NeuroList

Podcasts

orielly

ML, Data, Geo

O'Reilly Radar tracks the technologies and people that will shape our world in the years to come. Each episode of O'Reilly Radar features an interview with an industry thought leader, with topics touching on everything from programming to data to experience design. We also take a step back from the breathless pace of the latest tech news to examine why new developments are important and what they might mean down the road.

O'Reilly Radar Podcast

This week, we talk to Cagla Eroglu about the eureka moments in scientific discovery, the role of astrocytes in synapse formation, and more!

Dr. Eroglu is an assistant professor of cell biology at Duke University.

NeuroTalk S2E7 Cagla Eroglu

This week, Marc Freeman talks to us about falling in love with biology, eating injured axons, and more!

Marc Freeman is an assistant professor of neurobiology at the University of Massachusetts Medical School. 

NeuroTalk S2E8 Marc Freeman

This week we speak with Kathleen Cullen about how our brains control our eyes and head, why astronauts get sick in space, and more!

Dr. Cullen is a professor of physiology, and the director of the Systems Neuroscience and Aerospace Medical Research Unit at McGill University. 

NeuroTalk S2E9 Kathleen Cullen

Rachel Roumeliotis talks to Intel's Chuck Freedman and Iman Saleh about the data analytics applications of the Trusted Analytics Platform (TAP).

The advantages of open source in analytics

Eliot Knudsen, Field Architect at Tamr, on the business value of prescriptive analytics and machine learning algorithms.

Machine learning for procurement analytics

In this episode of the Hardware Podcast, we talk with Rob Chandhok, president of Helium, a startup with a platform for connecting sensors and control elements over the Internet. We talk about the process of taking a prototype to production, and the blend of hardware and software that allows smart edge devices to last an eternity on a single battery.

Rob Chandhok on Connecting Sensors to the Internet

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap.

Marc Warner on AI's Fundamental Shift: Supplemental Thinking

In this episode, I sit down with Brad Knox, founder and CEO of Emoters, a startup building a product called bots_alive—animal-like robots that have a strong illusion of life. We chat about the approach the company is taking, why robots or agents that pass themselves off as human without any transparency should be illegal, and some challenges and applications of reinforcement learning and interactive machine learning.

Brad Knox on Creating a Strong Illusion of Life

This week we're featuring a conversation from earlier this year—O'Reilly's Mary Treseler chats with Giles Colborne, managing director of cxpartners. They talk about the transformative effects of AI on design, designing for natural language interactions, and why designers need to nurture the ability to reinvent themselves.

Giles Colborne on AI's Move From Academic Curiosity to Mainstream Tech

On this week's episode of the Radar Podcast, O'Reilly's Mac Slocum chats with award-winning author Pagan Kennedy about the art and science of serendipity—how people find, invent, and see opportunities nobody else sees, and why serendipity is actually a skill rather than just dumb luck.

Pagan Kennedy on How People Find, Invent, and See Opportunities Nobody Else Sees

This week, I sit down with anthropologist, futurist, Intel Fellow, and director of interaction and experience research at Intel, Genevieve Bell. We talk about what she’s learning from current AI research, why the resurgence of AI is different this time, and five things that are missing from the AI conversation.

Genevieve Bell on Moving From Human-Computer Interactions to Human-Computer Relationships

This week, I sit down with Tom Davenport. Davenport is a professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a fellow at the MIT Center for Digital Business, and a senior advisor for Deloitte Analytics. He also pioneered the concept of “competing on analytics.” We talk about how his ideas have evolved since writing the seminal work on that topic, Competing on Analytics: The New Science of Winning; his new book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, which looks at how AI is impacting businesses; and we talk more broadly about how AI is impacting society and what we need to do to keep ourselves on a utopian path.

Tom Davenport on Mitigating AI's Impact on Jobs and Business

In this week's Radar Podcast, O’Reilly’s Mac Slocum chats with Sara Watson, a technology critic and writer in residence at Digital Asia Hub. Watson is also a research fellow at the Tow Center for Digital Journalism at Columbia and an affiliate with the Berkman Klein Center for Internet and Society at Harvard. They talk about how to optimize personalized experience for consumers, the role of machine learning in this space, and what will drive the evolution of personalized experiences.

Sara Watson on Optimizing Personalized Experiences

This week, I sit down with David Beyer, an investor with Amplify Partners. We talk about machine learning and artificial intelligence, the challenges he’s seeing in AI adoption, and what he thinks is missing from the AI conversation.

AI Adoption at the Atomic Level of Jobs and Work

This week, I sit down with Aman Naimat, senior vice president of technology at Demandbase, and co-founder and CTO of Spiderbook. We talk about his project to build a knowledge graph of the entire business world using natural language processing and deep learning. We also talk about the role AI is playing in those companies today and what’s going to drive AI adoption in the future.

Aman Naimat on Building a Knowlege Graph of the Entire Business World

In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.

Scaling Machine Learning

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.