Can Robots Learn to Read the Room? With Angelica Lim | Approximately Correct Podcast

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.Approximately Correct: An AI Podcast from Amii is hosted by Alona Fyshe and Scott Lilwall, produced by Lynda Vang, with video production by Zachariah Connelly.

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.On the latest episode of Approximately Correct, Stephanie Enders joins to discuss how the conversation around AI has shifted in recent years.

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’.