Fundamental Theory and Parallel Inference for Probabilistic Programming (10.3.1 Integrated Intelligence)

Abstract

This project was conceived with three major goals: (i) develop new probabilistic programming technology that addressed limitations of first-generation languages such as Church; (ii) demonstrate the capabilities of knowledge based AI systems written using these new probabilistic programming languages, emphasizing reflective uses of probabilistic programming; and (iii) develop mathematical theory that addresses fundamental questions associated with probabilistic programs. Over the past four years, we have accomplished all three of these goals.

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Document Details

Document Type
Technical Report
Publication Date
Jul 07, 2017
Accession Number
AD1057510

Entities

People

  • Joshua B. Tenenbaum
  • Vikash Mansinghka

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Autonomous Systems
  • Bayesian Networks
  • Computer Programming
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Generative Models
  • Information Science
  • Machine Learning
  • Monte Carlo Method
  • Neural Networks
  • Probabilistic Models
  • Probability

Fields of Study

  • Computer science

Readers

  • Computer Science.
  • Operations Research
  • Theoretical Analysis.

Technology Areas

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