Episode 8: Trust in Enterprise AI with Susan Etlinger

Episode 8: Trust in Enterprise AI with Susan Etlinger

In this episode of the In Context podcast, we welcome Susan Etlinger, an industry analyst at Altimeter Group who focuses on data, conversational business, and ethics in the age of artificial intelligence. In their conversation, she and Kathryn Hume look at how AI is unlocking the promise of design thinking to enable amazing customer experiences, how the rapidly evolving technology landscape is changing consumer expectations around trust, and why enterprises should view ethics as a competitive differentiator rather than merely a compliance exercise. Find out how you can break these issues down and start thinking about them clearly for your business.

Episode 7: Understanding the Marketplace for Data Products with Clare Corthell and Sarah Catanzaro

Episode 7: Understanding the Marketplace for Data Products with Clare Corthell and Sarah Catanzaro

In our latest episode of the In Context Podcast, we welcome Clare Corthell, the Founder of Luminant Data, and Sarah Catanzaro, a Principal at Amplify Partners. Join us for a fascinating discussion about data products and where value and work occurs in machine learning pipelines. Find out about the competitive concerns that many companies have as they think about disclosing their data to third parties and the challenges and opportunities companies face as they consider entering this highly competitive space. 

Episode 6: Making AI Teachable with Randy Goebel and Osmar Zaiane

Episode 6: Making AI Teachable with Randy Goebel and Osmar Zaiane

This episode of the In Context Podcast features a conversation with artificial intelligence researchers Randy Goebel and Osmar Zaiane, both professors at the Alberta Machine Intelligence Institute (AMII). We discuss the limitations of deep supervised learning and discuss alternatives to make it easier to understand, explain, and teach machine learning systems.

Episode 5: New frontiers in deep learning research with Graham Taylor and David Duvenaud

Episode 5: New frontiers in deep learning research with Graham Taylor and David Duvenaud

This episode features a conversation with machine learning researchers Graham Taylor (University of Guelph) and David Duvenaud (University of Toronto). We discuss how deep learning enables us to exploit the creative potential of framing tasks and phenomena as optimization problems. We cover a broad set of examples, like machine creativity, automating the design of neural network architecture, variational inference (that is, finding a good proxy representation of a tricky data set to make it usable for machine learning), and the mathematical structure behind making hard choices.

Episode 4: Machine Learning, Cancer, and the Genome with Quaid Morris

Episode 4: Machine Learning, Cancer, and the Genome with Quaid Morris

 

This episode of In Context features Quaid Morris, Principal Investigator at The Morris Lab at the University of Toronto and a member of the Vector Institute. Morris and his research team apply machine learning to understand biology, with projects ranging from cancer genomics to personalized medicine. The discussion includes a brief primer on genomics and DNA sequencing, insights into the challenges of working with genome data, examples of how new machine learning algorithms are changing genomics research, and thoughts on why translators are needed to realize the value machine learning can provide to science and in the enterprise.

Episode 3: Applying AI to Improve Sales Relationships: A Conversation with Steve Woods

Episode 3: Applying AI to Improve Sales Relationships: A Conversation with Steve Woods

This episode of In Context features Steve Woods, co-founder and CTO of Nudge, which applies AI to help sales professionals build deeper relationships. A veteran entrepreneur, Steve was also co-founder and CTO of Eloqua, a market leader in marketing automation eventually acquired by Oracle, and wrote Digital Body Language, a work about deciphering customer intentions online. In this wide-ranging conversation, Steve shares insights on why great sales is about theory of mind, what sales tasks should be automated and what will always remain the provenance of human creativity, why and how data science will eventually become just another form of software, and why Toronto is becoming a unique tech ecosystem. 

Episode 2: A Conversation with George Smith and Daniel Dennett

Episode 2: A Conversation with George Smith and Daniel Dennett

This episode of In Context features George Smith and Daniel Dennett, both professors in the department of philosophy at Tufts University. George Smith focuses on philosophy of science and logic, with particular focus on transforming data into evidence. Co-editor of the Cambridge Companion to Newton, he's an expert on how Isaac Newton transformed notions of high-quality evidence. Daniel Dennett focuses on philosophy of mind and cognitive science, and has written multiple books on consciousness. 

Throughout this conversation, George and Dan share their thoughts on how theories turn data into evidence, what contributions Descartes, Newton, Hume, and Darwin made to the history of philosophy, what consciousness is and isn't, why we fall prey to anthropomorphizing machines, and how we should engage with AI systems.

Episode 1: A Conversation with Rich Sutton

Episode 1: A Conversation with Rich Sutton

 

 

This episode of In Context features Rich Sutton, a pioneer in the field of reinforcement learning, a computational approach to learning where an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. A professor in the department of Computing Science at the University of Alberta and advisor for RBC and DeepMind, Professor Sutton has devoted much of his life to AI research. His 1998 textbook, Reinforcement Learning: An Introduction, is widely used today. 

Throughout this conversation, Rich shares his insights on the history of reinforcement learning, current developments in temporal difference learning (a subfield of reinforcement learning), the fundamental difference between reinforcement learning and supervised learning, the importance of intuition and critical thinking in scientific research, and the parallels between learning algorithms and the big choices we make in life.