Episode notes
Series: AI Papers
Building Machines That Learn and Think Like People — Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman
Lake and colleagues ask what artificial intelligence can learn from human learning and thought.
They argue that strong pattern recognition alone does not reproduce human intelligence.
Causal models and intuitive accounts of physics and psychology can support richer understanding.
Compositional representations and learning-to-learn help explain rapid adaptation from limited experience.
The paper proposes research directions that combine neural networks with structured cognitive models.
Explore: AI & Computer Science · Mind & Psychology · AI Papers
Related episodes: Bayesian Theories of Cognition (Griffiths, Chater & Tenenbaum) · V-JEPA (LeCun)