Book List
From The Transhumanist Wiki
This is the SL4 book list. It follows more or less the syllabus in So You Want To Be A Seed AI Programmer.
- Cognitive Science
- Functional neuroanatomy
- Functional neuroimaging studies
- Neuropathology; studies of lesions and deficits
- Tracing functional pathways for complete systems
- Computational neuroscience
- Suggestions: Take a look at the cerebellum, and the visual cortex
- Computing in single neurons
- Cognitive psychology
- Cognitive psychology of categories - Lakoff and Johnson
- Cognitive psychology of reasoning - Tversky and Kahneman
- Sensory modalities
- Human visual neurology. Big, complicated, very instructive; knock yourself out.
- Linguistics
- Natural Language Processing
- Natural Language Understanding (2nd Edition) by James Allen [ISBN: 0805303340]
- Word Net: An Electronic Lexical Database by Christiane Fellbaum [ISBN: 026206197X]
- How Children Learn the Meanings of Words by Paul Bloom [ISBN: 0262523299]
- Natural Language Processing
- Note: Some computer scientists think "Cognitive Science" is about Aristotelian logic, programs written in Prolog, semantic networks, philosophy of "semantics", and so on. This is not useful except as a history of error. What we call "Cognitive Science" they call "brain science". I mention this in case you try to take a "Cognitive Science" course in college - be sure what you're getting into.
- Functional neuroanatomy
- Evolutionary Psychology
- Popular evolutionary psychology; dating and mating; Robert Wright and Matt Ridley
- Formal evolutionary psychology; neo-darwinian population genetics and complex adaptation; Tooby and Cosmides
- Game theory; nonzero-sum and zero-sum games
- Evolutionarily stable strategies for social organisms
- Tit-for-tat, the evolution of cooperation, the evolution of cognitive altruism
- Evolutionary psychology of human "significantly more general" intelligence
- Mostly this means reading LOGI; there's not much else out there.
- But see also Lawrence Barsalou and Terrence Deacon.
- Evolutionary Biology
- Incrementally adaptive pathways; levels of organization; etc.
- Biology (a complex system not designed by humans)
- Genetics
- Gene regulatory networks (another good look at evolution's bizarre signature, and also a look at the way humans actually get constructed)
- Quantitative genetics
- Anthropology - the good old days
- Information Theory
- Shannon communication theory
- Shannon entropy
- Shannon information content
- Shannon mutual information
- Kolmogorov complexity
- Solomonoff induction
- Bayesian statistics
- Interpretation of human thought as Bayesian inference - see Jaynes.
- Any other kind of statistics
- Utilitarian Bayesian decisionmaking
- Decision theory is not classically part of "information theory", but does, in fact, belong together with the other items in this category
- Child goals, parent goals, "supergoals" (intrinsic desirability)
- Actions and desirability - read Creating Friendly AI as a preliminary to the longer story.
