«Les progrès de la robotique et de l’intelligence artificielle permettent d’envisager une infrastructure à l’échelle du continent pour numériser, analyser, reconstituer notre patrimoine millénaire. […] Les Big Data nous promettent une société où nous pourrions anticiper l’avenir, grâce à la puissance de calcul des superordinateurs et la collecte massive de données» – Frédéric Kaplan.
Source : La machine vénitienne à remonter le temps se tourne vers l’Europe – Le Temps
«Re-Engineering Humanity brings a pragmatic if somewhat dystopic perspective to the technological phenomena of our age. Humans are learning machines and we learn from our experiences. This book made me ask myself whether the experiences we are providing to our societies are in fact beneficial in the long run» – Vint Cerf.
Source : Home | Re-Engineering Humanity
«La question de la réduction du temps de travail deviendra alors incontournable pour faire face à cette révolution numérique. Sans doute faudra-t-il l’envisager non plus à l’échelle de la semaine, mais sur l’ensemble de la carrière professionnelle, afin de permettre aux salariés une reconversion et une adaptation à ces nouveaux métiers».
«The time between calling 911 and the ambulance arriving can be critical for saving heart attack victims, but the person on the phone may not know what’s happening: this AI parses non-verbal clues to help diagnose from a distance».
Source : Having A Heart Attack? This AI Helps Emergency Dispatchers Find Out
«Facebook espérait développer une IA qui pourrait réaliser automatiquement toutes les tâches demandées par les utilisateurs. Mais les humains auront été nécessaires jusqu’au bout pour faire fonctionner le service, ce qui a coûté cher à l’entreprise».
Source : Facebook ferme M, l’assistant personnel qui n’a jamais pu se passer des humains – Tech – Numerama
«Goodfellow’s friends were just as adamant that this method wouldn’t work, either. So when he got home that night, he built the thing. « I went home still a little bit drunk. And my girlfriend had already gone to sleep. And I was sitting there thinking: ‘My friends at the bar are wrong!' » he remembers. « I stayed up and coded GANs on my laptop. » The way he tells it, the code worked on the first try. « That was really, really lucky, » he says, « because if it hadn’t of worked, I might have given up on the idea. »»
Source : Google’s Dueling Neural Networks Spar to Get Smarter, No Humans Required | WIRED
«Arsenal’s smart assistant AI suggests settings based on your subject and environment. It uses an advanced neural network to pick the optimal settings for any scene (using similar algorithms to those in self driving cars). Like any good assistant, it then lets you control the final shot. Here’s how it works…»
Source : Meet Arsenal, the Smart Camera Assistant | Features
«You can listen to some of the Tacotron 2 audio samples that demonstrate the results of our state-of-the-art TTS system. In an evaluation where we asked human listeners to rate the naturalness of the generated speech, we obtained a score that was comparable to that of professional recordings».
Source : Research Blog: Tacotron 2: Generating Human-like Speech from Text
«Watson ne fait pas le travail à la place du collaborateur, il l’assiste dans ses choix, l’aide à s’orienter dans son enquête ». « Notre objectif est de régler davantage de dossiers, affirme Mme Bigot. Pas de réduire le nombre des collaborateurs qui travaillent sur les contrats non réclamés».
Source : Quand l’intelligence artificielle enquête sur les assurances-vie non réclamées
«Neural nets are just thoughtless fuzzy pattern recognizers, and as useful as fuzzy pattern recognizers can be—hence the rush to integrate them into just about every kind of software—they represent, at best, a limited brand of intelligence, one that is easily fooled. A deep neural net that recognizes images can be totally stymied when you change a single pixel, or add visual noise that’s imperceptible to a human. Indeed, almost as often as we’re finding new ways to apply deep learning, we’re finding more of its limits. Self-driving cars can fail to navigate conditions they’ve never seen before. Machines have trouble parsing sentences that demand common-sense understanding of how the world works.Deep learning in some ways mimics what goes on in the human brain, but only in a shallow way—which perhaps explains why its intelligence can sometimes seem so shallow».
Source : Is AI Riding a One-Trick Pony? – MIT Technology Review
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