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Polaron AI materials design tool wins inaugural Manchester Prize

Polaron is the first-ever winner of the Manchester Prize. Launched in 2023, the first year of the Manchester Prize called upon the innovators, academics, entrepreneurs and disruptors in the U.K. to enter AI solutions that would deliver public good, receiving nearly 300 entries.

Princeton Precision Health: An interdisciplinary, AI-driven approach to tackling big questions about health and disease

PPH researchers apply cutting-edge AI and computational models to massive datasets to develop a deep understanding of the factors that shape health and illness.

Primech AI Launches Hytron Lite Smart Cleaning Robot - Optimized for Narrow Spaces and Speed

The new Hytron Lite, featuring the powerful NVIDIA Jetson Orin Nano Super System-on-Module (SoM), represents a significant advancement in Primech AI's mission to revolutionize the cleaning industry. At 30% smaller than the original Hytron,

Robotics 7month
AI recognizes the mass of the most energetic particles of cosmic radiation

The use of artificial intelligence (AI) scares many people as neural networks, modeled after the human brain, are so complex that even experts do not understand them. However, the risk to society of applying opaque algorithms varies depending on the application.

Cybersecurity 7month
They're not from this world - More than 2 million discovered in America

Over two million have been found in America; they are not from this world. Google DeepMind's materials AI has already discovered new crystals

What's that microplastic? Advances in machine learning make identifying plastics in the environment more reliable

Microplastics—the tiny particles of plastic shed when litter breaks down—are everywhere, from the deep sea to Mount Everest, and many researchers worry that they could harm human health.

Deep learning model boosts plasma predictions in nuclear fusion by 1,000 times

A research team, led by Professor Jimin Lee and Professor Eisung Yoon in the Department of Nuclear Engineering at UNIST, has unveiled a deep learning-based approach that significantly accelerates the computation of a nonlinear Fokker-Planck-Landau (FPL) collision operator for fusion plasma.

Deep Learning 7month
Deep Learning Model Amplifies Plasma Predictions 1,000x

Abstract The nonlinear collision operator consumes a significant amount of computation time in tokamak whole-volume modeling, and in current numerical

Deep Learning 7month