AI 2.0: Machine-Generated Content, Intelligent Automation, and the Future of Academic Publishing
/by KnowledgeWorks Global Ltd.
Erin Cox, Mike Groth, Jim Longo, Niels Peter Thomas, and Henning Schoenenberger at the 2019 Frankfurt Book Fair
Every year at the Frankfurt Book Fair, there is a buzzword or phrase that continues to pop up on panels, in articles, and in conversations and meetings. In the past, we have seen ‘big data’ and ‘blockchain’ dominate the headlines, but this year’s buzz word (or acronym) was ‘AI,’ as publishers, information professionals, service providers, and the media debated how this technology can be used in the industry.
Because machine learning and artificial intelligence are integral to KGL’s work helping to alleviate pain points for publishers, we partnered with Springer Nature to host a panel entitled “AI 2.0: Machine-Generated Content, Intelligent Automation, and the Future of Academic Publishing.” On the panel, speakers from KGL (Cenveo at the time), HighWire, and Springer Nature talked about everything from workflow automation and high-speed publishing, to companies that use machine learning and AI for discovery, peer review, and even highlighted emerging technologies which allow publishers to offer a broader range of tools and services to serve researchers and authors. Here are some highlights from the panel:
Nicely setting the scene for us, on Thursday, October 17, solutions provider, Unsilo released the results of a survey on AI in Academic Publishing which noted that the reason some publishers haven’t been using AI is because there is “not enough time” to look into the technology or that they are “not sure of the business results.” Our panel sought to highlight why publishers should reconsider their strategies and explore examples of real AI applications in publishing happening now.
KGL’s Marketing Director, Mike Groth, discussed the benefits of KGL’s Smart Suite tools and how intelligent automation of language quality analysis and copyediting have helped publishers like Taylor & Francis reduce time to publication, boost immediacy of science, improve editorial quality, automate workflows, and decrease manual effort.
Jim Longo, VP of Product Management, HighWire, highlighted the work the platform provider is testing across its hosted content with partners such as Meta, Unsilo, Yewno, and Semantic Scholar, to increase discoverability, improve UX, automate tedious clerical work, analyze trends, and expand the peer review pool.
In April, Springer Nature with Goethe University Frankfurt/Main, created Beta Writer, an algorithm which pulled content to create the first-ever machine-generated book, Lithium-Ion Batteries, which we mentioned in an earlier blog post.
Henning Schoenenberger, Director of Product Data and Metadata Management, Springer Nature stressed the importance of transparency when talking about the algorithm behind Beta Writer. He noted that the process is not just about technology, but also publishing. All of the issues of who is the author, how we handle peer review, etc. play an important role as the industry looks at new ways of creating content. In scientific publishing, AI can help identify research we wouldn’t be able to find among many millions of articles and book chapters. This development can fundamentally change the role of publishers.
And Niels Peter Thomas, Managing Director, Springer Nature, highlighted why AI is so important to the industry. “The move towards using more AI in publishing has two applications. One is to make things faster and the second is to do something completely different, but in a responsible way.”
The speakers suggested that, at the forefront of AI development in the future, we will see scalability without a decrease in quality, a greater understanding and interpretation of data, and a personalization of user experience in navigating content and research.
Although the future of AI is one of freeing up editor time, discovering more information, and bettering the publishing process, one challenge that came up in the discussion is the issue of bias. Who is writing code and helping machines learn offers a limited perspective and that means that a natural, though unintended, bias may skew outcomes. Other industries who are using AI and machine learning more widely are starting to encounter these issues, and we will be highlighting trust and bias as it relates to both publishing and AI in the wider world in our next blog installment.
KnowledgeWorks Global Ltd. (formerly Cenveo Publisher Services) is the industry leader in editorial and production services for every stage of the content lifecycle. We are your source for intelligent automation, high-speed publishing, accessibility compliance, digital learning solutions and more. Email us at info@kwglobal.com.
