Note: OpenAI leads the general AI space, but AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research. Perhaps you have faculty or students asking you to present these tools to classes.
How will AI tools affect our traditional bibliographic databases? Will we see GenAI being put into our search platforms? Can we stop it?
Artificial intelligence encompasses broader goals of simulating human intelligence, followed by machine learning, which focuses on learning patterns from data, and deep learning, which uses layered neural networks. These systems learn through supervised, unsupervised, and reinforcement models, each suited to different types of data and problem-solving. At the core of deep learning are neural networks, which enable advances in natural language processing which is a key area for libraries and research involving text analysis, summarization, and retrieval. Modern NLP is driven by large language models (LLMs), which rely on transformer architectures, tokens and introduce challenges like hallucination. Information retrieval has evolved through embeddings and semantic searching, improving discovery beyond keyword and controlled searching.
Issues of bias, fairness, and explainability are critical, especially in scholarly and clinical contexts. Evaluating AI requires metrics such as precision and recall to assess performance. Generative AI now enables creation of text, images, and audio, raising important ethical concerns. As a result, governance and policy addressing copyright, misinformation, and accountability are essential for responsible AI use in libraries and beyond.
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
