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

The show is part of a series that I’m really excited about, in part because I’ve been working to bring them to you for quite a while now. The focus of the series is a sampling of the interesting work being done over at OpenAI, the independent AI research lab founded by Elon Musk, Sam Altman and others. In this episode, I’m joined by Greg Brockman, OpenAI Co-Founder and CTO. Greg and I touch on a bunch of topics in the show. We start with the founding and goals of OpenAI, before diving into a discussion on Artificial General Intelligence, what it means to achieve it, and how we going about doing so safely and without bias. We also touch on how to massively scale neural networks and their training training and the evolution of computational frameworks for AI. This conversation is not only informative and nerd alert worthy, but we cover some very important topics, so please take it all in, enjoy, and send along your feedback! To find the notes for this show, visit twimlai.com/talk/74 For more info on this series

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Sam Ritchie, a software engineer at Stripe. I caught up with Sam RIGHT after his talk at the conference, where he covered his team’s work on explaining black box predictions. In our conversation, we discuss how Stripe uses black box predictions for fraud detection, and he gives a few use case scenarios. We discuss Stripe’s approach for explaining those predictions as well as other approaches, and briefly mention Carlos Guestrin’s work on LIME paper, which he and I discuss in TWiML Talk #7. The notes for this show can be found at twimlai.com/talk/73 For more series info, visit twimlai.com/STLoop

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Allison Parrish, Poet and Professor at NYU in the Interactive Telecommunications dept. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference talk on “Experimental Creative Writing with the Vectorized Word”. In our time together, we discuss some of her research into computational poetry generation, actually performing AI-produced poetry, and some of the methods and processes she uses for generating her work. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference ta

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Matthew Taylor, Open Source Manager at Numenta. You might remember hearing a bit about Numenta from an interview I did with Francisco Weber of Cortical.io, for TWiML Talk #10, a show which remains the most popular show on the podcast. Numenta is basically trying to reverse-engineer the neocortex, and use what they learn to develop a neocortical theory for biological and machine intelligence called Hierarchical Temporal Memory. Matt joined me at the conference to discuss his talk “The Biological Path Towards Strong AI”. In our conversation, we discuss the basics of HTM, it’s biological inspiration, and how it differs from

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show I speak with Soumith Chintala, a Research Engineer in the Facebook AI Research Lab (FAIR). Soumith joined me at Strange Loop before his talk on Pytorch, the deep learning framework. In this talk we discuss the market evolution of deep learning frameworks and tools, different approaches to programming deep learning frameworks, Facebook’s motivation for investing in Pytorch, and much more. This was a fun interview, I hope you enjoy! The notes for this show can be found at twimlai.com/talk/70 For series information, visit twimlai.com/stloop

This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show you'll hear from Nexosis founders Ryan Sevey and Jason Montgomery. Ryan, Jason and I discuss how they got their start by applying ML to identify cheaters in video games, the application of ML for time-series data analysis, and of course the Nexosis Machine Learning API. Of course, if you like what you hear, they invite you to get your free Nexosis API key and discover what they can bring to your next project at nexosis.com/twiml. The notes for this show can be found at twimlai.com/talk/69 For series information, visit twimlai.com/stloop

We close out our NYU Future Labs AI Summit interview series with Ross Fadely, a New York based AI lead with Insight Data Science. Insight is an interesting company offering a free seven week post-doctoral training fellowship helping individuals to bridge the gap between academia and careers in data science, data engineering and AI. Ross joined me backstage at the Future Labs Summit after leading a Machine Learning Primer for attendees. Our conversation explores some of the knowledge gaps that Insight has identified in folks coming out of academia, and how they structure their program to address them. If you find yourself looking to make this transition, you’ll definitely want to check out this episode. The notes for this show can be found at twimlai.com/talk/68 For series information, visit twimlai.com/ainexuslab2

We continue our NYU Future Labs AI Summit interview series with Dennis Mortensen, founder and CEO of X.ai, a company whose AI-based personal assistant Amy helps users with scheduling meetings. I caught up with Dennis backstage at the Future Labs event a few weeks ago, right before he went on stage to talk about “Investing in AI from the Startup POV.” Dennis gave shares some great insight into building an AI-first company, not to mention his vision for the future of scheduling, something no one actually enjoys doing, and his thoughts on the future of human-AI interaction. This was a fun interview, which I’m sure you’ll enjoy. A quick warning though… This might not be a show to listen to in the car with the kiddos, as this episode does contain a few expletives. The notes for this show can be found at twimlai.com/talk/67 For series information, visit twimlai.com/ainexuslab2

