Advertise on podcast: McGill AI Podcast

Send us Fan Mail VIDEO ACCESS: To watch the video version, please check out our YouTube!  https://www.youtube.com/watch?v=C_vOHUWDo30&t=20s MAIS is thrilled to have been given the opportunity to record at ALL IN, Canada's largest tech and AI event. Today we share a short conversation with Emma Frejinger: Professor at Université de Montréal, optimization researcher, CN Chair in Railway Operations, Mila member, and more. Take a listen to learn more about her work on machine learning and optimization in transportation, the state and future of AI in research, and how experts deal with the problem of trust in AI. Host(s): Sam Knowlton

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Send us Fan Mail Yves is the founder and CEO of The Python Quants and has written several books, including Python for Finance. In recent years, AI has been at the forefront of computational finance and algorithmic trading. As the accessibility of AI increases, building our own financial models has become easier with programming languages like Python and the support of various available libraries. In this episode, we examine AI’s rise in the world of finance, exploring its applications across various facets of financial analysis. We discuss the possibility of AI models predicting market movement and what this means in terms of the Efficient Market Hypothesis. Yves also talks about the implications of AI for smaller companies as they try to compete with the bigger players in the industry, as well as the impacts of the growing accessibility of individual finance and trading.

Send us Fan Mail Ignacio Cofone is a professor at McGill’s Faculty of Law and is the Canada Research Chair in AI Law and Data Governance. With the rapid evolution of artificial intelligence in recent years, there are also many concerns at the forefront. This episode tackles current topics like the effects of bias in AI, the intersection between AI and privacy law, as well as AI regulations. We will examine how the transition between the current privacy regulation and the new Bill C-27 addresses these concerns and also its potential blind spots, while covering the challenges and uncertainties around Canada’s first comprehensive attempt at AI regulation.

Send us Fan Mail he podcast team is excited to announce they have released the 5th episode of the McGill AI Podcast, featuring Doina Precup 🎉🎉 Embark on an enlightening journey into the world of reinforcement learning with Prof. Doina Precup in this captivating podcast episode. As a distinguished professor at McGill University and the Head of DeepMind Montreal, Prof. Precup brings a wealth of expertise to our discussion. Join us as we delve into the intricacies of hierarchical reinforcement learning, navigating the complexities of decision-making processes. Explore the realm of continual reinforcement learning, where adaptability harmonizes with efficiency, and gain invaluable insights into new possibilities and challenges posed by large models. Whether you're an AI aficionado or newcomer, this episode promises an illuminating expedition into the forefront of reinforcement learning, guided by a true trailblazer in the field.

Send us Fan Mail 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 👏🏻👏🏻

Send us Fan Mail 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.

Send us Fan Mail 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

Send us Fan Mail 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

Send us Fan Mail HECTIC FIRST WEEK OF SCHOOL? REFRESH YOURSELF WITH MCGILL AI PODCAST’S EPISODE 2 WITH BLAKE RICHARDS, NOW AVAILABLE ON ALL PLATFORMS! 🥶🥶 Professor Blake Richards is a prominent researcher whose work bridges the gap between neuroscience and artificial intelligence. He is an Associate Professor at McGill University, holding appointments in both the School of Computer Science and the Department of Neurology and Neurosurgery. Additionally, he serves as a Core Faculty Member at Mila (the Quebec Artificial Intelligence Institute) and is a Research Scientist Manager with the Paradigms of Intelligence team at Google.

Send us Fan Mail Professor David Rolnick! Professor Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila. He also serves as the co-founder and chair of Climate Change AI and the scientific co-director of Sustainability in the Digital Age. “Certainly, this kind of thinking is not being taught to people sufficiently well. It’s not being taught to people at the level of how can you shape your algorithms to make them have the desired effect to be as beneficial as possible for society. It’s also not being taught to people in terms of you choosing the problems that you work on, and choosing not to work on certain problems at all. Certain things should not have AI.”