The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

My guest this week is Brendan Frey, Professor of Engineering and Medicine at the University of Toronto and Co-Founder and CEO of the startup Deep Genomics. Brendan and I met at the Re-Work Deep Learning Summit in San Francisco last month, where he delivered a great presentation called “Reprogramming the Human Genome: Why AI is Needed.” In this podcast we discuss the application of AI to healthcare. In particular, we dig into how Brendan’s research lab and company are applying machine learning and deep learning to treating and preventing human genetic disorders. The show notes can be found at twimlai.com/talk/12

My guest this time is Hilary Mason. Hilary was one of the first “famous” data scientists. I remember hearing her speak back in 2011 at the Strange Loop conference in St. Louis. At the time she was Chief Scientist for bit.ly. Nowadays she’s running Fast Forward Labs, which helps organizations accelerate their data science and machine intelligence capabilities through a variety of research and consulting offerings. Hilary presented at the O'Reilly AI conference on “practical AI product development” and she shares a lot of wisdom on that topic in our discussion. The show notes can be found at twimlai.com/talk/11.

My guest this time is Francisco Webber, founder and General Manager of artificial intelligence startup Cortical.io. Francisco presented at the O’Reilly AI conference on an approach to natural language understanding based on semantic representations of speech. His talk was called “AI is not a matter of strength but of intelligence.” My conversation with Francisco was a bit technical and abstract, but also super interesting. The show notes can be found at twimlai.com/talk/10.

My guest this time is Pascale Fung, professor of electrical & computer engineering at Hong Kong University of Science and Technology. Pascale delivered a presentation at the recent O'Reilly AI conference titled "How to make robots empathetic to human feelings in real time," and I caught up with her after her talk to discuss teaching computers to understand and respond to human emotions. We also spend some time talking about the (information) theoretical foundations of modern approaches to speech understanding. The notes for this show can be found at twimlai.com/talk/9.

My guest this time is Diogo Almeida, senior data scientist at healthcare startup Enlitic. Diogo and I met at the O'Reilly AI conference, where he delivered a great presentation on in-the-trenches deep learning titled “Deep Learning: Modular in theory, inflexible in practice,” which we discuss in this interview. Diogo is also a past 1st place Kaggle competition winner, and we spend some time discussing the competition he competed in and the approach he took as well. The notes for this show can be found at twimlai.com/talk/8.

My guest this time is Carlos Guestrin, the Amazon professor of Machine Learning at the University of Washington. Carlos and I recorded this podcast at a conference, shortly after Apple's acquisition of his company Turi. Our focus for this podcast is the explainability of machine learning algorithms. In particular, we discuss some interesting new research published by his team at U of W. The notes for this show can be found at twimlai.com/talk/7.

My guest this time is Angie Hugeback, who is principal data scientist at Spare5. Spare5 helps customers generate the high-quality labeled training datasets that are so crucial to developing accurate machine learning models. In this show, Angie and I cover a ton of the real-world practicalities of generating training datasets. We talk through the challenges faced by folks that need to label training data, and how to develop a cohesive system for achieving performing the various labeling tasks you’re likely to encounter. We discuss some of the ways that bias can creep into your training data and how to avoid that. And we explore the some of the popular 3rd party options that companies look at for scaling training data production, and how they differ. Spare5 has graciously sponsored this episode; you can learn more about them at spare5.com. The notes for this show can be found at twimlai.com/talk/6.

My guest this time is Joshua Bloom. Josh is professor of astronomy at the University of California, Berkeley and co-founder and Chief Technology Officer of machine learning startup Wise.io. In this wide-ranging interview you’ll learn how Josh and his research group at Berkeley pioneered the use of machine learning for the analysis of images from robotic infrared telescopes. We discuss the founding of his company, Wise.io, which uses machine learning to help customers deliver better customer support. That wasn’t where the company started though, and you’ll hear why and how they evolved to serve this market. We talk about his company’s technology stack and data science pipeline in fair detail, and discuss some of the key technology decisions they’ve made in building their product. We also discuss some interesting open research challenges in machine learning and AI. The notes for this show can be found at twimlai.com/talk/5.

My guest this time is Charles Isbell, Jr., Professor and Senior Associate Dean in the College of Computing at Georgia Institute of Technology. Charles and I go back a bit… in fact he’s the first AI researcher I ever met. His research focus is what he calls “interactive artificial intelligence,” a discipline of AI focused specifically on the interactions between AIs and humans. We explore what this means and some of the interesting research results in this field. One part of this discussion I found particularly interesting was the intersection between his AI research and marketing and behavioral economics. Beyond his research, Charles is well known in the ML and AI worlds for his popular Machine Learning course sequence on Udacity, which he teaches with Brown University professor Michael Littman, and for the Online Master’s of Science in Computer Science program that he helped launch at Georgia Tech. We also spend quite a bit of time talking about what’s really missing in machine learning education and how to make it more accessible. The notes for this show can be found at twimlai.com/talk/4.

My guest this time is Xavier Amatriain. Xavier is a former researcher who went on to lead the machine learning recommendations team at Netflix, and is now the vice president of engineering at Quora, the Q&A site. We spend quite a bit of time digging into each of these experiences in the interview. Here are just a few of the things we cover in our discussion: Why Netflix invested $1 million in the Netflix Prize, but didn’t use the winning solution; What goes into engineering practical machine learning systems; The problem Xavier has with the deep learning hype; And, what the heck is a multi-arm bandit and how can it help us. The notes for this show can be found at https://twimlai.com/talk/3.