Multi-Device, Multi-Use-Case Optimization with Jeff Gehlhaar - #587

We are back with our third show this week, episode 3 of our Autonomous Vehicles Series. My guest this time is Katie Driggs-Campbell, PostDoc in the Intelligent Systems Lab at Stanford University’s Department of Aeronautics and Astronautics. Katie joins us to discuss her research into human behavioral modeling and control systems for self-driving vehicles. Katie also gives us some insight into her process for collecting training data, how social nuances come into play for self-driving cars, and more. The notes for this show can be found at twimlai.com/talk/59 For Series info, visit twimlai.com/av2017

27 Loka 201733min

We are back with our second show this week, episode 2 of our Autonomous Vehicles Series. This time around we are joined by Jianxiong Xiao of AutoX, a company building computer vision centric solutions for autonomous vehicles. Jianxiong, a PhD graduate of MIT’s CSAIL Lab, joins me to discuss the different layers of the autonomous vehicle stack and the models for machine perception currently used in self-driving cars. If you’re new to the autonomous vehicles space I’m confident you’ll learn a ton, and even if you know the space in general, you’ll get a glimpse into why Jianxiong thinks AutoX’s direct perception approach is superior to end-to-end processing or mediated perception. The notes for this show can be found at twimlai.com/talk/58 For Series info, visit twimlai.com/av2017

25 Loka 201741min

The episode you are about to hear is the first of a new series of shows on Autonomous Vehicles. We all know that self-driving cars is one of the hottest topics in ML & AI, so we had to dig a little deeper into the space. To get us started on this journey, I’m excited to present this interview with Daryn Nakhuda, CEO and Co-Founder of MightyAI. Daryn and I discuss the many challenges of collecting training data for autonomous vehicles, along with some thoughts on human-powered insights and annotation, semantic segmentation, and a ton more great stuff. For the notes for this show, Visit twimlai.com/talk/57. For series info, visit twimlai.com/AV2017

23 Loka 201747min

As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. I sat down with James Guszcza, US Chief Data Scientist at Deloitte Consulting to talk about human factors in machine intelligence. James was in San Francisco to give a talk at the O’Reilly AI Conference on “Why AI needs human-centered design.” We had an amazing chat, in which we explored the many reasons why the human element is so important in ML and AI, along with useful ways to build algorithms and models that reflect this human element, while avoiding out problems like group-think and bias. This was a very interesting conversation. I enjoyed it a ton, and I’m sure you will too! The notes for this episode can be found at twimlai.com/talk/56

16 Loka 201742min

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. My guest for this show is Paul Tepper, worldwide head of cognitive innovation and product manager for machine learning & AI at Nuance Communications. Paul gave a talk at the conference on critical factors in building successful AI-powered conversational interfaces. We covered a bunch of topics, like voice UI design, behavioral biometrics and a ton of other interesting things that Nuance has in the works. The notes for this show can be found at twimlai.com/talk/52

6 Loka 201736min

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. This time around, I speak with Mo Patel, practice director of AI & deep learning and Laura Frølich, data scientist, of Think Big Analytics. Mo and Laura joined me at the AI conference after their session on “Training vision models with public transportation datasets.” We talked over a bunch of use cases they’ve worked on involving image analysis and deep learning, including an assisted driving system. We also talk through a bunch of practical challenges faced when working on real machine learning problems, like feature detection, data augmentation, and training data. The notes for this show can be found at twimlai.com/talk/54

6 Loka 201745min

In this episode, I talk to Naveen Rao, VP and GM of Intel’s AI Products Group, and Scott Apeland, director of Intel’s Developer Network. It's been a few months since we last spoke to Naveen, so he gives us a quick update on what Intel’s been up to and we discuss his perspective on some recent developments in the AI ecosystem. Scott and I dig into Intel Nervana’s new DevCloud offering, which was announced at the conference. We also discuss the Intel Nervana AI Academy, a new portal offering hands-on learning tools and other resources for various aspects of machine learning and AI. The notes for this show can be found at twimlai.com/talk/51

6 Loka 201737min

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. In this episode, I talk with Ion Stoica, professor of computer science & director of the RISE Lab at UC Berkeley. Ion joined us after he gave his talk “Building reinforcement learning applications with Ray.” We dive into Ray, a new distributed computing platform for RL, as well as RL generally, along with some of the other interesting projects RISE Lab is working on, like Clipper & Tegra. This was a pretty interesting talk. Enjoy! The notes for this show can be found at twimlai.com/talk/55

5 Loka 201728min