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.

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.

Computer scientist Scott Garrabrant discusses the challenge of building learning agents for real-world goals. The podcast explores the concept of embedded agents, the four complications of embedded agency, and open problems in world models and subsystem alignment. It also delves into the conflicts that arise when spinning up sub-agents with different goals.

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.

Jesse Clifton, a researcher in transformative artificial intelligence, discusses the potential consequences and risks of TAI. They explore cooperation failures, social dilemmas, and the strategic landscape and governance of TAI systems. Topics include bargaining models of war, simulated conflict, and the importance of credible commitments in multi-agent systems.