{"id":12743,"date":"2023-06-29T09:30:19","date_gmt":"2023-06-29T07:30:19","guid":{"rendered":"https:\/\/www.toulouse-biotechnology-institute.fr\/?p=12743"},"modified":"2023-06-29T09:33:25","modified_gmt":"2023-06-29T07:33:25","slug":"deep-learning-du-sudoku-au-design-de-proteines","status":"publish","type":"post","link":"https:\/\/www.toulouse-biotechnology-institute.fr\/en\/deep-learning-du-sudoku-au-design-de-proteines\/","title":{"rendered":"Deep learning: from Sudoku to protein design"},"content":{"rendered":"
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Scientists from the Applied Mathematics and Informatics research unit (MIAT) and Toulouse Biotechnology Institute (TBI) at the INRAE Occitanie-Toulouse center have developed a hybrid artificial intelligence (AI) learning method for solving difficult puzzles and designing new proteins.<\/strong><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Published on 17 July 2023<\/em><\/p>\n\n

Artificial intelligence (AI) language models impress with their ability to respond in a coherent, structured way. But when it comes to reasoning and logic, they are still limited. For example, when ChatGPT is asked to solve a grid of the famous Sudoku puzzle, the solutions it comes up with do not respect the rules, and sometimes even modify the original numbers in the grid.<\/p>\n\n

Cette difficult\u00e9 de raisonnement logique n’est pas sp\u00e9cifique aux mod\u00e8les de langage mais est commune \u00e0 la plupart des r\u00e9seaux de neurones. Ils sont tr\u00e8s efficaces pour extraire des informations \u00e0 partir de donn\u00e9es, mais beaucoup moins pour raisonner \u00e0 partir de celles-ci. Mais combin\u00e9e \u00e0 des m\u00e9thodes de raisonnement automatique, une IA g\u00e9n\u00e9rale devrait \u00eatre capable \u00e0 la fois d’apprendre et de raisonner, comme le cerveau humain.<\/p>\n\n

Learning to play Sudoku<\/h2>\n\n

Pour le raisonnement automatique, la r\u00e9solution de grilles de Sudoku, m\u00eame tr\u00e8s difficiles, est une t\u00e2che triviale lorsque les r\u00e8gles sont donn\u00e9es. Mais lorsqu\u2019il faut au pr\u00e9alable les apprendre, il s’agit d’un processus en deux \u00e9tapes. D’abord, l’IA doit d\u00e9duire les r\u00e8gles en observant des exemples de puzzles termin\u00e9s, puis elle doit utiliser ces r\u00e8gles pour remplir de nouveaux puzzles. Les scientifiques des laboratoires Math\u00e9matiques et informatique appliqu\u00e9es Toulouse (MIAT) et Toulouse biotechnology institute (TBI) ont donc combin\u00e9 deux types d\u2019IA pour apprendre \u00e0 jouer au Sudoku : un r\u00e9seau de neurones pour apprendre les r\u00e8gles \u00e0 partir d’exemples, et un \u00ab prouveur \u00bb pour appliquer ces r\u00e8gles \u00e0 la r\u00e9solution de nouvelles \u00e9nigmes.<\/p>\n\n

Leur m\u00e9thode hybride apprend vite (en 15 minutes !) et est minimaliste : elle ne n\u00e9cessite que 200 exemples de grilles compl\u00e8tes, soit une fraction de ce qu’exigent les autres approches. De plus, elle est transparente : la d\u00e9cision prise par le prouveur peut \u00eatre comprise en analysant les r\u00e8gles qui ont \u00e9t\u00e9 apprises. Cette \u00ab\u00a0fen\u00eatre\u00a0\u00bb sur le processus en permet un meilleur contr\u00f4le et une meilleure compr\u00e9hension.<\/p>\n\n

From Sudoku to protein design<\/h2>\n\n

Proteins are macro-molecules essential to any kind of life as they fulfill many biological and biochemical functions in all living organisms. Designing new proteins has applications in health and green chemistry, among others.<\/p>\n\n

From the proof-of-principle obtained on Sudoku grid solving, scientists have exploited its analogy with protein design. Seen as a \"puzzle\" in which the aim is to place atoms in space, learning protein models leads to learning its \"rules of the game\", which can then be used by automatic reasoning tools to design new proteins.<\/p>\n\n

Ces recherches combinent la reconnaissance intuitive de l’apprentissage profond avec la logique m\u00e9ticuleuse du raisonnement automatis\u00e9. En plus de sa rapidit\u00e9 et sa transparence, le v\u00e9ritable atout de la m\u00e9thode d\u00e9velopp\u00e9e est sa capacit\u00e9 \u00e0 mod\u00e9liser des probl\u00e8mes vari\u00e9s, allant de la r\u00e9solution de Sudoku \u00e0 des probl\u00e9matiques r\u00e9elles comme le design de nouvelles prot\u00e9ines.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\tArticle AIhub Bridging the gap between learning and reasoning\n\nM. Defresne, S. Barbe and T. Schiex. Scalable Coupling of Deep Learning with Logical Reasoning. Proceedings of the Thirty-second International International Joint Conference on Artificial Intelligence, IJCAI\u20192023. https:\/\/arxiv.org\/abs\/2305.07617 \t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\tSophie Barbe, Directrice de recherche INRAE
\nMarianne Defresne Doctorante MIAT \/ TBI\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Des scientifiques des laboratoires Math\u00e9matiques et informatique appliqu\u00e9es Toulouse (MIAT) et Toulouse biotechnology institute (TBI) du centre INRAE Occitanie-Toulouse ont d\u00e9velopp\u00e9 une m\u00e9thode hybride d\u2019apprentissage par intelligence artificielle (IA) pour r\u00e9soudre des casse-t\u00eates difficiles et ainsi concevoir de nouvelles prot\u00e9ines. A lire sur le site INRAE Occitanie Publi\u00e9 le 27 juin 2023 Les mod\u00e8les de […]<\/p>\n","protected":false},"author":1,"featured_media":12744,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[35],"class_list":["post-12743","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actus","tag-equipe-cimes"],"yoast_head":"\nDeep learning : du Sudoku au design de prot\u00e9ines | TBI, Toulouse Biotechnology Institute<\/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:\/\/www.toulouse-biotechnology-institute.fr\/en\/deep-learning-du-sudoku-au-design-de-proteines\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning : du Sudoku au design de prot\u00e9ines | TBI, Toulouse Biotechnology Institute\" \/>\n<meta property=\"og:description\" content=\"Des scientifiques des laboratoires Math\u00e9matiques et informatique appliqu\u00e9es Toulouse (MIAT) et Toulouse biotechnology institute (TBI) du centre INRAE Occitanie-Toulouse ont d\u00e9velopp\u00e9 une m\u00e9thode hybride d\u2019apprentissage par intelligence artificielle (IA) pour r\u00e9soudre des casse-t\u00eates difficiles et ainsi concevoir de nouvelles prot\u00e9ines. 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