$GTCH researching implementation of AI techniques for

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$GTCH researching implementation of AI techniques for optical networks to significantly improve performance, quality, reliability and security. Artificial intelligence has created a whole range of possibilities within computerized systems by emulating advanced biological processes such as learning, reasoning and decision making. Today’s optical networks drive advanced communication solutions, enabling the ever demand for faster performance, larger bandwidth, and security, for example 5G technology.

GBT has been implementing AI technology within a variety of domains, among them are information agents, imaging analysis, tracking and the health arena. This research will cover implementing AI techniques for ultra-high-performance optical networks targeting characterization and operation of network components to increase performance, cybersecurity, reliability and quality control.


GBT aims to build AI based applications for optical network control, switching and management, including cloud scenarios evaluations, WDM (Wavelength Division Multiplexing) a fiber-optic transmission technique that enables the use of multiple light wavelengths/colors for sending data and 5G communications. As the internet/cellular traffic has been exponentially growing, and more diversified services emerged, the capacity and reliability of optical networks have to expand accordingly. Better efficiency and flexibility are needed as many users access the network at the same time. Another crucial aspect is security to protect user’s data and privacy. GBT believes that implementing an AI management system for optical networks will provide our era’s desired properties of high performance, reliable and secured optical networking.

"We aim to implement our AI methods for efficient packet switching protocol to dramatically improve dynamic bandwidth sharing, flexibility and security among vast number of users. The Unicorn Network, (an internal project name), once developed will be an AI management system for optical networks, using neural network and cognitive capabilities to learn an optical network characteristic, among them are traffic jams and blocking weak spots, large communication capacity demand, and providing a rapid solution in real time, transparent for the users. As the system upon development will identify or predict potential issues, it may provide an on-the-fly solution so end users will not suffer network’s latency, low speed, or delays. By enabling a constant AI control and monitoring we will enable seamlessly high speed, bandwidth, and a robust degree of reliability. By adding constant security monitoring, the system will offer a high degree of cybersecurity measures that can be widely deployed within existing physical optical network infrastructures. The research introduces new approach that has the potential to offer a major enhancement for optical communication systems, bringing flawless higher bandwidth, push more data, with much higher efficiency, reliability and security.” Said Danny Rittman, the Company’s CTO.
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