Season 4 of our Theory and Practice podcast investigates the powerful new world of AI applications and what it means to be human in the age of human-like artificial intelligence. Episode 6 explores what happens when AI is explicitly used to understand humans.In this episode, we’re joined by Jim DiCarlo, the Peter de Florez Professor of Neuroscience at Massachusetts Institute of Technology and Director of the MIT Quest for Intelligence. Trained in biomedical engineering and medicine, Professor DiCarlo brings a technical mindset to understanding the machine-like processes that occur in human brains. His focus is on the machinery that enables us to see. “Anything that our brain is achieving is because there's a machine in there. It's not magic; there's some kind of machine running. So that means there is some machine that could emulate what we do. And our job is to figure out the details of that machine. So the problem is someday tractable. It's just a question of when.”Professor DiCarlo unpicks how well convolutional neural networks (CNNs), a form of deep learning, mimic the human brain. These networks excel at finding patterns in images to recognize objects. One key difference with humans, though, is that our vision feeds information forward into different areas of the brain but it also receives feedback. For example, you see something that triggers a feeling and you look again to validate the feeling whereas computers are currently unable to do that. Despite this, Professor DiCarlo argues that CNNs help him and his team understand how our brains gather vast amounts of information in a millisecond glimpse, from a limited field of vision — just the 10 degrees of our central gaze.Alex and Anthony also discuss the potential clinical applications of machine learning — from using an ECG to determine a person’s biological age to understanding a person’s cardiovascular health from retina images.