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We Have Not Yet Begun to Learn

This article explores the difficulties in creating AI systems that truly understand and learn like humans and animals, emphasizing the necessity of verifying knowledge for reliability. The text delves into the limitations and complexities of current AI systems and proposes a focus on transition knowledge, emphasizing the importance of verification for achieving powerful AI capabilities.

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We Have Not Yet Begun to Learn

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  1. We Have Not Yet Begun to Learn Rich Sutton AT&T Labs

  2. We Have Not Yet Begun to Learn None of our ML and RL systems learn anything like the things that people animals know and use everyday

  3. Intelligence can be definedas using knowledge flexiblyto achieve goals/purposes A working definition that matches our intuitions Sufficiently sufficient

  4. AI systems Knowledge • Chess-playing programs • Planners • Heuristic search • Dynamic programming • SOAR • Calendar agents • Reactive robots • Navigating robots • Theorem Provers • CYC • Quality of knowledge • amount • scope • relevance/utility • accuracy • Flexibility with which knowledge is used • Kinds of knowledge • policy • transition • implication • value

  5. So What Holds AI Back? • For the problems of interest,it is hard to get the knowledge right • people must manually tune it to make it right • and often it’s not quite clear what “right” means • may be “right” mainly because of what it causes to happen • The complex web of knowledge becomes unwieldy • brittle • difficult to change • unreliable • More responsibility needs to be given to the machine • To autonomously maintain and verify its knowledge

  6. Reliable Knowledge Requires Verification • To know something reliably, robustly,you have to be able to tell, by yourself, whether it is correct • policies do they get reward? • predictions do they come true? • theorems do they have valid proofs? • plans do they achieve their goals?

  7. Conclusion:Reliable Knowledge Requires Verification • We can distinguish • 1. Having knowledge • 2. Having the ability to verify knowledge • I.e., there is somethingbeyondhaving knowledgewhich we might call understanding its meaningand which is key in practice to building powerful AIs

  8. Let’s Focus on Transition Knowledge • Projective/predictive knowledge of what follows what • Strips Operators • Action models • Physics, dynamics, causation • The key kind of knowledge in planners/search systems • chess players, state-based planning, SOAR • A paradigm case in knowledge that can be verifiedand learned from experience

  9. Verifying Transition Knowledge • Must have experience • Knowledge must be expressed in term of experience • Verification must be in terms of experience • “Dyna” and successors did this for 1-step transitions • But 1-step predictions are not expressive enough • Need predictions of arbitrary experiments • a closed-loop policy • with closed-loop, temporally-flexible termination • Need something like option models + options

  10. Anatomy of a Super-Prediction 1 Predictor (option model) Recognizes the conditions, makes the prediction 2 Experiment (option) - policy - termination condition - measurement function(s) knowledge verifier

  11. “Reliable Knowledge Requires Verification”is an Example of Purposive Design • “Purposive” = control by consequences • Fixed ends achieved by variable means • Widely seen as the hallmark of mind • “the mark and criterion of mentality” William James, 1890 • “Purposive Behavior in Animals and Man” Edward Tolman, 1932 • Achieving systems vs Purposive systems achieve the fixed end more often, by varying means. requires ability to verify/recognize the fixed end achieve the fixed end

  12. There is a tension betweenachievement and purposive design • It’s always easier to build than to meta-build • easier to write a policy than a policy-learner • easier to plan than to write a planner • easier to add knowledge than to add its verifier • Even at the policy level, many advocate direct building • reactive systems • simple expert systems • At the knowledge level, it is easier to rely on human interpretation than to write explicit verifiers • can talk at human level - objects, times, properties, space • rather than experience level - actions, observations, rewards

  13. Conclusion:Reliable Knowledge Requires Verification • We can distinguish • 1. Having knowledge • 2. Having the ability to verify knowledge • I.e., there is somethingbeyondhaving knowledgewhich we might call understanding its meaningand which is key in practice to building powerful AIs

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