three challenges for computational models of cognition
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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: - PowerPoint PPT PresentationTRANSCRIPT
Three challenges for computational models of
cognition
Charles Kemp
CMU
Humans vs machines
Outstanding
Not so good
Machine Human
Performance
Three challenges
1. Composition
2. Generativity
3. Putting it all together
StructuredModels
Neural network/continuous space
models
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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)
Liang et al, Learning dependency based compositional semantics
“A major focus of this work isour semantic representation, DCS,which offers a new perspective oncompositional semantics.”
Socher et al, Semantic compositionality through recursive matrix vector spaces
Opportunities/Challenges
1. Compositional systems that work with fuzzy concepts.
Generativity
“Mr. and Mrs. Dursley, of number four Privet Drive, were proud to say that they were perfectly normal, thank you very much.”
Computational models
(Hofstadter et al, Letter Spirit)
(Cohen, AARON)
Hinton et al, A fast learning algorithm for deep belief nets
Training:
…Z NX
MQ JX
MQ JD
B
Test: Generate another
Z ND
B
HumanModel
Jern & Kemp, A probabilistic account of exemplar and category generation
Fleuret et al, Synthetic Visual Reasoning Test
Category 1
Category 2
Opportunities/Challenges
1. Compositional systems that work with fuzzy concepts.
2. Avoid “cargo cult” science via benchmark engineering.
One problem, many settings
(Salakhutdinov, Tenenbaum, Torralba)
Psychological data: categorization (Canini et al) causal learning (Kemp et al)
One setting, many problems
Generalization, Categorization, Identification, Recognition …
(Shepard; Nosofsky; Ashby; Kemp & Jern…)
Many settings, many problems
• Cognitive architectures (ACT-R, SOAR)
• Artificial general intelligence
Opportunities/Challenges
1. Compositional systems that work with fuzzy concepts.
2. Avoid “cargo cult” science via benchmark engineering
3. Systems that solve many different problems in many different settings
Three challenges
1. Composition
2. Generativity
3. Putting it all together
StructuredModels
Neural network/continuous space
models
✓
✓
✓
✓✓
✓