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US HEAT-ML breakthrough accelerates fusion plasma heat protection

A public-private team of fusion pioneers – Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory – has unveiled an AI breakthrough that could reshape the future of fusion plasma research.

The new system, called HEAT-ML, can identify safe zones inside a reactor in milliseconds, replacing a process that once took more than 30 minutes.

By protecting sensitive components from the blistering heat of superheated plasma, this advance could accelerate the design and operation of next-generation fusion power plants.


An AI tool, developed by CFS, PPPL, and Oak Ridge, maps fusion plasma heat in milliseconds, protecting reactors and advancing clean energy.

Theoretical particle physicist tackles machine learning’s black box

From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.

Despite its omnipresence in society, we’re just beginning to understand the mechanisms driving the technology. In a recent study, Zhengkang (Kevin) Zhang, assistant professor in the University of Utah’s Department of Physics & Astronomy, demonstrated how physicists can play an important role in unraveling its mysteries.

“People used to say is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do. It feels like magic because we don’t really know how it works,” said Zhang. “Now that we’re using AI across many critical sectors of society, we have to understand what our machine learning models are really doing—why something works or why something doesn’t work.”

Discover How AI is Transforming Quantum Computing

Quantum technologies have had a meteoric rise and become a key area of prioritization for governments, academics, and businesses. Government funding commitments total almost $40 billion, while private investments since 2021 total nearly $8 billion. The US agency, National Institute of Standards and Technology, released this year three new post-quantum security standards, which governments classify as ‘critical resources’ for the economy and national defense. Meanwhile, users of quantum technologies experiment with them, from industry applications in drug development and materials science to energy grid optimization and logistics efficiency.

Yet, besides a few areas, such as quantum sensing, practical and impactful quantum technologies haven’t matured for widespread use. However, when combined with classical machine learning, practical use cases emerge.

This article delves into the impact and potential of artificial intelligence and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in quantum technologies and AI, as a potential collaborator for startups to deliver investment, resources, global networks, and tailored accelerator and incubator programs.


This article covers AI and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in emerging technologies.

Small But Mighty: How is Nanotechnology Powering AI?

The limitations of conventional semiconductor technology have become increasingly apparent as AI applications require exponentially larger computational resources. Once the engines of rapid technological advances, silicon-based transistors are now encountering fundamental physical constraints at the nanoscale that inhibit further scaling and performance enhancement. Moore’s law, which predicted the doubling of transistors on a chip every two years, is running out of space.

On top of that, the breakdown of Dennard scaling, which once enabled simultaneous improvements in speed, power efficiency, and density, has further intensified the need for alternative materials and device architectures capable of sustaining AI-driven workloads.

This is where nanotechnology comes in. Working on a nanoscale offers a pathway to overcome the constraints of conventional tech, enabling the precise manipulation of materials at the atomic and molecular levels, typically within the one to 100 nanometer range.

At this minute scale, materials exhibit unique physical, chemical, and electrical characteristics. These small-scale properties can enable faster operation, lower energy consumption, and can be used to deliver complex functionalities within a single nanoscale architecture.


Discover how nanotechnology is advancing AI with energy-efficient chips, in-memory computing, neuromorphic hardware, and nanoscale data storage solutions.

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