{"id":5143,"date":"2023-09-07T11:38:29","date_gmt":"2023-09-07T11:38:29","guid":{"rendered":"https:\/\/dailyai.com\/?p=5143"},"modified":"2023-09-07T11:38:29","modified_gmt":"2023-09-07T11:38:29","slug":"microsofts-algorithm-of-thoughts-will-make-ai-even-smarter","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/09\/microsofts-algorithm-of-thoughts-will-make-ai-even-smarter\/","title":{"rendered":"L'algorithme de pens\u00e9e de Microsoft rendra l'IA encore plus intelligente"},"content":{"rendered":"<p><strong>Les chercheurs de Microsoft ont mis au point un algorithme de pens\u00e9e (AoT) qui constitue un changement de paradigme dans la mani\u00e8re dont l'IA r\u00e9sout les probl\u00e8mes.<\/strong><\/p>\n<p>L'AoT a \u00e9t\u00e9 d\u00e9velopp\u00e9 pour que les LLM pensent davantage comme des humains et deviennent plus efficaces dans la r\u00e9solution de probl\u00e8mes. Microsoft affirme que son <a href=\"https:\/\/arxiv.org\/pdf\/2308.10379.pdf\">nouvelle approche<\/a> combine les \"nuances du raisonnement humain et la pr\u00e9cision disciplin\u00e9e des m\u00e9thodologies algorithmiques\".<\/p>\n<p>Le processus actuel de la cha\u00eene de pens\u00e9e utilis\u00e9 par les LLM tels que ChatGPT s'appuie sur des mod\u00e8les statistiques pour passer de l'invite \u00e0 la sortie. Il s'agit d'une progression tr\u00e8s lin\u00e9aire du probl\u00e8me \u00e0 la solution, car le LLM divise la solution en \u00e9tapes plus petites.<\/p>\n<p>Le probl\u00e8me de cette approche est que les donn\u00e9es d'apprentissage ne sont pas toujours suffisantes et qu'il manque parfois certaines \u00e9tapes. Dans ce cas, le LLM fait preuve de cr\u00e9ativit\u00e9 et <a href=\"https:\/\/dailyai.com\/fr\/2023\/07\/is-chatgpt-getting-worse-heres-everything-we-know-so-far\/\">hallucine<\/a> de combler les lacunes par une r\u00e9ponse incorrecte.<\/p>\n<p>Une technique plus avanc\u00e9e utilis\u00e9e par certains LLM consiste \u00e0 trouver une solution \u00e0 l'aide de l'approche de l'arbre de pens\u00e9e. Le LLM suit plusieurs chemins lin\u00e9aires du probl\u00e8me \u00e0 la solution et s'arr\u00eate lorsqu'il tombe sur une solution non viable.<\/p>\n<p>Mais cela implique un grand nombre de requ\u00eates et est tr\u00e8s gourmand en m\u00e9moire et en ressources informatiques.<\/p>\n<h2>Qu'est-ce qui rend AoT meilleur ?<\/h2>\n<p>Avec l'AoT, l'algorithme \u00e9value les premi\u00e8res \u00e9tapes d'une solution potentielle et d\u00e9cide d'embl\u00e9e si une approche vaut la peine d'\u00eatre poursuivie ou non. Cela lui \u00e9vite de s'ent\u00eater dans une voie manifestement erron\u00e9e et de devoir ensuite inventer quelque chose.<\/p>\n<p>En outre, au lieu d'une approche lin\u00e9aire, l'AoT donne au m\u00e9canisme d'apprentissage tout au long de la vie la possibilit\u00e9 de rechercher de multiples solutions potentielles et m\u00eame de revenir en arri\u00e8re si n\u00e9cessaire. Au lieu de recommencer depuis le d\u00e9but lorsqu'il se trouve dans une impasse, il peut revenir \u00e0 l'\u00e9tape pr\u00e9c\u00e9dente et continuer \u00e0 explorer.<\/p>\n<p>L'approche actuelle des LLM revient \u00e0 conduire de son domicile \u00e0 sa destination, \u00e0 se perdre, puis \u00e0 retourner chez soi pour essayer un autre itin\u00e9raire. L'approche de l'AoT consiste \u00e0 revenir sur ses pas jusqu'\u00e0 la derni\u00e8re erreur de parcours et \u00e0 essayer un nouvel itin\u00e9raire \u00e0 partir de l\u00e0.