{"id":3464,"date":"2023-08-01T16:34:30","date_gmt":"2023-08-01T16:34:30","guid":{"rendered":"https:\/\/dailyai.com\/?p=3464"},"modified":"2023-08-02T15:13:13","modified_gmt":"2023-08-02T15:13:13","slug":"ai-matches-college-undergrads-solving-reasoning-problems","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"L'IA fait correspondre des \u00e9tudiants de premier cycle \u00e0 la r\u00e9solution de probl\u00e8mes de raisonnement"},"content":{"rendered":"

Une \u00e9tude men\u00e9e par des psychologues de l'UCLA a montr\u00e9 que le GPT-3 est \u00e0 peu pr\u00e8s aussi performant que les \u00e9tudiants du premier cycle universitaire pour r\u00e9soudre des probl\u00e8mes de raisonnement.<\/strong><\/p>\n

Nous savons que les LLM comme le GPT-3 sont capables de g\u00e9n\u00e9rer des r\u00e9ponses bas\u00e9es sur les donn\u00e9es sur lesquelles ils ont \u00e9t\u00e9 form\u00e9s, mais leur capacit\u00e9 \u00e0 raisonner est discutable. Le raisonnement analogique est la capacit\u00e9 qu'ont les humains de prendre ce qu'ils apprennent d'une exp\u00e9rience sans rapport avec la leur et de l'appliquer \u00e0 un probl\u00e8me auquel ils n'ont pas \u00e9t\u00e9 confront\u00e9s auparavant.<\/span><\/p>\n

C'est sur cette capacit\u00e9 que vous vous appuyez lorsque vous devez r\u00e9pondre \u00e0 une question que vous n'avez jamais vue auparavant. Vous pouvez raisonner en vous basant sur les probl\u00e8mes que vous avez d\u00e9j\u00e0 r\u00e9solus. D'apr\u00e8s les recherches, il semble que le GPT-3 ait \u00e9galement d\u00e9velopp\u00e9 cette capacit\u00e9.<\/span><\/p>\n

Les <\/span>Chercheurs de l'UCLA<\/span><\/a> Le GPT-3 a \u00e9t\u00e9 mis au travail sur une s\u00e9rie de probl\u00e8mes similaires \u00e0 ceux de la <\/span>Matrices progressives de Raven<\/span><\/a> qui consiste \u00e0 pr\u00e9dire l'image suivante d'une s\u00e9rie d'images. En voici un facile \u00e0 essayer.<\/span><\/p>\n

\"Exemple
Exemple de matrice progressive Raven - Source : Wikipedia (en anglais)<\/a><\/figcaption><\/figure>\n

GPT-3 s'est montr\u00e9 \u00e0 la hauteur des 40 \u00e9tudiants de l'UCLA \u00e0 qui l'on a demand\u00e9 de passer le m\u00eame test. L'IA a trouv\u00e9 les bonnes r\u00e9ponses dans 80% des cas, alors que les 40 \u00e9tudiants ont obtenu une moyenne de 60%. Les meilleurs \u00e9tudiants ont obtenu \u00e0 peu pr\u00e8s les m\u00eames r\u00e9sultats que GPT-3.<\/span><\/p>\n

Hongjing Lu, professeur de psychologie \u00e0 l'UCLA et auteur principal de l'\u00e9tude, a d\u00e9clar\u00e9 : \"Il est surprenant de constater que non seulement le GPT-3 s'est comport\u00e9 \u00e0 peu pr\u00e8s aussi bien que les humains, mais qu'il a \u00e9galement commis des erreurs similaires\".<\/span><\/p>\n

Les chercheurs ont \u00e9galement demand\u00e9 au GPT-3 de r\u00e9soudre des probl\u00e8mes d'association de mots. Par exemple, \"La voiture est \u00e0 la route ce que le bateau est \u00e0 quel mot ? La r\u00e9ponse est \u00e9videmment \"eau\", mais ce genre de questions peut \u00eatre difficile \u00e0 r\u00e9soudre pour une IA.<\/span><\/p>\n

Du moins, les chercheurs pensaient-ils que cela pouvait \u00eatre d\u00e9licat. Il s'av\u00e8re que le GPT-3 a obtenu de meilleurs r\u00e9sultats que la moyenne des candidats \u00e0 l'universit\u00e9 lors de leur SAT.<\/span><\/p>\n

Le GPT-3 s'attaque \u00e0 des probl\u00e8mes qui sont faciles \u00e0 r\u00e9soudre pour les humains.<\/h2>\n

