{"id":87879,"date":"2024-10-02T17:20:01","date_gmt":"2024-10-02T15:20:01","guid":{"rendered":"https:\/\/intercoaching.fr\/?p=87879"},"modified":"2024-10-02T19:06:00","modified_gmt":"2024-10-02T17:06:00","slug":"ai-facing-its-own-limits-a-wake-up-call-for-the-industry","status":"publish","type":"post","link":"https:\/\/intercoaching.fr\/en\/ai-facing-its-own-limits-a-wake-up-call-for-the-industry\/","title":{"rendered":"AI facing its own limits: a wake-up call for the industry?"},"content":{"rendered":"<figure class=\"wp-block-table\">\n<table>\n<tbody>\n<tr>\n<td>\n            <p><strong>IN BRIEF<\/strong><\/p>\n            <ul>\n                <li><strong>AI<\/strong> confronted with its own <strong>boundaries<\/strong>.<\/li>\n                <li>Impact on<strong>industry<\/strong> and its practices.<\/li>\n                <li>Importance of a <strong>regulation<\/strong> appropriate.<\/li>\n                <li>Risks linked to a <strong>excessive dependence<\/strong>.<\/li>\n                <li>Potentiality of <strong>bias<\/strong> in algorithms.<\/li>\n                <li>Call for a <strong>collaboration<\/strong> between experts and regulators.<\/li>\n                <li>Reflection on the future of<strong>innovation<\/strong> technological.<\/li>\n            <\/ul><p>\n        <\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n<figure class=\"wp-block-image size-full\">\n<img decoding=\"async\" width=\"2040\" height=\"1152\" src=\"https:\/\/intercoaching.fr\/wp-content\/uploads\/2024\/07\/LIA-face-a-ses-propres-limites-un-signal-dalarme-pour-lindustrie-.png\" class=\"attachment-full size-full\" alt=\"discover in this article how artificial intelligence, despite its spectacular advances, comes up against worrying limits. we analyze the issues and implications of these restrictions for the industry, a real warning signal that should not be ignored.\">\n<\/figure>\n\n\n<p>Artificial intelligence, a real catalyst for innovation in many sectors, also raises crucial questions about its limits. While technological advances appear promising, it is essential to examine the inherent weaknesses of these systems, particularly in terms of bias, ethics and reliance on data. These challenges pose risks not only to the reliability of automated decisions, but also to the safety of users and the sustainability of the industries concerned. Faced with these challenges, it would be imprudent to ignore the warning signals that AI emits, thus inviting players in the sector to reconsider their approach and anticipate the future with caution.<\/p>\n\n\n<h2 class=\"wp-block-heading\">Self-degradation of AI models<\/h2>\n\n\n<figure class=\"wp-block-image size-full\">\n<img decoding=\"async\" width=\"2040\" height=\"1152\" src=\"https:\/\/intercoaching.fr\/wp-content\/uploads\/2024\/07\/LIA-face-a-ses-propres-limites-un-signal-dalarme-pour-lindustrie-1-1.png\" class=\"attachment-full size-full\" alt=\"discover how artificial intelligence, despite its spectacular advances, faces critical limits. This article explores the implications of these restrictions for the industry and raises critical questions about the future of the technology.\">\n<\/figure>\n\n\n<p>A recent study published in the journal Nature reveals a new threat to the future of artificial intelligence. Researchers have discovered that <strong>AI models<\/strong> trained with data generated by other AIs could suffer gradual degradation. This degradation could transform the generated content into a <strong>unrecoverable gibberish<\/strong> in just a few generations.<\/p>\n\n\n<h2 class=\"wp-block-heading\">The phenomenon of model collapse<\/h2>\n\n\n<figure class=\"wp-block-image size-full\">\n<img decoding=\"async\" width=\"2040\" height=\"1152\" src=\"https:\/\/intercoaching.fr\/wp-content\/uploads\/2024\/07\/LIA-face-a-ses-propres-limites-un-signal-dalarme-pour-lindustrie-1-2.png\" class=\"attachment-full size-full\" alt=\"explore the challenges and limitations of artificial intelligence in industry. This article highlights the warning signs that these limits represent for business innovation and sustainability. an essential reflection on the future of AI.\">\n<\/figure>\n\n\n<p>This phenomenon of<strong>collapse of models<\/strong> occurs when algorithms are fed too much synthetic data. According to researchers at the University of Oxford, this overabundance of artificial data can lead to a loss of variance and, ultimately, a complete drop in the performance of AI models.