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
Overview
Syllabus
Intro
AI successes
convolutional neural network
person detectors
going beyond the data
challenging problem
advice for person detector
mature mathematics
next week
people in scenes
classification learning
scene understanding
the set of problems
the Bayesian approach
Bayesian cognitive neuroscience
Summary
Bayesian Inference
Representation
Questions
Bayesian analysis
Taught by
MITCBMM
