{"id":256671,"date":"2025-03-11T13:26:26","date_gmt":"2025-03-11T13:26:26","guid":{"rendered":"https:\/\/news.talkwithrattan.com\/index.php\/2025\/03\/11\/the-meaning-of-artificial-general-intelligence-remains-unclear\/"},"modified":"2025-03-11T13:26:26","modified_gmt":"2025-03-11T13:26:26","slug":"the-meaning-of-artificial-general-intelligence-remains-unclear","status":"publish","type":"post","link":"https:\/\/news.talkwithrattan.com\/index.php\/2025\/03\/11\/the-meaning-of-artificial-general-intelligence-remains-unclear\/","title":{"rendered":"The meaning of artificial general intelligence remains unclear"},"content":{"rendered":"<div style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/i0.wp.com\/www.sciencenews.org\/wp-content\/uploads\/2025\/03\/030625_ll_agi_feat.jpg?fit=800%2C450&amp;ssl=1\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"The meaning of artificial general intelligence remains unclear\" title=\"The meaning of artificial general intelligence remains unclear\" \/><\/div> \r\n<br><br><div data-component=\"video-embed\">\n\t\t\t\t\n\n\n\n\n<p>When Chinese AI startup DeepSeek burst onto the scene in January, it sparked intense chatter about its efficient and cost-effective approach to generative AI. But like its U.S. competitors, DeepSeek\u2019s main goal is murkier than just efficiency: The company aims to create the first true artificial general intelligence, or AGI.<\/p>\n\n\n\n<p>For years, AI developers \u2014 from small startups to big tech companies \u2014 have been racing toward this elusive endpoint. AGI, they say, would mark a critical turning point, enabling computer systems to replace human workers, making AI more trustworthy than human expertise and positioning artificial intelligence as the ultimate tool for societal advancement.<\/p>\n\n\n<aside class=\"sn-conversion rich-text rich-text--with-sidebar\">\n<style><![CDATA[\n.email-conversion {\n  border: 1px solid #ffcccb;\n  color: white;\n  margin-top: 50px;\n  background-image: url(\"\/wp-content\/themes\/sciencenews\/client\/src\/images\/cta-module@2x.jpg\");\n  padding: 20px;\n  clear: both;\n}\n\n]]><\/style>\n\n\n\n                        <style class=\"is-layout-flow 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var(--zephr-input-borderColor);--zf-payment-options-borderRadius:calc(var(--zephr-input-borderRadius) * 1px);--zf-payment-options-highlighted-color:var(--zephr-color-text-main);--zf-payment-options-highlighted-bgColor:var(--zephr-color-accent-main);--zf-payment-summary-fontSize:var(--zf-subtext-fontSize);--zf-payment-summary-color:var(--zephr-color-text-main);--zf-payment-summary-bgColor:var(--zf-input-bgColor);--zf-payment-summary-border:calc(var(--zephr-input-borderWidth) * 1px) solid var(--zephr-input-borderColor);--zf-payment-summary-borderRadius:calc(var(--zephr-input-borderRadius) * 1px);--zf-list-subscriptions-container-maxWidth:630px;--zf-list-subscriptions-container-padding:24px;--zf-list-subscriptions-section-padding:16px 0px;--zf-list-subscriptions-container-border:calc(var(--zephr-input-borderWidth) * 1px) solid var(--zephr-input-borderColor);--zf-list-subscriptions-container-borderRadius:calc(var(--zephr-input-borderRadius) * 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5px}.zephr-form-input-inner-text{color:var(--zephr-color-text-main);font-size:var(--zf-subtext-fontSize);font-weight:bold;font-family:var(--zf-root-fontFamily)}\n]]><\/style>\n                        \n                        <style><![CDATA[label {\n    display: none !important;\n}\n\n#coppa {\n    font-size: 10px;\n    font-weight: normal;\n    color: white;\n    padding: 10px;\n}\n\n#coppa>a {\n    color: white !important;\n}\n\n.zephr-form-button {\n    background-color: #d4441f !important;\n    text-transform: uppercase !important;\n    font-weight: 900 !important;\n    letter-spacing: 0.075em;\n    font-size: 1.2em !important;\n    border: 2px solid black !important;\n}\n\n.zephr-form {\n    border: 1px solid #ffcccb;\n    background-size: cover;\n    background-position: left