Why AI Needs to Stay Weird with Kate Compton | Approximately Correct Podcast

The latest episode of Approximately Correct, we're joined by Kate Compton. Kate is an expert in generative AI, an artist, and a self-described “weird futurist.” She’s long been fascinated by creativity, and how people use digital tools to express themselves.She joins Alona Fyshe and Scott Lilwall to talk about the time when AI was small, strange and shareable, and explains why we need to keep that energy going to allow people to use it as a creative tool.

Class is back in session: this month, we're catching up again with Jill Kowalchuk to talk about the monumental shifts in AI and education over the past couple years.As Amii's Manager of AI Literacy, Jill has seen the way that students and educators have dealt with the introduction of AI in the classroom, both the opportunities and the pitfalls. She talks with hosts Alona Fyshe and Scott Lilwall about how AI means we need to rethink the way we evaluate learning, and how she sees AI as less than a tool and more of a "thought partner."

We’re taking a bit of a break this month, so no new episode of Approximately Correct. Instead, we wanted to revisit one of our favourite episodes from the past year. So let’s talk survival prediction with Russ Greiner. How can machine learning revolutionize healthcare? In this episode, Amii Fellow and Canada CIFAR AI Chair Russ Greiner explores how AI is transforming survival prediction, giving doctors and patients personalized health insights that were never before possible. From creating tailored survival curves to improving treatment decisions, Greiner reveals the groundbreaking potential of AI in medicine.

Ever wonder what exactly plants get up to all day? It’s much more than just sitting around to soak up the sun. Now, machine learning is helping to unlock the mysteries of how plants change over the course of a day, and the impact it could have on how we grow our food. Biochemist  Dr. Glen Uhrig joins hosts Alona Fyshe and Scott Lilwall to talk about how his lab is using machine learning to study how plants grow in different lighting conditions of the course of the day. This could lead to applications with big impacts for indoor farming in northern climates, and perhaps even feeding astronauts during space missions. Listen to discover the potential that artificial intelligence has in advancing scientific research in biochemistry, agriculture, and other fields.

Should powerful foundational AI models be kept under lock and key, or shared openly with the world? In this episode of Approximately Correct, we sit down with Joelle Pineau, a professor at McGill University, former head of AI research at Meta and current chief AI officer at Cohere.. She led the development and release of the early versions of Meta’s Llama models, a series of open-weight models that challenged the closed-door approach of other AI teams. Pineau argues that openness is the fastest path to better, safer AI, and that diversity in foundational models is essential to developing a limited ‘algorithm monoculture’.

On this episode of Approximately Correct, we talk with Michael Littman about the importance of making AI accessible and fun for everyone. A former division director for the AI division at the National Science Foundation, Michael shares his unique perspective on AI policy, communication, and his career in reinforcement learning. He also discusses his new role as Associate Provost of Artificial Intelligence at Brown University, where he is working to coordinate AI research and teaching across the entire university.

This AI doesn't replace jobs; it collaborates with human experts to get the job done.On this episode of Approximately Correct, we talk with Revan McQueen on the future of industrial control and AI's role in making it safer and better

Is it possible to make building a video game as easy as writing a story? What if artificial intelligence could be more than just a tool, and instead become a true creative partner?In this episode of Approximately Correct, we dive into computational creativity with Amii Fellow and Canada CIFAR AI Chair, Matthew Guzdial. We explore how AI is being developed to collaborate with artists and designers, breaking down the technical barriers that can stand in the way of a great idea. Learn about the future of human-AI collaboration, the philosophical questions behind AI art, and how research that starts with video games can end up solving problems in finance and even medicine.

How can we teach robots to safely navigate our unpredictable world?On this special live episode of Approximately Correct recorded at Upper Bound 2025, we talk with Mo Chen about combining classical and modern AI to create smarter, safer, and more robust robots.

Is AI this year's MVP? How is machine learning changing sports?On this episode of Approximately Correct, we talk with Chicago Blackhawks' David Radke about how machine learning is transforming the analysis of sports like hockey.