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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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Andreessen Horowitz's Chris Dixon and Balaji Srinivasan discuss the state of the Bitcoin ecosystem now that the Mt. Gox dust has settled. What's next in crypto-currency, and a theory on Bitcoin creator Satoshi (not Dorian) Nakamoto. 

a16z Podcast: The State of the Bitcoin Ecosystem, and a Theory on Satoshi

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 datacenter has long been -- there's no nice way to put this -- a bit of a snoozer. Expensive boxes running expensive software. No more, says a16z General Partner Peter Levine. Along with Chris Dixon, Levine lays out a vision for the datacenter of the future. Building on the technology established by companies like Facebook and Google, Levine and Dixon describe a software-led transformation of the datacenter, one where the mobile supply chain and fast-moving companies are reimagining everything -- from the underlying architecture to new business models. Be prepared to get in the weeds, hear Levine talk about the next opportunity, “hosted instances,” Dixon describe the “the dream within the dream,” and discover why the datacenter is about to get exciting.

a16z Podcast: Datacenter of the Future

Open source software has permeated practically every nook of the software world. The biggest companies and largest-scale systems all lean heavily on open source code. Yet, with the exception of Red Hat, no one has built a great business on top of open source software. That’s because what companies should be selling isn’t necessarily the software, or even support, says a16z’s Peter Levine, but a service that leverages open source. One example of such a service is DigitalOcean, which has built a cloud environment tailored to the needs of developers. DigitalOcean CEO  and cofcounder Ben Uretsky joins Levine from his New York City HQ for a discussion about open source-as-a-service, how companies of all sizes should think about leveraging open source, and whether we’ll start to see a slew of specialized clouds geared toward different verticals.

a16z Podcast: An Open Source Business Model That Works

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

With the rise of mobile phones equipped with GPS, we're meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques. A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

For more on our interview, visit O'Reilly Radar: http://oreil.ly/1t7EcOQ

The science of moving dots

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

O'Reilly's Ben Lorica chats with Sarah Meiklejohn about analytic applications for blockchain and cryptocurrency technology.

Clustering Bitcoin Accounts Using Heuristics

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

O’Reilly’s big data guru Ben Lorica sits down with prominent data scientist DJ Patil to talk about his new ebook (written with Hilary Mason) and other topics in data science and big data.

A Brief Look At Data Science's Past And Future

O'Reilly's Ben Lorica talks with Dato co-founder Carlos Guestrin about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.

The Evolution of GraphLab

O’Reilly’s Ben Lorica chats with Kira Radinsky, CTO and co-founder of SalesPredict, about predicting events using machine learning, NLP, and semantic analysis.

Forecasting events, from disease outbreaks to sales to cancer research

O'Reilly's Ben Lorica chats with David Blei, co-author of the seminal paper on topic models, and who remains one of the leading researchers in the field. They talk about the origins of topic models, their applications, improvements to the underlying algorithms, and his new role in training data scientists at Columbia University.

Topic models: Past, present, and future

Chris Clark discusses the blockchain's potential to disrupt the financial industry, from contracts to mortgages to government taxation.

Blockchain and decentralization hold big implications for society

Conrad Barski on the bitcoin and blockchain landscape and how the technology will evolve over the next 10 years.

For more on the interview, visit O'Reilly Radar: oreil.ly/15A9cCp

Bitcoin's road to democratization lies in decentralization

Ethereum founder Vitalik Buterin outlines the main applications of blockchain technology

Support the show, consider donating: 3JE6C12CPukvgaFXqjTd2ZSkrv23Evr45T (http://bit.ly/1gmt1Tw)

Close to two years after the initial whitepaper, the revolutionary Ethereum network has finally launched. The decentralized smart contract platform is, without a doubt, the most exciting cryptocurrency project since Bitcoin. The versatile platform with its turing-complete scripting language aspires to power the distributed applications of the future.

Ethereum Founder Vitalik Buterin joined us for the second time to discuss the Frontier Launch, future plans for scalability, governance and the road ahead for the organization and the protocol.

Topics covered included:
- Ethereum's frontier launch and the upcoming development stages
- The plans for how to create a scalable blockchain architecture
- The thinking around transitioning to proof-of-stake
- The state and new board of the Ethereum Foundation
- How Ethereum's development will be funded in the longer term
- His thoughts on the governance of Ethereum and Bitcoin
- When private blockchains make sense and if Ethereum
- What mistakes were made during the development of Ethereum

Review us on iTunes and WIN a Free T-Shirt!

