Can Machines Learn Like Humans?

Artificial intelligence has pervaded our lives, from computer vision to ChatGPT, but how effectively can machines emulate human thought? Some machines have gotten impressively good at predicting outcomes, yet the ability to truly reason and ask “what if” remains elusive. In this pair of talks and moderated discussion, three experts spanning the fields of cognitive science and machine learning come together to discuss the next frontier at the intersection of natural and artificial intelligence.

Zenna Tavares, PhD, Alan Kanzer Innovation Scholar and Associate Research Scientist at Columbia University’s Zuckerman Institute and Data Science Institute and Co-Founder and Director of Basis Research Institute, will open our event by sharing his work on a fundamental yet complex question: how do we make sense of the world? By studying how our brains reason, he explores what would it take for a machine to “think” like, or even better than, a human.

Kimberly Stachenfeld, PhD, Senior Research Scientist at Google DeepMind and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University, will then speak about her research deciphering the math behind how the mind works. The brain acts as a simulator for our mental experience of the world, but can a computer model faithfully capture its essence? In what ways can a deeper understanding of this process unlock new potential for artificial intelligence?

Following the two talks, Emily Mackevicius, PhD, Associate Research Scientist at Columbia University’s Zuckerman Institute, and Co-Founder and Director of Basis Research Institute, will moderate a discussion and Q&A with the speakers. Audience questions are welcomed, either submitted during registration or live during the event.

Please RSVP by Monday, September 11, 2023.