I have heard of AIXI but haven't looked deeply into it. I'm curious about it. What are some results you think are cool in this field ?
AIXI isn't isn't a practically realisable model due to its incomputability, but there's nice optimality results, and it gives you an ideal model of intelligence that you can approximate (https://arxiv.org/abs/0909.0801). It uses a universal Bayesian mixture over environments, using the Solomonoff prior (in some sense the best choice of prior) to learn, (in a way you can make formal) as fast as possible, as fast as any agent possibly could. There's some recent work done on trying to build practical approximations using deep learning instead of the CTW mixture (https://arxiv.org/html/2401.14953v1).
(Sorry for the lazy formatting, I'm on a phone right now. Maybe now is the time to get around to making a website for people to link)
I think the biggest thing I like about it is that it exists! Someone tried to make a fully formalized agent model, and it worked. As mentioned above it's got some big problems, but it helps enormously to have some ground to stand on to try to build on further.
1I haven't watched it yet, but there is also a recent technical discussion/podcast episode about AIXI and relatedd topics with Marcus Hutter: https://www.youtube.com/watch?v=7TgOwMW_rnk
Marcus Hutter and his PhD students David Quarel and Elliot Catt have just published a new textbook called An Introduction to Universal Artificial Intelligence.
"Universal AI" refers to the body of theory surrounding Hutter's AIXI, which is a model of ideal agency combining Solomonoff induction and reinforcement learning. Hutter has previously published a book-length exposition of AIXI in 2005, called just Universal Artificial Intelligence, and first introduced AIXI in a 2000 paper. I think UAI is well-written and organized, but it's certainly very dense. An introductory textbook is a welcome addition to the canon.
I doubt IUAI will contain any novel results, though from the table of contents, it looks like it will incorporate some of the further research that has been done since his 2005 book. As is common, the textbook is partly based on his experiences teaching the material to students over many years, and is aimed at advanced undergraduates.
I'm excited for this! Like any rationalist, I have plenty of opinions about problems with AIXI (it's not embedded, RL is the wrong frame for agents, etc) but as an agent foundations researcher, I think progress on foundational theory is critical for AI safety.
Basic info
Table of contents:
Part I: Introduction
1. Introduction
2. Background
Part II: Algorithmic Prediction
3. Bayesian Sequence Prediction
4. The Context Tree Weighting Algorithm
5. Variations on CTW
Part III: A Family of Universal Agents
6. Agency
7. Universal Artificial Intelligence
8. Optimality of Universal Agents
9. Other Universal Agents
10. Multi-agent Setting
Part IV: Approximating Universal Agents
11. AIXI-MDP
12. Monte-Carlo AIXI with Context Tree Weighting
13. Computational Aspects
Part V: Alternative Approaches
14. Feature Reinforcement Learning
Part VI: Safety and Discussion
15. AGI Safety
16. Philosophy of AI