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

Today, I'm joined by Kenneth Stanley, Professor in the Department of Computer Science at the University of Central Florida and senior research scientist at Uber AI Labs. Kenneth studied under TWiML Talk #47 guest Risto Miikkulainen at UT Austin, and joined Uber AI Labs after Geometric Intelligence, the company he co-founded with Gary Marcus and others, was acquired in late 2016. Kenneth’s research focus is what he calls Neuroevolution, applies the idea of genetic algorithms to the challenge of evolving neural network architectures. In this conversation, we discuss the Neuroevolution of Augmenting Topologies (or NEAT) paper that Kenneth authored along with Risto, which won the 2017 International Society for Artificial Life’s Award for Outstanding Paper of the Decade 2002 - 2012. We also cover some of the extensions to that approach he’s created since, including, HyperNEAT, which can efficiently evolve very large networks with connectivity patterns that look more like those of the human and that are generally m

Today, I'm joined by Davide Venturelli, science operations manager and quantum computing team lead for the Universities Space Research Association’s Institute for Advanced Computer Science at NASA Ames. Davide joined me backstage at the NYU Future Labs AI Summit a while back to give me some insight into a topic that I’ve been curious about for some time now, quantum computing. We kick off our discussion about the core ideas behind quantum computing, including what it is, how it’s applied and the ways it relates to computing as we know it today. We discuss the practical state of quantum computers and what their capabilities are, and the kinds of things you can do with them. And of course, we explore the intersection between AI and quantum computing, how quantum computing may one day accelerate machine learning, and how interested listeners can get started down the quantum rabbit hole. The notes for this show can be found at twimlai.com/talk/93

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I speak with Yael Niv, professor of neuroscience and psychology at Princeton University. Yael joined me after her invited talk on “Learning State Representations.” In this interview Yael and I explore the relationship between neuroscience and machine learning. In particular, we discusses the importance of state representations in human learning, some of her experimental results in this area, and how a better understanding of representation learning can lead to insights into machine learning problems such as reinforcement and transfer learning. Did I mention this was a nerd alert show? I really enjoyed this interview and I know you will too. Be sure to send over any thoughts or feedback via the show n

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew’s research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creat

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy!

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode i'm joined by Sara Jennings, Timothy Seabrook and Andres Rodriguez to discuss NASA’s Frontier Development Lab or FDL. The FDL is an intense 8-week applied AI research accelerator, focused on tackling knowledge gaps useful to the space program. In our discussion, Sara, producer at the FDL, provides some insight into its goals and structure. Timothy, a researcher at FDL, describes his involvement with the program, including some of the projects he worked on while on-site. He also provides a look into some of this year’s FDL projects, including Planetary Defense, Solar Storm Prediction, and Lunar Water Location. Last but not least, Andres, Sr. Principal Engineer at Intel's AIPG, joins us to detail Intel

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I sit down with Timnit Gebru, postdoctoral researcher at Microsoft Research in the Fairness, Accountability, Transparency and Ethics in AI, or FATE, group. Timnit is also one of the organizers behind the Black in AI group, which held a very interesting symposium and poster session at NIPS. I’ll link to the group’s page in the show notes. I’ve been following Timnit’s work for a while now and was really excited to get a chance to sit down with her and pick her brain. We packed a ton into this conversation, especially keying in on her recently released paper “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US”. Timnit describes the pipeline she developed for this res

This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. Today we’re joined by Aaron Ames, Professor of Mechanical & Civil Engineering at Caltech. Aaron joined me before his talk at the Deep Learning Summit “Eye, Robot: Computer Vision and Autonomous Robotics” and I had a ton of questions for him. While he considers himself a “hardware guy”, we got into a great discussion centered around the intersection of Robotics and ML Inference. We cover a range of topics, including Boston Dynamics backflipping robot (If you haven't seen it, check out the show notes), Humanoid Robotics, His work on motion primitives and transitions and he even gives us a few predictions on the future of robotics.

This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode we’re joined by Chris Adzima, Senior Information Analyst for the Washington County Sheriff’s Department. While Chris is not a traditional data scientist, he comes to us with a very interesting use case using AWS’s Rekognition. Chris is using Rekognition to identify suspects in the Portland area by running their mugshots through the software. In our conversation, he details how he is using Rekognition, while giving us few use cases along the way. We discuss how bias affects the work he is doing, and how they try to remove it from their process, not only from a software developer standpoint, but from a law enforcement standpoint and what his next steps are with the Rekognition software. This was a pretty interesting discussion

This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. This time around we’re joined by Kristen Grauman, a professor in the department of computer science at UT Austin. Kristen specializes in Computer Vision and joined me leading up to her talk at the Deep Learning Summit “Learning where to look in video”. Kristen & I cover the details from her talk, like exploring how a vision system can learn how to move and where to look. Kristen considers how an embodied vision system can internalize the link between “how I move” and “what I see”, explore policies for learning to look around actively, and learn to mimic human videographer tendencies, automatically deciding where to look in unedited 360 degree video. The notes for this show can be found at twimlai.com/talk/85. For series details, visit twiml

