What about actionable recourse that provides actionable explanations? isn't that a good way to go? https://arxiv.org/abs/1809.06514   

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

It Will Rain by Bruno Mars

"When we fell in with the Acid Tests we a started having the most fun we had ever had."
- Jerry Garcia

Interview by Joe Smith
May 23, 1988 
Cassette Tape

Recorded during the writing of Off the Record
Hear the full interview catalog at The Library of Congress

Watch this episode as part of our animation series with PBS -->>
http://blankonblank.org/interviews/jerry-garcia-on-the-acid-tests/

Jerry Garcia on the Acid Tests

The visit of America’s speaker of the House of Representatives Nancy Pelosi has Chinese tempers flaring. We ask what the trip suggests about <a href="https://www.economist.com/leaders/2022/08/02/nancy-pelosis-trip-to-taiwan-highlights-americas-incoherent-strategy?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">American policy</a> and what it means for Taiwan. Crowdfunding is making a <a href="https://www.economist.com/the-economist-explains/2022/07/27/how-crowdfunding-is-shaping-the-war-in-ukraine?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">real difference</a> in the war in Ukraine—but its effects vary between the two sides. And a close listen to a young pianist’s <a href="https://www.economist.com/culture/2022/07/21/the-wonder-of-lim-yun-chans-performance-of-rach-3?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">prizewinning</a> Rachmaninoff-concerto performance.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> See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for privacy and opt-out information.

Nancy meeting you here: a tetchy Taiwan trip

Two years in the making, the country’s new foundational document was summarily <a href="https://www.economist.com/the-americas/2022/09/05/common-sense-prevails-as-chileans-reject-a-new-constitution?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">swatted down</a> in a referendum. We ask how it went so wrong, and what comes next. Data show a long-held view on fertility and prosperity is <a href="https://www.economist.com/finance-and-economics/2022/09/15/richer-societies-mean-fewer-babies-right?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">not as straightforward</a> as thought; we examine the policy implications. And learning about <a href="https://www.economist.com/the-economist-explains/2022/09/11/what-are-harm-the-air-to-surface-missiles-destroying-russian-air-defence-radar?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">HARM</a>—the missiles causing so much harm to Russian forces.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> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.

The first draft is history: Chile’s rejected constitution

İl Başkanı Mustafa Köse’den Veda 11 Ocak Pazar günü Başbakan Ahmet Davutoğlu Köse’nin Milletvekilini adaylığını ilan edecek http://ift.tt/14AKbWW

İl Başkanı Mustafa Köse’den Veda 11 Ocak Pazar günü Başbakan Ahmet Davutoğlu Köse’nin Milletvekilini adaylığını ilan edecek

The diary of a girl in the 1980s who-- forbidden to go out on dates-- resorts to finding love via the phone. To learn more about Mortified visit getmortified.com.  The Mortified Podcast is a proud member of Radiotopia from PRX. 

02 Sakena: Mr. Telephone Man

BLOOD

Explorer George Kourounis finds himself growing increasingly anxious as he prepares to enter a fiery sinkhole known as the “Doorway to Hell.”

George Kourounis is a renowned global explorer and storm chaser who specializes in documenting extreme forces of nature including: tornadoes, hurricanes, volcanoes, deserts, caves, avalanches and more. He is an Explorer In Residence for The Royal Canadian Geographical Society, the Chairman of the Explorers Club Canadian Chapter, and has received several awards and medals for his efforts. He frequently finds himself driving into the eye of fierce storms, or descending ropes into actively erupting volcanic craters, often while hosting television programs including “Angry Planet” and others.  He has given four TEDx talks, and has addressed the United Nations Environmental Emergencies Forum. George’s expeditions have taken him to 65 countries on all seven continents to such far-flung places as: Madagascar, Turkmenistan, Vanuatu, Greenland, North Korea, Myanmar, and Antarctica.

This story originally aired on The Story Collider's podcast on October 26, 2018, in an episode titled "Fear: Stories of daring adventures." Find the transcript and other information here: https://www.storycollider.org/stories/2018/8/24/fear-stories-of-daring-adventures

George Kourounis: The Doorway to Hell

In this episode of the Data Show, I spoke with Forough Poursabzi-Sangdeh, a postdoctoral researcher at Microsoft Research New York City. Poursabzi works in the interdisciplinary area of interpretable and interactive machine learning. As models and algorithms become more widespread, many important considerations are becoming active research areas: fairness and bias, safety and reliability, security and privacy, and Poursabzi’s area of focus—explainability and interpretability.

It's Time for Data Scientists to Collaborate With Researchers in Other Disciplines

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

Today Is Bad

Learan is a widower who lost his wife to breast cancer a year ago. Now he is a single parent to two boys. This is part of a larger group of audio portraits called Squirrels Stories of people talking about living with cancer. You can hear more stories at www.SquirrelsStories.com. 

You can also follow along at https://www.facebook.com/SquirrelsStories. 

The caretaker

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.

It's Time for Data Scientists to Collaborate With Researchers in Other Disciplines

O'Reilly Radar

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