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Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
Samuel J Gershman1, Eric J Horvitz2, Joshua B Tenenbaum3
1Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. gershman@fas.harvard.edu horvitz@microsoft.com jbt@mit.edu.
Science (New York, N.Y.)
|July 18, 2015
Summary
Artificial intelligence (AI), cognitive science, and neuroscience are merging, focusing on computational intelligence. This reconvergence drives innovation in perception, action, and decision-making under uncertainty.
Area of Science:
- Interdisciplinary research at the intersection of artificial intelligence (AI), cognitive science, and neuroscience.
Background:
- Historically separate fields are now converging on shared computational theories of intelligence.
- This reconvergence fosters cross-disciplinary collaboration and knowledge exchange.
Purpose of the Study:
- To chart advances in understanding perception and action under uncertainty through computation.
- To explore the development of computational rationality for optimal decision-making.
Main Methods:
- Reviewing decades of progress in computational approaches to intelligence.
- Examining representations and inference for probabilistic reasoning.
- Analyzing machinery for computational trade-offs (effort, precision, timeliness).
Main Results:
- Development of large-scale probabilistic inference methods.
- Machinery enabling reflection on computational trade-offs.
- Application of computational rationality to complex, real-world problems.
Conclusions:
- The convergence of AI, cognitive science, and neuroscience offers a unified view of intelligence.
- Computational approaches provide powerful tools for addressing perception, action, and decision-making challenges.
- Interchange between these fields holds significant potential for future scientific discovery.
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