{"id":48901,"date":"2021-07-21T10:00:00","date_gmt":"2021-07-21T14:00:00","guid":{"rendered":"https:\/\/cifar661.wpengine.com\/cifarnews\/2021\/07\/19\/irina-rish-wants-to-solve-one-of-ais-biggest-challenges\/"},"modified":"2021-07-26T14:20:42","modified_gmt":"2021-07-26T18:20:42","slug":"irina-rish-veut-resoudre-lune-des-plus-grandes-difficultes-de-lia","status":"publish","type":"post","link":"https:\/\/cifar.ca\/fr\/cifarnews\/2021\/07\/21\/irina-rish-veut-resoudre-lune-des-plus-grandes-difficultes-de-lia\/","title":{"rendered":"Irina Rish veut r\u00e9soudre l\u2019une des plus grandes difficult\u00e9s de l\u2019IA"},"content":{"rendered":"

Au cours des derni\u00e8res d\u00e9cennies, des avanc\u00e9es extraordinaires ont \u00e9t\u00e9 r\u00e9alis\u00e9es en ce qui a trait au mode d\u2019apprentissage des syst\u00e8mes d\u2019intelligence artificielle (IA). L\u2019une de leurs limites est toutefois leur capacit\u00e9 \u00e0 acqu\u00e9rir de nouvelles connaissances tout en pr\u00e9servant les informations pertinentes existantes, comme le font les humains qui apprennent tout au long de leur vie. Irina Rish, titulaire d\u2019une chaire en IA Canada-CIFAR, est une pionni\u00e8re dans ce domaine \u00e9mergent connu sous le nom d\u2019apprentissage continu.<\/span><\/p>\n

Les syst\u00e8mes d\u2019IA sont toujours plus performants et plus rapides que les humains lorsqu\u2019ils effectuent des t\u00e2ches comme l\u2019identification de mod\u00e8les et la pr\u00e9diction de r\u00e9sultats. Cependant, ces syst\u00e8mes manquent souvent de polyvalence et ne r\u00e9ussissent pas \u00e0 appliquer les comp\u00e9tences acquises \u00e0 de nouvelles situations. Irina Rish se sp\u00e9cialise dans l\u2019utilisation des propri\u00e9t\u00e9s les plus robustes et invariantes de diff\u00e9rentes r\u00e9partitions de donn\u00e9es pour permettre aux syst\u00e8mes d\u2019apprendre continuellement au fil du temps, sans entra\u00eenement suppl\u00e9mentaire.\u00a0<\/span><\/p>\n

\u00ab\u00a0Les syst\u00e8mes peuvent s\u2019adapter \u00e0 de nouvelles donn\u00e9es, mais lorsqu\u2019ils sont confront\u00e9s \u00e0 de nouveaux ensembles de donn\u00e9es et \u00e0 de nouvelles t\u00e2ches, ils exp\u00e9rimentent souvent un oubli catastrophique, un ph\u00e9nom\u00e8ne o\u00f9 les nouvelles informations effacent les anciennes, explique Irina Rish. \u00c0 l\u2019inverse, l\u2019oubli chez les humains et les animaux semble se produire de fa\u00e7on plus graduelle et moins catastrophique.\u00a0\u00bb<\/span><\/p>\n

Irina Rish est membre du corps professoral de Mila, l\u2019Institut qu\u00e9b\u00e9cois d\u2019intelligence artificielle, et professeure agr\u00e9g\u00e9e au d\u00e9partement d\u2019informatique et de recherche op\u00e9rationnelle de l\u2019Universit\u00e9 de Montr\u00e9al. En septembre 2020, elle a d\u00e9croch\u00e9 une Chaire d\u2019excellence en recherche du Canada en IA autonome dot\u00e9e d\u2019un budget de 34 millions de dollars. Auparavant, elle \u00e9tait chercheuse chez IBM \u00e0 Yorktown Heights, dans l\u2019\u00c9tat de New York.<\/span><\/p>\n

