Veille technologique concernant les intelligences artificielles
How SETI uses off-the-shelf AI to search for extraterrestrial life. The Search for Extraterrestrial Intelligence Institute (SETI) is using off-the-shelf machine learning technology from Nvidia and Google to advance its mission of finding other forms of life in the universe.
Using these tools, a newly hired graduate student last fall discovered the richest signal dataset found to date. “I didn’t believe him, actually, but eventually that was borne out to be true. And in fact the thing that took the longest was actually writing the paper and getting it through peer review,” Berkeley SETI Research Center director Andrew Siemion said. This AI system can generate images of artificial galaxies. Picture this: star clusters, nebulas, and other interstellar phenomena created out of whole cloth unsupervised, by a computer.
It might sound like the description for a futuristic holodeck, but researchers at the University of Edinburgh’s Institute for Perception and Institute for Astronomy have designed such a system with the help of artificial intelligence (AI). In a paper published on the preprint server Arxiv.org (“Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks“), they describe an AI model capable of generating high-resolution images of synthetic galaxies that closely follow the distributions of real galaxies.
“Astronomy of the 21st century finds itself with extreme quantities of data, with most of it filtered out during capture to save on memory storage,” they wrote. Google and university researchers are using deep learning to discover exoplanets. A robot has figured out how to use tools. Learning to use tools played a crucial role in the evolution of human intelligence.
It may yet prove vital to the emergence of smarter, more capable robots, too. New research shows that robots can figure out at least the rudiments of tool use, through a combination of experimenting and observing people. Chelsea Finn, a researcher at Google Brain, and Sergey Levine, an assistant professor at UC Berkeley, developed the robotic system together with several of Levine’s students. Boston Dynamics buys a better brain for its robots. It is famous for making acrobatic, eerily lifelike, and somewhat accident-prone robots.
Now Boston Dynamics is buying technology that could give its creations the brains they need to navigate the real world. Watch 10 Boston Dynamics Robo-Dogs Pull a Truck in a Parking Lot. Robot ATLAS from Boston Dynamics can backflip and do parkour. Are we one step closer to the robo apocalypse?
Boston Dynamics, the mechanical wizards behind door-opening robots and electronic dogs, released a new video Thursday showing its humanoid robot Atlas running and jumping like a parkour pro through an obstacle course. “Atlas does parkour. The control software uses the whole body including legs, arms and torso, to marshal the energy and strength for jumping over the log and leaping up the steps without breaking its pace,” the company wrote. “Atlas uses computer vision to locate itself with respect to visible markers on the approach to hit the terrain accurately.”
The video shows the robot jogging and jumping over a large log without breaking pace, then hopping diagonally up three large steps and running forward. DeepMind wants to teach AI to play a card game that’s harder than Go. If you’ve ever played the card game Hanabi, you’ll understand when I say it’s unlike any other.
It’s a collaborative game in which you have full view of everyone else’s hands but not your own. To win the game, each player must give the others hints about their hands over a limited number of rounds to arrange all the cards in a specific order. It’s an intense exercise in strategy, inference, and cooperation. An AI is playing Pictionary to figure out how the world works. It might be a frivolous after-dinner game to you, but Pictionary could perhaps give AI programs a deeper understanding of the world.
AI’s lack of common sense is one of the main obstacles to the development of chatbots and voice assistants that are genuinely useful. What’s more, while AI programs can trounce the best human players of many games, including chess, Go, and (more recently) StarCraft, mastering them offers only a narrow measure of artificial intelligence. Learning to play chess, for instance, does nothing to help a computer play Sudoku. Researchers at the Allen Institute for AI (Ai2) believe that Pictionary could push machine intelligence beyond its current limits. To that end, they have devised an online version of the game that pairs a human player with an AI program. Pourquoi la victoire d'une intelligence artificielle aux jeux vidéo sera plus importante que celle sur le jeu de Go. SCIENCE - Petit à petit, les humains se sont inclinés face aux machines dans la plupart des jeux les plus complexes inventés par nos civilisations, des échecs au jeu de Go, en passant par Othello ou les dames.
Ironiquement, c'est dans le virtuel que le biologique résiste le mieux au mécanique, où l'intelligence artificielle ne nous a pas surclassé. L'intelligence artificielle Nvidia crée un jeu vidéo à partir d'images réelles. Intelligence artificielle - IA - AI. Intelligence artificielle : le test de turing est-il dépassé ? The Rise of AI. Les différentes applications de l'intelligence artificielle. Les programmes de reconnaissance vocale ou de parole, les agents conversationnels (ou chat bots) et les autres applications liées au sont des technologies du domaine appelé « traitement du langage naturel langage », une branche de l’Intelligence Artificielle.
Ces outils utilisent généralement des méthodes innovantes comme le machine learning et les réseaux de neurones convolutifs pour accélérer le traitement du langage dans différents domaines (défense militaire, renseignements généraux, justice, communication entre particuliers à travers le téléphone portable, … bref, tout ce qui peut nécessiter l’usage de la parole et des mots !). A Tour of The Top 10 Algorithms for Machine Learning Newbies. Essentials of Machine Learning Algorithms (with Python and R Codes) 4 Machine Learning Techniques You Should Recognize. Previously, we discussed what machine learning is and how it can be used.
