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1
Intro
2
AI successes
3
convolutional neural network
4
person detectors
5
going beyond the data
6
challenging problem
7
advice for person detector
8
mature mathematics
9
next week
10
people in scenes
11
classification learning
12
scene understanding
13
the set of problems
14
the Bayesian approach
15
Bayesian cognitive neuroscience
16
Summary
17
Bayesian Inference
18
Representation
19
Questions
20
Bayesian analysis
Description:
Explore the fascinating world of artificial intelligence and cognitive science in this comprehensive lecture on the development of intelligence. Delve into Josh Tenenbaum's insights on Bayesian inference and its applications in AI. Begin with an introduction to recent AI successes, including convolutional neural networks and person detectors, before examining the challenges of going beyond existing data. Discover mature mathematical concepts and their relevance to scene understanding and classification learning. Investigate the Bayesian approach and its role in cognitive neuroscience. Gain a deeper understanding of Bayesian inference, representation, and analysis through detailed explanations and examples. Conclude with a summary and engage in a thought-provoking Q&A session to solidify your knowledge of this cutting-edge field.

Development of Intelligence - Josh Tenenbaum: Bayesian Inference

MITCBMM
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