Each week we will be giving away an Epicenter Bitcoin t-shirt to one lucky listener! All you need to do is leave us a review on iTunes. Then send us an email at show@epicenterbitcoin.com with the review text so we can identify you and we’ll draw one winner per week who will get a t-shirt.

Sponsors: 

- Ledger Nano: Smart card security for your Bitcoins - Get 10% off your first order when you use the code EB09 at http://ledgerwallet.com (valid until 09/30/15)

Show notes: http://epicenterbitcoin.com/podcast/091
YouTube: https://youtu.be/3uvbIyzsCLY


Epicenter Bitcoin is hosted by Brian Fabian Crain & Sébastien Couture.
 
- Visit our website: http://epicenterbitcoin.com
- Subscribe to our newsletter: http://epicenterbitcoin.com/newsletter
- Twitter: http://twitter.com/epicenterbtc

EB91 – Vitalik Buterin: Ethereum Frontier Launch, Scalability And The Road Ahead

Support the show, consider donating: 3DD22464EcibJksVGJh5CGR767SNpmmioZ (http://bit.ly/1IpY86t)

In a long-overdue episode, we finally had the chance to dive into one of the most known but poorly understood cryptocurrency/blockchain project: Ripple. Reviled by many in the Bitcoin space, we put aside any prejudices and sat down for a fascinating conversation with Ripple Labs CTO Stefan Thomas.

We talked about his early days in the Bitcoin space, how Ripple came about, what the Ripple network looks like today and how its consensus protocol works.

Topics covered included:
- Stefan Thomas' early experiences in the Bitcoin space
- How Ripple was created
- The problem Ripple is trying to solve
- How the Ripple consensus algorithm works
- Whether Ripple is decentralized or not
- What the correspondent banking system is and how Ripple wants to disrupt it
- The recent FinCen fine and what it means for Ripple

Links mentioned in this episode: 

- Ripple http://bit.ly/1hgMTaV
- Ripple Labs http://bit.ly/1Msvnfe
- Ripple Labs Fined $700,000 by FinCen http://bit.ly/1WwHyMx
- Richard Gendal Brown:  The Deep Insight at Ripple's Cores http://bit.ly/1UMG7YC
- Peter Todd Ripple Protocol Consensus Algorithm Review http://bit.ly/1hgN0U0
- Ripple Consensus Protocol Whitepaper http://bit.ly/1HQ4202

Review us on iTunes and WIN a Free T-Shirt!

Each week we will be giving away an Epicenter Bitcoin t-shirt to one lucky listener! All you need to do is leave us a review on iTunes. Then send us an email at show@epicenterbitcoin.com with the review text so we can identify you and we’ll draw one winner per week who will get a t-shirt.

Sponsors: 

- Shapeshift: Buy and sell alt coins instantly at http://ShapeShift.io

Show notes: http://epicenterbitcoin.com/podcast/092
YouTube: https://youtu.be/KMydq_m9f_4


Epicenter Bitcoin is hosted by Brian Fabian Crain & Sébastien Couture.
 
- Visit our website: http://epicenterbitcoin.com
- Subscribe to our newsletter: http://epicenterbitcoin.com/newsletter
- Twitter: http://twitter.com/epicenterbtc

EB92 – Stefan Thomas: Understanding Ripple

Slock.it was the winner of the IoT competition at 4YFN, for 2016. Their system allows anyone to rent, sell or share anything without the use of a middleman. They have set up an ethereum-based locking system -based on blockchain technology- that can be opened with money. Stephan Tual, Founder & COO at Slock.it, gives an example of its use, explaining how someone could rent their house, without having to put it on a platform like AirBnb; which might not be available in every country and in the end takes a commission. What they would be renting out is the actual door of the apartment -an autonomous object connected to the Internet- using a decentralised database of sort, allowing the users both types of access; to rent and use. Everything can be done through a phone and obviously the prospects are enormous and, may we dare say, quite democratic. Took a lot of imagination to realise the potential of considering every object autonomous, thus transferable, but the guys seem to be onto something. A startup worth keeping an eye on. Start by listening to the interview Sakis Triantafyllakis conducted for Tech Talks Central.


TTC #259 IoT & Blockchain Technology for Renting, Selling, Sharing

Vitalik Buterin Interview- Ethereum & Blockchain Technology Talk (EXCLUSIVE)

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.