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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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The author of Appcelerator Titanium: Up and Running describes how Titanium can be used to generate native mobile apps from JavaScript code. He distinguishes the Titanium platform from native API programming and from other popular JavaScript platforms for mobile devices. We look at the way Titanium exploits the expressiveness and flexibility of JavaScript, and some of the directions that the Appcelerator company is taking Titanium."

Building native apps from JavaScript using Titanium - John Anderson

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 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 Michael Stack to get an update on the Apache HBase project.

Coming Full Circle With Bigtable and HBase

"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 Anima Anandkumar about tensor decomposition techniques for machine learning.

The Tensor Renaissance In Data Science

O'Reilly's Jenn Webb chats with Filament co-founder and CEO Eric Jennings about how a decentralized Internet would work, what we need to do to get there, and why it will become necessary as the Internet of Things begins to scale. Eric also talks about why his company recently shifted its focus from the maker community to industrial manufacturing.

A Vision of a Decentralized IoT Stack

O'Reilly's Mary Treseler talks with information architect Jorge Arango about the state of IA and the importance of designers' understanding of context and perspective.

Information Architecture's Role in UX Design

O'Reilly's Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.

Data Science Makes an Impact on Wall Street

O'Reilly's Jenn Webb chats with Cait O’Riordan, VP of product, music and platforms at Shazam, about the current state of predictive analytics and how Shazam is able to predict the success of a song, and how the Internet of Things affects Shazam’s product life cycles as well as the behaviors of their users. Jenn also chats with Francine Bennett, CEO and cofounder of Mastodon C, about unintentional uses of data for evil and how data scientists can ensure their work stays on the data for good side of that line.

How Shazam Predicts Pop Hits

Speaker(s): Professor Ed Glaeser | Residents of big cities typically earn higher wages, but are they any happier? According to surveys on life satisfaction, American cities were once less happy than rural areas. Industrial areas seem once to have paid wages that were high enough for their residents to put up with a little misery, but this is no longer true. The unhappier cities of America's industrial heartland have shrunk, while the happier cities have grown, and today there is no relationship between city size and self-reported life satisfaction within the U.S. The developing world today appears to be reversing the Western industrial pattern of happy farms/unhappy cities, with far higher levels of life satisfaction in urban areas. Ed Glaeser is Fred and Eleanor Glimp Professor of Economics at Harvard University. Ricky Burdett (@BURDETTR) is Professor of Urban Studies, and director of LSE Cities and the Urban Age Programme. LSE Cities (@LSECities) is an international centre at the London School of Economics and Political Science that carries out research, education and outreach activities in London and abroad. Its mission is to study how people and cities interact in a rapidly urbanising world, focussing on how the design of cities impacts on society, culture and the environment. Credits: Tom Sturdy (Audio Post-Production), LSE AV Services (Audio Recording).

The Happiness of Cities [Audio]

Marcus Schössow is back on his own imprint Code Red with 'Smile'. A happy disco influenced house track that once again proves the label stands for groovy house music. "I want our releases to have a good vibe and sexy feel", Schössow explains. 

With 'Smile', Code Red's founder shows again he isn't afraid to explore different genres and sounds. Schössow says Code Red has to become a community rather than just the next record label by a dj. "We are all our own A&R's, so why limit our taste and creativity?" 

Code Red's second release goes the name 'Smile' because it actually makes people smile. Not only because Schössow says so, but also according all the positive reactions online. A smile Marcus will bring to the USA and Asia during his Code Red tour this summer.

www.facebook.com/marcusschossow
twitter.com/marcusschossow
@marcusschossow

www.facebook.com/coderedworld
twitter.com/coderedworld
@coderedworld

Download

Marcus Schossow - Smile // OUT NOW

O'Reilly's Ben Lorica chats with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.

