AI as a Precise Art

1. AI as a Precise Art Eliezer Yudkowsky Singularity Institute for Artificial Intelligence singinst.org

2. Cards 70% blue, 30% red, randomized sequence • Subjects paid 5¢ for each correct guess • Subjects only guessed blue 76% of the time (on average) • Optimal strategy is "Always guess blue" • Strategy need not resemble cards - noisy strategy doesn't help in noisy environment (Tversky, A. and Edwards, W. 1966. Information versus reward in binary choice. Journal of Experimental Psychology, 71, 680-683.) Eliezer Yudkowsky Singularity Institute for AI

3. Vernor Vinge: Can't predict any entity smarter than you, or you would be that smart • Deep Blue played better chess than its programmers, from which it follows that programmers couldn't predict exact move • Why go to all that work to write a program whose moves you couldn't predict? Why not just use a random move generator? • Takes vast amount of work to craft AI actions predictably so good you can't predict them • We run a program because we know something about the output and we don't know the output Eliezer Yudkowsky Singularity Institute for AI

4. Gilovich: If we wish to disbelieve, we ask if the evidence compels us to accept the discomforting belief. If we wish to believe, we ask if the evidence prohibits us from keeping our preferred belief. • The less you know, the less likely you are to get good results, but the easier it is to allow yourself to believe in good results. (Gilovich, T. 2000, June. Motivated skepticism and motivated credulity: Differential standards of evidence in the evaluation of desired and undesired propositions. Address presented at the 12th Annual Convention of the American Psychological Society, Miami Beach, Florida. Quoted in Brenner, L. A., Koehler, D. J. and Rottenstreich, Y. 2002. "Remarks on support theory: Recent advances and future directions." In Gilovich, T., Griffin, D. and Kahneman, D. eds. 2003. Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge, U.K.: Cambridge University Press.) Eliezer Yudkowsky Singularity Institute for AI

5. Mind Projection Fallacy: If I am ignorant about a phenomenon, this is a fact about my state of mind, not a fact about the phenomenon. Confusion exists in the mind, not in reality. There are mysterious questions. Never mysterious answers. (Inspired by Jaynes, E.T. 2003. Probability Theory: The Logic of Science. Cambridge: Cambridge University Press.) Eliezer Yudkowsky Singularity Institute for AI

6. "The influence of animal or vegetable life on matter is infinitely beyond the range of any scientific inquiry hitherto entered on. Its power of directing the motions of moving particles, in the demonstrated daily miracle of our human free-will, and in the growth of generation after generation of plants from a single seed, are infinitely different from any possible result of the fortuitous concurrence of atoms... Modern biologists were coming once more to the acceptance of something and that was a vital principle." -- Lord Kelvin Eliezer Yudkowsky Singularity Institute for AI

7. Intelligence Explosion • Hypothesis: The smarter you are, the more creativity you can apply to the task of making yourself even smarter. • Prediction: Positive feedback cycle rapidly leading to superintelligence. • Extreme case of more common belief that reflectivity / self-modification is one of the Great Keys to AI. (Good, I. J. 1965. Speculations Concerning the First Ultraintelligent Machine. Pp. 31-88 in Advances in Computers, 6, F. L. Alt and M. Rubinoff, eds. New York: Academic Press.) Eliezer Yudkowsky Singularity Institute for AI

8. If a transistor operates today, the chance that it will fail before tomorrow is greater than 10-6 (1 failure per 3,000 years) Eliezer Yudkowsky Singularity Institute for AI

9. If a transistor operates today, the chance that it will fail before tomorrow is greater than 10-6 (1 failure per 3,000 years) • But a modern chip has millions of transistors Eliezer Yudkowsky Singularity Institute for AI

10. If a transistor operates today, the chance that it will fail before tomorrow is greater than 10-6 (1 failure per 3,000 years) • But a modern chip has millions of transistors • Possible because most causes of transistor failure not conditionally independent for each transistor Eliezer Yudkowsky Singularity Institute for AI

