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Classroom Contents
Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum
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- 1 Introduction
- 2 AI is a uniquely exciting time
- 3 Point the gap
- 4 Myths of machine learning
- 5 Human intelligence
- 6 Core knowledge
- 7 Alan Turing
- 8 Commonsense core knowledge
- 9 Intuitive psychology
- 10 probabilistic programming
- 11 game engines
- 12 simulation
- 13 probabilistic simulation
- 14 probabilistic simulation demo
- 15 intuitions
- 16 building and thinking
- 17 learning from scratch
- 18 babylike learning
- 19 learning in game engines
- 20 examples
- 21 perception
- 22 plan interactions
- 23 lowlevel learning
- 24 simulation engine
- 25 physics engine
- 26 amortized inference
- 27 a physics engine
- 28 simple shape parameters
- 29 tackling problems
- 30 looking around
- 31 trial and error
- 32 virtual tools game
- 33 trial error learning
- 34 SM model
- 35 Learning simulation engines
- 36 Hard problem of learning
- 37 Childrens learning
- 38 Oneshot learning
- 39 Omniglot domain
- 40 Inverse motor program
- 41 Bayesian inference
- 42 probabilistic programs
- 43 classification task
- 44 human scale
- 45 human version
- 46 generative models
- 47 drawing styles
- 48 more structure
- 49 more interesting model
- 50 learn neural components
- 51 wakesleep algorithm
