At HAI’s fall conference, scholars discussed novel ways AI can learn from human intelligence – and vice versa.
Can we teach robots to generalize their learning? How can algorithms become more commonsensical? Can a child’s learning style influence AI?
Stanford Institute for Human-Centered Artificial Intelligence’s fall conference considered those and other questions to understand how to mutually improve and better understand artificial and human intelligence. The event featured the theme of “triangulating intelligence” among the fields of AI, neuroscience, and psychology to develop research and applications for large-scale impact.
HAI faculty associate directors Christopher Manning, a Stanford professor of machine learning, linguistics, and computer science, and Surya Ganguli, a Stanford associate professor of neurobiology, served as hosts and panel moderators for the conference, which was co-sponsored by Stanford’s Wu-Tsai Neurosciences Institute, Department of Psychology, and Symbolic Systems program.
Speakers described cutting-edge approaches—some established, some new—to create a two-way flow of insights between research on human and machine-based intelligence, for powerful application. Here are some of their key takeaways.