Three challenges for
computational models of
cognition
Charles Kemp
CMU
Humans vs machines
Outstanding
Performance
Not so good
Machine
Human
Three challenges
Structured Neural network/
Models continuous space
models
✓
✓
2. Generativity
✓
✓
3. Putting it all together
✓
✓
1. Composition
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 is
our semantic representation, DCS,
which offers a new perspective on
compositional 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
Jern & Kemp, A probabilistic account of
exemplar and category generation
Training:
X
Z
X
N
M
Q
D
J
M
Q
J
…
B
Test: Generate another
Model
D
Z
N
B
Human
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
Structured Neural network/
Models continuous space
models
✓
✓
2. Generativity
✓
✓
3. Putting it all together
✓
✓
1. Composition