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

Pieter Abbeel, a leading researcher in deep reinforcement learning and Assistant Professor at UC Berkeley, shares his expertise in AI and robotics. He discusses the evolution of deep reinforcement learning and the integration of established physics into modern systems. Pieter elaborates on how games aid training and the intricacies of transfer learning. He also analyzes Q-learning and policy gradients, emphasizing their significance in decision-making processes. This conversation dives deep into the technical challenges and future possibilities of AI.

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 twimlai.com/talk/27, and for more information on the Industrial AI Series, visit twimlai.com/IndustrialAI.

This week I’ve invited my friend Ben Lorica onto the show. Ben is Chief Data Scientist for O’Reilly Media, and Program Director of Strata Data & the O'Reilly A.I. conference. Ben has worked on analytics and machine learning in the finance and retail industries, and serves as an advisor for nearly a dozen startups. In his role at O’Reilly he’s responsible for the content for 7 major conferences around the world each year. In the show we discuss all of that, touching on how publishers can take advantage of machine learning and data mining, how the role of “data scientist” is evolving and the emergence of the machine learning engineer, and a few of the hot technologies, trends and companies that he’s seeing arise around the world. The notes for this show can be found at twimlai.com/talk/26

This week on the show my guest is Deep Varma, Vice President of Data Engineering at real estate startup Trulia. Deep has run data engineering teams in silicon valley for well over a decade, and is now responsible for the engineering efforts supporting Trulia’s Big Data Technology Platform, which encompasses everything from Data acquisition & management to Data Science & Algorithms. In the show we discuss all of that, with an emphasis on Trulia’s data engineering pipeline and their personalization platform, as well how they use computer vision, deep learning and natural language generation to deliver their product. Along the way, Deep offers great insights into what he calls offensive vs defensive data science, and the difference between data-driven decision making vs products. Another great interview, and i'm sure you’ll enjoy it. The notes for this show can be found at twimlai.com/talk/25 Subscribe! iTunes ➙ https://itunes.apple.com/us/podcast/this-week-in-machine-learning/id1116303051?mt=2 Soundcloud ➙ https://soundcloud.com/twiml Google Play ➙ http://bit.ly/2lrWlJZ Stitcher ➙ http://www.stitcher.com/s?fid=92079&refid=stpr RSS ➙ https://twimlai.com/feed Lets Connect! Twimlai.com ➙ https://twimlai.com/contact Twitter ➙ https://twitter.com/twimlai Facebook ➙ https://Facebook.com/Twimlai Medium ➙ https://medium.com/this-week-in-machine-learning-ai

Danny Lange, VP of Machine Learning & AI at Unity Technologies, shares his extensive experience in ML and AI from companies like Uber and Amazon. He discusses the transformative power of reinforcement learning in gaming, which allows AI to enhance character movements through trial-and-error. Lange highlights the future of personalized gameplay, where games adapt to players' preferences. The conversation also touches on the integration of AR/VR technologies to create immersive experiences while addressing current development challenges.

I think you’re really going to enjoy today’s show. Our guest this week is Dominique Simmons, Applied research Scientist at AI tools vendor Dimensional Mechanics. Dominique brings an interesting background in Cognitive Psychology and psycholinguistics to her work and research in AI and, well, to this podcast. In our conversation, we cover the implications of cognitive psychology for neural networks and AI systems, and in particular how an understanding of human cognition impacts the development of AI models for media applications. We also discuss her research into multimodal training of AI models, and how our understanding of the human brain has influenced this work. We also explore the debate around the biological plausibility of machine learning and AI models. It was a great conversation. The show notes can be found at twimlai.com/talk/23.

Today we bring you our final interview from backstage at the NYU FutureLabs AI Summit. Our guest this week is Matt Zeiler. Matt graduated from the University of Toronto where he worked with deep learning researcher Geoffrey Hinton and went on to earn his PhD in machine learning at NYU, home of Yann Lecun. In 2013 Matt’s founded Clarifai, a startup whose cloud-based visual recognition system gives developers a way to integrate visual identification into their own products, and whose initial image classification algorithm achieved top 5 results in that year’s ImageNet competition. I caught up with Matt after his talk “From Research to the Real World”. Our conversation focused on the birth and growth of Clarifai, as well as the underlying deep neural network architectures that enable it. If you’ve been listening to the show for a while, you’ve heard me ask several guests how they go about evolving the architectures of their deep neural networks to enhance performance. Well, in this podcast Matt gives the most satisfying answer I’ve received to date by far. Check it out. I think you’ll enjoy it. The show notes can be found at twimlai.com/talk/22.

Today we bring you the second of three interviews we did backstage from the NYU FutureLabs AI Summit, this time with Ruchir Puri. Ruchir is the Chief Architect at IBM Watson as well as an IBM Fellow. I caught up with Ruchir after his talk on “engineering the Future of AI for Businesses”. Our conversation focused on cognition and reasoning, and we explored what these concepts represent, how enterprises really want to consume them, and how IBM Watson seeks to deliver them. The show notes can be found at twimlai.com/talk/21.

This week's guest is Kathryn Hume. Kathryn is the President of Fast Forward Labs, which is an independent machine intelligence research company that helps organizations accelerate their data science and machine intelligence capabilities. If Fast Forward Labs sounds familiar, that's because we had their founder, Hilary Mason on a few months ago. We’ll link to that in the show notes. My discussion with Kathryn focused on AI adoption within the enterprise. She shared several really interesting examples of the kinds of things she’s seeing enterprises do with machine learning and AI, and we discussed a few of the various challenges enterprises face and some of the lessons her company has learned in helping them. I really enjoyed our conversation and I know you will too! You can find the notes for todays show here: https://twimlai.com/talk/20

This week my guest is Scott Stephenson. Scott is co-Founder & CEO of Deepgram, which has developed an AI-based platform for indexing and searching audio and video. Scott and I cover a ton of interesting topics including applying machine learning techniques to particle physics, his time in a lab 2 miles below the surface of the earth, applying neural networks to audio, and the Deep Learning Framework Kur that his company open-sourced. The show notes can be found at twimlai.com/talk/19.