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Cool computing—why the future of electronics could lie in the cold

Modern computer chips generate a lot of heat—and consume large amounts of energy as a result. A promising approach to reducing this energy demand could lie in the cold, as highlighted by a new Perspective article by an international research team coordinated by Qing-Tai Zhao from Forschungszentrum Jülich. Savings could reach as high as 80%, according to the researchers.

The work was conducted in collaboration with Prof. Joachim Knoch from RWTH Aachen University and researchers from EPFL in Switzerland, TSMC and National Yang Ming Chiao Tung University (NYCU) in Taiwan, and the University of Tokyo. In the article published in Nature Reviews Electrical Engineering, the authors outline how conventional CMOS technology can be adapted for cryogenic operation using and intelligent design strategies.

Data centers already consume vast amounts of electricity—and their are expected to double by 2030 due to the rising energy demands of artificial intelligence, according to the International Energy Agency (IEA). The computer chips that around the clock produce large amounts of heat and require considerable energy for cooling. But what if we flipped the script? What if the key to energy efficiency lay not in managing heat, but in embracing the cold?

Agentic AI And The Future Of Autonomous Enterprises

Agentic architecture is the foundation of the current evolution of AI. It’s an AI system development approach that emphasizes autonomy, self-direction and self-improvement. This architecture supports multi-agent collaboration, integration with key enterprise systems and self-learning ecosystems. Instead of being programmed for specific tasks, AI agents in an agentic architecture continually evolve, shifting from task-based automation to proactive, AI-driven decision making.

Why Business And Technology Leaders Should Care

The shift to agentic AI represents a strategic transition from viewing AI as a tool to recognizing it as a strategic partner. This fundamentally alters how companies function and will redefine the roles of business and technology leaders and their interactions with AI moving forward.

Tool automatically separates training and test data to improve AI evaluation

A new tool has been developed to better assess the performance of AI models. It was developed by bioinformaticians at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS).

“DataSAIL” automatically sorts training and test data so that they differ as much as possible from each other, allowing for the evaluation of whether AI models work reliably with different data. The researchers have now presented their approach in the journal Nature Communications.

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set—the former is used for the model to learn, and the latter is used to check its reliability.