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 ...

Demographic bias gaps are closing in face recognition, but how training images are sourced is becoming the field’s biggest privacy fight.

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.

These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance ...

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 ...