Magazine article
Thinking Fast and Slow in Human and Machine Intelligence
Communications of the ACM, Vol.68(8), pp.72-79
07/25/2025
Web of Science ID: WOS:001543415400015
Metrics
2 File views/ downloads
20 Record Views
Abstract
When working to build machines that have a form of intelligence, it is natural to be inspired by human intelligence. Of course, humans are very different from machines, in their embodiment and myriad other ways. Humans exploit their bodies to experience the world, create an internal model of it, and use this model to reason, learn, and make contextual and informed decisions. Machines lack the same embodiment, but often have access to both more memory and more computing power. Despite these crucial disanalogies, it is still useful to leverage our knowledge of how the human mind reasons and makes decisions to design and build machines that demonstrate behaviors similar to that of a human. In this article, we present a novel AI architecture, Slow and Fast AI (SOFAI), that is inspired by the “thinking fast and slow” cognitive theory of human decision making. SOFAI is a multi-agent architecture that employs both “fast” and “slow” solvers underneath a metacognitive agent that is able to both choose among a set of solvers as well as reflect on and learn from past experience. Experimental results on the behavior of two instances of the SOFAI architecture show that, compared to using just one of the two decision modalities, SOFAI is markedly better in terms of decision quality, resource consumption, and efficiency.
Files and links (2)
CC BY V4.0, Open Access
Related links
Details
- Title
- Thinking Fast and Slow in Human and Machine Intelligence
- Publication Details
- Communications of the ACM, Vol.68(8), pp.72-79
- Resource Type
- Magazine article
- Publisher
- Association for Computing Machinery
- Number of pages
- 8
- Grant note
- Harold L. and Heather E. Jurist Center of Excellence for Artificial Intelligence at Tulane University: RI-2339880, CNS-SCC-2427237 Tulane University Center for Community-Engaged Artificial Intelligence
Nicholas Mattei was supported in parts by NSF Awards IIS-RI-2007955, IIS-III-2107505, IIS-RI-2134857, IISRI-2339880 and CNS-SCC-2427237 as well as the Harold L. and Heather E. Jurist Center of Excellence for Artificial Intelligence at Tulane University and the Tulane University Center for Community-Engaged Artificial Intelligence.r RI-2339880 and CNS-SCC-2427237 as well as the Harold L. and Heather E. Jurist Center of Excellence for Artificial Intelligence at Tulane University and the Tulane University Center for Community-Engaged Artificial Intelligence.
- Copyright
- © 2025 Copyright held by the owner/author(s).
- Identifiers
- WOS:001543415400015; 99381474084606600
- Academic Unit
- Institute for Human and Machine Cognition; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English