Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...

How a team at UC Berkeley devised a multi-sensor smell system and combined it with machine learning to create a more ...

Biologically plausible learning now reaches 96.7% on MNIST and 61.7% on CIFAR-10 without backpropagation, as Sakana AI ...

Energy-efficient visible-band intelligent vision processors demands both optical neural network (ONN) design and high-speed nanofabrication advances. Chinese researchers proposed a random-projection ...

Written By Or Shalom Over the past decade—and particularly in recent years—the lower airspace domain has evolved from a ...

The landscape of high-performance computing (HPC) storage is undergoing significant change. Traditional simulation and data engineering workloads are increasingly running alongside generative AI, ...

Master of Information and Data Science (MIDS) alums Beth McBride, Ishani Cheshire, Indri Adisoemarta, Chase Martin, and Ambro ...

Cut through AI jargon with this practical AI glossary. Learn essential AI terms like LLMs, hallucination, tokens, and more in ...

Part 2 of this series on ethical AI looks at operationalizing trust with the clear prompting framework and robust data ...

Syntiant Corp., a leading provider of full-stack, low-power physical AI solutions comprising sensors, processors and ML models, today announced the expansion of its ongoing collaboration with PRADCO ...

Effective counter-drone operations begin with timely detection. Leonardo combines complementary sensing technologies to ...