Navigating the AI-Driven Future of Scholarly Publishing

MODERATOR: Chhavi Chauhan American Society for Investigative Pathology SPEAKERS: Jay Patel Cactus Communications Jennifer Regala Wolters Kluwer George Currie eLife Daniella Thoren Wolters Kluwer REPORTER: Maya Workowski Wolters Kluwer Over the past few years, there has been a long development cycle for artificial intelligence (AI). AI includes many iterations, such as machine learning, neural networks, deep learning, and now, generative AI (Figure). In this session, panelists discussed ways to navigate this ever-changing landscape and use AI in a productive and responsible way. What is Generative AI? Generative AI (Gen AI) is a word prediction engine. It figures out what words go best together using mathematics, not consciousness. There are several reasons to integrate AI into workflows; however, there are also several realities to be aware of when approaching AI. Some realities of AI to consider: It cannot predict the future It amplifies biases Hallucinations exist Copyright issues exist Energy consumption is increasing (but technology will become more efficient and models will improve) AI and Scientific Publishing Even though these considerations exist, there are ways to work around them. For example, users can ask the model to use plain language, to cite sources, and for a specific format. Giving the model constraints such as these to adhere to gives less leeway for the model to take liberties. Authors and editors can prompt AI to summarize a manuscript, check guidelines, generate keywords, or improve the readability of a text. Advanced prompting can include role-prompting, few-shot learning, reflexion, and chain of thought. […]