top;\n    background-image:url(\"https:\/\/www.sciencenews.org\/wp-content\/uploads\/2024\/11\/cta_background_webb-tarantula-neb.jpg\");\n    padding: 20px;\n    padding-top: 40px;\n}\n\n.zephr-form {\n    --zf-container-bgColor: none;\n    --zf-container-maxWidth: 900px;\n}\n\n.zephr-form-input {\n    font-size: 1.5rem !important;\n    font-family: monospace !important;\n    filter: drop-shadow(black .1rem .1rem 5px);\n}\n\n.zephr-form-error-message {\n    background-color:white; \n    padding:5px; \n    font-weight:bold\n}\n\n.zephr-form-input::placeholder {\n    font-family: \"Courier New\" !important;\n    color: #999 !important;\n}]]><\/style>\n                        \n                        \n<\/aside>\n\n\n<p>Yet, years into the AI race, AGI remains a poorly defined and contentious concept. Some computer scientists and companies frame it as a threshold for AI\u2019s potential to transform society. Tech advocates suggest that once we have superintelligent computers, day-to-day life could fundamentally change, affecting work, governance and the pace of scientific discovery.<\/p>\n\n\n\n<p>But many experts are skeptical about how close we are to an AI-powered utopia and the practical utility of AGI. There\u2019s limited agreement about what AGI means, and no clear way to measure it. Some argue that AGI functions as little more than a marketing term, offering no concrete guidance on how to best use AI models or their societal impact.<\/p>\n\n\n\n<p>In tech companies\u2019 quest for AGI, the public is tasked with navigating a landscape filled with marketing hype, science fiction and actual science, says Ben Recht, a computer scientist at the University of California, Berkeley. \u201cIt becomes very tricky. That\u2019s where we get stuck.\u201d Continuing to focus on claims of imminent AGI, he says, could muddle our understanding of the technology at hand and obscure AI\u2019s current societal effects.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The definition of AGI is unclear<\/h2>\n\n\n\n<p>The term \u201cartificial general intelligence\u201d was coined in the mid-20th century. Initially, it denoted an autonomous computer capable of performing any task a human could, including physical activities like making a cup of coffee or fixing a car.<\/p>\n\n\n\n<p>But as advancements in robotics lagged behind the rapid progress of computing, most in the AI field shifted to narrower definitions of AGI: Initially, this included AI systems that could autonomously perform tasks a human could at a computer, and more recently, machines capable of executing most of only the \u201c<a href=\"https:\/\/openai.com\/our-structure\/\" target=\"_blank\" rel=\"noopener\">economically valuable<\/a>\u201d tasks a human could handle at a computer, such as coding and writing accurate prose. Others think AGI should encompass flexible reasoning ability and <a href=\"https:\/\/arxiv.org\/abs\/2311.02462\" target=\"_blank\" rel=\"noopener\">autonomy when tackling a number of unspecified tasks<\/a>.<\/p>\n\n\n\n<p>\u201cThe problem is that we don\u2019t know what we want,\u201d says Arseny Moskvichev, a machine learning engineer at Advanced Micro Devices and computer scientist at the Santa Fe Institute. \u201cBecause the goal is so poorly defined, there\u2019s also no roadmap for reaching it, nor reliable way to identify it.\u201d<\/p>\n\n\n\n<p>To address this uncertainty, researchers have been developing benchmark tests, similar to student exams, to evaluate how close systems are to achieving AGI. <\/p>\n\n\n\n<p>For example, in 2019, French computer scientist and former Google engineer Francois Chollet released the <a href=\"https:\/\/arcprize.org\/arc\" target=\"_blank\" rel=\"noopener\">Abstract Reasoning Corpus for Artificial General Intelligence<\/a>, or ARC-AGI. In this test, an AI model is repeatedly given some examples of colored squares arranged in different patterns on a grid. For each example set, the model is then asked to generate a new grid to complete the visual pattern, a task intended to assess flexible reasoning and the model\u2019s ability to acquire new skills outside of its training. This setup is similar to Raven\u2019s Progressive Matrices, a test of human reasoning.