tech

orielly

Playq

podcasts

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

Sometimes little bets are the ones that really pay off. Author Peter Sims talks about how surprisingly small moves have helped launch everything from Facebook to Pixar to the comedy routines of Chris Rock.

Big Ideas Start Small

David Cranor and Jon Bruner talk with Sanjit Biswas, founder and CEO of the industrial sensor company Samsara.

Sanjit Biswas on Industrial Sensors

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

CSIS senior fellow and Deputy Director of the Strategic Technologies Program, Denise Zheng, talks with Marty Edwards about the "Industrial Internet of Things". Marty Edwards is the Director of the Industrial Control Systems Cyber Emergency Response Team (ICS-CERT) at the Department of Homeland Security.
 (Read more at https://csis.org/podcasts/strategic-technologies-podcast/security-and-industrial-internet-things-conversation-marty)

Security and the Industrial Internet of Things: A Conversation with Marty Edwards

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 I spoke with Vikash Mansinghka, research scientist at MIT, where he leads the Probabilistic Computing Project, and co-founder of Empirical Systems. I’ve long wanted to introduce listeners to recent developments in probabilistic programming, and I found the perfect guide in Mansinghka.

Introducing Model-Based Thinking Into AI Systems

Speaker(s): Michael Jacobs, Professor Mariana Mazzucato | Western capitalism is in trouble. For decades investment has been falling, living standards have stagnated or declined, and inequality has risen dramatically. Economic policy has neither reformed the financial system nor restored stable growth. Climate change meanwhile poses increasing risks to future prosperity. In this joint lecture, Mariana Mazzucato and Michael Jacobs will propose new ways of thinking about capitalism. Drawing on their new book, Rethinking Capitalism: Economics and Policy for Sustainable and Inclusive Growth they will show how today’s deep economic problems reflect the inadequacies of orthodox economic theory and the failure of economic policies informed by it.  They will show how alternative economic approaches can better explain how capitalism works, why it often doesn’t, and how it can be made more innovative, inclusive and sustainable. Michael Jacobs (@michaelujacobs) is Visiting Professor in the School of Public Policy and Department of Political Science at University College London and Visiting Fellow at the Institute for Public Policy Research. An environmental economist and political theorist, his work has focused on the political economy of environmental change. He was formerly General Secretary of the Fabian Society, Co-Editor of the The Political Quarterly, and Senior Adviser to the Global Commission on the Economy and Climate, which he helped to found. Mariana Mazzucato (@MazzucatoM) holds the RM Phillips chair in the Economics of Innovation at SPRU in the University of Sussex. Her book The Entrepreneurial State: debunking public vs. private sector myths was on the 2013 Books of the Year list of the Financial Times. Professor Mazzucato is winner of the 2014 New Statesman SPERI Prize in Political Economy, the 2015 Hans-Matthöfer-Preis and in 2013 the New Republic called her one of the '3 most important thinkers about innovation'. Professor Mazzucato advises policy makers around the world on innovation-led growth. Robin Archer is Director of the Ralph Miliband Programme at LSE. The Ralph Miliband Programme (@RMilibandLSE) is one of LSE's most prestigious lecture series and seeks to advance Ralph Miliband's spirit of free social inquiry.

Rethinking Capitalism [Audio]

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.

Introducing Model-Based Thinking Into AI Systems

O'Reilly Radar