This Week in Machine Learning & AI Podcast

This Week in Machine Learning & AI Podcast


This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.


Reinforcement Learning: The Next Frontier of Gaming with Danny Lange - TWiML Talk #24  

My guest on the show this week is Danny Lange, VP for Machine Learning & AI at video game technology developer Unity Technologies. Danny is well traveled in the world of ML and AI, and has had a hand in developing machine learning platforms at companies like Uber, Amazon and Microsoft. In this conversation we cover a bunch of topics, including How ML & AI are being used in gaming, the importance of reinforcement learning in the future of game development, the intersection between AI and AR/VR and the next steps in natural character interaction. The notes for this show can be found at

Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47  

My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we've received a ton of requests for -- evolutionary algorithms. During our talk we discuss some of the things Sentient is working on in the financial services and retail fields, and we dig into the technology behind it, evolutionary algorithms, which is also the focus of Risto’s research at UT. I really enjoyed this interview and learned a ton, and I’m sure you will too! Notes for this show can be found at

Agile Machine Learning - Jennifer Prendki - TWiML Talk #46  

My guest this week is Jennifer Prendki. That name might sound familiar, as she was one of the great speakers from my Future of Data Summit back in May. At the time, Jennifer was senior data science manager and principal data scientist at Walmart Labs, but she's since moved on to become head of data science at Atlassian. Back at the summit, Jennifer gave an awesome talk on what she calls Data Mixology, the slides for which you can find on the show notes page. My conversation with Jennifer begins with a recap of that talk. After that, we shift our focus to some of the practices she helped develop and implement at Walmart around the measurement and management of machine learning models in production, and more generally, building agile processes and teams for machine learning. The notes for this show can be found at

LSTMs, Plus a Deep Learning History Lesson - Jürgen Schmidhuber - TWiML Talk #44  

This week we have a very special interview to share with you! Those of you who’ve been receiving my newsletter for a while might remember that while in Switzerland last month, I had the pleasure of interviewing Jurgen Schmidhuber, in his lab IDSIA, which is the Dalle Molle Institute for Artificial Intelligence Research in Lugano, Switzerland, where he serves as Scientific Director. In addition to his role at IDSIA, Jurgen is also Co-Founder and Chief Scientist of NNaisense, a company that is using AI to build large-scale neural network solutions for “superhuman perception and intelligent automation.” Jurgen is an interesting, accomplished and in some circles controversial figure in the AI community and we covered a lot of very interesting ground in our discussion, so much so that I couldn't truly unpack it all until I had a chance to sit with it after the fact. We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years. Along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years. It was like walking back in time with the 3 eyed raven. I know you’re really going to enjoy this one, and by the way, this is definitely a nerd alert show! For the show notes, visit

Machine Teaching for Better Machine Learning with Mark Hammond - TWiML Talk #43  

Today’s show, which concludes the first season of the Industrial AI Series, features my interview with Bonsai co-founder and CEO Mark Hammond. I sat down with Mark at Bonsai HQ a few weeks ago and we had a great discussion while I was there. We touched on a ton of subjects throughout this talk, including his starting point in Artificial intelligence, how Bonsai came about & more. Mark also describes the role of what he calls “machine teaching” in delivering practical machine learning solutions, particularly for enterprise or industrial AI use cases. This was one of my favorite conversations, I know you’ll enjoy it! The notes for this show can be found at

Marrying Physics-Based and Data-Driven ML Models with Josh Bloom - TWiML Talk #42  

Recently I had a chance to catch up with a friend and friend of the show, Josh Bloom, vice president of data & analytics at GE Digital. If you’ve been listening for a while, you already know that Josh was on the show around this time last year, just prior to the acquisition of his company by GE Digital. It was great to catch up with Josh on his journey within GE, and the work his team is doing around Industrial AI, now that they’re part of the one of the world’s biggest industrial companies. We talk about some really interesting things in this show, including how his team is using autoencoders to create training datasets, and how they incorporate knowledge of physics and physical systems into their machine learning models. The notes for this show can be found at

Cognitive Biases in Data Science with Drew Conway - TWiML Talk #39  

This show features my interview with Drew Conway, whose Wrangle keynote could have been called “Confessions of a CIA Data Scientist.” The focus of our interview, and of Drew’s presentation, is an interesting set of observations he makes about the role of cognitive biases in data science. If your work involves making decisions or influencing behavior based on data-driven analysis--and it probably does or will--you’re going to want to hear what he has to say. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at

Data Pipelines at Zymergen with Airflow with Erin Shellman - TWiML Talk #41  

The show you’re listening to features my interview with Erin Shellman. Erin is a statistician and data science manager with Zymergen, a company using robots and machine learning to engineer better microbes. If you’re wondering what exactly that means, I was too, and we talk about it in the interview. Our conversation focuses on Zymergen’s use of Apache Airflow, an open-source data management platform originating at Airbnb, that Erin and her team uses to create reliable, repeatable data pipelines for its machine learning applications. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at

