Artificial neural networks, as physical systems, are implemented through hardware substrates that realize interconnected processing units and weighted connections using electronic, optical, or neuromorphic architectures. In conventional digital hardware, they are instantiated as configurations of logic gates, memory arrays, and interconnects on CPUs, GPUs, or specialized accelerators (e.g., TPUs), where weights reside in volatile or non-volatile memory and computation is executed via parallel multiply-accumulate operations. Emerging neuromorphic and analog implementations encode synaptic weights in device conductances (e.g., memristors, phase-change materials) and exploit device physics for in-memory computation, enabling high spatiotemporal parallelism, low-latency signal propagation, and energy-efficient approximation of neural operations in a physically embedded network topology.
AI can predict rainfall intensity better than several widely used forecasting models in tests using historical weather data from India. The new model reported in the International Journal of Mobile Communications shows that ...
Environment
May 14, 2026
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In early phases of drug development, new active substances are tested in animals—alongside numerous other experimental methods. Researchers face a dilemma: on the one hand, for ethical reasons, they aim to keep the number ...
Other
May 11, 2026
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Protein language models are artificial intelligence tools which help engineer proteins with useful properties, including completely new structures never seen before in nature.
Biotechnology
May 11, 2026
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Modern biology is awash in data. Scientists can sequence DNA, track gene activity cell-by-cell, map proteins in space, and image tissues at microscopic resolution. However, it is a struggle to put all that information together ...
Biotechnology
May 5, 2026
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As a part of a study testing out a new type of implanted brain-computer interface (BCI), three rhesus monkeys controlled movements in a virtual reality (VR) world using only brain signals. The study, published in Science ...
Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based models to simulate large-scale ...
Earth Sciences
Feb 7, 2026
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Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial intelligence (AI)-based method to dramatically tame the flood of data generated by particle detectors at ...
General Physics
Feb 2, 2026
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Recent advances in the field of artificial intelligence (AI) have opened new exciting possibilities for the rapid analysis of data, the sourcing of information and the generation of use-specific content. To run AI models, ...
A research team from the Institute of Modern Physics of the Chinese Academy of Sciences and Lanzhou University has obtained important experimental evidence for revealing brain memory mechanisms and developing a new type of ...
Bio & Medicine
Jan 7, 2026
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Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development of computational models inspired by the brain's layered organization, also known as deep neural ...
