{"id":208500,"date":"2026-03-19T20:14:47","date_gmt":"2026-03-19T19:14:47","guid":{"rendered":"https:\/\/liora.io\/en\/google-deepmind-framework-measure-agi"},"modified":"2026-08-09T18:33:41","modified_gmt":"2026-08-09T17:33:41","slug":"google-deepmind-framework-measure-agi","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/google-deepmind-framework-measure-agi","title":{"rendered":"Google DeepMind reveals cognitive framework to finally measure AGI"},"content":{"rendered":"\n<p><strong>\n<a href=\"https:\/\/liora.io\/en\/google-deepmind-creates-ai-that-revolutionizes-sorting-algorithms\">Google DeepMind<\/a> unveiled a comprehensive framework Monday to measure progress toward Artificial General Intelligence, breaking intelligence into 10 core cognitive abilities and launching a $200,000 Kaggle competition to develop new AI benchmarks. The initiative, running through April 16, invites researchers worldwide to create evaluation tools for underassessed areas like metacognition and social cognition, marking a shift from task-based to theory-driven AI assessment.\n<\/strong><\/p>\n\n\n<p>The framework represents a fundamental departure from existing AI benchmarks like <strong>MMLU<\/strong>, <strong>BIG-bench<\/strong>, and <strong>HELM<\/strong> by grounding evaluation in formal cognitive science rather than collecting vast arrays of tasks, according to Google DeepMind. The new approach introduces a three-stage evaluation protocol that measures whether AI systems exhibit human-like problem-solving patterns, match average human capabilities, and eventually surpass top human experts in specific domains.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"breaking-down-intelligence-into-core-abilities\">Breaking Down Intelligence Into Core Abilities<\/h2>\n\n\n<p>The taxonomy identifies <strong>10 fundamental cognitive abilities<\/strong> essential for AGI, ranging from basic perception and attention to complex metacognition and social cognition. According to the framework published on Google&#8217;s blog, these include perception for processing sensory information, attention for filtering distractions, memory for information storage and retrieval, and learning for acquiring new knowledge.<\/p>\n\n\n<p>Higher-level abilities encompass executive functions for planning and decision-making, reasoning for logical thinking and problem-solving, and metacognition for awareness of one&#8217;s own thought processes. The framework also recognizes language understanding, action for interacting with physical or virtual worlds, and social cognition for understanding other agents.<\/p>\n\n\n<p>The <strong>Kaggle hackathon<\/strong>, running from March 17 to April 16, 2026, specifically targets five abilities with the largest evaluation gaps: <strong>learning, metacognition, attention, executive functions, and social cognition<\/strong>. Hosted on Kaggle&#8217;s Community Benchmarks platform, the competition offers researchers worldwide the chance to develop new assessment tools that will be integrated into DeepMind&#8217;s evaluation suite.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"critical-gaps-and-industry-response\">Critical Gaps and Industry Response<\/h2>\n\n\n<figure class=\"wp-block-image size-full\" style=\"margin-top:32px;margin-bottom:32px\"><img alt=\"Two people engaged in a discussion over research data, analyzing graphs on a laptop and surrounded by notes.\" class=\"wp-image-208491\" decoding=\"async\" height=\"572\" loading=\"lazy\" sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-1024x572.jpg\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-56x56.jpg 56w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-115x64.jpg 115w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-150x150.jpg 150w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-210x117.jpg 210w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-300x167.jpg 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-410x270.jpg 410w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-440x246.jpg 440w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-448x448.jpg 448w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-587x510.jpg 587w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-768x429.jpg 768w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-785x438.jpg 785w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-1024x572.jpg 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-1250x590.jpg 1250w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-1440x680.jpg 1440w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-1536x857.jpg 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-2048x1143.jpg 2048w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2026\/03\/collaborative-discussion-research-data-analysis-scaled.jpg 2560w\" style=\"width:100%;height:auto\" width=\"1024\"\/><\/figure>\n\n\n<p>Despite the comprehensive approach, the framework notably lacks measures to prevent benchmark gaming, where models optimize for metrics without genuine capability improvement. The initial announcement, according to DeepMind&#8217;s documentation, focuses exclusively on measuring AI capabilities with no discussion of evaluating <a href=\"https:\/\/liora.io\/en\/google-creates-the-first-regulation-for-artificial-intelligence\">AI safety<\/a> or alignment with human values.<\/p>\n\n\n<p>Early reactions from within Google&#8217;s ecosystem and the broader AI community have been positive, with endorsements appearing on LinkedIn from Isabelle Hau and Erin Mote. However, prominent researchers from competing AI labs including <strong>OpenAI<\/strong> and <a href=\"https:\/\/liora.io\/en\/chinas-massive-heist-of-anthropics-claude-data-exposed\">Anthropic<\/a> have not yet offered public commentary on the framework&#8217;s methodology or potential limitations.<\/p>\n\n\n<p>The absence of a governance model for maintaining and updating benchmarks raises questions about long-term viability as AI technology advances, potentially rendering static benchmarks obsolete within months of deployment.<\/p>\n\n\n<h3 class=\"wp-block-heading\" id=\"sources\">\n    Sources\n  <\/h3>\n\n\n<ul class=\"wp-block-list\">\n<li>blog.google<\/li><li>kaggle.com<\/li>\n<\/ul>\n\n","protected":false},"excerpt":{"rendered":"<p>Google DeepMind unveiled a comprehensive framework Monday to measure progress toward Artificial General Intelligence, breaking intelligence into 10 core cognitive abilities and launching a $200,000 Kaggle competition to develop new AI benchmarks. The initiative, running through April 16, invites researchers worldwide to create evaluation tools for underassessed areas like metacognition and social cognition, marking a shift from task-based to theory-driven AI assessment.<\/p>\n","protected":false},"author":87,"featured_media":208495,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2417],"class_list":["post-208500","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/208500","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/87"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=208500"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/208500\/revisions"}],"predecessor-version":[{"id":210881,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/208500\/revisions\/210881"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/208495"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=208500"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=208500"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}