Artificial brains are helping scientists study the real thing

The striking progress in artificial intelligence over the past decade is mostly down to advances in machine learning, whereby computers teach themselves complicated tasks by crunching large quantities of data, rather than having to be programmed directly by humans. This approach has driven rapid progress in computer vision, language translation and, most recently, the human-like conversational skills of chatbots such as GPT-4.

The learning is done by software models called “artificial neural networks” (ANNs). The standard description of an ANN is that it is loosely inspired by the networks of neurons in the human brain. It is de rigueur to follow that description with an immediate disclaimer, in which both computer scientists and neuroscientists jump in nervously to point out that the analogy is very rough, that ANNs are mere cartoons of real brains (if even that) and that they fail to capture the complexity of the biological organ.