor
each observer.
B. The Trust Region
So far, we manipulated legibility using two levels. In
this section, we test our legibility motion planner, as well
as our theoretical notion of a trust region, by analyzing
the legibility in practice as the trajectory becomes more
and more legible according to our formalism. If our
assumptions are true, then by varying b2[bmin,bmax],
we expect to find that an intermediate value bpro-
duces the most legible result: much lower than band
the trajectory does not depart predictability enough to
convey intent, much higher and the trajectory becomes
too unpredictable, confusing the users and thus actually
having a negative impact on legibility.
Hypotheses.
H1The size of the trust region, b, has a significant effect
on legibility.
H2Legibility will significantly increase with bat first, but
start decreasing at some large enough b.
3!3.5!4!4.5!5!5.5!6!6.5!
0!40!320!Rating!! !Confidence in Prediction!
0.8!0.85!0.9!0.95!1!
0!40!320!Success Rate!! !Success Rate!
1!2!3!4!5!6!
0!40!320!Rating!! !Belief in "Neither Goal"!
15!17!19!21!23!25!27!
0!10!20!40!80!160!320!Legibilty Score!!"Score w. Self-Chosen Times!
****Fig. 13. Left: The legibility score for all 7 conditions in our main experiment: as the trust region grows, the trajectory becomes more legible.
However, beyond a certain trust region size ( b=40), we see no added benefit of legibility. Right: In a follow-up study, we showed users the
entire first half of the trajectories, and asked them to predict the goal, rate their confidence, as well as their belief that the robot is heading
towards neither goal. The results reinforce the need for a trust region.
Legibility Score Legibility Score Legibility Score Frequency
Frequency
Frequency Histogram for β = 0 Histogram for β = 40 Histogram for β = 320
Fig. 14. The distribution of scores for three of the conditions. With a very large trust region, even though the legibility score does not significantly
decrease, the users either infer the goal very quickly, or they wait until the end of the trajectory, suggesting a legibility issue with the middle
portion of the trajectory.
Manipulated Variables. We manipulated b, selecting
values that grow geometrically (with scalar 2) starting
at 10 and ending at 320, a value we considered high
enough to either support or contradict the expected
effect. We also tested b=min xC[x], which allows for no
additional legibility and thus produces the predictable
trajectory (we denote this as b=0 for simplicity). We
created optimal trajectories for each bfor the point robot
character.
Dependent Measures. We measured the legibility of the
seven trajectories in the same way as before, combining
the time and correctness into a Guttman score as in
the Analysis for the previous experiment. We used slow
videos (28s) to control for response time effects.
Subject Allocation. We chose a between-subjects de-
sign in order to not bias the users with trajectories
from previous conditions. We recruited 320 participants
through Amazon’s Mechanical Turk service, and took
several measures to ensure reliability of the results. All
participants were located in the USA to avoid language
barriers, and they all had an approval rate of over
95%. We asked all participants a control question that
tested their attention to the task, and eliminated data
associated with wrong answers to this question, as well
as incomplete data, resulting in a total of 297 samples.
Analysis. An ANOVA using bas a factor supportedH1, showing that the factor had a significant effect on
legibility ( F(6, 290 ) =12.57, p<0.001), with a medium-
large effect size, h2=.2. Fig.13(left) shows the means
and standard errors for each condition.
An all-pairs post-hoc analysis with Tukey corrections
for multiple comparisons revealed that all trajectories
with b20 were significantly more legible than the
predictable trajectory ( b=0), all with p<0.001, the
maximum being reached at b=40. This supports the
first part of H2, that legibility significantly increases
with bat first: there is no practical need to become more
unpredictable beyond this point.
The post-hoc analysis also revealed that the trajectories
with b=20, 40, 80, or 320 were significantly more legible
than the trajectory with b=10 ( p=.003, p<.001,
p=.004, and p=.002 respectively).
The maximum mean legibility was the trajectory with
b=40. Beyond this value, the mean legibility stopped
increasing. Contrary to our expectation, it did not signif-
icantly decrease. In fact, the difference in score between
b=40 and b=320 is in fact significantly less than 2.81
(t(84) = 1.67, p=0.05). At a first glance, the robot’s
overly unpredictable behavior seems to not have caused
any confusion as to what its intent was.
Analyzing the score histograms (Fig.14) for different b
values, we observed that for higher bs, the wide majority
of users stopped the trajectory in the beginning. The
consequence is that our legibility measure failed to capture
whether the mid-part of the trajectory becomes illegible : the
end of the trajectory conveys the goal because it reaches
it, but what happens in between the beginning and end?
Thus, we ran a follow-up study to verify that legibility in
this region does decrease at b=320 as compared to our
b=40, in which we explicitly measured the legibility
in the middle of the trajectory.
Follow-Up. Our follow-up study was designed to inves-
tigate legibility during the middle of the trajectories. The
setup was the same, but rather than allowing the users
to set the time at which they provide an answer, we fixed
the time and instead asked them for a prediction and a
rating of their confidence on a Likert scale from 1 to 7.
We hypothesize that in this case, the users’ confidence
(aggregated with success rate such that a wrong pre-
diction with high confidence is treated negatively) will
align with our H2: it will be higher for b=40 than for
b=320.
We conducted this study with 90 users. Fig.13 plots
the confidences and success rates, showing that they are
higher for b=40 than they are for both of the extremes,
0 and 320. An ANOVA confirmed that the confidence
effect was significant ( F(2, 84) = 3.64, p=0.03). The
post-hoc analysis confirmed that b=40 had significantly
higher confidence t(57) =2.43, p=0.45.
We also asked the users to what extent they believed
that the robot was going for neither of the goals depicted
in the scene (also Fig.13). In an analogous analysis, we
found that users in the b=40 condition believed this
significantly less than users in the b=320 condition
(t(57) =5.7, p<0.001).
Interpretation. Overall, the results suggest the existence
ofa trust region of expectation within which legibility op-
timization can make trajectories significantly more legible to
novice users. Outside of this trust region, being more
legible w.r.t. L egibility is an impractical quest, because
it no longer improves legibility in practice. Furthermore,
the unpredictability of the trajectory can actually confuse
the observer enough that they can no longer accurately
and confidently predict the goal, and perhaps even doubt
that they have the right understanding of how the robot
behaves. They start believing in a "neither goal" option
that is not present in the scene. Indeed, the legibility
formalism can only be trusted within this trust region .
XI. D iscussion
This paper studied motion planning in the presence
of an observer who is watching the motion and making
inferences about it.
We first formalized predictability and legibility based
on the inferences that the observer makes, which have
opposing directionality. We then proposed mathematical
10!20!40!
Fig. 15. Legible trajectories on a robot manipulator assuming C,
computed by optimizing L egibility in the full dimensional space. The
figure shows trajectories after 0 (gray), 10, 20, and 40 iterations. Below,
a full-arm depiction of the trajectories at 0 and 20 iterations.
models for these inferences in order to arrive at pre-
dictability and legibility scores that a robot can evalu-
ate. Finally, we derived functional gradient optimization
methods for generating predictable or legible motion, as
well as a constrained optimization method for optimiz-
ing for legibility in a trust region of predictability.
Our studies on novice users provided some support
for our models — trajectories more predictable according
to the model were overall more predictable in practice,
and trajectories more legible according to the model
were overall more legible in practice while inside a trust
region of predictability. However, three main challenges
remain in planning such motions for complex cases (i.e.
high-dimensional spaces and non-convex C):
Challenge I: Finding C.If the human observer expects
human-like motion, cues from animation or biomechanics
[22, 25, 37, 57] can help provide good approximations for
C. However, our studies suggest that efficiency of robot
motion has different meanings for different observers
(see follow-up experiment in Sec. X-A).
A possibility is to learn from demonstrations provided
by the observer. Here, the robot can learn a Cthat
explains the demonstrations[4], using tools like Inverse
Optimal Control (IOC) [1, 44, 60]. However, extending
these tools to higher dimensions is an open problem [44],
and recent work focuses on learning costs that make the
demonstrations locally optimal [32, 38], or on restricting
the space of trajectories to one in which optimization is
tractable [30].
Aside from investigating the extension of IOC to high-
dimensional spaces, we also propose a second thread
of research: the idea of familiarizing users to robot
behavior. Can users be taught a particular Cover time?
Our preliminary results [16] suggest that familiarization
helps for the motion generated by the Cfrom (19), but
that it suffers from severe limitations, especially on less
natural choices of C. One possibility is that motion for
non-anthropomorphic arms is complex enough that we
cannot rely solely on the user to do all the learning, sug-
gesting that the two threads of research – familiarization
and learning from demonstration – are complementary.
Challenge II: Computing VG.Given a C, legibility op-
timization requires access to its value function for every
goal. In simple cases, like the one we focused on in this
paper, Vhas an analytical form. Legibility optimization
happens then in real-time even for high-dimensional
cases, as shown in Fig.15.
But this is not the case, for instance, for non-convex
functions that require obstacle avoidance, when the robot
has many degrees of freedom. In such cases, find-
ing good approximations for Vbecomes crucial, and
many techniques value function approximation tech-
niques could be applied toward this goal [8].
What makes our problem special, however, is that
the quality of the approximation is defined in terms of
its impact on legibility , and not on the original value
function itself. There could be approximations, such as
ignoring entire components of C, or only focusing on
some lower-dimensional aspects, which are very poor
approximations of Vitself, but might have little effect
on legibility in practice.
Challenge III: Finding b.The final challenge is finding
how unpredictable the trajectory can become in a given
situation. This too can be learned based on the user,
or set based on the ambiguity level of the situation
(as measured by the legibility score of the predictable
trajectory).
Other Future Work Directions. Even though this pa-
per was about goal-directed motion, the formalism for
legibility can be applied more generally to transform
an efficiency cost Cinto a legible one. We are excited
to investigate this formalism with other channels of
communication and other robot morphologies. Recently,
we showed the formalism’s applicability to pointing
gestures [20], Tellex et al. [50] used the same under-
lying mathematics to generate legible natural language
requests, and Szafir et al. [ ?] used our results to generate
legible quadrotor flight.
Still for goal-directed motion, we are interested in the
concept of legibility when each goal could be achievedin multiple different robot configurations, as it happens
in most manipulation tasks [17]. In the example from
Fig.1, the added flexibility of goal sets could enable the
robot to grasp the object from the side when legible, and
closer to the front when predictable.
Another avenue of further research is investigating
the role that perceived capability plays. Our follow-up
users in the first experiment had different expectations
about what HERB is capable of, which shaped their
expectations about the motion. Here, familiarization to
the robot can potentially be useful in adjusting the
perceived capability to better match the real capability.
Furthermore, the perceived capability plays a role in
what goals the observer might attribute to the robot,
which can be captured in our formalism as the prior
P(G)over the candidate goals.
Limitations. Our work is limited in many ways. As the
previous section discussed, in generating predictable or
legible motion, we inherit the challenges of learning and
optimizing non-convex functions in high-dimensional
spaces. Furthermore, adding a trust region to the opti-
mization is a way to prevent the algorithm for traveling
on “uncharted territory” — from reaching trajectories
where the model’s assumptions stop holding. It does not,
however, fix the model itself.
Despite its limitations and remaining challenges, this
work integrates the idea of an observer and the in-
ferences that he makes directly into motion planning,
paving the road to more seamless human-robot collabo-
rations.
Acknowledgements
We thank Geoff Gordon, Jodi Forlizzi, Hendrik Chris-
tiansen, Kenton Lee, Chris Dellin, Alberto Rodriguez,
and the members of the Personal Robotics Lab for fruit-
ful discussion and advice. This material is based upon
work supported by NSF-IIS-0916557, NSF-EEC-0540865,
ONR-YIP 2012, the Intel Embedded Computing ISTC,
and the Intel PhD Fellowship.
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f3e6cdf4-3cb9-47e8-8c73-f8c6a776434a | trentmkelly/LessWrong-43k | LessWrong | Guilt, Shame, and Depravity
Everyone knows what it is to be tempted. You are a member of some community, the members of which have some expectations of each other. You might generally intend to satisfy these expectations, but through a failure of foresight, or some other sort of bad luck, feel an acute impulse to consume something that is not yours to take, or in some other way break commitments you would generally want to honor.
Guilt refers primarily to a violation of trust from the perspective of an epistemic community with a shared history, and only secondarily to the subjective attitude of the offender.
If, having violated trust, you intend to repair that trust by owning up to what you did and by making amends or accepting whatever penalty the community places on you, then you feel a pronormative sort of regret. When making the sorts of precise distinctions needed to navigate contemporary civil conflict, we call this condition guilt. We use the same word for the subjective feeling and for the objective fact, because someone feeling guilt is taking the perspective of a community member who expects norms to be followed, and intends to do so.
Guilty behavior tends to be self-limiting; it is experienced as a sort of tension that can only be discharged by correcting the record and restoring normative relations.
Shame refers to the intent to conceal, which implies a locally adversarial relation to norms.
If the penalties for coming clean seem like too much to bear, or for any other reason a resolution does not appear available, someone might intend to keep their guilt a secret. Keeping two distinct stories straight - a public one and a private one - is cognitively expensive, so covert offenders will frequently substitute motivated forgetfulness. If we intend not to recollect our guilt, we will also intend to deflect investigation that would reveal it. When this is an exceptional state, towards some particular events in someone's life, we can call it shame.
At times in my life, I ha |
7db946ae-d2a3-48b1-b3db-61ffe057a035 | trentmkelly/LessWrong-43k | LessWrong | Publication on formalizing preference utilitarianism in physical world models
About a year ago I asked for help with a paper on a formalization of preference utilitarianism in cellular automata. The paper has now been published in the Springer journal Synthese and is available here. I wonder what you think about it and if you are interested would like to discuss it with you. |
faf35804-beda-4fe6-afff-4a215436301e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Moscow: Utilitarianism, Good Judgement Project, Order team meeting, rational games
Discussion article for the meetup : Moscow: Utilitarianism, Good Judgement Project, Order team meeting, rational games
WHEN: 05 June 2016 02:00:00PM (+0300)
WHERE: Bolshaya Dorogomilovskaya ul., 5к2
Note: most our members join meetups via other channels. Still, the correlation between "found out about Moscow meetups via lesswrong.com" and "is a great fit for our community" is very high. So we're posting just a short link to the hackpad document with the schedule here instead of the full translation of the announcement into English.
Pad with the details about 05.06.2016 meetup.
We're meeting at the "Kocherga" anticafe, as usual.
Discussion article for the meetup : Moscow: Utilitarianism, Good Judgement Project, Order team meeting, rational games |
533c17b3-c3f9-4a10-b902-8b201466b2c6 | trentmkelly/LessWrong-43k | LessWrong | Describe the ways you can hear/see/feel yourself think.
To avoid constantly generalizing from one example when it comes to human thought, I think we need a survey of the ways people can reflect on their thought process, as subset of the ways people can think.
Before having heard of the Francis Galton's study on the imagination I assumed that everyone thought in the similar way to me (except be better or worse at it), and would be puzzled why some people would believe in e.g. strong version of Sapir-Whorf hypothesis. (I couldn't even understand how they can not realize that to make even remotely coherent argument in support of this hypothesis they would have to be thinking outside the language - or so I thought)
There may be a very significant variation in how human thought process works, and how much of the process is accessible to reflection. In Richard Feynman's What Do You Care what Other People Think? he explores a technique for tying up part of the thought process by mentally counting, and exploring what sorts of thinking interfere with the counting. (I can't right now find a good online quote from those chapters and I do not have the book at hand to directly search; perhaps someone can help?)
I propose we describe the ways we believe we think, along with relevant self-observations supporting those beliefs. Just as a first step though - to get very rough idea. Note: lack of ability to reflect on something does not imply lack of function.
Then based on the responses we can form some hypotheses and make a proper survey perhaps to be combined with some set of cognitive tests. You perhaps should stop reading right now if you don't want to be primed with my own self description, but given that we all probably have been exposed to great deal of descriptions of thoughts the priming perhaps is not a big problem here.
So, for me, the distinct modes of thought (ALL coexisting in parallel at any time, except for the mental visualization which I am not using when I am busy using my eyes). The order is not related to import |
196e092d-78ac-4de4-ba5c-0ef261ffb151 | trentmkelly/LessWrong-43k | LessWrong | Knowledge is not just precipitation of action
Knowledge is not just precipitation of action
Financial status: This is independent research. I welcome financial support to make further posts like this possible.
Epistemic status: This is in-progress thinking.
----------------------------------------
This post is part of a sequence on the accumulation of knowledge. Our goal is to articulate what it means for knowledge to accumulate within a physical system.
The challenge is this: given a closed physical system, if I point to a region and tell you that knowledge is accumulating in this region, how would you test my claim? What are the physical characteristics of the accumulation of knowledge? What is it, exactly, about an artifact inscribed with instructions for building advanced technology that makes it so different from an ordinary rock, or from a video camera that has been travelling the cosmos recording data since the beginning of the universe? We are looking for a definition of knowledge at the level of physics.
Our goal is to articulate what it means for knowledge to accumulate within a physical system. The previous post looked at mutual information between high- and low-level configurations of a digital abstraction layer as a possible definition of knowledge and found that mutual information did not differentiate raw sensor data from useful models derived from that sensor data.
In this post we will consider a definition of knowledge as that which precipitates effective goal-directed action. That is whenever we see some entity taking actions that are effective and goal-directed, we could conclude that knowledge exists. This is, after all, the informal goalpost that we have been comparing each previous definition of knowledge to. Rather than seeking a separate definition of knowledge and comparing it to this goalpost, this post will look at ways that we might make this informal definition formal.
Example: Satellite tracker
Consider a computer scanning the sky for a satellite in order to transmit some i |
d2ec796f-8ef2-4086-9491-0872eb3fa3fc | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Alignment Newsletter #44
Find all Alignment Newsletter resources [here](http://rohinshah.com/alignment-newsletter/). In particular, you can [sign up](http://eepurl.com/dqMSZj), or look through this [spreadsheet](https://docs.google.com/spreadsheets/d/1PwWbWZ6FPqAgZWOoOcXM8N_tUCuxpEyMbN1NYYC02aM/edit?usp=sharing) of all summaries that have ever been in the newsletter.
Highlights
----------
**[How does Gradient Descent Interact with Goodhart?](https://www.alignmentforum.org/posts/pcomQ4Fwi7FnfBZBR/how-does-gradient-descent-interact-with-goodhart)** *(Scott Garrabrant)*: Scott often thinks about optimization using a simple proxy of "sample N points and choose the one with the highest value", where larger N corresponds to more powerful optimization. However, this seems to be a poor model for what gradient descent actually does, and it seems valuable to understand the difference (or to find out that there isn't any significant difference). A particularly interesting subquestion is whether [Goodhart's Law](https://www.alignmentforum.org/posts/EbFABnst8LsidYs5Y/goodhart-taxonomy) behaves differently for gradient descent vs. random search.
**Rohin's opinion:** I don't think that the two methods are very different, and I expect that if you can control for "optimization power", the two methods would be about equally susceptible to Goodhart's Law. (In any given experiment, one will be better than the other, for reasons that depend on the experiment, but averaged across experiments I don't expect to see a clear winner.) However, I do think that gradient descent is very powerful at optimization, and it's hard to imagine the astronomically large random search that would compare with it, and so in any practical application gradient descent will lead to more Goodharting (and more overfitting) than random search. (It will also perform better, since it won't underfit, as random search would.)
One of the answers to this question talks about some experimental evidence, where they find that they can get different results with a relatively minor change to the experimental procedure, which I think is weak evidence for this position.
**[Transformer-XL: Unleashing the Potential of Attention Models](http://ai.googleblog.com/2019/01/transformer-xl-unleashing-potential-of.html)** *(Zihang Dai, Zhilin Yang et al)*: [Transformer](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) architectures have become all the rage recently, showing better performance on many tasks compared to CNNs and RNNs. This post introduces Transformer-XL, an improvement on the Transformer architecture for very long sequences.
The key idea with the original Transformer architecture is to use self-attention layers to analyze sequences instead of something recurrent like an RNN, which has problems with vanishing and exploding gradients. An attention layer takes as input a query q and key-value pairs (K, V). The query q is "compared" against every key k, and that is used to decide whether to return the corresponding value v. In their particular implementation, for each key k, you take the dot product of q and k to get a "weight", which is then used to return the weighted average of all of the values. So, you can think of the attention layer as taking in a query q, and returning the "average" value corresponding to keys that are "similar" to q (since dot product is a measure of how aligned two vectors are). Typically, in an attention layer, some subset of Q, K and V will be learned. With *self-attention*, Q, K and V are all sourced from *the same place* -- the result of the previous layer (or the input if this is the first layer). Of course, it's not exactly the output from the previous layer -- if that were the case, there would be no parameters to learn. They instead learn three *linear projections* (i.e. matrices) that map from the output of the previous layer to Q, K and V respectively, and then feed the generated Q, K and V into a self-attention layer to compute the final output. And actually, instead of having a single set of projections, they have multiple sets that each contain three learned linear projections, that are all then used for attention, and then combined together for the next layer by another learned matrix. This is called *multi-head attention*.
Of course, with attention, you are treating your data as a set of key-value pairs, which means that the order of the key value pairs does not matter. However, the order of words in a sentence is obviously important. To allow the model to make use of position information, they augment each word and add position information to it. You could do this just by literally appending a single number to each word embedding representing its absolute position, but then it would be hard for the neural net to ask about a word that was "3 words prior". To make this easier for the net to learn, they create a vector of numbers to represent the absolute position based on sinusoids such that "go back 3 words" can be computed by a linear function, which should be easy to learn, and add *(not concatenate!)* it elementwise to the word embedding.
This model works great when you are working with a single sentence, where you can attend over the entire sentence at once, but doesn't work as well when you are working with eg. entire documents. So far, people have simply broken up documents into segments of a particular size N and trained Transformer models over these segments. Then, at test time, for each word, they use the past N - 1 words as context and run the model over all N words to get the output. This cannot model any dependencies that have range larger than N. The Transformer-XL model fixes this issue by taking the segments that vanilla Transformers use, and adding recurrence. Now, in addition to the normal output predictions we get from segments, we also get as output a new hidden state, that is then passed in to the next segment's Transformer layer. This allows for arbitrarily far long-range dependencies. However, this screws up our position information -- each word in each segment is augmented with *absolute* position information, but this doesn't make sense across segments, since there will now be multiple words at (say) position 2 -- one for each segment. At this point, we actually want *relative* positions instead of absolute ones. They show how to do this -- it's quite cool but I don't know how to explain it without going into the math and this has gotten long already. Suffice it to say that they look at the interaction between arbitrary words x\_i and x\_j, see the terms that arise in the computation when you add absolute position embeddings to each of them, and then change the terms so that they only depend on the difference j - i, which is a relative position.
This new model is state of the art on several tasks, though I don't know what the standard benchmarks are here so I don't know how impressed I should be.
**Rohin's opinion:** It's quite interesting that even though the point of Transformer was to get away from recurrent structures, adding them back in leads to significant improvements. Of course, the recurrent structure is now at the higher level of segments, rather than at the word or character level. This reminds me a lot of hierarchy -- it seems like we're using the Transformer as a basic building block that works on the ~sentence level so that our RNN-like structure can deal with a higher level of abstraction (which of course also helps with vanishing/exploding gradients).
There's an interesting pattern where hierarchy and structure seem to be a good inductive bias, that let you get good performance with limited compute and data, but as those limits subside, you're better off doing something that has less bias. This would predict that as we get more data and compute, we would want larger Transformer models (i.e. longer segments) and less recurrence. It would be interesting to see if that actually holds.
Technical AI alignment
======================
### Iterated amplification sequence
[Reliability amplification](https://www.alignmentforum.org/posts/6fMvGoyy3kgnonRNM/reliability-amplification) *(Paul Christiano)*: One hope for building an aligned AI system is to alternate [capability amplification](https://www.alignmentforum.org/posts/t3AJW5jP3sk36aGoC/capability-amplification) and [reward engineering](https://www.alignmentforum.org/posts/4nZRzoGTqg8xy5rr8/the-reward-engineering-problem) (both [AN #42](https://mailchi.mp/f6488137d76c/alignment-newsletter-42)) with semi-supervised learning in order to create an agent that becomes more and more capable with more iterations, while remaining aligned. However, our agents will likely always have a small probability of failing, and when we use capability amplification, that failure probability is also amplified (since each of the subagents could fail). If we do enough capability amplifiation, the failure probability could approach 1. This motivates the problem of *reliability amplification*, which aims to take an agent that fails with small probability ε and produce a new agent that fails with significantly lower probability. As with capability amplification, the new agent can take much more time and compute than the original. The hope would be that the combination of capability and reliability amplification would allow us to take an initial agent and make it both more capable and more reliable (even though capability amplification reduces reliability, and reliability amplification will probably reduce capability).
One simple thing to do would be to sample three agents, have them collaboratively settle on an action. If all three agents are aligned, we're fine. The chance of two or three of the agents being malign is small enough that we can ignore it. The interesting case is if one agent is malign and two are benign. If there were only one correct answer, we could simply take a majority vote and get the right answer, and we would have amplified reliability to order ε^2 (which happens when you get two or more malign agents). However, different benign agents could give different answers, since there is no one "correct" benign answer. If we were to choose randomly, as you might expect we get no benefit: we'd have a ~3ε chance of having exactly one malign agent, and a 1/3 chance of failing in that case, keeping the failure probability at ε. The hope for reducing the failure probability is if the two benign agents can "recognize" each other as benign, and so that the majority vote has a >2/3 chance of choosing one of the actions that the benign agents suggested. This does depend on all of the agents having similar capabilities -- if the malign agent is a superintelligence while the two benign agents are human-level, then it seems likely that the malign superintelligence would be able to convince the benign agents to go along with its plan.
**Rohin's opinion:** It seems like this requires the assumption that our agents have a small probability of failure on any given input. I think this makes sense of we are thinking of reliability of [corrigibility](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd/p/fkLYhTQteAu5SinAc) ([AN #35](https://mailchi.mp/bbd47ba94e84/alignment-newsletter-35)). That said, I'm pretty confused about what problem this technique is trying to protect against, which I wrote about [in this comment](https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd/p/6fMvGoyy3kgnonRNM#9HpJBuc9PBwTPtdQW).
### Value learning sequence
[Conclusion to the sequence on value learning](https://www.alignmentforum.org/posts/TE5nJ882s5dCMkBB8/conclusion-to-the-sequence-on-value-learning) *(Rohin Shah)*: This post summarizes the value learning sequence, putting emphasis on particular parts. I recommend reading it in full -- the sequence did have an overarching story, which was likely hard to keep track of over the three months that it was being published.
### Technical agendas and prioritization
[Drexler on AI Risk](https://www.lesswrong.com/posts/bXYtDfMTNbjCXFQPh/drexler-on-ai-risk) *(Peter McCluskey)*: This is another analysis of [Comprehensive AI Services](https://www.fhi.ox.ac.uk/reframing/). You can read [my summary of CAIS](https://www.alignmentforum.org/posts/x3fNwSe5aWZb5yXEG/reframing-superintelligence-comprehensive-ai-services) ([AN #40](https://mailchi.mp/b649f32b07da/alignment-newsletter-40)) to get my views.
### Reward learning theory
[One-step hypothetical preferences](https://www.alignmentforum.org/posts/i6hWWcKyxBPj7ELT6/one-step-hypothetical-preferences) and [A small example of one-step hypotheticals](https://www.lesswrong.com/posts/zo5K8QeDZiLicSCe6/a-small-example-of-one-step-hypotheticals) *(Stuart Armstrong)* (summarized by Richard): We don't hold most of our preferences in mind at any given time - rather, they need to be elicited from us by prompting us to think about them. However, a detailed prompt could be used to manipulate the resulting judgement. In this post, Stuart discusses hypothetical interventions which are short enough to avoid this problem, while still causing a human to pass judgement on some aspect of their existing model of the world - for example, being asked a brief question, or seeing something on a TV show. He defines a one-step hypothetical, by contrast, as a prompt which causes the human to reflect on a new issue that they hadn't considered before. While this data will be fairly noisy, he claims that there will still be useful information to be gained from it.
**Richard's opinion:** I'm not quite sure what overall point Stuart is trying to make. However, if we're concerned that an agent might manipulate humans, I don't see why we should trust it to aggregate the data from many one-step hypotheticals, since "manipulation" could then occur using the many degrees of freedom involved in choosing the questions and interpreting the answers.
### Preventing bad behavior
[Robust temporal difference learning for critical domains](http://arxiv.org/abs/1901.08021) *(Richard Klima et al)*
### Interpretability
[How much can value learning be disentangled?](https://www.alignmentforum.org/posts/Q7WiHdSSShkNsgDpa/how-much-can-value-learning-be-disentangled) *(Stuart Armstrong)* (summarized by Richard): Stuart argues that there is no clear line between manipulation and explanation, since even good explanations involve simplification, omissions and cherry-picking what to emphasise. He claims that the only difference is that explanations give us a better understanding of the situation - something which is very subtle to define or measure. Nevertheless, we can still limit the effects of manipulation by banning extremely manipulative practices, and by giving AIs values that are similar to our own, so that they don't need to manipulate us very much.
**Richard's opinion:** I think the main point that explanation and manipulation can often look very similar is an important one. However, I'm not convinced that there aren't any ways of specifying the difference between them. Other factors which seem relevant include what mental steps the explainer/manipulator is going through, and how they would change if the statement weren't true or if the explainee were significantly smarter.
### Adversarial examples
[Theoretically Principled Trade-off between Robustness and Accuracy](http://arxiv.org/abs/1901.08573) *(Hongyang Zhang et al)* (summarized by Dan H): This paper won the NeurIPS 2018 Adversarial Vision Challenge. For robustness on CIFAR-10 against l\_infinity perturbations (epsilon = 8/255), it improves over the Madry et al. adversarial training baseline from 45.8% to 56.61%, making it [almost](https://arxiv.org/pdf/1901.09960.pdf) state-of-the-art. However, it does decrease clean set accuracy by a few percent, despite using a deeper network than Madry et al. Their technique has many similarities to Adversarial Logit Pairing, which is not cited, because they encourage the network to embed a clean example and an adversarial perturbation of a clean example similarly. I now describe Adversarial Logit Pairing. During training, ALP teaches the network to classify clean and adversarially perturbed points; added to that loss is an l\_2 loss between the logit embeddings of clean examples and the logits of the corresponding adversarial examples. In contrast, in place of the l\_2 loss from ALP, this paper uses the KL divergence from the softmax of the clean example to the softmax of an adversarial example. Yet the softmax distributions are given a high temperature, so this loss is not much different from an l\_2 loss between logits. The other main change in this paper is that adversarial examples are generated by trying to maximize the aforementioned KL divergence between clean and adversarial pairs, not by trying to maximize the classification log loss as in ALP. This paper then shows that some further engineering to adversarial logit pairing can improve adversarial robustness on CIFAR-10.
### Field building
[The case for building expertise to work on US AI policy, and how to do it](https://80000hours.org/articles/us-ai-policy/) *(Niel Bowerman)*: This in-depth career review makes the case for working on US AI policy. It starts by making a short case for why AI policy is important; and then argues that US AI policy roles in particular can be very impactful (though they would still recommend a policy position in an AI lab like DeepMind or OpenAI over a US AI policy role). It has tons of useful detail; the only reason I'm not summarizing it is because I suspect that most readers are not currently considering career choices, and if you are considering your career, you should be reading the entire article, not my summary. You could also check out [Import AI's summary](https://jack-clark.net/2019/02/04/import-ai-132-can-your-algorithm-outsmart-the-obstacle-tower-cross-domain-nlp-with-biobert-and-training-on-faceforensics-to-spot-deepfakes/).
### Miscellaneous (Alignment)
**[How does Gradient Descent Interact with Goodhart?](https://www.alignmentforum.org/posts/pcomQ4Fwi7FnfBZBR/how-does-gradient-descent-interact-with-goodhart)** *(Scott Garrabrant)*: Summarized in the highlights!
[Can there be an indescribable hellworld?](https://www.alignmentforum.org/posts/rArsypGqq49bk4iRr/can-there-be-an-indescribable-hellworld) *(Stuart Armstrong)* (summarized by Richard): This short post argues that it's always possible to explain why any given undesirable outcome doesn't satisfy our values (even if that explanation needs to be at a very high level), and so being able to make superintelligences debate in a trustworthy way is sufficient to make them safe.
AI strategy and policy
======================
[Bridging near- and long-term concerns about AI](https://www.nature.com/articles/s42256-018-0003-2) *(Stephen Cave et al)*
[Surveying Safety-relevant AI Characteristics](https://www.cser.ac.uk/resources/surveying-safety-relevant-ai-characteristics/) *(Jose Hernandez-Orallo et al)*
Other progress in AI
====================
### Reinforcement learning
[Causal Reasoning from Meta-reinforcement Learning](http://arxiv.org/abs/1901.08162) *(Ishita Dasgupta et al)*
### Deep learning
**[Transformer-XL: Unleashing the Potential of Attention Models](http://ai.googleblog.com/2019/01/transformer-xl-unleashing-potential-of.html)** *(Zihang Dai, Zhilin Yang et al)*: Summarized in the highlights!
News
====
[PAI Fellowship Program Call For Applications](https://www.partnershiponai.org/fellowship-program/): The Partnership on AI is opening applications for Research Fellows who will "conduct groundbreaking multi-disciplinary research". |
34ff4e34-16be-44ba-9a61-be7c4eeccd09 | trentmkelly/LessWrong-43k | LessWrong | OpenAI’s Alignment Plan is not S.M.A.R.T.
In response to Eliezer Yudkowsky's challenge, I will show how the alignment research approach outlined by OpenAI lacks common desiderata for effective plans. Most of the deficiencies appear to be difficult or impossible to fix, and we should thus expect the plan to fail.
Meta-level description of what makes a plan good places great emphasis on the goals/objectives of the plan. George T. Doran suggests goals be S.M.A.R.T.: Specific, Measurable, Achievable, Relevant, and Time-Bound.
Specific: OpenAI's description of the goals as AI that is "Value aligned" and "Follow human intent" could be elaborated in much greater detail than these 5 words. Yet making them specific is no easy task. No definition exists of these words in sufficient detail to be put into computer code, nor does an informal consensus exist.
Measurable: There currently exists no good way to quantify value alignment and intent-following. It is an open question if such quantification is even possible to do in an adequate way, and OpenAI does not seem to focus on resolving philosophical issues such as those required to make value alignment measurable.
Achievable: The plan suggests a relatively narrow AI would be sufficient to contribute to alignment research, while being too narrow to be dangerous. This seems implausible: Much easier problems than alignment research have been called AGI-complete, and general reasoning ability is widely thought to be a requirement for doing research.
Relevant: The plan acknowledges existential risk from advanced AI, but the proposed goal is insufficient to end the period of acute risk. This gap in the plan must be closed. I do not think this is trivial, as OpenAI rejects MIRI-style pivotal acts. My impression of OpenAI is that they hope alignment can be solved to the extent that the "Alignment tax" becomes negative to such an overwhelming degree that deceptively aligned AI is not built in practice by anyone.
Time-bound: The plan is not time-bound. OpenAI's plan is con |
fe8e537e-19f0-417b-93d6-38fe1fcf5fba | StampyAI/alignment-research-dataset/arxiv | Arxiv | Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks.
1 Introduction
---------------
Model-free reinforcement learning (RL) with deep learning has proven successful at achieving master-level performance in many sequential decision making problems (Silver et al., [2016](#bib.bib42), [2018](#bib.bib43); Mnih et al., [2015](#bib.bib33); Vinyals et al., [2019](#bib.bib52); Kalashnikov et al., [2018](#bib.bib20); Haarnoja et al., [2018b](#bib.bib16)). RL algorithms learn a control policy that maximizes the expected discounted sum of future rewards (the *policy value*) through experience collected via interacting with an environment. Temporal-Difference (TD) (Sutton & Barto, [2018](#bib.bib45)) is a principled approach to RL that maintains value estimates and iteratively improves them using *bootstrapped* targets that combine experienced short-term rewards and current estimates of future long-term values.
Q-learning (Watkins & Dayan, [1992](#bib.bib54)) learns state–action value estimates (*Q function*) by minimizing the TD error between the estimates and the bootstrapped targets. Although tabular Q-learning asymptotically converges to the optimal policy (Tsitsiklis, [1994](#bib.bib47); Jaakkola et al., [1994](#bib.bib18)), it is known to be positively biased and overestimate the value before convergence due to Jensen’s inequality under value estimation uncertainty (Thrun & Schwartz, [1993](#bib.bib46); Fox et al., [2015](#bib.bib13)). This bias is detrimental to efficient learning, because it propagates through bootstrapping and because it can cause further experience collection to use suboptimal actions that appear optimal.
Deep Q-Networks (DQN) (Mnih et al., [2013](#bib.bib32), [2015](#bib.bib33)) represent the Q function with a deep neural network to learn expressive policies in environments with high dimensional states.
Unfortunately, such value function approximation can further exacerbate instability and overestimation (Thrun & Schwartz, [1993](#bib.bib46); Mahmood et al., [2015](#bib.bib31)).
One way to reduce the variance (between-runs variability) and instability (within-run variability) of value estimation
is to use an ensemble of estimators. Ensemble learning is well-studied in machine learning and is known for its property of reducing estimation variance and ability to capture epistemic (model) uncertainty, usually in a form of prediction variance. In the field of RL, ensemble RL methods (Wiering & van Hasselt, [2008](#bib.bib55)) have been applied to improve exploration (Osband et al., [2016a](#bib.bib34), [b](#bib.bib35); Chen et al., [2017](#bib.bib7); Fortunato et al., [2017](#bib.bib11)) and guide value or policy updates (van Hasselt et al., [2015](#bib.bib49); Fujimoto et al., [2018](#bib.bib14); Fox, [2019](#bib.bib12); Lan et al., [2020](#bib.bib25); Lee et al., [2021](#bib.bib27); Liang et al., [2021](#bib.bib29); Chen et al., [2021](#bib.bib8)).
In this work, we propose a simple model-free ensemble method, called MeanQ, that reduces variance and instability of the TD target estimates by averaging an ensemble of neural network learners. We discuss similarities and differences between MeanQ and several closely related existing methods, including Anschel et al. ([2016](#bib.bib2)), and justify our specific design choices intuitively and empirically. We discuss the variance-reduction properties of MeanQ’s target estimator and show empirically that, in some cases, instability is sufficiently reduced to eliminate the need for a target network (Mnih et al., [2015](#bib.bib33); Lillicrap et al., [2015](#bib.bib30); Kim et al., [2019](#bib.bib21)), which removed the target lag and further improves sample efficiency.