Apache Spark: Powering Applications On-Premise and in the Cloud

<img src="http://ift.tt/1ydqO3F" align="right" hspace="20" vspace="20" border="0" title="Tech News Today" alt="Tech News Today"/>Hosts: <a href="http://ift.tt/1e2tW9E" target="_blank">Mike Elgan</a>,<a href="http://ift.tt/1gKEqeW" target="_blank"> Joe Panettieri</a>
Guests:<a href="https://twitter.com/bizcarson?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" target="_blank"> Biz Carson</a>,<a href="https://twitter.com/kwameopam?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" target="_blank"> Kwame Opam</a>, <a href="https://twitter.com/nicholasadeleon" target="_blank">Nicholas A. Deleon</a>, <a href="https://twitter.com/mcwm?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor" target="_blank">Mike Murphy</a>
10/20/15 : Uber now has StreetView cars that drive around mapping things, Google's delivery drone is spotted in the wild, the New York Times is going to deliver virtual reality along with the morning paper, and more...
Check out the<a href="http://ift.tt/1KmSmmS" target="_blank"> full shownotes</a> for this episode.
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 Published: October 20, 2015 at 12:55PM

Tech News Today 1370: Uber Streetview Cars Spotted in the Wild

<img src="http://ift.tt/1ydqO3F" align="right" hspace="20" vspace="20" border="0" title="Security Now" alt="Security Now"/>Hosts: <a href="http://ift.tt/1UYFDzE">Leo Laporte</a>, <a href="http://ift.tt/1Np88B8">Steve Gibson</a>
An emergency Adobe FLASH vunerability, sneaking naughty iOS apps pas Apple's scrutiny and a look at four examples (from this week) of companies getting security wrong.
We invite you to read our <a href="http://ift.tt/1M6P5iZ" target="_blank">show notes</a>.
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For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: <a href="http://ift.tt/LBBHjd" target="_blank">grc.com</a>, also the home of the best disk maintenance and recovery utility ever written <a href="http://spinrite.info/" target="_blank">Spinrite 6</a>.
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 Published: October 20, 2015 at 06:15PM

Security Now 530: Doing It Wrong

<img src="http://ift.tt/1ydqO3F" align="right" hspace="20" vspace="20" border="0" title="Tech News 2Night" alt="Tech News 2Night"/>Anchor: <a href="http://ift.tt/1fcLArN">Megan Morrone</a>
Guest: <a href="http://twitter.com/charlesarthur">Charles Arthur</a>
Crunching Google's mobile search math, Consumer Reports reassesses the Tesla Model S, Uber gets better PR, managing our Force Awakens expectations, your news in virtual reality, and more...
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 Published: October 20, 2015 at 06:17PM

Tech News 2Night 449: Winning the Battle for the Internet

<img src="http://ift.tt/1ydqO3F" align="right" hspace="20" vspace="20" border="0" title="Tech News Today" alt="Tech News Today"/>Anchor: <a href="http://ift.tt/1aRrzUg" target="_blank">Mike Elgan</a> and Co-Anchor: <a href="https://twitter.com/KevinCTofel" target="_blank">Kevin Tofel</a>
10/21/15: 22 of the biggest tech companies have come out against CISA, it's 'Back to the Future' Day, Magic Leap shows a real-life demo of its HoloLens killer, Apple tells the government it's impossible extract user data from a locked iPhone, and more...
Guests: <a href="https://twitter.com/Cory_Bennett" target="_blank">Cory Bennett</a>, <a href="https://twitter.com/lindsaygellman" target="_blank">Lindsay Gellman</a>, and <a href="https://twitter.com/Chris_Perkins1" target="_blank">Chris Perkins</a>
Check out the full <a href="http://ift.tt/1W6i0Iv" target="_blank">show notes</a> for today's episode.
Download or subscribe to this show at <a href="http://twit.tv/tnt" target="_blank">twit.tv/tnt</a>.
Thanks to <a href="http://cachefly.com/" target="_blank">Cachefly</a> for the bandwidth for this show.

 Published: October 21, 2015 at 12:00PM

Tech News Today 1371: Silicon Valley Slams Cybersecurity Bill

<img src="http://ift.tt/1ydqO3F" align="right" hspace="20" vspace="20" border="0" title="Triangulation" alt="Triangulation"/>Steve Jurczyk earned bachelor and master of science degrees in electrical engineering from the University of Virginia in 1984 and 1986. Jurczyk began his NASA career at Langley in 1988 as an electronics engineer in the electronic systems branch. Steve is currently the agency's Associate Administrator for the Space Technology Mission Directorate. He's responsible for innovating, developing, testing and flying hardware for use on future NASA missions.
 
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 Published: October 26, 2015 at 02:24PM

Triangulation 222: Steve Jurczyk from NASA

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 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

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.