Non-technological barriers: the last frontier towards AI-powered intelligent optical networks

• Published in: Nature communications Vol. 15; no. 1; pp. 5995 - 9
• Main Author: Khan, Faisal Nadeem
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 17.07.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Accountability; Artificial intelligence; Bioinformatics; Computer vision; Fiber optics; Learning algorithms; Machine learning; Natural language processing; Networks; Optics; Security management; Speech recognition; Transparency; Workforce
• ISSN: 2041-1723; 2041-1723
• 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.