Jabe Wilson addresses the challenges facing researchers these days, and how publishers are trying to help them find and develop new knowledge on R&D Magazine:
As scholarly publishing has become a digital enterprise, the move to create semantic data that captures knowledge has increased significantly. Another way to describe this trend is as a shift in focus from reading articles as a whole, towards finding individual, semantic ‘facts’ reported in publications. This has been driven by the maturing of automated approaches to identifying and extracting these facts; as well as the steps to bring AI into fruition in the form of machine learning. In essence, recent years have seen a step-change from human curation in isolation to rules-based automated indexing approaches, and then to the applications of statistical approaches such as deep learning and machine reasoning. These approaches are helping researchers to access insights in a far shorter time period, greatly improving productivity.
Semantic data is important to R&D, because it means we can link facts that are related across papers, and over different domains of knowledge – enabling us to deliver insights that might not be obvious from reading one paper alone. To do so requires normalizing the terminology with taxonomies, to allow a network to be created. The increasing reliance on linked facts mean the demands of the modern researcher are changing; researchers need bespoke analytics products for their specific needs, built on robust semantic databases. In today’s world, this more often than not means solutions that combine semantic technology methods, augmented with machine learning and machine reasoning approaches.
The support tools are growing ever more sophisticated. Does this mean they’re also more and more expensive to design, build, and maintain? How hard is it to bring a new engineer on board and have her up to speed within, say, 6 months? But in our current world of science, this is necessary because …
These techniques offer the ability to aim AIs at problems we are interested in solving, and having the means to understand and interpret the answers the AIs provide. Together, these two factors of growing interest in AI and greater collaboration will become increasingly important if we are to overcome the productivity crises that many disciplines of science and research are experiencing. Research at Stanford University has indicated that since the 1930s, the effective number of researchers at work has increased by a factor of 23, but annual growth in productivity has declined. As a result, new ideas are becoming more expensive to find; using AI to augment researchers will be a key weapon in the fight to overcome these issues.
Because, of course, a decline in growth indicates a moral failing in the researchers, or at least the money-driven managers would have us believe. I’d say it’s a matter of a paucity of low-hanging fruit, myself, and now we have to start climbing the trees to get the delicious fruit that beckons us.
And AI will be the guy who boosts our valiant researchers up the tree, I think. I wonder if they’re teaching courses at university on how to work with AI augmentation tools….
