The high-performance generative artificial intelligence (GAI) represents the
latest evolution of computational intelligence, while the blessing of future 6G
networks also makes edge intelligence (EI) full of development potential. The
inevitable encounter between GAI and EI can unleash new opportunities, where
GAI's pre-training based on massive computing resources and large-scale
unlabeled corpora can provide strong foundational knowledge for EI, while EI
can harness fragmented computing resources to aggregate personalized knowledge
for GAI. However, the natural contradictory features pose significant
challenges to direct knowledge sharing. To address this, in this paper, we
propose the GAI-oriented synthetical network (GaisNet), a collaborative
cloud-edge-end intelligence framework that buffers contradiction leveraging
data-free knowledge relay, where the bidirectional knowledge flow enables GAI's
virtuous-cycle model fine-tuning and task inference, achieving mutualism
between GAI and EI with seamless fusion and collaborative evolution.
Experimental results demonstrate the effectiveness of the proposed mechanisms.
Finally, we discuss the future challenges and directions in the interplay
between GAI and EI.

高性能生成人工智能与边缘智能之间的相互作用提供了新的机会，而基于大量计算资源和大规模未标记语料的 GAI 预训练为 EI 提供了强大的基础知识，而 EI 可以利用碎片化计算资源为 GAI 聚合个性化知识。本文提出了一种面向 GAI 的综合网络（GaisNet），它利用无数据的知识传递来缓解矛盾，并通过双向的知识流实现 GAI 的循环模型微调和任务推断，实现了 GAI 和 EI 之间无缝融合和协同进化。实验结果验证了所提机制的有效性。最后，我们讨论了 GAI 和 EI 之间相互作用的未来挑战和方向。