Pédagogie & Technologie

Neurospell est un outil de correction automatique expérimental très impressionnant qui fait appel à l'intelligence artificielle.

Artificial Intelligence (AI) has been quietly infiltrating society in every way. Tom shares how AI is being used to improve education.

Via Grant Montgomery

L’intelligence artificielle conduira à des transformations profondes du travail. Pour s’y préparer, le rapport passe au crible trois secteur

Via François GARREAU, Patrick Bouillaud

L'année 2018 va voir se multiplier les applications opérationnelles de l'IA. Marketing, maintenance, logistique

Via Jean-Pierre Blanger

La transformation digitale des entreprises, l’apparition des robots dans tous les secteurs d’activité, l’omniprésence de l’Intelligence artificielle, autant de mutations qui enthousiasment ou inquiètent, selon les cas, mais qui posent toutes débat. Jean-Frédéric Real, Directeur de l’Innovation de Scalian, un groupe français spécialiste des systèmes numériques, apporte sa pierre à cette réflexion. Pour lui, rien ne peut se faire sans mettre l’humain au centre de tous les dispositifs.

Via François Pellerin

Large-scale data gathering and quantum leaps in processing power have set the table for major advancement in artificial intelligence. Yet there’s a growing body of evidence that the field of AI is poised to move into a whole new dimension, one where AI not only imagines the real world, but can begin to make accurate decisions on what’s real and important, what’s not — and thus predict what’s coming next. “Computers are really good at memorization,” Carl Vondrick, research scientist at Google Inc., said during a presentation at the Re-Work Deep Learning Summit in San Francisco Thursday. “The problem is teaching them how to forget.” Vondrick’s research has focused on one of the most vexing challenges in today’s online world: how to make use of the massive database of unlabeled videos that clog nearly every corner of the web. It’s one thing to swoon over a cute baby or funny cat video. It’s another to learn from it. The Google research team decided the best approach was to use millions of unlabeled video hours to train deep learning neural networks to reach a better understanding of the world. By drawing on the vast cache of freely available footage, the AI-enabled network could correctly interpret not only what it saw, but what would happen next. In examples presented at the conference, Vondrick showed videos of people approaching each other and then the network decided, mostly correctly, what action would occur as a result. A hug, handshake or “high five” was often the behavior based on the human interaction captured on video. The deep learning research is important because growing dependence on robots will demand that the machines be able to interpret human actions as observed. If a human reaches for a doorknob, it would be highly inconvenient if the robot decides to slam the door. Intriguingly, the Google researchers have been extending the deep learning model to include sound as well. Summit attendees heard a clip of people singing “happy birthday” and as the video image was revealed, it showed that the network correctly predicted there would be an image of a candle in the segment. At 74 percent accuracy, deep learning systems are coming along well at predicting action, getting closer to human rates approximately 10 percentage points higher, according to Vondrick. “This task is still pretty hard and we don’t always get it right,” Vondrick admitted. Vondrick’s research is based on a methodology known as adversarial learning, which essentially pits two networks in competition with each other. One network generates real images and the other is tasked with analyzing them and rendering a decision on whether they are genuine or fake. This technique has also been recently employed by Ian Goodfellow, staff research scientist at Google Brain, who has become a leading authority in “generative adversarial networks” or GANs. In Goodfellow’s work, GANs create photos and sounds of the real world. “GANs are generative models based on game theory,” Goodfellow explained. “They open the door to a wide range of engineering tasks.” These tasks encompass a variety of deep learning models where machines can be asked to turn a brown horse into a zebra.

88% des dirigeants interrogés utilisent des technologies AI comme l'analyse prédictive ou la reconnaissance vocale dans le milieu professionnel.

Via Gilles Le Page, elearning at eCampus ULg

“ En thèse CIFRE convention industrielle, Caroline Etienne est doctorante de l'université Paris-Saclay. Son laboratoire de rattachement est le LIMSI-CNRS et son entreprise est DREAMQUARK, une start-up de logiciel deep-learning pour les banques et les assurances.”
Via CNRS Sciences informatiques, Claude Tran

“Human plus machine isn't the future, it's the present,” Garry Kasparov said in a recent TED talk.

 

And this “present” is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards.

 

We have already witnessed the rise and impact of education technology especially through a multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge.

Via Edumorfosis, Fernando de la Cruz Naranjo Grisales

L'IA sera un vrai défi pour innover dans la formation. Objectifs : permettre aux apprenants de mieux se former et aux formateurs de pouvoir les accompagner.

Via Bhushan Thapliyal

Via juandoming, Juergen Wagner, Yashy Tohsaku

Aux Etats-Unis comme en Europe, des outils intégrant l’intelligence artificielle prétendent répondre aux difficultés scolaires. Individualiste et technocratique, l’approche néglige les finalités éducatives essentielles : l’esprit critique, la réflexion et l’argumentation. IA
Via Sandrine Decamps

Via ESR_Info

AI isn't going to be the major job killer that many fear.

Nothing is “artificial” about this technology. It is made by us, for us.

Via Jean-Pierre Blanger

...AlphaGo a appris en copiant le raisonnement humain via l’usage du deep learning. C’est à dire en analysant un volume considérable de parties afin de détecter les automatismes qui lui ont permis de...

Via Patrick Bouillaud

According to an Oxford University analysis, close to half of all jobs will be taken over by robots in the next 25 years. No wonder the press is full of handwringing about how workers will adjust and the best way to prepare the next generation for this A.I.-filled future.

But not everyone is alarmed about the prospect of radical change in the labor market. After all, this has happened before (for instance, when mechanization replaced the vast majority of farmers) and it turned out OK. Plus, a lot of today's jobs are soul-crushingly boring and repetitive. Losing them just might be a blessing. 

Among these optimists are IT service company Cognizant. In a recent report (hat tip to Business Insider for the pointer), the consultancy notes that while creative destruction has always been with us, so has reinvention. Sure, robots will take jobs away, but they'll also create new ones.

What will these new gigs look like exactly? The report imagines detailed job ads for 21 future careers that Cognizant thinks may emerge in the next 10 years.

Via John Evans

D’ici 2030, la Chine ambitionne de devenir le numéro un mondial dans le domaine de l'intelligence artificielle, menaçant la domination des Etats-Unis.

Insatisfait de la couverture médiatique de l’Intelligence Artificielle, le chercheur Zachary C. Lipton a lancé le blog Approximately Correct pour faire entendre une voix dissonante. Selon lui, de nombreux articles consacrés au sujet pêchent par approximation et relaient des prophéties spectaculaires au détriment des véritables enjeux. Explications.

Via Rémi Brossard