{"id":13552,"date":"2024-07-23T13:12:56","date_gmt":"2024-07-23T13:12:56","guid":{"rendered":"https:\/\/dailyai.com\/?p=13552"},"modified":"2024-07-23T13:19:34","modified_gmt":"2024-07-23T13:19:34","slug":"googles-ai-predicts-weather-using-fraction-of-computing-power","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2024\/07\/googles-ai-predicts-weather-using-fraction-of-computing-power\/","title":{"rendered":"L'IA de Google pr\u00e9dit la m\u00e9t\u00e9o en utilisant une fraction de la puissance de calcul"},"content":{"rendered":"<p><strong>Google a d\u00e9voil\u00e9 NeuralGCM, un mod\u00e8le hybride de pr\u00e9vision m\u00e9t\u00e9orologique qui combine l'apprentissage automatique et les techniques de pr\u00e9vision traditionnelles et qui pr\u00e9sente des avantages surprenants.<\/strong><\/p>\n<p>La pr\u00e9cision des pr\u00e9visions m\u00e9t\u00e9orologiques s'est consid\u00e9rablement am\u00e9lior\u00e9e, mais les techniques traditionnelles n\u00e9cessitent de vastes ressources informatiques pour faire fonctionner des algorithmes de plus en plus complexes.<\/p>\n<p>Les mod\u00e8les de circulation g\u00e9n\u00e9rale (MCG) sont \u00e0 la base des pr\u00e9visions climatiques et m\u00e9t\u00e9orologiques qui vous permettent de savoir si vous aurez besoin d'un parapluie demain.<\/p>\n<p>Les MCG sont des simulateurs bas\u00e9s sur la physique qui utilisent des \u00e9quations math\u00e9matiques fond\u00e9es sur les lois de la physique pour simuler la fa\u00e7on dont l'air, l'eau et l'\u00e9nergie se d\u00e9placent sur la plan\u00e8te.<\/p>\n<p>Les MCG typiques divisent la surface de la Terre en une grille de cellules allant jusqu'\u00e0 100 kilom\u00e8tres, comme un \u00e9chiquier g\u00e9ant. L'algorithme traite chaque case selon une approche progressive afin de pr\u00e9dire comment les conditions atmosph\u00e9riques sont susceptibles de changer.<\/p>\n<p>Les \u00e9quations qui sous-tendent les MCG sont incroyablement complexes et occupent certains des plus grands superordinateurs du monde.<\/p>\n<p>Les mod\u00e8les d'apprentissage automatique pour les pr\u00e9visions m\u00e9t\u00e9orologiques ont montr\u00e9 un potentiel important, mais ils sont essentiellement bas\u00e9s sur des donn\u00e9es.<\/p>\n<p>Un mod\u00e8le de pr\u00e9vision m\u00e9t\u00e9orologique ML a une bonne id\u00e9e des donn\u00e9es m\u00e9t\u00e9orologiques historiques, mais ne dispose pas de la compr\u00e9hension inh\u00e9rente des lois physiques r\u00e9gissant l'atmosph\u00e8re qui sont mod\u00e9lis\u00e9es dans un MCG.<\/p>\n<p>Les mod\u00e8les ML sont rapides et peuvent fournir des pr\u00e9visions pr\u00e9cises \u00e0 court terme, mais ils ont souvent du mal \u00e0 assurer la stabilit\u00e9 \u00e0 long terme et la raret\u00e9 des \u00e9v\u00e9nements m\u00e9t\u00e9orologiques extr\u00eames ou des sc\u00e9narios climatiques futurs.<\/p>\n<p>NeuralGCM, d\u00e9velopp\u00e9 par une \u00e9quipe de Google Research, combine la pr\u00e9cision et les capacit\u00e9s de pr\u00e9vision \u00e0 long terme des MCG traditionnels avec la r\u00e9solution am\u00e9lior\u00e9e, l'efficacit\u00e9 et la vitesse des mod\u00e8les ML.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">NeuralGCM est disponible gratuitement et nous sommes impatients de voir comment les scientifiques l'utiliseront.<\/p>\n<p>Pour plus de d\u00e9tails, voir mon article de blog d\u00e9crivant le travail, et notre code source ouvert :<a href=\"https:\/\/t.co\/AfqLagoYDs\">https:\/\/t.co\/AfqLagoYDs<\/a><a href=\"https:\/\/t.co\/MSlFg6pgfj\">https:\/\/t.co\/MSlFg6pgfj<\/a><\/p>\n<p>- Stephan Hoyer (@shoyer) <a href=\"https:\/\/twitter.com\/shoyer\/status\/1815453663424639354?ref_src=twsrc%5Etfw\">22 juillet 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>Le document indique que la pr\u00e9cision de NeuralGCM est comparable ou sup\u00e9rieure \u00e0 celle des mod\u00e8les GCM les plus r\u00e9cents. Il indique que NeuralGCM est \"le premier mod\u00e8le bas\u00e9 sur l'apprentissage automatique \u00e0 \u00e9tablir des pr\u00e9visions m\u00e9t\u00e9orologiques d'ensemble pr\u00e9cises, avec un meilleur CRPS que les mod\u00e8les bas\u00e9s sur la physique les plus r\u00e9cents\".<\/p>\n<p>Le CRPS est un score qui compare les pr\u00e9visions m\u00e9t\u00e9orologiques avec les conditions m\u00e9t\u00e9orologiques r\u00e9elles.<\/p>\n<p>Les chercheurs affirment que \"NeuralGCM est comp\u00e9titif par rapport aux mod\u00e8les d'apprentissage automatique pour les pr\u00e9visions \u00e0 un ou dix jours, et par rapport aux pr\u00e9visions d'ensemble du Centre europ\u00e9en pour les pr\u00e9visions m\u00e9t\u00e9orologiques \u00e0 moyen terme pour les pr\u00e9visions \u00e0 un ou quinze jours\".