
An Introduction to Universal Artificial Intelligence
Description
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The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?
This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background. You can also visit the author website: http://www.hutter1.net/ai/uaibook2.htm.
Reviews / Votes
"Is it possible to mathematically define and study artificial superintelligence? If that sounds like an interesting question, then this is definitely the book for you. Starting with probability theory, complexity theory and sequence prediction, it takes you right through to the safety of superintelligent machines."- Shane Legg, co-founder of DeepMind
"This is seminal work!"
- Roman Yampolskiy, Tenured Associate Professor at the University of Louisville, USA
"This is an important, timely, high-quality book by highly respected authors."
- Juergen Schmidhuber, Director of the AI Initiative at King Abdullah University of Science and Technology, Scientific Director at the Swiss AI Lab IDSIA, Co-Founder & Chief Scientist at NNAISENSE
"Clearly very strongly based on mathematical foundations. This offers a theoretical depth which will be of value in research, education (at an appropriate level), and for advanced practitioners."
- Alan Dix, Director of the Computational Foundry at Swansea University and Professorial Fellow at Cardiff Metropolitan University
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Persons
David Quarel is completing a PhD at the ANU. He holds a BSc in mathematics and MSc in computer science, specialising in artificial intelligence and machine learning. David has several years' experience in developing course content and distilling complex topics suitable for a wide range of academic audiences, as well as having delivered guest lectures at the ANU, and spent two years as a full-time tutor before starting his PhD.
Elliot Catt is a Research Scientist at DeepMind London and has previously completed a PhD in Universal Artificial Intelligence. He holds a BSc and MSc in mathematics and a PhD in computer science. Elliot has lectured on the topic of Advanced Artificial Intelligence at the ANU and published several pieces of work on the topic of Universal Artificial Intelligence. https://catt.id/
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