2 min
LW - New intro textbook on AIXI by Alex Altair The Nonlinear Library
-
- Education
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New intro textbook on AIXI, published by Alex Altair on May 12, 2024 on LessWrong.
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
Hutter's website
Releasing on May 28th 2024
Available in hardcover, paperback and ebook
496 pages
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
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: New intro textbook on AIXI, published by Alex Altair on May 12, 2024 on LessWrong.
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
Hutter's website
Releasing on May 28th 2024
Available in hardcover, paperback and ebook
496 pages
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
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org
2 min