Modern biology is awash in data. Scientists can sequence DNA, track gene activity cell-by-cell, map proteins in space, and ...

In the last decade, convolutional neural networks (CNNs) have been the go-to architecture in computer vision, owing to their powerful capability in learning representations from images/videos.

Researchers at KAUST have proposed a 'super transformer' AI architecture designed to integrate diverse biological data types—such as DNA sequences, gene activity, and tissue images—into a single model ...

Computer vision continues to be one of the most dynamic and impactful fields in artificial intelligence. Thanks to breakthroughs in deep learning, architecture design and data efficiency, machines are ...

Transformers, first proposed in a Google research paper in 2017, were initially designed for natural language processing (NLP) tasks. Recently, researchers applied transformers to vision applications ...

The rapid ascent of large language models (LLMs)—and their growing role in everyday life—masks a fundamental problem: ...

Chinese AI startup Zhipu AI aka Z.ai has released its GLM-4.6V series, a new generation of open-source vision-language models (VLMs) optimized for multimodal reasoning, frontend automation, and ...

In 2015, the launch of YOLO — a high-performing computer vision model that could produce predictions for real-time object detection — started an avalanche of progress that sped up computer vision’s ...