Khan Faisal Nadeem
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China.
Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Nat Commun. 2024 Jul 17;15(1):5995. doi: 10.1038/s41467-024-50307-y.
Machine learning (ML) has been remarkably successful in transforming numerous scientific and technological fields in recent years including computer vision, natural language processing, speech recognition, bioinformatics, etc. Naturally, it has long been considered as a promising mechanism to fundamentally revolutionize the existing archaic optical networks into next-generation smart and autonomous entities. However, despite its promise and extensive research conducted over the last decade, the ML paradigm has so far not been triumphant in achieving widespread adoption in commercial optical networks. In our perspective, this is primarily due to non-addressal of a number of critical non-technological issues surrounding ML-based solutions' development and use in real-world optical networks. The vision of intelligent and autonomous fiber-optic networks, powered by ML, will always remain a distant dream until these so far neglected factors are openly confronted by all relevant stakeholders and categorically resolved.
近年来，机器学习（ML）在变革众多科学和技术领域方面取得了显著成功，包括计算机视觉、自然语言处理、语音识别、生物信息学等。自然而然地，长期以来它一直被视为一种有前景的机制，能够从根本上把现有的陈旧光网络转变为下一代智能和自主实体。然而，尽管其前景广阔，且在过去十年中进行了广泛研究，但到目前为止，ML范式在商业光网络中尚未成功实现广泛应用。在我们看来，这主要是由于围绕基于ML的解决方案在实际光网络中的开发和使用，一些关键的非技术问题未得到解决。由ML驱动的智能和自主光纤网络的愿景，将始终只是一个遥远的梦想，直到所有相关利益攸关方公开面对这些迄今为止被忽视的因素并彻底解决它们。
