{"id":2000107558,"date":"2026-04-06T13:20:08","date_gmt":"2026-04-06T11:20:08","guid":{"rendered":"https:\/\/new.igihe.com\/?p=2000107558"},"modified":"2026-04-06T13:20:09","modified_gmt":"2026-04-06T11:20:09","slug":"revolutionary-ai-breakthrough-cuts-energy-use-by-100x-while-boosting-accuracy","status":"publish","type":"post","link":"https:\/\/new.igihe.com\/english\/revolutionary-ai-breakthrough-cuts-energy-use-by-100x-while-boosting-accuracy\/","title":{"rendered":"Revolutionary AI breakthrough cuts energy use by 100x while boosting accuracy"},"content":{"rendered":"\n

Recognizing this problem, researchers at Tufts University have developed a new approach that could transform how AI systems are designed and used, making them both much more energy efficient and significantly smarter.<\/p>\n\n\n\n

Traditional AI systems, like the large language models many people are familiar with, learn by processing vast amounts of data through trial and error. This method works, but it requires huge amounts of computing power, which translates into high energy demands and large carbon footprints. <\/p>\n\n\n\n

To address this, the Tufts research team focused on combining the strengths of two different types of AI reasoning: neural networks (which learn patterns from data) and symbolic reasoning (which uses explicit logic and rules).<\/p>\n\n\n\n

The result is called neuro\u2011symbolic AI, a hybrid system that mimics how humans think by breaking problems down into meaningful rules while still learning from experience. In practical tests, including classic problem\u2011solving tasks like the Tower of Hanoi puzzle, the neuro\u2011symbolic AI outperformed more traditional systems. <\/p>\n\n\n\n

It solved complex problems more accurately and did so with far less energy. In one example, the new AI used only 1\u202f% of the energy required by a conventional system while still achieving a 95\u202f% success rate in solving difficult tasks.<\/p>\n\n\n\n

This breakthrough has wide\u2011ranging implications. For robotics and visual\u2011language\u2011action systems   which combine perception and physical movement  the energy savings could make real\u2011world applications far more practical and affordable. The researchers say that by making AI thinking more structured and logical, systems don\u2019t need to rely as heavily on brute\u2011force data processing, which is a major source of inefficiency in today\u2019s models.<\/p>\n\n\n\n

The development of more efficient AI is not only important for reducing environmental impact, but also for promoting broader access to advanced technology. <\/p>\n\n\n\n

As AI continues to influence medicine, transportation, education, and industry, making these systems less energy\u2011intensive could help ensure they are both sustainable and widely available. This new research points toward a future where AI innovations are not just powerful, but also environmentally and economically responsible. <\/p>\n\n\n\n

\"\"
AI breakthrough cuts energy use by 100x while boosting accuracy.<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

Artificial intelligence (AI) has become one of the most powerful technologies of our time, but its rapid growth has also brought a major challenge: enormous energy consumption. In fact, AI systems today use a surprisingly large amount of electricity, accounting for more than 10\u202f% of total power usage in the United States and putting increasing strain on energy resources. <\/p>\n","protected":false},"author":139,"featured_media":2000107559,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[151,69],"byline":[201],"hashtag":[],"class_list":["post-2000107558","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science-technology","tag-editors-choice","tag-homehighlights","byline-rania-umutoni"],"bylines":[{"id":201,"name":"Rania Umutoni","slug":"rania-umutoni","description":"","image":{"id":0,"url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&f=y&r=g","alt":"Default avatar","title":"Default avatar","caption":"","mime_type":"image\/jpeg","sizes":[]},"user_id":139}],"contributors":[{"id":201,"name":"Rania Umutoni","slug":"rania-umutoni","description":"","image":{"id":0,"url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&f=y&r=g","alt":"Default avatar","title":"Default avatar","caption":"","mime_type":"image\/jpeg","sizes":[]},"user_id":139}],"featured_image":{"id":2000107559,"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","alt":"","caption":"","mime_type":"image\/jpeg","width":1920,"height":1080,"sizes":{"thumbnail":{"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","width":150,"height":84},"medium":{"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","width":300,"height":169},"medium_large":{"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","width":768,"height":432},"large":{"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","width":1024,"height":576},"full":{"url":"https:\/\/new.igihe.com\/english\/wp-content\/uploads\/2026\/04\/server-facility-at-sandia-national-laboratory.jpg","width":1920,"height":1080}}},"_links":{"self":[{"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/posts\/2000107558","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/users\/139"}],"replies":[{"embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/comments?post=2000107558"}],"version-history":[{"count":2,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/posts\/2000107558\/revisions"}],"predecessor-version":[{"id":2000107628,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/posts\/2000107558\/revisions\/2000107628"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/media\/2000107559"}],"wp:attachment":[{"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/media?parent=2000107558"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/categories?post=2000107558"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/tags?post=2000107558"},{"taxonomy":"byline","embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/byline?post=2000107558"},{"taxonomy":"hashtag","embeddable":true,"href":"https:\/\/new.igihe.com\/english\/wp-json\/wp\/v2\/hashtag?post=2000107558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}