{"id":169,"date":"2023-10-12T12:00:44","date_gmt":"2023-10-12T12:00:44","guid":{"rendered":"https:\/\/www.techopedia.com\/fr\/"},"modified":"2024-09-03T08:29:09","modified_gmt":"2024-09-03T08:29:09","slug":"how-liquid-neural-networks-can-shrink-the-world-of-ai","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/fr\/reseaux-neuronaux-liquides-reduire-ressources-ai","title":{"rendered":"Comment les r\u00e9seaux neuronaux liquides peuvent r\u00e9duire le monde de l’IA"},"content":{"rendered":"

Les r\u00e9seaux neuronaux liquides comptent parmi les composants \u00e9mergents les plus importants et les plus uniques du paysage de l’intelligence artificielle (IA).<\/p>\n

Lorsqu’une machine ou un robot doit r\u00e9agir \u00e0 un stimulus ou \u00e0 des donn\u00e9es externes, il peut \u00eatre extr\u00eamement gourmand en ressources. Ce qui provoque un goulot d’\u00e9tranglement si l’on essaie de faire tenir de l’intelligence dans un espace tr\u00e8s restreint.<\/p>\n

VentureBeat d\u00e9crit comment un r\u00e9seau neuronal classique pourrait avoir besoin de 100 000 neurones artificiels pour maintenir la voiture stable dans une t\u00e2che telle que la conduite d’un v\u00e9hicule sur une route.<\/p>\n

Cependant, l’\u00e9quipe du CSAIL du MIT, qui d\u00e9veloppe des r\u00e9seaux neuronaux liquides, est parvenue \u00e0 r\u00e9aliser la m\u00eame t\u00e2che avec seulement 19 neurones.<\/p>\n

L’inspiration derri\u00e8re les r\u00e9seaux de neurones liquides<\/p>\n

Les r\u00e9seaux neuronaux liquides sont un type d’architecture d’apprentissage en profondeur mis au point pour r\u00e9soudre le probl\u00e8me des robots effectuant des t\u00e2ches et des apprentissages complexes, dans le but de contourner le probl\u00e8me de la d\u00e9pendance \u00e0 l’\u00e9gard du cloud ou du stockage interne limit\u00e9.<\/p>\n

Daniela Rus, directrice du MIT CSAIL, a d\u00e9clar\u00e9 \u00e0 VentureBeat : “L’inspiration pour les r\u00e9seaux neuronaux liquides a \u00e9t\u00e9 de r\u00e9fl\u00e9chir aux approches existantes de l’apprentissage automatique et de voir comment elles s’adaptent au type de syst\u00e8mes critiques en mati\u00e8re de s\u00e9curit\u00e9 qu’offrent les robots et les appareils de pointe.<\/p>\n

“Sur un robot, il n’est pas possible d’ex\u00e9cuter un mod\u00e8le linguistique de grande taille, car il n’y a pas vraiment la [puissance] de calcul et l’espace [de stockage] n\u00e9cessaires.<\/p><\/blockquote>\n

L’\u00e9quipe de chercheurs a trouv\u00e9 une solution \u00e0 leur probl\u00e8me dans la recherche sur les neurones biologiques pr\u00e9sents dans de minuscules organismes.<\/p>\n

Qu’est-ce qu’un r\u00e9seau de neurones liquides ?<\/span><\/h2>\n

Les r\u00e9seaux de neurones liquides sont comparables aux cellules interconnect\u00e9es d’un cerveau humain qui s’unissent pour traiter des informations et produire des r\u00e9sultats.<\/p>\n

Le cerveau humain est un agencement de cellules tr\u00e8s complexe qui effectue des calculs extr\u00eamement complexes.<\/p>\n

Les r\u00e9seaux de neurones liquides se concentrent sur les applications critiques pour la s\u00e9curit\u00e9, telles que les v\u00e9hicules et les robots autopilot\u00e9s, qui ont besoin d’un flux continu de donn\u00e9es.<\/p>\n

Selon Daniela Rus, “en g\u00e9n\u00e9ral, les r\u00e9seaux liquides donnent de bons r\u00e9sultats lorsque nous disposons de s\u00e9ries de donn\u00e9es temporelles… il faut une s\u00e9quence pour que les r\u00e9seaux liquides fonctionnent bien”.<\/p>\n

“Cependant, si vous essayez d’appliquer la solution du r\u00e9seau liquide \u00e0 une base de donn\u00e9es statique comme ImageNet, cela ne fonctionnera pas tr\u00e8s bien.”<\/p><\/blockquote>\n

Avantages et limites<\/span><\/h2>\n

L’\u00e9quipe de recherche du Computer Science and Artificial Intelligence Laboratory du MIT (CSAIL) a constat\u00e9 les avantages suivants sur la base de son exp\u00e9rience.<\/p>\n

    \n
  • Compacit\u00e9<\/strong><\/li>\n<\/ul>\n

    Les r\u00e9seaux neuronaux liquides peuvent fonctionner avec un nombre de neurones nettement inf\u00e9rieur \u00e0 celui des r\u00e9seaux neuronaux classiques.<\/p>\n

