In our latest episode of the In Context Podcast, we're joined by Eric Colson, the Chief Algorithms Officer for Stitch Fix, an online subcription personal shopping service whose tech blog, Multithreaded, may have the coolest algorithms tour on the internet. In a fascinating discussion, he and Kathryn Hume analyze how Stitch Fix's engineering culture works, including what they value, what they look for in new hires, and how they’ve architected their platform to enable astounding success. You'll also hear about the critical role that autonomy plays in how Eric organizes his data science teams.
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.
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.
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.
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.