McGill AI Podcast

What does it mean for an artificial intelligence model to do the right thing? What are researchers and governments doing to ensure that the use of AI technologies does not infringe on human rights? These are two of the questions that we touch on in this conversation with Dr. Su Lin Blodgett and Prof. Fenwick McKelvey. The world of AI has seen an explosion of progress in recent years and this has opened up exciting new applications for these models. However, the issue of reflecting human biases, which has long been plaguing these models, is far from solved. On top of this, the applications in which such models can be used remain poorly regulated, at least within Canada. In this episode, spanning across fields like computer science, media, and technology policy, we try to understand why these questions are essential to address, and how we can start thinking about solutions that take into account a plurality of perspectives.

Yoshua Bengio, co-laureate of the 2018 ACM AM Turing Award for his pioneering contributions in deep learning and recognized worldwide as one of the leading experts in artificial intelligence, joins us on the McGill AI Podcast to discuss his experience in pioneering deep learning, the work towards achieving human-level artificial intelligence, and advice to students wanting to do research in AI.
He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.
In 2019, he was awarded the prestigious Killam Prize and in 2022, became the computer scientist with the highest h-index in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada.

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

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.

Professor David Rolnick is an assistant professor at McGill, Canada CIFAR AI Chair, Mila researcher and a member of MIT Technology Review’s 35 Innovators Under 35. He joins the podcast to talk about how machine learning can be applied to tackling climate change, deep learning theory, AI in the public sector, ethics and advice for students.