    Comme indiqu\u00e9 plus haut, un r\u00e9seau neuronal classique \u00e0 apprentissage profond aurait besoin de 100 000 neurones pour maintenir une voiture autopilot\u00e9e dans sa voie, alors qu’un r\u00e9seau neuronal liquide n’a besoin que de 19 neurones.<\/p>\n

      \n
    • Causalit\u00e9<\/strong><\/li>\n<\/ul>\n

      Les r\u00e9seaux neuronaux liquides g\u00e8rent mieux la causalit\u00e9 que les r\u00e9seaux neuronaux classiques \u00e0 apprentissage profond. Ils peuvent rep\u00e9rer une relation claire entre la cause et les effets, ce que les r\u00e9seaux neuronaux classiques d’apprentissage profond ont du mal \u00e0 faire.<\/p>\n

      Par exemple, les r\u00e9seaux neuronaux classiques \u00e0 apprentissage profond peuvent identifier de mani\u00e8re coh\u00e9rente les relations de cause \u00e0 effet entre les \u00e9v\u00e9nements dans divers contextes, et ce plus efficacement que les r\u00e9seaux neuronaux classiques.<\/p>\n

        \n
      • Interpr\u00e9tabilit\u00e9<\/strong><\/li>\n<\/ul>\n

        Comprendre l’interpr\u00e9tation des donn\u00e9es par un syst\u00e8me d’IA est l’un des plus grands d\u00e9fis de l’IA.<\/p>\n

        Les mod\u00e8les d’apprentissage profond classiques pr\u00e9sentent souvent des bases d’interpr\u00e9tation des donn\u00e9es peu profondes, peu claires ou erron\u00e9es, alors que les r\u00e9seaux neuronaux liquides peuvent expliquer leur base d’interpr\u00e9tation des donn\u00e9es.<\/p>\n

          \n
        • Cependant…<\/strong><\/li>\n<\/ul>\n

          Les r\u00e9seaux neuronaux liquides ne constituent pas une solution compl\u00e8te pour tout.<\/p>\n

          S’ils g\u00e8rent bien les flux de donn\u00e9es continus tels que les flux audio, les donn\u00e9es de temp\u00e9rature ou les flux vid\u00e9o, ils ont du mal avec les donn\u00e9es statiques ou fixes, qui conviennent mieux \u00e0 d’autres mod\u00e8les d’IA.<\/p>\n

          La ligne de fond<\/span><\/h2>\n

          Dans le paysage de l’IA, les r\u00e9seaux neuronaux liquides font partie des mod\u00e8les \u00e9mergents les plus critiques.<\/p>\n

          Ils coexistent avec les r\u00e9seaux neuronaux classiques \u00e0 apprentissage profond, mais semblent mieux adapt\u00e9s \u00e0 des t\u00e2ches extr\u00eamement complexes telles que les v\u00e9hicules autonomes, la lecture de la temp\u00e9rature ou du climat, ou les \u00e9valuations boursi\u00e8res, alors que les r\u00e9seaux neuronaux classiques \u00e0 apprentissage profond sont plus performants avec les donn\u00e9es statiques ou ponctuelles.<\/p>\n

          Les chercheurs du Computer Science and Artificial Intelligence Laboratory du MIT (CSAIL) ont essay\u00e9 d’\u00e9tendre les capacit\u00e9s des r\u00e9seaux neuronaux liquides \u00e0 d’autres cas d’utilisation, mais cela prendra du temps.<\/p>\n

          Les r\u00e9seaux neuronaux liquides et les r\u00e9seaux neuronaux classiques d’apprentissage profond ont tous deux leur r\u00f4le d\u00e9fini dans le cadre plus large de l’IA, et il s’agit certainement d’un cas o\u00f9 deux mod\u00e8les valent mieux qu’un.<\/p>\n","protected":false},"excerpt":{"rendered":"

          Les r\u00e9seaux neuronaux liquides comptent parmi les composants \u00e9mergents les plus importants et les plus uniques du paysage de l’intelligence artificielle (IA). Lorsqu’une machine ou un robot doit r\u00e9agir \u00e0 un stimulus ou \u00e0 des donn\u00e9es externes, il peut \u00eatre extr\u00eamement gourmand en ressources. Ce qui provoque un goulot d’\u00e9tranglement si l’on essaie de faire […]<\/p>\n","protected":false},"author":7870,"featured_media":59,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[],"class_list":["post-169","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-intelligence-artificielle"],"acf":[],"yoast_head":"\nComment les r\u00e9seaux neuronaux liquides peuvent r\u00e9duire le monde de l'IA - Techopedia Fran\u00e7ais<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comment les r\u00e9seaux neuronaux liquides peuvent r\u00e9duire le monde de l'IA\" \/>\n<meta property=\"og:description\" content=\"Les r\u00e9seaux neuronaux liquides comptent parmi les composants \u00e9mergents les plus importants et les plus uniques du paysage de l’intelligence artificielle (IA). 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