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1
Introduction
2
Computational Neuroscience
3
Biology and AI
4
Learning from the brain
5
Humanlevel AI
6
Humanlike AI
7
I learned Atari games
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Three questions
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Five key ingredients
10
Intuitive theories
11
Examples
12
developmentally
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intuitive physics
14
intuitive psychology
15
compositionality
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benefits
17
Montezumas Revenge
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Learning to Learn
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Causality
20
Caption Generation
21
Full Circle
22
Psychology in AI
23
Applications
24
Biological plausibility
25
Cognitive plausibility
Description:
Explore the fascinating intersection of cognitive science and artificial intelligence in this 28-minute talk by Sam Gershman from Harvard University. Delve into the concept of building machines that learn and think like humans, examining key ingredients such as intuitive theories, compositionality, and learning to learn. Discover how developmental psychology, intuitive physics, and intuitive psychology contribute to human-like AI. Analyze examples like Atari games and Montezuma's Revenge to understand the challenges and progress in creating more cognitively plausible AI systems. Investigate the role of causality in machine learning and its applications in caption generation. Gain insights into the biological and cognitive plausibility of AI models and their potential real-world applications.

Building Machines that Learn and Think Like People

MITCBMM
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