Bayesian Program Learning and Concept Induction

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 21, 2019
Accession Number
AD1096503

Entities

People

  • Joshua B. Tenenbaum

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognition
  • Cognitive Science
  • Computer Programming
  • Information Processing
  • Information Systems
  • Language
  • Machine Learning
  • Military Research
  • Natural Languages
  • Neural Networks
  • Scientific Research

Readers

  • Gender and Food Studies
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy