“Insights for AI from the Human Mind”
Communications of the ACM, January 2021, Vol. 64 No. 1, Pages 38-41
By Gary Marcus, Ernest Davis
“Much work in evolutionary and developmental psychology points in the same direction; the mind is not one thing, but many.”
What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.
—Marvin Minsky, The Society of Mind
Artificial intelligence has recently beaten world champions in Go and poker and made extraordinary progress in domains such as machine translation, object classification, and speech recognition. However, most AI systems are extremely narrowly focused. AlphaGo, the champion Go player, does not know that the game is played by putting stones onto a board; it has no idea what a “stone” or a “board” is, and would need to be retrained from scratch if you presented it with a rectangular board rather than a square grid.
To build AIs able to comprehend open text or power general-purpose domestic robots, we need to go further. A good place to start is by looking at the human mind, which still far outstrips machines in comprehension and flexible thinking.
Here, we offer 11 clues drawn from the cognitive sciences—psychology, linguistics, and philosophy.
- No Silver Bullets
- Rich Internal Representations
- Abstraction and Generalization
- Highly Structured Cognitive Systems
- Multiple Tools for Simple Tasks
- Top-Down and Bottom-Up Information, Integrated
- Concepts Embedded in Theories
- Causal Relations
- Tracking Individuals
- Innate Knowledge
The discoveries of the cognitive sciences can tell us a great deal in our quest to build artificial intelligence with the flexibility and generality of the human mind. Machines need not replicate the human mind, but a thorough understanding of the human mind may lead to major advances in AI.
About the Authors:
Gary Marcus is Founder and CEO of Robust.AI, and Professor Emeritus at NYU.
Ernest Davis is Professor of Computer Science at NYU. This Viewpoint is adapted from their new book, Rebooting AI: Building Artificial Intelligence We Can Trust.