<\/p>\n\n\n\n<p>The test results are part of what OpenAI and other tech companies use to guide model development and assessment. Recently, OpenAI\u2019s soon-to-be released o3 model <a href=\"https:\/\/arcprize.org\/blog\/oai-o3-pub-breakthrough\" target=\"_blank\" rel=\"noopener\">achieved vast improvement<\/a> on ARC-AGI compared to previous AI models, leading some researchers to view it as a breakthrough in AGI. Others disagree.<\/p>\n\n\n<aside class=\"sn-conversion rich-text rich-text--with-sidebar\">\n<p class=\"has-text-align-center wp-elements-27c40654034fbeecef6418d6adfe0794\" style=\"color:gray; margin-bottom:0px; font-size:.9rem;\">Sponsor Message<\/p>\n<!-- Tag ID: sciencenews-org_leaderboard_incontent -->\n\n<\/aside>\n\n\n<p>\u201cThere\u2019s nothing about ARC that\u2019s general. It\u2019s so specific and weird,\u201d Recht says.<\/p>\n\n\n\n<p>Computer scientist Jos\u00e9 Hern\u00e1ndez-Orallo of the Universitat Polit\u00e9cnica de Val\u00e8ncia in Spain says that it\u2019s possible ARC-AGI just assesses a model\u2019s ability to recognize images. Previous generations of language models could solve similar problems with high accuracy if the visual grids were described using text, he says. That context makes o3\u2019s results seem less novel. <\/p>\n\n\n\n<p>Plus, there\u2019s a limited number of grid configurations, and some AI models with tons of computing power at their disposal <a href=\"https:\/\/techcrunch.com\/2024\/12\/09\/a-test-for-agi-is-closer-to-being-solved-but-it-may-be-flawed\/\" target=\"_blank\" rel=\"noopener\">can \u201cbrute force\u201d<\/a> their way to correct responses simply by generating all possible answers and selecting the one that fits best \u2014 effectively reducing the task to a multiple-choice problem rather than one of novel reasoning.<\/p>\n\n\n\n<p>To tackle each ARC-AGI task, o3 uses an enormous amount of <a href=\"https:\/\/www.scientificamerican.com\/article\/do-chatbots-just-need-more-time-to-think\/\" target=\"_blank\" rel=\"noopener\">computing power (and money) at test time<\/a>. Operating in an efficient mode, it costs about $30 per task, Chollet says. In a less-efficient setting, one task can cost about $3,000. Just because the model <em>can <\/em>solve the problem doesn\u2019t mean it\u2019s practical or feasible to routinely use it on similarly challenging tasks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI tests don\u2019t capture real-world complexity<\/h2>\n\n\n\n<p>It\u2019s not just ARC-AGI that\u2019s contentious. Determining whether an AI model counts as AGI is complicated by the fact that <em>every<\/em> available test of AI ability is flawed. Just as Raven\u2019s Progressive Matrices and other IQ tests are imperfect measures of human intelligence and <a href=\"https:\/\/www.discovermagazine.com\/mind\/understanding-the-flaws-behind-the-iq-test\" target=\"_blank\" rel=\"noopener\">face constant criticism<\/a> for their biases, so too do AGI evaluations, says Amelia Hardy, a computer scientist at Stanford University. \u201cIt\u2019s really hard to know that we\u2019re measuring [what] we care about.\u201d<\/p>\n\n\n\n<p>Open AI\u2019s o3, for example, correctly responded to more than a quarter of the questions in a collection of exceptionally difficult problems called the Frontier Math benchmark, says company spokesperson Lindsay McCallum. These problems <a href=\"https:\/\/epoch.ai\/frontiermath\" target=\"_blank\" rel=\"noopener\">take professional mathematicians hours to solve<\/a>, according to the benchmark\u2019s creators. On its face, o3 seems successful. But this success may be partly due to OpenAI funding the benchmark\u2019s development and having <a href=\"https:\/\/www.searchenginejournal.com\/openai-secretly-funded-frontiermath-benchmarking-dataset\/537760\/\" target=\"_blank\" rel=\"noopener\">access to the testing dataset<\/a> while developing o3. Such data contamination is a continual difficulty in assessing AI models, especially for AGI, where the ability to generalize and abstract beyond training data is considered crucial.