Guest Post — Hanging in the Balance: Generative AI Versus Scholarly Publishing
Balancing the anxiety and the excitement over the use of Large Language Models (LLMs) in scholarly publishing.
Balancing the anxiety and the excitement over the use of Large Language Models (LLMs) in scholarly publishing.
The short story “The Library of Babel” by Jorge Luis Borges provides an opportunity to consider the veracity of AI-generated information.
Themes and ideas from the Fortune Brainstorm AI. “People won’t lose their jobs to AI; they’ll lose their jobs to people that are using AI.”
Introducing the AI in Scholarly Publishing Community of Interest (CoIN), the SSP’s latest offering to all its members to explore and engage in all matters AI as they relate to scholarly publishing.
Academia has developed an amazing tree of knowledge which is arguably the most important data for Large Language Models to be trained on. Where does the scholarly communication community fit in?
Reflecting on the Charleston Conference Vendor Showcase @lisalibrarian share what she did — and didn’t — see.
Separately, both open research and AI are considered disrupters, causes of disorder in the normal continuance of scholarly publishing. But approaching them in a synchronized way can offer more productivity gains and efficiencies than taking them on individually.
Generative AI wants to make information cheap, but will people want to read it? Are we ready for more productive writers?
Today, Alice Meadows and Roger Schonfeld introduce a new interview series – Kitchen Essentials – featuring leaders of some of the key scholarly infrastructure organizations globally.
Functional silos lead to customer data silos. Can you get a full view of customer engagement without re-architecting your whole organization?
A report of the Chef’s panel on AI, Open content, and research integrity during the Frankfurt Book Fair.
Some beautiful winners in this year’s Nikon Small World in Motion video microscopy competition.
With yet another stumble from Twitter/X, Angela Cochran looks at the numbers and asks whether all the efforts journals have put into building and maintaining journal Twitter accounts have been worth it.
Julie Zhu reflects on the IEEE’s journey with the Open Discovery Initiative (ODI) and the benefits of ODI conformance statements.
Are there enough reviewers though to meet demand and is the peer review process efficient enough to handle the sheer volume of papers being published? How can a combination of human expertise and AI make the peer review process more efficient?