Neural networks have emerged as versatile computational frameworks that mimic the functionality of biological brains, while time-delay systems capture the essence of processes where responses are ...

Scientists from Tomsk Polytechnic University, together with their colleagues, analyzed various methods of planning experiments to determine the optimal technological parameters of polymer scaffold ...

For more than seventy years, deep learning has relied on a simplified model of brain function. Now, a Pittsburgh startup thinks the AI field is due for an update.

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 ...

Wastewater treatment is energy-intensive, with aeration and pumping among the largest cost drivers. The review details how AI ...

Graph neural networks (GNNs) are powerful artificial intelligence (AI) models designed for analyzing complex, unstructured graph data.

Energy and memory: A new neural network paradigm A dynamic energy landscape is at the heart of theorists' new model of memory retrieval Date: May 14, 2025 Source: University of California - Santa ...

The idea of thinking machines (Turing, 1950) and the term “artificial intelligence” were introduced in the 1950s (McCarthy, 2007). The 1960s and 1970s saw the development of neural networks. The 1980s ...

Security researchers have devised a technique to alter deep neural network outputs at the inference stage by changing model ...