The gap between who you are and who the machine thinks you are has always been an issue in search. After all, this gap is an alignment problem before it’s an AI problem, per se. AI has finally made it ...

Abstract: To improve the accuracy of graph neural network recommendation algorithms, research mainly integrates multi head attention mechanism and GRU, which is to construct a graph neural network ...

This isn't going to end well, is it?

Meta Platforms Inc. today debuted an image generation model that can write code and search the web. Muse Image is the second ...

Genomic surveillance—the process of monitoring and sequencing pathogens—is one of the most important tools for detecting ...

New Iterative Block Particle Filter algorithm makes genomic surveillance faster, cheaper and more scalable, improving early ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

The premise is straightforward — we are awash in biological data. The rapid growth of multiomics datasets (genomics, transcriptomics, proteomics, metabolomics, and radiomics) together with ...

Abstract: Graph cut algorithms are popular in optimization tasks related to min-cut and max-flow problems. However, modern FPGA graph cut algorithm accelerators still need performance and memory ...

ABSTRACT: A new nano-based architectural design of multiple-stream convolutional homeomorphic error-control coding will be conducted, and a corresponding hierarchical implementation of important class ...