{"id":10786,"date":"2024-03-18T09:35:31","date_gmt":"2024-03-18T09:35:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=10786"},"modified":"2024-03-28T09:35:17","modified_gmt":"2024-03-28T09:35:17","slug":"apple-reveals-mm1-its-first-family-of-multimodal-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/","title":{"rendered":"Apple d\u00e9voile MM1, sa premi\u00e8re famille de LLM multimodaux"},"content":{"rendered":"

Apple n'a pas encore officiellement lanc\u00e9 de mod\u00e8le d'IA, mais un nouveau document de recherche donne un aper\u00e7u des progr\u00e8s r\u00e9alis\u00e9s par l'entreprise dans le d\u00e9veloppement de mod\u00e8les dot\u00e9s de capacit\u00e9s multimodales de pointe.<\/strong><\/p>\n

Le document<\/a>intitul\u00e9 \"MM1 : Methods, Analysis & Insights from Multimodal LLM Pre-training\", pr\u00e9sente la famille de MLLM d'Apple appel\u00e9e MM1.<\/p>\n

MM1 affiche des capacit\u00e9s impressionnantes en mati\u00e8re de sous-titrage d'images, de r\u00e9ponse \u00e0 des questions visuelles (VQA) et d'inf\u00e9rence en langage naturel. Les chercheurs expliquent que le choix judicieux des paires d'images et de l\u00e9gendes leur a permis d'obtenir des r\u00e9sultats sup\u00e9rieurs, en particulier dans les sc\u00e9narios d'apprentissage en quelques images.<\/p>\n

Ce qui distingue MM1 des autres MLLM, c'est sa capacit\u00e9 sup\u00e9rieure \u00e0 suivre des instructions sur plusieurs images et \u00e0 raisonner sur les sc\u00e8nes complexes qui lui sont pr\u00e9sent\u00e9es.<\/p>\n

Les mod\u00e8les MM1 contiennent jusqu'\u00e0 30B param\u00e8tres, soit trois fois plus que le GPT-4V, le composant qui donne au GPT-4 de l'OpenAI ses capacit\u00e9s de vision.<\/p>\n

Voici quelques exemples des capacit\u00e9s du MM1 en mati\u00e8re de VQA.<\/p>\n

\"\"
Test de la capacit\u00e9 de MM1 \u00e0 raisonner sur des images et des textes. Source : arXiv<\/figcaption><\/figure>\n

MM1 a fait l'objet d'un pr\u00e9apprentissage multimodal \u00e0 grande \u00e9chelle sur \"un ensemble de donn\u00e9es de 500 millions de documents image-texte entrelac\u00e9s, contenant 1 milliard d'images et 500 milliards de jetons de texte\".<\/p>\n

L'ampleur et la diversit\u00e9 de son pr\u00e9apprentissage permettent \u00e0 MM1 d'effectuer d'impressionnantes pr\u00e9dictions en contexte et de suivre un formatage personnalis\u00e9 \u00e0 partir d'un petit nombre d'exemples. Voici des exemples de la fa\u00e7on dont MM1 apprend le r\u00e9sultat et le format souhait\u00e9s \u00e0 partir de seulement 3 exemples.<\/p>\n

\"\"
MM1 peut compter des objets, effectuer une reconnaissance optique de caract\u00e8res sur des zones sp\u00e9cifiques d'une image, appliquer un raisonnement de bon sens \u00e0 des objets et effectuer des fonctions math\u00e9matiques de base. Source : arXiv<\/figcaption><\/figure>\n

La cr\u00e9ation de mod\u00e8les d'IA capables de \"voir\" et de raisonner n\u00e9cessite un connecteur vision-langage qui traduit les images et le langage en une repr\u00e9sentation unifi\u00e9e que le mod\u00e8le peut utiliser pour un traitement ult\u00e9rieur.<\/p>\n

Les chercheurs ont constat\u00e9 que la conception du connecteur vision-langage n'\u00e9tait pas un facteur d\u00e9terminant pour les performances de MM1. Il est int\u00e9ressant de noter que c'est la r\u00e9solution de l'image et le nombre de jetons d'image qui ont eu le plus d'impact.<\/p>\n

Il est int\u00e9ressant de constater l'ouverture d'Apple \u00e0 partager ses recherches avec l'ensemble de la communaut\u00e9 de l'IA. Les chercheurs d\u00e9clarent que \"dans cet article, nous documentons le processus de construction du MLLM et tentons de formuler des le\u00e7ons de conception qui, nous l'esp\u00e9rons, seront utiles \u00e0 la communaut\u00e9\".<\/p>\n

Les r\u00e9sultats publi\u00e9s permettront probablement d'orienter les autres d\u00e9veloppeurs de MMLM en ce qui concerne l'architecture et le choix des donn\u00e9es de pr\u00e9-entra\u00eenement.<\/p>\n

Reste \u00e0 savoir comment les mod\u00e8les MM1 seront mis en \u0153uvre dans les produits Apple. Les exemples publi\u00e9s des capacit\u00e9s de MM1 laissent penser que Siri deviendra beaucoup plus intelligent lorsqu'il apprendra \u00e0 voir.<\/p>","protected":false},"excerpt":{"rendered":"

Apple n'a pas encore officiellement pr\u00e9sent\u00e9 de mod\u00e8le d'IA, mais un nouveau document de recherche donne un aper\u00e7u des progr\u00e8s r\u00e9alis\u00e9s par l'entreprise dans le d\u00e9veloppement de mod\u00e8les dot\u00e9s de capacit\u00e9s multimodales de pointe. L'article, intitul\u00e9 \"MM1 : Methods, Analysis & Insights from Multimodal LLM Pre-training\", pr\u00e9sente la famille de MLLM d'Apple appel\u00e9e MM1. MM1 pr\u00e9sente des capacit\u00e9s impressionnantes en mati\u00e8re de sous-titrage d'images, de r\u00e9ponse \u00e0 des questions visuelles (VQA) et d'inf\u00e9rence en langage naturel. Les chercheurs expliquent qu'un choix judicieux des paires d'images et de l\u00e9gendes leur a permis d'obtenir des r\u00e9sultats sup\u00e9rieurs, en particulier dans les sc\u00e9narios d'apprentissage en peu d'images. Ce qui distingue MM1 des autres MLLM, c'est sa capacit\u00e9 sup\u00e9rieure \u00e0 suivre des instructions \u00e0 travers plusieurs images et<\/p>","protected":false},"author":6,"featured_media":10790,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[126,166],"class_list":["post-10786","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-apple","tag-computer-vision"],"yoast_head":"\nApple reveals MM1, its first family of multimodal LLMs | 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\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apple reveals MM1, its first family of multimodal LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Apple is yet to officially release an AI model, but a new research paper gives an insight into the company\u2019s progress in developing models with state-of-the-art multimodal capabilities. The paper, titled \u201cMM1: Methods, Analysis & Insights from Multimodal LLM Pre-training\u201d, introduces Apple\u2019s family of MLLMs called MM1. MM1 displays impressive abilities in image captioning, visual question answering (VQA), and natural language inference. The researchers explain that careful choices of image-caption pairs enabled them to achieve superior results, especially in few-shot learning scenarios. What sets MM1 apart from other MLLMs is its superior ability to follow instructions across multiple images and\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/fr\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-18T09:35:31+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-28T09:35:17+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Apple-MM1.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" 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