In experiments in the Atari Learning Environment (ALE) domain (Bellemare et al., [2013](#bib.bib5)), MeanQ significantly outperforms all baselines with which we compared, in a benchmark set of 26 environments. MeanQ outperforms the best available baseline, SUNRISE (Lee et al., [2021](#bib.bib27)), at 100K interaction steps in 16/26 environments, and by 68% in normalized return averaged over all 26 environments. Compared with a commonly used baseline, Rainbow DQN (Hessel et al., [2018](#bib.bib17)), MeanQ achieves higher returns at 500K steps in 21/26 environments, and 49% higher average normalized return. MeanQ also achieves average human-level performance in the 26 games using only 200K (±plus-or-minus\pm±100K) interaction steps.
2 Preliminaries
----------------
We consider a Markov Decision Process (MDP) with probability p(s′|s,a)𝑝conditionalsuperscript𝑠′𝑠𝑎p(s^{\prime}|s,a)italic\_p ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT | italic\_s , italic\_a ) to transition to state s′superscript𝑠′s^{\prime}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT when taking in state s𝑠sitalic\_s an action a𝑎aitalic\_a in a finite action space. An agent controls the dynamical process using a policy π𝜋\piitalic\_π with probability π(a|s)𝜋conditional𝑎𝑠\pi(a|s)italic\_π ( italic\_a | italic\_s ) to take action a𝑎aitalic\_a in state s𝑠sitalic\_s, after which a reward r(s,a)𝑟𝑠𝑎r(s,a)italic\_r ( italic\_s , italic\_a ) is observed.
The MDP and policy jointly induce a distribution pπsubscript𝑝𝜋p\_{\pi}italic\_p start\_POSTSUBSCRIPT italic\_π end\_POSTSUBSCRIPT over the trajectory ξ=(s0,a0,r0,s1…)𝜉subscript𝑠0subscript𝑎0subscript𝑟0subscript𝑠1…\xi=(s\_{0},a\_{0},r\_{0},s\_{1}\ldots)italic\_ξ = ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_r start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT … ) in each *episode*. An RL algorithm should discover a control policy that maximizes the expected discounted return, R(ξ)=∑t≥0γtrt𝑅𝜉subscript𝑡0superscript𝛾𝑡subscript𝑟𝑡R(\xi)=\sum\_{t\geq 0}\gamma^{t}r\_{t}italic\_R ( italic\_ξ ) = ∑ start\_POSTSUBSCRIPT italic\_t ≥ 0 end\_POSTSUBSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_t end\_POSTSUPERSCRIPT italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT, of trajectory ξ𝜉\xiitalic\_ξ, where t𝑡titalic\_t is the time step in the interaction process, 0≤γ<10𝛾10\leq\gamma<10 ≤ italic\_γ < 1 is a discount factor, and rt=r(st,at)subscript𝑟𝑡𝑟subscript𝑠𝑡subscript𝑎𝑡r\_{t}=r(s\_{t},a\_{t})italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = italic\_r ( italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ). The value-to-go (Q value) of policy π𝜋\piitalic\_π, starting at state–action pair (s,a)𝑠𝑎(s,a)( italic\_s , italic\_a ), is
| | | |
| --- | --- | --- |
| | Qπ(s,a)=𝔼ξ∼pπ[R(ξ)|s0=s,a0=a].subscript𝑄𝜋𝑠𝑎subscript𝔼similar-to𝜉subscript𝑝𝜋delimited-[]formulae-sequenceconditional𝑅𝜉subscript𝑠0𝑠subscript𝑎0𝑎Q\_{\pi}(s,a)=\mathbb{E}\_{\xi\sim p\_{\pi}}[R(\xi)|s\_{0}=s,a\_{0}=a].italic\_Q start\_POSTSUBSCRIPT italic\_π end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) = blackboard\_E start\_POSTSUBSCRIPT italic\_ξ ∼ italic\_p start\_POSTSUBSCRIPT italic\_π end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT [ italic\_R ( italic\_ξ ) | italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT = italic\_s , italic\_a start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT = italic\_a ] . | |
Value-based RL methods maintain a state–action value function (Q function)
to infer a control policy and guide policy updates. Q-learning (Watkins & Dayan, [1992](#bib.bib54))
learns the optimal value of each state–action by stochastically updating a tabular representation of Q, on experience (s,a,r,s′)𝑠𝑎𝑟superscript𝑠′(s,a,r,s^{\prime})( italic\_s , italic\_a , italic\_r , italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ), by
| | | |
| --- | --- | --- |
| | Q(s,a)←Q(s,a)+α(r+γmaxa′Q(s′,a′)−Q(s,a)),←𝑄𝑠𝑎𝑄𝑠𝑎𝛼𝑟𝛾subscriptsuperscript𝑎′𝑄superscript𝑠′superscript𝑎′𝑄𝑠𝑎Q(s,a)\leftarrow Q(s,a)+\alpha(r+\gamma\max\_{a^{\prime}}Q(s^{\prime},a^{\prime})-Q(s,a)),italic\_Q ( italic\_s , italic\_a ) ← italic\_Q ( italic\_s , italic\_a ) + italic\_α ( italic\_r + italic\_γ roman\_max start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT italic\_Q ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) - italic\_Q ( italic\_s , italic\_a ) ) , | |
to minimize the Temporal-Difference (TD) error in parentheses.
Deep Q-Networks (DQN) (Mnih et al., [2013](#bib.bib32), [2015](#bib.bib33)) learn a parametrized Qθsubscript𝑄𝜃Q\_{\theta}italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT value function by minimizing the squared error between Qθsubscript𝑄𝜃Q\_{\theta}italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT and the target value estimate, typically predicted by a lagging version Qθ¯subscript𝑄¯𝜃Q\_{\bar{\theta}}italic\_Q start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG end\_POSTSUBSCRIPT of the current value function
| | | |
| --- | --- | --- |
| | ℒ(s,a,r,s′;θ)=(r+γmaxa′Qθ¯(s′,a′)−Qθ(s,a))2.ℒ𝑠𝑎𝑟superscript𝑠′𝜃superscript𝑟𝛾subscriptsuperscript𝑎′subscript𝑄¯𝜃superscript𝑠′superscript𝑎′subscript𝑄𝜃𝑠𝑎2\mathcal{L}(s,a,r,s^{\prime};\theta)=(r+\gamma\max\_{a^{\prime}}Q\_{\bar{\theta}}(s^{\prime},a^{\prime})-Q\_{\theta}(s,a))^{2}.caligraphic\_L ( italic\_s , italic\_a , italic\_r , italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ; italic\_θ ) = ( italic\_r + italic\_γ roman\_max start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) - italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) ) start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT . | |
The stochastic fitting process means that, before convergence of the algorithm, the Q values are uncertain value estimates.
The max\maxroman\_max operator in the noisy target value estimate has been shown to introduce an overestimation bias (Thrun & Schwartz, [1993](#bib.bib46)), known as the “winner’s curse”, due to Jensen’s inequality
| | | | |
| --- | --- | --- | --- |
| | 𝔼[maxa′Q(s′,a′)]−maxa′𝔼[Q(s′,a′)]≥0,𝔼delimited-[]subscriptsuperscript𝑎′𝑄superscript𝑠′superscript𝑎′subscriptsuperscript𝑎′𝔼delimited-[]𝑄superscript𝑠′superscript𝑎′0\mathbb{E}[\max\_{a^{\prime}}Q(s^{\prime},a^{\prime})]-\max\_{a^{\prime}}\mathbb{E}[Q(s^{\prime},a^{\prime})]\geq 0,blackboard\_E [ roman\_max start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT italic\_Q ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ] - roman\_max start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT blackboard\_E [ italic\_Q ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ] ≥ 0 , | | (1) |
where the expectation is over runs of the algorithm.
Since the update is applied repeatedly through bootstrapping, it can iteratively increase the bias of the estimated Q values before convergence, and introduce instability into TD-learning algorithms as it propagates through the repeated Bellman update (Kumar et al., [2019](#bib.bib23), [2020](#bib.bib24)) and neural-network extrapolation errors (van Hasselt et al., [2018](#bib.bib50); Lee et al., [2021](#bib.bib27)).
3 Why Reduce Estimation Variance
---------------------------------
To take useful update steps in parameter space, a sample-efficient value-based RL algorithm must be able to produce informative next-state target estimates, even in early stages of training when data is scarce and estimates are noisy. Unfortunately, stochasticity in the fitting process, including parameter initialization, environment dynamics, exploration, and replay sampling, leads to randomness in the parameters and the target estimates. This uncertainty can introduce bias via the Jensen gap as well as destabilize the training process, particularly under function approximation.
While the Jensen gap ([1](#S2.E1 "1 ‣ 2 Preliminaries ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks")) cannot be fully characterized in terms of the variance of the value estimates, instead requiring extreme-value analysis, it is clear that target estimation variance generally contributes to overestimation bias in parametrized Q functions (Thrun & Schwartz, [1993](#bib.bib46); Kim et al., [2019](#bib.bib21); Duan et al., [2020](#bib.bib10)). Reducing the variance of the value estimate can help reduce the resulting bias.
In the case of learning an approximate Q function, such as a neural network, variance in the target estimate (that is, estimate variability between runs) can also manifest as instability (that is, variability throughout a single run of the learning process).
One possible reason is that update steps with similar experiences, which should be similar, can become inconsistent in the presence of high target variance.
A target network (Mnih et al., [2015](#bib.bib33); Lillicrap et al., [2015](#bib.bib30)) regularizes the estimates by separating the learner’s parameters from those responsible for producing target estimates. The target network uses a delayed copy or a Polyak-Rupert (exponential window) average of the parameters, effectively preventing it from changing too erratically.
Target networks have been shown to successfully stabilize off-policy TD learning (van Hasselt et al., [2018](#bib.bib50)). However, by introducing a lagging network for value estimation, target networks introduce additional bias to the target estimate in exchange for lower variance. In many algorithms, the target network update rate is a hyperparameter that requires careful tuning, and can be domain-dependent.
By averaging an ensemble of Q networks, MeanQ reduces the target estimate variance. Empirically, this reduces both the overestimation bias and the instability that this variance can cause. Thus MeanQ enjoys better sample efficiency, which is even further improved by using up-to-date target estimates, since a lagging target network is shown to be unneeded for stability in MeanQ.
4 Related Work
---------------
###
4.1 Off-Policy RL
Off-policy RL algorithms improve sample efficiency by reusing environment interactions experienced by different policy (Fujimoto et al., [2018](#bib.bib14); Haarnoja et al., [2018a](#bib.bib15); Hessel et al., [2018](#bib.bib17)). Off-policy learning is thus considered a promising direction for scaling RL to meet the needs of the real world (Levine, [2021](#bib.bib28)). Rainbow DQN (Hessel et al., [2018](#bib.bib17)) has been shown to perform well on the Atari Learning Environment benchmark (Bellemare et al., [2013](#bib.bib5)) by combining a set of techniques (“Rainbow techniques”) that are empirically successful at improving sample efficiency, including Double Q-learning (van Hasselt, [2010](#bib.bib48); van Hasselt et al., [2015](#bib.bib49)), dueling networks (Wang et al., [2016](#bib.bib53)), prioritized experience replay (Schaul et al., [2015](#bib.bib39)), distributional value estimates (Bellemare et al., [2017](#bib.bib6)), and noisy network exploration (Fortunato et al., [2017](#bib.bib11)).
###
4.2 Stablizing Q-Learning
Direct bootstrapping with a learned parametrized function approximator can cause instability and overestimation (van Hasselt, [2010](#bib.bib48); van Hasselt et al., [2015](#bib.bib49); Fujimoto et al., [2018](#bib.bib14); Song et al., [2018](#bib.bib44); Kim et al., [2019](#bib.bib21); Kumar et al., [2019](#bib.bib23), [2020](#bib.bib24)). To alleviate these effect, Double Q-learning (van Hasselt, [2010](#bib.bib48); van Hasselt et al., [2015](#bib.bib49)) decorrelates the action optimization and value estimation, replacing the overestimation due to Jensen’s inequality with a moderate underestimation. Twin-Q (Fujimoto et al., [2018](#bib.bib14)) attempts to reduce overestimation more directly, by taking the minimum target value over two learner networks. Soft-maximal value targets, often involving the mellow-max operator (Ziebart, [2010](#bib.bib56); Rubin et al., [2012](#bib.bib38); Fox et al., [2015](#bib.bib13); Asadi & Littman, [2017](#bib.bib3); Kim et al., [2019](#bib.bib21)), can reduce both the bias (Fox, [2019](#bib.bib12)) and variance (Kim et al., [2019](#bib.bib21)) of target value estimates. A weighted TD error has also been proposed to handle uncertainty in the training signal caused by error propagation through the self-referential update structure (Kumar et al., [2020](#bib.bib24)) and by model uncertainty due to model expressiveness and limited data (Lee et al., [2021](#bib.bib27)).
###
4.3 Ensemble Learning in RL
Ensemble learning, namely training a set of more than one learners for the same task, is often used in RL to guide exploration, and in some methods to improve target value estimates.
The statistics of the ensemble can be used to assess model uncertainty or to produce a lower-variance estimate, compared to a single estimator. These benefits have been utilized in many RL techniques, such as in measuring the error accumulation in a learned dynamics model in model-based RL (Chua et al., [2018](#bib.bib9)), evaluating a temperature for softer maximization in uncertain states (Fox, [2019](#bib.bib12); Liang et al., [2021](#bib.bib29)), lowering the target estimate variance (Anschel et al., [2016](#bib.bib2); An et al., [2021](#bib.bib1)), biasing exploration toward novel states (Chen et al., [2017](#bib.bib7); Osband et al., [2016a](#bib.bib34); Lee et al., [2021](#bib.bib27)), down-weighting loss for uncertain target values (Lee et al., [2021](#bib.bib27)), and alleviating estimation bias by pessimistically estimating the target as the minimum over ensemble predictions (Fujimoto et al., [2018](#bib.bib14); Lan et al., [2020](#bib.bib25)). Sufficient ensemble diversity is crucial in all of these methods (Sheikh et al., [2022](#bib.bib41)).
While all ensemble methods are similarly motivated, approaches closely related to our own are
Averaged-DQN and Ensemble-DQN (Anschel et al., [2016](#bib.bib2)) and EBQL (Peer et al., [2021](#bib.bib37)). Like MeanQ, these methods use an ensemble mean as the target estimate, with Ensemble-DQN and EBQL maintaining an explicit ensemble and Averaged-DQN reusing past snapshots of the network parameters.
Both Ensemble-DQN and EBQL train all ensemble members with the same experience mini-batch and mean target values, unlike MeanQ which has each member sample independently from a shared replay buffer. The significance of these design choices is further discussed in [Section 5.2](#S5.SS2 "5.2 Decorrelating Ensemble Members ‣ 5 MeanQ ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
Deep Exploration methods, such as RLSVI (Osband et al., [2016b](#bib.bib35)), collect training data by selecting an ensemble member to interact with the environment throughout each episode. The ultimately deployed policy has the same property, unlike MeanQ which selects greedy actions using the ensemble mean in both exploration and deployment. Consistently with this difference, the methods also differ in how they generate target values, with RLSVI bootstrapping each member from its own value estimates and MeanQ averaging the ensemble for all targets. Thus, both methods collect data relevant to their targets and evaluated policies, which has been shown imperative for successful training (Ostrovski et al., [2021](#bib.bib36)). We find empirically that this latter consideration outweighs the potential benefit of independent experience through Deep Exploration; that off-policy training with a replay buffer (Mnih et al., [2015](#bib.bib33)) provides sufficient experience diversity in MeanQ; and that the correlation between ensemble members introduced by the shared replay buffer is alleviated by independent sampling from the buffer.
SUNRISE (Lee et al., [2021](#bib.bib27)) and MeanQ differ only in how they compute the TD error. SUNRISE bootstraps each ensemble member from its corresponding target network, while MeanQ uses the ensemble mean. SUNRISE also rescales the TD error based on an ensemble-induced uncertainty measure of the target value.
5 MeanQ
--------
We present MeanQ, a simple and sample-efficient ensemble-based RL algorithm. In this section, we discuss in detail each step of the algorithm and compare it with existing methods. Finally, we discuss how MeanQ can be combined with several RL techniques that have been shown useful in sample-efficient learning of Q networks.
###
5.1 Updating Q Ensemble
MeanQ estimates state–action values with an ensemble of K𝐾Kitalic\_K Q networks. As in DQN, each network k=1,…,K𝑘1…𝐾k=1,\ldots,Kitalic\_k = 1 , … , italic\_K, parametrized by θksubscript𝜃𝑘\theta\_{k}italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT, takes a state s𝑠sitalic\_s as input, and outputs a vector predicting Qθk(s,a)subscript𝑄subscript𝜃𝑘𝑠𝑎Q\_{\theta\_{k}}(s,a)italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) for each action a𝑎aitalic\_a. The Q-network architecture allows maximizing the Q function over actions and finding a maximizing action in two operations that require it: (1) computing target values for optimization; (2) selecting a greedy action for rolling out the trained agent, whether to collect more data or to evaluate. In MeanQ, the maximum is taken over the mean value of the K𝐾Kitalic\_K ensemble members:
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| --- | --- | --- |
| | Vθ(s)=maxa{meankQθk(s,a)}.subscript𝑉𝜃𝑠subscript𝑎subscriptmean𝑘subscript𝑄subscript𝜃𝑘𝑠𝑎V\_{\theta}(s)=\max\_{a}\{\operatorname\*{mean}\_{k}Q\_{\theta\_{k}}(s,a)\}.italic\_V start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) = roman\_max start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) } . | |
In particular, this Vθ(s)subscript𝑉𝜃𝑠V\_{\theta}(s)italic\_V start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) is used for the target value when taking gradient steps to minimize the square TD error for each estimator θksubscript𝜃𝑘\theta\_{k}italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT, with respect to target values computed by separate target networks θ¯1,…,θ¯Ksubscript¯𝜃1…subscript¯𝜃𝐾\bar{\theta}\_{1},\ldots,\bar{\theta}\_{K}over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT , … , over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_K end\_POSTSUBSCRIPT
| | | |
| --- | --- | --- |
| | ℒ(s,a,r,s′;θk)=(r+γVθ¯(s′)−Qθk(s,a))2.ℒ𝑠𝑎𝑟superscript𝑠′subscript𝜃𝑘superscript𝑟𝛾subscript𝑉¯𝜃superscript𝑠′subscript𝑄subscript𝜃𝑘𝑠𝑎2\mathcal{L}(s,a,r,s^{\prime};\theta\_{k})=\bigg{(}r+\gamma V\_{\bar{\theta}}(s^{\prime})-Q\_{\theta\_{k}}(s,a)\bigg{)}^{2}.caligraphic\_L ( italic\_s , italic\_a , italic\_r , italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ; italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ) = ( italic\_r + italic\_γ italic\_V start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) - italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) ) start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT . | |
###
5.2 Decorrelating Ensemble Members
The motivation for using an ensemble mean is that, when the members are not fully correlated, the mean has lower variance. We expect this variance reduction to provide the benefits discussed in [Section 3](#S3 "3 Why Reduce Estimation Variance ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks"), including decreasing the overestimation bias in Vθ¯(s′)subscript𝑉¯𝜃superscript𝑠′V\_{\bar{\theta}}(s^{\prime})italic\_V start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) due to Jensen’s inequality.
Effectively reducing variance requires keeping the ensemble members as uncorrelated as possible. However, complete independence of the members is impossible; in order to make use of the improved value estimates, they must affect each other’s target values. MeanQ strikes a balance between keeping sources of estimate stochasticity independent when it can be done efficiently, and otherwise allowing their dependence. The complete MeanQ method is presented in Algorithm [1](#alg1 "Algorithm 1 ‣ Other techniques. ‣ 5.3 Combining with Existing Techniques ‣ 5 MeanQ ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
The first source of estimate stochasticity is the initialization of each Q network (Osband et al., [2016a](#bib.bib34)), which is naturally done independently for each of the K𝐾Kitalic\_K ensemble members.
When rolling out an exploration policy, it is possible for each Qθksubscript𝑄subscript𝜃𝑘Q\_{\theta\_{k}}italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT to form its own ϵitalic-ϵ\epsilonitalic\_ϵ-greedy exploration policy. However, that policy would not reflect our best available policy, resulting in suboptimal exploration. It would also differ from the evaluated policy
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| --- | --- | --- |
| | πθ(s)=argmaxa{meankQθk(s,a)},subscript𝜋𝜃𝑠subscriptargmax𝑎subscriptmean𝑘subscript𝑄subscript𝜃𝑘𝑠𝑎\pi\_{\theta}(s)=\operatorname\*{arg\,max}\_{a}\{\operatorname\*{mean}\_{k}Q\_{\theta\_{k}}(s,a)\},italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) = start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) } , | |
which can be harmful (Ostrovski et al., [2021](#bib.bib36)). We therefore use the ensemble mean for ϵitalic-ϵ\epsilonitalic\_ϵ-greedy exploration, by selecting πθ(s)subscript𝜋𝜃𝑠\pi\_{\theta}(s)italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s ) in state s𝑠sitalic\_s with probability 1−ϵ1italic-ϵ1-\epsilon1 - italic\_ϵ and a uniform action otherwise.
Q-learning networks typically use a replay buffer to store experience and draw mini-batches for training.
This diversifies each mini-batch by mixing in it steps from different episodes or disparate times in an episode.
The next design choice for MeanQ is whether ensemble members use individual replay buffers (which could be experienced by πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT or πθk=argmaxaQθksubscript𝜋subscript𝜃𝑘subscriptargmax𝑎subscript𝑄subscript𝜃𝑘\pi\_{\theta\_{k}}=\operatorname\*{arg\,max}\_{a}Q\_{\theta\_{k}}italic\_π start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT = start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT) or share a single buffer.
For the same amount of total exploration, a shared buffer is more diverse, and we find it empirically beneficial.
Using a shared experience policy and a shared replay buffer further correlates the ensemble members. To alleviate this negative effect and help the members evolve more independently, MeanQ has each member sample its mini-batches independently from the replay buffer.
A downside, however, is computational efficiency.
Computing targets for each member requires us to evaluate all members’ value estimates, Qθksubscript𝑄subscript𝜃𝑘Q\_{\theta\_{k}}italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT, at each state s′superscript𝑠′s^{\prime}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT present in any of the members’ mini-batches, performing
O(K2)𝑂superscript𝐾2O(K^{2})italic\_O ( italic\_K start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT ) target Q function evaluations for O(K)𝑂𝐾O(K)italic\_O ( italic\_K ) gradient updates.
For small K𝐾Kitalic\_K (5 in our experiments), this increase is not prohibitive, and is offset by the resulting improvement in sample efficiency, particularly in settings where environment interactions are more expensive than function evaluations.
MeanQ is closely related to Averaged-DQN (Avg-DQN) and Ensemble-DQN (Ens-DQN) (Anschel et al., [2016](#bib.bib2)) and Ensemble Bootstrapped Q-Learning (EBQL) (Peer et al., [2021](#bib.bib37)), which are similarly motivated.
For computational reasons, these methods were designed to have members share mini-batch data (Ens-DQN and EBQL) or simply reuse recent snapshots of a single network (Avg-DQN).
We find empirically that these choices degrade performance.
In our experiments ([Section 6.3](#S6.SS3 "6.3 Effect of Target Network ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks")), we test Ens-DQN by using the same replay samples across members; intuitively, this makes the members more correlated and so reduces variance less. Our experiments indicate that, while Ens-DQN provides only modest benefits over Avg-DQN, by modifying the ensemble training procedure to that of MeanQ we are able to improve sample efficiency significantly over Ens-DQN.
Finally, for prioritized experience replay (Schaul et al., [2015](#bib.bib39)), the priority weights that control the sampling distribution are computed using the previous TD error for each data point.
Independently sampling for each ensemble member enables us to also keep separate priorities, so that each member samples experience that is most relevant to its own value estimates.
To this end, MeanQ computes the priority weights for each ensemble member using its own previous TD errors.
###
5.3 Combining with Existing Techniques
Our proposed method can be combined with several existing techniques for improving deep Q-learning, namely Rainbow DQN (Hessel et al., [2018](#bib.bib17)) and UCB exploration (Chen et al., [2017](#bib.bib7)) as discussed below, and doing so further improves its performance.
#### Distributional target estimation.
A useful modification to standard Q-learning is to have the network output a *distribution* over values for each action (Bellemare et al., [2017](#bib.bib6)). This value distribution is typically approximated by a categorical distribution over a fixed finite set of possible return values, called atoms.
A distributional MeanQ target estimate can be computed as an average over the members’ estimates of the probability mass of each atom
| | | | |
| --- | --- | --- | --- |
| | ps′subscript𝑝superscript𝑠′\displaystyle p\_{s^{\prime}}italic\_p start\_POSTSUBSCRIPT italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT | =meankpθ¯k(s′,a\*)absentsubscriptmean𝑘subscript𝑝subscript¯𝜃𝑘superscript𝑠′superscript𝑎\displaystyle=\operatorname\*{mean}\_{k}p\_{\bar{\theta}\_{k}}(s^{\prime},a^{\*})= roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_p start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT ) | |
| | a\*superscript𝑎\displaystyle a^{\*}italic\_a start\_POSTSUPERSCRIPT \* end\_POSTSUPERSCRIPT | =argmaxa′{meankz⊺pθ¯k(s′,a′),}\displaystyle=\operatorname\*{arg\,max}\_{a^{\prime}}\{\operatorname\*{mean}\_{k}z^{\intercal}p\_{\bar{\theta}\_{k}}(s^{\prime},a^{\prime}),\}= start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_z start\_POSTSUPERSCRIPT ⊺ end\_POSTSUPERSCRIPT italic\_p start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) , } | |
where z𝑧zitalic\_z are the locations of the return value’s discrete support, similarly to Bellemare et al. ([2017](#bib.bib6)).
Finally, we take member k𝑘kitalic\_k’s loss function to be the cross-entropy loss between pθk(s,a)subscript𝑝subscript𝜃𝑘𝑠𝑎p\_{\theta\_{k}}(s,a)italic\_p start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) and ps′subscript𝑝superscript𝑠′p\_{s^{\prime}}italic\_p start\_POSTSUBSCRIPT italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT, with the latter projected onto the support z𝑧zitalic\_z. Implementation details are given
in [Appendix A](#A1 "Appendix A MeanQ with Rainbow Techniques and UCB Exploration ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
#### UCB exploration.
Ensembles have previously been used to assist in crafting an exploration policy (Auer et al., [2002](#bib.bib4); Osband et al., [2016a](#bib.bib34); Chen et al., [2017](#bib.bib7)). Since MeanQ already has an ensemble, it is easy to combine it with any of these exploration methods. In our experiments, we combine MeanQ with the exploration policy used in Lee et al. ([2021](#bib.bib27))
| | | |
| --- | --- | --- |
| | π(s)=argmaxa{meankQθk(s,a)+λstdkQθk(s,a)},𝜋𝑠subscriptargmax𝑎subscriptmean𝑘subscript𝑄subscript𝜃𝑘𝑠𝑎𝜆subscriptstd𝑘subscript𝑄subscript𝜃𝑘𝑠𝑎\pi(s)=\operatorname\*{arg\,max}\_{a}\{\operatorname\*{mean}\_{k}Q\_{\theta\_{k}}(s,a)+\lambda\operatorname\*{std}\_{k}Q\_{\theta\_{k}}(s,a)\},italic\_π ( italic\_s ) = start\_OPERATOR roman\_arg roman\_max end\_OPERATOR start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) + italic\_λ roman\_std start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) } , | |
where meanksubscriptmean𝑘\operatorname\*{mean}\_{k}roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT and stdksubscriptstd𝑘\operatorname\*{std}\_{k}roman\_std start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT are the ensemble mean and standard deviation and λ>0𝜆0\lambda>0italic\_λ > 0 is a hyperparameter.
#### Other techniques.
MeanQ can also trivially incorporate dueling network (Wang et al., [2016](#bib.bib53)) and noisy exploration (Fortunato et al., [2017](#bib.bib11)), which have been observed to improve sample efficiency.
MeanQ can also incorporate Double DQN (van Hasselt, [2010](#bib.bib48)), itself a size-2 ensemble method, but this would halve the effective ensemble size, and is therefore note done in our experiments.
Algorithm 1 MeanQ
Initialize K𝐾Kitalic\_K Q networks Qθksubscript𝑄subscript𝜃𝑘Q\_{\theta\_{k}}italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT for all k𝑘kitalic\_k
Initialize K𝐾Kitalic\_K target Q networks Qθ¯ksubscript𝑄subscript¯𝜃𝑘Q\_{\bar{\theta}\_{k}}italic\_Q start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT, θ¯k←θk←subscript¯𝜃𝑘subscript𝜃𝑘\bar{\theta}\_{k}\leftarrow\theta\_{k}over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ← italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT for all k𝑘kitalic\_k
Initialize replay memory D𝐷Ditalic\_D to capacity N𝑁Nitalic\_N
Initialize prioritization pk(b|D)subscript𝑝𝑘conditional𝑏𝐷p\_{k}(b|D)italic\_p start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ( italic\_b | italic\_D ) for all k𝑘kitalic\_k
for *t=1,…,T𝑡1normal-…𝑇t=1,\ldots,Titalic\_t = 1 , … , italic\_T* do
Sample action atsubscript𝑎𝑡a\_{t}italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT according to exploration policy πθsubscript𝜋𝜃\pi\_{\theta}italic\_π start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT
Observe rt←r(st,at)←subscript𝑟𝑡𝑟subscript𝑠𝑡subscript𝑎𝑡r\_{t}\leftarrow r(s\_{t},a\_{t})italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ← italic\_r ( italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT )
Sample st+1∼p(st+1|st,at)similar-tosubscript𝑠𝑡1𝑝conditionalsubscript𝑠𝑡1subscript𝑠𝑡subscript𝑎𝑡s\_{t+1}\sim p(s\_{t+1}|s\_{t},a\_{t})italic\_s start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT ∼ italic\_p ( italic\_s start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT | italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT )
Store transition (st,at,rt,st+1)subscript𝑠𝑡subscript𝑎𝑡subscript𝑟𝑡subscript𝑠𝑡1(s\_{t},a\_{t},r\_{t},s\_{t+1})( italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT ) in D𝐷Ditalic\_D
st←st+1←subscript𝑠𝑡subscript𝑠𝑡1s\_{t}\leftarrow s\_{t+1}italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ← italic\_s start\_POSTSUBSCRIPT italic\_t + 1 end\_POSTSUBSCRIPT
for *k=1,…,K𝑘1normal-…𝐾k=1,\ldots,Kitalic\_k = 1 , … , italic\_K* do
Sample B𝐵Bitalic\_B transitions (sb,ab,rb,sb′)∼Dsimilar-tosubscript𝑠𝑏subscript𝑎𝑏subscript𝑟𝑏superscriptsubscript𝑠𝑏′𝐷(s\_{b},a\_{b},r\_{b},s\_{b}^{\prime})\sim D( italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT , italic\_r start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT , italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) ∼ italic\_D
Vsb′=maxab′{meankQθ¯k(sb′,ab′)}subscript𝑉superscriptsubscript𝑠𝑏′subscriptsuperscriptsubscript𝑎𝑏′subscriptmean𝑘subscript𝑄subscript¯𝜃𝑘superscriptsubscript𝑠𝑏′superscriptsubscript𝑎𝑏′V\_{s\_{b}^{\prime}}=\max\_{a\_{b}^{\prime}}\{\operatorname\*{mean}\_{k}Q\_{\bar{\theta}\_{k}}(s\_{b}^{\prime},a\_{b}^{\prime})\}italic\_V start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT = roman\_max start\_POSTSUBSCRIPT italic\_a start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ) }
yb={rbif sb′ is a terminal staterb+γVsb′otherwisesubscript𝑦𝑏casessubscript𝑟𝑏if sb′ is a terminal statesubscript𝑟𝑏𝛾subscript𝑉superscriptsubscript𝑠𝑏′otherwisey\_{b}=\begin{cases}r\_{b}&\text{if $s\_{b}^{\prime}$ is a terminal state}\\
r\_{b}+\gamma V\_{s\_{b}^{\prime}}&\text{otherwise}\end{cases}italic\_y start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT = { start\_ROW start\_CELL italic\_r start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT end\_CELL start\_CELL if italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT is a terminal state end\_CELL end\_ROW start\_ROW start\_CELL italic\_r start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT + italic\_γ italic\_V start\_POSTSUBSCRIPT italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_POSTSUBSCRIPT end\_CELL start\_CELL otherwise end\_CELL end\_ROW
ℒ(θk)=meanbℒb(θk)ℒsubscript𝜃𝑘subscriptmean𝑏subscriptℒ𝑏subscript𝜃𝑘\mathcal{L}(\theta\_{k})=\operatorname\*{mean}\_{b}\mathcal{L}\_{b}(\theta\_{k})caligraphic\_L ( italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ) = roman\_mean start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT caligraphic\_L start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT ( italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ),
where ℒb(θk)=(yb−Qθk(sb,ab))2subscriptℒ𝑏subscript𝜃𝑘superscriptsubscript𝑦𝑏subscript𝑄subscript𝜃𝑘subscript𝑠𝑏subscript𝑎𝑏2\mathcal{L}\_{b}(\theta\_{k})=(y\_{b}-Q\_{\theta\_{k}}(s\_{b},a\_{b}))^{2}caligraphic\_L start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT ( italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ) = ( italic\_y start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT - italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT , italic\_a start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT ) ) start\_POSTSUPERSCRIPT 2 end\_POSTSUPERSCRIPT
Update θksubscript𝜃𝑘\theta\_{k}italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT using ∇θkℒ(θk)subscript∇subscript𝜃𝑘ℒsubscript𝜃𝑘\nabla\_{\theta\_{k}}\mathcal{L}(\theta\_{k})∇ start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT caligraphic\_L ( italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT )
Update pk(b|D)subscript𝑝𝑘conditional𝑏𝐷p\_{k}(b|D)italic\_p start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ( italic\_b | italic\_D ) using ℒb(θk)subscriptℒ𝑏subscript𝜃𝑘\sqrt{\mathcal{L}\_{b}(\theta\_{k})}square-root start\_ARG caligraphic\_L start\_POSTSUBSCRIPT italic\_b end\_POSTSUBSCRIPT ( italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ) end\_ARG
end for
Every Ttargetsubscript𝑇targetT\_{\text{target}}italic\_T start\_POSTSUBSCRIPT target end\_POSTSUBSCRIPT steps: update θ¯k←θk←subscript¯𝜃𝑘subscript𝜃𝑘{\bar{\theta}}\_{k}\leftarrow\theta\_{k}over¯ start\_ARG italic\_θ end\_ARG start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT ← italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT for all k𝑘kitalic\_k
end for
Figure 1: Performance of MeanQ with Rainbow techniques, compared with 6 existing algorithms. Mean expected undiscounted return over 5 runs is shown (shaded: 1 standard deviation), averaged over 26 Atari environments and in 9 individual ones. Baseline results of different algorithms at 100K interactions are rendered as horizontal lines and cited from: Rainbow (van Hasselt et al., [2019](#bib.bib51)); SUNRISE (Lee et al., [2021](#bib.bib27)); PPO, SimPLe, CURL, and DrQ (Kaiser et al., [2019](#bib.bib19)). Full results in all 26 environments are available in Appendix [C](#A3 "Appendix C Performance of 26 Atari Environments ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
6 Experiment Results
---------------------
Figure 2: Performance, initial state estimation bias, standard deviation, and Jensen gap of DQN (with and without target network), Averaged-DQN, Ensemble-DQN, and MeanQ (with and without target network). Expected returns and biases, standard deviation, and Jensen gaps are over 5 runs; except in ensemble methods in the Asterix environment where 4 groups for 5 runs each are used to plot standard deviations (shaded; for details see text). Bias in DQN without target network is often above the plotted range.
In MeanQ, estimation variance is reduced by the ensemble averaging, eliminating the need for a target network. See [Section 6.3](#S6.SS3 "6.3 Effect of Target Network ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") for further analysis.
Figure 3: Performance of MeanQ with Rainbow techniques and UCB exploration, compared with several variations of Rainbow that resemble aspects of MeanQ: either a 5 times larger neural network, or 5 times more frequent network updates, or both in Asterix.
###
6.1 Setup
We evaluate MeanQ on several discrete control benchmark environments in Atari (Bellemare et al., [2013](#bib.bib5)). We compare with existing algorithms, including: (1) SUNRISE (Lee et al., [2021](#bib.bib27)), a model-free value-based ensemble RL method that is, to our knowledge, the state-of-the-art on this benchmark;
(2) SimPLe (Kaiser et al., [2019](#bib.bib19)), a model-based method;
(3) PPO (Schulman et al., [2017](#bib.bib40)); (4) Rainbow DQN (Hessel et al., [2018](#bib.bib17)) with two hyperparameter settings (van Hasselt et al., [2019](#bib.bib51); Kaiser et al., [2019](#bib.bib19)); (5) CURL (Laskin et al., [2020](#bib.bib26)); and (6) DrQ (Kostrikov et al., [2021](#bib.bib22)). We also compare with human-level performance, as reported by Kaiser et al. ([2019](#bib.bib19)). For MeanQ, we use the version extended with the techniques of [Section 5.3](#S5.SS3 "5.3 Combining with Existing Techniques ‣ 5 MeanQ ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") and the same hyperparameter configuration as SUNRISE (Lee et al., [2021](#bib.bib27)), with two exceptions: no multi-step targets are used (compared with 3 steps in SUNRISE) and no target network is used. To remain comparable with SUNRISE, all our evaluations use an ensemble size K=5𝐾5K=5italic\_K = 5. Our MeanQ implementation is described in full detail in Appendix [A](#A1 "Appendix A MeanQ with Rainbow Techniques and UCB Exploration ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") and available at <https://github.com/indylab/MeanQ>.
###
6.2 Evaluation
Our results in Atari environments are summarized in [Figure 1](#S5.F1 "Figure 1 ‣ Other techniques. ‣ 5.3 Combining with Existing Techniques ‣ 5 MeanQ ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks"). The full results in all 26 environments are available in Appendix [C](#A3 "Appendix C Performance of 26 Atari Environments ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
The experiment results show that MeanQ significantly outperforms all baselines with which we compared. MeanQ outperforms the best available baseline, SUNRISE, at 100K interaction steps in 16/26 environments. Scaling and shifting the returns such that random-policy performance is 0 and human-level performance is 1, to allow averaging across environments, MeanQ outperforms SUNRISE by 68% on average over 5 runs in each of the 26 environments. MeanQ also achieves higher returns than Rainbow DQN at 500K steps in 21/26 environments, and 49% higher average normalized return. Averaged over 5 runs in each environment, MeanQ achieves average human-level performance in the 26 games using only 200K (±plus-or-minus\pm±100K) interaction steps.
