Asset Details
Do you wish to reserve the book?
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
Do you wish to request the book?
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
2024
Overview
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.
The author sheds light on critical non-technological barriers that significantly limit the broad utilization of machine learning in optical networks and presents several prospective solutions. Various pathways are discussed for the evolving machine learning potential for its desired penetration, credibility, and impact in real-world optical networks.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
