Zoom link: https://mit.zoom.us/j/2711902511

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Speaker: Nathan Cloos

Affliation: Fiete Lab

Speaker Bio:
Nathan is a second-year PhD student at MIT Brain and Cognitive Sciences. He is generally interested in rapid learning and systematic generalization in brains and machines. Before his PhD, Nathan completed his master’s in Applied Mathematics and Physics at UCLouvain in Belgium. He subsequently worked as a research assistant in the lab of Robert Yang at MIT, where he developed a pipeline to scale up the evaluation of Recurrent Neural Network models using quantitative similarity measures. He also had the chance to work in the lab of Omri Barak at the Technion. Besides his scientific work, he likes building software to try to make scientific modelling more scalable and accessible.

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Speaker: Ced Zhang

Affiliation: CoCoSci Group and Computational Psycholinguistics Laboratory

Speaker Bio:
Ced is a third-year PhD student at the Department of Brain and Cognitve Sciences at Massachusetts Institute of Technology, advised by Josh Tenenbaum and Roger Levy. Previously, he completed his undergraduate degree at UC Berkeley with backgrounds in cognitive science, computer science, logic, and philosophy. He is broadly interested in studying artificial intelligence, computational cognitive science, computational linguistics, and philosophy from an interdisciplinary perspective. Some questions he likes to think about these days: What are the strengths and limitations of LLMs? What are good approaches to combine LLMs and symbolic techniques? In what sense are humans better learners, reasoners, and communicators than state-of-the-art AI? What are world models? Is there a Language of Thought, and what might that look like? On top of these interests and questions, his long-term reaserach goal is to (1) build general AI systems in a human-inspired and human-compatible way with a focus on language, and (2) advance the understanding of intelligence through theories and models.