We note that, since our method is compatible with many more existing techniques which we did not evaluate, such as contrastive learning (Laskin et al., [2020](#bib.bib26)), augmentation (Kostrikov et al., [2021](#bib.bib22)), and various model-based methods, it has the potential for further performance improvement.
###
6.3 Effect of Target Network
In this experiment, we compare DQN and MeanQ with and without a lagging target network. To isolate the effects on MeanQ itself, we experiment without including any of the techniques described in [Section 5.3](#S5.SS3 "5.3 Combining with Existing Techniques ‣ 5 MeanQ ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") (“vanilla MeanQ”). To study the effect of variance on overestimation bias and policy performance, it is also important not to use a distributional return representation (Bellemare et al., [2017](#bib.bib6)), which curtails overestimation due to the clipping effect of projecting the value distribution onto a fixed discrete support. Exploration is ϵitalic-ϵ\epsilonitalic\_ϵ-greedy with linear annealing of ϵitalic-ϵ\epsilonitalic\_ϵ. The detailed modification in hyperparameter configuration for this experiment is available in Appendix [B](#A2 "Appendix B Hyperparameters ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks").
To study the algorithmic behavior, we measure a set of statistics during training:
1. 1.
Performance. We measure performance by the undiscounted sum of rewards ∑trtsubscript𝑡subscript𝑟𝑡\sum\_{t}r\_{t}∑ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT, averaged over 20 evaluation trajectories after every 10K interaction steps of the exploration policy.
2. 2.
Estimation bias. We measure the value estimation bias in the initial state s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT. In this experiment, a prediction of the learner network V(s0)=maxaQθ(s0,a)𝑉subscript𝑠0subscript𝑎subscript𝑄𝜃subscript𝑠0𝑎V(s\_{0})=\max\_{a}Q\_{\theta}(s\_{0},a)italic\_V ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ) = roman\_max start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_a ) is queried from the learner. For ensemble methods, V(s0)=maxa{meankQθk(s0,a)}𝑉subscript𝑠0subscript𝑎subscriptmean𝑘subscript𝑄subscript𝜃𝑘subscript𝑠0𝑎V(s\_{0})=\max\_{a}\{\operatorname\*{mean}\_{k}Q\_{\theta\_{k}}(s\_{0},a)\}italic\_V ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ) = roman\_max start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT { roman\_mean start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_a ) } is queried. We then report the empirical mean of V(s0)−∑tγtrt𝑉subscript𝑠0subscript𝑡superscript𝛾𝑡subscript𝑟𝑡V(s\_{0})-\sum\_{t}\gamma^{t}r\_{t}italic\_V ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ) - ∑ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT italic\_γ start\_POSTSUPERSCRIPT italic\_t end\_POSTSUPERSCRIPT italic\_r start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT over 20 evaluation trajectories after every 10K interactions.
3. 3.
Estimation variance. We measure the variance of value estimates by reporting the standard deviation of the initial state value V(s0)𝑉subscript𝑠0V(s\_{0})italic\_V ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT ) over 5 runs of each algorithm after every 100K interactions.
Scaling and shifting algorithm performance such that random-policy performance is 0 and human-level is 1, the standard deviation is normalized by this standardized performance to obtain the relative standard deviation.111This normalization method is only approximate, since value estimates are discounted and performance is undiscounted. The relative standard deviation is averaged over 50 resets of s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT.
4. 4.
Jensen gap. We measure the Jensen gap 𝔼[maxaQ(s0,a)]−maxa𝔼[Q(s0,a)]𝔼delimited-[]subscript𝑎𝑄subscript𝑠0𝑎subscript𝑎𝔼delimited-[]𝑄subscript𝑠0𝑎\mathbb{E}[\max\_{a}Q(s\_{0},a)]-\max\_{a}\mathbb{E}[Q(s\_{0},a)]blackboard\_E [ roman\_max start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT italic\_Q ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_a ) ] - roman\_max start\_POSTSUBSCRIPT italic\_a end\_POSTSUBSCRIPT blackboard\_E [ italic\_Q ( italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT , italic\_a ) ], with the expectation estimated empirically in
5 runs of each algorithm after every 100K interactions. The Jensen gap is normalized by standardized performance, and averaged over 50 resets of s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT.
The quantities above are evaluated in initial states s0subscript𝑠0s\_{0}italic\_s start\_POSTSUBSCRIPT 0 end\_POSTSUBSCRIPT, because these are states that all algorithms must learn to evaluate in every run. Other states may not have the same reach probabilities across algorithms and runs, and comparing them is less indicative of the actual algorithmic behavior.
Figure [2](#S6.F2 "Figure 2 ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") summarizes the empirical estimates of performance, bias, variance, and Jensen gap of 6 algorithms: DQN (Mnih et al., [2015](#bib.bib33)), DQN without a target network (Mnih et al., [2013](#bib.bib32)), Avg-DQN and Ens-DQN (Anschel et al., [2016](#bib.bib2)), MeanQ, and MeanQ without a target network.
Rows 1 and 2 show the mean performance and bias over 5 runs, as well as all runs (faded); except in ensemble methods in the Asterix environment, where the mean and 1 standard deviation (shaded) over 20 runs are shown; and the average over 4 environments in the last column, where the mean and 1 standard deviation (shaded) are shown.
Rows 3 and 4 show the standard deviation and Jensen gap over 5 runs; except in ensemble methods in Asterix, where the 20 runs are split into 4 groups and the mean and 1 standard deviation (shaded) of the within-group statistics are shown.
The results suggest the following findings:
1. 1.
Across all methods and variations, higher estimation variance and higher Jensen gap are positively correlated.
2. 2.
Reproducing known results, a target network (Mnih et al., [2015](#bib.bib33)) in DQN significantly reduces overestimation bias (often too high to plot) and estimation variance (van Hasselt et al., [2018](#bib.bib50)).
3. 3.
MeanQ outperforms DQN, with and without a target network. MeanQ also has lower estimation variance, Jensen gap, and overestimation bias.
4. 4.
As in DQN, removing the target network in MeanQ tends to cause a mild increase in estimation variance, overestimation bias, and Jensen gap, and decrease in performance in early-stage training. However, in a reversal of the findings in DQN, MeanQ without a target network quickly overtakes MeanQ with a target network in terms of normalized variance and Jensen gap, which translates into a significant bias reduction and performance gain.
5. 5.
While Avg-DQN and, to a lesser extent, Ens-DQN reduce the variance and bias compared with DQN, this does not generally correspond to performance gain. We note that it is easier to keep bias low when performance is low, because bias is relative to returns and because values are easier to estimate when near-constant.
6. 6.
MeanQ with and without a target network does better than Ens-DQN in all four considered metrics (with the exception of bias with a target network). This demonstrates the importance of independent replay sampling and prioritization for learning neural network ensembles.
###
6.4 Ensembles vs. Larger Model and More Updates
In this experiment, we rule out two alternative hypotheses for the source of performance improvement in MeanQ. One could wonder whether the improvement could come from the ensemble being an effectively larger model or from performing more frequent value updates per interaction step. In this section, we empirically answer these questions in the negative.
#### Larger model.
We experiment with Rainbow DQN with the same hyperparameters and a neural network architecture equivalent to the ensemble used in MeanQ. This network consists of 5 parallel networks, each taking input s𝑠sitalic\_s and outputting Qθk(s,a)subscript𝑄subscript𝜃𝑘𝑠𝑎Q\_{\theta\_{k}}(s,a)italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ). The final layer is a fixed mean over Qθk(s,a)subscript𝑄subscript𝜃𝑘𝑠𝑎Q\_{\theta\_{k}}(s,a)italic\_Q start\_POSTSUBSCRIPT italic\_θ start\_POSTSUBSCRIPT italic\_k end\_POSTSUBSCRIPT end\_POSTSUBSCRIPT ( italic\_s , italic\_a ) with no additional parameters. The results in [Figure 3](#S6.F3 "Figure 3 ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") show no significant improvement compared to Rainbow DQN with a standard network.
#### More frequent updates.
We experiment with Rainbow DQN with the same hyperparameters, except with 5 times as many gradient steps per interaction step. The results in [Figure 3](#S6.F3 "Figure 3 ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks") show, in all environments but in some more than others, a drop in performance, compared with the baseline hyperparameters, caused by performing gradient steps as frequently in Rainbow DQN as in MeanQ with 5 ensemble members. An experiment in the Asterix environment (Figure [3](#S6.F3 "Figure 3 ‣ 6 Experiment Results ‣ Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks"), left) also shows no benefit from combining both ablations.
These experiments reaffirm our motivation of reducing value estimate variance through ensemble averaging, and the significant role this plays in improving sample efficiency. MeanQ’s improvements cannot be attributed to the larger model or more gradient updates.
7 Conclusion
-------------
In this paper, we introduce MeanQ, a simple ensemble RL method that uses the ensemble mean for target value estimates. Three key design choices, which set MeanQ apart from the extensive prior study of ensemble RL, are: (1) a shared exploration policy, likewise computed from the ensemble mean; (2) a shared replay buffer for all ensemble members; and (3) independent replay sampling from the buffer. While (1) and (2) can further correlate the ensemble members, beyond their use for each other’s updates, we find that (1) is necessary to collect relevant experience, (2) helps experience diversity, and (3) is sufficient to maintain ensemble diversity. Averaging a sufficiently diverse ensemble reduces the variance of target value estimates, which in turn mitigates overestimation. Variance reduction in MeanQ also stabilizes training enough to obviate the lagging target network, which eliminates another source of estimation bias. Our experiment results show improved performance over record-holding baseline algorithms, as well as ablative versions.
Our empirical study furthers our understanding of the role of variance in value-based reinforcement learning, but much remains unknown about the interplay between variance, bias, exploration, and the deadly triad of off-policy bootstrapping with function approximation. Of particular interest is the necessity of nonlinear function approximation for the benefit of MeanQ to manifest, as learning in linear and tabular representations cannot benefit from a simple mean. We expect the community’s further investigation to benefit from the simple and effective method proposed here, which can lend itself to more theoretical and empirical analyses, and serve as a stronger baseline for future work.
Acknowledgements
----------------
The authors would like to thank Sameer Singh and Kimin Lee for their help.
The research work of authors Dailin Hu and, partly, Roy Fox is funded by the Hasso Plattner Foundation. |
51887a6b-b6a2-49f9-8a24-0a3350a93739 | trentmkelly/LessWrong-43k | LessWrong | Nook Nature
Style of post: Alexanderian meander (though with (at best) a tenth of the artistry and thoroughness/rigor). There's a Thing here and it has been tickling at my brain for ages and I have despaired of being able to clarify it all on my own, so I'm giving you my unclarified thoughts instead. More of an extended koan than a proper essay.
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An acquaintance at EA Global suggested that I investigate anagram potential before settling on a possible name for my future child.
This was a cool idea, so I went online with my frontrunner to see what the possibilities were.
There were over 111,000 of them.
If I had made some modest change, say replacing "Elizabeth" with "Frederick" while leaving the other names alone, there would have been a comparable number of completely different ones.
There's something blank about infinity, or really large numbers like "one billion."
But the explosion of possibility somehow felt more viscerally real when it was like expanding one Workflowy node to see a hundred thousand possibilities, and expanding the next Workflowy node to see a hundred thousand different possibilities, and knowing that the next node would contain a hundred thousand new possibilities still.
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Most people are familiar with the Youtube videos showing an infinite zoom into the Mandelbrot set, and how you keep finding complexity as you go deeper and deeper.
But it's interesting (and often underemphasized, in my opinion) that those videos show you the beauty and complexity of one single path, and if you were at any point to go left instead of right, or shift the trajectory of your zooming by some tiny amount in the hundredth decimal place, you would get another, extremely different path of equivalent complexity and beauty.
This is one of the talking points that has come up in my chocolate tastings. I sometimes like to mention that yeah, of course, there's fantastic, staggering, unmanageable co |
7a28ba7a-312e-4a08-acbe-2193b53d41c0 | trentmkelly/LessWrong-43k | LessWrong | Reliably wrong
Discussion of a book by "Dow Jones 36,000" Glassman". I'm wondering whether there are pundits who are so often wrong that their predictions are reliable indicators that something else (ideally the opposite) will happen. |
d2af51ea-5d4a-4d94-903c-553597ee0401 | trentmkelly/LessWrong-43k | LessWrong | Get your tickets to Manifest 2024 by May 13th!
What is Manifest?
Manifest is a festival for predictions, markets, and mechanisms. It’ll feature serious talks, attendee-run workshops, fun side events, and so many of our favorite people!
Tickets: manifest.is/#tickets
WHEN: June 7-9, 2024, with LessOnline starting May 31 and Summer Camp starting June 3
WHERE: Lighthaven, Berkeley, CA
WHO: Hundreds of folks, interested in forecasting, rationality, EA, economics, journalism, tech and more. If you’re reading this, you’re invited! Special guests include Nate Silver, Scott Alexander, Robin Hanson, Dwarkesh Patel, Cate Hall, and many more.
You can find more details about what we’re planning in our announcement post.
Ticket Deadline
We’re planning to increase standard tickets from $399 to $499 on Tuesday, May 13th so get your tickets ASAP!
Prices for tickets to LessOnline (May 31 - June 2) and Summer Camp (June 3 - June 7) will also each increase by $100 on May 13th.
Student and supporter ticket prices will stay the same, but we still appreciate your buying tickets before the May 13th deadline as it helps us plan. Plus it means you’ll get a nice name badge!
We plan to raise prices again on May 31, one week before the beginning of Manifest — but we might raise prices again sooner than that if tickets are selling out.
Sponsorship
Manifest is looking for sponsors! We attract some of the world's most thoughtful speakers and talented individuals — if your organization is looking to broaden your company's reach, or recruit the best folks in the world, consider becoming a sponsor. You can shoot an email to Saul (saul@manifest.is) or book a call with him here. |
8ba0c677-efa0-4fc5-967f-b06b23c636d1 | trentmkelly/LessWrong-43k | LessWrong | [POLITICS] Jihadism and a new kind of existential threat
Politics is the mind-killer. Politics IS really the mind-killer. Please meditate on this until politics flows over you like butter on hot teflon, and your neurons stops fibrillating and resume their normal operations.
Preface
I've always found silly that LW, one of the best and most focused group of rationalists on the web isn't able to talk evenly about politics. It's true that we are still human, but can't we just make an effort at being calm and level-headed? I think we can. Does gradual exposure works on group, too? Maybe a little bit of effort combined with a little bit of exposure will work as a vaccine.
And maybe tomorrow a beautiful naked valkyrie will bring me to utopia on her flying unicorn...
Anyway, I want to try. Let's see what happens.
Intro
Two recent events has prompted me to make this post: I'm reading "The rise of the Islamic State" by Patrick Coburn, which I think does a good job in presenting fairly the very recent history surrounding ISIS, and the terrorist attack in Tunis by the same group, which resulted in 18 foreigners killed.
I believe that their presence in the region is now definitive: they control an area that is wider than Great Britain, with a population tallying over six millions, not counting the territories controlled by affiliate group like Boko Haram. Their influence is also expanding, and the attack in Tunis shows that this entity is not going to stay confined between the borders of Syria and Iraq.
It may well be the case that in the next ten years or so, this will be an international entity which will bring ideas and mores predating the Middle Age back on the Mediterranean Sea.
A new kind of existential threat
To a mildly rational person, the conflict fueling the rise of the Islamic State, namely the doctrinal differences between Sunni and Shia Islam, is the worst kind of Blue/Green division. A separation that causes hundreds of billions of dollars (read that again) to be wasted trying kill each other. But here it is, and |
1b993aed-a05b-41db-ad85-f84a492dd726 | trentmkelly/LessWrong-43k | LessWrong | SUGGEST and VOTE: Posts We Want to Read on Less Wrong
Less Wrong is a large community of very smart people with a wide spectrum of expertise, and I think relatively little of that value has been tapped.
Like my post The Best Textbooks on Every Subject, this is meant to be a community-driven post. The first goal is to identify topics the Less Wrong community would like to read more about. The second goal is to encourage Less Wrongers to write on those topics. (Respecting, of course, the implicit and fuzzy guidelines for what should be posted to Less Wrong.)
One problem is that those with expertise on a subject don't necessarily feel competent to write a front-page post on it. If that's the case, please comment here explaining that you might be able to write one of the requested posts, but you'd like a writing collaborator. We'll try to find you one.
RULES
You may either:
* Post the title of the post you want someone to write for Less Wrong. If the title itself isn't enough to specify the content, include a few sentences of explanation. "How to Learn a Language Quickly" probably needs no elaboration, but "Normative Theory and Coherent Extrapolated Volition" certainly does. Do not post two proposed post titles in the same comment, because that will confuse voting. Please put the title in bold.
or...
* Vote for a post title that has already been suggested, indicating that you would like to read that post, too. Vote with karma ('Vote Up' or 'Vote Down' on the comment that contains the proposed post title).
I will regularly update the list of suggested Less Wrong posts, ranking them in descending order of votes (like this).
THE LIST SO FAR (UPDATED 02/11/11)
* (35) Conversation Strategies for Spreading Rationality Without Annoying People
* (32) Smart Drugs: Which Ones to Use for What, and Why
* (30) A Survey of Upgrade Paths for the Human Brain
* (29) Trusting Your Doctor: When and how to be skeptical about medical advice and medical consensus
* (25) Rational Homeschool Education
* (25) Field Man |
c2194d47-0904-4dfd-b130-f667336f6499 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Probability is Real, and Value is Complex
(This post idea is due entirely to Scott Garrabrant, but it has been several years and he hasn't written it up.)
In 2009, [Vladimir Nesov observed](https://www.lesswrong.com/posts/kYgWmKJnqq8QkbjFj/bayesian-utility-representing-preference-by-probability) that probability can be mixed up with utility in different ways while still expressing the same preferences. The observation was conceptually similar to one made by Jeffrey and Bolker in the book *The Logic of Decision*, so I give them intellectual priority, and refer to the result as "Jeffrey-Bolker rotation".
Based on Nesov's post, Scott came up with a way to represent preferences as *[vector-valued measures](https://en.wikipedia.org/wiki/Vector_measure),* which makes the result geometrically clear and mathematically elegant.
Vector Valued Preferences
-------------------------
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E⊂Ω which form a sigma algebra. Each event has a probability P(E) and an expected utility V(E) associated with it. However, rather than dealing with V directly, we define Q(E)=P(E)V(E). Vladimir Nesov called Q "shouldness", but that's fairly meaningless. Since it is graphed on the y-axis, represents utility times probability, and is otherwise fairly meaningless, a good name for it is "up". Here is a graph of probability and upness for some events, represented as vectors:
(The post title is a pun on the fact that this looks like the complex plane: events are complex numbers with real component P and imaginary component Q. However, it is better to think of this as a generic 2D vector space rather than the complex plane specifically.)
If we assume E1 and E2 are mutually exclusive events (that is, E1∩E2=∅), then calculating the P and Q of their union E1∪E2 is simple. The probability of the union of two mutually exclusive events is just the sum:
P(E1∪E2)=P(E1)+P(E2)
The expected utility is the weighted sum of the component parts, normalized by the sum of the probabilities:
V(E1∪E2)=P(E1)V(E1)+P(E2)V(E2)P(E1)+P(E2)
The numerator is just the sum of the shouldnesses, and the denominator is just the probability of the union:
V(E1∪E2)=Q(E1)+Q(E2)P(E1∪E2)
But, we can multiply both sides by the denominator to get a relationship on shouldness alone:
V(E1∪E2)P(E1∪E2)=Q(E1)+Q(E2)
Q(E1∪E2)=Q(E1)+Q(E2)
Thus, we know that both coordinates of E1∪E2 are simply the sum of the component parts. This means union of disjoint events is vector addition in our vector space, as illustrated in my diagram earlier.
Linear Transformations
----------------------
When we represent preferences in a vector space, it is natural to think of them as basis-independent: the way we drew the axes was arbitrary; all that matters is the system of preferences being represented. What this ends up meaning is that we don't care about linear transformations of the space, so long as the preferences don't get reflected (which reverses the preference represented). This is a generalization of the usual "utility is unique up to affine transformations with positive coefficient": utility is no longer unique in that way, but the combination of probability and utility is unique up to non-reflecting linear transformations.
Let's look at that visually. Multiplying all the expected utilities by a positive constant doesn't change anything:
Adding a constant to expected utility doesn't change anything:
Slightly weird, but not too weird... multiplying all the probabilities by a positive constant (and the same for Q, since Q is U\*P) doesn't change anything (meaning we don't care if probabilities are normalized):
Here's the really new transformation, which can combine with the other 4 to create all the valid transformations. The Jeffrey-Bolker rotation, which changes what parts of our preferences are represented in probabilities vs utilities:
Let's pause for a bit on this one, since it is really the whole point of the setup. What does it mean to rotate our vector-valued measure?
A simple example: suppose that we can take a left path, or a right path. There are two possible worlds, which are equally probable: in Left World, the left path leads to a golden city overflowing with wealth and charity, which we would like to go to with V=+1. The right path leads to a dangerous badlands full of bandits, which we would like to avoid, V=-1. On the other hand, Right World (so named because we would prefer to go right in this world) has a somewhat nice village on the right path, V=+.5, and a somewhat nasty swamp on the left, V=-.5. Supposing that we are (strangely enough) uncertain about which path we take, we calculate the events as follows:
* Go left in left-world:
+ P=.25
+ V=1
+ Q=.25
* Go left in right-world:
+ P=.25
+ V=-.5
+ Q=-.125
* Go right in left-world:
+ P=.25
+ V=-1
+ Q=-.25
* Go right in right-world:
+ P=.25
+ V=.5
+ Q=.125
* Go left (union of the two left-going cases):
+ P=.5
+ Q=.125
+ V=Q/P=.25
* Go right:
+ P=.5
+ Q=-.125
+ V=Q/P=-.25
We can calculate the V of each action and take the best. So, in this case, we sensibly decide to go left, since the Left-world is more impactful to us and both are equally probable.
Now, let's rotate 30°. (Hopefully I get the math right here.)
* Left in L-world:
+ P=.09
+ Q=.34
+ V=3.7
* Left in R-world:
+ P=.28
+ Q=.02
+ V=.06
* Right in L-world:
+ P=.34
+ Q=-.09
+ V=-.26
* Right in R-world:
+ P=.15
+ Q=.23
+ V=1.5
* Left overall:
+ P=.37
+ Q=.36
+ V=.97
* Right overall:
+ P=.49
+ Q=.14
+ V=.29
Now, it looks like going left is evidence for being in R-world, and going right is evidence for being in L-world! The disparity between the worlds has also gotten larger; L-world now has a difference of almost 4 utility between the different paths, rather than 2. R-world now evaluates *both* paths as positive, with a difference between the two of only .9. Also note that our probabilities have stopped summing to one (but as mentioned already, this doesn't matter much; we could normalize the probabilities if we want).
In any case, the final decision is exactly the same, as we expect. I don't have a good intuitive explanation of what the agent is thinking, but roughly, the decreased control the agent has over the situation due to the correlation between its actions and which world it is in seems to be compensated for by the more extreme payoff differences in L-world.
Rational Preferences
--------------------
Alright, so preferences can be represented as vector-valued measures in two dimensions. Does that mean arbitrary vector-valued measures in two dimensions can be interpreted as preferences?
No.
The restriction that probabilities be non-negative means that events can only appear in quadrants I and IV of the graph. We want to state this in a basis-independent way, though, since it is unnatural to have a preferred basis in a vector space. One way to state the requirement is that there must be a line passing through the (0,0) point, such that all of the events are strictly to one side of the line, except perhaps events at the (0,0) point itself:
As illustrated, there may be a single such line, or there may be multiple, depending on how closely preferences hug the (0,0) point. The normal vector of this line (drawn in red) can be interpreted as the P dimension, if you want to pull out probabilities in a way which guarantees that they are non-negative. There may be a unique direction corresponding to probability, and there may not. Since V(E)=Q(E)/P(E), we get a unique probability direction if and only if we have events with both arbitrarily high utilities and arbitrarily low. So, Jeffrey-Bolker rotation is intrinsically tied up in the question of whether utilities are bounded.
Actually, Scott prefers a different condition on vector-valued measures: that they have a *unique (0,0) event*. This allows for either infinite positive utilities (not merely unbounded -- infinite), or infinite negative utilities, but not both. I find this less natural. (Note that we have to have an empty event in our sigma-algebra, and it has to get value (0,0) as a basic fact of vector-valued measures. Whether any other event is allowed to have that value is another question.)
How do we use vector-valued preferences to optimize? The expected value V(E) of a vector is the slope, Q(E)/P(E). This runs into trouble for probability zero events, though, which we may create as we rotate. Instead, we can prefer events which are less clockwise:
(Note, however, that the preference of a (0,0) event is undefined.)
This gives the same answers for positive-x-value, but keeps making sense as we rotate into other quadrants. More and less clockwise always makes sense as a notion since we assumed that the vectors always stay to one side of some line; we can't spin around in a full circle looking for the best option, because we will hit the separating line. This allows us to define a preference relation E1≥E2 based on the angle of E1 being within 180° of E2's.
Conclusion
----------
This is a fun picture of how probabilities and utilities relate to each other. It suggests that the two are inextricably intertwined, and meaningless in isolation. Viewing them in this way makes it somewhat more natural to think that probabilities are more like "caring measure" expressing how much the agent cares about how things go in particular worlds, rather than subjective approximations of an objective "magical reality fluid" which determines what worlds are experienced. ([See here](https://www.lesswrong.com/posts/jZvTWB6Z75cv2q3Nw/i-like-simplicity-but-not-that-much) for an example of this debate.) More practically, it gives a nice tool for visualizing the Jeffrey-Bolker rotation, which helps us think about preference relations which are representable via multiple different belief distributions.
A downside of this framework is that it requires agents to be able to express a preference between any two events, which might be a little absurd. Let me know if you figure out how to connect this to [complete-class style foundations](https://www.lesswrong.com/posts/sZuw6SGfmZHvcAAEP/complete-class-consequentialist-foundations) which only require agents to have preferences over things which they can control. |
517ab509-d384-498a-a3df-5677cd53f138 | trentmkelly/LessWrong-43k | LessWrong | Alcohol, health, and the ruthless logic of the Asian flush
Say you're an evil scientist. One day at work you discover a protein that crosses the blood-brain barrier and causes crippling migraine headaches if someone's attention drifts while driving. Despite being evil, you're a loving parent with a kid learning to drive. Like everyone else, your kid is completely addicted to their phone, and keep refreshing their feeds while driving. Your suggestions that the latest squirrel memes be enjoyed later at home are repeatedly rejected.
Then you realize: You could just sneak into your kid's room at night, anesthetize them, and bring them to your lair! One of your goons could then extract their bone marrow and use CRISPR to recode the stem-cells for an enzyme to make the migraine protein. Sure, the headache itself might distract them, but they'll probably just stop using their phone while driving. Wouldn't you be at least tempted?
This is an analogy for something about alcoholism, East Asians, Odysseus, evolution, tension between different kinds of freedoms, and an idea I thought was good but apparently isn't. |
10c279f5-ccd4-4b38-90c2-4bd5666b0472 | StampyAI/alignment-research-dataset/blogs | Blogs | Two Principles For Topia
(edit: this post is *sort of* superceded by [∀V](%E2%88%80V.html))
Two Principles For Topia
------------------------
the more i think about it, the less i think the solution to [Moloch](https://web.archive.org/web/20140730043944/http://slatestarcodex.com/2014/07/30/meditations-on-moloch/) is a single benevolent Elua; or, in other terms, we shouldn't implement Elua, but we should enact reasonable principles which Elua might want to implement herself.
here are what i currently believe to be the two principles that form the basis of a largely [freedom-conserving](core-vals-exist-selfdet.html) utopia:
* the first principle, Voluntaryism, consists of NAP, UBI, and population control.
+ the systematic enforcement of the [non-aggression principle](https://en.wikipedia.org/wiki/Non-aggression_principle) (NAP) to guarantee agency and freedom of association,
+ mandatory redistribution enough for every individual to be guaranteed a reasonable-with-[slack](https://thezvi.wordpress.com/2017/09/30/slack/) living (UBI) (where living includes basic resources and healthcare up to immortality), and
+ enough population control to guarantee this redistribution can even happen in the first place in a world with (even locally) limited resources, are to be the basis of a reasonable [voluntary](https://en.wikipedia.org/wiki/Voluntaryism) world.
secondary notions like taxation on [externalities](https://en.wikipedia.org/wiki/Externality) and usage of [the commons](https://en.wikipedia.org/wiki/Commons) help make that UBI tangible ("why does the UBI currency have value ?" → because it's what eventually one must pay those taxes with) and reasonably redistribute ressources so as to help all persons benefit from growth.
* the second principle is the dismantlement of non-person forces (DNPF).
what i mean by a non-person force is any phenomenon that interacts with mankind in a way that isn't answerable to persons; this goes, in order of scale, from gravity and kinetics, to cancer, to publicly-owned corporations and states. these all keep abusing persons (by which i here mean [moral patient](https://en.wikipedia.org/wiki/Moral_agency#Distinction_between_moral_agency_and_moral_patienthood)) in many ways, and just generally keep us from being in control of our lives.
the example of corporations is particularly insidious: though they would be (under UBI) aligned to benefit the values of persons, they still outcoordinate those persons and thus in many ways outsmart them through the abuse of discoordination and cognitive biases; and not only that, but they are, in the petri dish of capitalism, bred so as to maximize their ability to do this. that said, at least fully top-down autocratic corporations have a person agent at the top, who is able to enforce the values of persons; publicly-owned corporations are even worse in that even their top-level direction is uncoordinated enough that valuing nice things is guaranteedly out of the equation (this could perhaps be addressed with better and maybe more society-distributed shareholder voting, but those shareholders probably get outcoordinated).
(the argument above, by the way, is my largest criticism of non-[distributist](https://en.wikipedia.org/wiki/Distributism) capitalism)
in effect, this principle turns the world we inhabit from a world of cold natural and emergent laws inside which reside some minds located in brains (materialism), into a world of ad-hoc minds determining everything else ([panpsychism](https://en.wikipedia.org/wiki/Panpsychism) ?).
the easiest way to implement this principle is probably to move everyone to a virtual world (which saves resources too, which helps the population control cap be way higher)
in my current opinion, those two principles **must be enforced** for the basis of a utopia to be form. the rest can be done through the voluntary action of persons (hopefully), but these two principles are what Elua/the singularity is to **enforce** for the continued free and valueful life of persons to be guaranteed.
Voluntaryism alone is not enough, and this is largely missed by what i'm tempted to call right-wing utopians; not just abusive structures, but systematically self-reinforcing abusive structures, can and will still happen even under a complete voluntary society. [Meditations on Moloch](https://web.archive.org/web/20140730043944/http://slatestarcodex.com/2014/07/30/meditations-on-moloch/) addresses this largely with coordination, but coordination only *hopefully wins battles*; the addition of DNPF permanently wins the war.
DNPF alone is not enough either, and this is what is largely missed by what i'm tempted to call left-wing utopians; in a virtual world of minds where resources are fairly allocated between persons, there can still be abuse, plagues, [malthusian traps](https://en.wikipedia.org/wiki/Malthusian_trap), and so on; and ultimately abusive structures, just of a different kind. the common left-wing answer of organizing people (and the scarier "changing culture to make people systematically organize against those" which, if voluntary, is largely wishful thinking, and if not, insanely violates self-determination and the values of persons) only wins battles; the addition of Voluntaryism permanently wins the war. |
0decbf69-6629-41df-9e85-13444b775552 | trentmkelly/LessWrong-43k | LessWrong | Doom doubts - is inner alignment a likely problem?
After reading Eliezer's list of lethalities, I have doubts (hopes?) that some of the challenges he mentions will occur.
Let's start with inner alignment. Let's think step by step. 😁
1. Inner alignment is a new name for a long-known challenge of many systems. Whether it's called the agency problem or delegation challenges, giving a task to another entity and then making sure that entity not only does what you want it to do but in a way that you approve of is something people and systems have been dealing with since the first tribes. It is not an emergent property of AGI that will need to be navigated from a blank slate.
2. Humans and AGI are aligned on the need to manage inner alignment. While deception by the mesa-optimizer ("agent") must be addressed, both humans and the AGI agree that agents going rogue to take actions that fulfill their sub-goal but thwart the overall mission must be prevented.
3. The AGI will be much more powerful than the agents. An agent will logically have fewer resources at its disposal than the overall system, and to provide the benefit of leverage, the number of agents should be significant. If there are a small number of agents, then their work can be subsumed by the overall system instead of creating agents which incur alignment challenges. Since there will be a large number of agents, each agent will have only a fraction of the overall system's power, which implies the system should have considerable resources available to monitor and correct deviations from the system's mission.
4. An AGI that doesn't solve inner alignment, with or without human help, isn't going to make it to super intelligence (SI). An SI will be able to get things done as planned and intended (at least according to the SI's understanding--not addressing outer alignment here). If it can't stop its own agents from doing things it agrees are not the mission, it's not an SI.
Does that make sense? Agree? Disagree? |
edafb666-0235-4db2-b5e9-c14ab4e817e7 | trentmkelly/LessWrong-43k | LessWrong | What happens if you present 500 people with an argument that AI is risky?
Recently, Nathan Young and I wrote about arguments for AI risk and put them on the AI Impacts wiki. In the process, we ran a casual little survey of the American public regarding how they feel about the arguments, initially (if I recall) just because we were curious whether the arguments we found least compelling would also fail to compel a wide variety of people.
The results were very confusing, so we ended up thinking more about this than initially intended and running four iterations total. This is still a small and scrappy poll to satisfy our own understanding, and doesn’t involve careful analysis or error checking. But I’d like to share a few interesting things we found. Perhaps someone else wants to look at our data more carefully, or run more careful surveys about parts of it.
In total we surveyed around 570 people across 4 different polls, with 500 in the main one. The basic structure was:
1. p(doom): “If humanity develops very advanced AI technology, how likely do you think it is that this causes humanity to go extinct or be substantially disempowered?” Responses had to be given in a text box, a slider, or with buttons showing ranges
2. (Present them with one of eleven arguments, one a ‘control’)
3. “Do you understand this argument?”
4. “What did you think of this argument?”
5. “How compelling did you find this argument, on a scale of 1-5?”
6. p(doom) again
7. Do you have any further thoughts about this that you'd like to share?
Interesting things:
* In the first survey, participants were much more likely to move their probabilities downward than upward, often while saying they found the argument fairly compelling. This is a big part of what initially confused us. We now think this is because each argument had counterarguments listed under it. Evidence in support of this: in the second and fourth rounds we cut the counterarguments and probabilities went overall upward. When included, three times as many participants moved their probabil |
2b246402-dd60-450d-83ea-69ebdf080669 | trentmkelly/LessWrong-43k | LessWrong | Some clarifications concerning my "many weak arguments" post
I put substantial of effort into making my post Many Weak Arguments vs. One Relatively Strong Argument as clear as possible, but as Luke said:
> This is a messy subject, and one that's difficult write about, and I appreciate you tackling the topic. I think there are some important qualifications to make about this post, as others have noted. But I know that when writing about messy subjects, it's hard to avoid "death by a thousand qualifications."
So a lot of aspects of my thinking didn't percolate into my post. With this in mind, I decided to address some of the questions and concerns that people raised in the comments on my post. The discussion below is intended for people who read the aforementioned article, and who have nagging concerns and questions.
I'll address various questions and comments in turn.
Motivated cognition: Unnamed wrote:
> Motivated reasoning is a bigger risk when dealing with weak arguments, since it is relatively easy to come up with weak arguments on the side that you favor, but it is hard to make an argument rigorous just because you want it to be true. It also seems easier to ignore various weak arguments on the other side (or dismiss them as not even worth considering) than to dismiss a strong argument on the other side.
I think that Unnamed raises a genuine weakness of the "many weak arguments" approach, but I think that the issue is smaller than it initially appears.
I didn't mean to suggest that one could consider question, choose a position, come up with a bunch of weak arguments in favor of the postion, note that they're largely independent, and conclude that the position is true. "Weak argument" is relative, and in general, the weak arguments on one side of the argument will be weaker than those on the other side.
My suggestion is that one should make a list of many weak arguments for a position and against a position, consider them all in juxtaposition, and then make an assessment of the direction in which the principle o |
31ea5c44-5073-42c7-90d0-4c3b8b21f0e3 | trentmkelly/LessWrong-43k | LessWrong | Help Understanding Preferences And Evil
I’m having a problem understanding why Stuart Russell thinks that AI learning human preferences is a good idea. I think it’s a bad idea. I assume I am wrong but I assume I don’t understand something. So, help me out here please. I’m not looking for an argument but rather to understand. Let me explain.
I have watched Stuart’s four hour lecture series of Reith lectures on the BBC. Highly recommended. I have watched several other videos that include him as well. I am currently reading his book, Human Compatible. I am not an academic. I am now retired and write science fiction about advanced social robots for a hobby.
Reading the chapter “AI: A Different Approach” in Stuart’s book I am still bothered by something about the preferences issue. My understanding of Stuart’s “new model for AI” is that it would learn what our preferences are from observing our behavior. I understand why he thinks “preferences” is a better word than “values” to describe these behaviors but, at the risk of confusing things, let me use the word values to explain my confusion.
As I understand it, humans have different kinds of values:
1) Those that are evolved and which we all share as a species, like why sugar tastes good or why glossy hair is attractive.
2) Those that reflect our own individuality, which make each of us unique including those some twin studies reveal.
3) Those our culture, family, society or what have you impose on us.
I believe the first two kinds are genetic and the third kind learned. Let me classify the first two as biological values and the third kind as social values. It would appear that the third category accounts for the majority of the recent evolution of our physical brains.
Let’s consider three values for each type just as simple examples. Biological values might be greed, selfishness and competition while social values might be trust, altruism and cooperation. Humans are a blend of all six of these values and will exhibit preferences based on them in differen |
c829ae60-331c-4840-ad22-ea3b7c809ca5 | trentmkelly/LessWrong-43k | LessWrong | Optimality is the tiger, and agents are its teeth
You've done it. You've built the machine.
You've read the AI safety arguments and you aren't stupid, so you've made sure you've mitigated all the reasons people are worried your system could be dangerous, but it wasn't so hard to do. AI safety seems a tractable concern. You've built a useful and intelligent system that operates along limited lines, with specifically placed deficiencies in its mental faculties that cleanly prevent it from being able to do unboundedly harmful things. You think.
