LEVERAGING AI FOR UNBIASED ANALYSIS OF EMERGING TECHNIQUES IN ACADEMIC LITERATURE

In the rapidly evolving landscape of artificial intelligence (AI), the challenge of quantifying knowledge drift and emerging techniques persists due to inherent biases in expert knowledge. AI engineers often apply techniques subjectively based on their understanding, making it difficult to measure knowledge evolution objectively. To address this, we propose leveraging AI to process extensive academic research data, providing an unbiased assessment of the emergence and application of techniques. This study employs natural language processing (NLP) techniques, including knowledge embedding, abstractions, named entity recognition (NER), and knowledge graphs, to extract cutting-edge knowledge using a large corpus of academic literature, enabled by techniques such as Retrieval-Augmented Generation (RAG) to generate summaries, keywords, and abstracts for analysis. NER facilitates identifying and classifying entities such as persons, organizations, and locations. Word2Vec measures the similarity and diversity of words and entities, uncovering new associations and patterns. Integrating knowledge graphs organizes the extracted knowledge into a coherent and comprehensive structure, allowing for easy querying, visualization, and reasoning. This approach enables a deeper understanding of techniques' emergence, application, and suitability within specific domains, helping identify inflection points and trends in knowledge evolution.