{"id":5054,"date":"2021-04-30T14:57:54","date_gmt":"2021-04-30T13:57:54","guid":{"rendered":"https:\/\/wip.geomatys.com\/?p=5054"},"modified":"2025-02-28T17:20:23","modified_gmt":"2025-02-28T16:20:23","slug":"intelligence-artificielle-du-hasard-et-de-la-necessite","status":"publish","type":"post","link":"https:\/\/www.geomatys.com\/en\/2021\/04\/30\/intelligence-artificielle-du-hasard-et-de-la-necessite\/","title":{"rendered":"Intelligence Artificielle \u2013 du hasard et de la n\u00e9cessit\u00e9"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"5054\" class=\"elementor elementor-5054\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-6775bde1 elementor-section-full_width elementor-section-height-default elementor-section-height-default exad-glass-effect-no wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no exad-sticky-section-no\" data-eae-slider=\"22234\" data-id=\"6775bde1\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a09297a exad-glass-effect-no exad-sticky-section-no\" data-eae-slider=\"70334\" data-id=\"a09297a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7384ca1a exad-sticky-section-no exad-glass-effect-no elementor-widget elementor-widget-shortcode\" data-id=\"7384ca1a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">\t\t<div 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class=\"trp-ls-language-name\">FR<\/span><\/span><\/a><\/li>\n<\/ul><\/div><\/nav>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-5cd25c2 exad-glass-effect-no exad-sticky-section-no\" data-eae-slider=\"49504\" data-id=\"5cd25c2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\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>\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<div class=\"has_eae_slider elementor-element elementor-element-2858b1dd e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no e-con e-parent\" data-eae-slider=\"74607\" data-id=\"2858b1dd\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-789222c exad-sticky-section-no exad-glass-effect-no elementor-widget elementor-widget-image\" data-id=\"789222c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"413\" src=\"https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?fit=800%2C413&amp;ssl=1\" class=\"attachment-large size-large wp-image-5055\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?w=1008&amp;ssl=1 1008w, https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?resize=300%2C155&amp;ssl=1 300w, https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?resize=768%2C397&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1a7dfc6 exad-sticky-section-no exad-glass-effect-no elementor-widget elementor-widget-heading\" data-id=\"1a7dfc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Intelligence artificielle : Du hasard et de la n\u00e9c\u00e9ssit\u00e9<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c77ef71 exad-sticky-section-no exad-glass-effect-no elementor-widget elementor-widget-text-editor\" data-id=\"1c77ef71\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<figure class=\"wp-block-post-featured-image\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"413\" src=\"https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?resize=800%2C413&#038;ssl=1\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"\" style=\"object-fit:cover;\" srcset=\"https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?w=1008&amp;ssl=1 1008w, https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?resize=300%2C155&amp;ssl=1 300w, https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?resize=768%2C397&amp;ssl=1 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/figure>\n<p>\u00a0<\/p>\n\n<p><em><strong>\u201cVivre, c\u2019est transformer en conscience une exp\u00e9rience aussi large que possible\u201d<\/strong><\/em>disait Andr\u00e9 Malraux. Nos Intelligences Artificielles contemporaines, souvent fantasm\u00e9es pour leurs capacit\u00e9s, sont bien loin de ces consid\u00e9rations.\u00a0<\/p>\n\n<p>Il ne s\u2019agit pas ici de nier, les r\u00e9sultats spectaculaires obtenus depuis le tournant des ann\u00e9es 2010, dans le domaine des algorithmes d\u2019apprentissage ou Machine Learning, d\u00fb en partie, d\u2019une part \u00e0 la d\u00e9mocratisation des capacit\u00e9s de calcul n\u00e9cessaires \u00e0 ces algorithmes et d\u2019autres part au verrou de la dimensionnalit\u00e9 qu\u2019ont su, si ce n\u2019est r\u00e9soudre, au moins grandement d\u00e9gripper les r\u00e9seaux de neurones convolutifs (ou CNN). La lib\u00e9ration de ces verrous a trouv\u00e9 des applications pour tous et dans tous les domaines , qui plus est de mani\u00e8re si rapide, que les capacit\u00e9s nouvelles de ces outils, ainsi mis en lumi\u00e8re, peuvent se transformer pour certains en miroir aux alouettes. Qui n\u2019a pas entendu ou lu depuis quelques ann\u00e9es des r\u00e9cits proph\u00e9tiques o\u00f9 les robots bient\u00f4t r\u00eaveraient.