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McGill AI Podcast - Guillaume Lajoie: Interfacing the Brain

Guillaume Lajoie: Interfacing the Brain

02/04/22 • 63 min

McGill AI Podcast

Guillaume is an assistant professor at Université de Montréal, Canada CIFAR AI Chair, Core Academic Member at Mila, is a research scholar at FRQS (Fonds de recherche du Québec), and has research center affiliations with UNIQUE (Unifying Neuroscience and Artificial Intelligence - Québec), CIRCA (Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage), and CRM (Centre de Recherches Mathématiques). He joins us to talk about how AI influences neuroscience, the mathematics behind AI, brain-computer interfaces, and thoughts on the future of AI regulation.

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Guillaume is an assistant professor at Université de Montréal, Canada CIFAR AI Chair, Core Academic Member at Mila, is a research scholar at FRQS (Fonds de recherche du Québec), and has research center affiliations with UNIQUE (Unifying Neuroscience and Artificial Intelligence - Québec), CIRCA (Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage), and CRM (Centre de Recherches Mathématiques). He joins us to talk about how AI influences neuroscience, the mathematics behind AI, brain-computer interfaces, and thoughts on the future of AI regulation.

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undefined - Jackie CK Cheung: AI & Language

Jackie CK Cheung: AI & Language

Jackie Cheung is an associate professor at McGill, Canada CIFAR AI Chair, researching with Mila, the Reasoning and Learning Lab, Centre for Research on Brain, Language and Music and the Centre for Intelligent Machines. He sits down with us to talk about the history and change in the field of NLP, common sense reasoning and summarization, AI deployment in practice, and advice for students looking to get into research.

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undefined - Dan Cervone: Machine Learning in Sports

Dan Cervone: Machine Learning in Sports

Dr. Dan Cervone is the principal data scientist at Zelus Analytics where they are building the world leading sports analytics platform. Prior to this, Dan spent three seasons with the Los Angeles Dodgers, most recently as Director of Quantitative Research. He completed his PhD in Statistics at Harvard University, and was then a Moore-Sloan Data Science Fellow at NYU. His work focuses on spatiotemporal data and hierarchical models, with particular application to sports analytics and player tracking data. Dan joins us today to talk about the field of sports analytics, his own research using machine learning in sports, and the future of sports analytics.
Music by Aria Khiabani with other music under the name SATRAP

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