<\/p>\n\n\n\n<p>AI models can also seem to perform very well on complex tasks, like <a href=\"https:\/\/www.nature.com\/articles\/s41598-024-55568-7\" target=\"_blank\" rel=\"noopener\">accurately responding to Ph.D.-level science questions<\/a>, while failing on more basic ones, like <a href=\"https:\/\/community.openai.com\/t\/incorrect-count-of-r-characters-in-the-word-strawberry\/829618\" target=\"_blank\" rel=\"noopener\">counting the number of r\u2019s<\/a> in \u201cstrawberry.\u201d This discrepancy indicates a fundamental misalignment in how these computer systems process queries and understand problems.<\/p>\n\n\n\n<p>Yet, AI developers aren\u2019t collecting and sharing the sort of information that might help researchers better gauge why, Hern\u00e1ndez-Orallo says. Many developers provide only a single accuracy value for each benchmark, as opposed to a detailed breakdown of which types of questions a model answered correctly and incorrectly. Without additional detail, it\u2019s impossible to determine where a model is struggling, why it\u2019s succeeding, or if any single test result demonstrates a breakthrough in machine intelligence, experts say.<\/p><p>Even if a model passes a specific, quantifiable test with flying colors, such as <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10506-024-09396-9\" target=\"_blank\" rel=\"noopener\">the bar exam<\/a> or <a href=\"https:\/\/ai.nejm.org\/doi\/full\/10.1056\/AIdbp2300192\" target=\"_blank\" rel=\"noopener\">medical boards<\/a>, there are few guarantees that those results will translate to expert-level human performance in messy, real-world conditions, says David Rein, a computer scientist at the nonprofit Model Evaluation and Threat Research based in Berkeley, Calif.<\/p>\n\n\n\n<p>For instance, when asked to write legal briefs, generative AI models still routinely fabricate information. Although one study of GPT-4 suggested that <a href=\"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2825395?utm_campaign=articlePDF&amp;utm_medium=articlePDFlink&amp;utm_source=articlePDF&amp;utm_content=jamanetworkopen.2024.40969#google_vignette\" target=\"_blank\" rel=\"noopener\">the chatbot could outperform human physicians in diagnosing patients<\/a>, more detailed research has found that comparable AI models perform far worse than actual doctors <a href=\"https:\/\/www.nature.com\/articles\/s41591-024-03097-1\" target=\"_blank\" rel=\"noopener\">when faced with tests that mimic real-world conditions<\/a>. And no study or benchmark result indicates that current AI models should be <a href=\"https:\/\/www.washingtonpost.com\/nation\/2025\/02\/06\/elon-musk-doge-ai-department-education\/?utm_campaign=wp_main&amp;utm_medium=social&amp;utm_source=bluesky\" target=\"_blank\" rel=\"noopener\">making major governance decisions<\/a> over expert humans.<\/p>\n\n\n\n<p>The benchmarks that OpenAI, DeepSeek and other companies report results from \u201cdo not tell us much about capabilities in the real world,\u201d Rein says, although they can provide reasonable information for comparing models to one another.<\/p>\n\n\n\n<p>So far, researchers have tested AI models largely by providing them with discrete problems that have known answers. However, humans don\u2019t always have the luxury of knowing what the problem before them is, whether it\u2019s solvable or in what time frame. People can identify key problems, prioritize tasks and, crucially, know when to give up. It\u2019s not yet clear <a href=\"https:\/\/www.platformer.news\/openai-operator-ai-agent-hands-on\/\" target=\"_blank\" rel=\"noopener\">that machines can or do<\/a>. The most advanced \u201cautonomous\u201d agents struggle to navigate ordering pizza or groceries online.