McGill AI Podcast

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The podcast team has released the 4th episode of the McGill AI Podcast featuring Chlöe Ryan, Katherine Beaulieu and Dr. Kussil Oumedjbeur 🥳

The advent of powerful generative AI models - particularly in the art space - has enabled limitless creative opportunities. In this episode, we discuss both the usage benefits and the importance of regulating generative AI in this space from a diverse stakeholder perspective. Chloë Ryan, CEO and founder of Acrylic Robotics - a fast-growing pre-seed art-tech startup, Katherine Beaulieu, a BCL/JD Candidate at the McGill Faculty of Law & member of Tech Law, and Dr. Kussil Oumedjbeur, a current MSc candidate in experimental surgery at McGill and popular digital artist, join us in a panel to discuss the ethical and regulatory challenges involved in the usage of generative AI in the art space.

The McGill AI Podcast is available on YouTube, Apple Podcast and Spotify. Check out the newest episode through the link in our bio 👏🏻👏🏻

The advent of powerful generative AI models - particularly in the art space - has enabled limitless creative opportunities. In this episode, we discuss both the usage benefits and the importance of regulating generative AI in this space from a diverse stakeholder perspective. Chloë Ryan, CEO and founder of Acrylic Robotics - a fast-growing pre-seed art-tech startup, Katherine Beaulieu, a BCL/JD Candidate at the McGill Faculty of Law & member of Tech Law, and Dr. Kussil Oumedjbeur, a current MSc candidate in experimental surgery at McGill and popular digital artist, join us in a panel to discuss the ethical and regulatory challenges involved in the usage of generative AI in the art space. 

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.

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The McGill AI Podcast team is thrilled to finally release this season's first episode on Machine Learning in Sports with Dr. Dan Cervone!🏅🏀⚾⚽

Dan is the Principal Data Scientist at Zelus Analytics, where they’re building the world's 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.

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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Professor Lajoie is an applied mathematician interested in the interactions and commonalities of biological and artificial neural computations. His research group works at the intersection of AI and Neuroscience, developing tools to better understand neural networks as well as algorithms for brain-machine interfaces for scientific and clinical use. Furthermore, he is an assistant professor at Université de Montréal, Canada CIFAR AI Chair, Core Academic Member at Mila, is a research scholar at FRQS, and has research center affiliations with UNIQUE, CIRCA, and CRM.

“I think we're starting to see in the computational neuroscience community, in the past five years, is the more prevalent use of deep learning architectures and ways to train models to essentially mimic tasks or behaviour of animals, and then looking at the emergent principles that are learned from an end to end perspective. So you train an artificial network to perform the same behavioural task as a mouse, and what people are starting to see is that oftentimes, you will get some neuroscience kind of system or, mechanism that emerges from learning that task.” -Guillaume Lajoie

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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Professor Cheung is an associate professor at McGill, Canada CIFAR AI Chair, researching with Mila, the Reasoning and Learning Lab, Center for Research on Brain, Language and Music and the Center for Intelligent Machines.

“Because of the recent, relative success of large, pre-trained language models, I think we tend to ignore the spectacular failures they have in certain cases. Generalizing for all situations is very important for the safe and ethical deployment of AI systems.” -Jackie CK Cheung