Selon elle, l\u2019entra\u00eenement des syst\u00e8mes d\u2019IA bas\u00e9s sur les r\u00e9seaux neuronaux implique un compromis entre \u00ab\u00a0stabilit\u00e9 et plasticit\u00e9\u00a0\u00bb, par exemple entre l\u2019adaptation et la m\u00e9morisation. Parfois, un syst\u00e8me retient des informations, mais n\u2019est pas capable de s\u2019adapter rapidement (trop de stabilit\u00e9 et pas assez de plasticit\u00e9)\u00a0tandis qu\u2019une bonne adaptation peut \u00eatre malheureusement associ\u00e9e \u00e0 un oubli rapide. L\u2019objectif ultime de l\u2019apprentissage continu est de parvenir \u00e0 une adaptation rapide tout en \u00e9vitant le probl\u00e8me de l\u2019oubli catastrophique.<\/span><\/p>\n

Les syst\u00e8mes d\u2019apprentissage continu sont prometteurs pour un large \u00e9ventail d\u2019applications, notamment en mati\u00e8re d\u2019IA au service la sant\u00e9, un domaine d\u2019int\u00e9r\u00eat pour Irina Rish. Elle con\u00e7oit des mod\u00e8les qui utilisent des donn\u00e9es d\u2019imagerie c\u00e9r\u00e9brale pour identifier diff\u00e9rents probl\u00e8mes de sant\u00e9 mentale, dont des troubles tels que la d\u00e9pendance et la schizophr\u00e9nie. L’apprentissage continu permettrait d\u2019\u00e9viter d\u2019avoir \u00e0 apprendre \u00e0 partir de z\u00e9ro avec de nouvelles donn\u00e9es en transf\u00e9rant les connaissances acquises aupr\u00e8s de diff\u00e9rents patients et h\u00f4pitaux, et d\u2019en extraire des sch\u00e9mas d’activit\u00e9 c\u00e9r\u00e9brale robustes associ\u00e9s \u00e0 des probl\u00e8mes de sant\u00e9 mentale qui sont invariants dans diff\u00e9rents ensembles de donn\u00e9es.\u00a0<\/span><\/p>\n

L\u2019apprentissage continu peut \u00e9galement \u00eatre utile pour les v\u00e9hicules autonomes, qui fonctionnent g\u00e9n\u00e9ralement tr\u00e8s bien dans un environnement contr\u00f4l\u00e9, mais qui ont du mal \u00e0 s\u2019adapter \u00e0 des environnements et \u00e0 des sc\u00e9narios qu\u2019ils ne connaissent pas.<\/span><\/p>\n

\u00ab\u00a0Nous visons \u00e0 d\u00e9velopper des algorithmes robustes capables d\u2019apprendre par eux-m\u00eames en r\u00e9utilisant les connaissances pass\u00e9es tout en s\u2019adaptant \u00e0 de nouvelles t\u00e2ches. En m\u00eame temps, l\u2019algorithme doit \u00eatre suffisamment intelligent pour d\u00e9tecter des situations o\u00f9 il n\u2019y a rien de similaire ou d\u2019invariant entre les anciennes et les nouvelles donn\u00e9es et pour d\u00e9velopper le mod\u00e8le au besoin, explique Irina Rish. Nos recherches permettront de combler le foss\u00e9 entre les capacit\u00e9s de g\u00e9n\u00e9ralisation des humains et des syst\u00e8mes d’IA.\u00a0\u00bb<\/span><\/p>\n

Irina Rish est charg\u00e9e de cours \u00e0 l\u2019\u00c9cole d\u2019\u00e9t\u00e9 sur l\u2019apprentissage profond et l\u2019apprentissage par renforcement du CIFAR<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Ses recherches permettront aux machines d\u2019apprendre de nouvelles connaissances plus efficacement.<\/p>\n","protected":false},"author":65,"featured_media":49016,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1771,4391,1842,3972],"tags":[5533,824,5113,4403,5114],"class_list":["post-48901","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nouvelles","category-percees","category-profil","category-strategie-pancanadienne-en-matiere-dia","tag-artificial-intelligence-fr","tag-intelligence-artificielle","tag-irina-rish-fr","tag-mila-fr","tag-university-of-montreal-fr"],"acf":[],"yoast_head":"\nCIFAR<\/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:\/\/cifar.ca\/fr\/cifarnews\/2021\/07\/21\/irina-rish-veut-resoudre-lune-des-plus-grandes-difficultes-de-lia\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Irina Rish veut r\u00e9soudre l\u2019une des plus grandes difficult\u00e9s de l\u2019IA - 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