11. If a transistor operates today, the chance that it will fail before tomorrow is greater than 10-6 (1 failure per 3,000 years) • But a modern chip has millions of transistors • Possible because most causes of transistor failure not conditionally independent for each transistor • Similarly, an AI that remains stable over millions of self-modifications cannot permit any significant probability of failure which applies independently to each modification Eliezer Yudkowsky Singularity Institute for AI

12. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory Eliezer Yudkowsky Singularity Institute for AI

13. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory • A formal proof of ten billion steps can still be correct (try this with informal proof!) Eliezer Yudkowsky Singularity Institute for AI

14. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory • A formal proof of ten billion steps can still be correct (try this with informal proof!) • Humans too slow to check billion-step proof Eliezer Yudkowsky Singularity Institute for AI

15. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory • A formal proof of ten billion steps can still be correct (try this with informal proof!) • Humans too slow to check billion-step proof • Automated theorem-provers don't exploit enough regularity in the search space to handle large theorems Eliezer Yudkowsky Singularity Institute for AI

16. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory • A formal proof of ten billion steps can still be correct (try this with informal proof!) • Humans too slow to check billion-step proof • Automated theorem-provers don't exploit enough regularity in the search space to handle large theorems • Human mathematicians can do large proofs Eliezer Yudkowsky Singularity Institute for AI

17. Modern chip may have 155 million interdependent parts, no patches after it leaves the factory • A formal proof of ten billion steps can still be correct (try this with informal proof!) • Humans too slow to check billion-step proof • Automated theorem-provers don't exploit enough regularity in the search space to handle large theorems • Human mathematicians can do large proofs • ...but not reliably Eliezer Yudkowsky Singularity Institute for AI

18. Solution: Human+AI • Human generates lemmas, mysteriously avoiding exponential explosion of search space • Complex theorem-prover generates formal proof leading to next lemma • Simple verifier checks proof • Could an AGI use a similar combination of abilities to carry out deterministic self-modifications? Eliezer Yudkowsky Singularity Institute for AI

19. Solution: Human/AI synergy • Human generates lemmas, mysteriously avoiding exponential explosion of search space • Complex theorem-prover generates formal proof • Simple verifier checks proof • Could an AGI use a similar combination of abilities to carry out deterministic self-modifications? Eliezer Yudkowsky Singularity Institute for AI

20. Inside of a chip is deterministic environment • Possible to achieve determinism for things that happen inside the chip Eliezer Yudkowsky Singularity Institute for AI

21. Inside of a chip is deterministic environment • Possible to achieve determinism for things that happen inside the chip • Success in external world not deterministic, but AI can guarantee that its future self will try to accomplish the same things – this cognition happens within the chip Eliezer Yudkowsky Singularity Institute for AI

22. Inside of a chip is deterministic environment • Possible to achieve determinism for things that happen inside the chip • Success in external world not deterministic, but AI can guarantee that its future self will try to accomplish the same things – this cognition happens within the chip • AI cannot predict future self's exact action, but knows criterion that future action will fit Eliezer Yudkowsky Singularity Institute for AI

23. Difficult to formalize argument! • Bayesian framework breaks down on infinite recursion • Not clear how to calculate the expected utility of changing the code that calculates the expected utility of changing the code... Eliezer Yudkowsky Singularity Institute for AI

24. Difficult to formalize argument! • Bayesian framework breaks down on infinite recursion • Not clear how to calculate the expected utility of changing the code that calculates the expected utility of changing the code... • Yet humans don't seem to break down when imagining changes to themselves • Never mind an algorithm that does it efficiently – how would you do it at all? Eliezer Yudkowsky Singularity Institute for AI

25. Wanted: Reflective Decision Theory • We have a deep understanding of: • Bayesian probability theory • Bayesian decision theory • Causality and conditional independence • We need equally deep understanding of: • Reflectivity • Self-modification • Designing AI will be a precise art when we know how to make an AI design itself Eliezer Yudkowsky Singularity Institute for AI

26. Thank you. Eliezer Yudkowsky Singularity Institute for Artificial Intelligence singinst.org