Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling ...

Google popularized the term "knowledge graph" in this 2012 blog post. Since then, there has been a massive momentum around ...

The potential for these kinds of machines to reshape computer processing, increase energy efficiency, and revolutionize medical testing has scientists excited. But when do we consider these cells to ...

The agentic AI solution, known internally as Weave, launched alongside a model benchmarking tool called LLM-IQ. It was built from the ground up using data sourced from carriers and operators to create ...

Google's new Graph Foundation Model delivers up to 40 times greater precision and has been tested at scale on spam detection.

Graph Neural Networks (GNNs) have recently achieved significant success in processing non-Euclidean datasets, such as social and protein-protein interaction networks. However, these datasets often ...

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the ...

Researchers from the University of Campinas (Campinas, Brazil) and the Waters Research Center (Budapest, Hungary) introduced ...

Since the 1940s, the meaning of the term 'artificial intelligence' has evolved significantly, as its applications have ...

Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node ...

Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...