GBT to develop AI models for its Motion Detection Wireless tech

Machine Learning models are targeted to be applied for IoT applications

GBT Technologies Inc. (OTC PINK: GTCH) (“GBT” or the “Company”) is seeking to develop application specific, AI models for its intelligent, wireless, motion detection patented technology, which has been assigned an internal code name “Apollo.” The goal of Apollo is to develop AI that would reprogram itself on-the-fly, adjusting its internal recognition data, constantly learning about its environment. Apollo is building up and updating its AI models with new data every nanosecond to improve its knowledge of its surroundings, maintaining a reliable, speed lightning response to changes.

GBT is now researching the development of specific AI models for the IoT domain. These models aim to adapt the Apollo system for IoT devices to identify changes within rapid-change environments, reason and make the necessary call to execute the best action according to circumstances. Apollo patented technology includes AI algorithms to control and analyze radio wave transmissions and their reflections, constructing accurate 3D/2D imaging in real time. The technology is designed to produce high-resolution images and live video, understanding its environment. The technology distinguishes between objects and living entities by detecting unique patterns and performing an intelligent surroundings mapping. The Company is researching the development of machine learning models for Apollo with the goal of enabling intelligent computer vision capabilities for IoT systems.

The Company released a short video explaining the basic of the Apollo system:

“We believe our Apollo technology can be an effective system for wide range of applications. We believe one of the major domains that Apollo can be adapted into, is the IoT arena. An intelligent autonomous vehicle computer vision implementation would be able to understand a sudden scenery change from multiple perspectives at the same time. Apollo is designed to analyze the data using multidisciplinary methods to understand the problem, reason and execute an appropriate response. We are now researching to develop AI models to work particularly designed within the IoT domain. Since Apollo’s AI is equipped with high resolution responsive practices, rapid scan updates, and integrated real time analysis, it could be a great fit for IoT applications in many fields. We believe we are developing Apollo to serve as an accurate, quick response where computer vision is needed. Apollo sends RF data and analyzes its reflections using machine learning techniques, constructing 3D/2D images and live motion videos in real time. Since it’s very sensitive to volume changes, it can wirelessly “see” all around. We believe the use of AI models targeted for IoT can be an excellent application to provide a radio based, real-time response computer vision system. Apollo has the capability to understand complex context of scanned images and can be implemented within a wide range of computer vision tasks. One of its main advantages upon other technologies is its capability to understand the big picture. For example, Apollo can determine if an upcoming road tunnel height is 12ft height or 20ft and make a judgement call if it is safe to go through it. Apollo’s great advantage is its capability to understanding its surroundings settings which makes it a great candidate for IoT computer vision solution,” stated Danny Rittman, the Company’s CTO.

There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.

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