Human-Level Machine Learning

1. Human-Level Machine Learning Selmer Bringsjord, Nick Cassimatis, Kostas Arkoudas, and Bettina SchimanskiDepartment of Cognitive ScienceDepartment of Computer ScienceRensselaer Polytechnic Institute (RPI)Troy NY 12180 USADecember 9 2004 @ NSF

2. RAIR Lab Sponsors Deontic/Doxastic Reasoning • -Cracking Project; “Superteaching” hypothesis generation; AI in support of IA “Poised-For” Learning Slate (Intelligence Analysis) test generation advanced synthetic charactrs synthetic characters/psychological time

3. The Problem: Machine learning is dominated by forms of learning that are impoverished relative to the human case. Humans often learn by leveraging an ensemble of “pre-established” heterogeneous reasoning mechanisms and vast amounts of prior knowledge. Solution/Goal: Formalize human learning and rich cognitive mechanisms that underlie and enable it. Implement these formalizations to produce “human-level” machine learning, and corresponding applications. Applications: Software and robotic applications; in our case, specifically Homeland defense/intelligence analysis tools Elder-care robots that are quickly adapt to their owners Improve learning in humans: Intelligent tutoring systems in math,/logic/computer science More precise understanding of learning disabilities for less traumatic interventions Overview

4. Formal Models of Human-Level Learning Can Help Close Learning Gaps • Learning gaps (esp in math) between: • US and other countries • The latest PISA and TIMSS point to an outright crisis! • 12.7.04 WSJ • High-achieving and low-achieving students within US • High-achieving and low-achieving schools within US • A precise, formal understanding of learning would enable us to • pinpoint the factors that enable rapid, explosive learning; • build machines able to augment human teaching (which for various reasons is failing) in the math/logic/comp sci area

5. Machine Learning Today:Costly Trial and Error • Traditional machine learning: • Learn only after many repetitions of trial and error • Stuck on function-based model • E.g., Language: WSJ Corpus, 1987-1989, with 39 million words • Explanation-Based Learning uses only primitive reasoning/knowledge compared to the full human-level arsenal of heterogeneous reasoning and knowledge • Hurts with applications: • Trial and error not good in cases where errors kill • Medical robotics • Thousands of learning trials can be expensive • Acquainting a robot with a new hospital would take days • Teaching people new software makes them less productive in the short-term. Machines train us now instead of us training them. • Learning trials often not available • Homeland security: Not thousands of people in flight schools • Robots and software therefore limited to narrow tasks and inflexible • We are forced to assemble machine knowledge manually • CYC has over a million facts and is not even remotely complete

6. Some Motivating Examples... Millions of students are currently learning primarily by reading -- and ditto e.g. for adult researchers like us!

7. Example 1: Suppose You Were Tasked to Learn About Astronomy! The scorpion lies between Libra and Sagittarius in the Milky Way. It is not hard to imagine this pattern of starts resembling a scorpion, with its claws and stinging tail. An arc of stars marks the curve of its raised tail and the fiery red star Antares lies at is heart...

8. Example 2: Human One-shot Learning(a simple example) USB CONVERTOR CUP

9. Insert movie here (Nick has a copy)

10. The traditional machine learning approach...

11. Behavior of Micro-PERI

12. Implications of One-Shot Learning and Learning by Reading • Learning by reading and one-shot learning examples require: • Rich set of representation and reasoning abilities early on • Where was speaker lookingwhen he said “USB Converter”. • Social reasoning to track where speaker was looking. • Spatial and temporalreasoning to infer what he was looking at. • Diagrammatic reasoning • Existing machine learning algorithms have no notion of space, time or human attention. • Statistical generalization just one of several learning strategies; also need: • Inference (deductive, abductive, inductive, ...) from single group of percepts • Analogy • Imitation • Instruction • Learning much more socially and physically interactive. • Ask questions: Why? How? What if? Physically test their own hypotheses about the world. • And, in learning by reading... • the best learners are those who “pre-test” themselves, and hence acquire “poised-for” knowledge that marks true learning

13. To Solve the Problem:A New (5-step) Research Program 1 Without flinching, study the human case -- humans (including kids) who learn rapidly, including learning by reading • Developmental psychology has shown that even infants and toddlers have rich notions of: • Time, place, causality, belief, desire, attention, number, etc., and of inference over these concepts 2 Develop formal theories that show how to use these factors to make learning faster and more effective 3 Develop machine learning algorithms using these formalizations that learn by: • Explicit reading and instruction • Analogical reasoning • Deduction, Abduction, etc. • Imitation • Visual reasoning 4 Build applications from these algorithms that have broad impact • Elder care • Homeland security 5 Trace out the implications of these algorithms for better teaching/learning in the human sphere, particularly in mathematics/logic instruction • address “Math Gap” • including intelligent tutoring systems and synthetic characters

14. Our Approach Forges a Bridge SBE CISE Behavioral & Cognitive Sciences Artificial Intelligence and Cognitive Science ? ?