<\/p>\n<p>Cette approche \"d'apprentissage en contexte\" permet au mod\u00e8le d'\u00eatre beaucoup plus structur\u00e9 et syst\u00e9matique dans la mani\u00e8re dont il r\u00e9sout les probl\u00e8mes. Elle est beaucoup moins gourmande en ressources et pourrait potentiellement \u00e9radiquer le probl\u00e8me d'hallucination des LLM.<\/p>\n<p>M\u00eame avec cette nouvelle approche, l'IA est encore loin de penser et de raisonner comme les humains. Avec l'AoT, il semble qu'un pas important ait \u00e9t\u00e9 fait dans cette direction.<\/p>\n<p>L'une des conclusions que les chercheurs ont tir\u00e9es de leurs exp\u00e9riences est que leurs \"r\u00e9sultats sugg\u00e8rent que l'instruction d'un LLM \u00e0 l'aide d'un algorithme peut conduire \u00e0 des performances sup\u00e9rieures \u00e0 celles de l'algorithme lui-m\u00eame\".<\/p>\n<p>C'est exactement ce que fait notre cerveau. Nous avons la capacit\u00e9 inh\u00e9rente d'apprendre de nouvelles comp\u00e9tences que nous ne connaissions pas auparavant. Imaginez qu'un outil comme ChatGPT soit capable d'apprendre par le raisonnement sans avoir besoin d'une formation suppl\u00e9mentaire.<\/p>\n<p>Cette nouvelle approche pourrait \u00e9galement permettre \u00e0 l'IA d'\u00eatre plus transparente dans son processus de \"r\u00e9flexion\", en nous donnant un aper\u00e7u de ce qui se passe r\u00e9ellement derri\u00e8re le code.<\/p>","protected":false},"excerpt":{"rendered":"<p>Les chercheurs de Microsoft ont mis au point un algorithme de pens\u00e9e (AoT) qui constitue un changement de paradigme dans la mani\u00e8re dont l'IA r\u00e9sout les probl\u00e8mes. L'AoT a \u00e9t\u00e9 d\u00e9velopp\u00e9 pour que les LLM pensent davantage comme les humains et deviennent plus efficaces dans la r\u00e9solution de probl\u00e8mes. Microsoft affirme que sa nouvelle approche combine les \"nuances du raisonnement humain et la pr\u00e9cision disciplin\u00e9e des m\u00e9thodologies algorithmiques\". Le processus actuel de la cha\u00eene de pens\u00e9e utilis\u00e9 par les LLM comme ChatGPT s'appuie sur des mod\u00e8les statistiques pour passer de l'invite \u00e0 la sortie. Il s'agit d'une progression tr\u00e8s lin\u00e9aire du probl\u00e8me \u00e0 la solution, car le LLM divise la solution en petites \u00e9tapes. Le probl\u00e8me avec<\/p>","protected":false},"author":6,"featured_media":2181,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[207,105,121],"class_list":["post-5143","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llm","tag-machine-learning","tag-microsoft"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Microsoft\u2019s Algorithm of Thoughts will make AI even smarter | 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\/2023\/09\/microsofts-algorithm-of-thoughts-will-make-ai-even-smarter\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microsoft\u2019s Algorithm of Thoughts will make AI even smarter | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Microsoft researchers have developed an Algorithm of Thoughts (AoT) which is a paradigm shift in how AI solves problems. The AoT was developed to make LLMs think more like humans do and to become more efficient at problem-solving. Microsoft claims that its new approach combines the &#8220;nuances of human reasoning and the disciplined precision of algorithmic methodologies.&#8221; The current Chain of Thought process that LLMs like ChatGPT use, relies on statistical patterns to go from prompt to output. It\u2019s a very linear progression from problem to solution as the LLM breaks the solution up into smaller steps. 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