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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.

Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.

Jun 23, 2017

This week we continue our series on industrial applications of machine learning and AI with a conversation with Chelsea Finn, a PhD student at UC Berkeley. Chelsea’s research is focused on machine learning for robotic perception and control. Despite being early in her career, Chelsea is an accomplished researcher with more than 14 published papers in the past 2 years, on subjects like Deep Visual Foresight , Model-Agnostic Meta-Learning and Visuomotor Learning to name a few, all of which we discuss in the show, along with topics like zero-shot, one-shot and few-shot learning. I’d also like to give a shout out to Shreyas, a listener who wrote in to request that we interview a current PhD student about their journey and experiences. Chelsea and I spend some time at the end of the interview talking about this, and she has some great advice for current and prospective PhD students but also independent learners in the field. During this part of the discussion I wonder out loud if any listeners would be interested in forming a virtual paper reading club of some sort. I’m not sure yet exactly what this would look like, but please drop a comment in the show notes if you’re interested. I'm going to once again deploy the Nerd Alert for this episode; Chelsea and I really dig deep into these learning methods and techniques, and this conversation gets pretty technical at times, to the point that I had a tough time keeping up myself. The notes for this page can be found at