I’m going to try and write a table of contents for the textbook, just because it seems like a fun exercise.
Epistemic status: unbridled speculation
Volume I: Foundation
Preface [mentioning, ofc, the infamous incident of 2041]
Chapter 0: Introduction
Part I: Statistical Learning Theory
Chapter 1: Offline Learning [VC theory and Watanabe’s singular learning theory are both special cases of what’s in this chapter]
Chapter 2: Online Learning [infra-Bayesianism is introduced here, Garrabrant induction too]
Chapter 3: Reinforcement Learning
Chapter 4: Lifelong Learning [this chapter deals with traps, unobservable rewards and long-term planning]
Part II: Computational Learning Theory
Chapter 5: Algebraic Classes [the theory of SVMs is a special case of what’s explained here]
Chapter 6: Circuits [learning various class of circuits]
Chapter 7: Neural Networks
Chapter 8: ???
Chapter 9: Reflective Learning [some version of Turing reinforcement learning comes here]
Part III: Universal Priors
Chapter 10: Solomonoff’s Prior [including regret analysis using algorithmic statistics]
Chapter 11: Bounded Simplicity Priors
Chapter 12: ??? [might involve: causality, time hierarchies, logical languages, noise-tolerant computation...]
Chapter 13: Physicalism and the Bridge Transform
Chapter 14: Intelligence Measures
Part IV: Multi-Agent Systems
Chapter 15: Impatient Games
Chapter 16: Population Games
Chapter 17: Space Bounds and Superrationality
Chapter 18: Language [cheap talk, agreement theorems...]
Part V: Alignment Protocols
Chapter 19: Quantilization
Chapter 20: Malign Capacity Bounds [about confidence thresholds and consensus algorithms]
Chapter 21: Value Learning [using the intelligence measures from chapter 14]
Chapter 22: ??? [debate and/or some version of IDA might be here, or not]
Chapter 23: ???
Volume II: Algorithms [about efficient algorithms for practical models of computation, and various trade-offs.]
???
Volume III: Applications
I’m going to try and write a table of contents for the textbook, just because it seems like a fun exercise.
Epistemic status: unbridled speculation
Volume I: Foundation
Preface [mentioning, ofc, the infamous incident of 2041]
Chapter 0: Introduction
Part I: Statistical Learning Theory
Chapter 1: Offline Learning [VC theory and Watanabe’s singular learning theory are both special cases of what’s in this chapter]
Chapter 2: Online Learning [infra-Bayesianism is introduced here, Garrabrant induction too]
Chapter 3: Reinforcement Learning
Chapter 4: Lifelong Learning [this chapter deals with traps, unobservable rewards and long-term planning]
Part II: Computational Learning Theory
Chapter 5: Algebraic Classes [the theory of SVMs is a special case of what’s explained here]
Chapter 6: Circuits [learning various class of circuits]
Chapter 7: Neural Networks
Chapter 8: ???
Chapter 9: Reflective Learning [some version of Turing reinforcement learning comes here]
Part III: Universal Priors
Chapter 10: Solomonoff’s Prior [including regret analysis using algorithmic statistics]
Chapter 11: Bounded Simplicity Priors
Chapter 12: ??? [might involve: causality, time hierarchies, logical languages, noise-tolerant computation...]
Chapter 13: Physicalism and the Bridge Transform
Chapter 14: Intelligence Measures
Part IV: Multi-Agent Systems
Chapter 15: Impatient Games
Chapter 16: Population Games
Chapter 17: Space Bounds and Superrationality
Chapter 18: Language [cheap talk, agreement theorems...]
Part V: Alignment Protocols
Chapter 19: Quantilization
Chapter 20: Malign Capacity Bounds [about confidence thresholds and consensus algorithms]
Chapter 21: Value Learning [using the intelligence measures from chapter 14]
Chapter 22: ??? [debate and/or some version of IDA might be here, or not]
Chapter 23: ???
Volume II: Algorithms [about efficient algorithms for practical models of computation, and various trade-offs.]
???
Volume III: Applications
???