The Advanced NLP (Natural Language Processing) capabilities to enhance the human-to-computer interaction experience, capable of making it significantly more user friendly, mainly intuitive.

GBT Technologies Inc. (OTC PINK: GTCH) ("GBT”, or the “Company”), is evaluating the use of advanced NLP algorithm model to enhance its AI based healthcare advisory system human interaction experience. The Text-To-Text Transfer Transformer (T5) model introduces an efficient technology to perform a wide variety of supervised Natural Language Processing (NLP) tasks such as classification, Q&A and summarization. Typically, most of the new deep learning NLP models are very large and include vast number of parameters. Normally the larger the NLP model, the more learning capacity it has, yet one of the main disadvantages is the huge dataset which may reduce the overall performance. The advanced NLP algorithm model is considered one of the most advanced, high performance NLP algorithms that includes a vast number of parameters, using significantly less memory, and provides high accuracy. GBT will be evaluating the T5, pre-trained model with the goal of using it in its Hippocrates healthcare advisory system, handling Q/A, text, summarization and compositional commonsense knowledge. The model allows more parallel processing than methods like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) which significantly increases data understanding and reasoning capabilities. For example, T5 model is capable of processing words together rather than on a word-by-word of a given text. As global data realm is estimated to reach zettabytes range in the near future, our deep learning computing will need powerful processing capabilities, comprehending and scrutinizing data, particularly in the huge, unstructured NLP domain. The system is designed to perform as a general health Q/A advisory system, providing first-line of health-related advice. We plan to further develop the system to include intelligent telemedicine capabilities that will assist patients and healthcare specialists through global connected devices.

“Natural Language processing (NLP) is currently broadly used for the various tasks involving sentiment analysis, for example spam email detection, textual/voice driven virtual assistants, chatbots, and similar. In the last decade, deep learning technology have become more powerful, allowing larger data and faster processing models to handle the constantly growing unstructured data. The amount of unstructured data has become a challenge for information analysis especially within the NLP arena due to compositional complexity and the demand for a human-alike interaction. Today’s voice and text digital assistants are expected to efficiently “understand” verbal or textual conversations, exactly as an in-person interaction. Artificial General Intelligence (AGI) is our machine learning solution to “understand” the data, reason and make decisions exactly as a human would do. One of the key challenges for such system is to comprehend humans natural language, interpret, and manipulate it, which is exactly what NLP is. Our Avant! deep neural networks already encapsulate vast computing power, allowing larger language models with faster processing. We are seeking new capabilities by combining new NLP models with our current technology to better “understand” users, their intent, and providing accurate, well summarized, response. We are evaluating if the possible implementation of the T5 model within our Hippocrates healthcare AI system may produce much more robust level of user’s chat interaction with more human-alike dialog, mainly intuitive. The Hippocrates healthcare advisory system is aimed to provide first line health related advice and to become an assisting tool for the general population and healthcare professionals. Its human interaction aspect, via a robust NLP technology, is one of the most important features to ensure viable and accurate health related advice information” provided 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.