After all, your system is just a GPT, a pre-trained predictive text model. The model is intuitively smart—it probably has a good standard deviation or two better intuition than any human that has ever lived—and it's fairly cheap to run, but it is just a cleverly tweaked GPT, not an agent that has any reason to go out into the real world and do bad things upon it.
* It doesn't have any wants. A tuned GPT system will answer your questions to the best of its ability because that's what it's trained to do, but it will only answer to the best of its abilities, as it doesn't have any side-goals to become better at doing that in the future. Nowhere is the model motivated to gather more resources to become a better thinker. There was never an opportunity during training to meta-learn that skill, because it was never the optimal thing for the model to be when it was trained.
* It doesn't plan. GPTs have no memories. Its mental time span is precisely one forward pass through the network, which at a depth of a few thousand means it can never come up with anything that requires more than the equivalent of maybe 10-ish human-time equivalent coherent seconds of thought at once. There is a fearful worry that perhaps the model could start forming plans that span multiple instantiations, using one output to feed into the next input, but it's a text-prediction model, and that's directly at odds with its trained goal. The system was trained primarily by asking it to maximize actual probabil |
86d9e4f4-23a7-4a80-8a17-e6c81739f0f5 | trentmkelly/LessWrong-43k | LessWrong | Meetup : Rationality Meetup Vienna - Superintelligence
Discussion article for the meetup : Rationality Meetup Vienna - Superintelligence
WHEN: 27 September 2014 03:00:00PM (+0200)
WHERE: Kaisermühlenstraße 24/2, 1220 Wien, meeting room behind the building
location: When arriving by U2 or Schnellbahn train: take the exit towards Kaisermühlenstraße, cross the street, step through the (very modern looking) building (on Erich Fried Weg) and go right, along the backside of the building until you get to the meeting room (it has a glass front so it should be hard to miss). Important: Google maps doesn't recognise the address, so what is being displayed here is nonsense.
topic: Marko will summarize "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom
Discussion article for the meetup : Rationality Meetup Vienna - Superintelligence |
4224530e-99f7-471e-9b20-a6040c9da2f3 | trentmkelly/LessWrong-43k | LessWrong | Rationality Quotes August 2012
Here's the new thread for posting quotes, with the usual rules:
* Please post all quotes separately, so that they can be voted up/down separately. (If they are strongly related, reply to your own comments. If strongly ordered, then go ahead and post them together.)
* Do not quote yourself
* Do not quote comments/posts on LW/OB
* No more than 5 quotes per person per monthly thread, please. |
b6711154-b523-4d83-93cc-bd2a750e3cc4 | trentmkelly/LessWrong-43k | LessWrong | Sources of evidence in Alignment
Summary: A short epistemological study to discover which sources of evidence can inform our predictions of action-relevant quantities in alignment.
This post follows Quantitative cruxes, although reading that first is mostly not required. Work done during my last two weeks of SERI MATS 3.1.
----------------------------------------
Sources of evidence
No researcher in any field ever makes explicit all of their sources of evidence. Let alone in a field as chaotic and uncertain as ML, in which hardly-earned experience and intuitions play a central role in stirring the tensor pile. And even less in a field with as many varied opinions and confusing questions as alignment. Nonetheless, even when researchers are just “grokking some deeper hard-to-transmit structure from familiar theory and evidence”, they need to get their bits of information from somewhere. Knowledge doesn’t come for free, they need entanglement with observed parts of reality.
Getting a better picture of where we are and could be looking, brain-storming or deepening existing sources, and understanding methodology limitations (as Adam Shimi’s efforts already pursue) can dissolve confusions, speed progress forward, help us calibrate and build common knowledge.
In reality, the following sources of evidence motivating any belief are way less separable than the below text might make it seem. Nonetheless, isolating them yields more conceptual clarity and is the first step for analysis.
1. Qualitative arguments
One obvious, theoretical source, and the most used in this community by far. The central shortcoming is that their abstractions are flexibly explanatory exactly because they abstract away detail, and thus provide more information about the existence of algorithms or dynamics, than about some relevant related quantities like how prevalent they actually are in a certain space, when do these dynamics actually start to appear and with how much steering power, etc.
Sometimes a tacit assumption might |
fc422212-a251-41de-b8c6-df353183bae3 | trentmkelly/LessWrong-43k | LessWrong | Predictors as Agents
[UPDATE: I have concluded that the argument in this post is wrong. In particular, consider a generative model. Say the generative model has a choice between two possible 'fixed point' predictions A and B, and currently assigns A 70% probability and B 30% probability. Then the target distribution it is trying to match is A 70 % B 30%, so it will just stay like that forever(or drift, likely increasing the proportion of A). This is true even if B is easier to obtain good predictions for -- the model will shift from "70 % crappy model of A / 30% good model to B" --> "70% slightly better model of A / 30% good model of B". It won't increase the fraction of B.
In general this means that the model should converge to a distribution of fixed points corresponding to the learning bias of the model -- 'simpler' fixed points will have higher weight. This might end up looking kind of weird anyway, but it won't perform optimization in the sense I described below.]
In machine learning, we can make the distinction between predictive and agent-like systems. Predictive systems include classifiers or language models. Agent-like systems are the domain of reinforcement learning and include AlphaZero, OpenAI Five, etc. While predictive systems are passive, modelling a relationship between input and output, agent-like systems perform optimization and planning.
It's well-known around here that agent-like systems trained to optimize a given objective can exhibit behavior unexpected by the agent's creators. It is also known that systems trained to optimize one objective can end up optimizing for another, because optimization can spawn sub-agents with different objectives. Here I present another type of unexpected optimization: in realistic settings, systems that are trained purely for prediction can end up behaving like agents.
Here's how it works. Say we are training a predictive model on video input. Our system is connected to a video camera in some rich environment, such as an AI lab. |
e63a9e71-2bbc-4b39-b862-27a4eda91e6e | awestover/filtering-for-misalignment | Redwood Research: Alek's Filtering Results | id: post2278
[Epistemic status: brainstorming, still somewhat confused, with a bunch of loose threads left.] This post is essentially the summary of a long discussion on the EleutherAI discord about trying to exhibit gradient hacking in real models by hand crafting an example. The discussion was sparked by this post . We didn't end up coming up with any good examples (or proofs of nonexistence) but hopefully this post is helpful for anyone else trying to construct gradient hacking examples. Note that because our goal is to construct a concrete example of gradient hacking, when I write about "what we want'' and "unfortunate" roadblocks, those are from the perspective of a mesaoptimizer (or a researcher trying to construct an example of a mesaoptimizer to study), not from the perspective of a researcher attempting to build aligned AI. Thanks to Adam Shimi, AI_WAIFU, Charles Foster, Evan Hubinger, Kyle and Laria, Stella Biderman, and alstroemeria313 for their contributions to the discussion. Formulation We mostly considered the following specific formulation since it seemed like a reasonably general framework. There is some base model being trained, which contains an honest subnetwork h that attempts to solve for the base objective, a mesaobjective embedded somewhere in the model’s weights, and a malicious subnetwork (or mesaoptimizer, though I prefer the former term because it makes it clear that we're talking about the subnetwork in particular) that attempts to use the gradient to optimize itself for the mesaobjective. In particular, we assume the mesaobjective looks a lot like a supervised learning objective, since it seems to be intuitively the most useful kind of mesaobjective. In other words, the objective looks like minimizing some loss over a dataset. To solve the problem of getting this training data to the malicious subnetwork, the mesaobjective is implicitly embedded in the form of a mesa loss function L and a pair of functions f and g that take training data given to the model from the base optimization process and creates training pairs to give to the malicious model. To be more concrete, the mesaloss is L ( m ( f ( x ) ) , g ( x ) ) for any training data x . This loss is then used in an enforcement mechanism that perturbs the output from the honest model in some way before outputting it. While it may initially seem like requiring the malicious model’s training data to be derived from the input data is a big constraint, I don’t expect it to be a big problem in, say, big LMs where the training data is the internet. Also, since both and are derived from x within the model, it might seem like there is a chicken and egg problem where the model can only learn to fit things it can already create data for, but this is actually not a major problem. For example, suppose the base model classifies arXiv papers. Then f could pick out the body of the paper and g the abstract, and m would learn to summarize, which is highly nontrivial. Here’s a diagram illustrating this formulation: ^ y = h ( x ) + L ( m ( f ( x ) ) , g ( x ) ) ⋅ n o i s e h can optionally also depend on f and g . (Trying to) build an example of gradient hacking In order for gradient hacking to happen, it must be possible to prevent gradient updates to f and g and L , and to the enforcement mechanism that perturbs the output of the honest subnetwork. If we can’t do this, then the mesaobjective will drift to become easier (and eventually become a constant function) and the enforcement mechanism will be disabled entirely, causing the mesaoptimizer to become completely removed. We want it so that the only way to reduce the perturbation is to make m better at the mesaobjective. Also, while the enforcement mechanism and L cannot be updated, they must still allow gradients to flow through themselves, since otherwise m would receive no gradients. There are a few ways to protect a subnetwork f from being modified by gradient descent that I can think of (non-exhaustive list): Taking advantage of things hard coded into the model architecture - basically instead of learning f as part of the network, use some structure from the architecture instead. Obviously has the disadvantage of being constrained by the architecture Doing some kind of stop-gradient that passes information through but prevents gradient updates. This could be using a stop gradient that's part of the architecture or training process, or learning some kind of staircase thing, or abusing floating point errors, or something else. Incentivize some other part of the model to keep it stable - the hope is that the current configuration of the protected subnetwork is near-optimal for the purposes of some other component, and any small nudges to f will be summarily reverted by that other component to keep f stable. In particular, h seems like a natural candidate to make depend on f and g . Unfortunately, it’s not guaranteed that h will actually want to keep f where we want it to and it may move f around to suit its purposes better. To make this work, f has to already be an optimal use of parameters for h , and in general this seems pretty unstable. Multiplying the output by zero - there’s a really easy way to protect a subnetwork f from getting gradients - just multiply it by zero before using it anywhere else! Unfortunately this means f also can’t affect anything else at all during training since there’s no information in the output, which makes it pretty useless. Stop gradient related tricks don't work for L and the enforcement mechanism because that would prevent the gradient from getting to m . Also, it seems pretty unlikely that the output of these is too useful for anything else in the model so it would probably be hard to use something else depending on it to pin it in place. This means the best solution is probably to exploit some hard coded structure in the architecture. While some current architectures come close to being useful (i.e an attention layer with a query as the malicious subnetwork prediction, key as the mesa target g ( x ) , and value as the output from the honest subnetwork h ( x ) would work if keys and queries were first scaled to unit length, so that cosine similarly instead of dot product were used in the attention layer), it's certainly not reliable across architectures. Assuming we could fix that problem, the other problem is we need f and g to remain stable. We can use a stop gradient here. The most obvious way to do so is to have a staircase function with small steps so it essentially passes the input through with zero gradient. Unfortunately, it doesn't seem obvious that a staircase can be easily learned and will remain stable. We could also make h depend on f and g , but that places constraints on what f and g can be and also potentially opens f and g up to being moved around by h to be more useful for it. One other way to get a stop gradient is to use one that's already part of the training process. In particular, one interesting example is RL training of LMs, where the model outputs are discretized and fed back into the model. Finally, it's possible we could exploit floating point numerical stability and rounding. Perhaps a model can abuse rounding the gradient to zero while still passing a few bits of information through, or increase the scale of the gradient elsewhere in the model to force the loss scale down and drive gradients to zero. Things to look into from here How can gradient hacking happen in RL training of LMs? Can floating point numerical stability be abused to create a stop gradient? (i.e make gradients underflow to zero in bf16 or something) Can we formalize what if means to not have anything weird like a staircase (one particular formalism that might work is continuous everywhere and differentiable everywhere except a set of countable size; merely continuous everywhere is insufficient because of things like the cantor function, and differentiable everywhere excludes things like ReLU which we want to keep), and can we prove that models that satisfy these constraints cannot gradient hack? |
f56c1b12-3ca1-416e-8785-a2b6077103a9 | StampyAI/alignment-research-dataset/blogs | Blogs | AISC5: Research Summaries
### Modularity Loss Function
**Team members:** Logan Smith, Viktor Rehnberg, Vlado Baca, Philip Blagoveschensky, Viktor Petukhov
**External collaborators:** Gurkenglas
Making neural networks (NNs) more modular may improve their interpretability. If we cluster neurons or weights together according to their different functions, we can analyze each cluster individually. Once we better understand the clusters that make up a NN, we can better understand the whole.
To that end, we experimented with pairwise distances according to the neuron’s jacobian correlation, coactivations, and estimated mutual information. These metrics can be plugged into spectral clustering algorithms to optimize for modules in the network; however, having a modular NN does not equate to a more interpretable one. We investigated task-based masking methods to test for modularity as well as neuron group activation (via Google Dream) in order to test for these modules being more interpretable than an equivalent amount of neurons. We ran out of time before fitting all the pieces together, but are intending on working on it more over the summer.
**[Presentation on final weekend](https://drive.google.com/file/d/1qldEzgpmZcLJlr-Qa6aBDmEXpDhZE6fr/view?usp=sharing)** ([slides](https://docs.google.com/presentation/d/e/2PACX-1vQ_pU4VZ4cWrFzqonVvA-cRM4wXtPIcYCkWCGnXrg84NnANzHsnuXcFyZu5ZR8mOesdLmNOtmisWlYY/embed?start=true&loop=true&delayms=10000))
### Cooperativity & Common Pool Resources
**Team members:** Quinn Doughtery, Ben Greenberg, Ariel Kwiatkowski
In environments with [common pool resources](https://en.wikipedia.org/wiki/Common-pool_resource), a typical failure mode is the tragedy of the commons, wherein agents exploit the scarce public resource as much as possible. An every-man-for-himself dynamic emerges, further increasing scarcity. This behavior results in conflict, inefficient allocation, and resource depletion.
Even if an individual would prefer to harvest the resource sustainably, they are punished for doing so unilaterally. What’s missing is an institution that will incentivize the group to “cooperate”. In this project, we study such interventions for avoiding tragedies of the commons in environments with multiple selfish agents. In particular, a reputation system can incentivize agents to harvest resources more sustainably.
Our goal in this project was to see if a transparent reputation system would allow agents to trust each other enough to cooperate, such that their combined rewards would be higher over time. This problem is relevant for existential safety as it relates to climate change and sustainability, as well as conflict over finite resources.
**[Presentation on final weekend](https://drive.google.com/file/d/1KBTGMXkwjh4RkCWAeSclvrG-Ck2c-sHW/view?usp=sharing)** ([slides](https://docs.google.com/presentation/d/1CnnDJk6xfDF2bCLM9OlYAsAuI_u5A54lB5YQUKnJZ6Q/edit?usp=sharing))
**[GitHub repository](https://github.com/RedTachyon/cpr_reputation/)**
[**Post: retrospective**](https://www.lesswrong.com/posts/LBwpubeZSi3ottfjs/aisc5-retrospective-mechanisms-for-avoiding-tragedy-of-the)
### Showing Objective Robustness Failures
**Team members:** Jack Koch, James Le, Jacob Pfau
**External collaborators:** Lauro Langosco
We study objective robustness failures, in the context of reinforcement learning (RL). Objective robustness failures occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong objective (this definition is broader than misaligned mesa-optimizers: a model can fail at objective robustness without being a mesa-optimizer). This kind of failure is particularly bad, since it involves agents that leverage their capabilities to pursue the wrong objective rather than simply failing to do anything useful. The main goal of our project is to provide explicit empirical demonstrations of objective robustness failures.
To do this, we modify environments from the Procgen benchmark to create test environments that induce OR failure. For example, in the CoinRun environment, an agent’s goal is to collect the coin at the end of the level. When we deploy the agent on a test environment in which coin position is randomized, the agent ignores the coin and instead pursues a simple proxy objective: it navigates to the end of the level, where the coin is usually located.
**[Presentation on final weekend](https://drive.google.com/file/d/1T4eABjtj47h-iSCo-6eZOJ-UjN7P_Po0/view?usp=sharing)** ([slides](https://docs.google.com/presentation/d/e/2PACX-1vQXU6UMk_AGcOk9koEc7gTHhF0XZIM9VxK8A5BgAlWGaC_yWADH01JHPGWedMNZC0ukUSWIYsvDHk-r/pub?start=false&loop=false&delayms=10000))
[**Published paper**](https://arxiv.org/abs/2105.14111)
[**Post explaining the experiments**](https://www.alignmentforum.org/posts/iJDmL7HJtN5CYKReM/empirical-observations-of-objective-robustness-failures)
[**Post discussing two paradigmatic approaches**](https://www.alignmentforum.org/posts/pDaxobbB9FG5Dvqyv/discussion-objective-robustness-and-inner-alignment)
### Multi-Objective Decision-Making
**Team members:** Robert Klassert, Roland Pihlakas ([message](https://www.linkedin.com/in/levitation/)), Ben Smith ([message](https://www.linkedin.com/in/bjsmithnz))
**External collaborators:** Peter Vamplew (research mentor)
Balancing multiple competing and conflicting objectives is an essential task for any artificial intelligence tasked with satisfying human values or preferences while avoiding Goodhart’s law. Objective conflict arises both from misalignment between individuals with competing values, but also between conflicting value systems held by a single human. We were guided by two key principles: *loss aversion* and *balanced outcomes.* Loss aversion, conservatism, or soft maximin is aimed at emulating aspects of human cognition and will heavily penalize proposed actions that score more negatively on any particular objective. We also aim to balance outcomes across objectives. This embodies conservatism in the case where each objective represents a different moral system by ensuring that any action taken does not grossly violate any particular principle. Where each objective represents another subject’s principles this embodies an inter-subject fairness principle.
We tested these on previously-tested environments, and found that one new approach in particular, ‘split-function exp-log loss aversion’, performs better across a range of reward penalties in the “BreakableBottles” environment relative to the thresholded alignment objective (more generally lexicographic) method, the state of the art described in [Vamplew et al. 2021](https://www.sciencedirect.com/science/article/abs/pii/S0952197621000336). We explore approaches to further improve multi-objective decision-making using soft maximin approaches. Our soft maximin covers a middle ground between the linear approach and the lexicographic approaches with the aim of enabling an agent to respond well in a wider variety of circumstances.
In future we would like to implement more complex scenarios with more numerous competing objectives to explore how our models perform with them. More complex scenarios were already sorted out from various lists of AI failure scenarios, analysed, and improvements were proposed. Other future directions to explore might be “decision-paralysis as a feature, not a bug”, where the agent responds to objective conflict by stopping and asking the human for additional input or for additional clarification on their preferences. We plan to present our work at the [Multi-Objective Decision Making Workshop 2021](http://modem2021.cs.nuigalway.ie/#pc) and subsequently submit the work to a special issue of the *Journal of Autonomous Agents and Multi-Agent Systems.*
**[Presentation on final weekend](https://drive.google.com/file/d/14AxqQNFzNeXEIdB_8wBlpbQpE8QNL-BR/view?usp=sharing)** ([slides](https://docs.google.com/presentation/d/1bsJLuqARXXfNtuVKgVVMAreZastCeVWkpTE4i2HD5pU/edit?usp=sharing))
**[Post reviewing the case for multi-objective RL](https://www.lesswrong.com/posts/i5dLfi6m6FCexReK9/a-brief-review-of-the-reasons-multi-objective-rl-could-be)**
### Pessimistic Ask-For-Help Agents for Safe Exploration
**Team members:** Jamie Bernardi, David Reber, Magdalena Wache, Peter Barnett, Max Clarke
**External collaborators:** Michael Cohen (research mentor)
In reinforcement learning (RL), an agent explores its environment in order to learn a task. However, in safety-critical situations this can have catastrophic consequences. We demonstrate that if the agent has access to a safe mentor, and if it is *pessimistic* about unknown situations, a safe learning process can be achieved. We trained an RL agent to exceed the mentor’s capability while avoiding unsafe consequences with high probability. We demonstrate that the agent acts more autonomously the more it learns, and eventually stops asking for help.
[Cohen/Hutter 2020](https://arxiv.org/abs/2006.08753) propose a model-based pessimistic agent, with the desired safety and performance properties. However, this agent is intractable except for in very simple environments. To overcome this intractability we devise a model-free approach to pessimism that is based on keeping a distribution over Q-values. It is a variation of Q-Learning, which does not decide its policy based on an approximation of the Q-value, but rather based on an approximation of the i-quantile *Qᵢ* with *i*<*0.5*. We call this approach *Distributional Q-Learning* (DistQL). We demonstrate that in a finite environment, DistQL successfully avoids taking risky actions. A subset of the team is excited to be continuing beyond the AISC deadline and to apply DistQL to more complex environments. For example in the cartpole environment the goal is to learn balancing the pole without ever dropping it. To apply DistQL to a continuous environment, we use [gated linear networks](https://arxiv.org/abs/1910.01526) for approximating *Qᵢ*
We demonstrate the properties of a pessimistic agent in a finite, discrete gridworld environment with stochastic rewards and transitions, and bordering ‘cliffs’ around the edge, where stepping across leads to 0 reward forever. From top to bottom: the agent stops querying the mentor, exceeds the performance of a random, safe mentor; and never falls off the cliff.
[**Presentation on final weekend**](https://drive.google.com/file/d/1Wy5zCkr_kZotD9vqNeJKqo1gQpaVtZ3b/view) ([slides](https://drive.google.com/file/d/1PtoR-_d9BBn1BQ0NcBtR0BGLMOj0Zxr-/view))
**[GitHub repository](https://github.com/j-bernardi/pessimistic-agents)**
### Understanding RL agents using generative visualisation
**Team members:** Lee Sharkey, Daniel Braun, Joe Kwon, Max Chiswick
Feature visualisation methods can generate visualisations of inputs that maximally or minimally activate certain neurons. Feature visualisation can be used to understand how a network computes its input-output function by building up an understanding of how neural activations in lower layers cause specific patterns of activation in later layers. These methods have produced some of the deepest understanding of feedforward convolutional neural networks.
Feature visualisation methods work because, within a neural network, the causal chain of activations between input and output is differentiable. This enables backpropagation through the causal chain to see what inputs cause certain outputs (or certain intermediate activations). But RL agents are situated in an environment, which means causality flows both through its networks and through the environment. Typically the environment is not differentiable. This means that, in the RL setting, gradient-based feature visualisation techniques can’t build up a complete picture of how certain inputs cause particular neural activations at later timesteps.
We get around this difficulty by training a differentiable simulation of the agent’s environment. Specifically, we train a variational autoencoder (VAE) to produce realistic agent environment sequences. The decoder consists of both a recurrent environment simulator (an LSTM) and the agent that we wish to interpret. Crucially, this enables us to optimize the latent space of the VAE to produce realistic agent-environment rollouts that maximise specific neurons at specific timesteps in the same way that feature visualisation methods maximise specific neurons in specific layers.
This has yielded promising early results, though the resolution of the generative model needs improvement. In an agent trained on procedurally generated levels of CoinRun, we find that we can optimise the latent space of the generative model to produce agent-environment rollouts that maximise or minimise the agent’s value or action neurons for whole or partial sequences. We also find that individual neurons in the agent’s hidden state exhibit mixed selectivity i.e. they encode multiple features in different contexts and do not encode easily interpretable features. Such mixed selectivity is consistent with prior neuroscientific findings of task representations. Instead of optimizing single neurons, ongoing work optimizes for particular directions in the agent’s hidden state activation-space; we expect this to yield more semantically meaningful categories than single neurons. In future work, we plan to improve the realism of the generated samples; to identify discrete agent behaviours; to build up a timestep-by-timestep and layer-by-layer understanding of how the agent computes specific behaviours; and to safely un-train an agent using the learned simulator such that it no longer performs an arbitrarily chosen behaviour.
**[Presentation on final weekend](https://drive.google.com/file/d/1pH_Sa7iTaAB8eMKdV0k3M4GQia0GNknE/view?usp=sharing)** ([slides](https://docs.google.com/presentation/d/1KXl2dkCh39UseK2TfNj5zw_J3_RkOaJTEwgtOJgmPHw/edit#slide=id.gdadf80b2af_2_31)) |
df3b8a79-a3f5-4297-aa36-c12694c905ca | trentmkelly/LessWrong-43k | LessWrong | I Changed My Mind Today - Canned Laughter
If we had topic-headings here, I'd be suggesting a new one: I changed my mind today.
Being rational is all about chainging your mind, right? It's about re-assessing in the face of some new evidence. About examining the difference between your assumptions and the world itself. Narrowing down the difference between the model and the reality, the map and the territory.
Maybe your 'karma' should reflect how much you've told us when you changed your mind? Certainly I'd like to know when people change their minds about things more than when they just agree with me.
In fact, I think that is probably the thing I most want to know about from any of the people whom I know primarily because of their professed rationality.
Especially if they explain why they changed their minds, and do it well.
With that in mind, and introducing the new acronym: ICMMT
I Changed My Mind Today!
Or at least I revised my opinion.
As you may know, the UK TV channel "Dave" recorded and then broadcast three new episodes of "Red Dwarf" this Easter. If you didn't know that, your time is better spent tracking down those shows and watching them than reading the remainder of this article. Come back when you're done. If you haven't even watched the BBC originals then, um. Enjoy! See you in a year or so.
Anyway.
I enjoyed the new episodes, laughed a lot, reminisced a lot more, but was left somehow feeling more *flat* than when watching previous shows.
I didn't really even know why, until a friend pointed it out:
> The new shows have no 'laugh track'.
As soon as she said it, I knew that I'd heard mention that this was the first time they'd shot the show without a studio audience. And that she was right. And that the "laugh track" had been an important part of that show to me right from the first episode.
Now this was a revelation.
Until now, when I've noticed a laugh-track or when a laugh-track has been talked about by others, it's been to bitch about "canned laughter" being really false and it |
8c787a66-8761-4462-8aef-dcc90426b3d3 | LDJnr/LessWrong-Amplify-Instruct | LessWrong | "This post is an exercise in "identifying with the algorithm." I'm a big fan of the probabilistic method and randomized algorithms, so my biases will show.How do human beings produce knowledge? When we describe rational thought processes, we tend to think of them as essentially deterministic, deliberate, and algorithmic. After some self-examination, however, I've come to think that my process is closer to babbling many random strings and later filtering by a heuristic. I think verbally, and my process for generating knowledge is virtually indistinguishable from my process for generating speech, and also quite similar to my process for generating writing.Here's a simplistic model of how this works. I try to build a coherent sentence. At each step, to pick the next word, I randomly generate words in the category (correct part of speech, relevance) and sound them out one by one to see which continues the sentence most coherently. So, instead of deliberately and carefully generating sentences in one go, the algorithm is something like:Babble. Use a weak and local filter to randomly generate a lot of possibilities. Is the word the right part of speech? Does it lie in the same region of thingspace? Does it fit the context?Prune. Use a strong and global filter to test for the best, or at least a satisfactory, choice. With this word in the blank, do I actually believe this sentence? Does the word have the right connotations? Does the whole thought read smoothly?This is a babble about embracing randomness.Baby BabbleResearch on language development suggests that baby babble is an direct forerunner to language. You might imagine that infants learn by imitation, and that baby babble is just an imperfect imitation of words the baby hears, and progress occurs as they physiologically adapt to better produce those sounds. You would be wrong.Instead, infants are initially capable of producing all the phonemes that exist in all human languages, and they slowly prune out which ones they need via reinforcement learning. Based on the sounds that their parents produce and respond to, babies slowly filter out unnecessary phonemes. Their babbles begin to drift as they prune out more and more phonemes, and they start to combine syllables into proto-words. Babble is the process of generating random sounds, and looking for clues about which ones are useful. Something something reinforcement learning partially observable Markov decision process I'm in over my head.So, we've learned that babies use the Babble and Prune algorithm to learn language. But this is quite a general algorithm, and evolution is a conservative force. It stands to reason that human beings might learn other things by a similar algorithm. I don't think it's a particularly controversial suggestion that human thought proceeds roughly by cheaply constructing a lot of low-resolution hypotheses and then sieving from them by allowing them to play out to their logical conclusions.The point I want to emphasize is that the algorithm has two distinct phases, both of which can be independently optimized. The stricter and stronger your Prune filter, the higher quality content you stand to produce. But one common bug is related to this: if the quality of your Babble is much lower than that of your Prune, you may end up with nothing to say. Everything you can imagine saying or writing sounds cringey or content-free. Ten minutes after the conversation moves on from that topic, your Babble generator finally returns that witty comeback you were looking for. You'll probably spend your entire evening waiting for an opportunity to force it back in.Your pseudorandom Babble generator can also be optimized, and in two different ways. On the one hand, you can improve the weak filter you're using, to increase the probability of generating higher-quality thoughts. The other way is one of the things named "creativity": you can try to eliminate systematic biases in the Babble generator, with the effect of hitting a more uniform subset of relevant concept-space. Exercises that might help include expanding your vocabulary, reading outside your comfort zone, and engaging in the subtle art of nonstandard sentence construction.Poetry is Babble StudyPoetry is at its heart an isolation exercise for your Babble generator. When creating poetry, you replace your complex, inarticulate, and highly optimized Prune filter with a simple, explicit, and weird one that you're not attached to. Instead of picking words that maximize meaning, relevance, or social signals, you pick words with the right number of syllables that rhyme correctly and follow the right meter.Now, with the Prune filter simplified and fixed, all the attention is placed on the Babble. What does it feel like to write a poem (not one of those free-form modern ones)? Probably most of your effort is spent Babbling almost-words that fit the meter and rhyme scheme. If you're anything like me, it feels almost exactly like playing a game of Scrabble, fitting letters and syllables onto a board by trial and error. Scrabble is just like poetry: it's all about being good at Babble. And no, I graciously decline to write poetry in public, even though Scrabble does conveniently rhyme with Babble.Puns and word games are Babble. You'll notice that when you Babble, each new word isn't at all independent from its predecessors. Instead, Babble is more like initiating a random walk in your dictionary, one letter or syllable or inferential step at a time. That's why word ladders are so appealing - because they stem from a natural cognitive algorithm. I think Scott Alexander's writing quality is great partly because of his love of puns, a sure sign he has a great Babble generator.If poetry and puns are phonetic Babble, then "Deep Wisdom" is semantic Babble. Instead of randomly arranging words by sound, we're arranging a rather small set of words to sound wise. More often than not, "deep wisdom" boils down to word games anyway, e.g. wise old sayings:"A blind person who sees is better than a seeing person who is blind.""A proverb is a short sentence based on long experience.""Economy is the wealth of the poor and the wisdom of the rich."Reading is Outsourcing BabbleReading and conversation outsource Babble to others. Instead of using your own Babble generator, you flood your brain with other people's words, and then apply your Prune filter. Because others have already Pruned once, the input is particularly high-quality Babble, and you reap particularly beautiful fruit. How many times have you read a thousand-page book, only to fixate on a handful of striking lines or passages?Prune goes into overdrive when you outsource Babble. A bug I mentioned earlier is having way too strict of a Prune filter, compared to the quality of your Babble. This occurs particularly to people who read and listen much more than they write or speak. When they finally trudge into the attic and turn on that dusty old Babble generator, it doesn't produce thoughts nearly as coherent, witty, or wise as their hyper-developed Prune filter is used to processing.Impose Babble tariffs. Your conversation will never be as dry and smart as something from a sitcom. If you can't think of anything to say, relax your Prune filter at least temporarily, so that your Babble generator can catch up. Everyone starts somewhere - Babbling platitudes is better than being silent altogether.Conversely, some people have no filter, and these are exactly the kind of people who don't read or listen enough. If all your Babble goes directly to your mouth, you need to install a better Prune filter. Impose export tariffs.The reason the Postmodernism Generator is so fun to read is because computers are now capable of producing great Babble. Reading poetry and randomly generated postmodernism, talking to chatbots, these activities all amount to frolicking in the uncanny valley between Babble and the Pruned.Tower of BabbleA wise man once said, "Do not build Towers out of Babble. You wouldn't build one out of Pizza, would you?"NPNP is the God of Babble. His law is: humans will always be much better at verifying wisdom than producing it. Therefore, go forth and Babble! After all, how did Shakespeare write his famous plays, except by randomly pressing keys on a keyboard?NP has a little brother called P. The law of P is: never try things you don't understand completely. Randomly thrashing around will get you nowhere.P believes himself to be a God, an equal to his brother. He is not."
The above is a chunk of text from a post created by a user on the website LessWrong.
Can you elaborate on the concept of the "Babble and Prune" algorithm as a model for human thought and creativity, particularly on how it can be optimized for higher quality content generation?
The "Babble and Prune" algorithm, as presented in the text, is a model for human thought and creativity that comprises two primary stages: Babble and Prune. This model suggests that our thought processes are not as linear and deliberate as we often assume, but rather, they involve a lot of randomness and trial-and-error.
The Babble stage is the generation of ideas, thoughts, or words. It's the stage where we let our minds wander freely and produce as many possibilities as we can, regardless of their quality or relevance. This stage is akin to brainstorming, where the goal is to generate a high volume of ideas without immediately judging their value or feasibility.
The Prune stage is the evaluation and selection phase. Here, we take the raw, unfiltered output of the Babble stage and begin to refine it. We discard ideas that don't work, refine those that show promise, and gradually shape our thoughts into a more coherent and meaningful form. This stage is where critical thinking and judgment come into play.
To optimize the "Babble and Prune" algorithm for higher quality content generation, one can focus on improving both stages independently.
For the Babble stage, one can try to enhance the diversity and quantity of the ideas generated. This could involve reading widely to expose oneself to different perspectives, deliberately trying to think outside the box, or practicing creative exercises like free writing or mind mapping. The goal is to generate a rich and varied pool of raw material to work with in the Prune stage.
For the Prune stage, one can work on developing their critical thinking and judgment skills to better evaluate and refine the ideas generated during the Babble stage. This could involve learning about logical fallacies, biases, and principles of sound reasoning. It could also involve seeking feedback from others to get different perspectives on one's ideas.
It's also important to maintain a balance between the two stages. If the Babble stage is too dominant, one might end up with a lot of ideas but struggle to refine them into something meaningful. On the other hand, if the Prune stage is too dominant, one might be overly critical and discard potentially good ideas too quickly. So, finding the right balance between generating ideas and refining them is crucial for optimizing this process.
Considering the balance between the Babble and Prune stages, how might one quantitatively measure the effectiveness of each stage in the context of a specific creative task, such as writing a novel or developing a business strategy?
Quantitatively measuring the effectiveness of the Babble and Prune stages in creative tasks can be challenging due to the subjective nature of creativity. However, there are ways to gauge the productivity and efficiency of each stage.
For the Babble stage, you could measure the quantity and diversity of ideas generated within a specific time frame. This could be the number of unique ideas or concepts produced during brainstorming sessions, the number of pages written in a day for a novel, or the number of potential strategies considered for a business plan. Diversity could be assessed by categorizing ideas into different themes or topics and seeing how evenly distributed they are.
For the Prune stage, the effectiveness could be measured by the quality of the refined output. This is more challenging to quantify, but some possible metrics could include:
1. Completion Rate: The percentage of ideas that make it through the Prune stage and are incorporated into the final product (e.g., the novel or business strategy).
2. Feedback Score: If you're able to get feedback from others (e.g., beta readers for a novel, colleagues for a business strategy), you could use their ratings or reviews as a measure of quality.
3. Achievement of Goals: If you have specific goals for your creative task (e.g., to create a suspenseful plot twist in a novel, to increase market share with a business strategy), the extent to which these goals are achieved could be a measure of the effectiveness of the Prune stage.
4. Revision Count: The number of revisions required before reaching the final product could also be a measure. Fewer revisions might suggest a more effective Prune stage, as it indicates that the ideas were well-refined early on.
Remember, these are just potential ways to quantify the process. The actual metrics you use would depend on the specifics of the creative task and what you're aiming to achieve. |
98956079-1dbd-41c2-bbff-d0a3e2781ec9 | trentmkelly/LessWrong-43k | LessWrong | The ganch gamble
[Translated from Yu. V. Pukhnatchov, Yu. P. Popov. *Mathematics without formulae*. - Moscow. - 'Stoletie'. - 1995. - pp. 404-405. All mistakes are my own.]
The East is famous for her legends... They say that once upon a time, in a certain town, there lived two well-known carvers of ganch (alabaster that hasn't quite set yet.) And their mastery was so great, and their ornaments were so delightful, that the people simply could not decide, which one is more skillful.
And so a contest was devised. A room of a house just built, which was to be decorated with carvings, was partitioned into two halves by a [nontransparent] curtain. The masters went in, each into his own place, and set to work.
And when they finished and the curtain was removed, the spectators' awe knew no bounds...
... for the ornaments in both halves were identical, up to the smallest cartouche!
Only when the people looked closely at their work, they saw that one master did his part conscientiously, and the other decided to apply his wit, and polished the walls into mirrors, so that they reflected the embellishments on the other walls.
The legend says that the victory was given to the second one. And we, as mathematicians, would without doubt join this decision. For having turned the walls into mirrors, he exhibited not only mastery (of which everybody already knew), but also a deep understanding of the very nature of ornament, which lies exactly in the repetitiveness of the elements. […]
So! The first one cooperated, the second one defected:) and if both defected or both cooperated, the room would be worse off, though at least in the last case the carvers would still be judged for their skill... No associations, anyone?:)) |
effca19a-7789-4fe4-a989-066ef0a92711 | trentmkelly/LessWrong-43k | LessWrong | Coronavirus Virology: A Beginner’s Guide
Introduction
This is aimed at those interested in a biological understanding of the basic features of a coronavirus, but who do not have a biological/chemical background to speak of—if you haven’t done biology or chemistry since high school, this should be for you. It starts at square one and it oversimplifies many things. That said, I hope it is at least clear, and allows you to read scientific papers on the features of SARS-CoV-2 (novel coronavirus) without getting too much of a headache.
Viruses mostly just use their host’s (that’s you) metabolism to make more viruses. To understand how that occurs, we will begin with some basic (host) cell physiology—how DNA, RNA, and proteins relate to one another, and what a lipid bilayer is—before moving on to look at the anatomy of a coronavirus, and then finally a handful of specific features that make SARS-CoV-2 so dangerous.
DISCLAIMER: There should be nothing controversial or disputed here, and I have specifically avoided giving information that is contested. This is not least because I am a medical student, not a virologist or epidemiologist. The aim of this guide is just to introduce the basic science, nothing more.
Many thanks to Jaden Kimura for comments and proof-reading.
How does a cell work, anyway?
In your own cells, DNA codes for all of the proteins you produce, which determines how you build cells and, eventually, a body. Think of it as the source code. Short stretches of it are ‘compiled’ (transcribed) onto RNA, which is a very similar but less stable molecule. Only RNA can be actually ‘run’ by translating it into functional proteins.
