{"id":31281,"date":"2021-02-23T10:32:56","date_gmt":"2021-02-23T09:32:56","guid":{"rendered":"https:\/\/www.atim.com\/intelligence-artificielle\/"},"modified":"2023-01-03T16:19:31","modified_gmt":"2023-01-03T15:19:31","slug":"artificial-intelligence","status":"publish","type":"page","link":"https:\/\/www.atim.com\/en\/artificial-intelligence\/","title":{"rendered":"Intelligence Artificielle EN"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"31281\" class=\"elementor elementor-31281 elementor-15184\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8f59980 elementor-section-full_width elementor-section-stretched elementor-section-height-default elementor-section-height-default wd-section-disabled\" data-id=\"8f59980\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fb8cf02\" data-id=\"fb8cf02\" 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-c90b097 elementor--h-position-center elementor--v-position-middle elementor-arrows-position-inside elementor-pagination-position-inside elementor-widget elementor-widget-slides\" data-id=\"c90b097\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;navigation&quot;:&quot;both&quot;,&quot;autoplay&quot;:&quot;yes&quot;,&quot;pause_on_hover&quot;:&quot;yes&quot;,&quot;pause_on_interaction&quot;:&quot;yes&quot;,&quot;autoplay_speed&quot;:5000,&quot;infinite&quot;:&quot;yes&quot;,&quot;transition&quot;:&quot;slide&quot;,&quot;transition_speed&quot;:500}\" data-widget_type=\"slides.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-swiper\">\n\t\t\t\t\t<div class=\"elementor-slides-wrapper elementor-main-swiper swiper\" role=\"region\" aria-roledescription=\"carousel\" aria-label=\"Slides\" dir=\"ltr\" data-animation=\"fadeInUp\">\n\t\t\t\t<div class=\"swiper-wrapper elementor-slides\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-repeater-item-7290cea swiper-slide\" role=\"group\" aria-roledescription=\"slide\"><div class=\"swiper-slide-bg elementor-ken-burns elementor-ken-burns--in\" role=\"img\" aria-label=\"ai_artifitial_intelligence_atim_sensors_iot_lpwan_acw\"><\/div><div class=\"elementor-background-overlay\"><\/div><div class=\"swiper-slide-inner\" ><div class=\"swiper-slide-contents\"><div class=\"elementor-slide-heading\">Artificial intelligence<\/div><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-57b9d7d elementor-section-boxed elementor-section-height-default elementor-section-height-default wd-section-disabled\" data-id=\"57b9d7d\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-a9d7309\" data-id=\"a9d7309\" 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-a4238ab elementor-widget elementor-widget-image\" data-id=\"a4238ab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"387\" height=\"216\" src=\"https:\/\/www.atim.com\/wp-content\/uploads\/2021\/08\/ai_logo_technologies_atim-3.png\" class=\"attachment-full size-full wp-image-29282\" alt=\"\" srcset=\"https:\/\/www.atim.com\/wp-content\/uploads\/2021\/08\/ai_logo_technologies_atim-3.png 387w, https:\/\/www.atim.com\/wp-content\/uploads\/2021\/08\/ai_logo_technologies_atim-3-150x84.png 150w, https:\/\/www.atim.com\/wp-content\/uploads\/2021\/08\/ai_logo_technologies_atim-3-300x167.png 300w\" sizes=\"(max-width: 387px) 100vw, 387px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f0b08e color-scheme-inherit text-left elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"7f0b08e\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;,&quot;_animation_delay&quot;:1000}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>There are different types of maintenance including <strong>Machine Learning<\/strong> in predictive maintenance.<\/p><h3><strong><span style=\"color: #fe753e;\">Corrective<\/span> Maintenance<\/strong><\/h3><p>The machine is in operation until a need for maintenance arises (breakdown, breakage&#8230;). The machine is repaired following the breakdown.<\/p><h3><strong><span style=\"color: #fe753e;\">Preventive<\/span> Maintenance<\/strong><\/h3><p>The machine is in operation for a defined period of time (in time, number of cycles). This duration is planned so that the machine does not have time to break down. At the end of this time, the machine is repaired, regardless of its state of wear or its operating cycle.<\/p><h3><strong><span style=\"color: #fe753e;\">Predictive<\/span> Maintenance<\/strong><\/h3><p>The machine is monitored during its operation, and maintenance is planned based on the data collected. Today, this last type of maintenance is the most promising for Factory 4.0. Indeed, it allows to have the lowest possible maintenance costs, since the machines are used at their maximum endurance, but the unexpected and untimely stops are reduced to the strict minimum.