This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode, I’ll be speaking with Nikita Shamgunov, co-founder and CEO of MemSQL, a company offering a distributed, memory-optimized data warehouse of the same name. Nikita and I take a deep dive into some of the features of their recently released 6.0 version, which supports built-in vector operations like dot product and euclidean distance to enable machine learning use cases like real-time image recognition, visual search and predictive analytics for IoT. We also discuss how to architect enterprise machine learning solutions around the data warehouse by including components like data lakes and Spark. Finally, we touch on some of the performance advantages MemSQL has seen by implementing vector operations using Intel’s latest AVX2 an

This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. If you missed the news coming out of re:Invent and want to know more about what one of the biggest AI platform providers is up to, you’ll want to say tuned, because we’ll discuss many of their new offerings in this episode, a Roundtable discussion I held with Dave McCrory VP of Software Engineering at Wise.io at GE Digital and Lawrence Chung, engagement lead at ThingLogix. We cover all of AWS’ most important news, including the new SageMaker and DeepLens, their Rekognition and Transcription services, Alexa for Business, GreenGrass ML and more. This kind of discussion is something a little new for the show, and is a bit reminiscent of my days covering news here on the podcast, so I hope you enjoy it!

This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. To close out our AI Summit New York Series, I speak with Sherif Mityas, head of Technology, Digital and Strategy at restaurant chain TGI Fridays. Sherif joins us to discuss how Fridays is utilizing conversational AI to enhance customer loyalty. Sherif wants Friday’s to be known as a tech company that happens to sell burgers and beer, and in this conversation we get an in-depth look at the technology landscape they’ve put in place to move the company in this direction. Sherif also shares some of the things on the horizon for Friday’s, as well as some of what they’ve learned along the way. Be sure to share your feedback or questions on the show n

This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. Today’s show continues our discussion of enterprise AI, with a conversation with Thierry Derungs, Chief Digital Officer at BNP Paribas, a multinational bank headquartered in Paris. Thierry joined me to discuss how BNP uses AI and some of the opportunities that have arisen with the changing AI landscape. We also discuss the innovation process that BNP has used to introduce AI to the bank, via what they call innovation incubators or “factories”. The notes for this show can be found at twimlai.com/talk/81.

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 show I’m joined by Jonas Schneider, Robotics Technical Team Lead at OpenAI. This episode features Durk Kingma, a Research Scientist at OpenAI. Although Durk is probably best known for his pioneering work on variational autoencoders, he joined me this time to talk through his latest project on block sparse kernels, which OpenAI just published this week. Block sparsity is a property of certain neural network representations, and OpenAI’s work on developing block sparse kernels helps make it more computationally efficient to take advantage of them. In addition to covering block sparse kernels themselves and the background required to understand them, we also discuss why they’re important and walk through s

This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. Today I'm joined by Brian Gross, Head of Digital Innovation for the Mexico City-based airline AeroMexico. AeroMexico is using AI techniques like neural nets to build a chatbot that responds to its customer’s inquiries. In our conversation, Brian describes how he views the chatbot landscape, shares his thoughts on the platform requirements that established enterprises like AeroMexico have for chatbots, and describes how AeroMexico plans to stay ahead of the curve. Be sure post any feedback or questions you may have to the show notes page, which you’ll find at twimlai.com/talk/79. For more info on this series, visit twimlai.com/aisummit.

This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest this time around is Mazin Gilbert, vice president of advanced technology & architecture with AT&T. Mazin and I have a really interesting discussion on what’s really required to scale AI in the enterprise, and you’ll learn about a new open source project that AT&T is working on to allow any enterprise to do this. You already know by now that I geek out when it comes to talking about the intersection of machine learning and cloud computing, and this conversation is no exception. Be sure to let us know what you think by posting your comments or questions to the show notes page at twimlai.com/talk/78. For more info on this series, visit tw

This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest for this first show in the series is, Hillery Hunter, IBM Fellow & Director of the Accelerated Cognitive Infrastructure group at IBM’s T.J. Watson Research Center. Hillery and I met a few weeks back in New York and I'm really glad that we were able to get her on the show. Hillery joins us to discuss her team's research into distributed deep learning, which was recently released as the PowerAI Distributed Deep Learning Communication Library or DDL. In my conversation with Hillery, we discuss the purpose and technical architecture of the DDL, it’s ability to offer fully synchronous distributed training of deep learning models, the advant

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 show I’m joined by Jonas Schneider, Robotics Technical Team Lead at OpenAI. While in San Francisco a few months ago, I spent some time with Jonas at the OpenAI office, during which we covered a lot of interesting ground around OpenAI’s work in robotics. We discuss OpenAI Gym, which was the first project he worked on at OpenAI, as well as how they approach setting up the infrastructure for their experimental work, including how they’ve set up a Robots-as-a-Service environment for their researchers and how they use the open source Kubernetes project to manage their compute environment. Check it out and let us know what you think! To find the notes for this show, visit twimlai.com/talk/76 For more info on

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 Dario Amodei, Team Lead for Safety Research at OpenAI. While in San Francisco a few months ago, I spent some time at the OpenAI office, during which I sat down with Dario to chat about the work happening at OpenAI around AI safety. Dario and I dive into the two areas of AI safety that he and his team are focused on--robustness and alignment. We also touch on his research with the Google DeepMind team, the OpenAI Universe tool, and how human interactions can be incorporated into reinforcement learning models. This was a great conversation, and along with the other shows in this series, this is a nerd alert show! To find the notes for this show, visit twimlai.com/talk/75 For more inf