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La police s'est adjoint les services d'un huissier pour compter en même temps qu'elle les manifestants qui défilaient jeudi à Albi contre la réforme des retraites et pour prendre en défaut ceux qui l'accusent de tricher sur les chiffres, a-t-on appris de source policière.

Résultat de l'opération : 4 380 manifestants selon l'huissier, 4 200 selon la police, 20 000 selon les syndicats.

Les cimetières et lieux de culte catholiques demeurent les plus visés.

Croix renversées et souillées de symboles nazis au sanctuaire dédié à la Vierge Marie à Saint-Loup (Jura), tombes et chapelle vandalisée au cimetière d'Hénin-Beaumont (Pas-de-Calais), statues arrachées dans l'église Saint-Géry de Valenciennes (Nord), lustres réduits en miettes et chemin de croix incendié à Saint-Pierre de Pouan-les-Vallées (Aube), classé et datant du XIIIe siècle… Dans l'indifférence, la France est le théâtre d'une profanation tous les deux jours. Selon une note de la direction générale de la gendarmerie nationale, que s'est procurée Le Figaro, pas moins de 184 dégradations de sépultures ont été recensées l'année dernière, soit à peine dix-neuf de moins qu'en 2008.

Kerviel condamné à 3 ans ferme et à rembourser 4,9 milliards

Urd a écrit :

Spoiler:

Quoique c'est peut-être possible de rembourser 5 milliards d'euros.

Since the start of the year, a team of researchers at Carnegie Mellon University — supported by grants from the Defense Advanced Research Projects Agency and Google, and tapping into a research supercomputing cluster provided by Yahoo — has been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computer — calculating 24 hours a day, seven days a week — that resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself.

“For all the advances in computer science, we still don’t have a computer that can learn as humans do, cumulatively, over the long term,” said the team’s leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department.

The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categories — cities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like “San Francisco is a city” and “sunflower is a plant.”

NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player (category). The Indianapolis Colts is a football team (category). By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Colts — even if it has never read that Mr. Manning plays for the Colts. “Plays for” is a relation, and there are 280 kinds of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily expand.

The learned facts are continuously added to NELL’s growing database, which the researchers call a “knowledge base.” A larger pool of facts, Dr. Mitchell says, will help refine NELL’s learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.

[...]

A helping hand from humans, occasionally, will be part of the answer. For the first six months, NELL ran unassisted. But the research team noticed that while it did well with most categories and relations, its accuracy on about one-fourth of them trailed well behind. Starting in June, the researchers began scanning each category and relation for about five minutes every two weeks. When they find blatant errors, they label and correct them, putting NELL’s learning engine back on track.

When Dr. Mitchell scanned the “baked goods” category recently, he noticed a clear pattern. NELL was at first quite accurate, easily identifying all kinds of pies, breads, cakes and cookies as baked goods. But things went awry after NELL’s noun-phrase classifier decided “Internet cookies” was a baked good. (Its database related to baked goods or the Internet apparently lacked the knowledge to correct the mistake.)

NELL had read the sentence “I deleted my Internet cookies.” So when it read “I deleted my files,” it decided “files” was probably a baked good, too. “It started this whole avalanche of mistakes,” Dr. Mitchell said. He corrected the Internet cookies error and restarted NELL’s bakery education.

His ideal, Dr. Mitchell said, was a computer system that could learn continuously with no need for human assistance. “We’re not there yet,” he said. “But you and I don’t learn in isolation either.”

Efreet a écrit :

Urd a écrit :

Spoiler:

Quoique c'est peut-être possible de rembourser 5 milliards d'euros.

J'ai lu je sais plus où qu'avec son salaire actuel il lui faudrait 170 000 ans pour rembourser tout ça, ou gagner 40 fois à l'euro millions... C'est tellement n'importe quoi que j'en rigole.

Résumé : Molex a décidé de couper les vivres de son ancienne usine près de Toulouse. Le groupe américain ne veut plus financer le plan social. Il n'a pas supporté que les salariés contestent leur licenciement devant les prud'hommes.

Me_Marcadet a écrit :Par contre, je comprends pas qu'on puisse se rejouir du malheur des autres