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

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machine learning

Shaun Weston talks to Zac Sheffer from Elsen about data analysis, machine learning, engineering, financial services and the startup scene in Boston.

Zac Sheffer of Elsen on data analysis and Boston's thriving fintech scene

This episode of the Data Show marks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born.

To mark this milestone, my colleague Paco Nathan, co-chair of Jupytercon, turned the tables on me and asked me questions about previous Data Show episodes. In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. I’m proud of how this show has reached so many people across the world, and I’m looking forward to sharing more conversations in the future.

The Evolution of Data Science, Data Engineering, and AI

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Why It's Hard to Design Fair Machine Learning Models

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Lessons Learned While Helping Enterprises Adopt Machine Learning

<img src="https://elroycdn.twit.tv/sites/default/files/styles/twit_album_art_144x144/public/images/shows/the_tech_guy/album_art/audio/ttg1400audio.jpg?itok=NYIIVc8B" align="right" hspace="20" vspace="20" border="0" title="The Tech Guy (Audio)" alt="The Tech Guy (Audio)"/>Is the new iPad a laptop replacement? Charging different devices with USB-C, is it worth fixing an LCD TV? Finding the right iPhone to last for a while, smart door locks, is Li-Fi coming to more cities? Should I get a MacBook Pro or a MacBook Air? And more of your calls! 
Host: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> 
Guests: <a href="https://twit.tv/people/scott-wilkinson">Scott Wilkinson</a>, <a href="http://www.johnnyjet.com/" target="_blank">Johnny Jet</a>, and <a href="https://twit.tv/people/dick-debartolo">Dick DeBartolo</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/the-tech-guy">https://twit.tv/shows/the-tech-guy</a>. 
For detailed show notes, visit <a href="http://techguylabs.com/" target="_blank">techguylabs.com</a>.
Sponsors:<ul>
<li><a href="http://LastPass.com/twit">LastPass.com/twit</a></li>
<li><a href="http://carbonite.com">carbonite.com</a></li>
<li><a href="http://capterra.com/techguy">capterra.com/techguy</a></li>
</ul>

The Tech Guy 1539

<img src="https://elroycdn.twit.tv/sites/default/files/styles/twit_album_art_144x144/public/images/shows/the_tech_guy/album_art/audio/ttg1400audio.jpg?itok=NYIIVc8B" align="right" hspace="20" vspace="20" border="0" title="The Tech Guy (Audio)" alt="The Tech Guy (Audio)"/>Explaining the power of the A12X Bionic chip in Apple's new iPad Pro, recommending the new MacBook Air to replace an old faulty one, showing off the new Mac mini, Gmail as an alternative to Yahoo mail, the inability to downgrade to iOS 11, the Hit Em Up app for mass bulk texting, the STIR/SHAKEN method for halting spam robocalls, Chris Marquardt's guide to camera symbols, and more of your calls! 
Host: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> 
Guest: <a href="https://chrismarquardt.com/" target="_blank">Chris Marquardt</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/the-tech-guy">https://twit.tv/shows/the-tech-guy</a>. 
For detailed show notes, visit <a href="http://techguylabs.com/" target="_blank">techguylabs.com</a>.
Sponsors:<ul>
<li><a href="http://OnDeck.com/techguy">OnDeck.com/techguy</a></li>
<li><a href="http://LastPass.com/twit">LastPass.com/twit</a></li>
<li><a href="http://grammarly.com/techguy">grammarly.com/techguy</a></li>
</ul>

The Tech Guy 1540

<img src="https://elroycdn.twit.tv/sites/default/files/styles/twit_album_art_144x144/public/images/shows/valley_of_genius/album_art/audio/vog_2048x2048.jpg?itok=y309Sxkt" align="right" hspace="20" vspace="20" border="0" title="Valley of Genius" alt="Valley of Genius"/>Joey Anuff created Suck.com, one of the earliest humor sites on the web. In his own words, he talks about working at Wired magazine's first website, HotWired, and creating Suck.com in secret. 
Hosts: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> and <a href="https://twitter.com/adamcfisher" target="_blank">Adam Fisher</a> 
Guest: <a href="https://wired.com" target="_blank">Joey Anuff</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/valley-of-genius">https://twit.tv/shows/valley-of-genius</a>. 
Get a copy of Adam Fisher's <a href="https://www.amazon.com/dp/1455559024/ref=as_li_ss_tl?tag=twitllc-20" target="_blank">Valley of Genius book</a> at a bookstore near you.

