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O'Reilly Radar

The O'Reilly Hardware Podcast is hosted by Jon Bruner and David Cranor, and focuses on the people, ideas, and technology that we feature at the O'Reilly Solid Conference (solidcon.com). We explore topics related to the Internet of Things, hardware, software, manufacturing, and the blurring of the physical and virtual worlds.

O'Reilly Hardware Podcast

In this special holiday episode of the O’Reilly Data Show, I look back at two conversations I had earlier this year at the Spark Summit in San Francisco. The first segment is an on-stage fireside chat with Ben Horowitz, co-founder of Andreessen Horowitz and author of The Hard Thing About Hard Things.

In the second segment, Reynold Xin, one of the architects of Apache Spark, explains the rise of Apache Spark in China.

Investing in Big Data Technologies

This will be a breakout year for JavaScript on embedded systems. Our guest on the Hardware Podcast this week is Peter Hoddie, who founded Kinoma, which makes software and hardware for building JavaScript-powered prototypes. Previously he was one of the original developers of QuickTime at Apple.

Peter Hoddie on JavaScript for Embedded Systems

In this episode of the O’Reilly Hardware Podcast, Jeff Bleiel and I speak with Gilad Rosner, a privacy and information policy researcher, and the founder of the Internet of Things Privacy Forum.  Rosner is also the author of the recently-published free O’Reilly ebook, “Privacy and the Internet of Things.”

Gilad Rosner on Privacy in the Age of The Internet of Things

With interest growing in field programmable gate arrays (FPGAs)—witness Amazon’s recent addition of AWS EC2 instances that include dedicated FPGAs—this episode of the O’Reilly Hardware Podcast looks at what FPGAs are and how their capabilities are different from microcontroller boards (such as Arduino and Raspberry Pi).Jeff Bleiel and I speak with Ryan Cousins, co-founder and CEO of KRTKL (pronounced “critical”), the makers of Snickerdoodle, a board that’s based on an ARM/FPGA hybrid chip.

Ryan Cousins on Field Programmable Gate Arrays

In this episode of the O’Reilly Hardware Podcast, Jeff Bleiel and I speak with Niko Triulzi, co-founder and CTO of AE Dreams, the makers of the children’s product Turtle Mail, a wooden mailbox with a WiFi-connected thermal printer inside. Triulzi explains the hardware and software workings of the device, which enables parents, relatives, and friends to send a message from a computer or mobile device. The child then receives the message, which is printed from the mailbox.

Niko Triulzi on IoT Toys

In this episode of the O’Reilly Hardware Podcast, Jeff Bleiel and I speak with Joel Johnson, co-founder and CEO of BoXZY, a startup that makes an all-in-one desktop CNC mill, 3D printer, and laser engraver. We discuss the BoXZY device’s software and hardware, including its CAD/CAM software (Autodesk’s Fusion 360) and controller board (Arduino Mega), as well as its shield, steppers, and firmware.

Joel Johnson on Desktop Fabrication

In this episode of the O’Reilly Hardware Podcast, Brian Jepson and I speak with Mike Vladimer, co-founder of the Orange IoT Studio at Orange Silicon Valley. Vladimer discusses how Internet of Things devices could benefit from connectivity options other than those provided by well-known technologies (including cellular, WiFi, and Bluetooth), and explains the LoRa wireless protocol, which supports long-range and lower-power applications.

Mike Vladimer on IoT Connectivity

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Lessons Learned While Helping Enterprises Adopt Machine Learning

In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning, as well as monitoring.

I wanted the perspective of someone who is already faced with having to support many models in production. The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. They recently took their learnings public and open sourced TransmogrifAI, a library for automated machine learning for structured data, which sits on top of Apache Spark.

Building Tools for Enterprise Data Science

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Tools for Generating Deep Neural Networks With Efficient Network Architectures

For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.

Trends in Data, Machine Learning, and AI

In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.

In the Age of AI, Fundamental Value Resides in Data

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.