(Update: We think the tone of this post was overly positive considering our somewhat weak results. You can read our latest post with more takeaways and followup results here.) This post motivates and summarizes this paper from Redwood Research, which presents results from the project first introduced here. We used adversarial training to improve high-stakes reliability in a task (“filter all injurious continuations of a story”) that we think is analogous to work that future AI safety engineers will need to do to reduce the risk of AI takeover. We experimented with three classes of adversaries – unaugmented humans, automatic paraphrasing, and humans augmented with a rewriting tool – and found that adversarial training was able to improve robustness to these three adversaries without affecting in-distribution performance. We think this work constitutes progress towards techniques that may substantially reduce the likelihood of deceptive alignment.Motivation Here are two dimensions along which you could simplify the alignment problem (similar to the decomposition at the top of this post): 1. Low-stakes (but difficult to oversee): Only consider domains where each decision that an AI makes is low-stakes, so no single action can have catastrophic consequences. In this setting, the key challenge is to correctly oversee the actions that AIs take, such that humans remain in control over time. 2. Easy oversight (but high-stakes): Only consider domains where overseeing AI behavior is easy, meaning that it is straightforward to run an oversight process that can assess the goodness of any particular action.Source:https://www.alignmentforum.org/posts/A9tJFJY7DsGTFKKkh/high-stakes-alignment-via-adversarial-training-redwoodNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Abstract:What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess. By probing for a broad range of human chess concepts we show when and where these concepts are represented in the AlphaZero network. We also provide a behavioural analysis focusing on opening play, including qualitative analysis from chess Grandmaster Vladimir Kramnik. Finally, we carry out a preliminary investigation looking at the low-level details of AlphaZero's representations, and make the resulting behavioural and representational analyses available online.Original text:https://arxiv.org/abs/2111.09259Narrated for AI Safety Fundamentals by TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

With the benefit of hindsight, we have a better sense of our takeaways from our first adversarial training project (paper). Our original aim was to use adversarial training to make a system that (as far as we could tell) never produced injurious completions. If we had accomplished that, we think it would have been the first demonstration of a deep learning system avoiding a difficult-to-formalize catastrophe with an ultra-high level of reliability. Presumably, we would have needed to invent novel robustness techniques that could have informed techniques useful for aligning TAI. With a successful system, we also could have performed ablations to get a clear sense of which building blocks were most important. Alas, we fell well short of that target. We still saw failures when just randomly sampling prompts and completions. Our adversarial training didn’t reduce the random failure rate, nor did it eliminate highly egregious failures (example below). We also don’t think we've successfully demonstrated a negative result, given that our results could be explained by suboptimal choices in our training process. Overall, we’d say this project had value as a learning experience but produced much less alignment progress than we hoped.Source:https://www.alignmentforum.org/posts/n3LAgnHg6ashQK3fF/takeaways-from-our-robust-injury-classifier-project-redwoodNarrated for AI Safety Fundamentals by TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Suppose you want to build a robot to achieve some real-world goal for you—a goal that requires the robot to learn for itself and figure out a lot of things that you don’t already know. There’s a complicated engineering problem here. But there’s also a problem of figuring out what it even means to build a learning agent like that. What is it to optimize realistic goals in physical environments? In broad terms, how does it work? In this series of posts, I’ll point to four ways we don’t currently know how it works, and four areas of active research aimed at figuring it out. This is Alexei, and Alexei is playing a video game. Like most games, this game has clear input and output channels. Alexei only observes the game through the computer screen, and only manipulates the game through the controller. The game can be thought of as a function which takes in a sequence of button presses and outputs a sequence of pixels on the screen. Alexei is also very smart, and capable of holding the entire video game inside his mind.Original text:https://intelligence.org/2018/10/29/embedded-agents/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Abstract:Neural network models have a reputation for being black boxes. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. This helps us better understand the roles and dynamics of the intermediate layers. We demonstrate how this can be used to develop a better intuition about models and to diagnose potential problems. We apply this technique to the popular models Inception v3 and Resnet-50. Among other things, we observe experimentally that the linear separability of features increase monotonically along the depth of the model.Original text:https://arxiv.org/pdf/1610.01644.pdfNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

