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Reinventing AI: Is It the Time for a New Paradigm?

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Title: Reinventing AI: Is It the Time for a New Paradigm?
Authors: Gori, Marco1 (AUTHOR) marco.gori@unisi.it
Source: Communications of the ACM. Nov2025, Vol. 68 Issue 11, p37-40. 4p.
Subjects: Artificial intelligence, Central processing units, Distributed computing, Language models, Machine learning, Cognitive robotics
Abstract: 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.
Database: Engineering Source
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  Data: 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.
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        Value: 10.1145/3731676
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        Text: English
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        Type: general
      – SubjectFull: Central processing units
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      – SubjectFull: Distributed computing
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      – SubjectFull: Language models
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              Text: Nov2025
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