machine learning

- Tell us something we don't know!
- Stranger Things ROCKS "Master of Puppets"
- An Iowan on Jeopardy? (Waukee!)
- What's next for the Big 12?

Stranger Things ROCKS "Master of Puppets"!

Athlétique et intrépide, Giovanna Amati n’a qu’une idée en tête: devenir pilote automobile. Pour l’heure, la jeune femme de 19 ans vit enchainée dans un caisson obscur et insonorisé de 4m². Daniel Nieto est son geôlier attitré. Une nuit, il entre dans la cage à visage découvert...

Enlèvement à l’italienne
Rome. Fin des années 1970. Daniel Nieto, un braqueur marseillais, est en cavale dans l’Italie des années de plomb où le rapt est une véritable industrie. Il devient kidnappeur et enlève la fille d’un riche industriel du cinéma. Une relation contre-nature se noue entre l’otage et son geôlier. Elle sera fatale à ce dernier.

Une série documentaire de Marine Vlahovic
Enregistrements: Août 2020 à mai 2022 - Entretiens: Marine Vlahovic - Montage : Marine Vlahovic et Sara Monimart - Réalisation : Marine Vlahovic et Samuel Hirsch - Musique originale & mixage: Samuel Hirsch - Illustration : Zaven Najjar - Production : Arte Radio

Enlèvement à l'italienne(4/7): Dans la cage

Wall Street Journal's Josh Zumbrun details how airlines get rid of certain passengers on a flight and how passengers can fight against this issue.

The Science Behind Who Airlines Bump From a Flight—and How to Exploit It

Listen to an excerpt from I'M GLAD MY MOM DIED by Jennette McCurdy, read by the author.

I'M GLAD MY MOM DIED Audiobook Excerpt

LC.12_Smoke_Everything Eaten in One Year

Liebe Hörerinnen und Hörer,

gestartet sind Jochen Gebauer und Dom Schott mit der gleichen, heiligen Mission: Einen treuen Kult errichten und vier böse Gottheiten vom Thron stoßen, das erklärte Ziel von Cult of the Lamb. Die Wege aber, die die beiden dafür einschlugen, hätten unterschiedlicher nicht sein können. Und so kommt das Duo nach vielen Stunden wieder zusammen, um über das große Kot-Müsli-Fiasko, Opferrituale, unheilige Festessen, Hamsterräder und süchtig machende Arbeitsroutinen zu sprechen - und ihren Kultführungsstil miteinander zu vergleichen.

Viel Spaß!
Dom & Jochen

Timecodes:

00:00 - Einstieg                         	
04:10 - Spielidee & Grafik               	
22:41 - Aufbau des Kults                 	
37:01 - Rituale                          	
53:28 - Die Runs                         	
1:17:45 - Probleme des Spieles             	
1:29:03 - Fazit

Auf ein Bier

Runde 387: Cult of the Lamb

Carl Frampton v Kiko Martinez II dominates the podcast with the Northern Irishman, Frampton, bidding to win his 19th bout in a row.
Mike Costello and Steve Bunce discuss the fight as well as who Carl Froch's next opponent should be and the future of Anthony Joshua. Sep 04, 2014

5lspecials: 5live Boxing: Frampton v Martinez 04 Sep 14

In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts within those companies. When I first heard that he was moving to Unity, I was curious as to why he decided to join a company whose core product targets game developers.

As you’ll glean from our conversation, Unity is at the forefront of some of the most exciting, practical applications of deep learning (DL) and reinforcement learning (RL). Realistic scenery and imagery are critical for modern games. GANs and related semi-supervised techniques can ease content creation by enabling artists to produce realistic images much more quickly. In a previous post, Lange described how reinforcement learning opens up the possibility of training/learning rather than programming in game development.

Lange explains why simulation environments are going to be important tools for AI developers. We are still in the early days of machine intelligence, and I am looking forward to more tools that can democratize AI research (including future releases by Lange and his team at Unity).

Unleashing the Potential of Reinforcement Learning

Le sommier qui grince. Les cris et gémissements de la porte à-côté. Le plafond qui menace de s'écrouler. Pourquoi la vie sexuelle de nos voisins est-elle aussi envahissante ? Comment une activité privée finit-elle par devenir publique ? Ils prennent du Viagra ou des amphétamines ? Et surtout : est-ce que par hasard... on n'aimerait pas entendre - et écouter - nos voisins ?

A retrouver aussi sur la chaîne Youtube ARTE Radio
Enregistrements : avril 18 - Mise en ondes & mix : Arnaud Forest - Texte & voix : Maïa Mazaurette - Illustration : Sjoerd van Leeuwen

Sex and sounds (n°26) : Les voisins bruyants

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Why It's Hard to Design Fair Machine Learning Models

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Lessons Learned While Helping Enterprises Adopt Machine Learning

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Tools for Generating Deep Neural Networks With Efficient Network Architectures

For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.

Trends in Data, Machine Learning, and AI

Guess Who

While shooting the cultish webserie “New York Minute”, Samuel Hirsch recorded some urban sounds and voices for this short Afro-American ballad.  
Recorded on March 2010 - Directed by Samuel Hirsch - Produced by http://arteradio.com
Balade sonore à New York, avec sons urbain et voix afro-américaines. Enregistrée et montée par Samuel Hirsch pendant le tournage de "New York Minute", la web série culte produite par ARTE Radio.  

Download : http://bit.ly/1gFFt92


NY state of mind

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.

Unleashing the Potential of Reinforcement Learning

O'Reilly Radar

Related tracks