Étiquette : machine learning (Page 7 of 10)

3 janvier 2017 / noflux

A Japanese insurance company has decided to get rid of 34 jobs and instead get artificial intelligence to take over assessing medical insurance claims over the phone.Starting from January 2017, Tokyo-based Fukoku Life Insurance Mutual Company is handing over the roles of 34 human insurance agents to IBM Watson Explorer, which is a cognitive search and content analysis platform that uses machine learning and language processing to analyse data for trends and patterns.

Source : Robots taking over jobs: Japanese insurance claim agents now being replaced by IBM Watson

3 janvier 2017 / noflux

Pour nous, la détection d’émotions est fascinante parce que c’est une partie manquante des communications entre les humains et les I.A. d’aujourd’hui.

Source : Découvrez Keymochi, le clavier qui veut détecter votre humeur – Tech – Numerama

3 janvier 2017 / noflux

While machine-learning technology can offer unexpected insights and new forms of convenience, we must address the current implications for communities that have less power, for those who aren’t dominant in elite Silicon Valley circles.Currently the loudest voices debating the potential dangers of superintelligence are affluent white men, and, perhaps for them, the biggest threat is the rise of an artificially intelligent apex predator.But for those who already face marginalization or bias, the threats are here.

Source : Artificial Intelligence’s White Guy Problem – The New York Times

3 janvier 2017 / noflux

Here’s how it would work: Passengers would step up to the kiosk and be asked a series of questions such as, « Do you have fruits or vegetables in your luggage? » or « Are you carrying any weapons with you? » Eye-detection software and motion and pressure sensors would monitor the passengers as they answer the questions, looking for tell-tale physiological signs of lying or discomfort. The kiosk would also ask a series of innocuous questions to establish baseline measurements so people are just nervous about flying, for example, wouldn’t be unduly singled out.

Source : The lie-detecting security kiosk of the future

31 décembre 2016 / noflux

« L’analyse automatique des textes se prête bien à l’apprentissage profond, capable de traiter efficacement de grandes quantités de données », explique Yoshua Bengio, directeur du MILA. « Grâce au Fonds Druide, nous pourrons accroitre d’environ 20 % notre budget de recherche pour l’analyse des textes.

Source : Druide donne un million de dollars à l’Université de Montréal | Druide

31 décembre 2016 / noflux

Si le système de DeepFace identifie automatiquement les utilisateurs sans stocker leurs données biométriques, Faceebook ne transgresse pas la loi de l’Illinois.

Source : Le système de reconnaissance faciale de Facebook confronté à la justice américaine – Politique – Numerama

19 décembre 2016 / noflux

« Evernote annonce que les changements de ses conditions d’utilisation, qui impliquaient la possibilité que ses salariés puissent lire, selon un protocole précis, certaines des notes écrites par ses utilisateurs, sont abandonnés ».

Source : Evernote renonce à autoriser ses employés à lire vos notes – Tech – Numerama

16 décembre 2016 / noflux

Suleyman also argued that the kind of General AI we see in movies today probably won’t look anything like the general AI systems we will get decades from now. “When it comes to imagining what the future will be like, a lot of that is fun and entertaining, but it doesn’t bear a great deal of resemblance to the systems that we are building,” he said. “I can’t really think of a film that makes me think: yeah – AI looks like that.”

Source : DeepMind’s Mustafa Suleyman says general AI is still a long way off | TechCrunch

Microsoft Translator erodes language barrier for in-person conversations

16 décembre 2016 / noflux

“Instead of the word being a thing by itself, it is represented by a 500-dimensional vector, or basically a 500 set of numbers, and each of those numbers capture some aspect of the word,” Menezes explained. To create a translation, neural networks model the meaning of each word within the context of the entire sentence in a 1,000-dimensional vector, whether the sentence is five or 20 words long, before translation begins. This 1,000-dimension model – not the words – is translated into the other language.

Source : Microsoft Translator erodes language barrier for in-person conversations – Next at Microsoft

14 décembre 2016 / noflux

Machine Learning (ML) is a fast-moving, competitive field. As important as good algorithms are to ML, the state-of-the-art algorithms are reliably available. Compute clusters are also an important ingredient, and they too are easy to access (especially using services like Amazon EC2 and Amazon EC2 Elastic GPUs). What isn’t reliably available are large-scale high quality data sets. The things you need to train your classifiers. That’s where MTurk comes into play.

Source : re:Invent 2016 recap — Machine Learning with Amazon Mechanical Turk

Étiquette : machine learning (Page 7 of 10)

Microsoft Translator erodes language barrier for in-person conversations

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