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">General intelligence doesn\u2019t dictate impact<\/h2>\n\n\n\n<p>Large language models and neural networks have <a href=\"https:\/\/www.sciencenews.org\/article\/ai-large-language-model-understanding\">improved dramatically<\/a> in recent months and years. \u201cThey\u2019re definitely useful in a lot of different ways,\u201d Recht says, pointing to the ability of newer models to summarize and digest data or produce serviceable computer code with few mistakes. But attempts like ARC-AGI to measure general ability don\u2019t necessarily clarify what AI models can and can\u2019t be used for. \u201cI don\u2019t think it matters whether or not they\u2019re artificially generally intelligent,\u201d he says.<\/p><p>What might matter far more, based on the recent DeepSeek news, is traditional metrics of cost per task. Utility is determined by both the quality of a tool and whether that tool is affordable enough to scale. Intelligence is only part of the equation.<\/p>\n\n\n\n<p>AGI is supposed to serve as a guiding light for AI developers. If achieved, it\u2019s meant to herald a major turning point for society, beyond which machines will function independently on equal or higher footing than humans. But so far, AI has had major societal impacts, both good and bad, without any consensus on whether we\u2019re nearing (or have already surpassed) this turning point, Recht, Hern\u00e1ndez-Orallo and Hardy say.<\/p>\n\n\n\n<p>For example, scientists are using AI tools <a href=\"https:\/\/www.sciencenews.org\/article\/ai-health-care-medicine-tech\">to create new, potentially lifesaving molecules<\/a>. Yet in classrooms worldwide, generative chatbots have disrupted assessments. A recent Pew Research Center survey found that more and more U.S. teens <a href=\"https:\/\/www.pewresearch.org\/short-reads\/2025\/01\/15\/about-a-quarter-of-us-teens-have-used-chatgpt-for-schoolwork-double-the-share-in-2023\/\" target=\"_blank\" rel=\"noopener\">are outsourcing assignments to ChatGPT<\/a>. And a 2023 study in <em>Nature<\/em> reported that growing AI assistance in university courses has made <a href=\"https:\/\/www.nature.com\/articles\/s41598-023-38964-3\" target=\"_blank\" rel=\"noopener\">cheating harder to detect<\/a>.<\/p>\n\n\n\n<p>To say that AI will become transformative once we reach AGI ignores all the trees for the forest.<\/p>\n\n\n\n\t\t\t<\/div>\r\n<br>\r\n<br><a href=\"https:\/\/www.sciencenews.org\/article\/artificial-general-intelligence-ai-unclear\">Source link <\/a>","protected":false},"excerpt":{"rendered":"<p>When Chinese AI startup DeepSeek burst onto the scene in January, it sparked intense chatter about its efficient and cost-effective approach to generative AI. But like its U.S. competitors, DeepSeek\u2019s main goal is murkier than just efficiency: The company aims to create the first true artificial general intelligence, or AGI. For years, AI developers \u2014 [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":256672,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","fifu_image_url":"https:\/\/i0.wp.com\/www.sciencenews.org\/wp-content\/uploads\/2025\/03\/030625_ll_agi_feat.jpg?fit=800%2C450&ssl=1","fifu_image_alt":"","footnotes":""},"categories":[606],"tags":[5758,1032,2538,32678,1260,60791],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/posts\/256671"}],"collection":[{"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/comments?post=256671"}],"version-history":[{"count":1,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/posts\/256671\/revisions"}],"predecessor-version":[{"id":256673,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/posts\/256671\/revisions\/256673"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/media\/256672"}],"wp:attachment":[{"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/media?parent=256671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/categories?post=256671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news.talkwithrattan.com\/index.php\/wp-json\/wp\/v2\/tags?post=256671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}