<\/p>\n\n<p>A Geomatys, peut \u00eatre en partie car un de ses fondateurs poss\u00e9dait un retour d\u2019exp\u00e9rience notable sur ces outils manipul\u00e9s durant sa th\u00e8se au milieu des ann\u00e9es 2000, de leurs avantages et de leurs limites, nous avons dans un premier temps, regard\u00e9 ce bel objet qu\u2019\u00e9taient les r\u00e9seaux de neurones convolutifs comme un prolongement de nos activit\u00e9s plut\u00f4t que comme un axe d\u2019activit\u00e9 \u00e0 part enti\u00e8re.<\/p>\n\n<p>Ainsi l\u2019avons nous mis en en \u0153uvre tr\u00e8s t\u00f4t pour des besoins de classification d\u2019objet dans des d\u2019image satellites, o\u00f9 \u00e0 d\u2019autres fins mais sans pour autant en faire l\u2019alpha et l\u2019om\u00e9ga de nos activit\u00e9s futures. Il faut sans doute y voir ici, un hasard conjoncturel o\u00f9 la connaissance de l\u2019outil nous a emp\u00each\u00e9 d\u2019adh\u00e9rer \u00e0 la mythologie collective se mettant en place. Ainsi avons-nous continu\u00e9 \u00e0 consolider nos fondamentaux quant \u00e0 la ma\u00eetrise de la gestion de l\u2019information spatiale pour les grosses infrastructures de donn\u00e9es, cet outil \u00e9tant un parmi tant d\u2019autres.<\/p>\n\n<p>Or aujourd\u2019hui, \u00e0 la ville comme \u00e0 la campagne, force est de constater qu\u2019il y a les entreprises qui en sont et celles qui n\u2019en sont pas. N\u00e9cessit\u00e9 faisant loi, nous faisons donc ici notre coming out communicationnel et pr\u00e9sentons ci-apr\u00e8s nos activit\u00e9s dans le domaine pour affirmer que oui, nous en sommes!<\/p>\n\n<p>Aujourd\u2019hui l\u2019IMINT mobilise grandement les r\u00e9seaux de neurones convolutifs pour automatiser tr\u00e8s efficacement les t\u00e2ches de reconnaissances d\u2019objets dans une image, et avec force donn\u00e9es d\u2019apprentissage cela fonctionne tr\u00e8s bien eu \u00e9gard aux types de corr\u00e9lations spatiales qu\u2019un CNN est capable de capturer.\u00a0<br \/><br \/>De nombreuses soci\u00e9t\u00e9s se sont donc positionn\u00e9es sur ce segment d\u2019activit\u00e9. Ayant rat\u00e9 le premier train, nous avons donc d\u00e9cid\u00e9 de construire nous m\u00eame notre locomotive et de nous positionner sur ce que nous pensons constituer le gros du potentiel encore sous exploit\u00e9 du Machine Learning, et avons d\u00e9marr\u00e9 depuis un an trois projets distincts sur chacun des domaines.<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Axe 1, l\u2019IA a des fins de pr\u00e9diction : En effet les CNN utilis\u00e9s \u00e0 des fins de classification automatique produisent leurs r\u00e9sultats sur une observation \u00e0 un temps, lors de son analyse, il s\u2019agit d\u00e9j\u00e0 du pass\u00e9. Ils ne permettent pas nativement de combiner les informations d\u00e9tect\u00e9es pour pr\u00e9voir et corr\u00e9ler un futur comportement. Or, devant le flux d\u2019information susceptible d\u2019\u00eatre d\u00e9tect\u00e9, un humain va les hi\u00e9rarchiser et les mettre en perspective avant de prendre une d\u00e9cision. Les outils de machine learning, qui plus est, les architectures telles que l\u2019apprentissage par renforcement ou les r\u00e9seaux de neurones g\u00e9n\u00e9ratifs, plus complexes ont cette capacit\u00e9. Un projet en cours, visant \u00e0 la production automatis\u00e9e de cartographie nautique complexe \u00e0 partir d\u2019information h\u00e9t\u00e9rog\u00e8ne et contradictoire, l\u2019arbitrage \u00e9tant aujourd\u2019hui r\u00e9alis\u00e9 humainement nous a permis de confirmer le potentiel des architectures de r\u00e9seaux de neurones complexes, sujet que nous poursuivons en parall\u00e8le pour de la production de carte de probabilit\u00e9 de pr\u00e9sence de population animale en fonction des conditions environnementales.<\/li>\n\n<li>Axe 2, le Edge Computing : Dans certains contextes, le transit d\u2019une image depuis son lieu d\u2019acquisition vers son lieu de traitement n\u2019est pas envisageable. Il convient donc, afin de minimiser le co\u00fbt du message, de classifier sur place l\u2019image pour ne transmettre que l\u2019information d\u00e9duite. Nous travaillons actuellement avec des partenaires aux tests en conditions r\u00e9elles de ce genre de dispositifs.