Valley of Genius 10: Joey Anuff

Visit http://www.squarespace.com/cooptional for a free trial and 10% off!

Watch The Co-Optional Podcast live at http://www.twitch.tv/gennabain on Tuesdays at 3pm ET.

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

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

Original air date: November 13th, 2018

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ManyATrueNerd
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GennaBain
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Jesse Cox
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The Co-Optional Podcast Ep. 233 ft. ManyATrueNerd

<img src="https://elroycdn.twit.tv/sites/default/files/styles/twit_album_art_144x144/public/images/shows/this_week_in_tech/album_art/audio/twit1400audio.jpg?itok=Z5d-Ojno" align="right" hspace="20" vspace="20" border="0" title="This Week in Tech (Audio)" alt="This Week in Tech (Audio)"/><ul><li>Facebook's latest crisis is... its reaction to its last crisis.</li><li>Waymo plans a driverless car service.</li><li>Amazon announces its new headquarters in Queens and North Virginia.</li><li>Google's "smart city" in Toronto gets some pushback.</li><li>Julian Assange has been charged with... something?</li><li>SpaceX gets approval to launch 7000 internet satellites.</li><li>Japan's cybersecurity chief is 100% unhackable - because he has never touched a computer.</li><li>Alphabet's Verily gives up on glucose-measuring contact lenses.</li></ul> 
Host: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> 
Guests: <a href="https://twitter.com/Spacekatgal" target="_blank">Brianna Wu</a>, <a href="http://www.larrysworld.com/" target="_blank">Larry Magid</a>, and <a href="https://twitter.com/Georgia_Dow" target="_blank">Georgia Dow</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/this-week-in-tech">https://twit.tv/shows/this-week-in-tech</a>
Sponsors:<ul>
<li><a href="http://MasterClass.com/TWIT">MasterClass.com/TWIT</a></li>
<li><a href="http://RocketMortgage.com/TWiT2">RocketMortgage.com/TWiT2</a></li>
<li><a href="http://WordPress.com/twit">WordPress.com/twit</a></li>
<li><a href="https://twit.cachefly.com">twit.cachefly.com</a></li>
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This Week in Tech 693: Big Boy Easy Bake Oven

with Adrienne Mayor (@amayor) and Hanne Tidnam (@omnivorousread)

Is it possible that ancient Greeks and Romans dreamed of technological innovations like robots and artificial intelligence millennia before those technologies became realities? In this episode of the a16z Podcast, Adrienne Mayor, historian of science and author of the just released Gods & Robots: Myths, Machines, and Ancient Dreams of Technology, discusses with Hanne Tidnam the earliest myths around ideas of technology and even artificial life from the ancient world -- from the first imagined robot to walk the earth, to actual historical technological wonders of the ancient world such as mechanical flying doves or a giant miles-long parade of 10-foot-tall automatons. What do these early imaginings of technological invention tell us about human nature? And what can we take from understanding the deep roots of this mythology for the era of technology, today?

Mayor is the 2018-19 Berggruen Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford, and author of The Amazons: Lives and Legends; Fossil Legends of the First Americans; and The Poison King, which was a National Book Award finalist.

a16z Podcast: Ancient Dreams of Technology, Gods & Robots

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Tools for Generating Deep Neural Networks With Efficient Network Architectures

For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.

Trends in Data, Machine Learning, and AI

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.

Lessons Learned While Helping Enterprises Adopt Machine Learning

O'Reilly Radar