Greetings from the ANU School of Computing, where Qingyun Wang from University of Illinois Urbana-Champaign is speaking on Accelerating and Democratizing Scientific Research Lifecycle. Much has been written about more research of lower quality being published. Rather than bemoaning the role of AI in making this worse, here the idea is to use "AI for Scientists" (AI4Scientist) to address it. The first part of this is to automatically analyze papers to provide structured data. This can be used to explain the paper to the reader and also fact check it.
This can be taken further to expand the generation of hypotheses, but so far in very restricted well defined fields, such as biomedical. AI could also be used to seek out "hidden treasures" in the scientific literature. This would be a boon, as there is a lot of papers which simply repeat previous research, with slight variations.
What I find most interesting is the possibility to use this to teach students to write better papers. It could also be used to help working researchers write more readable papers, with fewer errors. It would be interesting to see how well this would work for non-STEM disciplines where the format of papers is not so constrained. A particular problem for human readers, and likely for AI, are interdisciplinary research.
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