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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

In this episode, I talk with Meredith Patterson, a software engineer and leader of the Langsec Conspiracy. We discuss the origins of LangSec, rigidity versus robustness, and game theory as it applies to organizational approaches to security.

Meredith Patterson on Using Language to Build Trustworthy Systems

In this episode, I talk with Amanda Berlin, security architect at Hurricane Labs. We discuss how to assess and develop defensive security policies when you’re new to the task, how to approach core security fundamentals like asset management, and generally how you can successfully improve your organization’s defensive security with limited time and resources.

Amanda Berlin on Defensive Security Fundamentals

In this episode of the Data Show, I spoke with Fabian Yamaguchi, chief scientist at ShiftLeft. His 2015 Ph.D. dissertation sketched out how the combination of static analysis, graph mining, and machine learning, can be used to develop tools to augment security analysts. In a recent post, I argued for machine learning tools to augment teams responsible for deploying and managing models in production (machine learning engineers). These are part of a general trend of using machine learning to develop and manage the software systems of tomorrow. Yamaguchi’s work is step one in this direction: using machine learning to reduce the number of security vulnerabilities in complex software products.

How Machine Learning Can Be Used to Write More Secure Computer Programs

In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.

Machine Learning Needs Machine Teaching

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

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TotalBiscuit and Jesse sit down with Sinvicta and Pyrion Flax for this episode of the Co-Optional Podcast. Enjoy!

Original air date: February 20th, 2018

Welcome to the Co-Optional Podcast 00:00:59
Highlander 2 wasn't very good 00:02:41
Disease 00:12:23
Filler 00:14:45
DOTA 00:15:33
Stellaris 00:16:35
Dynasty Warriors 9 00:33:50
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Welcome back 00:59:33
ECO 01:10:29
Civ 6: Rise and Fall 01:12:41
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Abandon Ship 01:49:39
PUBG Cheating 01:55:52
Heroes matchmaking problems 02:07:15
PayDay 2 Switch 02:19:00
Releases 02:29:01

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Sinvicta
https://www.youtube.com/user/sinvicta316
https://twitter.com/Sinvicta

Pyrion Flax
https://www.youtube.com/pyrionflax
https://twitter.com/PyrionFlax

Jesse Cox
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https://www.youtube.com/user/OMFGCata
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The Co-Optional Podcast Ep. 207 ft. Sinvicta & Pyrion Flax  [strong language] -  February 22nd, 2018

Who are the best-supporting characters in all of comics? Listen as we break it all down in a LIVE panel recorded at Planet Comicon in Kansas City! Special guests include Patrick Gleason and Stephen Schleicher.

Be careful! This podcast contains SPOILERS. So if you don't want to know what happens, then you should come back later.

Read our first comic book mini-series -- JUPITER JET! https://goo.gl/MdEYmD

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Thanks for showing up to class today. Class dismissed!

200: Comic's Best Supporting Characters LIVE!

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.

Graphs as the Front End for Machine Learning

O'Reilly Radar