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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

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows.  If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast.  You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on.  This is technical debt, and it's particularly easy to accrue with machine learning workflows.  That's the premise of this episode's paper.

Machine Learning: The High Interest Credit Card of Technical Debt

Picking up after last week's episode about maximal margin classifiers, this week we'll go into the kernel trick and how that (combined with maximal margin algorithms) gives us the much-vaunted support vector machine.

The Kernel Trick and Support Vector Machines

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

This episode is sponsored by Audible - Go to http://www.audible.com/cynical for your free 30 day trial. Audible paid us to promote their product. Listen to the show to find out some of our recommendations. 

Support Julian, the podcast animator: http://www.patreon.com/cooptionalanimation

Discuss the podcast on our official subreddit: https://www.reddit.com/r/cynicalbritofficial/comments/7rbfu9/the_cooptional_podcast_ep_202_ft_nerdcubed_strong/

TB, Dodger and Jesse sit down with NerdCubed for this episode of the Co-Optional Podcast. Enjoy!

Original air date: January 16th, 2018

Welcome to the Co-Optional Podcast 00:00:45
CRAZY!!!1!1!! 00:08:50
Star Wars: Secrets of the Empire 00:11:10
WWE 2K18 on Switch 00:20:04
Switch 00:33:35
Audible promo 00:45:20
Welcome back 00:51:20
Slay the Spire 00:52:43
Supreme Commander 2 01:01:02
They are Billions 01:08:42
Creeper World 01:13:21
Antihero 01:15:25
Jaipur 01:17:05
Fallout: The Board Game 01:19:54
Rusty Lake 01:24:10
Oxenfree 01:25:54
Deep Sky Derelicts 01:28:37
Y2K 01:30:43
Omensight 01:33:34
Claybook 01:38:43
Democracy 3 01:42:31
Terraforming Mars 01:48:18
PayDay 2 VR 01:58:29
Opus Magnum 02:07:29
We Were Here 02:08:45
Getting Over It 02:11:23
EDF 4.1 02:13:29
Depth 02:18:38
Releases 02:20:32

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NerdCubed
https://www.youtube.com/user/OfficialNerdCubed
https://twitter.com/DanNerdCubed

Dodger
https://twitter.com/dexbonus
https://www.youtube.com/user/presshearttocontinue

Jesse Cox
https://twitter.com/JesseCox
https://www.youtube.com/user/OMFGCata
---------

Thanks for watching The Co-Optional Podcast!

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The Co-Optional Podcast Ep. 202 ft. NerdCubed [strong language] - January 18th, 2018

Apple heeft recent toegegeven iPhones langzamer te maken. Daarvoor krijgt het bedrijf flink de wind van voren. Maar waarom doet Apple het eigenlijk en wat gaat het bedrijf eraan doen?

Daarnaast hebben we het over Nintendo's nieuwe product: karton!

Aflevering 160: Apple maakt iPhones langzamer

This episode of the a16z Podcast goes deep on various trends in cryptocurrencies -- from mental models for understanding tokens and what may give them long-term value; to the role of stablecoins in the ecosystem; to scaling, on-chain and off-chain protocols, forks, and more.

The discussion features general partner Chris Dixon in conversation with Nick Tomaino, the founder of early-stage crypto venture fund 1confirmation, editor of The Control, and former business development at Coinbase. (He is also an investor in Basecoin, Cosmos, Ethereum, and MakerDAO.) You can also check out past episodes in this series, covering everything from investing in cryptocurrencies and protocols to accelerating research and practical applications to why crypto tokens matter -- as well as a video covering the building blocks of all things crypto -- at a16z.com/crypto.

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a16z Podcast: Mental Models for Understanding Crypto Tokens

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.

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

O'Reilly Radar