Are Prosthetics About to Get a WHOLE LOT Smarter? with Patrick M. Pilarski

How do you build a robot that had empathy? On this episode of Approximately Correct, Amii Canada CIFAR AI Chair Angelica Lim joins hosts Alona Fyshe and Scott Lilwall to discuss the complex world of social robotics. As the director of the ROSIE Lab at Simon Fraser University, Lim is focused on making robots friendlier, more fun, and more useful, allowing them to interact with humans more effectively and react to our social cues. She dives into the challenges of adding empathy to artificial intelligence, why cultural diversity if important when it comes to training social machines, and why making robots into artificial friends might not actually be the ideal goal.

There are all kinds of big promises about how artificial intelligence will change everything. But there's a very real transformation quietly happening in healthcare that is happening just under the surface. In this episode of Approximately Correct, hosts Alona Fyshe and Scott Lilwall sit down with Amii Fellow and Canada CIFAR AI Chair Dr. Amber Simpson to explore how machine learning is making a tangible difference in cancer detection, research and everyday medicine.

How do we know where we are, and where we are going? While we use our eyes to take in the information, it is our brains that are doing the heavy computing as we navigate the world. Now, new research using machine learning models is perhaps giving us a glimpse into what is going on inside our minds, and how the cells needed to navigate the world might form. On this episode of Approximately Correct, we are joined by two Amii Fellows and Canada CIFAR AI Chairs — Marlos C. Machado and Quinn Lee — who combined their expertise in machine learning and neuroscience to better understand the mysteries of navigation.

What happens when you give an economics final to ChatGPT? On a special episode of Approximately Correct, filmed live at Upper Bound 2026, we're joined by Canada CIFAR AI Chair Kevin Leyton-Brown. His work focuses on the underexplored intersection of machine learning and microeconomics — two fields that might seem wildly different, but have a lot in common. Tune in for a wide-ranging discussion on multi-agent systems, the reasoning abilities of LLMS, and how AI is a crucial part of Canada’s economic future.

In just five years, Upper Bound has grown from a 400-person birthday party to one of Canada’s largest AI-centred events, drawing in thousands of people each year. But it isn’t the only thing that has changed: the way we talk about and use machine learning is also completely different in 2026.