MIRI is releasing a paper introducing a new model of deductively limited reasoning: “Logical induction,” authored by Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch, myself, and Jessica Taylor. Readers may wish to start with the abridged version. Consider a setting where a reasoner is observing a deductive process (such as a community of mathematicians and computer programmers) and waiting for proofs of various logical claims (such as the abc conjecture, or “this computer program has a bug in it”), while making guesses about which claims will turn out to be true. Roughly speaking, our paper presents a computable (though inefficient) algorithm that outpaces deduction, assigning high subjective probabilities to provable conjectures and low probabilities to disprovable conjectures long before the proofs can be produced. This algorithm has a large number of nice theoretical properties. Still speaking roughly, the algorithm learns to assign probabilities to sentences in ways that respect any logical or statistical pattern that can be described in polynomial time. Additionally, it learns to reason well about its own beliefs and trust its future beliefs while avoiding paradox. Quoting from the abstract: "These properties and many others all follow from a single logical induction criterion, which is motivated by a series of stock trading analogies. Roughly speaking, each logical sentence φ is associated with a stock that is worth $1 per share if φ is true and nothing otherwise, and we interpret the belief-state of a logically uncertain reasoner as a set of market prices, where ℙn(φ)=50% means that on day n, shares of φ may be bought or sold from the reasoner for 50¢. The logical induction criterion says (very roughly) that there should not be any polynomial-time computable trading strategy with finite risk tolerance that earns unbounded profits in that market over time."Original text:https://intelligence.org/2016/09/12/new-paper-logical-induction/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Transformative artificial intelligence (TAI) may be a key factor in the long-run trajectory of civilization. A growing interdisciplinary community has begun to study how the development of TAI can be made safe and beneficial to sentient life (Bostrom 2014; Russell et al., 2015; OpenAI, 2018; Ortega and Maini, 2018; Dafoe, 2018). We present a research agenda for advancing a critical component of this effort: preventing catastrophic failures of cooperation among TAI systems. By cooperation failures we refer to a broad class of potentially-catastrophic inefficiencies in interactions among TAI-enabled actors. These include destructive conflict; coercion; and social dilemmas (Kollock, 1998; Macy and Flache, 2002) which destroy value over extended periods of time. We introduce cooperation failures at greater length in Section 1.1. Karnofsky (2016) defines TAI as ''AI that precipitates a transition comparable to (or more significant than) the agricultural or industrial revolution''. Such systems range from the unified, agent-like systems which are the focus of, e.g., Yudkowsky (2013) and Bostrom (2014), to the "comprehensive AI services’’ envisioned by Drexler (2019), in which humans are assisted by an array of powerful domain-specific AI tools. In our view, the potential consequences of such technology are enough to motivate research into mitigating risks today, despite considerable uncertainty about the timeline to TAI (Grace et al., 2018) and nature of TAI development.Original text:https://www.alignmentforum.org/s/p947tK8CoBbdpPtyK/p/KMocAf9jnAKc2jXriNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. This article focuses on feature visualization. While feature visualization is a powerful tool, actually getting it to work involves a number of details. In this article, we examine the major issues and explore common approaches to solving them. We find that remarkably simple methods can produce high-quality visualizations. Along the way we introduce a few tricks for exploring variation in what neurons react to, how they interact, and how to improve the optimization process.Original text:https://distill.pub/2017/feature-visualization/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Richard Ngo compiles a number of resources for thinking about careers in alignment research.Original text:https://docs.google.com/document/d/1iFszDulgpu1aZcq_aYFG7Nmcr5zgOhaeSwavOMk1akw/edit#heading=h.4whc9v22p7tbNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

By Tom Everitt, Ryan Carey, Lewis Hammond, James Fox, Eric Langlois, and Shane LeggAbout 2 years ago, we released the first few papers on understanding agent incentives using causal influence diagrams. This blog post will summarize progress made since then. What are causal influence diagrams? A key problem in AI alignment is understanding agent incentives. Concerns have been raised that agents may be incentivized to avoid correction, manipulate users, or inappropriately influence their learning. This is particularly worrying as training schemes often shape incentives in subtle and surprising ways. For these reasons, we’re developing a formal theory of incentives based on causal influence diagrams (CIDs).Source:https://deepmindsafetyresearch.medium.com/progress-on-causal-influence-diagrams-a7a32180b0d1Narrated for AI Safety Fundamentals by TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.