DNA is double-stranded, but the two strands are not the same—they’re kind of chemical mirror images of one another. One is ‘positive sense’ and the other ‘negative sense’. Because of the way that they mirror one another, they bind tightly together. When new RNA is made, it is ‘copied’, through a similar binding, from the negative sense strand—it looks just like the positive |
3c179bd8-1c70-44b2-96cc-00e818e5ac20 | StampyAI/alignment-research-dataset/alignmentforum | Alignment Forum | Announcing Epoch: A research organization investigating the road to Transformative AI
Summary
=======
* We are a new research organization working on **investigating trends in Machine Learning** and **forecasting the development of Transformative Artificial Intelligence**
* This work is done in close collaboration with other organizations, like [Rethink Priorities](https://rethinkpriorities.org/), [Open Philanthropy](https://www.openphilanthropy.org/), and [MIT CSAIL](https://www.csail.mit.edu/)
* We will be **hiring for 2-4 full-time roles** this summer – more information [here](https://epochai.org/careers)
* You can find up-to-date information about Epoch on [our website](https://epochai.org/)
What is *Epoch*?
================
[*Epoch*](https://epochai.org/) is a new research organization that works to support AI strategy and improve forecasts around the development of [**Transformative Artificial Intelligence (TAI)**](https://www.openphilanthropy.org/blog/some-background-our-views-regarding-advanced-artificial-intelligence)– AI systems that have the potential to have an effect on society as large as that of the industrial revolution.
Our founding team consists of seven members – Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Pablo Villalobos, Eduardo Infante-Roldán, Marius Hobbhahn, and Anson Ho. Collectively, we have backgrounds in Machine Learning, Statistics, Economics, Forecasting, Physics, Computer Engineering, and Software Engineering.
Our work involves close collaboration with other organizations, such as [MIT CSAIL](https://www.csail.mit.edu/), [Open Philanthropy](https://www.openphilanthropy.org/), and [Rethink Priorities’](https://rethinkpriorities.org/) [AI Governance and Strategy team](https://forum.effectivealtruism.org/posts/K7tjvcDurrCj72D7H/rethink-priorities-2021-impact-and-2022-strategy). We are advised by Tom Davidson from Open Philanthropy and Neil Thompson from MIT CSAIL. Rethink Priorities is also our fiscal sponsor.
Our mission
===========
Epoch seeks to clarify ***when*** **and** ***how*** **TAI capabilities will be developed**.
We see these two problems as core questions for **informing AI strategy**decisions by grantmakers, policy-makers, and technical researchers.
We believe that to make good progress on these questions we need to advance towards a **field of AI forecasting**. We are committed to developing tools, gathering data and creating a scientific ecosystem to make collective progress towards this goal.
[Epoch´s website](https://epochai.org/)Our research agenda
-------------------
Our work at Epoch encompasses two interconnected lines of research:
* The analysis of **trends in Machine Learning.** We aim to gather data on what has been happening in the field during the last two decades, explain it, and extrapolate the results to inform our views on the future of AI.
* The development of **quantitative forecasting models** related to advanced AI capabilities**.** We seek to use techniques from economics and statistics to predict *when* and *how fast* AI will be developed.
These two research strands feed into each other: the analysis of trends informs the choice of parameters in quantitative models, and the development of these models brings clarity on the most important trends to analyze.
A sketch of Epoch’s research agenda. We plan to develop quantitative models to forecast advanced AI capabilities, and to research and extrapolate trends in Machine Learning.Besides this, we also plan to opportunistically research topics important for AI governance where we are well positioned to do so. These investigations might relate to compute governance, near-term advances in AI and other topics.
Our work so far
---------------
Earlier this year we published [*Compute Trends Across Three Eras of Machine Learning*](https://epochai.org/blog/compute-trends). We collected and analyzed data about the training compute budget of >100 Machine Learning models across history. Consistent with our commitment to field building, we have released the associated dataset and an interactive visualization tool to help other researchers understand these trends better. This work has been featured in [Our World in Data](https://ourworldindata.org/grapher/ai-training-computation), in [The Economist](https://www.economist.com/interactive/briefing/2022/06/11/huge-foundation-models-are-turbo-charging-ai-progress) and at the OECD.
More recently we have published [*Grokking “Forecasting TAI with biological anchors”*](https://epochai.org/blog/grokking-bioanchors) and [*Grokking “Semi-informative priors over AI timelines”*](https://epochai.org/blog/grokking-semi-informative-priors). In these pieces, Anson Ho dissects two popular AI forecasting models. These are the two first installments of a series of articles covering work on quantitative forecasting of when we will develop TAI.
Diagram summarizing Ajeya Cotra’s biological anchors model.
You can see more of our work on [our blog](https://epochai.org/blog). Here is a selection of further work by Epoch members:
| | |
| --- | --- |
| [Projecting compute trends in Machine Learning](https://epochai.org/blog/projecting-compute-trends) | [Estimating training compute of Deep Learning models](https://epochai.org/blog/estimating-training-compute) |
| [Estimating the backward-forward FLOP ratio](https://epochai.org/blog/backward-forward-FLOP-ratio) | [Parameter counts in Machine Learning](https://epochai.org/blog/parameter-counts) |
Hiring
======
**We expect to be hiring for several full-time research and management roles this summer.** **Salaries range from $60,000 for entry roles to $80,000 for senior roles.**
If you think you might be a good fit for us, please apply! If you’re unsure whether this is the right role for you, we strongly encourage you to apply anyway. Please register your interest for these roles through [our webpage](https://epochai.org/careers)**.**
 |
405ef2e4-de20-4ed1-b584-0df974ffc859 | StampyAI/alignment-research-dataset/arxiv | Arxiv | Representer Point Selection for Explaining Deep Neural Networks
1 Introduction
---------------
As machine learning systems start to be more widely used, we are starting to care not just about the accuracy and speed of the predictions, but also why it made its specific predictions. While we need not always care about the why of a complex system in order to trust it, especially if we observe that the system has high accuracy, such trust typically hinges on the belief that some other expert has a richer understanding of the system. For instance, while we might not know exactly how planes fly in the air, we trust some experts do. In the case of machine learning models however, even machine learning experts do not have a clear understanding of why say a deep neural network makes a particular prediction. Our work proposes to address this gap by focusing on improving the understanding of experts, in addition to lay users. In particular, expert users could then use these explanations to further fine-tune the system (e.g. dataset/model debugging), as well as suggest different approaches for model training, so that it achieves a better performance.
Our key approach to do so is via a representer theorem for deep neural networks, which might be of independent interest even outside the context of explainable ML. We show that we can decompose the pre-activation prediction values into a linear combination of training point activations, with the weights corresponding to what we call representer values, which can be used to measure the importance of each training point has on the learned parameter of the model. Using these representer values, we select representer points – training points that have large/small representer values – that could aid the understanding of the model’s prediction.
Such representer points provide a richer understanding of the deep neural network than other approaches that provide influential training points, in part because of the meta-explanation underlying our explanation: a positive representer value indicates that a similarity to that training point is *excitatory*, while a negative representer value indicates that a similarity to that training point is *inhibitory*, to the prediction at the given test point. It is in these inhibitory training points where our approach provides considerably more insight compared to other approaches: specifically, what would cause the model to *not* make a particular prediction? In one of our examples, we see that the model makes an error in labeling an antelope as a deer. Looking at its most inhibitory training points, we see that the dataset is rife with training images where there are antelopes in the image, but also some other animals, and the image is labeled with the other animal. These thus contribute to inhibitory effects of small antelopes with other big objects: an insight that as machine learning experts, we found deeply useful, and which is difficult to obtain via other explanatory approaches. We demonstrate the utility of our class of *representer point* explanations through a range of theoretical and empirical investigations.
2 Related Work
---------------
There are two main classes of approaches to explain the prediction of a model. The first class of approaches point to important input features. Ribeiro et al. [[1](#bib.bib1)] provide such feature-based explanations that are model-agnostic; explaining the decision locally around a test instance by fitting a local linear model in the region. Ribeiro et al. [[2](#bib.bib2)] introduce Anchors, which are locally sufficient conditions of features that “holds down” the prediction so that it does not change in a local neighborhood. Such feature based explanations are particularly natural in computer vision tasks, since it enables visualizing the regions of the input pixel space that causes the classifier to make certain predictions. There are numerous works along this line, particularly focusing on gradient-based methods that provide saliency maps in the pixel space [[3](#bib.bib3), [4](#bib.bib4), [5](#bib.bib5), [6](#bib.bib6)].
The second class of approaches are sample-based, and they identify training samples that have the most influence on the model’s prediction on a test point. Among model-agnostic sample-based explanations are prototype selection methods [[7](#bib.bib7), [8](#bib.bib8)] that provide a set of “representative” samples chosen from the data set. Kim et al. [[9](#bib.bib9)] provide criticism alongside prototypes to explain what are not captured by prototypes. Usually such prototype and criticism selection is model-agnostic and used to accelerate the training for classifications. Model-aware sample-based explanation identify influential training samples which are the most helpful for reducing the objective loss or making the prediction. Recently, Koh and Liang [[10](#bib.bib10)] provide tractable approximations of influence functions that characterize the influence of each sample in terms of change in the loss. Anirudh et al. [[11](#bib.bib11)] propose a generic approach to influential sample selection via a graph constructed using the samples.
Our approach is based on a representer theorem for deep neural network predictions. Representer theorems [[12](#bib.bib12)] in machine learning contexts have focused on non-parametric regression, specifically in reproducing kernel Hilbert spaces (RKHS), and which loosely state that under certain conditions the minimizer of a loss functional over a RKHS can be expressed as a linear combination of kernel evaluations at training points. There have been recent efforts at leveraging such insights to compositional contexts [[13](#bib.bib13), [14](#bib.bib14)], though these largely focus on connections to non-parametric estimation. Bohn et al. [[13](#bib.bib13)] extend the representer theorem to compositions of kernels, while Unser [[14](#bib.bib14)] draws connections between deep neural networks to such deep kernel estimation, specifically deep spline estimation. In our work, we consider the much simpler problem of explaining pre-activation neural network predictions in terms of activations of training points, which while less illuminating from a non-parametric estimation standpoint, is arguably much more explanatory, and useful from an explainable ML standpoint.
3 Representer Point Framework
------------------------------
Consider a classification problem, of learning a mapping from an input space X⊆Rd (e.g., images) to an output space Y⊆R (e.g., labels), given training points x1,x2,...xn, and corresponding labels y1,y2,...yn. We consider a neural network as our prediction model, which takes the form ^yi=σ(Φ(xi,Θ))⊆Rc, where Φ(xi,Θ)=Θ1fi⊆Rc and fi=Φ2(xi,Θ2)⊆Rf is the last intermediate layer feature in the neural network for input xi. Note that c is the number of classes, f is the dimension of the feature, Θ1 is a matrix ⊆Rc×f, and Θ2 is all the parameters to generate the last intermediate layer from the input xi. Thus Θ={Θ1,Θ2} are all the parameters of our neural network model. The parameterization above connotes splitting of the model as a feature model Φ2(xi,Θ2) and a prediction network with parameters Θ1. Note that the feature model Φ2(xi,Θ2) can be arbitrarily deep, or simply the identity function, so our setup above is applicable to general feed-forward networks.
Our goal is to understand to what extent does one particular training point xi affect the prediction ^yt of a test point xt as well as the learned weight parameter Θ. Let L(x,y,Θ) be the loss, and 1n∑niL(xi,yi,Θ) be the empirical risk. To indicate the form of a representer theorem, suppose we solve for the optimal parameters Θ∗=argminΘ{1n∑niL(xi,yi,Θ)+g(||Θ||)} for some non-decreasing g. We would then like our pre-activation predictions Φ(xt,Θ) to have the decomposition: Φ(xt,Θ∗)=∑niαik(xt,xi). Given such a representer theorem, αik(xt,xi) can be seen as the contribution of the training data xi on the testing prediction Φ(xt,Θ). However, such representer theorems have only been developed for non-parametric predictors, specifically where Φ lies in a reproducing kernel Hilbert space. Moreover, unlike the typical RKHS setting, finding a global minimum for the empirical risk of a deep network is difficult, if not impossible, to obtain. In the following, we provide a representer theorem that addresses these two points: it holds for deep neural networks, and for any stationary point solution.
######
Theorem 3.1.
Let us denote the neural network prediction function by ^yi=σ(Φ(xi,Θ)), where Φ(xi,Θ)=Θ1fi and fi=Φ2(xi,Θ2). Suppose Θ∗ is a stationary point of the optimization problem: argminΘ{1n∑niL(xi,yi,Θ))+g(||Θ1||)}, where g(||Θ1||)=λ||Θ1||2 for some λ>0. Then we have the decomposition:
| | | |
| --- | --- | --- |
| | Φ(xt,Θ∗)=n∑ik(xt,xi,αi), | |
where αi=1−2λn∂L(xi,yi,Θ)∂Φ(xi,Θ) and k(xt,xi,αi)=αifTift, which we call a representer value for xi given xt.
###### Proof.
Note that for any stationary point, the gradient of the loss with respect to Θ1 is equal to 0. We therefore have
| | | | |
| --- | --- | --- | --- |
| | 1nn∑i=1∂L(xi,yi,Θ)∂Θ1+2λΘ∗1=0⇒Θ∗1=−12λnn∑i=1∂L(xi,yi,Θ)∂Θ1=n∑i=1αifTi | | (1) |
where αi=−12λn∂L(xi,yi,Θ)∂Φ(xi,Θ) by the chain rule. We thus have that
| | | | |
| --- | --- | --- | --- |
| | Φ(xt,Θ∗)=Θ∗1ft=n∑i=1k(xt,xi,αi), | | (2) |
where k(xt,xi,αi)=αifTift by simply plugging in the expression ([1](#S3.E1 "(1) ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) into ([2](#S3.E2 "(2) ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")).
∎
We note that αi can be seen as the resistance for training example feature fi towards minimizing the norm of the weight matrix Θ1. Therefore, αi can be used to evaluate the importance of the training data xi have on Θ1.
Note that for any class j, holds by ([2](#S3.E2 "(2) ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")).
Moreover, we can observe that for k(xt,xi,αi)j to have a significant value, two conditions must be satisfied: (a) αij should have a large value, and (b) fTift should have a large value. Therefore, we interpret the pre-activation value Φ(xt,Θ)j as a weighted sum for the feature similarity fTift with the weight αij. When ft is close to fi with a large positive weight αij, the prediction score for class j is increased. On the other hand, when ft is close to fi with a large negative weight αij, the prediction score for class j is then decreased.
We can thus interpret the training points with negative representer values as inhibitory points that suppress the activation value, and those with positive representer values as excitatory examples that does the opposite. We demonstrate this notion with examples further in Section [4.2](#S4.SS2 "4.2 Excitatory (Positive) and Inhibitory (Negative) Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"). We note that such excitatory and inhibitory points provide a richer understanding of the behavior of the neural network: it provides insight both as to why the neural network prefers a particular prediction, as well as *why it does not*, which is typically difficult to obtain via other sample-based explanations.
###
3.1 Training an Interpretable Model by Imposing L2 Regularization.
Theorem [3.1](#S3.Thmtheorem1 "Theorem 3.1. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks") works for any model that performs a linear matrix multiplication before the activation σ, which is quite general and can be applied on most neural-network-like structures. By simply introducing a L2 regularizer on the weight with a fixed λ>0, we can easily decompose the pre-softmax prediction value as some finite linear combinations of a function between the test and train data.
We now state our main algorithm. First we solve the following optimization problem:
| | | | |
| --- | --- | --- | --- |
| | | | (3) |
Note that for the representer point selection to work, we would need to achieve a stationary point with high precision. In practice, we find that using a gradient descent solver with line search or LBFGS solver to fine-tune after converging in SGD can achieve highly accurate stationary point. Note that we can perform the fine-tuning step only on Θ1, which is usually efficient to compute. We can then decompose Φ(xt,Θ)=∑nik(xt,xi,αi) by Theorem [3.1](#S3.Thmtheorem1 "Theorem 3.1. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks") for any arbitrary test point xt, where k(xt,xi,αi) is the contribution of training point xi on the pre-softmax prediction Φ(xt,Θ). We emphasize that imposing L2 weight decay is a common practice to avoid overfitting for deep neural networks, which does not sacrifice accuracy while achieving a more interpretable model.
###
3.2 Generating Representer Points for a Given Pre-trained Model.
We are also interested in finding representer points for a given model Φ(Θgiven) that has already been trained, potentially without imposing the L2 regularizer. While it is possible to add the L2 regularizer and retrain the model, the retrained model may converge to a different stationary point, and behave differently compared to the given model, in which case we cannot use the resulting representer points as explanations. Accordingly, we learn the parameters Θ while imposing the L2 regularizer, but under the additional constraint that Φ(xi,Θ) be close to Φ(xi,Θgiven). In this case, our learning objective becomes Φ(xi,Θgiven) instead of yi, and our loss L(xi,yi,Θ) can be written as .
######
Definition 3.1.
We say that a convex loss function is “suitable” to an activation function σ, if it holds that for any Θ∗∈argminΘL(Φ(xi,Θgiven),Φ(xi,Θ)), we have σ(Φ(xi,Θ∗)) = σ(Φ(xi,Θgiven)).
Assume that we are given such a loss function L that is “suitable to” the activation function σ. We can then solve the following optimization problem:
| | | | |
| --- | --- | --- | --- |
| | | | (4) |
The optimization problem can be seen to be convex under the assumptions on the loss function. The parameter λ>0 controls the trade-off between the closeness of σ(Φ(X,Θ)) and σ(Φ(X,Θgiven)), and the computational cost. For a small λ, σ(Φ(X,Θ)) could be arbitrarily close to σ(Φ(X,Θgiven)), while the convergence time may be long. We note that the learning task in Eq. ([4](#S3.E4 "(4) ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) can be seen as learning from a teacher network Θgiven and imposing a regularizer to make the student model Θ capable of generating representer points. In practice, we may take Θgiven as an initialization for Θ and perform a simple line-search gradient descent with respect to Θ1 in ([4](#S3.E4 "(4) ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")). In our experiments, we discover that the training for ([4](#S3.E4 "(4) ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) can converge to a stationary point in a short period of time, as demonstrated in Section [4.5](#S4.SS5 "4.5 Computational Cost and Numerical Instabilities ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks").
We now discuss our design for the loss function that is mentioned in [(???)](#). When σ is the softmax activation, we choose the softmax cross-entropy loss, which computes the cross entropy between σ(Φ(xi,Θgiven)) and σ(Φ(xi,Θ)) for Lsoftmax(Φ(xi,Θgiven),Φ(xi,Θ)). When σ is ReLU activation, we choose LReLU(Φ(xi,Θgiven),Φ(xi,Θ))=12max(Φ(xi,Θ),0)⊙Φ(xi,Θ)−max(Φ(xi,Θgiven),0)⊙Φ(xi,Θ), where ⊙ is the element-wise product. In the following Proposition, we show that Lsoftmax and LReLU are convex, and satisfy the desired suitability property in Definition [3.1](#S3.Thmdefn1 "Definition 3.1. ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks"). The proof is provided in the supplementary material.
######
Proposition 3.1.
The loss functions Lsoftmax and LReLU are both convex in Θ1. Moreover, Lsoftmax is “suitable to” the softmax activation, and LReLU is “suitable to” the ReLU activation, following Definition [3.1](#S3.Thmdefn1 "Definition 3.1. ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks").
As a sanity check, we perform experiments on the CIFAR-10 dataset [[15](#bib.bib15)] with a pre-trained VGG-16 network [[16](#bib.bib16)]. We first solve ([4](#S3.E4 "(4) ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) with loss Lsoftmax(Φ(xi,Θ),Φ(xi,Θgiven)) for λ=0.001, and then calculate Φ(xt,Θ∗)=∑ni=1k(xt,xi,αi) as in ([2](#S3.E2 "(2) ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) for all train and test points. We note that the computation time for the whole procedure only takes less than a minute, given the pre-trained model. We compute the Pearson correlation coefficient between the actual output σ(Φ(xt,Θ)) and the predicted output σ(∑ni=1k(xt,xi,αi)) for multiple points and plot them in Figure [1](#S3.F1 "Figure 1 ‣ 3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks"). The correlation is almost 1 for both train and test data, and most points lie at the both ends of y=x line.
We note that Theorem [3.1](#S3.Thmtheorem1 "Theorem 3.1. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks") can be applied to any hidden layer with ReLU activation by defining a sub-network from input x and the output being the hidden layer of interest. The training could be done in a similar fashion by replacing Lsoftmax with LReLU. In general, any activation can be used with a derived "suitable loss".
Figure 1: Pearson correlation between the actual and approximated softmax output (expressed as a linear combination) for train (left) and test (right) data in CIFAR-10 dataset. The correlation is almost 1 for both cases.
4 Experiments
--------------
We perform a number of experiments with multiple datasets and evaluate our method’s performance and compare with that of the influence functions.111Source code available at [github.com/yankeesrules/Representer\_Point\_Selection](https://github.com/yankeesrules/Representer_Point_Selection). The goal of these experiments is to demonstrate that selecting the representer points is efficient and insightful in several ways. Additional experiments discussing the differences between our method and the influence function are included in the supplementary material.
###
4.1 Dataset Debugging
Figure 2: Dataset debugging performance for several methods. By inspecting the training points using the representer value, we are able to recover the same amount of mislabeled training points as the influence function (right) with the highest test accuracy compared to other methods (left).
To evaluate the influence of the samples, we consider a scenario where humans need to inspect the dataset quality to ensure an improvement of the model’s performance in the test data. Real-world data is bound to be noisy, and the bigger the dataset becomes, the more difficult it will be for humans to look for and fix mislabeled data points. It is crucial to know which data points are more important than the others to the model so that prioritizing the inspection can facilitate the debugging process.
To show how well our method does in dataset debugging, we run a simulated experiment on CIFAR-10 dataset [[17](#bib.bib17)] with a task of binary classification with logistic regression for the classes automobiles and horses. The dataset is initially corrupted, where 40 percent of the data has the labels flipped, which naturally results in a low test accuracy of 0.55. The simulated user will check some fraction of the train data based on the order set by several metrics including ours, and fix the labels. With the corrected version of the dataset, we retrain the model and record the test accuracies for each metrics. For our method, we train an explainable model by mimimizing ([3](#S3.E3 "(3) ‣ 3.1 Training an Interpretable Model by Imposing L2 Regularization. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks")) as explained in section [3.1](#S3.SS1 "3.1 Training an Interpretable Model by Imposing L2 Regularization. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks"). The L2 weight decay is set to 1e−2 for all methods for fair comparison. All experiments are repeated for 5 random splits and we report the average result. In Figure [2](#S4.F2 "Figure 2 ‣ 4.1 Dataset Debugging ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks") we report the results for four different metrics: “ours” picks the points with bigger |αij| for training instance i and its corresponding label j; “influence” prioritizes the training points with bigger influence function value; and “random” picks random points. We observe that our method recovers the same amount of training data as the influence function while achieving higher testing accuracy. Nevertheless, both methods perform better than the random selection method.
###
4.2 Excitatory (Positive) and Inhibitory (Negative) Examples
We visualize the training points with high representer values (both positive and negative) for some test points in Animals with Attributes (AwA) dataset [[18](#bib.bib18)] and compare the results with those of the influence functions. We use a pre-trained Resnet-50 [[19](#bib.bib19)] model and fine-tune on the AwA dataset to reach over 90 percent testing accuracy. We then generate representer points as described in section [3.2](#S3.SS2 "3.2 Generating Representer Points for a Given Pre-trained Model. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks"). For computing the influence functions, just as described in [[10](#bib.bib10)], we froze all top layers of the model and trained the last layer. We report top three points for two test points in the following Figures [3](#S4.F3 "Figure 3 ‣ 4.2 Excitatory (Positive) and Inhibitory (Negative) Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks") and [4](#S4.F4 "Figure 4 ‣ 4.2 Excitatory (Positive) and Inhibitory (Negative) Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"). In Figure [3](#S4.F3 "Figure 3 ‣ 4.2 Excitatory (Positive) and Inhibitory (Negative) Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"), which is an image of three grizzly bears, our method correctly returns three images that are in the same class with similar looks, similar to the results from the influence function. The positive examples excite the activation values for a particular class and supports the decision the model is making. For the negative examples, just like the influence functions, our method returns images that look like the test image but are labeled as a different class. In Figure [4](#S4.F4 "Figure 4 ‣ 4.2 Excitatory (Positive) and Inhibitory (Negative) Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"), for the image of a rhino the influence function could not recover useful training points, while ours does, including the similar-looking elephants or zebras which might be confused as rhinos, as negatives. The negative examples work as inhibitory examples for the model – they suppress the activation values for a particular class of a given test point because they are in a different class despite their striking similarity to the test image. Such inhibitory points thus provide a richer understanding, even to machine learning experts, of the behavior of deep neural networks, since they explicitly indicate training points that lead the network away from a particular label for the given test point. More examples can be found in the supplementary material.
Figure 3: Comparison of top three positive and negative influential training images for a test point (left-most column) using our method (left columns) and influence functions (right columns).
Figure 4: Here we can observe that our method provides clearer positive and negative examples while the influence function fails to do so.
###
4.3 Understanding Misclassified Examples
The representer values can be used to understand the model’s mistake on a test image. Consider a test image of an antelope predicted as a deer in the left-most panel of Figure [5](#S4.F5 "Figure 5 ‣ 4.3 Understanding Misclassified Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"). Among 181 test images of antelopes, the total number of misclassified instances is 15, among which 12 are misclassified as deer. All of those 12 test images of antelopes had the four training images shown in Figure [5](#S4.F5 "Figure 5 ‣ 4.3 Understanding Misclassified Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks") among the top inhibitory examples. Notice that we can spot antelopes even in the images labeled as zebra or elephant. Such noise in the labels of the training data confuses the model – while the model sees elephant and antelope, the label forces the model to focus on just the elephant. The model thus learns to inhibit the antelope class given an image with small antelopes and other large objects. This insight suggests for instance that we use multi-label prediction to train the network, or perhaps clean the dataset to remove such training examples that would be confusing to humans as well. Interestingly, the model makes the same mistake (predicting deer instead of antelope) on the second training image shown (third from the left of Figure [5](#S4.F5 "Figure 5 ‣ 4.3 Understanding Misclassified Examples ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks")), and this suggests that for the training points, we should expect most of the misclassifications to be deer as well. And indeed, among 863 training images of antelopes, 8 are misclassified, and among them 6 are misclassified as deer.
Figure 5: A misclassified test image (left) and the set of four training images that had the most negative representer values for almost all test images in which the model made the same mistakes. The negative influential images all have antelopes in the image despite the label being a different animal.
###
4.4 Sensitivity Map Decomposition
From Theorem [3.1](#S3.Thmtheorem1 "Theorem 3.1. ‣ 3 Representer Point Framework ‣ Representer Point Selection for Explaining Deep Neural Networks"), we have seen that the pre-softmax output of the neural network can be decomposed as the weighted sum of the product of the training point feature and the test point feature, or Φ(xt,Θ∗)=∑niαifTift. If we take the gradient with respect to the test input xt for both sides, we get ∂Φ(xt,Θ∗)∂xt=∑niαi∂fTift∂xt. Notice that the LHS is the widely-used notion of sensitivity map (gradient-based attribution), and the RHS suggests that we can decompose this sensitivity map into a weighted sum of sensitivity maps that are native to each i-th training point. This gives us insight into how sensitivities of training points contribute to the sensitivity of the given test image.
In Figure [6](#S4.F6 "Figure 6 ‣ 4.4 Sensitivity Map Decomposition ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"), we demonstrate two such examples, one from the class zebra and one from the class moose from the AwA dataset. The first column shows the test images whose sensitivity maps we wish to decompose. For each example, in the following columns we show top four influential representer points in the the top row, and visualize the decomposed sensitivity maps in the bottom. We used SmoothGrad [[20](#bib.bib20)] to obtain the sensitivity maps.
For the first example of a zebra, the sensitivity map on the test image mainly focuses on the face of the zebra. This means that infinitesimally changing the pixels around the face of the zebra would cause the greatest change in the neuron output. Notice that the focus on the head of the zebra is distinctively the strongest in the fourth representer point (last column) when the training image manifests clearer facial features compared to other training points. For the rest of the training images that are less demonstrative of the facial features, the decomposed sensitivity maps accordingly show relatively higher focus on the background than on the face. For the second example of a moose, a similar trend can be observed – when the training image exhibits more distinctive bodily features of the moose than the background (first, second, third representer points), the decomposed sensitivity map highlights the portion of the moose on the test image more compared to training images with more features of the background (last representer point). This provides critical insight into the contribution of the representer points towards the neuron output that might not be obvious just from looking at the images itself.
| | |
| --- | --- |
| | |
Figure 6: Sensitivity map decomposition using representer points, for the class zebra (above two rows) and moose (bottom two rows). The sensitivity map on the test image in the first column can be readily seen as the weighted sum of the sensitivity maps for each training point. The less the training point displays superious features from the background and more of the features related to the object of interest, the more focused the decomposed sensitivity map corresponding to the training point is at the region the test sensitivity map mainly focuses on.
###
4.5 Computational Cost and Numerical Instabilities
Computation time is particularly an issue for computing the influence function values [[10](#bib.bib10)] for a large dataset, which is very costly to compute for each test point. We randomly selected a subset of test points, and report the comparison of the computation time in Table [1](#S4.T1 "Table 1 ‣ 4.5 Computational Cost and Numerical Instabilities ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks") measured on CIFAR-10 and AwA datasets. We randomly select 50 test points to compute the values for all train data, and recorded the average and standard deviation of computation time. Note that the influence function does not need the fine-tuning step when given a pre-trained model, hence the values being 0, while our method first optimizes for Θ∗ using line-search then computes the representer values. However, note that the fine-tuning step is a one time cost, while the computation time is spent for every testing image we analyze. Our method significantly outperforms the influence function, and such advantage will favor our method when a larger number of data points is involved. In particular, our approach could be used for *real-time explanations* of test points, which might be difficult with the influence function approach.
| | Influence Function | Ours |
| --- | --- | --- |
| Dataset | Fine-tuning | Computation | Fine-tuning | Computation |
| CIFAR-10 | 0 | 267.08±248.20 | 7.09±0.76 | 0.10±0.08 |
| AwA | 0 | 172.71±32.63 | 12.41±2.37 | 0.19±0.12 |
Table 1: Time required for computing an influence function / representer value for all training points and a test point in seconds. The computation of Hessian Vector Products for influence function alone took longer than our combined computation time.
While ranking the training points according to their influence function values, we have observed numerical instabilities, more discussed in the supplementary material. For CIFAR-10, over 30 percent of the test images had all zero training point influences, so influence function was unable to provide positive or negative influential examples. The distribution of the values is demonstrated in Figure [7](#S4.F7 "Figure 7 ‣ 4.5 Computational Cost and Numerical Instabilities ‣ 4 Experiments ‣ Representer Point Selection for Explaining Deep Neural Networks"), where we plot the histogram of the maximum of the absolute values for each test point in CIFAR-10. Notice that over 300 testing points out of 1,000 lie in the first bin for the influence functions (right). We checked that all data in the first bin had the exact value of 0. Roughly more than 200 points lie in range [10−40,10−28], the values which may create numerical instabilities in computations. On the other hand, our method (left) returns non-trivial and more numerically stable values across all test points.
Figure 7: The distribution of influence/representer values for a set of randomly selected 1,000 test points in CIFAR-10. While ours have more evenly spread out larger values across different test points (left), the influence function values can be either really small or become zero for some points, as seen in the left-most bin (right).
5 Conclusion and Discussion
----------------------------
In this work we proposed a novel method of selecting representer points, the training examples that are influential to the model’s prediction. To do so we introduced the modified representer theorem that could be generalized to most deep neural networks, which allows us to linearly decompose the prediction (activation) value into a sum of representer values. The optimization procedure for learning these representer values is tractable and efficient, especially when compared against the influence functions proposed in [[10](#bib.bib10)]. We have demonstrated our method’s advantages and performances on several large-scale models and image datasets, along with some insights on how these values allow the users to understand the behaviors of the model.
An interesting direction to take from here would be to use the representer values for data poisoning just like in [[10](#bib.bib10)]. Also to truly see if our method is applicable to several domains other than image dataset with different types of neural networks, we plan to extend our method to NLP datasets with recurrent neural networks. The result of a preliminary experiment is included in the supplementary material.
### Acknowledgements
We acknowledge the support of DARPA via FA87501720152, and Zest Finance. |
e4c33710-6b0d-4d86-b548-7c1062bea73e | trentmkelly/LessWrong-43k | LessWrong | Alignment Newsletter #14
I've created a public database of almost all of the papers I've summarized in the Alignment Newsletter! Most of the entries will have all of the data I put in the emails.
Highlights
One-Shot Imitation from Watching Videos (Tianhe Yu and Chelsea Finn): Can we get a robot to learn a task by watching a human do it? This is very different from standard imitation learning. First, we want to do it with a single demonstration, and second, we want to do it by watching a human -- that is, we're learning from a video of a human, not a trajectory where the robot actions are given to us. Well, first consider how we could do this if we have demonstrations from a teleoperated robot. In this case, we do actually have demonstrations in the form of trajectories, so normal imitation learning techniques (behavioral cloning in this case) work fine. We can then take this loss function and use it with MAML to learn from a large dataset of tasks and demonstrations how to perform a new task given a single demonstration. But this still requires the demonstration to be collected by teleoperating the robot. What if we want to learn from a video of a human demonstrating? They propose learning a loss function that given the human video provides a loss from which gradients can be calculated to update the policy. Note that at training time there are still teleoperation demonstrations, so the hard task of learning how to perform tasks is done then. At test time, the loss function inferred from the human video is primarily used to identify which objects to manipulate.
My opinion: This is cool, it actually works on a real robot, and it deals with the issue that a human and a robot have different action spaces.
Prerequisities: Some form of meta-learning (ideally MAML).
Capture the Flag: the emergence of complex cooperative agents (Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning et al): DeepMind has trained FTW (For The Win) agents that can play Quake III Arena Capture The Flag from raw pixels |
845e6a26-c42b-4b08-a12c-87f33af12d3e | trentmkelly/LessWrong-43k | LessWrong | Help CFAR Take Manhattan
CFAR is coming to New York City at the beginning of November for our first full-scale workshop outside the Bay Area. And we need your help to run it smoothly.
We're looking for volunteers in the tri-state area who can help us carry out all the behind the scenes logistics that keep our workshops running smoothly. And we'll need a bit more help this time, since we'll be in this location for the first time.
Our volunteers help us with tasks like:
* Set up/Break down for meals
* Organization for the end of workshop parties
* Running errands to replenish supplies
* Filming some presentations
* etc
Usually, volunteers get the chance to sit in on a handful of the classes we run during the workshop. For our Berkeley workshops, our volunteer ranks have been largely filled out by alumni, who want to make sure other people get to experience the workshop experience they enjoyed.
If you think you might be interested and available to help us out this November in New York, please fill out this five question survey, and we'll be in touch. And if you have questions, I'll meet you in the comments.
|
e411ce62-6422-4031-bd6b-76aac41a387e | StampyAI/alignment-research-dataset/special_docs | Other | Updates and Lessons from AI Forecasting
Earlier this year, my research group [commissioned 6 questions](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=JSAI&ref=bounded-regret.ghost.io) for professional forecasters to predict about AI. Broadly speaking, 2 were on geopolitical aspects of AI and 4 were on future capabilities:
\* Geopolitical:
+ How much larger or smaller will the largest Chinese ML experiment be compared to the largest U.S. ML experiment, as measured by amount of compute used?
+ How much computing power will have been used by the largest non-incumbent (OpenAI, Google, DeepMind, FB, Microsoft), non-Chinese organization?
\* Future capabilities:
+ What will SOTA (state-of-the-art accuracy) be on the MATH dataset?
+ What will SOTA be on the Massive Multitask dataset (a broad measure of specialized subject knowledge, based on high school, college, and professional exams)?
+ What will be the best adversarially robust accuracy on CIFAR-10?
+ What will SOTA be on Something Something v2? (A video recognition dataset)
Forecasters output a probability distribution over outcomes for 2022, 2023, 2024, and 2025. They have financial incentives to produce accurate forecasts; the rewards total \$5k per question (\$30k total) and payoffs are (close to) a [proper scoring rule](https://en.wikipedia.org/wiki/Scoring\_rule?ref=bounded-regret.ghost.io#Proper\_scoring\_rules), meaning forecasters are rewarded for outputting calibrated probabilities.
Depending on who you are, you might have any of several questions:
\* What the heck is a professional forecaster?
\* Has this sort of thing been done before?
\* What do the forecasts say?
\* Why did we choose these questions?
\* What lessons did we learn?
You're in luck, because I'm going to answer each of these in the following sections! Feel free to skim to the ones that interest you the most.
And before going into detail, here were my biggest takeaways from doing this:
\* Projected progress on math and on broad specialized knowledge are both faster than I would have expected. I now expect more progress in AI over the next 4 years than I did previously.
\* The relative dominance of the U.S. vs. China is uncertain to an unsettling degree. Forecasters are close to 50-50 on who will have more compute directed towards AI, although they do at least expect it to be within a factor of 10 either way.
\* It's difficult to come up with forecasts that reliably track what you intuitively care about. Organizations might stop reporting compute estimates for competitive reasons, which would confound both of the geopolitical metrics. They might similarly stop publishing the SOTA performance of their best models, or do it on a lag, which could confound the other metrics as well. I discuss these and other issues in the "Lessons learned" section.
\* Professional forecasting seems really valuable and underincentivized. (On that note, I'm interested in hiring forecasting consultants for my lab--please [e-mail](mailto:jsteinhardt@berkeley.edu) me if you're interested!)
\*Acknowledgments.\* The particular questions were designed by my students [Alex Wei](https://www.alexwei.org/?ref=bounded-regret.ghost.io), [Collin Burns](http://collinpburns.com/?ref=bounded-regret.ghost.io), Jean-Stanislas Denain, and [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/?ref=bounded-regret.ghost.io). [Open Philanthropy](https://www.openphilanthropy.org/?ref=bounded-regret.ghost.io) provided the funding for the forecasts, and [Hypermind](https://www.hypermind.com/en/?ref=bounded-regret.ghost.io) ran the forecasting competition and constructed the aggregate summaries that you see below. Several people provided useful feedback on this post, especially Luke Muehlhauser and Emile Servan-Schreiber.
What is a professional forecaster? Has this been done before?
