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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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First up in this episode: a crash course in natural language processing, and important steps if you want to use machine learning techniques on text data.  Then we'll take that NLP know-how and talk about a really cool analysis of State of the Union text, which analyzes the topics and word choices of every President from Washington to Obama.

Relevant link:
https://civisanalytics.com/blog/data-science/2016/01/15/data-science-on-state-of-the-union-addresses/

Text Analysis on the State Of The Union

Speaker(s): Professor Richard H Thaler | Richard Thaler, described by The Spectator as ‘the godfather of behavioural economics’, will be in conversation with LSE Director Craig Calhoun about his book Misbehaving, an authoritative and entertaining history of behavioural economics. Richard Thaler has spent his career studying the radical notion that the central agents in the economy are humans—predictable, error-prone individuals. Traditional economics assumes rational actors. Early in his research, Thaler realized these Spock-like automatons were nothing like real people. Whether buying an alarm clock, selling football tickets, or applying for a mortgage, we all succumb to biases and make decisions that deviate from the standards of rationality assumed by economists. In other words, we misbehave. Dismissed at first by economists as an amusing sideshow, the study of human miscalculations and their effects on markets now drives efforts to make better decisions in our lives, our businesses, and our governments. Coupling recent discoveries in human psychology with a practical understanding of incentives and market behaviour, Thaler enlightens readers about how to make smarter decisions in an increasingly mystifying world, revealing how behavioural economic analysis opens up new ways to look at everything. Laced with antic stories of Thaler’s spirited battles with the bastions of traditional economic thinking, Misbehaving is a singular look into profound human foibles. When economics meets psychology, the implications for individuals, managers, and policy makers are both profound and entertaining. Richard H. Thaler (@R_Thaler) is the Ralph and Dorothy Keller Distinguished Service Professor of Behavioural Science and Economics and the director of the University of Chicago Booth School of Business. He is co-the author of Nudge: Improving Decisions About Health, Wealth and Happiness and has worked in the US with Barack Obama and with David Cameron's 'Nudge Unit' in the UK. Craig Calhoun (@craigjcalhoun) is Director and President of LSE. He is a world-renowned social scientist whose work connects sociology to culture, communication, politics, philosophy and economics. The Behavioural Research Lab (@LSEBehavioural) is a purpose-built facility set up by the Department of Management (@LSEManagement) for the use of researchers examining organisational behaviour and decision making. The BRL’s state-of-the-art facilities include 20 workstations for individual computer-mediated studies and four bespoke discussion rooms with built-in audio-visual equipment for studies in social dynamics. Since its opening in 2011, over 18000 participants have taken part in more than 120 studies.  The BRL caters to researchers across LSE, including Management, Economics, Geography/Grantham Institute, Philosophy, Social Policy, Social Psychology and Government, and offers a large diverse participant pool to its researchers.

Misbehaving: the making of behavioural economics [Audio]

Nothing will drain your startup's talent pool faster than a bad manager. So what makes for a good one? Kim Scott, author of the bestselling book Radical Candor: Be a kickass boss without losing your humanity, explains how to create a culture of giving and receiving feedback that will help your team do its best, most fulfilling work.

Kim Scott, author of Radical Candor

In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific computing framework. PyTorch is a relatively new deep learning framework that is fast becoming popular among researchers. Like Chainer, PyTorch supports dynamic computation graphs, a feature that makes it attractive to researchers and engineers who work with text and time-series.

Why AI and Machine Learning Researchers are Beginning to Embrace PyTorch

Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution.  It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE.  And boy oh boy can it be tough to explain.  But we're trying our hardest in this episode!

KL Divergence

Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes.  Today we have a roundup of a few successful efforts to create robust adversarial examples, including what it means for an adversarial example to be robust and what this might mean for machine learning in the future.

Adversarial Examples

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Disciplined Data Science

In this episode of the Data Show, I spoke with Neha Narkhede, co-founder and CTO of Confluent. As I noted in a recent post on “The Age of Machine Learning,” data integration and data enrichment are non-trivial and ongoing challenges for most companies. Getting data ready for analytics—including machine learning—remains an area of focus for most companies. It turns out, “data lakes” have become staging grounds for data; more refinement usually needs to be done before data is ready for analytics. By making it easier to create and productionize data refinement pipelines on both batch and streaming data sources, analysts and data scientists can focus on analytics that can unlock value from data.

The Current State of Apache Kafka

If literature, as romantic poet John Keats said, is for soul making, how will our souls be made and remade in a digital age when most reading, if it happens at all, occurs on screens and at light speed? What are the threats to knowledge acquisition, modes of teaching, learning and wisdom?

