How AI evolved from quest for a mathematical theory of the mind |
Below, Tom Griffiths shares five key insights from his new book, The Laws of Thought: The Quest for a Mathematical Theory of the Mind.
Griffiths is a professor of psychology and computer science at Princeton University and director of the Princeton Laboratory for Artificial Intelligence.
What’s the big idea?
How can we study something we can’t see or touch? Mathematics allows us to develop rigorous theories about how minds work. It also lets us use those theories to build artificial intelligence systems. Just as physicists seek to identify Laws of Nature, cognitive scientists hope to discover the Laws of Thought.
Listen to the audio version of this Book Bite—read by Griffiths himself—in the Next Big Idea app.
1. The story of AI goes back hundreds of years.
For many people, AI seems to have come out of nowhere. In late 2022, it suddenly became possible for anyone to have a conversation with chatbots that could draw on more knowledge than any human. Dig a little deeper and you might discover that the approach behind those chatbots—building bigger and bigger artificial neural networks—had its first dramatic demonstration in 2012, when it was used to significantly improve how well computers identify images. But the story goes back much further than that.
When Enlightenment thinkers, like René Descartes or Gottfried Wilhelm Leibniz, first began using mathematics to effectively describe the physical world around us, they also suggested that the same kind of approach might be used to describe the mental world inside us. Those early efforts led to the development of mathematical logic and digital computers, which in turn led to the creation of cognitive science by psychologists who used mathematical ideas to come up with new theories about the mind. Modern AI springs from that tradition: Key advances in the development of artificial neural networks came from psychologists seeking to understand how the human mind works.
2. No single piece of mathematics describes the mind.
Cognitive scientists started using mathematical logic to describe thought, but after a couple of decades realized that wasn’t going to work. Concepts have fuzzy edges that logic just can’t capture. Artificial neural networks were developed in parallel and became much more powerful after a group of psychologists showed how they could be used to learn more complex relationships than anyone had thought possible.
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