Reseach虚拟专刊｜生成式人工智能（Generative AI）前沿研究

Reseach虚拟专刊 | 生成式人工智能（Generative AI）前沿研究

在Transformer等深度学习模型的持续演进和Scaling Law所带来的数据规模效应推动下，生成式人工智能正逐步突破智能系统的既有约束，迈向更为开放和广阔的应用场景。通过对海量数据的模式进行学习并挖掘内部关联，生成式人工智能在文本、图像和声音等多模态内容创作中展现出强大的自主性，并在工业、医疗、自动驾驶、机器人等垂直领域展现出了巨大潜力。近年来，这一领域的迅猛发展正深刻影响着产业界和学术界，成为推动人工智能技术与产业应用深度融合的重要力量。本虚拟专刊收录的四篇论文围绕自动驾驶、语义通信、工业互联网和4D光场等领域，探讨了生成式人工智能的前沿研究与实践进展。希望通过这些内容，能够为读者了解该领域的整体趋势提供有益参考，并进一步激发关于生成式人工智能的思考与研究热情。

Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces.

Xiao Wang,Jun Huang,Yonglin Tian,Chen Sun,Lie Yang,Shanhe Lou,Chen Lv,Changyin Sun,and Fei-Yue Wang.

https://spj.science.org/doi/10.34133/research.0349

Cross-Modal Graph Semantic Communication Assisted by Generative AI in the Metaverse for 6G.

Mingkai Chen,Minghao Liu,Congyan Wang, Xingnuo Song,Zhe Zhang,Yannan Xie,and Lei Wang

https://spj.science.org/doi/10.34133/research.0342

A Deep Generative Model with Multiscale Features Enabled Industrial Internet of Things for Intelligent Fault Diagnosis of Bearings.

He-xuan Hu,Yicheng Cai,Qiang Hu,and Ye Zhang

https://spj.science.org/doi/10.34133/research.0176

Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations.

Mianzhao Wang,Fan Shi,Xu Cheng,and Shengyong Chen

https://spj.science.org/doi/10.34133/research.0328