<\/p>\n\n\n<figure class=\"wp-block-embed is-type-rich is-provider-twitter wp-block-embed-twitter\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"twitter-tweet\" data-width=\"550\" data-dnt=\"true\"><p lang=\"fr\" dir=\"ltr\">Le concept de 'limites plan\u00e9taires' s'impose peu \u00e0 peu comme un nouveau paradigme \u00e9cologique. Dans cette note pour la <a href=\"https:\/\/twitter.com\/FondationNH?ref_src=twsrc%5Etfw\">@FondationNH<\/a>, j'en explore certains points aveugles, et notamment la difficult\u00e9 de r\u00e9aliser des arbitrages entre diff\u00e9rentes limites. <a href=\"https:\/\/t.co\/YdOLuvNhs9\">https:\/\/t.co\/YdOLuvNhs9<\/a><\/p>\u2014 Fran\u00e7ois Gemenne (@Gemenne) <a href=\"https:\/\/twitter.com\/Gemenne\/status\/1811677675766862129?ref_src=twsrc%5Etfw\">July 12, 2024<\/a><\/blockquote><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script>\n<\/div><\/figure>\n\n\n<h2 class=\"wp-block-heading\">The most vulnerable model types<\/h2>\n\n\n<p>THE <strong>LLM (major linguistic models)<\/strong>, such as those used in <strong>chatbots<\/strong> and the <strong>AI assistants<\/strong>, are particularly at risk. These models, specialized in generating and interpreting text, can easily degrade if they are continually trained on lower quality data that they have produced themselves.<\/p>\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\n<div class=\"wp-block-embed__wrapper\">\n<iframe title=\"La minute IA - Quand l'IA s'invite dans l'industrie pharmaceutique\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/IsbMBjwgspY?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<\/figure>\n\n\n<h2 class=\"wp-block-heading\">The dangers of web saturation<\/h2>\n\n\n<p>There <strong>web saturation<\/strong> by content generated by AI could exacerbate this phenomenon. AI-created articles can quickly flood the internet, making it difficult for models to distinguish high-quality data from background noise. As a result, models risk training on inefficient data, thereby amplifying their degradation.<\/p>\n\n\n<h2 class=\"wp-block-heading\">The implications for the industry<\/h2>\n\n\n<p>This degradation in AI model performance poses serious problems for the industry. Not only does it compromise the reliability of <strong>AI tools<\/strong>, but it also raises questions of<strong>equity<\/strong>. Minority groups and less mainstream viewpoints could be underrepresented, or even erased, from AI model databases.<\/p>\n\n\n<figure class=\"wp-block-table\">\n<table>\n<tbody>\n<tr>\n<td>Phenomenon observed<\/td>\n<td>Consequence<\/td>\n<\/tr>\n<tr>\n<td>Saturation by synthetic data<\/td>\n<td>Loss of variance<\/td>\n<\/tr>\n<tr>\n<td>Training on generated data<\/td>\n<td>Progressive degeneration<\/td>\n<\/tr>\n<tr>\n<td>Reduced access to original data<\/td>\n<td>Performance drop<\/td>\n<\/tr>\n<tr>\n<td>Web saturation with AI content<\/td>\n<td>Difficulty identifying reliable sources<\/td>\n<\/tr>\n<tr>\n<td>Ignorance of minority data<\/td>\n<td>Underrepresentation of minority viewpoints<\/td>\n<\/tr>\n<tr>\n<td>Collapse at the end of the cycle<\/td>\n<td>Zero performance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n<ul class=\"wp-block-list\">\n\n<li><strong>Saturation by AI content: <\/strong>Increased risk of data quality loss<\/li>\n\n\n<li><strong>Proliferation of synthetic data: <\/strong>Difficulty maintaining variance<\/li>\n\n\n<li><strong>Gradual degradation: <\/strong>Impact on the reliability of AI tools<\/li>\n\n\n<li><strong>Underrepresentation of minority views: <\/strong>Fairness Issues in AI<\/li>\n\n<\/ul>\n\n\n<h2 class=\"wp-block-heading\">FAQs<\/h2>\n\n\n<p><strong>What is AI model collapse?<\/strong> This is a phenomenon where the performance of AI models degrades when they are fed too much synthetic data.<\/p>\n\n\n<p><strong>Which AI models are most affected?<\/strong> Large language models (LLM) like those used in chatbots and virtual assistants.<\/p>\n\n\n<p><strong>Why is web saturation a problem?<\/strong> It makes it difficult for AI models to distinguish reliable data from noisy data, accentuating their degradation.<\/p>\n\n\n<p><strong>What are the implications for the industry?<\/strong> A reduction in the reliability of AI tools and fairness issues due to underrepresentation of minority views.<\/p>\n\n\n<p><strong>How to prevent AI models from collapsing?<\/strong> It is recommended to train models with original data sources and practice rigorous data filtering.<\/p>\n\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;87879&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Notez cet article&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;AI facing its own limits: a wake-up call for the industry?&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; 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