In the last few years, computer algorithms inspired by the brain have succeeded in beating the best humans at Go, diagnosing skin cancer better than dermatologists, and designing new fashions for Amazon. But why do these same algorithms fail so badly at what many of us consider trivial tasks — things simple enough for a two-year-old to perform? Join us to discuss what makes humans and their biological brains so good at many of the tasks currently stymying computers. We’ll ask why human preschoolers are so good at generalizing concepts, learning from demonstrations, and understanding social contexts and what this tells us about how our own brains compute.
About our speaker
John Michael Pearson is an Assistant Research Professor in the Duke Institute for Brain Sciences. His research focuses on the application of machine learning methods to the analysis of brain data and behavior. He has a special interest in the neurobiology of reward and decision-making, particularly issues surrounding foraging, impulsivity, and self-control. He collaborates with clinicians at Duke to study these processes in patients undergoing brain surgery, smokers, and those with acute fear.