Business
Gopher Concluded Phase One of Avant! AI
Gopher Concluded Phase One of Avant! AI.
About this update from Gbt Technologies Inc.
[{"type":"text","content":"\nMoves into Phase Two - Semantic Segmentation Machine Learning System Development\n SAN DIEGO, CA, July 10, 2018 (GLOBE NEWSWIRE) -- Gopher Protocol Inc. (OTCQB:GOPH) (\"Gopher\"), a company specializing in the creation of Internet of Things (IoT) and Artificial Intelligence enabled mobile technologies, is proud to announced that it successfully completed Phase 1 in the development of Avant! AI infrastructure. Gopher is now moving into Phase 2 - semantic segmentation machine learning system development. Avant! AI engine infrastructure is the base foundation for the system and was successfully completed with advanced automation and manual features. This means that Avant! AI will be able to make its “own decisions” based on experience and also will be able to receive manual information by humans, in order to increase its knowledge base. In this way, Avant! AI has the capability to expand on its own and with the assistance of humans if necessary. This is an essential infrastructure feature in order to enable Avant! to learn from its experience and human experts as well. For example, Avant! will learn about Gopher GEO tracking technology and, in addition, Gopher's expert engineers will able to feed it additional information in order to enhance Avant! knowledge in the field. In addition, Avant! will be able to connect to other core AI engines to collaborate and share information across network, upon necessary permissions.  Avant! development is now moving to the next phase, which is the development of its semantic segmentation machine learning engine. Semantic segmentation is a machine learning technique that enables the learning and identification the extent of individual objects within an image. Semantic segmentation gives machine learning computerized systems the human-like ability to analyze and understand the content of an image. It enables machine learning algorithms to locate the precise boundaries of objects and their details. One efficient usage of semantic segmentation is for autonomous driving machines or surveillance/security drones. For example, it enables computers to learn and identify streets objects, pedestrians, vehicles and other robotic driving related objects. In the medical field it can learn an image of a heart, liver, kidney or any other body organ in order to assist with a diagnostic or...