Reinventing AI: Is It the Time for a New Paradigm?
The article proposes a paradigm shift in artificial intelligence (AI) methodologies, moving intelligence from centralized cloud systems to billions of small devices with onboard central processing units (CPUs), reflecting a potential return to the era of distributed computing. By embedding learning...
| Publicado en: | Communications of the ACM Vol. 68; no. 11; pp. 37 - 41 |
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| Formato: | Artículo |
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Association for Computing Machinery
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=188947690&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 188947690 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2025 vid: 68 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 188947690 10.1145/3731676 ppf: 37 ppct: 4 formats: tig: atl: Reinventing AI: Is It the Time for a New Paradigm? aug: au: Gori, Marco affil: University of Siena, DIISM, Siena, Italy, Italy su: Artificial intelligence Central processing units Distributed computing Language models Machine learning Cognitive robotics sug: subj: Artificial intelligence Central processing units Distributed computing Language models Machine learning Cognitive robotics ab: The article proposes a paradigm shift in artificial intelligence (AI) methodologies, moving intelligence from centralized cloud systems to billions of small devices with onboard central processing units (CPUs), reflecting a potential return to the era of distributed computing. By embedding learning processes directly within these devices, AI systems could interact continuously with their environments, allowing robots and agents to develop cognitive abilities through experiential engagement, akin to developmental robotics in nature. Time becomes a central factor, as learning and evaluation occur simultaneously, enabling adaptive and context-sensitive intelligence rather than reliance on static training-test separations. Integrating large language models and meta-learning mechanisms with environmental interaction frameworks further enhances the potential for scalable, actionable, and socially distributed AI systems. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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