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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Arabic Nokia Ringtone

Arabic Nokia Ringtone (Lo-Fi remix)

After a five-month hiatus, violence has returned to the northern region of Tigray—but that is just one of the <a href="https://www.economist.com/middle-east-and-africa/2022/08/25/two-ethnic-revolts-rack-ethiopia-at-the-same-time?utm_campaign=a.io&amp;utm_medium=audio.podcast.np&amp;utm_source=theintelligence&amp;utm_content=discovery.content.anonymous.tr_shownotes_na-na_article&amp;utm_term=sa.listeners" rel="noopener noreferrer" target="_blank">conflicts</a> threatening to pull the country to pieces. China’s Belt and Road Initiative has made it a prominent developing-world lender. <a href="https://www.economist.com/china/2022/08/24/faced-with-an-overseas-debt-crisis-will-china-change-its-ways?utm_campaign=a.io&amp;utm_medium=audio.podcast.np&amp;utm_source=theintelligence&amp;utm_content=discovery.content.anonymous.tr_shownotes_na-na_article&amp;utm_term=sa.listeners" rel="noopener noreferrer" target="_blank">How will it deal</a> with so many of its loans souring? And our obituaries editor reflects on Issey Miyake’s fashion-for-the-masses philosophy.For full access to print, digital and audio editions of The Economist, subscribe here <a href="http://www.economist.com/intelligenceoffer" rel="noopener noreferrer" target="_blank">www.economist.com/intelligenceoffer</a> <hr> Our GDPR privacy policy was updated on August 8, 2022. Visit <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.

No quiet on any front: Ethiopia’s clash of conflicts

Mekki, Benjamin et Grace ont fait des études universitaires en France avant de tenter d’intégrer le monde du travail. Qu’on soit Algérien, Camerounais ou Franco-Congolaise, les opportunités en tant que Noir ou Maghrébin sont-elles différentes d’un pays à l’autre, d’un continent à l’autre ?

La route de l'Eldorado
Ils et elles ont quitté leur pays pour la France avant de choisir de s’installer au Canada. Ils et elles nous racontent la France, son accueil, ses opportunités, mais aussi ses blocages. Après leur expérience française ils ont finalement atterri à Montréal, Ottawa ou Vancouver, où Halima Elkhatabi les a rencontrés. Après la Route du Bled, Halima Elkhatabi tisse avec finesse onze récits qui racontent un double exil avec émotion et lucidité. Une coproduction ARTE Radio - Radio Canada qui permet de passionnants regards croisés sur la France et la Canada.

Enregistrements : 2021 - Entretiens et prises de son : Halima Elkhatabi - Réalisation et mixage : Charlie Marcelet - Musiques originales : Samuel Hirsch - Illustration : Lola Penicaud - Production : Radio-Canada OHdio, ARTE Radio

La route de l'Eldorado (3/5) : Une lumière au bout du voyage

Gifted children stand trial for summertime crimes.

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Wheel of Misfortune

In 1974, oral historian Studs Terkel published a book with an unwieldy title: "Working: People talk about what they do all day and how they feel about what they do." This collective portrait of America was based on more than a hundred interviews Studs did around the country. And after "Working" came out, something surprising happened. It became a bestseller. It even inspired a Broadway musical. Something about ordinary people talking about their daily lives, struck a cord. Studs recorded all of his interviews on a reel-to-reel tape recorder, but after the book came out the tapes were packed away in boxes and few have ever been heard. On this episode of The Radio Diaries Podcast, we're bringing you eleven stories from the book. There's the telephone switchboard operator, the Chicago police officer, the private eye, the hotel piano player and many more.

Radio Diaries

The Working Tapes of Studs Terkel (Labor Day Special)

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

Dancing on the moon

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.

Acquiring and Sharing High-Quality Data

O'Reilly Radar

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