Les mod\u00e8les d'IA ont du mal \u00e0 r\u00e9soudre les probl\u00e8mes n\u00e9cessitant une compr\u00e9hension visuelle de l'espace physique. Si vous pr\u00e9sentez \u00e0 GPT-3 une liste d'outils tels qu'un marteau, un clou et un tableau, il n'est pas en mesure de trouver la solution \u00e9vidente pour accrocher le tableau au mur.<\/span><\/p>\n

Ce type de probl\u00e8me est facile \u00e0 r\u00e9soudre pour les humains car nous pouvons voir, tenir et sentir des objets physiques dans un espace que nous occupons. Ces exp\u00e9riences permettent \u00e0 notre cerveau d'apprendre et de r\u00e9soudre facilement les probl\u00e8mes, ce que les mod\u00e8les d'IA ne peuvent pas faire. Cela dit, le GPT-4 s'am\u00e9liore aujourd'hui dans ce type de raisonnement.<\/span><\/p>\n

Si les chercheurs ont pu mesurer les performances du GPT-3, ils n'ont aucune id\u00e9e du processus de \"r\u00e9flexion\" qu'il suit pour obtenir les r\u00e9ponses. Suit-il un processus de pens\u00e9e similaire \u00e0 celui des humains ou fait-il quelque chose de compl\u00e8tement diff\u00e9rent ? Le GPT-3 \u00e9tant un mod\u00e8le ferm\u00e9, il n'est pas possible de voir ce qui se passe sous le capot.\u00a0<\/span><\/p>\n

Le r\u00e9sultat surprenant de cette recherche est que le GPT-3 semble \u00eatre capable de r\u00e9soudre des probl\u00e8mes nouveaux sans aucun entra\u00eenement direct. Cette capacit\u00e9 est \u00e9troitement li\u00e9e \u00e0 la mani\u00e8re dont les humains r\u00e9solvent les nouveaux probl\u00e8mes. On s'attend \u00e0 ce que le GPT-4 soit encore plus performant pour ces probl\u00e8mes et qui sait quelles autres capacit\u00e9s de \"r\u00e9flexion\" pourraient \u00e9merger \u00e0 l'issue de tests plus pouss\u00e9s.<\/span><\/p>\n

Alors que le raisonnement spatial est un d\u00e9fi pour les LLM, ces d\u00e9fis pourraient \u00eatre r\u00e9solus par des mod\u00e8les visuels tels que RT-2 de Google<\/a> qui a \u00e9t\u00e9 annonc\u00e9 r\u00e9cemment. Lorsque les mod\u00e8les d'IA pourront commencer \u00e0 \"voir\" et \u00e0 interagir physiquement avec leur environnement, leurs capacit\u00e9s de r\u00e9solution de probl\u00e8mes s'am\u00e9lioreront de mani\u00e8re exponentielle.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Une \u00e9tude men\u00e9e par des psychologues de l'UCLA a montr\u00e9 que le GPT-3 est \u00e0 peu pr\u00e8s aussi performant que les \u00e9tudiants de premier cycle universitaire pour r\u00e9soudre des probl\u00e8mes de raisonnement. Nous savons que les LLM comme le GPT-3 sont capables de g\u00e9n\u00e9rer des r\u00e9ponses bas\u00e9es sur les donn\u00e9es sur lesquelles ils ont \u00e9t\u00e9 entra\u00een\u00e9s, mais leur capacit\u00e9 \u00e0 raisonner est discutable. Le raisonnement analogique est la capacit\u00e9 qu'ont les humains de prendre ce qu'ils apprennent d'une exp\u00e9rience sans rapport avec la leur et de l'appliquer \u00e0 un probl\u00e8me auquel ils n'ont jamais \u00e9t\u00e9 confront\u00e9s auparavant. C'est sur cette capacit\u00e9 que vous vous appuyez lorsque vous devez r\u00e9pondre \u00e0 une question que vous n'avez jamais vue auparavant. Vous pouvez raisonner en vous basant sur<\/p>","protected":false},"author":6,"featured_media":3467,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[266,207,105,93],"class_list":["post-3464","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-gpt","tag-llm","tag-machine-learning","tag-openai"],"yoast_head":"\nAI matches college undergrads solving reasoning problems | 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\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI matches college undergrads solving reasoning problems | DailyAI\" \/>\n<meta property=\"og:description\" content=\"A study by psychologists at UCLA has shown that GPT-3 is about as good as college undergraduate students at solving reasoning problems. We know that LLMs like GPT-3 are good at generating responses based on the data they\u2019ve been trained on, but their ability to reason is questionable. Analogical reasoning is the ability humans have to take what we learn from an unrelated experience and apply it to a problem we haven\u2019t been faced with before. That ability is what you rely on when you have to answer a question you\u2019ve never seen before. 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