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GBT Seeking to Develop a New LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network)
[January 08, 2020]

GBT Seeking to Develop a New LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network)


SAN DIEGO, Jan. 08, 2020 (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK: GTCH) ("GBT”, or the “Company”), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight™ wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that it is now working on the development of a new LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network).

The new RNN will be based on GBT’s upcoming communication IC patent (patent grant expected Q1 2020) with a goal of dramatically expanding its AI-controlled microchip technology platform.

GBT targets AI-controlled communications markets which are constantly expanding, and to address this expansion, GBT is designing a new architecture of RNN that is based on a proprietary LSTR (Long/Short Term Memory) algorithm. The new RNN will seek to offer robust training capabilities along with vast computational power. The new RNN is designed to store and distribute vast amounts of information about past and present events efficiently.

In addition, its design includes a non-linear flow that allows dynamic status updating of an event, which allows efficient, real-time response. This is particularly important for autonomous vehicles that require vast data analysis in real time. The new system is being designed to rapidly and efficiently learns about events, with the goal of creating a huge amount of possible responses, analyzing for the best one, and implementing it. It is expected that the entire process will last pico-seconds as the system captures the events, generates the knowledge base data, and runs a multi-solution solver in parallel to produce an optimal outcome.

The LSTM feature is being designed to enable the RNN to classify and remember an event's data for a long/short time, according to event's significance and impact. The new algorithms are expected to take into account appearing/vanishing factors by using an advanced optimizer that can detect changes with a tiny gradient. The new RNN memory method is being designed to create a dynamic long-short term memory network based on database logistics and multiplicative interactions.

The new, expected architecture may include various combination styles designed to work within GBT's GopherInsight™ communication AI microchip and can be used in a wide variety of applications. The new RNN will be implemented via hardware and software within the Company's communication microchip patent expected in Q1 2020. In addition, it is planned to be an integral part of GBT’s future multi-planner microchip technology, which is currently in design with the goal to enable the design of 3D, multi-planner processing components inside a chip.

"Our goal is for this new RNN to enable high-capacity, high-speed AI based analytics and data processing, which is crucial for advanced computing for use in autonomous vehicles and similar applications," stated Danny Rittman, GBT's CTO. "As we evolve towards vast, ever-changing conditions, it becomes crucial to be able to build a fast, self-learning, self-training system that is capable of remembering past events for long time. Decisions will have to be taken rapidly based on an event's point of time, importance and priority.



“Imagine an autonomous vehicle with ever-changing events. We are developing our new RNN to learn on its own about these events, performing an analysis, evaluate numerous amount of possible solutions, make an optimal decision and executing it. That's is why we are creating a new architecture LSTM hierarchical algorithm in order to enable high-speed, high-capacity data input with point of time consideration. The world of computing has evolved dramatically over the past decade as we generate data at a faster rate than we can analyze it. The new RNN is targeted to be implemented within GBT's microchip's technologies. It’s a powerful AI platform targeted to a wide variety of applications including autonomous vehicles, robotics, medicine and civil/military security systems. GBT foresee an on-going development for this new family of RNN algorithms with the goal of creating superior real-time, accurate decision making systems." 

About GBT Technologies Inc.


GBT Technologies Inc. (OTC PINK: GTCH) (“GBT”) ( http://gopherprotocol.com/ ) is a development-stage company which considers itself a native IoT creator, developing Internet of Things (IoT) and Artificial Intelligence (AI) enabled mobile technology platforms. GBT has a portfolio of Intellectual Property that, when commercialized, will include smart microchips, mobile and security applications and protocols, and supporting cloud software. GBT’s system envisions the creation of a global mesh network. The core of the system will be its advanced microchip technology that can be installed in any mobile or fixed device worldwide. GBT envisions this system as a low-cost, secure, private mesh network between any enabled devices, providing shared processing, advanced mobile database management/sharing and enhanced mobile features as an alternative to traditional carrier services.

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About GBT Technologies, S.A.

GBT Technologies, S.A., a private Costa Rican corporation (GBT - http://gbttechnologies.com/) is a development-stage company in the business of the strategic management of BPO (Business Process Outsourcing) digital communications processing for enterprises and startups; distributed ledger technology development, AI development and fintech software development and applications. 

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Forward-Looking Statements

Certain statements contained in this press release may constitute "forward-looking statements". Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors as disclosed in our filings with the Securities and Exchange Commission located at their website (http://www.sec.gov). In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, governmental and public policy changes, the Company’s ability to raise capital on acceptable terms, if at all, the Company’s successful development of its products and the integration into its existing products and the commercial acceptance of the Company’s products. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release.

Contact:
Dr. Danny Rittman, CTO
GBT Technologies Inc.
Media: press@gopherprotocol.com

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