<\/li>\n\n<li>Axe 3 : La d\u00e9tection de signaux faibles implique d\u2019explorer des masses de donn\u00e9es cons\u00e9quentes, et l\u2019apprentissage sur ce type de signaux implique des masses de donn\u00e9es d\u2019autant plus cons\u00e9quentes. Or dans certains cas, la centralisation des donn\u00e9es en une infrastructure unique n\u2019est pas r\u00e9aliste soit pour des raisons de confidentialit\u00e9 de la donn\u00e9e ou parfois plus pragmatiquement encore pour des raisons de taille d\u2019entrep\u00f4t mobilis\u00e9. D\u00e9porter une partie du mod\u00e8le d\u2019apprentissage au plus pr\u00e8s de la donn\u00e9e et en f\u00e9d\u00e9rer les r\u00e9sultats est alors une solution que nous mettons actuellement en \u0153uvre dans le cadre d\u2019un projet couplant g\u00e9olocalisation et donn\u00e9es sant\u00e9.<\/li>\n<\/ul>\n\n<p>Coupl\u00e9 aux 15 ann\u00e9es d\u2019expertises de Geomatys dans le domaine de l\u2019interop\u00e9rabilit\u00e9, du traitement et des infrastructures massives de donn\u00e9es g\u00e9ospatiales, et de consolidations de cette expertise dans sa gamme logiciel Examind, nous oeuvrons d\u00e9sormais \u00e0 transformer nos exp\u00e9riences dans le domaine du machine learning en des fonctionnalit\u00e9s facilement re-mobilisables pour nos client. Ce n\u2019est pas Malraux mais ce n\u2019est pas mal non plus.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-4d2cac1d elementor-section-full_width elementor-section-height-default elementor-section-height-default exad-glass-effect-no wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no exad-sticky-section-no\" data-eae-slider=\"99007\" data-id=\"4d2cac1d\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-545c9320 exad-glass-effect-no exad-sticky-section-no\" data-eae-slider=\"90868\" data-id=\"545c9320\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6021b0d exad-sticky-section-no exad-glass-effect-no elementor-widget elementor-widget-shortcode\" data-id=\"6021b0d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5276\" class=\"elementor elementor-5276\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-267b5fb elementor-section-boxed elementor-section-height-default elementor-section-height-default exad-glass-effect-no wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-equal-height-no exad-sticky-section-no\" data-eae-slider=\"92549\" 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data-widget_type=\"navigation-menu.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"hfe-nav-menu hfe-layout-vertical hfe-nav-menu-layout vertical\" data-layout=\"vertical\">\n\t\t\t\t<div role=\"button\" class=\"hfe-nav-menu__toggle elementor-clickable\" tabindex=\"0\" aria-label=\"Menu Toggle\">\n\t\t\t\t\t<span class=\"screen-reader-text\">Menu<\/span>\n\t\t\t\t\t<div class=\"hfe-nav-menu-icon\">\n\t\t\t\t\t\t<i aria-hidden=\"true\"  class=\"fas fa-align-justify\"><\/i>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<nav class=\"hfe-nav-menu__layout-vertical hfe-nav-menu__submenu-arrow\" data-toggle-icon=\"&lt;i aria-hidden=&quot;true&quot; tabindex=&quot;0&quot; class=&quot;fas fa-align-justify&quot;&gt;&lt;\/i&gt;\" data-close-icon=\"&lt;i aria-hidden=&quot;true&quot; tabindex=&quot;0&quot; class=&quot;far fa-window-close&quot;&gt;&lt;\/i&gt;\" data-full-width=\"yes\">\n\t\t\t\t\t<ul id=\"menu-1-ff0bb56\" class=\"hfe-nav-menu\"><li 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class=\"elementor-screen-only\">Youtube<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-youtube\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\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>\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":"<p>Intelligence artificielle : Du hasard et de la n\u00e9c\u00e9ssit\u00e9 \u00a0 \u201cVivre, c\u2019est transformer en conscience une exp\u00e9rience aussi large que possible\u201ddisait Andr\u00e9 Malraux. Nos Intelligences Artificielles contemporaines, souvent fantasm\u00e9es pour leurs capacit\u00e9s, sont bien loin de ces consid\u00e9rations.