Related papers: Thinking Fast and Slow in AI

Thinking Fast and Slow in AI

URL: http://arxiv.org/abs/2010.06002v2
Date: Tue, 15 Dec 2020 21:12:08 GMT
Title: Thinking Fast and Slow in AI
Authors: Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jon Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava
Abstract summary: This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI, we may obtain similar capabilities in an AI system.
Score: 38.8581204791644
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.

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