Machine Super Intelligence

My thesis is now available at lulu.com.  As promised, it’s at cost, which works out at $18 plus shipping.  It’s all under a creative commons licence and in a few months I’ll put the pdf online for free.  I’ll also write a post shortly on all the tricks involved in publishing on lulu.com with LaTeX, in case you plan on doing something similar.

Table of Contents

Preface
Thesis outline
Prerequisite knowledge
Acknowledgements

1 Nature and Measurement of Intelligence
1.1 Theories of intelligence
1.2 Definitions of human intelligence
1.3 Definitions of machine intelligence
1.4 Intelligence testing
1.5 Human intelligence tests
1.6 Animal intelligence tests
1.7 Machine intelligence tests
1.8 Conclusion

2 Universal Artificial Intelligence
2.1 Inductive inference
2.2 Bayes’ rule
2.3 Binary sequence prediction
2.4 Solomonoff’s prior and Kolmogorov complexity
2.5 Solomonoff-Levin prior
2.6 Universal inference
2.7 Solomonoff induction
2.8 Agent-environment model
2.9 Optimal informed agents
2.10 Universal AIXI agent

3 Taxonomy of Environments
3.1 Passive environments
3.2 Active environments
3.3 Some common problem classes
3.4 Ergodic MDPs
3.5 Environments that admit self-optimising agents
3.6 Conclusion

4 Universal Intelligence Measure
4.1 A formal definition of machine intelligence
4.2 Universal intelligence of various agents
4.3 Properties of universal intelligence
4.4 Response to common criticisms
4.5 Conclusion

5 Limits of Computational Agents
5.1 Preliminaries
5.2 Prediction of computable sequences
5.3 Prediction of simple computable sequences
5.4 Complexity of prediction
5.5 Hard to predict sequences
5.6 The limits of mathematical analysis
5.7 Conclusion

6 Temporal Difference Updating without a Learning Rate
6.1 Temporal difference learning
6.2 Derivation
6.3 Estimating a small Markov process
6.4 A larger Markov process
6.5 Random Markov process
6.6 Non-stationary Markov process
6.7 Windy Gridworld
6.8 Conclusion

7 Discussion
7.1 Are super intelligent machines possible?
7.2 How could intelligent machines be developed?
7.3 Is building intelligent machines a good idea?

Appendix
A Notation and Conventions
B Ergodic MDPs admit self-optimising agents
B.1 Basic definitions
B.2 Analysis of stationary Markov chains
B.3 An optimal stationary policy
B.4 Convergence of expected average value
C Definitions of Intelligence
C.1 Collective definitions
C.2 Psychologist definitions
C.3 AI researcher definitions

Bibliography
Index