{"id":306743,"date":"2025-06-13T09:00:09","date_gmt":"2025-06-13T16:00:09","guid":{"rendered":"https:\/\/cms-articles.softonic.io\/en\/?p=306743"},"modified":"2025-07-01T14:24:00","modified_gmt":"2025-07-01T21:24:00","slug":"researchers-propose-a-more-human-approach-to-evaluating-ais","status":"publish","type":"post","link":"https:\/\/cms-articles.softonic.io\/en\/researchers-propose-a-more-human-approach-to-evaluating-ais\/","title":{"rendered":"Researchers propose a more human approach to evaluating AIs"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">A new study from Rensselaer Polytechnic Institute and City University of Hong Kong suggests&nbsp;<strong>rethinking how we build and evaluate artificial neural networks<\/strong>. Instead of focusing solely on scaling models outward with more layers and data, the researchers propose a&nbsp;<strong>more biologically inspired and introspective approach<\/strong>, which could transform the efficiency and intelligence of AI systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A vertical leap in AI architecture<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Current AI models rely heavily on expanding horizontally\u2014adding more layers and parameters to boost performance. But this new framework introduces a&nbsp;<strong>vertical dimension and feedback loops<\/strong>, mimicking how the human brain processes information in three dimensions. This internal structure allows networks to&nbsp;<strong>relate, reflect, and refine outputs<\/strong>\u2014leading to smarter, more adaptable systems with lower resource demands.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Toward brain-inspired intelligence<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Inspired by biological cognition, this new architecture could allow neural networks to&nbsp;<strong>learn and adapt more efficiently<\/strong>, making them not just faster but more insightful. It paves the way for&nbsp;<strong>real-time applications in fields like healthcare, robotics, and education<\/strong>, while also helping researchers better understand neurological conditions such as epilepsy or Alzheimer\u2019s.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">More sustainable and explainable AI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Beyond performance, the study emphasises the importance of&nbsp;<strong>creating sustainable and accessible AI technologies<\/strong>. By requiring fewer computational resources, these brain-like models could reduce environmental impact and expand global access. Additionally, the feedback mechanisms offer greater transparency, moving toward&nbsp;<strong>more explainable and trustworthy AI<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A new standard for AI evaluation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The researchers argue that we need new ways to evaluate these advanced systems\u2014<strong>ones that reflect their internal reasoning and capacity for self-improvement<\/strong>. This shift could mark a turning point, not just in how AI works, but in how we understand and trust its decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new study from Rensselaer Polytechnic Institute and City University of Hong Kong suggests&nbsp;rethinking how we build and evaluate artificial neural networks. Instead of focusing solely on scaling models outward with more layers and data, the researchers propose a&nbsp;more biologically inspired and introspective approach, which could transform the efficiency and intelligence of AI systems. A &hellip; <a href=\"https:\/\/cms-articles.softonic.io\/en\/researchers-propose-a-more-human-approach-to-evaluating-ais\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Researchers propose a more human approach to evaluating AIs&#8221;<\/span><\/a><\/p>\n","protected":false},"author":9317,"featured_media":306744,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","wpcf-pageviews":0},"categories":[1015],"tags":[],"usertag":[],"vertical":[],"content-category":[],"class_list":["post-306743","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/306743","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/users\/9317"}],"replies":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/comments?post=306743"}],"version-history":[{"count":1,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/306743\/revisions"}],"predecessor-version":[{"id":306745,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/306743\/revisions\/306745"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/media\/306744"}],"wp:attachment":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/media?parent=306743"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/categories?post=306743"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/tags?post=306743"},{"taxonomy":"usertag","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/usertag?post=306743"},{"taxonomy":"vertical","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/vertical?post=306743"},{"taxonomy":"content-category","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/content-category?post=306743"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}