This Sydney Ideas panel discussion took place on 15 October 2018: https://sydney.edu.au/news-opinion/sydney-ideas/2018/reading-and-writing-slowly-in-a-digital-age.html

Reading and writing slowly in a digital age

Speaker(s): Professor David Spiegelhalter | In his new book, The Art of Statistics, David Spiegelhalter guides us through the essential principles we need in order to derive knowledge from data, showing us why data can never speak for itself. He explains the basic concepts, from regression to P-values (without using mathematics), and introduces the intellectual ideas that underpin statistics. Drawing on numerous real world examples, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if the skeleton in the Leicester car park really was Richard III. Sir David Spiegelhalter is a British statistician and Chair of the Winton Centre for Risk and Evidence Communication in the Statistical Laboratory at the University of Cambridge. Spiegelhalter is one of the most cited and influential researchers in his field, and was elected as President of the Royal Statistical Society for 2017-18. Fiona Steele is a Professor of Statistics and Deputy Head of the Department of Statistics at LSE. Fiona first joined in LSE in 1996 as Lecturer in Statistics and Research Methodology.  She then worked at the Institute of Education, University of London 2001-2005, followed by the University of Bristol 2005-2013 where she was Professor of Social Statistics and Director of the Centre for Multilevel Modelling. She returned to LSE in 2013. The Department of Statistics (@StatsDeptLSE) offers a vibrant research environment and a comprehensive programme of undergraduate and postgraduate degrees.

Learning from Data: the art of statistics [Audio]

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

<img src="https://elroycdn.twit.tv/sites/default/files/styles/twit_album_art_2048x2048/public/images/shows/this_week_in_enterprise_tech/album_art/audio/twiet_albumart_audio.jpg?itok=z6GcMTSv" align="right" hspace="20" vspace="20" border="0" width="300" height="300" title="This Week in Enterprise Tech (Audio)" alt="This Week in Enterprise Tech (Audio)"/>WhatsApp security flaws allow for bot responses, AT&amp;T workers took bribes to unlock phones, iMessage hack revealed at Blackhat, Wordpress is about to auto-update content systems, Russian attack group tries to breach corporate networks with phones and printers, AMD making deals to gain market share, Apple and Google stop listening to voice assistant requests temporarily and Jeff Schmidt, CEO of Ignited talks about transforming STEM education. 
Hosts: <a href="https://twit.tv/people/louis-maresca">Louis Maresca</a> and <a href="https://twit.tv/people/brian-chee">Brian Chee</a> 
Guest: <a href="https://www.igniteducation.org/about/" target="_blank">Jeff Schmidt</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/this-week-in-enterprise-tech">https://twit.tv/shows/this-week-in-enterprise-tech</a>.
Sponsors:<ul>
<li><a href="http://WWT.COM/TWIT">WWT.COM/TWIT</a></li>
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This Week in Enterprise Tech 354: Ignited STEM

with @astrange @jeff_jordan and @smc90

This is episode #5 of our new show, 16 Minutes, where we quickly cover recent headlines of the week, the a16z way -- why they're in the news; why they matter from our vantage point in tech -- and share our experts' views on these trends as well.

This week we cover, with the following a16z experts:

* Federal Reserve real-time payment and settlement service FedNow, the U.S. payments rail, and fintech -- with a16z general partner Angela Strange;
* Barney's bankruptcy, the "death of retail", and ecommerce -- with a16z general partner Jeff Jordan;

...hosted by Sonal Chokshi.

16 Minutes on the News #5: Fed Real-time Payments, Death of Retail

with @julesyoo @smc90

This is episode #6 of our new show, 16 Minutes, where we quickly cover recent headlines of the week, the a16z way -- why they're in the news; why they matter from our vantage point in tech -- and share our experts' views on these trends as well.

This week we cover, with the following a16z experts:

* health claims, insurance & big tech, and healthcare data liquidity -- with a16z bio partner Julie Yoo;
* Capital One data breach, cloud security, and corporate hacks -- with a16z operating partner for security Joel de la Garza;

...hosted by Sonal Chokshi.

16 Minutes on the News #6: Health Claims, Corporate Breaches

Ever since the FCC announced its proposed settlement with Facebook, the headlines have focused on the largest-ever privacy fine that came with it — but few people paid attention to the many, many important details. This week, we've got the first half of a two-part podcast with lawyer Joshua de Larios-Heiman, who helps us go through the entire settlement from start to finish, and pick apart what it means.

The Techdirt Podcast

What's Really In The FTC's Facebook Settlement, Part One

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.

Make Data Science More Useful

O'Reilly Radar