{"id":19382,"date":"2025-02-24T07:28:29","date_gmt":"2025-02-24T06:28:29","guid":{"rendered":"https:\/\/thesmartcityjournal.cibeles.net\/sin-categoria\/truly-autonomous-ai-is-on-the-horizon\/"},"modified":"2025-02-23T19:04:20","modified_gmt":"2025-02-23T18:04:20","slug":"truly-autonomous-ai-is-on-the-horizon","status":"publish","type":"post","link":"https:\/\/www.thesmartcityjournal.com\/en\/artificial-intelligence\/truly-autonomous-ai-is-on-the-horizon","title":{"rendered":"Truly autonomous AI is on the horizon"},"content":{"rendered":"<p class=\"lead\">A new algorithm significantly improves how AI can independently learn and uncover patterns in data<\/p>\n<p>Researchers have developed a new AI algorithm, called Torque Clustering, that is much closer to natural intelligence than current methods. It significantly improves how AI systems learn and uncover patterns in data independently, without human guidance.<\/p>\n<p>Torque Clustering can efficiently and autonomously analyse vast amounts of data in fields such as biology, chemistry, astronomy, psychology, finance, and medicine, revealing new insights such as detecting disease patterns, uncovering fraud, or understanding behaviour.<\/p>\n<p>\u201cIn nature, animals learn by observing, exploring, and interacting with their environment, without explicit instructions. The next wave of AI, \u2018unsupervised learning\u2019 aims to mimic this approach,\u201d said <a href=\"https:\/\/profiles.uts.edu.au\/Chin-Teng.Lin\" target=\"_blank\" rel=\"nofollow noopener\">Distinguished Professor CT Lin<\/a> from the University of Technology Sydney (UTS).<\/p>\n<p>\u201cNearly all current AI technologies rely on \u2018supervised learning\u2019, an AI training method that requires large amounts of data to be labelled by a human using predefined categories or values, so that the AI can make predictions and see relationships.<\/p>\n<p>\u201cSupervised learning has a number of limitations. Labelling data is costly, time-consuming, and often impractical for complex or large-scale tasks. Unsupervised learning, by contrast, works without labelled data, uncovering the inherent structures and patterns within datasets.\u201d<\/p>\n<p>A paper detailing the Torque Clustering method, <a href=\"https:\/\/url.au.m.mimecastprotect.com\/s\/CNtXCVARNyFxqq0q0fzhyTEuDwb?domain=link.mediaoutreach.meltwater.com\" target=\"_blank\" rel=\"nofollow noopener\">Autonomous clustering by fast find of mass and distance peaks<\/a>, has just been published in <em>IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>, a leading journal in the field of artificial intelligence.<\/p>\n<p>The Torque Clustering algorithm outperforms traditional unsupervised learning methods, offering a potential paradigm shift. It is fully autonomous, parameter-free, and can process large datasets with exceptional computational efficiency.<\/p>\n<p>It has been rigorously tested on 1,000 diverse datasets, achieving an average adjusted mutual information (AMI) score \u2013 a measure of clustering results \u2013 of 97.7%. In comparison, other state-of-the-art methods only achieve scores in the 80% range.<\/p>\n<p>\u201cWhat sets Torque Clustering apart is its foundation in the physical concept of torque, enabling it to identify clusters autonomously and adapt seamlessly to diverse data types, with varying shapes, densities, and noise degrees,\u201d said first author Dr Jie Yang.<\/p>\n<p>\u201cIt was inspired by the torque balance in gravitational interactions when galaxies merge. It is based on two natural properties of the universe: mass and distance. This connection to physics adds a fundamental layer of scientific significance to the method.<\/p>\n<p>\u201cLast year\u2019s Nobel Prize in physics was awarded for foundational discoveries that enable supervised machine learning with artificial neural networks. Unsupervised machine learning \u2013 inspired by the principle of torque \u2013 has the potential to make a similar impact,\u201d said Dr Yang.<\/p>\n<p>Torque Clustering could support the development of general artificial intelligence, particularly in robotics and autonomous systems, by helping to optimise movement, control, and decision-making. It is set to redefine the landscape of unsupervised learning, paving the way for truly autonomous AI. The open-source code has been made available to researchers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new algorithm significantly improves how AI can independently learn and uncover patterns in data Researchers have developed a new AI algorithm, called Torque Clustering, that is much closer to natural intelligence than current methods. It significantly improves how AI systems learn and uncover patterns in data independently, without human guidance. Torque Clustering can efficiently\u2026<\/p>\n","protected":false},"author":2,"featured_media":19381,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_scj_primary_category_id":55,"_scj_featured":false,"_scj_featured_from":"","_scj_featured_until":"","_scj_featured_order":0,"_scj_visibility_class":"current","_scj_layout_family":"standard","_scj_article_style":"","_scj_display_overrides":[],"_scj_intro_image_id":19381,"_scj_intro_alt":"Torque Clustering: Autonomous AI algorithm optimizing unsupervised learning and pattern detection","_scj_intro_caption":"","_scj_intro_class":"","_scj_intro_float":"","_scj_full_image_id":19381,"_scj_full_alt":"Torque Clustering: Autonomous AI algorithm optimizing unsupervised learning and pattern 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