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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

O'REILLY

How can ecommerce companies deal with the conflicting expectations of consumers and logistics realities of an on-demand economy? How can they compete with ecommerce giant Amazon, which just gets bigger every year? And given all this, how is physical retail faring?

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
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a16z Podcast: Ecommerce and the Holiday Shopping Collision

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

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 Mary Treseler sits down with Jeff Veen, vice president of products at Adobe, and CEO and founder of Typekit to talk about leadership, hiring designers, and the significance of trends.

Pace Of Change As A Metaphor For Full Stack Design

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

Dean Wampler on bounded and unbounded data processing and analytics.

Building Enterprise Data Applications With Open Source Components

Don Norman wrote Design of Everyday Things in 1988. Now, 25 years later, he revised it. This podcast is a reflection on the many changes that have taken place in the industry in these 25 years 
UX-radio hosts podcasts about Information Architecture, User Experience and Design. Listen to inspiring conversations with industry experts. The purpose of this show is to educate, inspire and provide valuable information architecture and user experience resources.

Reflections from Design of Everyday Things with Don Norman

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

Machine learning for procurement analytics

Collaboration tools in the open-source Trusted Analytics Platform (TAP) address the human side of data analytics. O'Reilly's Rachel Roumeliotis speaks with Chuck Freedman, Chief Developer Advocate for TAP. 

Making data analytics easier for data scientists, developers, and DevOps

In this episode of the Data Show, I spoke with Mike Tung, founder and CEO of Diffbot - a company dedicated to building large-scale knowledge databases. Diffbot is at the heart of many web applications, and it’s starting to power a wide array of intelligent applications. We talked about the challenges of building a web-scale platform for doing highly accurate, semi-supervised, structured data extraction. We also took a tour through the AI landscape, and the early days of self-driving cars.

Using AI to Build a Comprehensive Database of Knowledge

In episode three of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Dennis Yang, co-founder and chief product officer of Dashbot, an analytics platform for bots. Bots are a new way for humans to interact with computers, and require new ways of thinking about measurement.

We discuss crucial differences between bots and conventional interfaces, how human writers are essential for setting the right tone in a bot, and why users ask bots to tell them jokes.

Dennis Yang on Bot Analytics

In episode five of the O’Reilly Bots podcast, Pete Skomoroch and I speak with Joshua Browder, the 19-year old founder and CEO of DoNotPay, a series of bots that help people with legal issues, including challenging parking tickets, challenging bank charges, and claiming government assistance for homelessness. Dubbed “the world’s first robot lawyer,” his bots have attracted 260,000 users and provided 175,000 successful parking-ticket appeals.

Joshua Browder on Bots That Fight Bureaucracy

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

Jon Bruner and Mike Loukides talk about the classical music publishing industry and how it's being disrupted by projects like the Petrucci Music Library (http://imslp.org/) that make public-domain scores available online.

The future of classical music

Jim Stogdill, Edd Dumbill and Roger Magoulas have a conversation about in-memory databases and the impact on real time analysis. Leave questions and comments for us at http://radar.oreilly.com/2013/05/talking-about-in-memory.html

Talking about in-memory

Jon Bruner, Mike Loukides and Jim Stogdill from O'Reilly Radar muse on the return of software to the physical world.

Software and the physical world

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about anthropology in big data, advances in JavaScript, searching for ancient civilizations, the importance of context, and good sites for checking the weather.

Related blog post: http://radar.oreilly.com/2013/06/radar-podcast-anthropology-big-data-and-the-importance-of-context.html

Anthropology, big data, and the importance of context

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about what's been on their minds this week: the NSA's surveillance programs, what defense contractors will do with their technology as defense budgets dry up, and a Californian who isn't doing what you think he's doing with hydroponics.

For more on these topics, and links to the things we talked about, see http://radar.oreilly.com/2013/06/radar-podcast-the-internet-of-things-prism-and-defense-technology-that-goes-civilian.html.

The Internet of Things, PRISM, and defense technology that goes civilian

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.