The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode, i'm joined by Romer Rosales, Director of AI at LinkedIn. We begin with a discussion of graphical models and approximate probability inference, and he helps me make an important connection in the way I think about that topic. We then review some of the applications of machine learning at LinkedIn, and how what Romer calls their ‘holistic approach’ guides the evolution of ML project...

In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials. Greg and I start out with an exploration of some of the challenges of the status quo in materials science, and what’s to be gained by introducing machine learning into this process. We discuss how limitations in materials manifest themselves...

In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how those advances benefit the customer. In our conversation, we look into a few of the many applications of AI at the bank, including fraud detection, money laundering, customer service, and automating b...

On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed training for deep neural networks and the idea of compressing the gradient exchange to allow it to be done more efficiently. Song details the evolution of distributed training systems based on this idea, an...

In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoregressive Flow, which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, in...

For today’s show, the last in our TrainAI series, I'm joined by Qazaleh Mirsharif, a machine learning scientist working on computer vision at Figure Eight. Qazaleh and I caught up at the TrainAI conference to discuss a couple of the projects she’s worked on in that field, namely her research into the classification of retinal images and her work on parking sign detection from Google Street View im...

Today we continue our TrainAI series with Sarah Aerni, Director of Data Science at Salesforce Einstein. Sarah and I sat down at the TrainAI conference to discuss her talk “Notes from the Field: The Platform, People, and Processes of Agile Data Science.” Sarah and I dig into the concept of agile data science, exploring what it means to her and how she’s seen it done at Salesforce and other places s...

In this episode of our TrainAI series, I sit down with Anima Anandkumar, Bren Professor at Caltech and Principal Scientist with Amazon Web Services. Anima joined me to discuss the research coming out of her “Tensorlab” at CalTech. In our conversation, we review the application of tensor operations to machine learning and discuss how an example problem–document categorization–might be approached us...

In today’s show, I sit down with David Van Valen, assistant professor of Bioengineering & Biology at Caltech. David joined me after his talk at the Figure Eight TrainAI conference to chat about his research using image recognition and segmentation techniques in biological settings. In particular, we discuss his use of deep learning to automate the analysis of individual cells in live-cell imaging...

In this episode I’m joined by legendary chess champion, author, and fellow at the Oxford Martin School, Garry Kasparov. Garry and I sat down after his keynote at the Figure Eight Train AI conference in San Francisco last week. Garry and I discuss his bouts with the chess-playing computer Deep Blue–which became the first computer system to defeat a reigning world champion in their 1997 rematch–and...

In this episode I’m joined by Taryn Southern - a singer, digital storyteller and Youtuber, whose upcoming album I AM AI will be produced completely with AI based tools. Taryn and I explore all aspects of what it means to create music with modern AI-based tools, and the different processes she’s used to create her singles Break Free, Voices in My Head, and more. She also provides a rundown of the m...

In this episode, i'm joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course. The courses are designed to make deep learning more accessible to those without the extensive math background...

In this episode, I'm joined by Jose Hernandez-Orallo, professor in the department of information systems and computing at Universitat Politècnica de València and fellow at the Leverhulme Centre for the Future of Intelligence, working on the Kinds of Intelligence Project. Jose and I caught up at NIPS last year after the Kinds of Intelligence Symposium that he helped organize there. In our conversat...

In this episode i'm joined by John Bohannan, Director of Science at AI startup Primer. As you all may know, a few weeks ago we released my interview with Google legend Jeff Dean, which, by the way, you should definitely check if you haven’t already. Anyway, in that interview, Jeff mentions the recent explosion of machine learning papers on arXiv, which I responded to jokingly by asking whether Goo...

In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada. Chang joined me to discuss Georgian’s new offering, Epsilon, a software product that embodies the research, development and lessons learned helps in helpi...

In this episode of our Differential Privacy series, I'm joined by Nicolas Papernot, Google PhD Fellow in Security and graduate student in the department of computer science at Penn State University. Nicolas and I continue this week’s look into differential privacy with a discussion of his recent paper, Semi-supervised Knowledge Transfer for Deep Learning From Private Training Data. In our conversa...

In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his ins...

In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its incep...

In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay. Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including...

In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight. In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and e...

Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory chal...

In this episode, I’m joined by Missy Cummings, head of Duke University’s Humans and Autonomy Lab and professor in the department of mechanical engineering. In addition to being an accomplished researcher, Missy also became one of the first female fighter pilots in the US Navy following the repeal of the Combat Exclusion Policy in 1993. We discuss Missy’s research into the infrastructural and opera...

In this episode, we're joined by Rob Walker, Vice President of decision management and analytics at Pegasystems, a leading provider of software for customer engagement and operational excellence. Rob and I discuss what’s required for enterprises to fully realize the vision of providing a hyper-personalized customer experience, and how machine learning and AI can be used to determine the next best...

In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, Da...

In this episode, I chat with Rob Munro, CTO of the newly branded Figure Eight, formerly known as CrowdFlower. Figure Eight’s Human-in-the-Loop AI platform supports data science & machine learning teams working on autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Rob and I had a really interesting discussion covering...