ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Compare the orchestration of parallel investigations with the strength of the biological evidence.
@misc{openai2026using,
title = {Using thousands of AI agents to uncover new leads in biological data},
author = {OpenAI and PerturbAI and Scripps Research},
year = {2026},
url = {https://openai.com/index/scaling-ai-research-for-complex-biological-data/}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.
Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.