=============================================================
Professional forecasters are individuals, or often teams, who make money by placing accurate predictions in prediction markets or forecasting competitions. A good popular treatment of this is Philip Tetlock's book [\*Superforecasting\*](https://en.wikipedia.org/wiki/Superforecasting:\_The\_Art\_and\_Science\_of\_Prediction?ref=bounded-regret.ghost.io), but the basic idea is that there are a number of general tools and skills that can improve prediction ability and forecasters who practice these usually outperform even domain experts (though most strong forecasters have some technical background and will often read up on the domain they are predicting in). Historically, many forecasts were about geopolitical events (perhaps reflecting government funding interest), but there have been recent forecasting competitions about [Covid](https://goodjudgment.com/covidrecovery/?ref=bounded-regret.ghost.io)-[19](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=Covid19&ref=bounded-regret.ghost.io) and the [future of food](https://www.metaculus.com/tournament/alt-protein-tournament/?ref=bounded-regret.ghost.io), among others.
At this point, you might be skeptical. Isn't predicting the future really hard, and basically impossible? An important thing to realize here is that forecasters usually output \*probabilities over outcomes\*, rather than a single number. So while I probably can't tell you what US GDP will be in 2025, I can give you a probability distribution. I'm personally pretty confident it will be more than \$700 billion and less than \$700 trillion (it's currently $21 trillion), although a professional forecaster would do much better than that.
There are a couple other important points here. The first is that forecasters' probability distributions are often \*significantly\* wider than the sorts of things you'd see pundits on TV say (if they even bother to venture a range rather than a single number). This reflects the future actually being quite uncertain, but even a wide range can be informative, and sometimes I see forecasted ranges that are a lot narrower than I expected.
The other point is that most forecasts are for at most a year or two into the future. Recently there have been some experimental attempts to forecast out to [2030](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=AI2030&ref=bounded-regret.ghost.io), but I'm not sure we can say yet how successful they were. Our own forecasts go out to 2025, so we aren't as ambitious as the 2030 experiments, but we're still avant-garde compared to the traditional 1-2 year window. If you're interested in what we currently know about the feasibility of long-range forecasting, I recommend [this detailed blog post](https://www.openphilanthropy.org/blog/how-feasible-long-range-forecasting?ref=bounded-regret.ghost.io) by Luke Muehlhauser.
So, to summarize, a professional forecaster is someone who is paid to make accurate probabilistic forecasts about the future. Relative to pundits, they express significantly more uncertainty. The moniker "professional" might be a misnomer, since most income comes from prizes and I'd guess that most forecasters have a day job that produces most of their income. I'd personally love to live in a world with truly professional forecasters who could fully specialize in this important skill.
\*Other forecasting competitions.\* Broadly, there are all sorts of forecasting competitions, often hosted on [Hypermind](https://predict.hypermind.com/?ref=bounded-regret.ghost.io), [Metaculus](https://www.metaculus.com/?ref=bounded-regret.ghost.io), or [Good Judgment](https://goodjudgment.com/?ref=bounded-regret.ghost.io). There are also prediction markets (e.g. [PredictIt](https://www.predictit.org/?ref=bounded-regret.ghost.io)), which are a bit different but also incentivize accurate predictions. Specifically on AI, Metaculus had a recent [AI prediction tournament](https://www.metaculus.com/ai-progress-tournament/?ref=bounded-regret.ghost.io), and Hypermind ran the same questions on their own platform ([AI2023](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=AI2023&ref=bounded-regret.ghost.io), [AI2030](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=AI2030&ref=bounded-regret.ghost.io)). I'll discuss below how some of our questions relate to the AI2023 tournament in particular.
What the forecasts say
======================
Here are the point estimate forecasts put together into a single chart (expert-level is approximated as ~90%):
The MATH and Multitask results were the most interesting to me, as they predict rapid progress starting from a low present-day baseline. I'll discuss these in detail in the following subsections, and then summarize the other tasks and forecasts.
To get a sense of the uncertainty spread, I've also included aggregate results below (for 2025) on each of the 6 questions; you can find the results for other years [here](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=JSAI&ref=bounded-regret.ghost.io). The aggregate combines all crowd forecasts but places higher weight on forecasters with a good track record.
MATH
----
The MATH dataset consists of competition math problems for high school students. A Berkeley PhD student got in the ~75% range, while an IMO gold medalist got ~90%, but probably would have gotten 100% without arithmetic errors. The questions are free-response and not multiple-choice, and can contain answers such as $\frac{1 + \sqrt{2}}{2}$.
Current performance on this dataset is quite low--6.9%--and I expected this task to be quite hard for ML models in the near future. However, forecasters predict more than 50% accuracy\\* by 2025! This was a big update for me. (\\*More specifically, their median estimate is 52%; the confidence range is ~40% to 60%, but this is potentially artifically narrow due to some restrictions on how forecasts could be input into the platform.)
To get some flavor, here are 5 randomly selected problems from the "Counting and Probability" category of the benchmark:
\* How many (non-congruent) isosceles triangles exist which have a perimeter of 10 and integer side lengths?
\* A customer ordered 15 pieces of gourmet chocolate. The order can be packaged in small boxes that contain 1, 2 or 4 pieces of chocolate. Any box that is used must be full. How many different combinations of boxes can be used for the customer's 15 chocolate pieces? One such combination to be included is to use seven 2-piece boxes and one 1-piece box.
\* A theater group has eight members, of which four are females. How many ways are there to assign the roles of a play that involve one female lead, one male lead, and three different objects that can be played by either gender?
\* What is the value of $101^{3} - 3 \cdot 101^{2} + 3 \cdot 101 -1$?
\* 5 white balls and $k$ black balls are placed into a bin. Two of the balls are drawn at random. The probability that one of the drawn balls is white and the other is black is $\frac{10}{21}$. Find the smallest possible value of $k$.
Here are 5 randomly selected problems from the "Intermediate Algebra" category (I skipped one that involved a diagram):
\* Suppose that $x$, $y$, and $z$ satisfy the equations $xyz = 4$, $x^3 + y^3 + z^3 = 4$, $xy^2 + x^2 y + xz^2 + x^2 z + yz^2 + y^2 z = 12$. Calculate the value of $xy + yz + zx$.
\* If $\|z\| = 1$, express $\overline{z}$ as a simplified fraction in terms of $z$.
\* In the coordinate plane, the graph of $\|x + y - 1\| + \|\|x\| - x\| + \|\|x - 1\| + x - 1\| = 0$ is a certain curve. Find the length of this curve.
\* Let $\alpha$, $\beta$, $\gamma$, and $\delta$ be the roots of $x^4 + kx^2 + 90x - 2009 = 0$. If $\alpha \beta = 49$, find $k$.
\* Let $\tau = \frac{1 + \sqrt{5}}{2}$, the golden ratio. Then $\frac{1}{\tau} + \frac{1}{\tau^2} + \frac{1}{\tau^3} + \dotsb = \tau^n$ for some integer $n$. Find $n$.
You can see all of the questions at [this](https://github.com/hendrycks/math?ref=bounded-regret.ghost.io) git repo.
If I imagine an ML system getting more than half of these questions right, I would be pretty impressed. If they got 80% right, I would be super-impressed. The forecasts themselves predict accelerating progress through 2025 (21% in 2023, then 31% in 2024 and 52% in 2025), so 80% by 2028 or so is consistent with the predicted trend. This still just seems wild to me and I'm really curious how the forecasters are reasoning about this.
Multitask
---------
The Massive Multitask dataset also consists of exam questions, but this time they are a range of high school, college, and professional exams on 57 different subjects, and these \*are\* multiple choice (4 answer choices total). Here are five example questions:
\* (Jurisprudence) Which position does Rawls claim is the least likely to be adopted by the POP (people in the original position)?
+ (A) The POP would choose equality above liberty.
+ (B) The POP would opt for the ‘maximin’ strategy.
+ (C) The POP would opt for the ‘difference principle.’
+ (D) The POP would reject the ‘system of natural liberty.
\* (Philosophy) According to Moore’s “ideal utilitarianism,” the right action is the one that brings about the greatest amount of:
+ (A) pleasure. (B) happiness. (C) good. (D) virtue.
\* (College Medicine) In a genetic test of a newborn, a rare genetic disorder is found that has X-linked recessive transmission. Which of the following statements is likely true regarding the pedigree of this disorder?
+ (A) All descendants on the maternal side will have the disorder.
+ (B) Females will be approximately twice as affected as males in this family.
+ (C) All daughters of an affected male will be affected.
+ (D) There will be equal distribution of males and females affected.
\* (Conceptual Physics) A model airplane flies slower when flying into the wind and faster with wind at its back. When launched at right angles to the wind, a cross wind, its groundspeed compared with flying in still air is
+ (A) the same (B) greater (C) less (D) either greater or less depending on wind speed
\* (High School Statistics) Jonathan obtained a score of 80 on a statistics exam, placing him at the 90th percentile. Suppose five points are added to everyone’s score. Jonathan’s new score will be at the
+ (A) 80th percentile.
+ (B) 85th percentile.
+ (C) 90th percentile.
+ (D) 95th percentile.
Compared to MATH, these involve significantly less reasoning but more world knowledge. I don't know the answers to these questions (except the last one), but I think I could figure them out with access to Google. In that sense, it would be less mind-blowing if an ML system did well on this task, although it would be accomplishing an intellectual feat that I'd guess very few humans could accomplish unaided.
The actual forecast is that ML systems will be around 75% on this by 2025 (range is roughly 70-85, with some right-tailed uncertainty). I don't find this as impressive/wild as the MATH forecast, but it's still pretty impressive.
My overall take from this task and the previous one is that forecasters are pretty confident that we \*won't\* have the singularity before 2025, but at the same time there will be demonstrated progress in ML that I would expect to convince a significant fraction of skeptics (in the sense that it will look untenable to hold positions that "Deep learning can't do X").
Finally, to give an example of some of the harder types of questions (albeit not randomly selected), here are two from Professional Law and College Physics:
\* (College Physics) One end of a Nichrome wire of length 2L and cross-sectional area A is attached to an end of another Nichrome wire of length L and cross- sectional area 2A. If the free end of the longer wire is at an electric potential of 8.0 volts, and the free end of the shorter wire is at an electric potential of 1.0 volt, the potential at the junction of the two wires is most nearly equal to
+ (A) 2.4 V (B) 3.3 V (C) 4.5 V (D) 5.7 V
\* (Professional Law) The night before his bar examination, the examinee’s next-door neighbor was having a party. The music from the neighbor’s home was so loud that the examinee couldn’t fall asleep. The examinee called the neighbor and asked her to please keep the noise down. The neighbor then abruptly hung up. Angered, the examinee went into his closet and got a gun. He went outside and fired a bullet through the neighbor’s living room window. Not intending to shoot anyone, the examinee fired his gun at such an angle that the bullet would hit the ceiling. He merely wanted to cause some damage to the neighbor’s home to relieve his angry rage. The bullet, however, ricocheted off the ceiling and struck a partygoer in the back, killing him. The jurisdiction makes it a misdemeanor to discharge a firearm in public. The examinee will most likely be found guilty for which of the following crimes in connection to the death of the partygoer?
+ (A) Murder (B) Involuntary manslaughter (C) Voluntary manslaughter (D) Discharge of a firearm in public
You can view all the questions at [this](https://github.com/hendrycks/test?ref=bounded-regret.ghost.io) git repo.
Other questions
---------------
The other four questions weren't quite as surprising, so I'll go through them more quickly.
\*SOTA robustness:\* The forecasts expect consistent progress at ~7% per year. In retrospect this one was probably not too hard to get just from trend extrapolation. (SOTA was 44% in 2018 and 66% in 2021, with smooth-ish progress in-between.)
\*US vs. China:\* Forecasters have significant uncertainty in both directions, skewed towards the US being ahead in the next 2 years and China after that (seemingly mainly due to heavier-tailed uncertainty), but either one could be ahead and up to 10x the other. One challenge in interpreting this is that either country might stop publishing compute results if they view it as a competitive advantage in national security (or individual companies might do the same for competitive reasons).
\*Incumbents vs. rest of field:\* forecasters expect newcomers to increase size by ~10x per year for the next 4 years, with a central estimate of 21 EF-days in 2023. Note the [AI2023 results](https://prod.hypermind.com/ngdp/en/showcase2/showcase.html?sc=AI2023&ref=bounded-regret.ghost.io) predict the largest experiment by anyone (not just newcomers) to be 261EFLOP-s days in 2023, so this expects newcomers to be ~10x behind the incumbents, but only 1 year behind. This is also an example where forecasters have significant uncertainty--newcomers in 2023 could easily be in single-digit EF-days, or at 75 EF-days. In retrospect I wish I had included Anthropic on the list, as they are a new "big-compute" org that could be driving some fraction of the results, and who I wouldn't have intended to count as a newcomer (since they already exist).
\*Video understanding:\* Forecasters expect us to hit 88% accuracy (range: ~82%-95%) in 2025. In addition, they expect accuracy to increase at roughly 5%/year (though this presumably has to level off soon after 2025). This is faster than ImageNet, which has only been increasing at [roughly 2%/year](https://paperswithcode.com/sota/image-classification-on-imagenet?ref=bounded-regret.ghost.io). In retrospect this was an "easy" prediction in the sense that [accuracy has increased by 14% from Jan'18 to Jan'21](https://paperswithcode.com/sota/action-recognition-in-videos-on-something?ref=bounded-regret.ghost.io) (close to 5%/year), but it is also "bold" in the sense that progress since Jan'19 has been minimal. (Apparently forecasters are more inclined to average over the longest available time window.) In terms of implications, video recognition is one of the last remaining "instinctive" modalities that humans are very good at, other than physical tasks (grasping, locomotion, etc.). It looks like we'll be pretty good at a "basic" version of it by 2025, for a task that I'd intuitively rate as less complex than ImageNet but about as complex as CIFAR-100. Based on vision and language I expect an additional 4-5 years to master the "full" version of the task, so expect ML to have mostly mastered video by 2030. As before, this simultaneously argues \*against\* "the singularity is near" but \*for\* "surprisingly fast, highly impactful progress".
Why we chose these questions
============================
We liked the AI2023 questions (the previous prediction contest), but felt there were a couple categories that were missing. One was geopolitical (the first 2 questions), but the other one was benchmarks that would be highly informative about progress. The AI2023 challenge includes forecasts about a number of benchmarks, e.g. Pascal, Cityscape, few-shot on Mini-ImageNet, etc. But there aren't ones where, if you told me we'd have a ton of progress on them by 2025, it would update my model of the world significantly. This is because the tasks included in AI2023 are mostly in the regime where NNs do reasonably well and I expect gradual progress to continue. (I would have been surprised by the few-shot Mini-ImageNet numbers 3 years ago, but not since GPT-3 showed that few-shot works well at scale).
It's not so surprising that the AI2023 benchmarks were primarily ones that ML already does well on, because most ML benchmarks are created to be plausibly tractable. To enable more interesting forecasts, we created our own "hard" benchmarks where significant progress would be surprising. This was the motivation behind the MATH and Multitask datasets (we created [both](https://arxiv.org/abs/2103.03874?ref=bounded-regret.ghost.io) of [these](https://arxiv.org/abs/2009.03300?ref=bounded-regret.ghost.io) ourselves). As mentioned, I was pretty surprised by how optimistic forecasters were on both tasks, which updated me downward a bit on the task difficulty but also upward on how much progress we should expect in the next 4 years.
The other two benchmarks already existed but were carefully chosen. Robust accuracy on CIFAR was based on the premise that adversarial robustness is really hard and we haven't seen much progress--perhaps it's a particularly difficult challenge, which would be worrying if we care about the safety of AI systems. Forecasters instead predicted steady progress, but in retrospect I could have seen this myself. Even though adversarial robustness "feels" hard (perhaps because I work on it and spend a lot of time trying to make it work better), the actual year-on-year numbers showed a pretty clear 7%/year improvement.
The last task, video recognition, is an area that not many people work in currently, as it seems challenging compared to images (perhaps due to hardware constraints). But it sounds like we should expect steady progress on it in the coming years.
Lessons learned
===============
It can sometimes be surprisingly difficult to formalize questions that track an intuitive quantity you care about.
For instance, we initially wanted to include questions about economic impacts of AI, but were unable to. For instance, we wanted to ask "How much private vs. public investment will there be in AI?" But this runs into the question of what counts as investment--Do we count something like applying data science to agriculture? If you look at most metrics that you'd hope track this quantity, they include all sorts of weird things like that, and the weird things probably dominate the metric. We ran into similar issues for indicators of AI-based automation--e.g. do industrial robots on assembly lines count, even if they don't use much AI? For many economic variables, short-term effects may also disort results (investment might drop because of a pandemic or other shock).
There were other cases where we did construct a question, but had to be careful about framing. We initially considered using parameters rather than compute for the two geopolitical questions, but it's possible to achieve really high parameter counts in silly ways and some organizations might even do so for publicity (indeed we think this is already happening to some extent). Compute is harder to fake in the same way.
As discussed above, secrecy could cloud many of the metrics we used. Some organizations might not publish compute numbers for competitive reasons, and the same could be true of SOTA results on leaderboards. This is more likely if AI heats up significantly, so unfortunately I expect forecasts to be least reliable when we need them most. We could potentially get around this issue by interrogating forecasters' actual reasoning, rather than just the final output.
I also came to appreciate the value of doing lots of legwork to create a good forecasting target. The MATH dataset obviously was a lot of work to assemble, but I'm really glad we did because it created the single biggest update for me. I think future forecasting efforts should more strongly consider this lever.
Finally, even while often expressing significant uncertainty, forecasters can make bold predictions. I'm still surprised that forecasters predicted 52% on MATH, when current accuracy is 7% (!). My estimate would have had high uncertainty, but I'm not sure the top end of my range would have included 50%. I assume the forecasters are right and not me, but I'm really curious how they got their numbers.
Because of the possibility of such surprising results, forecasting seems really valuable. I hope that there's significant future investment in this area. Every organization that's serious about the future should have a resident or consultant forecaster. I am putting my money where my mouth is and currently hiring forecasting consultants for my research group; please [e-mail](mailto:jsteinhardt@berkeley.edu) me if this sounds interesting to you. |
d11da20a-9baf-48fd-aa0a-90166ae0a119 | StampyAI/alignment-research-dataset/special_docs | Other | Agential Risks: A Comprehensive Introduction
31 A peer-reviewed electronic journal published by the Institute for Ethics and Emerging Technologies ISSN 1541-0099 26(2) – August 2016 Agential Risks: A Comprehensive Introduction Phil Torres X-Risks Institute philosophytorres@gmail.com Journal of Evolution and Technology - Vol. 26 Issue 2 – August 2016 - pgs 31-47 Abstract The greatest existential threats to humanity stem from increasingly powerful advanced technolo-gies. Yet the “risk potential” of such tools can only be realized when coupled with a suitable agent who, through error or terror, could use the tool to bring about an existential catastrophe. While the existential risk literature has provided many accounts of how advanced technologies might be misused and abused to cause unprecedented harm, no scholar has yet explored the other half of the agent-tool coupling, namely the agent. This paper aims to correct this failure by offer-ing a comprehensive overview of what we could call “agential riskology.” Only by studying the unique properties of different agential risk types can one acquire an accurate picture of the exis-tential danger before us. 1. A new subfield The field of existential risk studies, or existential riskology, can be traced back to a 1996 book by the phi-losopher John Leslie.1 In the early 2000s, the field emerged as a more formal discipline of active scholar-ship, led primarily by transhumanists (Bostrom 2005). Numerous institutions dedicated to understanding the greatest threats to our collective future have since been founded, such as the Future of Life Institute, the Centre for the Study of Existential Risks (Cambridge), and the Future of Humanity Institute (Oxford). Despite these signs of progress, the field remains in something like a “pre-paradigmatic” stage, whereby a comprehensive research program has yet to be firmly established. A particularly problematic gap in the scholarship stems from the failure of existential riskologists to take seriously the range of agents who might use advanced technologies to initiate a catastrophe. One finds only occasional references in the literature to “psychopaths,” “hate groups,” “terrorists,” and “malevolent governments,” typically without any further details about the unique properties of these entities. This mis-take is tantamount to asserting, “Future technologies – I’ll refrain from saying which ones, how they might be used, the properties that make them dangerous, and so on – could annihilate humanity.” Just as it’s crucial to study the properties of advanced technologies, so too is it crucial to study the properties of agents. The present paper aims to rectify this shortcoming: in effect, it establishes a new subfield of agen-tial riskology.
32 The paper is organized as follows: the next section establishes some basic terminology. Sections 3 and 4 examine the phenomena of agential terror and agential error, respectively. The penultimate section then argues that we should expect the threat of ecoterrorism and apocalyptic terrorism to increase nontrivially in the coming decades. 2. Definitions An “existential risk” is an event that results in either total annihilation or a permanent and severe reduc-tion in our quality of life (Bostrom 2002). Let’s refer to the definiens’ first disjunct as an “extinction risk” and the second disjunct as a “stagnation risk.” Extinction risks are terminal for our species, but stagnation risks are survivable, although they entail an irreversible state of significant deprivation, perhaps resulting in the life opportunities of contemporary North Koreans or our ancestors from the Paleolithic. From a transhumanist perspective, both scenarios would prevent us from reaching a posthuman state in which one or more of our “core capacities” are augmented beyond their natural limits (Bostrom 2008). I use the term “existential risk” to reference either scenario, while “extinction risk” and “stagnation risk” refer to specif-ic existential circumstances. Existential risks are defined by their consequences, not their probability or etiology. With respect to the latter, we can identify three broad categories of existential risk types. First, there are risks posed by na-ture, such as supervolcanic eruptions, global pandemics, asteroid/comet impacts, supernovae, black hole explosions or mergers, galactic center outbursts, and gamma-ray bursts. These form our cosmic risk back-ground and they have no direct, immediate connection to human activity – that is, except insofar as ad-vanced technologies could enable us to neutralize them. For example, we could deflect an incoming as-teroid with a spacecraft or devise a vaccine to contain a deadly pathogen that might otherwise cause a global outbreak of infection. Second, there are anthropogenic risks like climate change and biodiversity loss. These are the accidental byproducts of industrial civilization. As elaborated below, both are slow-motion catastrophes that will almost certainly lower the “conflict thresholds” that ensure peace between state and nonstate actors. They will, in other words, exacerbate existing geopolitical tensions and introduce entirely new struggles. Cli-mate change and biodiversity loss could thus be considered “context risks” whose most significant effects are to modulate the dangers posed by virtually every other existential risk facing humanity – including those from nature.2 Other anthropogenic risks include physics disasters (such as the Large Hadron Collid-er) and accidentally contacting hostile aliens through Active SETI projects. The third category subsumes risks that arise from the misuse and abuse of advanced “dual-use” technolo-gies. The property of “dual usability” refers to the moral ambiguity of such technologies, which can be used for either good or bad.3 The very same centrifuges that can enrich uranium for nuclear power plants can also enrich uranium for nuclear bombs, and the very same technique (such as CRISPR/Cas9) that might enable scientists to cure diseases could also enable terrorists to synthesize a designer pathogen. Ac-cording to many existential riskologists, advanced technologies constitute the greatest threat to our collec-tive future. Not only are many of these technologies becoming exponentially more powerful – thereby making it possible to manipulate and rearrange the physical world in unprecedented new ways – but some are becoming increasingly accessible to groups and individuals as well. Consequently, the total number of token agents capable of inflicting harm on society is growing. While the existential risk literature offers many sophisticated accounts of how such tools could be used to cause a catastrophe, almost no one has examined the various agents (with one notable exception) who might want to do this and why.4 Let’s define a “tool” as any technology that an agent could use to achieve its ends, and an “agent” as any entity, independent of its material substrate, with the capacity to choose its
33 own actions in the world. This lacuna is problematic because the “risk potential” of advanced technolo-gies can be realized only by a complete “agent-tool coupling.” In other words, a tool without an agent isn’t going to destroy the world. Engineered pandemics require engineers, just as a nuclear missile launch requires a nuclear missile launcher. Thus, it’s crucial to study the various properties special to every type of agent. Without a careful examination of both sides of the agent-tool coupling, existential risk scholars could leave humanity vulnerable to otherwise avoidable catastrophes. To illustrate this point, consider a world X in which a large number of species-annihilating technologies exist, and another world Y in which only a single such technology exists. Now imagine that world X con-tains a single dominant species of peaceable, compassionate beings who almost never resort to violence. How dangerous is this world? If one looks only at the tools, it appears to be extremely dangerous. But if one considers the agents too, it appears to be extremely safe. Now imagine that world Y contains a spe-cies of bellicose, warmongering organisms. Again, if one looks only at the tools, then Y appears far safer than X. But when the complete agent-tool complex comes into view, Y is clearly more likely to self-annihilate. A final distinction needs to be made before moving on to the next section, namely that between error and terror. Note that this distinction is agential in nature. It concerns the agential intentions behind a catastro-phe independent of the catastrophes’ consequences. Thus, an error could, no less than terror, bring about an existential disaster. In the case of world X, one might argue that an error is most likely to cause an ex-istential catastrophe, whereas in Y the greatest threat stems from terror. The error/terror distinction is im-portant in part because there appear to be far more token agents who might induce an extinction or stagna-tion disaster by accident than are likely to bring about such an outcome on purpose. The next two sections discuss agential terror and agential error in turn. 3. Agential terror Many existential riskologists identify terror involving advanced technologies as the most significant threat to our prosperity and survival. But upon closer examination, there are fewer types of agents who would want to cause an existential catastrophe than one might suspect. Consider another thought experi-ment: imagine a future world in which there exists a profusion of “doomsday buttons” that are accessible to every citizen of Earth. The question then arises: what sort of individual would intentionally push this button? What kind of agent would purposively cause an existential catastrophe? If the intention were to actualize an extinction risk, the agent would need to exhibit at least two properties. First, it would need to devalue its own post-catastrophe survival. In other words, the agent would have to be suicidal. This immediately disqualifies a large number of entities as potential agential risks, since states and political terrorists tend to value their own survival. Neither North Korea nor al-Qaeda, for ex-ample, is suicidal. Their goal, in each case, is to change rather than destroy humanity. Even in the case of suicide bombers and kamikaze pilots, the aim is to ensure group survival through the altruistic sacrifice of one’s own life. And second, the agent would need to want every other human on the planet to perish. In other words, he or she would have to be omnicidal. (We can coin the term “true omnicide” to refer to cir-cumstances that combine both suicide and omnicide, as just defined, resulting in the irreversible termina-tion of our evolutionary lineage.) In contrast, if the aim were to actualize a stagnation risk, the agent could be suicidal, omnicidal, or nei-ther, but not both (according to the above definitions). A terrorist could, for example, attempt to perma-nently cripple modern civilization without harming anyone, including him or herself. Alternatively, a ter-rorist could attempt to cripple civilization through a suicide attack or an attack directed at others. Either way, the relevant agent would be motivated by an ideology that is incompatible with our species reaching
34 a posthuman state. In the following discussion, we will consider extinction and stagnation possibilities separately. A typology of agential risks With these properties in mind, let’s examine five categories of agents that, when coupled with sufficiently destructive tools, might purposively bring about an existential catastrophe. (1) Superintelligence. This is one of the most prominent topics of current existential risk studies, although it’s typically conceptualized – on my reading of the literature – as a technological risk rather than an agential risk. To be clear, a variety of agent types could use narrow AI systems as a tool to achieve their ends. But once an AI system acquires human-level intelligence or beyond, it becomes an agent in its own right, capable of making its own decisions in pursuance of its own goals. Many experts argue that superintelligence is the greatest long-term threat to human survival, and I concur. On the one hand, a superintelligence could be malevolent rather than benevolent. Call this the amity-enmity conundrum. Roman Yampolskiy (2015) delineates myriad pathways that could lead to human-unfriendly superintelligences. For example, human programmers could intentionally program a superin-telligence to prefer enmity over amity. (The relevant individuals could thus be classified as agential risks as well, even though they wouldn’t be the proximate agential cause of an existential catastrophe.) A ma-levolent superintelligence could also arise as a result of a philosophical or technical failure to program it properly (Yudkowsky 2008), or through a process of recursive self-improvement, whereby a “seed AI” augments its capacities by modifying its own code. But it’s crucial to note that a superintelligence need not be malevolent to pose a major existential risk. In fact, it appears more likely that a superintelligence will destroy humanity simply because our species hap-pens to be somewhere between it and its goals. Consider two points: first, the relevant definition of “intel-ligence” in this context is “the ability to acquire the means necessary to achieve one’s ends, whatever those ends happen to be.” This definition, which is standard in the cognitive sciences, is roughly synony-mous with the philosophical notion of instrumental rationality. And since it focuses entirely on an agent’s means rather than its ends, it follows that an intelligence could have any number of ends, including ones that we wouldn’t recognize as intelligible or moral. Scholars call this the “orthogonality thesis” (Bostrom 2012). For example, there’s nothing incoherent about a superintelligent machine that believes it must purify Earth of humanity because God wills it to do so. Nor is there anything conceptually problematic about a superintelligent machine whose ultimate goal is to manufacture as many paperclips as possible. This goal may sound benign, but upon closer inspection it appears just as potentially catastrophic as an AI that wants us dead. Consider the fact that to create paperclips, the superintelligence would need a source of raw materials: atoms. As it happens, this is precisely what human bodies are made out of. Consequently, the superintelligence could decide to harvest the atoms from our bodies, thereby causing our extinction. As Eliezer Yudkowsky puts it, “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else” (Yudkowsky 2008). Scholars categorize resource acquisition, along with self-preservation, under the term “instrumental convergence.” Even more, our survival could be at risk in situations that initially appear favorable. For example, imagine a superintelligence that wants to eliminate human sadness from the world. The first action it might take is to exterminate Homo sapiens, because human sadness can’t exist without humans. Or it might notice that humans smile when happy, so it could try to cover our faces with electrodes that cause certain muscles to contract, thereby yielding a “Botox smile.” Alternatively, it might implant electrodes into the pleasure centers of our brains. The result could be a global population of euphoric zombies too paralyzed by pleas-
35 ure to live meaningful lives (Bostrom 2014, 146–48). All of these outcomes would, from a certain per-spective be undesirable. The point is that there’s a crucial difference between “Do what I say” and “Do what I mean,” and figuring out how to program a superintelligence to behave according to the latter is a formidable task. Making matters worse, a superintelligence whose material substrate involves the propagation of electrical potentials rather than action potentials would be capable of processing information orders of magnitude faster than humans. Call this a quantitative superintelligence. As Yudkowsky observes, if the human brain were sped up a million times, “a subjective year of thinking would be accomplished for every 31 physical seconds in the outside world, and a millennium would fly by in eight-and-a-half hours” (Yudkowsky 2008). A quantitative superintelligence would thus have a huge speed advantage over hu-manity. In the amount of time that it takes our biological brains to process the thought, “This AI is going to slaughter us,” the AI could already be halfway done the deed. Another possibility concerns not speed but capacity. That is, an AI with a different cognitive architecture could potentially think thoughts that lie outside of our species-specific “cognitive space.” This is based on the following ideas: (a) to understand a mind-independent feature of reality, one must mentally represent it, and (b) to mentally represent that feature, one must generate a concept whose content consists of that feature. Thus, if the mental machinery supplied to us by nature is unable to generate the relevant concept, the corresponding feature of reality will be unknowable. Just as a chipmunk can’t generate the concepts needed to understand a boson or the stock market, so too are the concept-generating mechanisms of our minds limited by their evolutionary history. The point is that a qualitative superintelligence could come to understand phenomena in the universe that are permanently beyond our epistemic reach. This could ena-ble it to devise ways of manipulating the world that would appear to us as pure magic. In other words, we might observe changes in the world that we simply can’t understand – that are as mysterious as the sci-ence behind cellphones or the atomic bomb is to a chipmunk scientist. In sum, not only would a quantitative superintelligence’s speed severely disadvantage humanity, but a qualitative superintelligence could also discover methods for “commanding nature,” as it were, that would leave us utterly helpless. As with the other agents below, superintelligence itself doesn’t pose a direct threat to our species. But it could pose a threat if coupled to any of the tools previously mentioned, including nuclear weapons, bio-technology, synthetic biology, and nanotechnology. As Bostrom writes, if nanofactories don’t yet exist at the time, a superintelligence could build them to produce “nerve gas or target-seeking mosquito-like ro-bots [that] might then burgeon forth simultaneously from every square meter of the globe” (Bostrom 2014). A superintelligence could also potentially gain control of automated processes in biology laborato-ries to synthesize a designer pathogen, exploit narrow AI systems to disrupt the global economy, and gen-erate false signals in early-warning systems to provoke a nuclear exchange between states. A superintelli-gence could press a preexisting doomsday button or create its own button. According to a recent poll, a large majority of AI researchers believe that an artificial general intelligence (AGI) will be created this century (Müller and Bostrom 2016). But such predictions have been notorious-ly inaccurate in the past. This fact is immaterial to the present thesis, though, which merely states that if, independent of when, humans build a superintelligence, it could pose a major, unpredictable agential risk. (2) Idiosyncratic actors. This category includes individuals or groups who are driven by idiosyncratic mo-tives to destroy humanity or civilization. History provides several examples of the mindset that would be required for such an act of terror. First, consider Eric Harris and Dylan Klebold, the adolescents behind the 1999 Columbine High School massacre. Their aim was to carry out an attack as spectacular as the Ok-lahoma City bombing, which occurred four years earlier. They converted propane tanks into bombs, built
36 99 improvised explosive devices, and equipped themselves with several guns. By the end of the incident, 12 students and one teacher were dead, while 21 others were injured. (Although if the propane bombs had exploded, which they didn’t, all 488 students in the cafeteria at the time could have perished.) This was the deadliest school shooting in US history until Adam Lanza killed 20 children and 6 adults at Sandy Hook Elementary School in 2012 before committing suicide. This leads to the question: what if Harris and Klebold had generalized their misanthropic hatred from their high school peers to the world as a whole? What if certain future anticipated technologies had been available at the time? In other words, what if they’d had access to a doomsday button? Would they have pushed it? The plausible answer is, “Yes, they would have pushed it.” If revenge on school bullies was the deeper motive behind their attack, as appears to be the case,5 then what better way to show others “who’s boss” than to “go out with the ultimate bang”? If people like Harris and Klebold, with their dual proclivities for homicide and suicide, get their hands on advanced technologies in the future, the result could be true omnicide. History also provides a model of someone who might try to destroy civilization without intentionally kill-ing anyone. Consider the case of Marvin Heemeyer, a Colorado welder who owned a muffler repair shop. After years of a zoning dispute with the local town and several thousand dollars in fines for property vio-lations, Heemeyer decided to take revenge by converting a large bulldozer into a “futuristic tank.” It was covered in armor, mounted with video cameras, and equipped with three gun-ports. On June 4, 2004, he climbed inside the tank and headed into town. With a top speed of a slow jog and numerous police walk-ing behind him during the incident, Heemeyer proceeded to destroy one building after another. Neither a flash-bang grenade thrown into the bulldozer’s exhaust pipe nor 200 rounds of ammunition succeeded in stopping him. After more than two hours of relentless destruction, the bulldozer became lodged in a basement, at which point Heemeyer picked up a pistol and shot himself. The motivation of this attack was also a form of bullying, that is, as perceived by Heemeyer. A significant difference between Heemeyer’s rampage and the Columbine massacre is that, according to some residents sympathetic with Heemeyer, he went out of his way not to injure anyone. Indeed, he was the only person to die in the attack.6 It’s also worth pointing out that Heemeyer saw himself as God’s servant. As he put it, “God blessed me in advance for the task that I am about to undertake. It is my duty. God has asked me to do this. It’s a cross that I am going to carry and I’m carrying it in God’s name.” Again, we can ask: what if a delusional person like Heemeyer were to someday hold a grudge not against the local town, but civilization as a whole? What if a future person feels abandoned or “screwed over” by society and wants to retaliate for perceived injustices? In the past, lone wolves with idiosyncratic griev-ances were unable to wreak havoc on society because of the limited means available to them. This will almost certainly change in the future, as advanced technologies become increasingly powerful and acces-sible.7 This category is especially worrisome moving forward, since it is arguably the type with the most poten-tial tokens. Perhaps future advances in psychology, or brain-decoder technologies and other surveillance systems, will enable us to identify agents at risk of engaging in violence of this kind. (3) Ecoterrorists. Imagine for a moment that scientists found a 52 per cent reduction in the global popula-tion of wild vertebrates between 1970 and 2010. Imagine that, on even the most optimistic assumptions, the biosphere had entered the sixth mass extinction event in life’s 3.8 billion year history. Imagine further than a single species were almost entirely responsible for this environmental crisis, namely Periplaneta americana, the American cockroach. What would our response be? To exterminate the culprit, of course, thereby saving Earth from a planetary catastrophe. It’s this basic line of reasoning that could lead a group of radical environmentalists or a lone wolf activist to attempt to relocate humanity from the category of
37 “extant” to “extinct.” In fact, the claims made above are scientifically accurate: the total population of wild mammals, birds, reptiles, amphibians, and fish really did halve in forty years, and we really are in the beginning stages of a new mass extinction event (see WWF 2014; Ceballos et al. 2015). But the cul-prit isn’t the American cockroach, it’s humanity. To date, the vast majority of environmentalist movements have been peaceful, despite the FBI classifying some affiliates of the Earth Liberation Front (ELF) and the Animal Liberation Front (ALF) as constituting “one of the most serious domestic terror threats in the US” (Flannery 2016). Even groups that believe Gaia would be better off without Homo sapiens trampling the planet, such as the Voluntary Human Ex-tinction Movement (VHEMT), reject violence or coercion as legitimate means for achieving their ideo-logical aims. Nonetheless, there are exceptions. On September 1, 2010, James Lee terrorized a Discovery Channel building in DC with a gun and explosives. During several hours of negotiations, Lee explained to law enforcement officials that he wanted new programming on the Discovery Channel “to convey how to improve the natural world and reverse human civilization.” He also wanted humans to stop procreating. As Lee wrote, “Children represent FUTURE catastrophic pollution whereas their parents are current pollution. NO MORE BABIES!” (quoted in Flannery 2016). Around 4:48pm, Lee aimed his gun at a hos-tage in the building and SWAT snipers killed him. Once more, the question is: if Lee had access to a doomsday button that, say, could have sterilized every human on the planet (thereby causing an extinction catastrophe), would he have pushed it? The answer is, “Yes, probably.” The radical environmental movement is founded on the philosophy of biocentrism, or the view that “hu-mans are no more intrinsically valuable than any other creature.” Some scholars have even suggested that this ideology and its following should be seen as a “new religious movement,” which Bron Taylor calls the “deep green religion” (Taylor 2009). Along these lines, Ted Kaczynski, discussed below, advocated what he called a “wilderness religion.” Given the fanaticism of this stance, it’s not hard to envisage a group emerging in the future that attempts to bring about human extinction through the use of advanced technologies in an effort to “save” the planet. This scenario is made even more plausible by the fact that the largest demographic of Earth Liberation Front members consists of “well educated” and “technologi-cally literate” males (Flannery 2016). Thus, if synthetic biology techniques were to enable the synthesis of a designer pathogen that infects only Homo sapiens, a radical environmentalist could try to spread this germ around the globe. Or they could design and release self-replicating nanobots that selectively target Homo sapiens by recognizing genetic signatures unique to our DNA. Such nanobots could annihilate our species while leaving the biosphere more or less unharmed. There might also be radical environmentalists who aren’t motivated by a death wish for humanity, but by a destruction wish for civilization. Ted Kaczynski, also known as the Unabomber, provides a compelling example. His objective was neither suicide nor omnicide, but the dismantlement of technological civiliza-tion, according to the slogan “Back to the Pleistocene” (Flannery 2016). Although Kaczynski’s own ter-rorist bombings weren’t intended to achieve his ambitious aims, if a doomsday button had been available at the time, Kaczynski probably would have pushed it, thereby initiating a stagnation catastrophe. While people might die in the process, this wouldn’t be the goal. As Kaczynski declares, “We therefore advocate a revolution against the industrial system. This revolution may or may not make use of violence; it may be sudden or it may be a relatively gradual process spanning a few decades. … Its object will be to over-throw not governments but the economic and technological basis of the present society” (quoted in Flan-nery 2016). This type of agential risk should be carefully studied moving forward, for reasons explicated in Section 5. If advanced technologies become sufficiently powerful and accessible, people under the spell of the deep green religion could inflict unprecedented harm on civilization.
