My Halloween lecture has been uploaded to youtube. The basic outline is:

* what is intelligence?
* Solomonoff induction
* Hutter’s AIXI
* Monte Carlo AIXI (here’s the missing video of it playing pac-man)
* universal intelligence measure
* what neuroscience can teach us about AGI design
* early 2020′s: the Halloween scenario

You can get the slides here. I talked for 2 hours, so it’s broken up into many parts on youtube: Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 Part 7 Part 8 Part 9 Part 10 Part 11 Part 12

Thanks to David Wood at ExtroBritannian for organising this, and all the people who attended — especially those who travelled from other cities and countries, the intelligent questions during my talk, and all the positive feedback I’ve received since. Thanks also to Anders Sandberg for the picture of me speaking that I stole from his flicker stream.

The basic idea goes as follows. In full AIXI you have an extended Solomonoff predictor to model the environment, and an expecti-max tree to compute the optimal action. In order to scale AIXI down and still have something of roughly the same form, you need to find a tractable way to replace both of these two items. Here’s what they did: in the place of extended Solomonoff induction a version of context tree weighting (CTW) is used. CTW has to be extended for this application similar to the way Hutter had to extend Solomonoff induction to active environments for AIXI. In the place of the expecti-max tree search a Monte Carlo tree search is used, similar to that used in Go playing programs: initial selection within the tree, tree expansion, a so called play-out policy, followed by a backup stage to propagate the new information back into the model. You have to be a bit careful here because as the agent imagines different future observations and actions it has to update its hypothetical beliefs to reflect these in order for its analysis and decision making to be consistent. Then, once this possible future has been evaluated, the effect of this on the agent’s model of the world has to be unwound so that the agent doesn’t, in effect, start confusing its fantasies with its present reality.

The algorithm is both embarrassingly-parallel and any-time, which is very nice. In less technical language: it would be fairly easy to get it to run efficiently on a massively parallel supercomputer, and it also has the property that it can be forced to decide what action to take at any moment always returning the best action it had been able to compute so far. Thus, if you want a smarter agent, just give it more time and/or CPUs. Already they have shown that MC-AIXI can learn to solve a bunch of basic POMDP problems, including playing a somewhat reasonable game of Pac-man. It would be interesting to see what it was capable of on a supercomputer with ten thousand times the resources of their desktop PC.

A key question for future research is to make better sequence predictors, in particular to be able to identify more complex types of patterns in the agent’s history. I guess all sorts of machine learning techniques could come into play here… and possibly combine to produce quite a powerful RL agent?

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

]]> http://www.vetta.org/2008/07/machine-super-intelligence/feed/ 7 http://www.vetta.org/2008/06/kolmogorov-solomonoff-aixi-etc-questions/ http://www.vetta.org/2008/06/kolmogorov-solomonoff-aixi-etc-questions/#comments Wed, 25 Jun 2008 16:14:45 +0000 Shane Legg http://www.vetta.org/?p=65 Continue reading →]]> Many people seem to have questions about Kolmogorov complexity, Solomonoff induction, algorithmic probability theory, AIXI, the universal intelligence measure and so on.  I don’t always have time to watch all the email lists where these things get discussed, but if you do have any questions, concerns, etc. that you’d like to put to me, feel free to post a question below and I’ll try to answer it.

The award is in “recognition of [my] efforts to improve AI theory” and is worth CAD $10,000.  This will certainly help my budget over the next two years while I study at the Gatsby Unit in London.  So, thank you to SIAI Canada, and to all the Canadians whose donations made this money available!

Speaking of my research, after a long weekend of final edits, corrections, formatting, indexing, embedding fonts and other complexity (I’ll write a blog post about what I had to do at some point), I’ve finally uploaded my thesis “Machine Super Intelligence” to lulu.com and have ordered a test copy.  Once I’ve checked that everything is ok I’ll let you know where copies can be ordered.  Copies should be USD $18 plus shipping for a 200 page casewrap hardcover.  Probably about in a month…