Thinking Fast and Slow in AI


  • Grady Booch IBM
  • Francesco Fabiano University of Udine
  • Lior Horesh IBM
  • Kiran Kate IBM
  • Jonathan Lenchner IBM
  • Nick Linck IBM
  • Andreas Loreggia European University Institute
  • Keerthiram Murgesan IBM
  • Nicholas Mattei Tulane University
  • Francesca Rossi IBM
  • Biplav Srivastava University of Southern Carolina



Neuro-symbolic AI, Cognitive Theories Of Human Reasoning, Combination Of Learning And Reasoning


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.




How to Cite

Booch, G., Fabiano, F., Horesh, L., Kate, K., Lenchner, J., Linck, N., Loreggia, A., Murgesan, K., Mattei, N., Rossi, F., & Srivastava, B. (2021). Thinking Fast and Slow in AI. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15042-15046.



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