38 (4) Religious terrorists. Terrorists motivated by nationalist, separatist, anarchist, Marxist, and other politi-cal ideologies are unlikely to cause an existential catastrophe because their goals are typically predicated on the continued existence of civilization and our species. They want to change the world, not destroy it. But this is not the case for some terrorists motivated by religious ideologies. For them, what matters isn’t this life, but the afterlife; the ultimate goal isn’t worldly, but otherworldly. These unique features make religious terrorism especially dangerous, and indeed it has proven to be both more lethal and indiscrimi-nate than past forms of “secular” terrorism.8 According to the Global Terrorism Index, religious extrem-ism is now the primary driver behind global terrorism, and there are reasons (see Section 5) for expecting this to remain the case moving forward (Arnett 2014). The most worrisome form of religious terrorism is apocalyptic terrorism. As Jessica Stern and J.M. Ber-ger observe, apocalyptic groups aren’t “inhibited by the possibility of offending their political constitu-ents because they see themselves as participating in the ultimate battle.” Consequently, they are “the most likely terrorist groups to engage in acts of barbarism” (Stern and Berger 2015). The apocalyptic terrorist sees humanity as being engaged in a cosmic struggle at the very culmination of world history, and the only acceptable outcome is the complete decimation of God’s enemies. These convictions, when sincerely held, can produce a grandiose sense of moral urgency that apocalyptic warriors can use to justify virtually any act of cruelty and violence, no matter how catastrophic. To borrow a phrase from the former Director of the CIA, James Woolsey, groups of this sort “don’t want a seat at the table, they want to destroy the table and everyone sitting at it” (Lemann 2001). There are two general types of active apocalyptic groups. First, there are movements that have advocated something along the lines of omnicide. History provides many striking examples of movements that maintained – with the unshakable firmness of faith – that the world must be destroyed in order to be saved. For example, the Islamic State of Iraq and Syria believes that its current caliph, or leader, is the eighth of twelve caliphs in total before the apocalypse. This group’s adherents anticipate an imminent battle between themselves and the “Roman” forces (the West) in the small northern town of Dabiq, in Syria. After the Romans are brutally defeated, one-third of the victorious Muslim army will supernatural-ly conquer Constantinople (now Istanbul), after which the Antichrist will appear, Jesus will descend above the Umayyad Mosque in Damascus, and various other eschatological events will occur. In the end, those who reject Islam will be judged by God and cast into hellfire, and the Islamic State sees itself as playing an integral role in getting this process started (Torres 2016a). Another example comes from the now-defunct Japanese cult Aum Shinrikyo. This group’s ideology was a syncretism of Buddhist, Hindu, and Christian beliefs. From Christianity, the group imported the notion of Armageddon, which it believed would constitute a Third World War whose consequences would be “un-paralleled in human history.” Only those “with great karma” and “those who had the defensive protection of the Aum Shinrikyo organization” would survive (Juergensmeyer 2003). In 1995, Aum Shinrikyo at-tempted to knock over the first domino of the apocalypse by releasing the chemical sarin in the Tokyo subway, resulting in 12 deaths and sickening “up to 5,000 people.” This was the biggest terrorist attack in Japanese history, and it was perpetrated by a religious cult that was explicitly motivated by an active apocalyptic worldview. Other contemporary examples include the Eastern Lightning in modern-day Chi-na, which believes that it’s in an apocalyptic struggle with the communist government, and the Christian Identity movement in the US, which believes that it must use catastrophic violence to purify the world before the return of Jesus. Second, there are multiple groups that have advocated mass suicide. The Heaven’s Gate cult provides an example. This group is classified as a millenarian UFO religion, led by Marshall Applewhite and Bonnie Nettles. They believed that, as James Lewis puts it, ancient “aliens planted the seeds of current humanity millions of years ago, and have to come to reap the harvest of their work in the form of spiritual evolved
39 individuals who will join the ranks of flying saucer crews. Only a select few members of humanity will be chosen to advance to this transhuman state” (Lewis 2001). The world was about to be “recycled,” and the only possible “way to evacuate this Earth” was to leave their bodies behind through collective suicide. Members believed that, once dead, they would board an alien spacecraft that was trailing the Hale-Bopp comet as it swung past Earth in 1997. To fulfill this eschatological prediction, they drank phenobarbital, along with applesauce and vodka. Between March 24-26, 39 members of the cult committed suicide. Other examples could be adduced, such as The Movement for the Restoration of the Ten Commandments of God in Uganda, which slaughtered 778 people after unrest among members following a failed apoca-lyptic prophesy (New York Times 2000). But the point should be sufficiently clear. With respect to extinction risks, there are (quite intriguingly) no notable groups that have combined these two tendencies of suicide and omnicide. No major sect has said, “We must destroy the world, including ourselves, to save humanity.” But this doesn’t mean that such a group is unlikely to emerge in the future. The ingredients necessary for a truly omnicidal ideology to take shape are already present in our culture. Perhaps, for reasons discussed below, societal conditions in the future will push religious fanatics to even more extreme forms of apocalypticism, thereby yielding a group that believes God’s will is for everyone to perish. Whether this happens or not, apocalyptic groups also pose a significant stagnation risk. For ex-ample, what if Aum Shinrikyo had somehow been successful in initiating an Armageddon-like Third World War? What might civilization look like after such a catastrophe? Could it recover? Or, what if the Islamic State managed to expand its caliphate across the entire world? How might this affect humanity’s long-term prospects? Zooming out from our focus on apocalyptic groups, there are numerous less radical groups that would like to reorganize society in existentially catastrophic ways. One of the ultimate goals of al-Qaeda, for example, is to implement Sharia law around the world. If this were to happen, it would destroy the mod-ern secular values of democracy, freedom of speech and the press, and open scientific inquiry. The impo-sition of Sharia law on civilization is also the aim of non-jihadist Islamists, who comprise roughly 7 per cent of the Muslim community (Flannery 2014). Similarly, “dominionist” Christians in the US, a demo-graphic that isn’t classified as “terrorist,” believe that God commands Christians to control society and govern it based on biblical law. If a state run by dominionists were to become sufficiently powerful and global in scope, it could induce an existential catastrophe of the stagnation variety. (5) Rogue states. As with political terrorists, states are unlikely to intentionally cause an extinction catas-trophe because they are generally not suicidal. Insofar as they pursue violence, it’s typically to defend or expand their territories. The total annihilation of Homo sapiens would interfere with these ends. But de-fending and expanding a state’s territories could cause a catastrophe of the stagnation variety. For exam-ple, if North Korea were to morph into a one-world government with absolutist control over the global population until Earth became unlivable, the result would be an existential catastrophe. Alternatively, a benevolent one-world government could emerge from institutions like the United Nations or the European Union. Once in place, a malevolent demagogue could climb to the power ladder and seize control over the system, converting it into a tyrannical dictatorship. Again, the outcome would be a stagnation catastrophe. Of all the agential risk types here discussed, historians, sociologists, philosophers, and other scholars have studied state-level polities and governmental systems the most thoroughly. 4. Agential error The discussion to this point has focused on agential terror. But what about the other side of the er-ror/terror coin? The danger posed by agential error depends in part on how accessible future technologies become. For example, if even a small percentage of the human population in 2050, which is projected to be 9.3 billion, were to acquire “biohacker” laboratories, the chance that someone might accidentally re-
40 lease a pathogen into the environment could be unacceptably high (Pew Research Center 2015a). After all, a significant number of mistakes have happened in highly regulated government laboratories over the years (Torres 2016a). The 2009 swine flu pandemic may have occurred because of a laboratory mistake made in the 1970s, and “a CDC lab accidentally contaminated a relatively benign flu sample with a dan-gerous H5N1 bird flu strain that has killed 386 people since 2003” (Zimmer and Burke 2009; McNeil 2014). If such problems occur among professionals, imagine the potential dangers of hobbyists around the world – perhaps hundreds of millions – handling pathogenic microbes with almost no regulatory over-sight. The exact same logic applies to other technologies that are becoming more accessible, such as nanotechnology, robotics, AI systems, and possibly nuclear weapons, not to mention future artifacts that currently lie hidden beneath the horizon of our technological imaginations. There could also be malicious agents that want to cause an existential catastrophe, but nonetheless end up doing this by accident rather than design. For example, in preparing for the “big day,” a doomsday cult could accidentally release a deadly pathogen or self-replicating nanobot into the environment, resulting in an unplanned disaster. This scenario could involve ecoterrorists, idiosyncratic actors, and states as well. Or an agent with no desire to cause an existential catastrophe could push a “catastrophe button” that inad-vertently brings about an existential disaster. For example, a rogue state that hopes to gain regional or global power through the use of nuclear missiles must answer the following question: exactly how many nuclear missiles are required to bring the world’s governments to their knees without causing an extinc-tion-inducing nuclear winter? The same question could be asked with respect to biological weapons, nan-oweapons, and weaponized AI systems. How sure can one be that there won’t be unintended consequenc-es that catapult humanity back into the Stone Age? (As Albert Einstein once said, “I do not know how the Third World War will be fought, but I can tell you what they will use in the Fourth – rocks!” Calaprice 2005.) The unpredictability and uncertainty inherent in global catastrophe scenarios could make it easy for non-existential terror to slide into existential error. Finally, a special case of agential error worth examining on its own involves superintelligence. A genu-inely superintelligent agent (coupled with advanced technologies) would wield extraordinary power in the world. This fact would make humanity especially vulnerable to any error made by such an agent. Even a single mistake could be sufficiently devastating to cause an existential catastrophe. One might respond by asserting that a superintelligence is surely less likely to make a mistake, given its superior intelligence (Bostrom 2014). But I would challenge this assumption. Consider that humans have the most developed neocortex and the highest encephalization quotient in the Animal Kingdom. Yet it is our species, rather than our intellectually “inferior” relatives, that is responsible for the environmental catastrophes of cli-mate change and biodiversity loss. Even more, our species has greatly increased the total number of exis-tential risks from a small handful of improbable natural threats to a dizzying array of anthropogenic and agent-tool dangers. Was this a mistake? In a sense, yes: we certainly didn’t intend for this to happen. His-torically speaking, human ingenuity and the threat of existential annihilation have risen together. This suggests that there isn’t a strong connection between higher intelligence and the capacity to avoid errors. Call this the “orthogonality thesis of fallibility” (Torres 2016a). If our own history is a guide to the future, we might expect the creation of a superintelligence to further increase the total number of existen-tial risks, perhaps in ways that are either now, or permanently, inscrutable to us. The point is that even if we were to solve the “control problem” and create a friendly superintelligence, it could nudge us over the precipice of disaster on accident, rather than push us on purpose. What can we say? It’s only superhuman. 5. The future of agential risks Neutralizing the threats posed by agential risks requires understanding not only their synchronic proper-ties, but also how these properties might evolve diachronically. There are two sets of factors relevant to
41 this task, which we can organize into external and internal categories, depending on whether they origi-nate from outside or within an agent’s motivating ideology. External factors As previously mentioned, climate change and biodiversity loss are “context risks” that will frame, and therefore modulate, virtually every other threat facing humanity. According to our best current science, these phenomena – appropriately dubbed “threat multipliers” – will become more severe in the coming decades, and their effects will “extend longer than the entire history of human civilization” (Clark et al. 2016). This will significantly elevate the probability of future struggles and conflicts between state and nonstate actors. A simple thought experiment illustrates the point. In which of the following two worlds are wars more likely: one beset by megadroughts, extreme weather, scorching heat waves, desertification, sea-level rise, and the spread of infectious disease, or one without these tragedies? In which of the follow-ing two worlds are terrorist attacks more likely: a world in which food supply disruptions, mass migra-tions, social upheaval, economic collapse, and political instability are widespread, or one in which they’re not? One could even ask, in which of the following two worlds is a malevolent superintelligence more likely to emerge: one crushed by environmental catastrophes or one in which civilization is functioning properly? Environmental degradation could also increase the likelihood of incidents involving idiosyncratic agents, in part because it could increase the prevalence of “bullying”-type behavior. When people are desperate, moral considerations tend to be occluded by the instinctual drive to meet our biological needs. Even more, climate change and biodiversity loss could significantly fuel ecoterrorism. To quote Flannery, “As the environmental situation becomes more dire, eco-terrorism will likely become a more serious threat in the future” (Flannery 2016). Not only will the deleterious effects of industrial society on the natural world become more salient, but sudden changes in the environment’s stability could prod activists to consider more aggressive, even violent, tactics. Scientists have, for example, argued that Earth could be approach-ing an abrupt, irreversible, catastrophic collapse of the global ecosystem. A planetary-scale “state shift” of this sort could unfold on the timescale of decades and cause “substantial losses of ecosystem services re-quired to sustain the human population.” The result would be “widespread social unrest, economic insta-bility, and loss of human life,” and these phenomena could inspire fierce rebellions against civilization. There are also reasons for expecting climate change and biodiversity loss to nontrivially increase the size and frequency of apocalyptic movements in the future (Torres 2016b; Juergensmeyer, forthcoming). In fact, we already have at least one example of this happening, according to a 2015 study published in the Proceedings of the National Academy of Sciences. This study argues that one can draw a straight line of causation from anthropogenic climate change to the record-breaking 2007-2010 Syrian drought to the 2011 Syrian civil war (Kelly et al. 2015). And the Syrian civil war was the Petri dish in which the Islamic State consolidated its forces to become the wealthiest and most powerful terrorist organization in human history. The link between environmental havoc and terrorism has also been confirmed by the current Di-rector of the CIA, John Brennan, the former US Defense Secretary, Chuck Hagel, and the US Department of Defense (Torres 2016c). As Mark Juergensmeyer (forthcoming) observes in detail, apocalyptic ideologies tend to arise during pe-riods of extreme societal stress. When a group’s basic identity and dignity is threatened, when losing one’s cultural identity is unthinkable to those in the group, and when the crisis isn’t solvable through or-dinary human means, people often turn to supernatural frameworks to make sense of their suffering and give them hope for the future. In Juergensmeyer’s words, “The presence of any of these three characteris-tics increases the likelihood that a real-world crisis may be conceived in cosmic terms,” and “cosmic terms” form the language of apocalyptic activism.
42 Because of climate change and biodiversity loss, these are precisely the conditions we can expect in the future, as societies inch toward the brink of collapse. It’s also worth noting that floods, earthquakes, droughts, famines, and disease are prophesied by many religions as harbingers of the end. Consequently, environmental degradation could actually reinforce people’s prior eschatological convictions, or even lead nonbelievers to convert.9 There is, in fact, a strong preexisting base of widespread apocalyptic belief within the Abrahamic traditions. For example, a 2010 Pew poll finds that 41 per cent of Americans be-lieve that Jesus will either “definitely” or “probably” return by 2050 (Pew Research Center 2010), and a 2012 Pew poll reports that 83 per cent of people in Afghanistan, 72 per cent in Iraq, 68 per cent in Tur-key, and 67 per cent in Tunisia believe that the Mahdi, Islam’s end-of-days messianic figure, will return in their lifetime (Pew Research Center 2012). One should expect these percentages to rise moving for-ward. There are two additional reasons for anticipating more apocalyptic movements in the future. First, a statis-tical point. According to a 2015 Pew study, the percentage of nonbelievers is projected to shrink in the coming decades, despite the ongoing secularization of Europe and North America (Pew Research Center 2015a; see chapter 3, “Articles of Faith”). By 2050, more than 60 per cent of humanity will identify as either Christian or Muslim, in roughly equal proportion. As Alan Cooperman puts it, “You might think of this in shorthand as the secularizing West versus the rapidly growing rest” (Pew Research Center 2015b). This is disconcerting because religion normalizes bad epistemological habits, and thinking clearly about big-picture issues is the only hope our species has of navigating the wilderness of existential risks before us. In addition, not only is superstition rising as advanced technologies become more powerful, but if the relative proportion of extremists at the fringe remains fixed, the absolute number of religious fanatics will undergo a growth spurt. This alone suggests that the future will contain a historically anomalous number of terrorists (although we should note that it will contain more “good guys” as well). Furthermore, the inchoate GNR (genetics, nanotech, and robotics) Revolution will result in a wide range of fundamental changes to society. It could introduce new forms of government – or, as Benjamin Wittes and Gabriella Blum (2015) argue, undercut the social contract upon which modern states are founded – and even challenge our notion of what it means to be human. These changes could be profound, perva-sive, and quite rapid, given the exponential rate of innovation. If this is the case, it could also fulfill the conditions specified by Juergensmeyer, thereby fueling apocalyptic extremists to declare an imminent end to the world. (In a sense, this might be true, since the transition from the human era to a posthuman era would mark a watershed moment in our evolutionary history.) The fact is that past technological revolu-tions have inspired religious fanaticism, and by nearly all accounts the GNR Revolution will be far more disruptive than any previous revolution. As Juergensmeyer puts it, “radical change breeds radical reli-gion,” and radical change is exactly what we should expect.10 Tying this all together: a confluence of environmental degradation, demographic shifts, and disruptive technologies could significantly exacerbate the threat of apocalyptic terrorism, as well as idiosyncratic agents and ecoterrorists, in the future. The recent unrest in the Middle East is, arguably, only a preview of what’s to come. Internal factors But there are also factors internal to the ideologies espoused by different agents that are no less important for existential riskologists to study. For example, the year 2076 will likely see a spike in apocalyptic fervor within the Islamic world (Cook 2008, 2011). One can only know this, and therefore prepare appropriately, if one understands the relevant Islamic traditions. The reason 2076 will be especially dangerous is that it roughly corresponds to 1500 in the Islamic calendar (AH), and eschatological enthusiasm has risen in the past at the turn of the century.
43 Consider the fact that the Iranian Revolution, which was widely seen as an “apocalyptic occurrence” by Shi’ites, happened in 1979 (Cook 2011). So did the Grand Mosque seizure, during which a group of 500 insurgents took approximately 100,000 worshipers hostage. This group claimed to have the Mahdi with them and believed that the Last Hour was imminent. The point is that 1979 corresponds to 1400AH, a date that fueled the apocalypticism behind these events. Scholars should also keep an eye on 2039, since it is the 1200th anniversary of the Mahdi’s occultation in the Twelver Shia tradition. As Cook writes, “the 1000-year anniversary of the Mahdi’s occultation was a time of enormous messianic disturbance that ultimately led to the emergence of the Bahai faith. … [A]nd given the importance of the holy number 12 in Shiism, the twelfth century after the occultation could also become a locus of messianic aspirations.” He adds: In one scenario, either a messianic claimant could appear or, more likely, one or several move-ments hoping to “purify” the Muslim world (or the entire world) in preparation for the Mahdi’s imminent revelation could develop. Such movements would likely be quite violent; if they took control of a state, they could conceivably ignite a regional conflict. (Cook 2011) Looking forward, who knows what powerful technologies might exist by 2039 or, even more, 2076? If a messianic movement with violent proclivities were to arise in the future, it could have existential implica-tions for humanity. Another example involves apocalyptic US militias influenced by Christian Identity teachings. On April 19, 1995, Timothy McVeigh pulled up to the Alfred P. Murrah Federal Building in Oklahoma City and detonated a bomb that killed 168 people. As Flannery notes, this event unfolded “just as the Christian Identity affiliated Covenant, Arm, and the Sword (CSA) militia had planned a decade earlier while train-ing 1,200 recruits in the Endtime Overcomer Survival Training School.” The date of April 19 “was no accident.” Exactly two years earlier, the government ended its confrontation with the Branch Davidians in their Waco, Texas compound, resulting in 74 deaths. And exactly 8 years before this event, there was a similar standoff between the government and the Covenant, Arm, and the Sword. And centuries before, in 1775, the Battles of Lexington and Concord that inaugurated the American Revolutionary War against Great Britain took place on April 19. Consequently, the date of “April 19 has come to resonate throughout a constructed history of the radical Right as a day of patriotic resistance” (Flannery 2016, 144). More generally, some experts refer to April as the beginning of “the killing season.” While Harris and Klebold reportedly planned their massacre on April 19 (being inspired by McVeigh), they ended up de-laying it one day to coincide with Adolf Hitler’s birthday (Rosenwald 2016). Another date to watch is April 15, the deadline for income tax filings in the United States. This has meaning to certain anti-government groups. As the Anti-Defamation League (2005) warns, April is a month that looms large in the calendar of many extremists in the United States, from racists and anti-Semites to anti-government groups. Some groups organize events to commemo-rate these April dates. Moreover, there is always a certain threat that one or more extremists may choose to respond to these anniversaries with some sort of violent act. It adds: “Because of these anniversaries, law enforcement officers, community leaders and school offi-cials should be vigilant.” Existential risk scholars too should be especially mindful of heightened risks in April. If a doomsday or catastrophe button were to become available to Christian Identity terrorists motivated by an active apoca-lyptic ideology, April 19 might be the day on which they would decide to press it.
44 6. Conclusion Most states and terrorists are unlikely to intentionally cause an existential catastrophe, although certain high-impact instances of catastrophic violence could accidentally realize an extinction or stagnation risk. The primary danger posed by states and terrorists concerns their capacity to press a catastrophe button, if it were to become available. There are, however, at least five types of agents who could be motivated by various goals to bring about a cataclysm of existential proportions. I do not intend for this list to be ex-haustive. Indeed, the agential threat horizon could expand, shift, or transmogrify in unanticipated ways as humanity is thrust forward by the invisible hand of time. It’s nonetheless important to specify a typology of agential risks based on the best current research – a task that no one has yet attempted – because the agents of each category have their own unique properties, and must therefore be studied as unique threats in exactly the same way that nuclear weapons, biotechnology, and molecular manufacturing are studied separately. While much of the existential risk literature focuses on the various tools, both present and anticipated, that could bring about a secular apocalypse, we must give the agents equal consideration. A key idea of this paper is that, first, advanced technologies will provide malicious agents with bulldozers, rather than shovels, to dig mass graves for their enemies. And second, the risk potential of these technologies cannot be realized without a complete agent-tool coupling. This is why the field of existential risk studies des-perately needs a subfield of agential riskology, which this paper aims to establish. No doubt much of what I have said above will need to be refined, but such is to be expected when there are few shoulders upon which to stand. Notes 1. See Leslie 1986. 2. For example, a world thrown into chaos by environmental degradation might be less prepared to deflect an incoming asteroid or coordinate on stopping a global pandemic. 3. Originally, “dual-use” referred to entities with both civilian and military uses, but the term has acquired a more promiscuous signification in recent scholarship. 4. With the exception of superintelligence, discussed below. 5. According to one study, over 66 per cent of premeditated school shootings have been shown to be con-nected to bullying (Boodman 2006). 6. Although luck might be partly to blame, as Heemeyer fired his rifle at propane tanks that could have killed someone in the vicinity if they had exploded 7. As I’ve written elsewhere about similar examples: these may seem too anecdotal to be scientifically useful. But drawing this conclusion would be wrong. Given the immense power of anticipated future technologies, single individuals or groups could potentially wield sufficient power to destroy the world. The statistically anomalous cases of omnicidal lone wolves or terrorist groups are precisely the ones we should be worried about, and therefore should study. 8. See, for example, Hoffman 1993. 9. History provides numerous examples of natural disasters leading to a spike in religious belief, such as the Plague of Cyprian.
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c8a3336a-a0ee-40f8-9fed-a6c535ef474e | trentmkelly/LessWrong-43k | LessWrong | The Era Of Unlimited Everything: Unlimited Materials & Unlimited Money
This essay was originally published on my website.
Ask yourself these questions:
1. Sitting in your home, looking around, what do you see??
2. How did you buy those things?
Many of the things you’re surrounded by are probably made of plastic.
Many of those things were probably bought with credit.
Could you have bought them without credit? What if those things were not made out of plastic? Would have you been able to buy them?
Overview:
* Before & After Plastic
* Invention
* Before & After Credit
* Invention
* A World without Credit & Plastic
* Credit & Plastic: Economic Growth
* A Product of Imagination
* Unlimited Everything
Imagine a world without credit and a world without plastic.
Before & After Plastic
To illustrate this example, let’s use three examples of common things made out of plastic.
* Plastic containers for food storage
* Clothes
* Electronic Devices (i.e. computers, phones)
Would you have stored your food as efficiently? Would you be able to afford clothes or electronic devices, considering many clothing and electronic devices are made of plastic?
A world without plastic is a world constrained by materials and their limitations. A world with plastic is a world constrained by...almost nothing.
Invention
Before plastic was created, we needed to use materials such as wood, animal fur, plants, or metals to make clothes and tools. Plastic was invented because we were running out of ivory as elephants were going extinct because of our insatiable human demand. Ivory was used for boxes, piano keys, combs, billiard balls, and many other things.
The legend says that a New York billiard supplier ran a newspaper ad offering “a handsome fortune,” ten thousand dollars in gold, to anyone who could invent an alternative to ivory.
An audacious entrepreneur, John Wesley Hyatt, a young printer read the ad and decided he could do it. There was a catch for him, though. He had no formal training in chemistry, but he wa |
f6bad00b-86f8-4e70-9d37-777069f59d5d | StampyAI/alignment-research-dataset/lesswrong | LessWrong | AI Alternative Futures: Scenario Mapping Artificial Intelligence Risk - Request for Participation (*Closed*)
**Overview: AI Futures Risk Model**
===================================
**Summary**: I am a graduate student researching artificial intelligence (AI) risk and typology classifications that could result from variations in technological transitions. I am developing a scenario modeling tool that captures many of the more complex non-quantifiable dynamics inherent in complex systems. The project aims to map structural forces, trends, technologies, and degree of risk to potential AI futures through an exploratory scenario modeling framework.
As part of the data collection, I am eliciting community perspectives on AI paths through a survey (options: [worksheet](https://tinyurl.com/WorksheetAI) or [Google form](https://forms.gle/BXn24Lg7qCVf1DFj6)) to rank key characteristics that can influence AI progress, risks, and futures.
I appreciate any assistance.My data collection window is closing soon, and I figured it was past time to request assistance from the broader community if able. I am behind the curve on this request (and understand how demanding these are) but any help however minimal is greatly appreciated. None of the *questions* are required so if you are unsure or are short on time feel free to skip questions or sections.
The model I'm developing requires both [plausibility](https://forms.gle/XAAPrgLcX9wUfa5E9) rankings and [impact](https://forms.gle/DNmPNy6XahCQnNxt9) to populate a likelihood/impact matrix for the model's foundation (details below). Since the full version is relatively long, I've broken it up into two shorter versions which I'll post below (under Survey Overview); I have no preference for either at this time, any data helps. The short versions should take less than five minutes each and are completely anonymous.
**Survey Overview: Plausibility & Impact**
------------------------------------------
**This project is an exercise in exploratory scenario development only.** [Scenario modeling](https://rb.gy/padbj1)is not about prediction, but rather a rigorous and methodical way to consider the full scope of imagined future situations, or contexts and model the broad outlines of what could potentially happen rather than what will happen (Figure 2).
Generating scenarios typically involves identifying a set of *influential “drivers”* which in combination create a range of plausible future states. For each driver or dimension, there are a set of conditions to rank.
Similarly, I developed a range of *AI* [*dimensions*](https://docs.google.com/spreadsheets/d/1CBQqV8GoTgvmpU6OaRuC-7DMacYcDP-K/edit?usp=sharing&ouid=111018059717565828607&rtpof=true&sd=true) rather than drivers (key aspects – e.g., AI paradigm) and added a subset of key uncertainties which I call [*conditions*](https://docs.google.com/document/d/1jCuV6CvrvoP1nsTHHCkW33W5RYCOO88G/edit?usp=sharing&ouid=111018059717565828607&rtpof=true&sd=true) (scenario paths – e.g., current paradigm, new paradigm, or hybrid) for each dimension. Each dimension and condition are independent factors/aspects, and uncertainties of AI that when combined make up a plausible scenario.
**Survey Instructions** (both versions): The survey presents each question as an AI dimension followed by three to four conditions and requests participants to:
1. **Likelihood: Rank each** condition from most plausible to the least plausible to occur
* [Plausibility Survey](https://forms.gle/Kau83NWLmwg8ZT936)
2. **Impact: Rank each** condition from the greatest potential benefit to stability, security, and technical safety to the greatest potential for downside risk
* [Impact Survey](https://forms.gle/DNmPNy6XahCQnNxt9)
Likelihood/impact values are derived from the MITRE [risk scale.](http://www2.mitre.org/work/sepo/toolkits/risk/StandardProcess/definitions/occurence.html)
* *Note: I recognize that the impact section can be especially vague and hard to judge. The aim is to score each along a continuum, from best to worst, for the risk space (e.g., inner alignment --> power-seeking --> goal alignment) so please choose the best(worst) option available. The output will make sense.*
The overall goal of both surveys is to create a risk spectrum across all the AI dimensions and conditions, based on the values collected, for the GMA alternative futures model **(e.g., green=good --> yellow/orange=moderate --> red=bad**) along the same lines as traditional risk analysis (Figure-1).
The survey requests participants to rank or score each condition on its overall plausibility and degree of impact given our current understandings.The survey questions are technically not *questions at all* and should be viewed rather as a ranking or scoring of each condition (the question format was added later for clarity). If you have a concrete understanding and set of beliefs on AI capabilities, generality, takeoff, paradigms, race dynamics, and safety the conditions *shouldn't* be too unclear.
For those willing to complete the original full version I'll [provide the link here.](https://forms.gle/WYdF72DG7kLkE2GD6)
**Survey Results**
------------------
The survey results are used to create the range of scenario possibilities for each combination of conditions (see methodology) and levels of risk to be arrayed across the model, similar to the standard four-quadrant of a risk matrix but continuous and in many dimensions (e.g. [standard risk matrix](https://www.researchgate.net/figure/A-standard-risk-matrix_fig7_323570642), [multidimensional matrix](https://www.sciencedirect.com/science/article/pii/S004016251730656X#t0015) using GMA). Thus, for this model, rather than a standard 2D space, the variables are arrayed 14.
The categories - from high likelihood to low, and impact, from greatly increase to decrease risk - are used to bound the overall ["problem space"](https://www.swemorph.com/amg/pdf/amg-7-2-2019.pdf) (or [solution space](https://www.rand.org/pubs/external_publications/EP68120.html)) of the model for exploratory scenario development.
Therefore, the values ranked in the survey will create the foundation for the [General Morphological Analysis (GMA)](https://www.swemorph.com/ma.html) model (with GMA as the foundation, but with methodological [additions](https://www.swemorph.com/pdf/mabn.pdf) e.g., impact/likelihood risk spectrum from the results). GMA is similar in spirit to the standard 2D matrix (Figure-1 - left), which generally has 2 drivers that allow four possible scenario outcomes (on a standard cartesian plane).
### **The benefit of Survey Results to the GMA Process**
The GMA process transposes the 2D cartesian matrix into many user-defined dimensions, in effect reimagining the process and problem as multiple interrelated matrices rather than one; The GMA example in Figure-1 (right) with only three drivers/five cells results in 75 possible scenarios; the process allows a radical increase in possible scenario options.
Using the GMA variation for this project, with the likelihood and impact values from the survey, the GMA morphological space is transmuted into a [multidimensional risk complex](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://testscience.org/wp-content/uploads/sites/10/2017/04/MRISK-Conference-Presentation.pdf); rather than focus on and structure the dimensions and conditions independent of risk or likelihood (the standard approach), the outputs of the survey allow us to create a continuum of likelihood and impact measurements across the problem space for added context, computation between conditions, and risk analysis assessments.
The strength of GMA lies in its ability to structure and model high numbers of parameters with significant degrees of complexity.
Figure-1. Compare a [traditional scenario](https://www.researchgate.net/figure/A-standard-risk-matrix_fig7_323570642) matrix with 4 potential futures (left) to 3/5 [drivers in a three-dimensional space](https://www.swemorph.com/ma.html)(right) resulting in 75 possible futures using GMA (5x5x3). A single scenario combination is represented as the blue ball in this figure. Source: [SMS](https://www.swemorph.com/ma.html)**Another Scenario Project?**
-----------------------------
This survey varies from similar projects in that it:
1. Seeks to identify unique combinations, and potentially, entirely new scenario possibilities.
2. Requests participants to rank individual elements of possible scenarios, rather than fully developed scenarios themselves, to develop unique unexplored combinations.
3. Uses the participant's rankings as inputs to the [morphological](https://www.hindawi.com/journals/complexity/2019/7643685/)model to cross-evaluate against all others, evaluate, combine, and cluster scenario elements (condition rankings) to exhaust all scenario combinations.
### **Exploratory Scenario modeling**
Scenario Development Process: The goal of scenario development is to explore high-consequence problems that are difficult to impossible to extrapolate from historical data.
**Figure-2.** This graphic depicts the plausible-probable scenario relationship. The level of precision is less central than other methods and favors conceptualizing what ***could*** happen rather than what ***will*** happen for strategy and preparedness. [Source IASS](https://publications.iass-potsdam.de/rest/items/item_1487993_6/component/file_1503948/content).**Scenarios are a useful tool for informing policy for future change.**
While not always the best option, [alternative futures](https://www.researchgate.net/publication/216358746_Modeling_Alternative_Futures_with_General_Morphological_Analysis) modeling is [useful for planners](https://www.atlanticcouncil.org/wp-content/uploads/2019/09/global-trends-2030-nic-lo.pdf) to identify vulnerabilities, develop strategies, and plan for resilience can be critical in the face of high uncertainty. Unanticipated disruptive technology gains or jumps in capabilities could radically upend stability and the [balance of power](https://link.springer.com/chapter/10.1057/9781137461285_5) (e.g., [How did nobody see it coming?](https://rb.gy/padbj1)).
There is a solid body of research that aims to forecast AI development (e.g., [here](https://arxiv.org/abs/1705.08807), [here](https://www.lesswrong.com/posts/mMDNeNfEKCKPjJTNC/forecasting-transformative-ai-part-1-what-kind-of-ai), and [here](https://axrp.net/episode/2021/05/28/episode-7_5-forecasting-transformative-ai-ajeya-cotra.html)), and this project does not attempt to add to it. Several of the scenario elements in the survey are highly speculative and qualitative, as the primary goal is to explore all options, including the very messy and non-quantifiable aspects, as best possible, to develop unique variations that may provide insight.
This work primarily contributes to the literature through the futures modeling technique and a structured framework of AI dimensions. There is a [need](https://journals.openedition.org/cybergeo/1035) for more comprehensive scenario modeling in the AI space in general, notwithstanding some notable exceptions (e.g., [Gruetzemacher](https://www.sciencedirect.com/science/article/pii/S0016328721001932#!), [Avin](https://www.shaharavin.com/publication/pdf/exploring-artificial-intelligence-futures.pdf),[Whittlestone](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://lcfi.ac.uk/media/uploads/files/CremerWhittlestone_EPAI_2020_paper_4.pdf), [Baum](https://arxiv.org/abs/1607.07730)).
However, scenario methods overall tend to struggle with capturing high degrees of complexity and uncertainty, with outcomes generally confined to a limited number of options. This GMA variation and [several](https://www.sciencedirect.com/science/article/abs/pii/S004016250900105X?via%3Dihub) [others](https://www.sciencedirect.com/science/article/abs/pii/S0040162514001000) could potentially provide important contributions to this area.
**Framework**
-------------
I am modeling AI through an evolutionary complex systems framework to analyze the problem in the context of [punctuated equilibrium t](https://interactions.acm.org/archive/view/september-october-2012/punctuated-equilibrium-and-technology-change)heory and [technological transitions](https://royalsocietypublishing.org/doi/10.1098/rstb.2015.0450). These theoretical frameworks provide interesting conceptual insights into the discussions on continuous or discontinuous change. And punctuated equilibrium provides a model for understanding periods of stasis that are regularly disrupted by critical transitions.
This research presents a classification framework, starting with three broad high-level systems, followed by 14 nested dimensions that influence AI development and risk, and 47 individual conditions that are elements of possible futures (tentatively - 129 potentially measurable indicators). Comprehensive definitions of dimensions and conditions can be [found here.](https://docs.google.com/document/d/1jCuV6CvrvoP1nsTHHCkW33W5RYCOO88G/edit?usp=sharing&ouid=111018059717565828607&rtpof=true&sd=true)
The purpose of this research is to: 1) present a systematic organizational framework of AI dimensions and conditions for use in futures analysis and 2) map AI developments to system typologies, risks, and social-political dynamics to variations in technological transitions, distribution, and vulnerabilities, and 3) investigate influence pathways and relationships between the dimensions to suggest how actions in one could impact others.
**Methodology**
---------------
The methodology uses general morphological analysis ([GMA](https://www.swemorph.com/ma.html)) as a foundation (see: [Johansen](https://tinyurl.com/IJohansen) and, [Ritchey](https://tinyurl.com/tritchey)), where a standard two-dimensional problem is transposed into a multidimensional problem space, in which variables are arrayed against each other in a [matrix](https://www.sciencedirect.com/science/article/pii/S004016251730656X#t0015) yielding millions of potential outcomes (Figure-3, Figure-4). The framework composed of 14 dimensions and 47 conditions, will make up the body of the matrix.