\u00a0 Il ne s\u2019agit pas ici de nier, les r\u00e9sultats spectaculaires obtenus depuis le tournant des ann\u00e9es 2010, dans le domaine des algorithmes d\u2019apprentissage ou Machine Learning, d\u00fb en partie, d\u2019une part \u00e0 la d\u00e9mocratisation des capacit\u00e9s de calcul n\u00e9cessaires \u00e0 ces algorithmes et d\u2019autres part au verrou de la dimensionnalit\u00e9 qu\u2019ont su, si ce n\u2019est r\u00e9soudre, au moins grandement d\u00e9gripper les r\u00e9seaux de neurones convolutifs (ou CNN). La lib\u00e9ration de ces verrous a trouv\u00e9 des applications pour tous et dans tous les domaines , qui plus est de mani\u00e8re si rapide, que les capacit\u00e9s nouvelles de ces outils, ainsi mis en lumi\u00e8re, peuvent se transformer pour certains en miroir aux alouettes. Qui n\u2019a pas entendu ou lu depuis quelques ann\u00e9es des r\u00e9cits proph\u00e9tiques o\u00f9 les robots bient\u00f4t r\u00eaveraient. A Geomatys, peut \u00eatre en partie car un de ses fondateurs poss\u00e9dait un retour d\u2019exp\u00e9rience notable sur ces outils manipul\u00e9s durant sa th\u00e8se au milieu des ann\u00e9es 2000, de leurs avantages et de leurs limites, nous avons dans un premier temps, regard\u00e9 ce bel objet qu\u2019\u00e9taient les r\u00e9seaux de neurones convolutifs comme un prolongement de nos activit\u00e9s plut\u00f4t que comme un axe d\u2019activit\u00e9 \u00e0 part enti\u00e8re. Ainsi l\u2019avons nous mis en en \u0153uvre tr\u00e8s t\u00f4t pour des besoins de classification d\u2019objet dans des d\u2019image satellites, o\u00f9 \u00e0 d\u2019autres fins mais sans pour autant en faire l\u2019alpha et l\u2019om\u00e9ga de nos activit\u00e9s futures. Il faut sans doute y voir ici, un hasard conjoncturel o\u00f9 la connaissance de l\u2019outil nous a emp\u00each\u00e9 d\u2019adh\u00e9rer \u00e0 la mythologie collective se mettant en place. Ainsi avons-nous continu\u00e9 \u00e0 consolider nos fondamentaux quant \u00e0 la ma\u00eetrise de la gestion de l\u2019information spatiale pour les grosses infrastructures de donn\u00e9es, cet outil \u00e9tant un parmi tant d\u2019autres. Or aujourd\u2019hui, \u00e0 la ville comme \u00e0 la campagne, force est de constater qu\u2019il y a les entreprises qui en sont et celles qui n\u2019en sont pas. N\u00e9cessit\u00e9 faisant loi, nous faisons donc ici notre coming out communicationnel et pr\u00e9sentons ci-apr\u00e8s nos activit\u00e9s dans le domaine pour affirmer que oui, nous en sommes! Aujourd\u2019hui l\u2019IMINT mobilise grandement les r\u00e9seaux de neurones convolutifs pour automatiser tr\u00e8s efficacement les t\u00e2ches de reconnaissances d\u2019objets dans une image, et avec force donn\u00e9es d\u2019apprentissage cela fonctionne tr\u00e8s bien eu \u00e9gard aux types de corr\u00e9lations spatiales qu\u2019un CNN est capable de capturer.\u00a0 De nombreuses soci\u00e9t\u00e9s se sont donc positionn\u00e9es sur ce segment d\u2019activit\u00e9. Ayant rat\u00e9 le premier train, nous avons donc d\u00e9cid\u00e9 de construire nous m\u00eame notre locomotive et de nous positionner sur ce que nous pensons constituer le gros du potentiel encore sous exploit\u00e9 du Machine Learning, et avons d\u00e9marr\u00e9 depuis un an trois projets distincts sur chacun des domaines. Coupl\u00e9 aux 15 ann\u00e9es d\u2019expertises de Geomatys dans le domaine de l\u2019interop\u00e9rabilit\u00e9, du traitement et des infrastructures massives de donn\u00e9es g\u00e9ospatiales, et de consolidations de cette expertise dans sa gamme logiciel Examind, nous oeuvrons d\u00e9sormais \u00e0 transformer nos exp\u00e9riences dans le domaine du machine learning en des fonctionnalit\u00e9s facilement re-mobilisables pour nos client. Ce n\u2019est pas Malraux mais ce n\u2019est pas mal non plus.<\/p>","protected":false},"author":1,"featured_media":5055,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_umich_oidc_access":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5054","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actus"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.geomatys.com\/wp-content\/uploads\/2023\/08\/AI-image.jpeg?fit=1008%2C521&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/posts\/5054","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/comments?post=5054"}],"version-history":[{"count":3,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/posts\/5054\/revisions"}],"predecessor-version":[{"id":9187,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/posts\/5054\/revisions\/9187"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/media\/5055"}],"wp:attachment":[{"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/media?parent=5054"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/categories?post=5054"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.geomatys.com\/en\/wp-json\/wp\/v2\/tags?post=5054"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}