Ishan Misra

To be accurate, many AI models need large amounts of human-labeled data. Research from Ishan Misra, 31, shows that it’s possible to train these models on visual data alone, skipping the human labels. Misra believes that such self-supervised models will greatly expand the types of problems that AI can solve. “In domains like medical imaging, where labeling is expensive, self-supervised models can play a major role in rapidly developing AI models at a fraction of the cost,” he says. “These models can also enable AI models to learn new skills continuously from the stream of data they observe, without human supervision.” That could be especially useful for robots operating in environments that constantly change.