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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

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Suppose you develop a new technology that is valuable to some industry. The old approach was to sell or license your technology to the existing companies in that industry. The new approach is to build a complete, end-to-end product or service that bypasses existing companies. Andreessen Horowitz’s Chris Dixon, Balaji Srinivasan and Benedict Evans discuss the reasons behind, and advantages of, going “full stack.”

a16z Podcast: The Rise of Full Stack Startups

The announcement by Apple of its new programming language Swift is prompting developers to consider yet again how to tailor their efforts in the battle between iOS and Android. Benedict Evans and Steven Sinofsky discuss the questionable history of cross-platform software, and strategies for startups building apps today. How developers can build great apps on both of the largest mobile platforms.  This platform question is one Sinofsky has been grappling with for a very long time, and which he also details in this post: http://blog.learningbyshipping.com/2013/07/08/juggling-multiple-platforms-and-the-bumpy-road-ahead/

a16z Podcast: The Promise (and Nightmare) of Cross-Platform Software

It’s well understood that bitcoin can be used to buy and sell things/services, and the ecosystem of people accepting the cryptocurrency as payment is expanding. But what else can we use the underlying protocol for? How can we expand the same system that enables bitcoin-the-currency to flow and transactions to be recorded? This discussion (from the a16z Academic Roundtable 2014) examines that question, and answers why the currency is just the beginning of the story when it comes to this technology. You can watch video of the entire discussion here: http://a16z.com/2014/10/24/the-bitcoin-network-effect/

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

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a16z Podcast: Beyond Bitcoin -- The Blockchain

In this week’s Design Podcast episode, I sit down with Joel Marsh, designer and author of UX for Beginners. We talk about design as a scientific way of thinking, what happens when you try to cash a check in Sweden, combining behavioral economics and design, and why learning design is like learning to play the piano.

Joel Marsh on the Science of Design

Intel's Trusted Analytics Platform (TAP) combines open source components to make data analytics and data applications easier to manage. O'Reilly's Rachel Roumeliotis and Intel's Chuck Freedman talk about TAP's functionality.

A data platform for data scientists, DevOps, and developers

In this episode of the O’Reilly Data Show, I spoke with Mikio Braun, delivery lead and data scientist at Zalando. After spending previous years in academia, Braun recently made the decision to switch to industry. He shared some observations about building large-scale systems, particularly deploying data applications in production systems. Given his longstanding background as a machine learning researcher and practitioner, I wanted to get his take on topics like deep learning, hybrid systems, feature engineering, and AI applications.

Practical Machine Learning Techniques for Building Intelligent Applications

Rachel Roumeliotis talks to Intel's Chuck Freedman and Iman Saleh about the data analytics applications of the Trusted Analytics Platform (TAP).

The advantages of open source in analytics

Eliot Knudsen, Field Architect at Tamr, on the business value of prescriptive analytics and machine learning algorithms.

Machine learning for procurement analytics

In this episode of the Hardware Podcast, we talk with Rob Chandhok, president of Helium, a startup with a platform for connecting sensors and control elements over the Internet. We talk about the process of taking a prototype to production, and the blend of hardware and software that allows smart edge devices to last an eternity on a single battery.

Rob Chandhok on Connecting Sensors to the Internet

In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world.

Building and Deploying Large-Scale Machine Learning Applications

In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap.

Marc Warner on AI's Fundamental Shift: Supplemental Thinking

Redis creator Salvatore Sanfilippo discusses the Redis Module System, which provides developers with access to the data structure.

Salvatore Sanfilippo discusses Redis Modules

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

Two years ago, the Othermill desktop CNC mill made machining radically more accessible. Priced at $2,200, it found its way beyond general small-scale milling and into electronics prototyping; it can mill copper away from an FR-1 PCB blank to make a circuit board. Other Machine’s latest work is the Othermill Pro, whose spindle speed and rapid movement are 60% and 70% faster than the original’s, respectively, and whose accuracy makes it possible to fabricate traces as small as six thousandths of an inch.

In this episode of the Hardware Podcast, we talk with Ezra Spier, the vice president of product & software at Other Machine Co., maker of the Othermill. Spier walks us through the development process for the improved machine and gives us an overview of the desktop prototyping market.

Ezra Spier on Desktop Milling Machines

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

Maria Popova is the editor of Brain Pickings (http://www.brainpickings.org), an inventory of cross-disciplinary interestingness and curiosity, bringing you things you didn’t know you were interested in — until you are. She has also written for Wired UK, The Atlantic, The New York Times, and Nieman Journalism Lab, among others, and is an MIT Futures of Entertainment Fellow. 

In this audio interview with Debbie Millman, Maria discusses curation, creativity as pattern-recognition, growing up in Bulgaria, and her grandmother following her Google Alerts.

Subscribe to Design Matters on iTunes for more great interviews with celebrated designers and creative minds: http://itunes.apple.com/us/podcast/design-matters-debbie-millman/id328074695

Design Matters with Debbie Millman: Maria Popova

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.

Practical Machine Learning Techniques for Building Intelligent Applications

O'Reilly Radar