In a paper published this week on the preprint server Arxiv.org, They say that CovidQA, which is a work in progress, could help gauge the accuracy of chatbots and search engines that answer topics about the novel coronavirus. Countries, health systems, and nonprofits around the world are employing AI natural language tools to triage potential COVID-19 patients. But as our investigation in early April revealed, chatbots, in particular, rely on inconsistent medical data sources and privacy practices. Data sets like CovidQA could be used to empirically compare the accuracy of the answers supplied by COVID-19 chatbots, exposing gaps in their knowledge and giving users greater peace of mind.

The White House and others have released the free COVID-19 Open Research Dataset -- known as CORD-19 -- and are asking researchers around the world to apply AI techniques like natural language processing to drum up new insights, which could help us better understand and fight the coronavirus. Specifically, they want AI experts to develop text and data mining tools, which could help the medical community answer high-priorty questions about the novel coronavirus. With more than 29,000 scholarly articles, the freely available dataset is the "most extensive collection of machine readable coronavirus literature to date," says U.S. CTO Michael Kratsios. Broken down, 13,000 of the articles are available in their entirety and machine-readable, while 16,000 articles have partial text and summaries. It will be constantly updated with new information. It's the work of Microsoft Research, the Allen Institute for AI, the Chan Zuckerberg Initiative, the National Library of Medicine at the National Institutes of Health (NIH), the White House Office of Science and Technology (OSTP), Georgetown University's Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and Google's Kaggle AI platform, which is also hosting the database and researchers' submissions.