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Physics model reveals fundamental trade-off between prediction and energy efficiency in intelligent systems
Artificial intelligence is increasingly becoming part of our everyday lives, from digital assistants to robots that respond to their surroundings. Each of these capabilities is based on physical computational processes that consume energy.
AI Summary
An international research team led by Hans Briegel of the University of Innsbruck’s Department of Theoretical Physics has published a study in Physical Review X that examines the physical limits of energy efficiency in intelligent systems. The paper focuses on how artificial intelligence, from digital assistants to responsive robots, relies on physical computational processes that consume energy. Briegel’s team investigated the trade‑off between predictive accuracy and energy consumption in these systems. The findings highlight fundamental constraints that could guide future design of more energy‑efficient AI technologies.
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