Bots

Data Show

data science

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

In this episode of the O’Reilly Data Show, I spoke with Yishay Carmiel, president of Spoken Labs. As voice becomes a common user interface, the need for accurate and intelligent speech technologies has grown. And although computer vision is a common entry point for deep learning, some of the most interesting commercial applications of deep neural networks are in speech recognition. Carmiel has spent several years building commercial speech applications, and along the way he has witnessed (and helped architect) massive improvements in speech technologies.

Commercial Speech Recognition Systems in the Age of Big Data and Deep Learning

In this episode of the O’Reilly Data Show, I spoke withJana Eggers, CEO of Nara Logics. Eggers’ involvement with AI dates back to her days as a researcher at the Los Alamos National Laboratory. Most recently she has been helping companies across many industries adopt AI technologies as a way to enable a range of intelligent data applications.

Enabling Enterprise Adoption of AI Technologies

We’re launching a new pop-up podcast about bots. In this first episode of the O’Reilly Bots Podcast, I’m joined by Peter Skomoroch to talk background: why everyone is suddenly interested in bots and what they promise to do, and what sorts of applications are beginning to emerge.

What are Bots? Here's the Background.

In this episode of the O’Reilly Data Show, I spoke with Adam Marcus, co-founder and CTO of B12, a startup focused on building human-in-the-loop intelligent applications. We talked about the open source platform Orchestra,for coordinating human-in-the-loop projects; the current wave of human-assisted AI applications; best practices for reviewing and scoring experts; and flash teams.

Building Human-Assisted AI Applications

Greylock Partners investor Sarah Guo joins us for episode two of our new pop-up podcast on bots and conversational interfaces. She’s written insightfully on bots and has worked on several investments in bot startups.

Sarah Guo on the Case for Bots

In this episode of the O’Reilly Data Show I sat down with O’Reilly author Dean Wampler, big data architect at Lightbend. We talked about new architectures for stream processing, Scala, and cloud computing.

Data Architectures for Streaming Applications

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 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.

Data Architectures for Streaming Applications

O'Reilly Radar