05a Generative Embeddings Network-based Semantic Inference and Search (GENESIS)

Despite expertise in their respective fields, scientists often encounter significant challenges when attempting to engage with other disciplines. This deficiency in cross-disciplinary understanding creates bottlenecks in research and impedes the development of comprehensive solutions to complex problems within the scientific community. The emergence of advanced artificial intelligence tools, particularly large language models, presents an opportunity to overcome this challenge and enhance interdisciplinary interactions by improving productivity, fostering innovation, and driving progress in scientific domains. This proposal outlines an efficient large language model-based approach by providing a user-friendly chatbot-like platform. This platform leverages advanced search and logical reasoning techniques to deliver accurate, reliable, and customized responses based on state-of-the-art language models and emphasizes its adaptability to the preexisting learning frameworks of experts in scientific domains. Additionally, the platform incorporates multimodal data from scientific literature, enriching the information available for analysis and learning, thereby harnessing the full potential of large language models to enhance scientific research and knowledge integration. In Phase I, a series of interviews will be conducted to understand the requirements and processes involved in cross-disciplinary learning for scientists and experts. This will lay the foundation for refining the system design and developing a retrieval architecture using vectorized embeddings and knowledge graphs that surfaces information most relevant to what is being asked. A user interface and user experience will be designed to prioritize usability and customizability for scientists. A prototype will be implemented, focusing on the aforementioned retrieval mechanisms and large language model-based response synthesisŚevaluation with subject matter experts will ensure the system aligns with real-world needs, usability, and effectiveness, validating its potential for interdisciplinary knowledge integration. The proposed solution can streamline research and development processes by providing quick access to relevant scientific literature, aiding in product innovation and decision-making. Furthermore, this technology can enhance education by serving as a powerful learning tool, helping students and educators access a wide range of scientific knowledge in an easily digestible format. It also has the potential to assist healthcare professionals in staying up-to-date on the latest medical research and advancements, ultimately improving patient care. The solution fosters collaboration and knowledge sharing across disciplines, driving innovation, and accelerating progress in various fields, thus benefiting society as a whole.