But what do machine learning and cloud computing mean for scientific research? How can these tools help researchers to manage and navigate the vast datasets generated by increasingly sophisticated detectors and experiments? At big science facilities, data streams that used to be megabits per second are now hundreds of times faster, sometimes even more, as detector upgrades come online and new instruments are installed. That’s a lot of data to analyse and verify.

At the same time, scientific discovery is increasingly a multidisciplinary endeavour that is changing the way that scientists work together. Even if researchers manage to carry their data back to their desktop loaded on armfuls of portable hard drives, how are they going to collaborate on the analysis?

We examine some of these questions in a two-part series produced by Physics World on behalf of Tessella – an international data science, analytics and AI technology consulting services provider with years of experience in partnering with leading research organizations. In the first, we find out how cloud computing is allowing scientists to collaborate on research projects by providing easy access to shared resources. The second article explores the potential of artificial intelligence to accelerate the process of scientific discovery and extract meaningful information from increasingly large datasets. We hear from experts across a variety of scientific fields to examine what it takes to deploy effective cloud and AI solutions, highlighting the challenges, rewards, and trends that will shape the research of the future.

Richard Layne. Tessella. Posted: October 03, 2019.