The podcast you’re about to hear is the fourth of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this show, I speak with Kul Singh, CEO and Founder of Second Mind. Second Mind is building an integration platform for businesses that allows them to bring augmented intelligence to voice conversations. We talk to Kul about the concept behind Second Mind, and how the company combines ambient listening with a low-latency matching system to help users eliminate an estimated 2.5 hours of manual searches per day! The notes for this show can be found at twimlai.com/talk/66 For series information, visit twimlai.com/ainexuslab2

The podcast you’re about to hear is the second of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City.In this episode, you’ll hear from Bite.ai, a startup founded by Vinay Anantharaman and Michal Wolski, founders who met working at Clarifai, another NYU Future Labs alumni, whose CEO Matt Zeiler I interviewed on TWiML Talk #22(Link on show notes page). Bite is using convolutional neural networks and other machine learning to help computers understand and reason about food. Their product is the app Bitesnap, which provides users with detailed nutritional information about the food they’re about to eat using just a photo and a serving size. We dive into the details of their app and service, the machine learning models and pipeline that enable it, and how they plan to compete with other apps targeting dieters, and more! The notes for this show can be found at twimlai.com/talk/65 For series information, visit twimlai.com/ainexuslab2.

The podcast you’re about to hear is the second of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this episode, I speak with Ron Fisher and Mike Wang, who, along with Vivek Sudarsan founded Bowtie Labs, a 24/7 AI-based receptionist designed to help businesses in the beauty, wellness, and fitness industries increase retail conversion rates. I’ve talked with a few startups in the conversational space recently and one common theme seems to be quickly outgrowing commercial conversational platforms. Ron and Mike shared their own experiences with decision, and shared some of the challenges they’re trying to overcome with their ML models, as well as some of the techniques they use to make their system as responsive as possible. The notes for this show can be found at twimlai.com/talk/64 For Series information, visit twimlai.com/ainexuslab2

The podcast you’re about to hear is the first of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. My guests this time around are James Villarrubia and Bernie Prat, CEO and COO respectively, of Mt. Cleverest, an online service for teachers and students, that can take any text via the web, and generate a quiz along with answers based on the content supplied. To do this, Bernie and James employ a pretty sophisticated natural language understanding pipeline, which we discuss in this interview. We also touch on the challenges they face in generating correct question answers, how they fine tune their ML models to improve those answers over time, and more. The notes for this show can be found at twimlai.com/talk/63 For Series information, visit twimlai.com/nexuslabs2

The podcast you’re about to hear is the third of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest this time is Graham Taylor, professor of engineering at the University of Guelph, who keynoted day two of the conference. Graham leads the Machine Learning Research Group at Guelph, and is affiliated with Toronto’s recently formed Vector Institute for Artificial Intelligence. Graham and I discussed a number of the most important trends and challenges in artificial intelligence, including the move from predictive to creative systems, the rise of human-in-the-loop AI, and how modern AI is accelerating with our ability to teach computers how to learn-to-learn. The notes for this show can be found at twimlai.com/talk/62. For series info, visit twimlai.com/GPPC2017

The podcast you’re about to hear is the second of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this interview is Kenneth Conroy, VP of data science at Vancouver, Canada-based Finn.ai, a company building a chatbot system for banks. Kenneth and I spoke about how Finn.AI built its core conversational platform. We spoke in depth about the requirements and challenges of conversational applications, and how and why they transitioned off of a commercial chatbot platform--in their case API.ai--and built their own custom platform based on deep learning, word2vec and other natural language understanding technologies. The notes for this show can be found at https://twimlai.com/talk/61

The podcast you’re about to hear is the first of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this show is Solmaz Shahalizadeh, Director of Merchant Services Algorithms at Shopify. Solmaz gave a great talk at the GPPC focused on her team’s experiences applying machine learning to fight fraud and improve merchant satisfaction. Solmaz and I dig into, step-by-step, the process they used to transition from a legacy, rules-based fraud detection system system to a more scalable, flexible one based on machine learning models. We discuss the importance of well-defined project scope; tips and traps when selecting features to train your models; and the various models, transformations and pipelines the Shopify team selected; and how they use PMML to make their Python models available to their Ruby-on-Rails web application. The notes for this show can be found at twimlai.com/talk/60 For Series info, visit twimlai.com/GPPC2017

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

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

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

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

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