Events

Tal Linzen is an Associate Professor of Linguistics and Data Science at New York University, and a Research Scientist at Google. He directs NYU's Computation and Psycholinguistics Lab, which uses behavioral experiments and computational methods to study how people learn and understand language. At both Google and NYU, he works on large language model post-training, evaluation and interpretability.

This research mission broadly aims to understand how children grasp new concepts from few examples, how children build upon layers of concepts to reach an understanding of the world and have the flexibility to solve an unbounded range of problems. Can we build AI that starts like a baby and learns like a child?

Daphna Buchsbaum is an Associate Professor of Cognitive and Psychological Sciences at Brown University. She directs the Computational Cognitive Development Lab, and its sister lab the Canine Cognition Lab.

The Brain-Score platform aims to yield accurate, machine-executable computational models of how the brain gives rise to the mind. Its benchmarking system enables researchers to sense the alignment of their model(s) to currently dozens of neural and behavioral measurements, and it provides these models to experimentalists to prototype new experiments and make sense of biological data.

Professors Nancy Kanwisher and Leslie Kaelbling will discuss the Embodied Intelligence Mission's recent research and upcoming projects.

Prof. Dinesh Jayaraman is an associate professor at UPenn’s GRASP lab, with a primary appointment in Computer and Information Science, and a secondary appointment in Electrical and Systems Engineering. He leads the Perception, Action, and Learning (PennPAL) Research Group, which works on problems at the intersection of robotics, machine learning, and computer vision.

Prof. Jennifer Hu is an Assistant Professor of Cognitive Science and Computer Science at Johns Hopkins University, where she directs the Group for Language and Intelligence. Her work aims to understand the computational principles that underlie human language, and how language and cognition might be achieved by artificial models. She approaches these questions by combining cognitive science and machine learning, with the dual goals of understanding the human mind and safely advancing artificial intelligence.