All Episodes

A must-listen episode with Dr. Dieuwke Hupkes from Meta AI Research, diving into AI generalization, LLM robustness, and model evaluation in large language models. We explore how LLMs handle grammar and hierarchy, how they generalize across tasks and languages, and what consistency tells us about AI alignment. We also talk about Dieuwke’s journey from physics to NLP, the challenges of peer review, and sustaining a career in research—plus, how pole dancing helps with focus

In this episode, Wanru Zhao discusses decentralized and collaborative AI methods, the limitations of scaling laws, fine-tuning strategies, data attribution challenges in LLMs, and multilingual learning in federated settings—all while reflecting on her experiences across UK and Canadian research ecosystems. She also shares her academic journey, vision for the future of AI, and how she navigates research roadblocks with creativity and curiosity.

In this episode, we speak with Dr. Faiza Khan Khattak, , the CTO of an innovative AI startup, with a rich background in both academia and industry. We dive deep into her journey and expertise across: fairness, explainability, and social bias in AI; reasoning and interpretability in LLMs; operationalizing ML in healthcare, machine unlearning and adversarial attacks. This is a must-listen for anyone passionate about building ethical, transparent, and impactful AI systems.

Can robots have feelings? In this fascinating episode, we speak with Dr. Angelica Lim, Assistant Professor at Simon Fraser University and leader of the SFU Rosie Lab. Dr. Lim discusses her pioneering work on developing artificial intelligence software that enables robots to interact with social intelligence and empathy. We explore the intersection of robotics, neuroscience, machine learning, and developmental psychology, and consider both the technical challenges and philosophical questions surrounding emotional AI. This conversation offers a glimpse into the future of human-robot interaction and the potential for machines to understand and respond to human emotions.

This episode features Aparna Balagopalan, a brilliant PhD student at MIT, discussing the intersection of AI and healthcare. Aparna shares her research on developing fair, interpretable, and robust models for healthcare applications. We explore the unique challenges of applying AI in medical contexts, including data privacy, regulatory considerations, and the critical importance of model transparency. The conversation covers both technical innovations and ethical frameworks necessary for responsible AI deployment in healthcare settings.

Join us for an insightful conversation with Dr. Amanda Cercas Curry, a researcher at CENTAI Institute, as we explore the critical issue of bias in AI systems. Dr. Cercas Curry shares her expertise on identifying, measuring, and mitigating various forms of bias in language models and other AI applications. We discuss the social and ethical implications of biased AI, and how researchers are working to create more fair and inclusive systems. This episode highlights the importance of diverse perspectives in AI development and the ongoing challenges in the field.

In this episode, we dive deep into the limitations of transformer models with Dr. Nouha Dziri, a research scientist at Allen Institute for AI (AI2). Dr. Dziri shares insights from her groundbreaking research on understanding the capabilities and constraints of large language models. We discuss the challenges in reasoning, factuality, and ethical considerations that come with deploying these powerful AI systems. This conversation explores both technical aspects and broader implications for the future of AI research.