Accession Number:

AD1096503

Title:

Bayesian Program Learning and Concept Induction

Descriptive Note:

Technical Report,15 Dec 2015,14 Feb 2018

Corporate Author:

Massachusetts Institute of Technology Cambridge United States

Personal Author(s):

• Tenenbaum,Josh

Report Date:

2019-09-21

Pagination or Media Count:

12.0

Abstract:

A long-standing dream in computing has been to build machines that learn like a child Turing 1950 that grow into all the kinds and forms of knowledge that human adults do, starting from much less. At a minimum, any such learning system must be able to acquire many different kinds of expertise. Every child becomes an expert in natural language, motor control, intuitive physics and social interaction, and many will grow into adults with specialized expertise in cooking, calculus, tennis, drawing pictures, or writing software. Despite great advances, artificial intelligence AI is still far from acquiring human-like expertise in any of these domains let alone all of them, as a person can.

Descriptors:

• probability
• statistical inference
• machine learning
• artificial intelligence
• bayesian networks
• artificial intelligence computing
• computer programming
• pattern recognition

Subject Categories:

• Statistics and Probability
• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE