Research Priorities for Robust and Beneficial Artificial Intelligence (original) (raw)

Abstract

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents —systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable suc- cesses in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

Department

Massachusetts Institute of Technology. Department of Physics; MIT Kavli Institute for Astrophysics and Space Research

Publisher

Association for the Advancement of Artificial Intelligence

Citation

Russell, Stuart; Dewey, Daniel and Tegmark, Max Erik. "Research Priorities for Robust and Beneficial Artificial Intelligence." AI Magazine 36, no. 4 (December 2015): 105-114. © 2015 Association for the Advancement of Artificial Intelligence.

Version:

Final published version