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 Subject : How A.I. Is Making Its Way into the Scholarly Workflow (4/5/2019).. 04/08/2019 02:11:38 PM 
Marcie Granahan
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Artificial Intelligence (AI) is being deployed by many companies to help them stay ahead of the competition, maintain a position of strength, and create solutions for common business challenges [see full article here]. Corporate giants like Google and Amazon have made significant investments in numerous areas from predicting patient medical outcomes to automating personal assistant functions for managing schedules and searching content.

Increasingly powerful computers—harnessed by algorithms refined over the past decade—are driving an explosion of applications in everything from finance and healthcare to content discovery [see full article here]. Machine learning, a subset of AI, can be applied where there are large and complex data sets that can be mined for correlations between the inputs and outputs. The US Department of Energy (DOE) hopes to empower scientists and engineers in the development of new technologies that can be used for commercial applications to improve the lives, health and security of all Americans. The DOE recently announced its plan to provide $40 million for research aimed at developing new algorithms and software for quantum computers [see full article here].

While AI is a driving force behind the next technological revolution, it is not without its problems. One only has to view the public backlash regarding the bias in Amazon’s facial recognition technology [see full article here] or YouTube’s struggle with wrangling toxic content [see full article here] as prime examples.

Investment in AI technology should not be taken lightly, and there is not a one-size-fits-all artificial intelligence solution that works for all industries and companies. Before taking a step toward AI implementation, C-level executives must objectively estimate the value an AI solution can offer and the new opportunities it may provide.

So how is AI being implemented in the scholarly publishing community? Springer Nature has recently published the first machine-generated e-book title in collaboration with Goethe University [see full article here], demonstrating that AI and machine learning algorithms can potentially offer promising opportunities in the generation of scientific content. Clarivate Analytics’ ScholarOne is also piloting a project that uses artificial intelligence, helping authors submit higher-quality papers and speeding up the editorial evaluation process by providing insightful statistics [see more information here]. NFAIS will address several of these advances at its May 15 -16 conference, Artificial Intelligence: Finding Its Place in Research, Discovery and Scholarly Publishing.

How will AI fit into the scholarly workflow? That is yet to be determined; however, AI will most certainly play an important role in research, search and discovery, and content delivery.


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