The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Data

Past, present, future

Radar

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O'Reilly Big Data Podcast

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In this illuminating lecture, bestselling author Ori Brafman explores subtle drivers that can help you spark immediate work, life, and romantic connections. Based on insights from his latest book, <i>Click: The Magic of Instant Connections</i>, Brafman teaches leaders and aspiring entrepreneurs how to make every chance meeting count, using examples from the workplace, collegiate life, and the battlefield.

Ori Brafman - How to Build Instant Connections

O'Reilly Ben Lorica chats with Ion Stoica, UC Berkeley Professor and Databricks CEO, about the rise of Apache Spark and Apache Mesos.

Apache Spark's Journey From Academia To Industry

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

O'Reilly's Ben Lorica talks with Dato co-founder Carlos Guestrin about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.

The Evolution of GraphLab

O’Reilly’s Ben Lorica chats with Kira Radinsky, CTO and co-founder of SalesPredict, about predicting events using machine learning, NLP, and semantic analysis.

Forecasting events, from disease outbreaks to sales to cancer research

We often talk about designing security into a product from the get go, but we don't often discuss what that means. In today's podcast John Kestner, a principle with Supermechanical, the company behind the Twine sensor and the Range thermometer, shares his thoughts on how he designed Twine and range to be more secure but also how we might design our devices to indicate that what was once a humble lock is now a connected computer with all the pros and cons that entails.

Before Kestner and I dig into security, Kevin Tofel and I talk about my experience playing with the Logitech Harmony Ultimate Remote and home hub and then we dig into CSRMesh and the Bluetooth SIG's creation of a working group to add mesh networking to the Bluetooth Smart standard. That could put Bluetooth in closer competition with thread, Zigbee and Z-wave. So kick back and listen up.

Hosts: Stacey Higginbotham and Kevin Tofel
Guests: John Kestner, principal with Supermechanical

Thoughts on the Logitech Harmony Hub and remote (it's pretty nifty, guys)
The Bluetooth SIG is forming a working group on mesh technologies. Why this is cool!
Should you rely on security by obscurity or hope your device doesn't do much that's scary as a way to be secure?
Thinking about how to convey connectedness in design

How to design security into your connected product

O'Reilly's Mary Treseler talks with Andy Goodman, designer and group director of Fjord’s US studios, about service design, embeddables, and predictive analytics.

Designing On A System Level

O'Reilly's Ben Lorica chats with David Blei, co-author of the seminal paper on topic models, and who remains one of the leading researchers in the field. They talk about the origins of topic models, their applications, improvements to the underlying algorithms, and his new role in training data scientists at Columbia University.

Topic models: Past, present, and future

The Internet of Things is challenging designers to rethink their craft. I recently sat down with Claire Rowland, independent designer and author of the forthcoming book Designing Connected Products to talk about the changing design landscape.

During our interview, Rowland brought up three points that resonated with me: interoperability and the Internet of Things, experience designers and networks, and the IoT’s latency problem.

For more on our interview and for related links, check out the related post on O’Reilly Radar: oreil.ly/1EoyfUV

The complexity of the IoT requires experience design solutions

John Follett, editor of "Designing for Emerging Technologies" sits down with Dirk Knemeyer, founder of Involution Studios, who contributed to the book. Knemeyer discusses the changing role of design and designers in emerging technology.

Designers are engineers

O'Reilly's Jenn Webb chats with John Carnahan, executive vice president of data science at Ticketmaster, about Ticketmaster's evolving approach to data analysis, the avenues of user engagement they're investigating, and how his genetics background is informing his work in the big data space. Also, Three Questions with Michael Abbott, a general partner at VC firm Kleiner Perkins Caufield & Byers.

Bridging the gap in big data silos

O'Reilly's Ben Lorica talks with Angie Ma, co-founder and president of ASI, about the concept of the ASI, the structure of their training programs, and the data and startup scene in the UK.

Turning Ph.D.s Into Industrial Data Scientists and Data Engineers

O'Reilly's Ben Lorica chats with Erich Nachbar, founder and CTO of Virtual Power Systems, about the ease with which Nachbar integrates new open source components into existing infrastructure, his contributions to Mesos, and his new “software-defined power distribution” startup.

Redefining Power Distribution Using Big Data

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

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.

Topic models: Past, present, and future

O'Reilly Radar