Understanding the mind is hard. Understanding AI isn’t much easier.
Today’s AI landscape is defined by the ways in which neural networks are unlike human brains. A toddler learns how to communicate effectively with only a thousand calories a day and regular conversation; meanwhile, tech companies are reopening nuclear power plants, polluting marginalized communities, and pirating terabytes of books in order to train and run their LLMs.
But neural networks are, after all, neural—they’re inspired by brains. Despite their vastly different appetites for energy and data, large language models and human brains do share a good deal in common. They’re both made up of millions of subcomponents: biological neurons in the case of the brain, simulated “neurons” in the case of networks. They’re the only two things on Earth that can fluently and flexibly produce language. And scientists barely understand how either of them works.
I can testify to those similarities: I came to journalism, and to AI, by way of six years of neuroscience graduate school. It’s a common view among neuroscientists that building brainlike neural networks is one of the most promising paths for the field, and that attitude has started to spread to psychology. Last week, the prestigious journal Nature published a pair of studies showcasing the use of neural networks for predicting how humans and other animals behave in psychological experiments. Both studies propose that these trained networks could help scientists advance their understanding of the human mind. But predicting a behavior and explaining how it came about are two very different things.
