Artificial Intelligence: Beyond Machine Learning
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The past decade or so has seen such rapid advances in supervised deep learning and neural networks that those areas, and machine learning more generally, have become almost synonymous with AI especially in popular media. However, there are other broad areas of research that have fed into AI historically and continue to be important today. In this talk, Red Hat’s Gordon Haff will place machine learning within this set of broader science and engineering specialties that include cognitive psychology, control theory, linguistics, and human factors. The goal is to provide attendees with a broader context for both learning and applying cross-disciplinary fields of study to their AI-related work.
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Artificial Intelligence: Beyond Machine Learning
- 1. 1 Artificial Intelligence: Beyond Machine Learning Gordon Haff Emerging Technology Evangelist @ghaff https://bitmason.blogspot.com September 2020
- 2. @ghaff https://bitmason.blogspot.com 2 Research Applied Machine Learning Deep Learning Neurophysiology and Cognitive Science Linguistics & NLP Human/machine interactions Supervised learning Unsupervised learning Reinforcement learning Domain expertise Robotics Data privacy Data science & statistics AI
- 3. @ghaff https://bitmason.blogspot.com 3 Research Applied Machine Learning Deep Learning Supervised learning Unsupervised learning Reinforcement learning Data science AI
- 4. @ghaff https://bitmason.blogspot.com 4 Thinking Humanly Cognitive modeling Informed by neurophysiology Thinking Rationally Logicist tradition Intelligence based on logical relationships Acting Humanly Turing Test Computer vision, robotics, language, reasoning Acting Rationally Rational agent approach Achieve best or best expected outcome Russell & Norvig Source: Artificial Intelligence: A Modern Approach by Russell and Norvig.
- 5. @ghaff https://bitmason.blogspot.com 5 Learning agent Source: Russell & Norvig
- 6. @ghaff https://bitmason.blogspot.com Behavioral Psychology How do humans/animals think & act?
- 7. @ghaff https://bitmason.blogspot.com 7 Source: James Somers September 29, 2017 https://www.technologyreview.com/s/608911/is-ai-ri ding-a-one-trick-pony/
- 8. @ghaff https://bitmason.blogspot.com 8 “We can report steady progress. All the way to the top of the tree.” Russell and Norvig
- 9. @ghaff https://bitmason.blogspot.com 9 Linguistics & NLP Neurophysiology and Cognitive Science Human/machine interactions Data privacy What is AI? Philosophy Mathematics Economics Control theory System engineering
- 10. @ghaff https://bitmason.blogspot.com Philosophy ● Can formal rules be used to draw valid conclusions? ● How does the mind arise from the physical brain? ● Where does knowledge come from? ● How does knowledge lead to action?
- 11. @ghaff https://bitmason.blogspot.com Mathematics formalizes the rules ● What are the formal rules to draw valid conclusions? ● What can be computed? ● How do we reason with uncertain information? Source: Wikimedia
- 12. @ghaff https://bitmason.blogspot.com Economics ● How should we make decisions to maximize payouts? ● How should we do this when others may not go along? ● How should we do this when the payout is far in the future?
- 13. @ghaff https://bitmason.blogspot.com System engineering and human/machine interactions ● How do we design in safety at the level of the complete system? ● How do we anticipate emergent behaviors in complex artifacts controlled by software? ● How do human and computer decisions interact in systems with embedded autonomy?
- 14. @ghaff https://bitmason.blogspot.com 14 Linguistics & NLP Neurophysiology and Cognitive Science Human/machine interactions Data privacy What is AI? Philosophy Mathematics Control theory System engineering Economics
- 15. @ghaff https://bitmason.blogspot.com 15 Thinking Humanly Cognitive modeling Informed by neurophysiology Thinking Rationally Logicist tradition Intelligence based on logical relationships Acting Humanly Turing Test Computer vision, robotics, language, reasoning Acting Rationally Rational agent approach Achieve best or best expected outcome Russell & Norvig
- 16. @ghaff https://bitmason.blogspot.com 1956: “Birth” of AI as field
- 17. @ghaff https://bitmason.blogspot.com Two months later… Birth of cognitive science IEEE Symposium at MIT ● Often dated to MIT workshop in September 1956 ○ The Magic Number Seven (Miller) on memory ○ Three Models of Language (Chomsky) ○ The Logic Theory Machine (Newell/Simon) ● Interdisciplinary field ● Term actually coined in 1973 by Christopher Longuet-Higgins Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg
- 18. What’s the computational basis for ● Learning concepts ● Judging similarity ● Inferring causal connections ● Forming perceptual representations ● Learning word meanings and syntactic principles in natural language ● Predicting the future ● Developing physical world intuitions ?
- 19. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg One notion of intelligence ● Classifying/recognizing/predicting data ○ Neural networks (can sometimes) do this effectively ○ Humans do this as well
- 22. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg A second notion of intelligence ● Explaining/understanding/modeling the world ○ Complementary to the first notion ○ But more powerful ● A good explanation: ○ Compact / unifying / non-arbitrary / ”hard to vary” ○ Causal / actionable for an endless range of tasks, via planning ○ Compositional / flexible / extensible
- 23. @ghaff https://bitmason.blogspot.com “How do we humans get so much from so little? Josh Tenenbaum (MIT) Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg
- 24. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg Where are the people?
- 25. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg Is there a hidden cause of clustering?
- 26. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg A “common sense core”? Some research areas ● Learning as “theory building”, not “data analysis” ● Human thought is structured around a basic understanding of physical objects, intentional agents, and their interactions ○ Intuitive physics (forces, masses…) ○ Intuitive psychology (desires, beliefs, plans…) ● Modeling engine built on probabilistic programs (but Bayesian networks not enough)
- 27. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg “Critiques” of cognitive science ● Neglects the role of emotions in human thinking ● Disregards the role of physical environments in human thinking ● Neglects the contribution of embodiment to human thought and action ● Human thought is inherently social in ways that cognitive science ignores ● Human thinking cannot be computational in the standard sense, so the brain must operate differently
- 28. @ghaff https://bitmason.blogspot.com Source: https://commons.wikimedia.org/wiki/File:Cognitive_Science_Hexagon.svg If you want to dive deeper ● Brain, Minds, and Machines Summer Course, MIT OpenCourseWare, 2015 https://ocw.mit.edu/resources/res-9-003-brains-minds-and-machines -summer-course-summer-2015/
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