15. The Right Time:Resurrection of Human-Level AI • Recognition of need for human-level AI and integrated cognitive systems growing: • Dedicated issue of AAAI’s journal of record (AI Magazine) to be devoted to human-level AI • Cassimatis editor, Bringsjord, Arkoudas, Schimanski contributors • AAAI Fall Symposium on Integrated Cognition (Cassimatis led) • “Grand Cognitive Challenges” under discussion @ DARPA’s Learning-Focused IPTO • “Psychometric AI” a candidate • Hundreds of studies in infant cognition give us a good idea of what the right substrate is. • Integrated cognitive models exist and are advancing every day • Computational infrastructure there: • Abundant computational power for multiple methods in one system • Formal methods exploding with new power (e.g., Athena) • Robot and machine vision infrastructure in place: • Object recognition • Face recognition, eye-tracking • Mobility and navigation • Robot manipulation So the time is ripe for human-level machine learning.

16. Applications

17. Some Applications • High-stakes applications where trial and error too dangerous. • Homeland security. • Hazardous waste removal. • Robots and software for less sophisticated or learning-challenged humans use them. • Disabled. • Elder care. • Elder-care robots easier to use by the older set. • Emerging Robotics Technologies & Applications Conference Proceedings, March 9-10, 2004, Cambridge, MA • Rodney Brooks mentioned Elderly Care as one of the current future trends in robotics: • Currently: None • Future: Robotic Assistants in Millions of Households • Less brittle, more general, easier-to-learn and use robots and software. • Better learning environments: • Direct/instruct robots (PERI) • More accurate pinpoint causes of problem learning.

18. A catalyst grant for ...? • Carry out proof-of-concept version of entire 5-step research agenda • Build team to implement this sequence • part of team that would presumably power full SLC on Human-Level Machine Learning • Build proof-of-concept • p-o-c would run all the way through our proposed 5-step R&D sequence, start to finish • application/implementation: • homeland defense • Elder care robot • ITS for math/logic/comp sci • Workshops/Symposia • Conference presentations • Publications • Web site from the very start

19. END

20. Objection • How is this an improvement over GOFAI? i.e., Why isn’t this the 1970s all over again? • Less knowledge of human learning then • Formal methods in their infancy • Nothing like Athena (used to prove a good part of Unix sound)! • Like two-layer neural networks compared to bigger ones • Formal infrastructure was fragmented. Not known how to combine logical and probabilistic knowledge? • So researchers were either using no representation and reasoning substrate or they were using the wrong one. • Integrated cognitive models for combining methods not developed, • Polyscheme, ACT-R, ... • These techniques were not interactive. • No question asking • No tracking or reasoning about human intent • No experimentation

21. PERIPsychometric Experimental Robotic Intelligence • Scorbot-ER IX • Sony B&W XC55 Video Camera • Cognex MVS-8100M Frame Grabber • Dragon Naturally Speaking Software • NL (Carmel & RealPro?) • BH8-260 BarrettHand Dexterous 3-Finger Grasper System

22. Our Assets • Background in intersection of reasoning and formal methods, and learning • Bringsjord, Cassimatis, Arkoudas, and Schimanski • Prior R&D in logic-based machine learning. • Bringsjord, Arkoudas • Background in child development. • Cassimatis • Integrated cognitive models • All four • Background in robotics • Cassimatis, Bringsjord, Schimanski

23. Prior Related Work on One-Shot Learning • There isn’t anything that maches up perfectly. • But, related, we have:

24. Prior Related Work on Learning by Reading • Ask for pointers from Ken Forbus...

25. Impact on Machine Learning and AI • More flexible and resourceful learning and reasoning algorithms • Intellectually flexible robots (again, e.g., PERI) • Quantum leap in machine learning • Learning in situations that were impossible before • Integration of reasoning community back into learning community • Impact back on education, including machine-assisted education (e.g., intelligent tutoring systems & synthetic characters)

26. Impact on Study of Human Learning • Existing empirical work hampered by vague theories that make results of simple experiments controversial. • Formal theory should help this • Develop better understanding of which instruction or learning techniques are best in which circumstances. • More specifically: • Will produce new pedagogy linking learning to reasoning (mathematics/logic a beneficiary) • Will produce revolutionary advances in intelligent tutoring systems, synthetic characters/simulation)