Generative AI for translational scholarly communication
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Start Date
1-11-2023 9:15 AM
Publication Date
2023-11-01
End Date
1-11-2023 9:45 AM
Description
Many valuable insights embedded in scientific publications are siloed and rarely translated into results that can directly benefit humans. These research-to-practice gaps impede the diffusion of innovation, undermine evidence-based decision making, and contribute to the disconnect between science and the public. Generative AI systems trained on decades of digitized scholarly publications and other human-produced texts are now capable of generating (mostly) high-quality and (sometimes) trustworthy text, images, and media. Applied in the context of scholarly communication, Generative AI can quickly summarize research findings, generate visual diagrams of scientific content, and simplify technical jargon. In essence, Generative AI has the potential to help tailor language, format, tone, and examples to make research more accessible, understandable, engaging, and useful for different audiences. In this talk, I'll discuss some uses of Generative AI in these contexts as well as challenges towards realizing the potential of these models, e.g., how to effectively design generated translational science communication artifacts, incorporate human feedback in the process, and mitigate the generation of harmful, misleading, or false information. Scholarly communication is undergoing a major transformation with the emergence of these new tools. By using them safely, we can help bridge the research-to-practice gap and maximize the impacts of scientific discovery.
Generative AI for translational scholarly communication
Many valuable insights embedded in scientific publications are siloed and rarely translated into results that can directly benefit humans. These research-to-practice gaps impede the diffusion of innovation, undermine evidence-based decision making, and contribute to the disconnect between science and the public. Generative AI systems trained on decades of digitized scholarly publications and other human-produced texts are now capable of generating (mostly) high-quality and (sometimes) trustworthy text, images, and media. Applied in the context of scholarly communication, Generative AI can quickly summarize research findings, generate visual diagrams of scientific content, and simplify technical jargon. In essence, Generative AI has the potential to help tailor language, format, tone, and examples to make research more accessible, understandable, engaging, and useful for different audiences. In this talk, I'll discuss some uses of Generative AI in these contexts as well as challenges towards realizing the potential of these models, e.g., how to effectively design generated translational science communication artifacts, incorporate human feedback in the process, and mitigate the generation of harmful, misleading, or false information. Scholarly communication is undergoing a major transformation with the emergence of these new tools. By using them safely, we can help bridge the research-to-practice gap and maximize the impacts of scientific discovery.