to watch

Technology

Machine Learning

it_listen

hadoop

MyList

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

tech

Shapenote singing is a tradition developed in the late 1700's and early 1800's that helped everyday people sing music even if they couldn't sight-read standard musical notation. Shapenote and the Sacred Harp songbook are still allowing people to share a musical experience until this day. Learn more about this tradition from Anne Heider, Robert from the Chicago Shapenote Singers, and Ruth Reveal.

You can learn more about Shapenote, the Sacred Harp, and find singings in your area at http://fasola.org.

Also, thanks to Kate Lumpkin for her help with this episodes.

54: Shapenote

Tim O'Reilly chats with Google X's Astro Teller about moonshots, the relationship between technology and society, the learning process for hardware, and more.

To Create the Future We Want, We Need More Moonshots

Google engineer Tyler Akidau on the evolution of stream processing, the challenges of building systems that scale to massive data sets, and the recent surge in interest in all things real time.

Building Systems for Massive Scale Data Applications

Quantum computing. The dream of the 1980s is alive in 2015. But this time is different, for a number of reasons. What are the applications, beyond cryptography? And, what’s the point of it all — are the benefits of quantum computing truly going to be a difference of kind vs. of degree? And if so, how will it change what we already know about computers, from languages to coding to UIs to everything?

While it’s counterintuitive for us to imagine something so infinitesimally small, Ilyas Khan of Cambridge Quantum Computing helps us make the quantum leap in our minds … in this special edition of the a16z Podcast, brought to you from London.

a16z Podcast: Quantum Leap

Jai Ranganathan, senior director of product management at Cloudera, on trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends

Jai Ranganathan On Architecting Big Data Applications In The Cloud

Emil Eifrem, CEO and co-founder of Neo Technology, on the early days of NoSQL, applications of graph databases, cloud computing, and company culture in the U.S. and Sweden.

Graph Databases are Powering Mission-Critical Applications

How Can Tiny Norway Afford to Buy So Many Teslas?

Freakonomics Radio - How Can Tiny Norway Afford to Buy So Many Teslas?

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 O’Reilly Data Show, Ben Lorica sat down with Vasant Dhar, a professor at the Stern School of Business and Center for Data Science at NYU, founder of SCT Capital Management, and editor-in-chief of the Big Data Journal (full disclosure: Lorica is a member of the editorial board). They talked about the early days of A.I. and data mining, and recent applications of data science to financial investing and other domains.


Is 2016 the Year You Let Robots Manage Your Money?

O'Reilly's Ben Lorica chats with Eric Colson, chief algorithms officer at Stitch Fix, and former VP of data science and engineering at Netflix. They talk about building and deploying mission-critical, human-in-the-loop systems for consumer Internet companies. 

Building a Business That Combines Human Experts and Data Science

David Beyer, co-founder and CEO of Chart.io, principal at Amplify Partners, and part of the founding team at Patients Know Best, chats with Risto Miikkulainen, professor of computer science and neuroscience at the University of Texas at Austin. They chat about evolutionary computation, its applications in deep learning, and how it’s inspired by biology.

Risto Miikkulainen on Evolutionary Computation and Making Robots Think For Themselves

In this episode of the O’Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor. We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain.

Using Apache Spark to Predict Attack Vectors Among Billions of Users and Trillions of Events

The long-awaited Raspberry Pi 3 was released last week, so David Cranor and I present an episode of the Hardware Podcast on the contentious question of whether the Raspberry Pi is a Serious Computer for Serious People, or not.

The Raspberry Pi 3: Is it Good Enough?

In this episode of the O’Reilly Data Show, I spoke with M.C. Srivas, co-founder of MapR and currently chief architect for data at Uber. We discussed his long career in data management and his experience building a variety of distributed systems. In the course of his career, Srivas has architected key components that now comprise many data platforms (distributed file system, database, query engine, messaging system, etc.).

Stream Processing and Messaging Systems for the IoT Age

In this O’Reilly Podcast, O'Reilly's Ben Lorica talks with Ryan Betts, CTO of VoltDB, about the IoT and streaming data. Their discussion covers unexpected use cases for IoT; building networked things that respond to IoT data (not just feeding data in); and the big picture of data management in the context of many, many networked things.

The IoT Is a Natural Ecosystem for Streaming Analytics

In this episode of the O’Reilly Data Show, O’Reilly’s online managing editor Jenn Webb speaks with Natalino Busa on the topic of predictive analytics, the challenges of feature engineering, and a new class of techniques that is enabling features to emerge from patterns within the data. They also discuss the relationship between predictive techniques and high-quality microservices, and how machine learning is being used to improve financial services.

Understanding Predictive Analytics

In this episode I spoke with Michael Franklin, co-director of UC Berkeley’s AMPLab and chair of the Department of Computer Science at the University of Chicago. AMPLab is well-known in the data community for having originated Apache Spark, Alluxio (formerly Tachyon) and many other open source tools. Today marks the start of a two-day symposium commemorating the end of AMPLab, and we took the opportunity to reflect on its impressive accomplishments.

Building the Next-Generation Big Data Analytics Stack

In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.

Deep Learning for Apache Spark

Thanks to freeways, cities became something to get through instead of something to get to. Now, as the next transportation revolution -- from rivers to trains to cars to autonomous cars -- promises to change the face of our cities, what happens to car culture, infrastructure, and more?

Who owns what, who pays? And what about the design -- and product management -- challenges, whether it's designing for user trust, city adoption, or an ever-moving target thanks to constantly evolving tech?

This episode of the podcast (in conversation with Sonal) covers all this and more, featuring: a16z's Frank Chen, who recently shared 16 questions about autonomous cars; Taggart Matthiesen, director of product at Lyft who covers the core platform as well as development/strategy for autonomous vehicles; and Carl Pope, former executive director and chairman of the Sierra Club -- and author (with former NYC mayor Mike Bloomberg) of the upcoming book Climate of Hope: How Cities Businesses and Citizens Can Save the Planet. Will curb space be the new shelf space? When we value the "iPhone-ness" over the "carness" of cars, what changes? And... will we all drive less, walk more?

a16z Podcast: Cars and Cities, the Autonomy Edition

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.

Scaling Machine Learning

In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of the Jeopardy! quiz show. Elemental Cognition (EC) is a research group focused on building an AI system that will be equipped with state-of-the-art natural language understanding technologies. Ferrucci envisions that EC will ship with foundational knowledge in many subject areas, but will be able to very quickly acquire knowledge in other (specialized) domains with the help of “human mentors.”

Having built and deployed several prominent AI systems through the years, I also wanted to get Ferrucci’s perspective on the evolution of AI technologies, and how enterprises can take advantage of all the exciting recent developments.

Language Understanding Remains One of AI's Grand Challenges

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.

Stream Processing and Messaging Systems for the IoT Age

O'Reilly Radar