Web Scale Engineering for Machine Learning with Sharath Rao - TWiML Talk #40  

The show you’re about to listen to features my interview with Sharath Rao, Tech Lead Manager & Machine Learning Engineer at Instacart I reached out to Sharath about being on the show and was blown away when he replied that not only had he heard about the show, but that he was a fan and an avid listener. My conversation with him digs into some of the practical lessons and patterns he’s learned by building production-ready, web-scale data products based on machine learning models, including the search and recommendation systems at Instacart. We also spend a few minutes discussing our upcoming TWiML Paper Reading Meetup! A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at

Deep Learning for Warehouse Operations with Calvin Seward - TWiML Talk #38  

This week, I’m happy to bring you my interview with Calvin Seward, a research scientist with Berlin, Germany based Zalando. While our American listeners might not know the name Zalando, they’re one of the largest e-commerce companies in Europe with a focus on fashion and shoes. Calvin is a research scientist there, while also pursuing his doctorate studies at Johannes Kepler University in Linz, Austria. Our discussion, which continues our Industrial AI series, focuses on how Calvin’s team tackled an interesting warehouse optimization problem using deep learning. Calvin also gives his thoughts on the distinction between AI and ML, and the four P’s that he focuses on: Prestige, Products, Paper, and Patents. The notes for this show can be found at

Deep Robotic Learning with Sergey Levine - TWiML Talk #37  

This week we continue our Industrial AI series with Sergey Levine, an Assistant Professor at UC Berkeley whose research focus is Deep Robotic Learning. Sergey is part of the same research team as a couple of our previous guests in this series, Chelsea Finn and Pieter Abbeel, and if the response we’ve seen to those shows is any indication, you’re going to love this episode! Sergey’s research interests, and our discussion, focus in on include how robotic learning techniques can be used to allow machines to acquire autonomously acquire complex behavioral skills. We really dig into some of the details of how this is done and I found that our conversation filled in a lot of gaps for me from the interviews with Pieter and Chelsea. By the way, this is definitely a nerd alert episode! Notes for this show can be found at

Smart Buildings & IoT with Yodit Stanton - TWiML Talk #36  

After a brief hiatus, the Industrial AI Series is making its triumphant return! Our guest this week is Yodit Stanton, a self-described Data Nerd, and the Founder & CEO of is a real-time data exchange for IoT, that enables anyone to publish and subscribe to real time open data in order to build higher order smart systems and better understand the world around them. Our discussion focuses on Smart Buildings and how they’re enabled by IoT and machine learning techniques. The notes for this show can be found at

Enhancing Customer Experiences With Emotional AI with Rana El Kaliouby - TWiML Talk #35  

My guest for this show is Rana el Kaliouby. Rana is co-founder and CEO of Affectiva. Affectiva, as Rana puts it, "is on a mission to humanize technology by bringing in artificial emotional intelligence". If you liked my conversation about Emotional AI with Pascale Fung from last year’s O’Reilly AI conference, you’re going to love this one. My conversation with Rana kind of picks up where the previous one left off, with a focus on how her company is bringing Artificial Emotional Intelligence services to market. Rana and her team have developed a machine learning / computer vision platform that can use the camera on any device to read your facial expressions in real time, then maps it to an emotional state. Using data science to mine the world’s largest emotion repository, Affectiva has collected over 5.5 million pieces of emotional expression data to date, from laptop, driving, cellular interactions. Understanding the importance of personal privacy, Rana and her Co-Founder Rosalind Wright Picard have vowed to shy away from partnerships that would subject consumers to unknowing surveillance, a commendable effort. The notes for this show can be found at

Intel Nervana Update + Productizing AI Research with Naveen Rao And Hanlin Tang - TWiML Talk #31  

I talked about Intel’s acquisition of Nervana Systems on the podcast when it happened almost a year ago, so I was super excited to have an opportunity to sit down with Nervana co-founder Naveen Rao, who now leads Intel’s newly formed AI Products Group, for the first show in our O'Reilly AI series. We talked about how Intel plans to extend its leadership position in general purpose compute into the AI realm by delivering silicon designed specifically for AI, end-to-end solutions including the cloud, enterprise data center, and the edge; and tools that let customers quickly productize and scale AI-based solutions. I also spoke with Hanlin Tang, an algorithms engineer at Intel’s AIPG, about two tools announced at the conference: version 2.0 of Intel Nervana’s deep learning framework Neon and Nervana Graph, a new toolset for expressing and running deep learning applications as framework and hardware-independent computational graphs. Nervana Graph in particular sounds like a very interesting project, not to mention a smart move for Intel, and I’d encourage folks to take a look at their Github repo. The show notes for this page can be found at

Expressive AI - Generated Music With Google's Performance RNN - Doug Eck - TWiML Talk #32  