<\/p><p>The <strong>Pr\u00e9dictive maintenance<\/strong> appeared long before there were any real IT solutions adapted to this need. Today, many industries still use the manual method of sending a technician to go around the machines to collect <strong>KPI<\/strong> <strong>to detect anomalies<\/strong>.<\/p><p>The appearance of machines and <strong>algorithms <\/strong>allowing to <strong>automate<\/strong> these tasks prompts the following question: what technique should be used to <strong>pr\u00e9dire<\/strong> the state of a <strong>industrial machine<\/strong> ?<\/p><p>Artificial intelligence is the discipline of replicating human thinking using an algorithm or other artificial decision making device. The <strong>Machine Learning<\/strong>, consists of programmed AI : we will give data to a Machine Learning algorithm, as well as the expected result for these data, and the algorithm will deduce the algorithmic rules: we call this\u00a0<strong>learning<\/strong>.<\/p><p>Within the framework of a predictive maintenance project, several artificial intelligence solutions are available to answer the problem:<\/p><p>The approach\u00a0<strong>Deep Learning<\/strong>, and algorithms of\u00a0<strong>Machine Learning<\/strong>\u00a0The philosophy behind this is to collect raw, unprocessed data and provide it to an algorithm that is much more complex and powerful than those used in Machine Learning. This algorithm provides <strong>models<\/strong> able to analyze raw data and to detect patterns, repeating forms, in order to assemble little by little bits of signal until being able to make a decision.<\/p><p>Each approach has its <strong>advantages<\/strong> and its drawbacks. In fact, Deep Learning tends to <strong>perform better<\/strong> but in return suffers from a <strong>computation time<\/strong> much more important than its competitor (it has to discover the patterns alone, where the Machine Learning model is built based on the patterns).<\/p><p>In Machine Learning, a nice aspect is that the model is not totally a black box, as it has been designed to solve a very well characterized problem, unlike a Deep Learning model, where it is much more complicated to understand the path followed by the algorithm.<\/p><p>The main limitation is the embedded implementation. Indeed, it is much easier to combine embedded, <strong>low consumption<\/strong>, with <strong>Machine Learning<\/strong> than with <strong>Deep Learning<\/strong>, mainly because of the high computation time required by the latter.<\/p><p>Their applications are also different, indeed, Deep Learning is very effective in areas where experts are lacking to build models, these are often very current and complex problems, such as <strong>object recognition<\/strong> on images,\u00a0<strong>medical imaging<\/strong> and<strong> military<\/strong>, the <strong>intelligent word processing<\/strong>, <strong>voice recognition<\/strong>&#8230; On the other hand\u0301, the Machine Learning approach is confined to well-known domains but in demand of automation, such as predictive maintenance, IoT, fraud detection, etc&#8230;<\/p><p>The\u00a0<strong>Tiny ML<\/strong>\u00a0is a Machine Learning technique implemented in low power systems, such as <strong>sensors<\/strong> and others <strong>IoT objects<\/strong>. These devices integrate reduced and optimized machine learning applications.\u00a0\u00a0One of the main advantages is that they do not need to be connected to the Internet, all the work is done locally. When the system detects an anomaly, it can then send a simple <strong>alert message<\/strong> to the<strong> user<\/strong>, we can use a technology\u00a0<strong>radio radio LPWAN<\/strong> low speed to send this message<br \/>(LoRa, Sigfox, NB-IoT, LTE-M, 2G\u2026).<\/p><p><strong>ATIM<\/strong> is working on this subject of TinyML and proposes evaluation kits, <b><span style=\"color: #fe753e;\"><a href=\"https:\/\/www.atim.com\/en\/contact\/\">contact us<\/a><\/span><\/b> if you want more information on AI ATIM solutions.<\/p><p><strong>\u00a0<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-9f565e8\" data-id=\"9f565e8\" 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","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence There are different types of maintenance including Machine Learning in predictive maintenance. Corrective Maintenance The machine is in<\/p>\n","protected":false},"author":19,"featured_media":37870,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-31281","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Artificial Intelligence | ATIM<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is the discipline of reproducing human thinking using an algorithm: Machine Learning.\" \/>\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.atim.com\/en\/artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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