Digital #MediaArt(s) Numérique(s)
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Digital #MediaArt(s) Numérique(s)
Media Arts Watch Lab - www.arts-numeriques.info - laboratoire de veille Arts Numériques - twitter @arts_numeriques - @processing_org - @DigitalArt_be - by @jacquesurbanska @_Transcultures
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#Deepdream : le rêve du réseau | conférence Gregory Chatonsky

#Deepdream : le rêve du réseau | conférence Gregory Chatonsky | Digital #MediaArt(s) Numérique(s) | Scoop.it

 

En juillet 2015, Google a diffusé des images hallucinants des chiens et des poissons. Rapidement adoptés par des milliers d’internautes, le code source a vu la multiplication de ces images où la machine est capable de pareidolia, en observant quelque chose elle voit autre chose. Nous regardons ces images et nous imaginons une pareidolia régressive, nous cherchons l’image qui est en dessous. Tout se passe comme si notre rêve était emboîté dans un autre rêve. Sommes-nous bien sûr d’être les acteurs de ce rêve ou n’en sommes-nous que les objets ? Sommes-nous certains qu’Internet est une production humaine ? Le réseau ne nous capture-t-il pas intégralement, enregistrant chacun de nos gestes dans des bases de données en vue d’halluciner le monde humain ?

 

Conférence dans le cadre de l’exposition co-workers, organisé par Philippe Riss, Hypersalon, Paris au Musée d’art moderne de la ville de Paris. Conférence soutenue par DAD, art device.

 

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A guy trained a machine to "watch" Blade Runner. Then things got seriously sci-fi - by Aja Romano

A guy trained a machine to "watch" Blade Runner. Then things got seriously sci-fi - by Aja Romano | Digital #MediaArt(s) Numérique(s) | Scoop.it

Last week, Warner Bros. issued a DMCA takedown notice to the video streaming website Vimeo. The notice concerned a pretty standard list of illegally uploaded files from media properties Warner owns the copyright to — including episodes of Friends and Pretty Little Liars, as well as two uploads featuring footage from the Ridley Scott movie Blade Runner.

 

Just a routine example of copyright infringement, right? Not exactly. Warner Bros. had just made a fascinating mistake. Some of the Blade Runner footage — which Warner has since reinstated — wasn't actually Blade Runner footage. Or, rather, it was, but not in any form the world had ever seen...

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Introducing #DeepText: Facebook's text understanding engine

Introducing #DeepText: Facebook's text understanding engine | Digital #MediaArt(s) Numérique(s) | Scoop.it

Text is a prevalent form of communication on Facebook. Understanding the various ways text is used on Facebook can help us improve people's experiences with our products, whether we're surfacing more of the content that people want to see or filtering out undesirable content like spam. 

 

in french >>> http://www.futura-sciences.com/magazines/high-tech/infos/actu/d/internet-deeptext-facebook-veut-lire-comprendre-tout-ce-vous-ecrivez-63022

 

With this goal in mind, we built DeepText, a deep learning-based text understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages.

 

DeepText leverages several deep neural network architectures, including convolutional and recurrent neural nets, and can perform word-level and character-level based learning. We use FbLearner Flow and Torch for model training. Trained models are served with a click of a button through the FBLearner Predictor platform, which provides a scalable and reliable model distribution infrastructure. Facebook engineers can easily build new DeepText models through the self-serve architecture that DeepText provides. ...

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Geoff Hinton, Yoshua Bengio, Yann Lecun talk - Deep Learning

Geoffrey Hinton designs machine learning algorithms. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. He received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member in Computer Science at Carnegie-Mellon.

Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Machine Learning Laboratory (MILA), CIFAR Program co-director of the CIFAR Neural Computation and Adaptive Perception program,  Canada Research Chair in Statistical Learning Algorithms, and he also holds the NSERC-Ubisoft industrial chair. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning (IFT6266) and supervises a large group of graduate students and post-docs. His research is widely cited (over 22000 citations found by Google Scholar in early 2015, with an H-index of 60).

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience.


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