This Week In Machine Learning & Artificial Intelligence (ai) Podcast

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 variation

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 https://twimlai.com/talk/3

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.

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 twimlai.com/talk/30.

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

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 twimlai.com/talk/28

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 ➙ http

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 twimlai.com/talk/24

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 sa

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.

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with AlphaVertex, a FinTech startup creating a worldwide financial knowledge graph to help investors predict stock prices. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Behold.ai, which uses computer vision and natural language processing techniques to bring efficiencies to the world of healthcare insurance billing. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Cambrian Intelligence, a company using AI to simplify the programming of industrial robots for the automotive industry. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Klustera, a company applying location-based intelligence and machine learning to help brands execute smarter marketing campaigns. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!

This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with HelloVera, a company applying artificial intelligence to the challenge of automating customer support experiences. The notes for this series can be found at https://twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!