Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559

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 https://twimlai.com/talk/40.

4 Aug 201731min

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 https://twimlai.com/talk/38.

31 Jul 201746min

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 twimlai.com/talk/37

24 Jul 201746min

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 Opensensors.io. OpenSensors.io 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 twimlai.com/talk/36

17 Jul 201753min

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 https://twimlai.com/talk/31

5 Jul 201738min

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/32.

5 Jul 201746min

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 https://twimlai.com/talk/33

5 Jul 201742min

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.

5 Jul 201752min