McGill AI Podcast Alexandre St-Onge, Duru Aran, Nicolas Bosteels, Ada Tur

The podcast team is excited to announce the McGill Artificial Intelligence Society’s 3rd podcast episode of the 2023-2024 school year featuring Yves Hilpisch. 

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 in 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 impacts of the growing accessibility of individual finance and trading. 

The McGill AI podcast is available on Apple Podcast and Spotify.

The podcast team is excited to announce the McGill Artificial Intelligence Society’s 2nd podcast episode of the 2023-2024 school year featuring Ignacio Cofone. 

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. 

The McGill AI podcast is available on Apple Podcast and Spotify.

Artem Kirsanov is a graduate student in neuroscience at New York University Center for Neural Science and a YouTuber with a following of over 100k subscribers. In this episode, we talk about modelling the human brain with artificial intelligence, how AI is being used to enhance neuroscience, and what a future alongside AI can look like for scientists.

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.

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.