Artificial intelligence is transforming many sectors, from healthcare to finance, by improving efficiency and automation. However, this revolution relies on increasingly complex and energy-intensive models. Since 2012, the computational requirements to train these models have nearly doubled every three to four months, far outpacing Moore’s Law predictions. This exponential growth poses a major challenge: how can we reconcile technological progress with environmental preservation?<\/p>\n
The environmental impact of AI is undeniable today. Training a single large model can consume as much electricity as hundreds of households in a year and emit thousands of tons of CO\u2082. For example, training GPT-3 required nearly 1,300 megawatt-hours of electricity, equivalent to the annual consumption of 120 American homes. These figures highlight the urgent need to rethink how we develop and use AI.<\/p>\n
In response to this reality, a new approach is emerging: green AI. Unlike traditional AI, which prioritizes performance at all costs, green AI seeks to reduce the ecological footprint of models while maintaining their efficiency. This involves lighter architectures, optimized algorithms, and better resource management. For example, models like EcoFormer or EfficientFormer-V2 have demonstrated that it is possible to reduce energy consumption by 60% without significant loss of accuracy.<\/p>\n
Green AI is not limited to technical optimization. It also incorporates social and economic dimensions by making models accessible to researchers and organizations with fewer resources. This helps democratize access to innovation and limits the concentration of technological power in the hands of a few large companies.<\/p>\n
To measure progress toward greener AI, precise indicators are needed. Energy consumption, carbon footprint, water usage for cooling data centers, and material resource efficiency are all criteria to consider. Tools like CarbonTracker or CodeCarbon help track these indicators and assess the environmental impact of models.<\/p>\n
However, the path to truly sustainable AI is fraught with challenges. Technical hurdles, such as the compatibility of measurement tools with different types of hardware, as well as economic and political obstacles, still hinder this transition. Nevertheless, recent advancements show that green AI is not a utopia but a necessity for the future of technology and the planet.<\/p>\n
DOI:<\/strong> https:\/\/doi.org\/10.1007\/s11831-026-10546-2<\/a><\/p>\n Title:<\/strong> Green Artificial Intelligence: A Comprehensive Review of Metrics, Tools, Challenges, Trends, and Future Prospects<\/p>\n Journal:<\/strong> Archives of Computational Methods in Engineering<\/p>\n Publisher:<\/strong> Springer Science and Business Media LLC<\/p>\n Authors:<\/strong> Pejman Peykani; Ali Emrouznejad; Sanly Ghanidel; Iman Javadi-Sisi; Seyedali Mirjalili<\/p>\n","protected":false},"excerpt":{"rendered":" Can Artificial Intelligence Become Eco-Friendly Without Sacrificing Performance? Artificial intelligence is transforming many sectors, from healthcare to finance, by improving efficiency and automation. However, this revolution relies on increasingly complex and energy-intensive models. Since 2012, the computational requirements to train these models have nearly doubled every three to four months, far outpacing Moore’s Law predictions.… Continue reading Can Artificial Intelligence Become Eco-Friendly Without Sacrificing Performance?<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,10],"tags":[],"class_list":["post-37","post","type-post","status-publish","format-standard","hentry","category-health","category-politics","entry"],"_links":{"self":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/37","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/comments?post=37"}],"version-history":[{"count":1,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/37\/revisions"}],"predecessor-version":[{"id":38,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/posts\/37\/revisions\/38"}],"wp:attachment":[{"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/media?parent=37"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/categories?post=37"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journalofartificialintelligence.com\/en\/wp-json\/wp\/v2\/tags?post=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}