<\/p>\n<p>Si le NeuralGCM permet d'obtenir des r\u00e9sultats de pr\u00e9vision comparables \u00e0 ceux des MCG, il est beaucoup moins gourmand en ressources informatiques et beaucoup moins complexe.<\/p>\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41586-024-07744-y\" target=\"_blank\" rel=\"noopener\">Le document<\/a> ne pr\u00e9cise pas la taille de NeuralGCM mais propose le mod\u00e8le de pr\u00e9vision m\u00e9t\u00e9orologique ML de Google <a href=\"https:\/\/dailyai.com\/fr\/2023\/11\/googles-graphcast-model-predicts-weather-better-than-the-rest\/\">GraphCast<\/a> \u00e0 titre de comparaison.<\/p>\n<p>GraphCast comprend environ 5 417 lignes, tandis que le mod\u00e8le atmosph\u00e9rique FV3 de la National Oceanic and Atmospheric Administration (NOAA) compte environ 376 578 lignes de code.<\/p>\n<p>Selon les chercheurs, NeuralGCM permet d'\u00e9conomiser \"3 \u00e0 5 ordres de grandeur en ressources informatiques\".<\/p>\n<p>Pour situer le contexte, le document explique que \"NeuralGCM-1.4\u00b0 simule 70 000 jours de simulation en 24 heures \u00e0 l'aide d'une seule unit\u00e9 de traitement tensoriel, contre 19 jours simul\u00e9s sur 13 824 c\u0153urs d'unit\u00e9 de traitement central avec X-SHiELD\", qui est un mod\u00e8le de pr\u00e9vision m\u00e9t\u00e9orologique \u00e0 haute r\u00e9solution.<\/p>\n<p>Les chercheurs affirment que leurs r\u00e9sultats montrent que leur mod\u00e8le poss\u00e8de d'impressionnantes capacit\u00e9s de mod\u00e9lisation du climat. L'article note que \"les mod\u00e8les NeuralGCM form\u00e9s sur des pr\u00e9visions \u00e0 72 heures sont capables de simulations pluriannuelles r\u00e9alistes\".<\/p>\n<p>La combinaison de l'apprentissage automatique avec les mod\u00e8les physiques traditionnels, comme l'a fait Google avec les pr\u00e9visions m\u00e9t\u00e9orologiques, \"a le potentiel de transformer la simulation pour un large \u00e9ventail d'applications, telles que la d\u00e9couverte de mat\u00e9riaux, le repliement de prot\u00e9ines et la conception d'ing\u00e9nierie multiphysique\".<\/p>\n<p>L'IA, tr\u00e8s gourmande en ressources, a fait des centres de donn\u00e9es une cible de choix. <a href=\"https:\/\/dailyai.com\/fr\/2024\/02\/ai-energy-usage-and-carbon-emission-stats-may-be-overblown\/\">critiques concernant leurs \u00e9missions de carbone et leur impact potentiel sur le climat.<\/a><\/p>\n<p>Le NeuralGCM est un bon exemple de la mani\u00e8re dont l'IA pourrait avoir un impact positif sur l'environnement en rempla\u00e7ant, ou en augmentant, les processus traditionnels inefficaces afin de r\u00e9duire la consommation d'\u00e9nergie informatique.<\/p>","protected":false},"excerpt":{"rendered":"<p>Google a d\u00e9voil\u00e9 NeuralGCM, un mod\u00e8le hybride de pr\u00e9vision m\u00e9t\u00e9orologique qui combine l'apprentissage automatique et les techniques de pr\u00e9vision traditionnelles et qui pr\u00e9sente des avantages surprenants. La pr\u00e9cision des pr\u00e9visions m\u00e9t\u00e9orologiques s'est consid\u00e9rablement am\u00e9lior\u00e9e, mais les techniques traditionnelles n\u00e9cessitent de vastes ressources informatiques pour faire fonctionner des algorithmes de plus en plus complexes. Les mod\u00e8les de circulation g\u00e9n\u00e9rale (MCG) sont \u00e0 la base des pr\u00e9visions climatiques et m\u00e9t\u00e9orologiques qui vous permettent de savoir si vous aurez besoin d'un parapluie demain. Les MCG sont des simulateurs bas\u00e9s sur la physique qui utilisent des \u00e9quations math\u00e9matiques fond\u00e9es sur les lois de la physique pour simuler la fa\u00e7on dont l'air, l'eau et l'\u00e9nergie se d\u00e9placent sur la plan\u00e8te. Les MCG typiques divisent la surface de la Terre en une grille de cellules allant de 0,5 \u00e0 1,5 m\u00e8tre.<\/p>","protected":false},"author":6,"featured_media":13555,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[102,105],"class_list":["post-13552","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-google","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google\u2019s AI predicts weather using fraction of computing power | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/fr\/2024\/07\/googles-ai-predicts-weather-using-fraction-of-computing-power\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s AI predicts weather using fraction of computing power | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google unveiled NeuralGCM, a hybrid weather prediction model that combines machine learning with traditional forecasting techniques and has surprising benefits. 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