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.
Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for uncovering patterns in large amounts of data that would otherwise be difficult to detect. Over the ...
Artificial intelligence is now better than humans at identifying many patterns, but evolutionary relationships have always been difficult for the technology to decipher. A team from the Bioinformatics Department at Ruhr University ...
Evolution
Sep 10, 2025
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Researchers at The University of Osaka have developed a computer graphics (CG) model, NeuraLeaf, capable of representing a wide variety of plant species and their deformations using a single, unified model. This innovative ...
Plants & Animals
Sep 4, 2025
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Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts ...
Biotechnology
Sep 3, 2025
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EPFL researchers have developed Systema, a new tool to evaluate how well AI models work when predicting the effects of genetic perturbations.
Biotechnology
Aug 28, 2025
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Cities are particularly vulnerable to heat stress because paved and densely built-up areas tend to store heat. More frequent and intense heat waves are a growing challenge for public health and urban infrastructure.
Earth Sciences
Aug 26, 2025
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Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety of biological applications, such as identifying drug targets and designing new therapeutic antibodies.
Biotechnology
Aug 19, 2025
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Deep learning models have shown great potential in predicting and engineering functional enzymes and proteins. Does this prowess extend to other fields of biology as well?
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate predictions by analyzing large ...
By integrating large-kernel convolutional blocks and a novel loss function, LKNet effectively addresses challenges such as overlapping targets, annotation bias, and variability in panicle structure across growth stages. Tested ...
Biotechnology
Aug 8, 2025
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