# Difficulties in training a neural network Given that our neural network functions as both a translator and a voice assistant, and additionally interfaces with Unreal Engine 5 for scene construction, we encounter some challenges in its training and enhancement. We possess a sizable user base, whose requests will facilitate the neural network's learning and boost its performance. However, this process could span anywhere from 6 to 24 months. Throughout this period, we will incessantly monitor and fine-tune the neural network to ensure it aligns with our users' needs. Moreover, we are also committed to augmenting the precision of our neural network. This endeavor necessitates substantial time and resources. We also need to evaluate and train our neural network predicated on genuine user requests. In the imminent future, Amberi will be accessible for testing on our website and Telegram channels. Embrace the future with us! With our dynamic, constantly learning and evolving neural network, we offer a unique blend of technology and user experience that is unparalleled. Despite the obstacles, we are relentlessly dedicated to improving Amberi and adapting it to the ever-changing needs of our users. Join us on this journey of technological evolution and be a part of the AI revolution with us! --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://hotel-of-secrets.gitbook.io/white-paper/problems/difficulties-in-training-a-neural-network.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.