The buzz surrounding artificial intelligence (AI) is hard to ignore. Huge data sets and large amounts of compute are the perfect match for deep learning, wowing us with algorithms that have beaten grandmasters at games of chess and Go. Today, with access to no more than a web browser, the benefits enabled by breakthroughs in image and speech recognition – not to mention machine translation and more – are just a few clicks away.

There’s the growth of the cloud to consider too. We’re depositing more and more of our data on virtual drives that make it easier to share, back-up and transfer information – providing us with new opportunities both in the office and at home.

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 is Head of Big Science at Tessella