- Shannon communication theory
- Good Old Fashioned AI
- General
- Artificial Intelligence: A Modern Approach (2nd Edition) by Stuart J. Russell, Peter Norvig [ISBN: 0137903952]
- Intelligent Systems Architecture, Design, and Control - Meystel, Albus
- Empirical Methods for Artificial Intelligence by Paul R. Cohen [ISBN: 0262032252]
- Machine Learning
- Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Ian H. Witten, Eibe Frank [ISBN: 1558605525]
- Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) by Richard S. Sutton, Andrew G. Barto [ISBN: 0262193981]
- Goal-Driven Learning - Graham, Leake
- Machine Learning by Tom M. Mitchell [ISBN: 0070428077]
- Elements of Machine Learning by Pat Langley, Michael B. Morgan [ISBN: 1558603018]
- General
- Computer programming
- Knowledge of many languages
- Java programming (that's probably what we'll end up doing it in)
- Being an excellent programmer
- Parallelism
- Multithreading
- Concurrent Programming in Java(TM): Design Principles and Pattern (2nd Edition) by Doug Lea [ISBN: 0201310090] (features incorporated into Java 1.5)
- Clustered and distributed computing - we may not need this for a while, but then again, we may
- The Grid - Foster, Kesselman [ISBN: 0470853190]
- The Grid 2: Blueprint for a New Computing Infrastructure by Ian Foster, Carl Kesselman [ISBN: 1558609334]
- Multithreading
- Any kind of experience working with complicated dynamic data patterns controlled by compact mathematical algorithms - some of the interior of the AI may end up looking like this
- Other stuff
- Computer security (experience with defensive caution; not that it's sufficient for Friendly AI or even a good attitude, but it's a start)
- Physics
- Thermodynamics
- The second law of thermodynamics
- Noncompressibility of phase space
- The arrow of time and the development of complex structure
- The second law of thermodynamics
- Traditional AI methods
- History of error; don't repeat past mistakes
- We might reuse one or two design patterns at some point
- Transhumanism or transhumanist SF - sufficient exposure to have a very high future shock tolerance; helps to "take it all in stride"
- Mathematics
- Engineering
- Replicating Systems
- "Kinematics of Self Replicating Machines", Freitas & Merkle http://www.molecularassembler.com/KSRM.htm
- Replicating Systems
Book List General Physics Sites
Book List General Physics Books
This is the stuff I've read recently that I thought was specifically relevant and reasonably useful (two stars or more) for seed & Friendly AI;
***** : 'Godel Escher Bach: An Eternal Golden Braid' (Douglas Hofstadter) ***** : 'Fluid Concepts and Creative Analogies' (Douglas Hofstadter) ***** : 'Levels of Organisation in General Intelligence' (Eliezer Yudkowsky) ***** : 'Collective Volition' and 'Dialogue On Friendliness' (Eliezer Yudkowsky) ***** : 'Probability Theory' (E.T. Jaynes) **** : Perceptual Symbol Systems' and related papers (Lawrence Barsalou) **** : 'Thinking and Deciding' (Jonathan Baron) **** : 'What Is Thought?' (Eric Baum) **** : 'Metamagical Themas' (Douglas Hofstadter) **** : 'The Origins of Virtue' (Matt Ridley) **** : 'Creating a Friendly AI' (Eliezer Yudkowsky) *** : 'The MIT Encyclopedia of the Cognitive Sciences' *** : 'The Adapted Mind' (Barkow, Cosmides, Tooby) *** : 'The Symbolic Species' (Terrence Deacon) *** : 'Consciousness Explained' (Daniel Dennett) *** : 'Sources of Power' (Gary Klein) *** : 'The Society of Mind' (Marvin Minsky) *** : 'Analogy Making as Perception' (Melanie Mitchell) *** : 'Induction: Processes of Inference, Learning and Discovery' (John Holland et al) ** : 'Bright Air, Brilliant Fire: On the Matter of the Mind' (Gerald Edelman) ** : 'Judgment Under Uncertainty' (D. Kahneman, P. Slovic, A. Tversky et al) ** : 'Metaphors We Live By' (G. Lakoff) ** : 'An Introduction to Genetic Algorithms' (Melanie Mitchell) ** : 'Unified Theories of Cognition' (Allen Newell) ** : 'Artificial Intelligence: A New Synthesis' (Nils Nilsson) ** : 'How the Mind Works' (Steven Pinker) ** : 'Consilience: The Unity of Knowledge' (Edward Wilson) ** : 'Simple Heuristics That Make Us Smart' (Gerd Gigerenzer, Peter M. Todd)
Pearl's 'Causality' looks good but I've only read a few chapters. Seed AI absolutely requires strong basic computer science knowledge; all the seemingly impractical stuff that a compsci degree teaches you but 90% of graduates promptly forget, along with at least competent programming and systems architecture. Basic logic, probability, information, game and decision theory are essential, plus evpsych mainly for avoiding countless common pitfalls.
- Starglider
