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Three challenges for computational models of cognition

Three challenges for computational models of cognition. Charles Kemp CMU. Humans vs machines. Outstanding. Performance. Not so good. Human. Machine. First order of business is to close this gap:

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Three challenges for computational models of cognition

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  1. Three challenges for computational models of cognition Charles Kemp CMU

  2. Humans vs machines Outstanding Performance Not so good Human Machine First order of business is to close this gap: Nagging worry for non-Bayesian models: maybe the model does badly because the learning component isn’t optimal. We have a good theoretical understanding of inductive inference. Bayesian approach puts you in a good position to explore what’s left.

  3. Three challenges Structured Models Neural network/ continuous space models • Composition • Generativity • Putting it all together ✓ ✓ ✓ ✓ ✓ ✓

  4. Composition: sentences • Given a database of geography facts, answer questions like: • “how many rivers run through the states bordering Colorado?” • “how many states border the state that borders the most states?” (Mooney, 1997)

  5. Liang et al, Learning dependency based compositional semantics “A major focus of this work is our semantic representation, DCS, which offers a new perspective on compositional semantics.”

  6. Socher et al, Semantic compositionality through recursive matrix vector spaces

  7. Opportunities/Challenges • Compositional systems that work with fuzzy concepts.

  8. Generativity “Mr. and Mrs. Dursley, of number four Privet Drive, were proud to say that they were perfectly normal, thank you very much.”

  9. (Hofstadter et al, Letter Spirit) Computational models (Cohen, AARON)

  10. Hinton et al, A fast learning algorithm for deep belief nets

  11. Jern & Kemp, A probabilistic account of exemplar and category generation Training: X X D … Z N Q J Q J M M B Test: Generate another Human Model D Z N B

  12. Fleuret et al, Synthetic Visual Reasoning Test Category 1 Category 2

  13. Opportunities/Challenges • Compositional systems that work with fuzzy concepts. • Avoid “cargo cult” science via benchmark engineering.

  14. One problem, many settings (Salakhutdinov, Tenenbaum, Torralba) Psychological data: categorization (Canini et al) causal learning (Kemp et al)

  15. One setting, many problems Generalization, Categorization, Identification, Recognition … (Shepard; Nosofsky; Ashby; Kemp & Jern…)

  16. Many settings, many problems • Cognitive architectures (ACT-R, SOAR) • Artificial general intelligence

  17. Opportunities/Challenges • Compositional systems that work with fuzzy concepts. • Avoid “cargo cult” science via benchmark engineering • Systems that solve many different problems in many different settings

  18. Three challenges Structured Models Neural network/ continuous space models • Composition • Generativity • Putting it all together ✓ ✓ ✓ ✓ ✓ ✓

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