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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

Headbuddy, Froks & DarkAlley conduct an hour long in depth interview with controversial freelance music writer Chris Ott for /mu/chellapoloozaroo

Follow Chris:
https://twitter.com/shallowrewards
https://www.youtube.com/user/shallowrewards

2013 Winter /mu/chella: Chris Ott (Shallow Rewards) Inteview

Halle's baby hair, Jill Scott's cape and more.

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Not My God

Professor Deborah E. Lipstadt, Dorot Professor of Modern Jewish History and Holocaust Studies at Emory University, will speak about ‘Holocaust Denial: A Flat Earth Theory or a Clear and Present Danger?’

Holocaust denial, the attempt to deny that the Third Reich engaged in the annihilation of approximately six million Jews, has been depicted as a serious threat to historical truth. Others have dismissed it as the equivalent of the theory that the earth is flat, i.e. absurd, illogical, and unworthy of serious consideration. In this presentation Professor Lipstadt will explore these two contradictory perspectives on Holocaust denial. In addition, she will explore the difference between “hardcore” or traditional Holocaust denial and the more recent phenomenon of “soft core” denial. 

Holocaust Denial: A Flat Earth Theory or a Clear and Present Danger?

El-P, legendary hip-hop producer and half of the celebrated rap duo Run the Jewels, and acclaimed new singer, rapper, songwriter, producer, musician and filmmaker Boots (Beyoncé, FKA twigs, Run the Jewels), sat down and recorded a Talkhouse Music Podcast while they were on tour, backstage at Cain’s Ballroom in Tulsa, Oklahoma. They talked about lots of things: their jazz-musician dads, the creative value of sampling, drugs and music, the internet’s fascination with cats, their doomsday scenarios and whether iconic film director Stanley Kubrick predicted the iPhone. We also find out how Boots got his nickname — and the answer will amaze you.

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Boots with El-P

It's Monday so it's time for another episode of Special Delivery!!! We cover the controversy surrounding the Flash & Arrow crossover and we wonder why Hollywood loves them some white Egyptians. Plus the mid season finale of Empire leaves Benhameen underwhelmed, Agents Of Shield might be the best comic book show out right now, and Chico wonders will The Leftovers get a third season after the ending of the second one. Plus much more including our thoughts on The Wiz & the film Creed! You need to see both if you haven't is the short answer.

Legends Of 70 Percent White Egyptians

In this week’s Design Podcast, I sit down with Jay Trimble, mission operations manager, ground data system manager, and resource prospector for the Lunar Rover Mission at NASA. We talk about applying Agile, adopting design thinking and user-centered design, and what he and his team rely on to design and build software for mission control.

Jay Trimble on User-Centered Design, Agile, and Design Thinking at NASA

In this week’s Design Podcast, I sit down with Ben Yoskovitz, investor, entrepreneur, and former VP of product at VarageSale and at GoInstant. We talk about using metrics in product development and why anyone building anything new needs to have both hubris and intellectual honesty. Yoskovitz is co-author of Lean Analytics, and is teaching a two-day course on product strategy as part of the upcoming O'Reilly Design Conference.

Ben Yoskovitz on Lean Product Development Using Metrics to Build Successful Products and Companies

This week, I sit down with Matt LeMay, product coach and consultant, and author of "Product Management in Practice." We talk about the four guiding principles of product management, what he has learned about himself as a product manager, and how to conduct meaningful research.

Matt LeMay on the Four Principles of Product Management

In this episode of the O’Reilly Programming Podcast, I talk with Ken Kousen, an author, instructor, and consultant who is presenting the live online training courses Functional Programming in Java 8 and Getting Started with Spring Boot in September and October. He is also the author of the newly published O’Reilly book Modern Java Recipes: Simple Solutions to Difficult Problems in Java 8 and 9.

Ken Kousen on Java, Spring, and Groovy

In this episode of the Data Show, I spoke with Christine Hung, head of data solutions at Spotify. Prior to joining Spotify, she led data teams at the NY Times and at Apple (iTunes). Having led teams at three different companies, I wanted to hear her thoughts on digital transformation, and I wanted to know how she approaches the challenge of building, managing, and nurturing data teams.

I also wanted to learn more about what goes into building a recommender system for a popular consumer service like Spotify. Engagement should clearly be the most important metric, but there are other considerations, such as introducing users to new or “long tail” content.

Machine Learning at Spotify: You Are What You Stream

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.

Machine Learning at Spotify: You Are What You Stream

O'Reilly Radar