But within machine learning, there are several techniques you can use to analyze your data. Today I’m going to walk you through some common ones so you have a good foundation for understanding what’s going on in that much-hyped machine learning world. If you are a data scientist, remember that this series is for the non-expert. But first, let’s talk about terminology. A Google Brain Program Is Learning How to Program. The idea of using machine learning to teach programs how to automatically write or modify code has always been tempting for computer scientists. The feat would not only greatly reduce engineering time and effort, but could also lead to the creation of novel and advanced intelligent agents.
In a new paper, Google Brain researchers propose using neural networks to model human source code editing. China's Google Equivalent Can Clone Voices After Seconds of Listening. The Google of China, Baidu, has just released a white paper showing its latest development in artificial intelligence (AI): a program that can clone voices after analyzing even a seconds-long clip, using a neural network. Not only can the software mimic an input voice, but it can also change it to reflect another gender or even a different accent. You can listen to some of the generated examples here, hosted on GitHub. Previous iterations of this technology have allowed voice cloning after systems analyzed longer voice samples. In 2017, the Baidu Deep Voice research team introduced technology that could clone voices with 30 minutes of training material. Adobe has a program called VoCo which could mimic a voice with only 20 minutes of audio.
While at first this may seem like an upgrade to tech that became popular in the 90s, with the help of “Home Alone 2” and the “Scream” franchise, there are actually some noble applications for this technology. This AI Can Clone Any Voice, Including Yours. Des chercheurs du CNRS/Thales créent le premier nano-neurone artificiel capable de reconnaissance vocale. Récemment, les algorithmes d’intelligence artificielle sont devenus performants pour la reconnaissance visuelle ou vocale.
Une expérience de traduction poétique : IA vs humain. La-start-up-francaise-deepomatic-specialiste-de-la-reconnaissance-d-images-leve-6-2-millions-de-dollars. AI-produced artwork is moving from the fringes into the high art world. In a month, prestigious auction house Christie’s will invite the upper crust of the art collector world to their Manhattan showroom to bid on a gold-framed impressionistic portrait of a European gentleman.
It would be an ordinary affair in the world of high art, except for one detail: The portrait for sale was generated by AI, not a famous painter. It will be the first AI-produced artwork to be sold at a major auction house — and is estimated to sell for $7,000 to $10,000. Google creates neuroaesthetic experience for Salone del Mobile. Should a self-driving car kill the baby or the grandma? Depends on where you’re from. In 2014 researchers at the MIT Media Lab designed an experiment called Moral Machine. The idea was to create a game-like platform that would crowdsource people’s decisions on how self-driving cars should prioritize lives in different variations of the “trolley problem.” In the process, the data generated would provide insight into the collective ethical priorities of different cultures.
The researchers never predicted the experiment’s viral reception. Four years after the platform went live, millions of people in 233 countries and territories have logged 40 million decisions, making it one of the largest studies ever done on global moral preferences. Faut-il limiter la vitesse des voitures autonomes ? Le logiciel d'une voiture autonome comprend un algorithme qui calcule le plus court chemin allant d'un point à un autre. Il est similaire à celui que nous utilisons déjà sur nos GPS, à la différence près que ses indications sont directement communiquées aux commandes de la voiture, sans l'intermédiaire d'un conducteur humain. Il est naturel de penser que, dans un tel cas, l'algorithme respecte, par construction, les limitations de vitesse. Ainsi, sur une route limitée à 80 kilomètres par heure, il ne peut jamais dépasser cette vitesse.
Le code de la route serait même partiellement inutile, puisque le code de l'algorithme exprimerait la même chose, dans un autre langage. Mais ce raisonnement repose sur une confusion entre deux significations du verbe « pouvoir » : sa signification aléthique, selon laquelle rouler à plus de 80 kilomètres par heure est matériellement impossible, et sa signification déontique, selon laquelle cela est interdit. Is Stephen Hawking Right? Could AI Lead To The End Of Humankind? The famous theoretical physicist, Stephen Hawking, has revived the debate on whether our search for improved artificial intelligence will one day lead to thinking machines that will take over from us. The British scientist made the claim during a wide-ranging interview with the BBC. Hawking has the motor neurone disease, amyotrophic lateral sclerosis (ALS), and the interview touched on new technology he is using to help him communicate.
It works by modelling his previous word usage to predict what words he will use next, similar to predictive texting available on many smart phone devices. But Professor Hawking also mentioned his concern over the development of machines that might surpass us. “Once humans develop artificial intelligence, it would take off on its own and re-design itself at an ever increasing rate,” he reportedly told the BBC.
“The development of full artificial intelligence could spell the end of the human race.” Elon Musk - A.I., Biology, Neuralink. Comment permettre à l’Homme de garder la main ? Rapport sur les enjeux éthiques des algorithmes et de l’intelligence artificielle. Intelligences artificielles, droit des robots : quels problèmes éthiques ?