With all the hype surrounding ChatGPT, most people are giddy with the promise of artificial intelligence, yet they are overlooking its pitfalls. If we want to have genuinely intelligent machines that understand their environments, learn continuously, and help us every day, we need to apply neuroscience to deep-learning A.I. models. Yet with a few exceptions, the two disciplines have remained surprisingly isolated for decades.
That wasn’t always the case. In the 1930s, Donald Hebb and others came up with theories of how neurons learn, inspiring the first deep-learning models. Then in the 1950s and ‘60s, David Hubel and Torsten Wiesel won the Nobel Prize for understanding how the brain’s perceptual system works. That had a big impact on convolutional neural networks, which are a big part of A.I. deep learning today.
