Data analysis

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.

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Data Analysis

Three themes from Detroit: Become Human, each focusing on one of the title's main characters, and each scored by a different composer.

Detroit: Become Human launches May 25, 2018 on PS4.

Detroit: Become Human — Connor Main Theme By Nima Fakhrara

In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China.

We had a great conversation spanning many topics, including:

Potential applications of data science in financial services.

The current state of data science in financial services in both the U.S. and China.

His experience recruiting, training, and managing data science teams in both the U.S. and China.

Applications of Data Science and Machine Learning in Financial Services

Meine Damen und Herren,

denken Sie auch gerade: Moooment mal? Ein zweieinhalb Jahre altes Spiel im heiligen Sonntagspodcast – und dazu noch eins, zu dem Jochen damals eine eigene Wertschätzung gemacht hat? Haben die etwa getrunken?
Die Antwort lautet natürlich „ja“, aber trotzdem gibt es gute Gründe, diesem Horizon: Zero Dawn noch einmal eine  Sonntagsfolge zu widmen. Zum Beispiel der Umstand, dass es ein verflixt gutes Spiel ist und das Kunststück fertigbringt, gleichzeitig eine mittelmäßige und eine großartige Geschichte zu erzählen. Außerdem hat Jochen echt lange gequengelt. 

Viel Spaß wünschen:

André & Jochen 

Timecodes:

00:00 - Einleitung
06:04 - Horizon Zero Dawn: Der Blick von heute, Spieleinstieg und Frauenfiguren
28:38 - Gameplay
01:02:40 - Writing, Spielwelt, Charakterzeichnung, Pacing und Gegnerdesign
01:50:01 - Fazit
01:55:34 - Spoilerteil

Auf ein Bier

Runde #221: Horizon – Zero Dawn

In this week's episode of the Data Show, we're featuring an interview Data Show host Ben Lorica participated in for the Software Engineering Daily Podcast, where he was interviewed by Jeff Meyerson. Their conversation mainly centered around data engineering, data architecture and infrastructure, and machine learning (ML).

Tools for Machine Learning Development

Alle Infos zur The-Pod-Livetour: https://www.gamespodcast.de/tour/

Jetzt Backer werden: https://www.gamespodcast.de/abo/

Köpfe abschießen, Saltos schlagen und mit Bananen sprechen: Das verspricht „My Friend Pedro“ und das hält es auch. Andre und Sebastian knöpfen sich die neueste Kuriosität aus dem Hause Devolver Digital einmal vor und nutzen die Gelegenheit, um dabei auch gleich über den Indie-Publisher und die konstante „wir sind die coolen Underdogs“-Masche seiner Gründer zu sprechen – und warum die vielleicht besser funktioniert, denn je. Oder funktioniert sie am Ende schon gar nicht mehr so gut?

Timecodes:

00:00 - Einleitung
12:09 - Devolver und E3- Anekdoten
50:17 - My Friend Pedro
01:16:03 - Warum das Spiel so gut ankommt, Fazit und Ausblick

Runde #222: Alles völlig Banane (My Friend Pedro & Devolver Digital)

Watch The Co-Optional Podcast live at http://www.twitch.tv/gennabain on Tuesdays at 3pm ET.

Genna, Dodger & Jesse are joined by iNcontroLTV in this episode of the Co-Optional Podcast. Enjoy!

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The Co-Optional Podcast Ep. 252 ft. iNcontroLTV

Liebe Freunde,

heute haben wir mal wieder so einen Meta-Podcast für euch. Den hat sich Sebastian gewünscht, denn er hält „Bewegung“ für einen ziemlich essenziellen Bestandteil von Spielen. Klar eigentlich, denn egal ob Mario, Call-of-Duty-Soldat, Prügelspielcharakter oder Rollenspielheld – alle werden vom Spieler bewegt und das mit einem teils stark unterschiedlichen Bewegungsrepertoire. Und wir betrachten da mal die Details – von der Air-Control und „Coyote-Time“ bei 2D-Hüpfspielen bis zu Bunny-Hopping und Circle-Strafing in Ego-Shootern. Oder man Denke an Animations-Systeme wie Euphoria. Oder an Bewegungs-Boni als Belohnung? Oder, oder, oder … es ist ein weites Feld, diese Bewegung in Spielen. 

Wir wünschen euch viel Spaß beim Zuhören!

Timecodes:

00:00 - Einführung
05:40 - Von der Wichtigkeit der Bewegung in Spielen, warum es so schwer ist, darüber zu sprechen und Bewegung in 2D- Spielen
52:40 - Der Schritt zu 3D und Erfordernisse verschiedener Genres

Runde 223: Bewegung!

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>.
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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.

Tools for Machine Learning Development

O'Reilly Radar