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<\/figure>\n\n\n\nIf this approach proves viable on a large scale, the impact could be remarkable. Many common AI tasks\u2014internal queries, document analysis, corporate assistants, administrative automation\u2014don't require massive models or millisecond responses.<\/p>\n\n\n\n
In these cases, processing information locally reduces costs, improves privacy, and alleviates pressure on central infrastructure. And, incidentally, it challenges the current hype that justifies the accelerated construction of data centers around the world.<\/p>\n\n\n\n
Not everything will be local, but neither will everything be centralized. It's important to be realistic. The most complex calculations, large foundational models, and certain critical applications will still require very powerful data centers. But that doesn't mean everything has to go through them.<\/p>\n\n\n\n
More and more researchers and companies are working on distributed AI, edge computing, and more efficient models capable of running closer to the user. It's a more balanced, less urgent, and almost certainly more sustainable approach.<\/p>\n\n\n\n
Artificial intelligence is still very young in terms of mass adoption. We've only recently begun to experience it as \"the hottest technology,\" and it wouldn't be surprising if its architecture evolves rapidly in the coming years.<\/p>\n\n\n\n
Perhaps the solution isn't to eliminate data centers, but to use them more efficiently and only when truly necessary. In the meantime, we should calmly observe how proposals like EPFL's progress. EPFL's project has already passed the prototype stage and is being tested in companies and government agencies in Switzerland, and I imagine we'll have more concrete news about its true viability before long.<\/p>\n\n\n\n
As is almost always the case with technology, time will tell, and things will fall into place.<\/p>\n\n\n\n
<\/p>","protected":false},"excerpt":{"rendered":"
Views: 2 La llegada de la inteligencia artificial ha provocado un crecimiento explosivo de los centros de datos en todo el mundo. Cada pregunta a un chatbot, cada imagen generada y cada modelo entrenado requiere enormes capacidades de c\u00e1lculo. El resultado es una carrera fren\u00e9tica por construir infraestructuras gigantescas, con un consumo energ\u00e9tico y de […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[128,147,174,135],"tags":[],"class_list":["post-4280","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-electronics","category-energy","category-technology"],"yoast_head":"\n