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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

Social Science, Small worlds and Big Data

How can ecommerce companies deal with the conflicting expectations of consumers and logistics realities of an on-demand economy? How can they compete with ecommerce giant Amazon, which just gets bigger every year? And given all this, how is physical retail faring?

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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/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Ecommerce and the Holiday Shopping Collision

In this episode, Ben Lorica chats with Jay Kreps, a highly regarded data engineer and former technical lead for Online Data Infrastructure at LinkedIn, and most recently CEO/co-founder of Confluent, about Apache Kafka, data integration, event data, and the Internet of Things.

Building Apache Kafka From Scratch

John Follett, editor of "Designing for Emerging Technologies" sits down with Dirk Knemeyer, founder of Involution Studios, who contributed to the book. Knemeyer discusses the changing role of design and designers in emerging technology.

Designers are Engineers

O'Reilly's Mary Treseler sits down with Jeff Veen, vice president of products at Adobe, and CEO and founder of Typekit to talk about leadership, hiring designers, and the significance of trends.

Pace Of Change As A Metaphor For Full Stack Design

O'Reilly Ben Lorica chats with Ion Stoica, UC Berkeley Professor and Databricks CEO, about the rise of Apache Spark and Apache Mesos.

Apache Spark's Journey From Academia To Industry

Ray Kurzweil‘s impact on my life in general but especially on what I have been doing for the past 3 or 4 years is hard to exaggerate. It is a simple fact that, if I haven’t read his seminal book The Singularity is Near, I would be neither blogging nor podcasting about exponential technologies, not to mention going to Singularity University. And so it was with great excitement and some trepidation that I went to interview Dr. Kurzweil in his office in Boston.

Part of my trepidation came from some technical concerns: I wish I could buy a better camera. I wish I could hire a team of audio and video professionals so that I can focus on the interview itself. I wish I did a better job with the set up. I wish I had noticed that Ray’s lavalier mic has slipped out of its holder… The list is exponential. Still, if there is one thing that I’ve learned since I started podcasting is that we do get better. But it takes time. Meanwhile, my consolation is that eventually I will have to do another interview with Ray Kurzweil just so I get it right that time. For now, however, I hope that the content of this one will make up for its technical deficiencies.

During our conversation with Dr. Kurzweil we cover a wide variety of topics such as: how and why at age 5 Ray decided to become an inventor; his unique background of being born to Jewish parents but brought up in a Unitarian Church; his early interest in issues such as religious tolerance, poverty, social inequality and justice; 3D printing, open source, patents, progress and intellectual property rights; Watson, artificial intelligence, the Turing Test and human rights for AI, the technological singularity and some criticism thereof; his upcoming book How To Create A Mind and his Pattern Recognition Theory of Mind; the evolutionary advantages of intelligence; the benefits of reverse-engineering the human brain for the creation of AI and whether the latter would be interested in pondering and solving humanity’s greatest problems.

My favorite quote that I will take away from this interview with Ray Kurzweil is:

“Don’t be too concerned about what’s practical. Follow your passion and be who you would like to be.”

Ray Kurzweil on the Singularity: Be Who You Would Like To Be

Gary Marcus is not only a professor in psychology but also a computer scientist, programmer, AI researcher and best selling author. He recently wrote a critical article titled Ray Kurzweil’s Dubious New Theory of Mind that was publsihed by The New Yorker. After reading his blog post I thought that it will be interesting to invite Gary on Singularity 1 on 1 so that he can talk more about his argument.   

During our conversation with Gary we cover a wide variety of topics such as: what is psychology and how he got interested in it; his theory of mind in general and the idea that the mind is a kluge in particular; why the best approach to creating AI is at the bridge between neuroscience and psychology; other projects such as Henry Markram‘s Blue Brain, Randal Koene‘s Whole Brain Emulation, and Dharmendra Modha‘s SyNAPSE; Ray Kurzweil’s Patern Recognition Theory of Mind; Deep Blue, Watson and the lessons thereof; his take on the technological singularity and the ethics surrounding the creation of friendly AI…

Gary Marcus: How do we bridge the mind with the brain?!…

"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it's become a buzzword that captures so many things)... 

On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe 'big data' in relation to 'machine learning' -- as well as what comes next with 'deep learning'. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.

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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/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

O'Reilly's Ben Lorica chats with Mike Cafarella, assistant professor of computer science at the University of Michigan and co-founder of both Hadoop and Nutch.

From Search to Distributed Computing to Large-scale Information Extraction

Recorded live at the Intercom on Capital event, Intercom CEO, Eoghan McCabe, talks to Mamoon Hamid, partner and co-founder at SocialCapital. They talk about raising capital, what Mamoon looks for in investments, and the future of venture capital.

Intercom Live: Mamoon Hamid, Partner & Co-Founder at SocialCapital

Recorded live at the Intercom on Capital event, Intercom CEO, Eoghan McCabe, talks to Ethan Kurzweil, Partner at Bessemer Venture Partners. Their conversation ranges from what VCs are looking for in their investments, to the effects of volatile public markets on Silicon Valley.

Intercom Live: Ethan Kurzweil, Partner at Bessemer Venture Partners

Recorded live at the Intercom on Capital event, Ethan Kurzweil, Mamood Hamid and Intercom CEO, Eoghan McCabe, talk about all things investment in a discussion led by Kathleen Estreich. 

They talk about the current landscape of high valuations, the competitive advantages of working outside the Valley, and the insider info VCs can give that nobody else can.

Intercom Live - Panel Discussion On Raising Capital

In episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning, the language made of shapes of words, and New England Machine Learning Day. We take a look at the workshops being presented at NIPS this year, and we take a listener question about changing the number of features your data has.

Workshops At NIPS And Crowdsourcing In Machine Learning

In this week’s Radar Podcast episode, O’Reilly’s Mac Slocum delves into the economy with two speakers from our recent Next:Economy conference. First, Slocum talks with Leah Busque, founder of TaskRabbit, about service networking, TaskRabbit’s goals, and issues facing the peer economy. In the second segment, Slocum talks with Dan Teran, co-founder of Managed by Q, about the on-demand economy and the future of work.


Leah Busque and Dan Teran on the Future of Work

In this special holiday episode of the O’Reilly Data Show, I look back at two conversations I had earlier this year at the Spark Summit in San Francisco. The first segment is an on-stage fireside chat with Ben Horowitz, co-founder of Andreessen Horowitz and author of The Hard Thing About Hard Things.

In the second segment, Reynold Xin, one of the architects of Apache Spark, explains the rise of Apache Spark in China.

Investing in Big Data Technologies

In this O’Reilly Podcast, Ben Lorica talks with John Hugg, founding engineer and manager of developer outreach at VoltDB. From the deluge of today's diverse data, Hugg describes how to extract meaningful information to make things cheaper and faster.

Streaming data + stream processing = information

This will be a breakout year for JavaScript on embedded systems. Our guest on the Hardware Podcast this week is Peter Hoddie, who founded Kinoma, which makes software and hardware for building JavaScript-powered prototypes. Previously he was one of the original developers of QuickTime at Apple.

Peter Hoddie on JavaScript for Embedded Systems

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.

Investing in Big Data Technologies

O'Reilly Radar