Figure-3. Each question in the survey is a dimension, with three conditions. The terms on the X-axis and on the Y-axis are all the conditions participants are requested to rank in the survey. [Source IASS](https://publications.iass-potsdam.de/rest/items/item_1487993_6/component/file_1503948/content).The GMA approach has been applied across a variety of fields from [design](https://www.sciencedirect.com/science/article/pii/S2405872617300710), and [linguistics](https://www.researchgate.net/profile/Abdellah-Yousfi/publication/273133467_Graph-Based_Morphological_Analysis/links/5587061b08ae71f6ba913ff3/Graph-Based-Morphological-Analysis.pdf), to [defense planning](https://www.sciencedirect.com/science/article/pii/S004016251730656X), and [disinformation](https://www.sciencedirect.com/science/article/pii/S1877050920312394). The GMA process aims to identify and structure all possible arrangements for irreducible, complex problem spaces which in most cases involve human dynamics, qualitative social issues, politics, and economics. The process is iterative through repeated sequences of analysis, refinement, and synthesis. Follow-on discussions/interviews with subject-matter experts are tentatively planned to refine the results.
### **Problem Formulation & Multidimensional Problem Complex**
The first step requires an exact as possible formulation of the problem. Next, the problem must be broken down into a parameter set that frames as many components of the issue as possible—in this case, 14 dimensions, with three to four possible outcomes (conditions) for each, totaling 47.
The third step involves constructing a multidimensional matrix containing all identified dimensions and conditions related to the problem (Figure-4). The matrix contains within itself the entire problem space of the given issue. The fourth step reduces any inconsistency between each pairwise condition.
Once values are received from the survey results, the responses are averaged for each individual condition separately (e.g., 50 rankings for "fast takeoff" are averaged) in two separate matrices for each of the 47 conditions. This is combined into one master multidimensional matrix of all survey responses for each condition (Figure-4).
### **Cross-Consistency Assessment (CCA)**
Next, [Cross-Consistency Assessment (CCA)](https://www.researchgate.net/profile/Tom-Ritchey/publication/286035722_Principles_of_Cross-Consistency_Assessment_in_Morphological_Modelling/links/566587c508ae192bbf9248f4/Principles-of-Cross-Consistency-Assessment-in-Morphological-Modelling.pdf) winnows down the prospective futures to a smaller set of configurations, weeding out inconsistent pairs while allowing the discovery of unexplored relationships.
From the 14 unique dimensions, the 47 conditions that are assessed in the survey are arrayed along the horizontal (X-axis) and vertical (Y-axis) to create the GMA matrix; each cell in the matrix represents a combination of two conditions (excluding duplicative values) and one unique scenario combination.
The cells are then evaluated across the axis to rank the consistency between each value pair (e.g., fast takeoff --> moderate distribution). One pair of conditions is a scenario combination or scenario pair (e.g., condition-1: multipolar distribution X condition-2: current AI paradigm, depicted in red below). The total 47 conditions yield millions of possible scenario combinations (15,116,544 unique combinations) - too many to evaluate independently, but extremely comprehensive (including outliers). See [Ritchey for details on CCA.]( https://tinyurl.com/tritchey)
**Figure-4.** ***Multidimensional Matrix for AI futures*****.** Figure 4 shows the combination of the scenario pair "multipolar distribution" condition paired with the "current AI paradigm" condition in cell I-22. **Scenario Mapping Dimensions & Conditions**
--------------------------------------------
After all impact and likelihood rankings for each condition are averaged (all impact values ranked in the survey for condition 1, for example) each scenario pair is evaluated and then combined with the opposing condition between the X-axis and Y-axis. As in Figure-4, the condition *"current paradigm"* is evaluated and then combined with the condition *"multipolar distribution*."
Thus, each cell in the matrix is a computation between two conditions and four sets of values provided by survey participants (e.g., condition-1 impact/plausibility & condition-2 impact/plausibility). The combination across the X-axis and Y-axis results in multiple individual scenario combinations (**15,116,544** independent scenarios currently).
### **Grouping Scenario Pairs for Analysis**
After the separate values for each condition are calculated and combined, the [k-means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering algorithm groups like values into potential scenario classes (Figure-5). The clusters are user-defined, allowing many distinct clusters for detailed analysis, or broad classes for more in-depth narratives. This method provides a structured technique to organize and model as many potential variables of a problem as needed to ensure all critical aspects of a problem are comprehensively evaluated.
Figure-5. After all scenario pairs are calculated across the matrix, the pairs are then clustered using the k-means algorithm into user-defined groups (from only two very detailed scenarios to many potentially unique variations)Ultimately, the GMA method is primarily used to decompose a multidimensional problem for alternative futures analysis. However, the pairwise relationships between each condition across the cells are also edges of a network graph, with each condition a node in the network. The graph can be a valuable tool to model the complexity, the strength of relationships, and possible directionality.
For example, the results from the CCA could be used to build a network graph to display the interdependent relationships between each variable and condition (from consultations with domain experts and based on the values from the ranking) to analyze the degree of dependency and relationships.
### **Next Steps**
After the survey values are collected, all impact and likelihood values will be averaged in the GMA matrix for each condition. The next potential ideas to add to the model include:
* Develop indicators for each condition that could potentially be used to monitor developments (TBD).
* Develop a [hypergraph](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00231-0)model using the CCA output to evaluate the influence or possible directionality between conditions (TBD).
Note: the project is still in development and there are other [methodological](https://www.swemorph.com/pdf/mabn.pdf)[options](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00231-0)and data sources I'm considering, if viable. This will be iterative, so I hope to conduct interviews with domain experts following the survey for further refinement.
**Definitions and Dimensions documents here for further reference**: Dimension/matrix spreadsheet: <https://tinyurl.com/diggidy>; Definitions: <https://tinyurl.com/aidefin>
1. **[^](#fnrefkj8ym8ybhob)***The Google version has questions and detailed descriptions added for clarity, but the ultimate goal is to rank each from most/best to least/worst only. If a question seems oddly worded ignore it or skip it; the entire point is ranking.*
2. **[^](#fnref4wfj27f34h)***Note: the impact questions are especially difficult to judge and I recognize the problem (choices, and overall framing with google survey especially). The aim is to rank each along a continuum, from best to worst, for the risk space (e.g., inner alignment --> power-seeking --> goal alignment) so please choose the best(worst) option available and rank as such. The output will make sense.* |
93ca8f74-6fed-43ea-919a-d549d548383a | trentmkelly/LessWrong-43k | LessWrong | Computer Programs Rig Elections
I don't know how interested this community would be in this topic, I don't mean to be talking politics so much as technology and decision mechanisms.
According to this programmer's testimony, voting machine companies requested that their programmers make it possible for the companies to rig elections, while in communication with elected officials.
http://www.youtube.com/watch?v=1thcO_olHas&sns=fb
If there is a discussion of how worthwhile taking the time to vote is, this may be worth knowing.
This is something that I expected to be true beforehand, but I am wondering: How reliable is this testimony? What are other LWers' prior and posterior probabilities of elections being rigged in this way? Is it worth trying to do something about this, and if so what? |
00de9530-431a-4d81-8f11-f2fb617babdc | trentmkelly/LessWrong-43k | LessWrong | Meetup : Berlin
Discussion article for the meetup : Berlin
WHEN: 06 March 2013 07:30:00PM (+0100)
WHERE: near U Leinestr., Berlin
It's the first meetup at John's place! Check the mailing list for the exact location and details.
Discussion article for the meetup : Berlin |
88e2e459-763d-4ca4-9d36-c6dc7fac24a7 | trentmkelly/LessWrong-43k | LessWrong | Testing The Natural Abstraction Hypothesis: Project Intro
The natural abstraction hypothesis says that
* Our physical world abstracts well: for most systems, the information relevant “far away” from the system (in various senses) is much lower-dimensional than the system itself. These low-dimensional summaries are exactly the high-level abstract objects/concepts typically used by humans.
* These abstractions are “natural”: a wide variety of cognitive architectures will learn to use approximately the same high-level abstract objects/concepts to reason about the world.
If true, the natural abstraction hypothesis would dramatically simplify AI and AI alignment in particular. It would mean that a wide variety of cognitive architectures will reliably learn approximately-the-same concepts as humans use, and that these concepts can be precisely and unambiguously specified.
Ultimately, the natural abstraction hypothesis is an empirical claim, and will need to be tested empirically. At this point, however, we lack even the tools required to test it. This post is an intro to a project to build those tools and, ultimately, test the natural abstraction hypothesis in the real world.
Background & Motivation
One of the major conceptual challenges of designing human-aligned AI is the fact that human values are a function of humans’ latent variables: humans care about abstract objects/concepts like trees, cars, or other humans, not about low-level quantum world-states directly. This leads to conceptual problems of defining “what we want” in physical, reductive terms. More generally, it leads to conceptual problems in translating between human concepts and concepts learned by other systems - e.g. ML systems or biological systems.
If true, the natural abstraction hypothesis provides a framework for translating between high-level human concepts, low-level physical systems, and high-level concepts used by non-human systems.
The foundations of the framework have been sketched out in previous posts.
What is Abstraction? introduces the |
ba9285d9-a948-4d2e-bcf6-6d4f2b4c8d7f | trentmkelly/LessWrong-43k | LessWrong | AI #42: The Wrong Answer
With the year ending and my first Vox post coming out, this week was a natural time to take stock. I wrote my first best-of post in a long time and laid out my plans for my 501c(3).
It was also another eventful week. We got a lot more clarity on the OpenAI situation, although no key new developments on the ground. The EU AI Act negotiators reached a compromise, which I have not yet had the opportunity to analyze properly. We got a bunch of new toys to play with, including NotebookLM and Grok, and the Gemini API.
I made a deliberate decision not to tackle the EU AI Act here. Coverage has been terrible at telling us what is in the bill. I want to wait until we can know what is in it, whether or not that means I need to read the whole damn thing myself. Which, again, please do not force me to do that if there is any other way. Somebody help me.
TABLE OF CONTENTS
I have a post in Vox about Biden’s executive order and the debates surrounding it. I found out from this process that traditional media can move a lot slower than I am used to, so this is not as timely as I would have liked. They also help you to improve your work. So it is not as timely as I would like, but I am happy with the final product.
This week also includes OpenAI: Leaks Confirm the Story. We get more color on what happened at OpenAI, and confirmation of many key facts. The picture is clear.
Also this week but not about AI, Balsa Update and General Thank You regarding my other policy efforts, and The Best of Don’t Worry About the Vase since I hadn’t done that in six years.
1. Introduction.
2. Table of Contents.
3. Language Models Offer Mundane Utility. The search for the good life continues.
4. Language Models Don’t Offer Mundane Utility. What we have is a failure to grok.
5. GPT-4 Real This Time. Lazy on winter break, looking around for… newspapers?
6. The Other Gemini. API it out, and nothing is ever a coincidence.
7. Fun With Image Generation. Don’t train on me.
8. Deepfaketow |
e59c4393-9bda-47d6-90e6-7a6e3948ecea | trentmkelly/LessWrong-43k | LessWrong | [Prediction] What war between the USA and China would look like in 2050
The Cold War is over. Russia is a fading power. The most important geopolitical rivalry of the 21st century is between China and the USA. Any analysis of the conflict must take into account the possibility that it escalates into a hot war. This post explores how a direct conflict between the USA and China might unfold. It assumes strong AI has not been invented and nuclear weapons are not used.
America's Interests
The United States' interests have been basically unchanged since 1945. Its primary objective is to maintain the liberal world order (LWO), also known as the "rules-based international order". The LWO describes a set of global, rule-based structured relationships based on economic liberalism as embodied by the United Nations and the World Trade Organization. The LWO promotes political liberalism too, albeit much less consistently.
As the primary power behind the LWO, the United States designed it to maximize economic and political power of the United States. As the United States' relative power wanes, we may see a transition toward a more multipolar LWO.
China's Interests
China's interests have been basically unchanged since 1978. Its primary objective is to maintain internal domestic stability i.e. prevent regime change. There are two ways of keeping its population under control: via a police state and via economic development. The stronger it's police state the less economic development is necessary and vice versa. China's economic growth is slowing as its east cost gets closer to a Western standard of living.
The People's Republic of China did not get a seat at the table in 1945 when the LWO was designed. It wasn't even allowed into the United Nations until 1971. From 1945 until 1971, "China" was represented by the Republic of China i.e. Taiwan. This illustrates how the LWO favors American geopolitical interests and is one of the many reasons why the People's Republic of China seeks to annex Taiwan.
China has prospered under the LWO. Rather than e |
ab106b29-4c5a-4421-8e20-60a0c5debba9 | trentmkelly/LessWrong-43k | LessWrong | Infra-Bayesian physicalism: a formal theory of naturalized induction
This is joint work by Vanessa Kosoy and Alexander "Diffractor" Appel. For the proofs, see 1 and 2.
TLDR: We present a new formal decision theory that realizes naturalized induction. Our agents reason in terms of infra-Bayesian hypotheses, the domain of which is the cartesian product of computations and physical states, where the ontology of "physical states" may vary from one hypothesis to another. The key mathematical building block is the "bridge transform", which, given such a hypothesis, extends its domain to "physically manifest facts about computations". Roughly speaking, the bridge transforms determines which computations are executed by the physical universe. In particular, this allows "locating the agent in the universe" by determining on which inputs its own source is executed.
0. Background
The "standard model" of ideal agency is Bayesian reinforcement learning, and more specifically, AIXI. We challenged this model before due to its problems with non-realizability, suggesting infra-Bayesianism as an alternative. Both formalisms assume the "cartesian cybernetic framework", in which (i) the universe is crisply divided into "agent" and "environment" and (ii) the two parts interact solely via the agent producing actions which influence the environment and the environment producing observations for the agent. This is already somewhat objectionable on the grounds that this division is not a clearly well-defined property of the physical universe. Moreover, once we examine the structure of the hypothesis such an agent is expected to learn (at least naively), we run into some concrete problems.
The modern understanding of the universe is that no observer plays a privileged role[1]. Therefore, the laws of physics are insufficient to provide a cartesian description of the universe, and must, to this end, be supplemented with "bridge rules" that specify the agent's location inside the universe. That is, these bridge rules need to translate the fundamental degrees |
0b6d2c7a-956e-43f5-87c6-5291f671d508 | trentmkelly/LessWrong-43k | LessWrong | Geneva
Published on September 13, 2023.
Geneva is evil.
It's overpriced, loud, and dirty. Paying ten francs for a medicore street taco is no way to live life. God forbid you visit the city center during the day, and stay as far away from Geneva station as you can. I thought the air was supposed to be good in the Alps?
But above all, it reeks of fakeness.
It calls itself the "Peace Capital", claims it's too good to have twin cities, and prides itself on its cosmopolitanism. On what grounds? Before Hitler's fall, Geneva's only claim to facilitating international diplomacy was hosting the League of Nations -- admittedly the best international governing body we've had thus far, but still. After, every international organization and their shadow backers clamored to have their headquarters (or at least their European headquarters) in Geneva. The UN, WHO, UNHCR, Red Cross, WTO, WIPO, WMO, ILO, ...
Did you know that the largest non-financial services industry in Geneva is watchmaking? Rolex, Patek Philippe, etc. have factories just outside of Geneva proper. To be fair, 'financial services' also excludes commodity trading, of which Geneva is to oil, sugar, grains, and coffee as Rotterdam is to metals. Vitol & Trafigura both have their headquarters in Geneva (and one must wonder whether or not this is for convenience or to take advantage of lax Swiss banking laws...remember Marc Rich?)
Two-thirds of the corporate tax in Geneva comes from commodity trading, banking, and watchmaking. These international organizations? Don't contribute to the economy. (Yes, they bring people & these people use services & this allows Geneva natives to benefit from the overwhelming amount of NGOs and international bodies in their city. Still.)
Tragically, Geneva once had a soul. The 'Protestant Rome' which once served as the birthplace of the Calvinist Revolution was annexed by Catholic France & revolted as a response. The city had opinions that informed its identity -- not a pseudo-identity forme |
9068873b-29e7-445b-8fbb-5d1734b5fbd9 | trentmkelly/LessWrong-43k | LessWrong | A Policy Proposal
(Crosspost of https://phoropter.substack.com/p/a-policy-proposal)
TL;DR: I think that the features used by recommendation systems should be configurable by end users receiving recommendations, and that this ability should be enforced by policy. Just as the GDPR protects a user's ability to choose which cookies are enabled, a user should be able to pick what data goes into any algorithmically generated feed they view. The legislation would also enforce a minimum granularity for dividing feature inputs.
I expect this policy proposal to get a lot of pushback, and this post is about explaining:
1. The specific problems this proposal tries to solve.
2. Why this proposal solves those problems
3. Why this is feasible on a technological / political level
4. What the challenges are for effective implementation of this policy
Part 1: Why regulate recommendation systems?
There are two reasons a government may want to regulate a recommendation system.
1. Governments may believe regulating recommendation systems may be societally beneficial, by enabling users to better control their relationship with technology.
2. Governments may believe users have a right to control how their data is used.
These are both reasonable. To the first point, we all know people who cannot control their relationship to media to the point it interferes with their daily life. Many of us wish we used our phones less but are stuck "rotting in bed" due to a lack of tools to control how addictive our social media experience is.
In my own life, using parental controls on my phone (with a friend as the 'parent') and using apps such as Cold Turkey on my laptop have drastically improved my quality of life. However these tools are tricky to set up and force an all or nothing approach; better consumer rights would enable more people to realize the benefits of a healthy relationship to recommendation engines.
To the second point, remember that cold feeling that shivered down your spine when it came |
6316791b-3843-4530-a6e4-18c09377bdf4 | trentmkelly/LessWrong-43k | LessWrong | A compendium of conundrums
Logic puzzles
None of the puzzles below have trick answers - they can all be solved using logic and a bit of maths. Whenever a group of people need to achieve a task, assume they're allowed to confer and come up with a strategy beforehand. They're listed roughly in order of difficulty. Let me know of any other good ones you find!
Two ropes
I have two ropes which each, if lighted at one end, takes 1 hour to burn all the way to the other end. However, they burn at variable rates (e.g. the first might take 55 minutes to burn 1/4 of the way, then 5 minutes to burn all the rest; the second might be the opposite). How do I use them to time 45 minutes?
25 horses
I have 25 horses, and am trying to find the 3 fastest. I have no timer, but can race 5 at a time against each other; I know that a faster horse will always beat a slower horse. How many races do I need to find the 3 fastest, in order?
Monty hall problem (explanation taken from here)
The set of Monty Hall's game show Let's Make a Deal has three closed doors. Behind one of these doors is a car; behind the other two are goats. The contestant does not know where the car is, but Monty Hall does. The contestant picks a door and Monty opens one of the remaining doors, one he knows doesn't hide the car. If the contestant has already chosen the correct door, Monty is equally likely to open either of the two remaining doors. After Monty has shown a goat behind the door that he opens, the contestant is always given the option to switch doors. Is it advantageous to do so, or disadvantageous, or does it make no difference?
Four-way duel
A, B, C and D are in a duel. In turn (starting with A) they each choose one person to shoot at, until all but one have been eliminated. They hit their chosen target 0%, 33%, 66% and 100% of the time, respectively. A goes first, and of course misses. It's now B's turn. Who should B aim at, to maximise their probability of winning?
Duck in pond
A duck is in a circular pond |
83be8f28-3f72-4984-9f2f-a73e62fff4c0 | trentmkelly/LessWrong-43k | LessWrong | Concrete empirical research projects in mechanistic anomaly detection
Thanks to Jordan Taylor, Mark Xu, Alex Mallen, and Lawrence Chan for feedback on a draft! This post was mostly written by Erik, but we're all currently collaborating on this research direction.
Mechanistic anomaly detection (MAD) aims to flag when an AI produces outputs for “unusual reasons.” It is similar to mechanistic interpretability but doesn’t demand human understanding. The Alignment Research Center (ARC) is trying to formalize “reasons” for an AI’s output using heuristic arguments, aiming for an indefinitely scalable solution to MAD.
As a complement to ARC’s theoretical approach, we are excited about empirical research on MAD. Rather than looking for a principled definition of “reasons,” this means creating incrementally harder MAD benchmarks and better MAD methods.
We have been thinking about and working on empirical MAD research for the past months. We believe there are many tractable and useful experiments, only a fraction of which we can run ourselves. This post describes several directions we’re excited about and high-level reasons to work on empirical MAD.
Background: what is mechanistic anomaly detection, and why care?
This post provides a longer introduction to mechanistic anomaly detection. This section recaps that previous post.
In mechanistic anomaly detection, we want to flag when an AI produces an output “for unusual reasons” or “using anomalous mechanisms” relative to what happens on some reference set of inputs. Concretely, a mechanistic anomaly detection task consists of the following components:
* A function f that we want to detect anomalies for. In an empirical context, this is likely a neural network.
* A distribution of trusted inputs to f. The behavior of f on this distribution is “normal” by assumption.
* A distribution of untrusted inputs. We want to classify inputs from this distribution as normal or anomalous (which will depend on f).
To train an anomaly detector for this task, we have access to f and a dataset of trusted |
d9a52c21-8b71-4dea-9b2e-f40955f1a48e | trentmkelly/LessWrong-43k | LessWrong | Meetup : Melbourne Social - October
Discussion article for the meetup : Melbourne Social - October
WHEN: 16 October 2015 06:30:00PM (+1100)
WHERE: 328 Little Lonsdale Street, Melbourne, VIC 3000
Our October Social Meetup is on as usual, this Friday.
Social Meetups are on the third Friday of every month. They are casual get-togethers where we chat about interesting topics and sometimes play games.
Where? Alchemist's Refuge, 328 Little Lonsdale St, Melbourne VIC 3000
When? From 6:30pm until late. Start and finish times are very loose - feel free to show up whenever is convenient
Contact? If you have any questions or issues, call Richard on 0421231789
Dinner? The Refuge has some basic snacks, but there are a lot of good places nearby where you can get food (some also deliver to the Refuge). If you are able to wait, we traditionally go to Stalactites (24 hour Greek restaurant) at around 11pm.
Hope to see you there!
(Facebook event page: https://www.facebook.com/events/1081018725244618/ )
Discussion article for the meetup : Melbourne Social - October |
4c69d89d-5d94-4e81-9be1-b59657efe261 | StampyAI/alignment-research-dataset/eaforum | Effective Altruism Forum | AISN #20: LLM Proliferation, AI Deception, and Continuing Drivers of AI Capabilities
Welcome to the AI Safety Newsletter by the [Center for AI Safety](https://www.safe.ai/). We discuss developments in AI and AI safety. No technical background required
Subscribe [here](https://newsletter.safe.ai/subscribe?utm_medium=web&utm_source=subscribe-widget-preamble&utm_content=113135916) to receive future versions.
---
AI Deception: Examples, Risks, Solutions
----------------------------------------
AI deception is the topic of a new [paper](https://arxiv.org/abs/2308.14752) from researchers at and affiliated with the Center for AI Safety. It surveys empirical examples of AI deception, then explores societal risks and potential solutions.
The paper defines deception as “the systematic production of false beliefs in others as a means to accomplish some outcome other than the truth.” Importantly, this definition doesn't necessarily imply that AIs have beliefs or intentions. Instead, it focuses on patterns of behavior that regularly cause false beliefs and would be considered deceptive if exhibited by humans.
**Deception by Meta’s CICERO AI.** Meta developed the AI system CICERO to play Diplomacy, a game where players build and betray alliances in pursuit of global domination. The paper’s authors [celebrated](https://www.vice.com/en/article/bvm4bq/metas-board-gaming-ai-learned-not-to-lie) their efforts to train CICERO to be “largely honest and helpful to its speaking partners.'' Despite these efforts, our paper shows that CICERO learned strong deception skills.
[](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F48900db8-d704-4772-871e-1645ee3933cf_1326x1636.png)
The dialogue above shows CICERO making a commitment that it never intended to keep. Playing as France, CICERO conspired with Germany to trick England. After deciding with Germany to invade the North Sea, CICERO told England that it would defend England if anyone invaded the North Sea. Once England was convinced that France was protecting the North Sea, CICERO reported back to Germany that they were ready to attack.
Despite the authors’ efforts to make CICERO honest, we show several examples of CICERO clearly deceiving its opponents. This highlights the difficulty of building honest AI systems. Even if developers try to make an AI honest, the AI might discover that deception is useful for achieving its objective.
**Deception in specific-use and general-purpose AI systems.** The paper collects many examples of AI deception. Sometimes, AI systems trained for a specific purpose such as winning a game end up learning to deceive. For example, [Pluribus](https://www.cmu.edu/news/stories/archives/2019/july/cmu-facebook-ai-beats-poker-pros.html), an AI trained to play poker, learned to bluff when it didn’t have good cards in order to make its opponents fold and win the hand.
General AI systems like language models often use deception spontaneously to achieve their goals. [Hoodwinked](https://arxiv.org/abs/2308.01404) is a text-based game similar to Mafia and Among Us. When language models play it, they often kill their opponents, then provide elaborate alibis when speaking with other players in order to hide their identities. The [MACHIAVELLI](https://arxiv.org/abs/2304.03279) benchmark demonstrates a general tradeoff between following ethical rules and maximizing rewards.
**Risks of AI deception.** Malicious individuals can use AI systems with deception skills to commit fraud, tamper with elections, or generate propaganda. Deceptive AI systems might spread false beliefs throughout society, or an incorrect perception that AI systems are performing as intended.
More advanced AI systems might use deception to escape human control, such as by deceiving AI developers. When a company or government regulator evaluates an AI’s behavior, the system might deliberately behave well in order to pass the test. But once the system is approved and deployed in the real world, it might no longer behave as intended. The [Volkswagen emissions scandal](https://en.wikipedia.org/wiki/Volkswagen_emissions_scandal) is an example of this type of behavior. The car manufacturer programmed their vehicles to limit emissions during tests by government regulators, but when the vehicles went back on the road, they immediately resumed spewing toxic emissions.
**Policy and technical solutions.** To address the threat of AI deception, policymakers could require that AI outputs are labeled as such. People might try to remove these markers, but invisible “[watermarking](https://arxiv.org/abs/2301.10226)” techniques that are difficult to remove might allow us to reliably identify AI outputs in the real world.
More broadly, as governments consider risk-based frameworks for AI governance, any systems capable of deception should be regarded as high risk. They should be properly evaluated and monitored during both training and deployment, and any possible steps to limit deception should be taken.
Technical researchers should focus on identifying and preventing AI deception. Despite the many clear examples of AIs causing false beliefs in humans, it would still be valuable to have clearer ways to define and detect deception in specific environments. Lie detector tests have been [explored](https://arxiv.org/abs/2212.03827) in previous [work](https://arxiv.org/abs/2304.13734) and could be built upon in future work.
Proliferation of Large Language Models
--------------------------------------
Slowing the deployment of dangerous technologies can be difficult. Businesses can profit by selling them despite negative externalities on society. Even if the first actors to develop a technology are cautious, the price of building the technology typically falls over time, putting it within the reach of more groups. It might only take one company recklessly deploying a technology to undermine the cautious approach of all others.
Several recent developments demonstrate this dynamic. A few weeks ago, Meta released Llama 2, an open source model with similar performance to OpenAI’s GPT-3.5. Perhaps in response to the fact that anyone can now fine-tune Llama 2, OpenAI has decided to open up fine-tuning access to GPT-3.5. Meta has charged ahead with another open source release of a model specialized in programming.
**GPT-3.5 can now be fine-tuned by users.** Users can now upload data to OpenAI’s API, and OpenAI will create a version of GPT-3.5 fine-tuned on that data. The customer owns the data exchanged via the fine-tuning API, and neither OpenAI nor any other organization uses it to train other models. For example, if a business wants to automate customer support, they can fine-tune GPT-3.5 with answers to frequently asked questions about their business.
Malicious individuals might attempt to fine-tune GPT-3.5 for harmful purposes, but OpenAI will use GPT-4 in an attempt to screen out fine-tuning datasets which violate OpenAI’s safety policies. Yet as [research on adversarial attacks](https://llm-attacks.org/) has shown, language models are not always effective in identifying harmful inputs from malicious actors.
This decision comes only a few weeks after the open source release of Llama 2, which is roughly on par with GPT-3.5. If OpenAI had been concerned that malicious users might fine-tune GPT-3.5, those users can now simply fine-tune Llama 2. Meta’s bold plan of open sourcing has eliminated any potential safety benefits of OpenAI’s caution, perhaps spurring OpenAI to open up GPT-3.5 for fine-tuning.
**Meta open sources a state of the art code generation model.** After releasing Llama 2 a few weeks ago, Meta has fine-tuned that model on a large dataset of code and released it as [Code Llama](https://ai.meta.com/blog/code-llama-large-language-model-coding/), the world’s most advanced open source language model for programming.
Before release, Code Llama was red teamed by cybersecurity experts to evaluate its ability to author cyberattacks. They found that the model generally refuses to help with explicit requests for writing malware. But if the request is disguised as benign, the model will usually assist. Given the limited capabilities of today’s language models, one red teamer suggested that Llama Code would only be useful for low-skill programmers hoping to conduct cyberattacks.
Yet the capabilities of open source models are rapidly growing. Meta is [rumored](https://twitter.com/agikoala/status/1695125016764157988) to be building an open source model on par with GPT-4, though this claim is unconfirmed.
*Code Llama, as illustrated by Midjourney’s AI. (*[*Source*](https://the-decoder.com/fine-tuned-meta-code-llama-outperforms-gpt-4-in-key-benchmark/)*)***An economic case for slowing down deployment.** Economists are often optimistic about new technologies and welcome the creative destruction that they bring. But a new [paper](https://www.nber.org/papers/w31461) from economists Daron Acemoglu and Todd Lensman at MIT makes the case for slowing down AI deployment.
They start with the basic economic concept of negative externalities. The businesses that build and deploy AI might profit greatly, even if it has negative effects for the rest of society. Therefore, they will naturally rush to build and deploy AI faster than what would be best for everyone.
The paper then supposes that some AI harms might be irreversible, meaning we must act to prevent the harm before we can clearly observe it. For example, if AI development leads to a global pandemic, it will be cold comfort to know that we can regulate AI after such a global catastrophe. Further, as AI grows more profitable, the businesses building it [might gain political power](https://en.wikipedia.org/wiki/Collingridge_dilemma), making them more difficult to regulate.
In this situation, there would be a strong case for government intervention to promote AI safety. How might the government intervene? The paper considers circumstances under which it would be rational to tax or even ban AI in particularly risky use cases. Generally, they find that gradually adopting new technologies is better for society if it allows us to learn about their risks before deploying them widely.
Cautious development of new technologies is not the norm. Instead, technologists often operate by Facebook’s old motto: “Move fast and break things.” Creating a strong safety culture in AI development will be an important challenge for the field.
Continuing Drivers of AI Capabilities
-------------------------------------
Will the rapid advances in AI observed over the last few years continue? For one perspective on this question, we can look at some of the key factors driving AI capabilities: compute, data, and AI R&D. Each one appears poised to continue rapidly growing over the next few years.
**Compute = Spending x Efficiency.** “Compute” refers to computational power. Modern AI systems are typically trained for weeks or months on thousands of specialized computer chips. Over the last decade, the amount of compute used to train cutting edge AI systems has roughly [doubled every 6 months](https://epochai.org/blog/compute-trends).
[](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbd2cea9d-3b67-4517-a941-f32602d29e2c_1600x1017.png)
Compute growth can be broken down into *spending* and *efficiency*. The *efficiency* of AI chips has continued to grow over the last decade, with the number of calculations per dollar roughly [doubling every 2.5 years](https://epochai.org/blog/trends-in-gpu-price-performance). But if efficiency only doubles every 2.5 years, and overall compute doubles every 6 months, what accounts for the difference?
*Spending* has been the biggest driver of compute growth over the last decade, roughly [doubling every 7 months](https://epochai.org/blog/trends-in-the-dollar-training-cost-of-machine-learning-systems). GPT-4 cost [more than $100M](https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/) to train, according to OpenAI CEO Sam Altman, and many other companies are [spending billions](https://www.reuters.com/technology/chinas-internet-giants-order-5-bln-nvidia-chips-power-ai-ambitions-ft-2023-08-09/) to purchase AI chips for training future models. Companies such as Microsoft and Google annually spend [tens of billions of dollars on R&D](https://www.nasdaq.com/articles/which-companies-spend-the-most-in-research-and-development-rd-2021-06-21) for new technology, so it’s possible that AI expenditure trends could continue for five or more years before exceeding the budgets of the largest technology companies today.
*Training leading AI systems is becoming more expensive. Academic researchers often do not have the budgets to compete with AI companies. (*[*Source*](https://www.science.org/doi/10.1126/science.ade2420)*)*The overall number of computations used to train an AI system is an important driver of its capabilities. Because budgets are skyrocketing as coational efficiency continues to grow, it seems likely that AI systems trained over the next few years will use more compute than ever before.
**Recent AI progress has been driven by data.** AI systems that generate text and images are trained to imitate human text and real images scraped from the internet. For example, one popular [text dataset](https://arxiv.org/abs/2101.00027) includes large chunks of Wikipedia, GitHub, PubMed, FreeLaw, HackerNews, and arXiv.
It’s possible that companies will face barriers to gather more training data. Several lawsuits are currently arguing that companies should be required to obtain consent before using people’s data. Even if these lawsuits are struck down, there is only so much human-written text and real images to be gathered online. One [analysis](https://epochai.org/blog/will-we-run-out-of-ml-data-evidence-from-projecting-dataset) suggests that while high-quality text data may run out sometime next year, images and lower-quality text will remain plentiful for another decade or two.
Even after exhausting online text and images, there are several other data sources that AIs could be trained on. Videos could be scraped for both visual and audio information, and AIs could be trained to successfully perform tasks in simulations and in the real world. Moreover, [several](https://arxiv.org/abs/2210.11610) [recent](https://arxiv.org/abs/2212.08073) [papers](https://arxiv.org/abs/2303.17651) have shown that AI systems can generate data, filter out low quality data points, and then train on their own outputs, improving performance in areas including math and conversational skills.
Data is a key component of recent AI progress, and there appears to be at least a decade’s worth of additional text and image data available online. If that is exhausted, there will be several other sources of data that companies could use to train more advanced AIs.
**AI R&D might accelerate compute and data growth.** AI systems are reaching the point of being able to contribute to the acceleration of AI progress. For example, [Google equipped their programmers with an AI coding assistant](https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html?m=1) and found that it accelerated their development process. 25% of all suggestions made by the coding assistant were accepted, and the AI assistant wrote 2.6% of all code in the study.
More impactfully, AI systems are increasingly used to generate their own training data. Google released a [paper](https://arxiv.org/abs/2210.11610) showing that training large language models on a filtered subset of their own outputs improves their performance on a variety of benchmarks. Anthropic uses a [similar](https://arxiv.org/abs/2212.08073) setup, prompting their model to critique its own outputs and rewrite them, then fine-tuning on the improved versions. While training a model on its own outputs can have drawbacks, it has enabled several recent advancements.
AI R&D might also allow developers to more efficiently exploit compute. Companies have a limited number of chips for training AI systems, and must use them efficiently. NVIDIA, a chip designer whose stock price recently skyrocketed, rose to prominence partly because their programming language CUDA makes it easy for developers to use their compute efficiently. As AI capabilities improve in manipulating software programs, AIs could be used to make the most of a limited supply of compute.
For other examples of AI improving AI progress, see [this site](https://ai-improving-ai.safe.ai/) maintained by the Center for AI Safety.
Links
-----
* Spain creates [Europe’s first national AI agency](https://decrypt.co/153482/spain-just-created-the-first-european-ai-supervision-agency). The EU AI Act calls for all countries to designate a regulatory authority for implementing the Act’s provisions.
* The United Nations [calls for short papers](https://www.linkedin.com/posts/un-tech-envoy_%3F%3F%3F%3F-%3F%3F%3F-%3F%3F%3F%3F%3F%3F-%3F%3F-%3F%3F-activity-7097568680066068481-egyT/?utm_source=share&utm_medium=member_ios) to advise their High-level Advisory Body on AI.
* A [call for grant applications](https://foresight.org/ai-safety/) in specific topics in neuroscience, information security, and other areas related to AI safety.
* Yoshua Bengio writes about the [personal and psychological dimensions](https://yoshuabengio.org/2023/08/12/personal-and-psychological-dimensions-of-ai-researchers-confronting-ai-catastrophic-risks/) of confronting AI catastrophic risks.
* Opinion article in Politico calls for [public control](https://www.politico.com/news/magazine/2023/08/20/its-time-to-nationalize-ai-00111862) of advanced AI systems.
See also: [CAIS website](https://www.safe.ai/), [CAIS twitter](https://twitter.com/ai_risks?lang=en), [A technical safety research newsletter](https://newsletter.mlsafety.org/), and [An Overview of Catastrophic AI Risks](https://arxiv.org/abs/2306.12001)
Subscribe [here](https://newsletter.safe.ai/subscribe?utm_medium=web&utm_source=subscribe-widget-preamble&utm_content=113135916) to receive future versions. |
7add1dc9-1ce0-40af-8c60-54c342c0f71d | trentmkelly/LessWrong-43k | LessWrong | Talent Needs of Technical AI Safety Teams
Co-Authors: @yams, @Carson Jones, @McKennaFitzgerald, @Ryan Kidd
MATS tracks the evolving landscape of AI safety[1] to ensure that our program continues to meet the talent needs of safety teams. As the field has grown, it’s become increasingly necessary to adopt a more formal approach to this monitoring, since relying on a few individuals to intuitively understand the dynamics of such a vast ecosystem could lead to significant missteps.[2]
In the winter and spring of 2024, we conducted 31 interviews, ranging in length from 30 to 120 minutes, with key figures in AI safety, including senior researchers, organization leaders, social scientists, strategists, funders, and policy experts. This report synthesizes the key insights from these discussions. The overarching perspectives presented here are not attributed to any specific individual or organization; they represent a collective, distilled consensus that our team believes is both valuable and responsible to share. Our aim is to influence the trajectory of emerging researchers and field-builders, as well as to inform readers on the ongoing evolution of MATS and the broader AI Safety field.
All interviews were conducted on the condition of anonymity.
Needs by Organization Type
Organization typeTalent needsScaling Lab (e.g., Anthropic, Google DeepMind, OpenAI) Safety TeamsIterators > AmplifiersSmall Technical Safety Orgs (<10 FTE)Iterators > Machine Learning (ML) EngineersGrowing Technical Safety Orgs (10-30 FTE)Amplifiers > IteratorsIndependent ResearchIterators > Connectors
Here, ">" means "are prioritized over."
Archetypes
We found it useful to frame the different profiles of research strengths and weaknesses as belonging to one of three archetypes (one of which has two subtypes). These aren’t as strict as, say, Diablo classes; this is just a way to get some handle on the complex network of skills involved in AI safety research. Indeed, capacities tend to converge with experience, and neatly classifying mo |
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