My guest for this second show in our O’Reilly AI series is Doug Eck of Google Brain. Doug did a keynote at the O’Reilly conference on Magenta, Google’s project for melding machine learning and the arts. Magenta’s goal is to produce open-source tools and models that help people in their personal creative processes. Doug’s research starts with using so-called “generative” machine learning models to create engaging media. Additionally, he is working on how to bring other aspects of the creative process into play. We talk about the newly announced Performance RNN project, which uses neural networks to create expressive, AI-generated music. We also touch on QuickDraw, a project by Google AI Experiments, in which users as Doug describes it, “play Pictionary” with a visual classifier. We dig into what he foresees as possibilities for Magenta, machine learning models eventually developing storylines, generative models for media and creative coding. The notes for this episode can be found at

The Power Of Probabilistic Programming with Ben Vigoda - TWiML Talk #33  

My guest for this third episode in the O'Reilly AI series is Ben Vigoda. Ben is the founder and CEO of Gamalon, a DARPA-funded startup working on Bayesian Program Synthesis. We dive into what exactly this means and how it enables what Ben calls idea learning in the show. Gamalon's first application structures unstructured data — input a paragraph or phrase of unstructured text and output a structured spreadsheet/database row or API call. This can be applicable to a wide range of data challenges, including enterprise product and customer information, AI or digital assistant, and many others. Before Gamalon, Ben was co-founder and CEO of Lyric Semiconductor, Inc., which created the first microprocessor architectures dedicated for statistical machine learning. The company was based on his PhD thesis at MIT and acquired by Analog Devices. In today’s talk we are discussing probabilistic programming, his new approach to deep learning, posterior distribution, and the difference between sampling methods and variational methods and how solvers work in the system. Nerd alert: We go pretty deep in this discussion. The notes for this show can be found at

Video Object Detection At Scale with Reza Zadeh - TWiML Talk #34  

My guest for the fourth show in the O'Reilly AI Series is Reza Zadeh. Reza is an adjunct professor of computational mathematics at Stanford University and founder and CEO of the startup Matroid. Reza has a background in machine translation and distributed machine learning, along with having helped build Apache Spark, and the"Who to Follow" feature on Twitter, which is based on a chapter from his PhD thesis. Our conversation focused on some of the challenges and approaches to scaling deep learning, both in general and in the context of his company’s video object detection service. Our conversation focused on some of the challenges and approaches to scaling deep learning, both in general and in the context of his company’s video object detection service. We also spoke about the advancement of computer vision technologies, using CPU's, GPU's, the upcoming shift to TPU's and we get below the surface on Apache Spark.

Natural Language Understanding for Amazon Alexa with Zornitsa Kozareva - TWiML Talk #30  

Our guest this week is Zornitsa Kozareva, Manager of Machine Learning with Amazon Web Services Deep Learning, where she leads a group focused on natural language processing and dialogue systems for products like Alexa and Lex, the latter of which we introduce in the podcast. We spend most of our time talking through the architecture of modern Natural Language Understanding systems, including the role of deep learning, and some of the various ways folks are working to overcome the challenges in this field, such as understanding human intent. If you’re interested in this field she mentions the AWS Chatbot Challenge, which you’ve still got a couple more weeks to participate in. The notes for this show can be found at

Robotic Perception and Control with Chelsea Finn - TWiML Talk #29  

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

Reinforcement Learning Deep Dive with Pieter Abbeel - TWiML Talk #28  

This week our guest is Pieter Abbeel, Assistant Professor at UC Berkeley, Research Scientist at OpenAI, and Cofounder of Gradescope. Pieter has an extensive background in AI research, going way back to his days as Andrew Ng’s first PhD student at Stanford. His research today is focused on deep learning for robotics. During this conversation, Pieter and I really dig into reinforcement learning, a technique for allowing robots (or AIs) to learn through their own trial and error. Nerd alert!! This conversation explores cutting edge research with one of the leading researchers in the field and, as a result, it gets pretty technical at times. I try to uplevel it when I can keep up myself, so hang in there. I promise that you’ll learn a ton if you keep with it. The notes for this show can be found at

Intelligent Autonomous Robots with Ilia Baranov - TWiML Talk #27  

Our first guest in the Industrial AI series is Ilia Baranov, engineering manager at Clearpath Robotics. Ilia is responsible for setting the engineering direction for all of Clearpath’s research platforms. Ilia likes to describe his role at the company as “both enabling and preventing the robot revolution.” He’s a longtime contributor to the Open Source Robotics Community and ROS, an open source robotic operating system. He is the also the managing engineer of the PR2 support team at Clearpath and leads the technical demonstration group. In our conversation we cover a lot of ground, including what it really means to field autonomous robots, the use of autonomous robots in research and industrial environments, the different approaches and challenges to achieving autonomy, and much more! The notes for this show are available at, and for more information on the Industrial AI Series, visit

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