Data vs dollars: the race for new AI research tools

Microsoft announces Accelerate Foundation Models Research, “a global research network and a resource platform that enables researchers in computer science and many other disciplines to engage with some of the greatest technical and societal challenges of our time.”

Good-o, but where will the data they search come from?

Presumably everything available through Open Access for a start and what is scrapeable from research sites. But does that not still leave a mass of research behind publisher paywalls?

The learned Danny Kingsley asked about this in March and got a bunch of informed but not consistent commentary in reply.

And she pointed to a core question for the future of research, “it has been hard enough for those in the Open Access movement to deal with the shapeshifting of commercial publishers from their role as publishers to that of data wranglers. What happens if it morphs further?

Elsevier is keen to find out – announcing in August, an AI for its Scopus database, which “provides easy-to-read topic summaries based on trusted content from over 27,000 academic journals, from more than 7,000 publishers worldwide, with over 1.8bn citations.”

Which is good but, unsurprisingly, will cost; “full product pricing and availability will be determined and shared in the future” Elsevier advises.

The question which offering represents the best deal, at least for open access search functions with embedded AI than services from no-cost competitors and the specialist services to come.

Google Bard searches Scholar, as well as Scopus, for material that isn’t paywalled. So does Microsoft Academic – which also claims a summary function that cites sources.

The AI challenge for Elsevier is how to keep consumers paying in a world where ever-more content is open access.

Much like journal publishing.



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