The rise and rise of big data promises to drive increasingly imaginative scientific computing approaches, 
says Felix Grant
Beth Harlen reports on the strides being made within the UK climate modelling community
Our ability to digitise the world around us now readily outstrips our ability to process the data we generate. 
Rob Baxter reports
John Barr offers his take on the future of cloud computing and HPC
A level of debate surrounds the question of how and when we might reach exascale computing. Industry experts share their views on the subject
Andrew Jones explains why, 
when it comes to high-performance computing, science/dollar is not 
the same as Flops/dollar
Are informatics solutions keeping up with trends in pharma? Beth Harlen speaks to industry figures
In January 2020 the American Diabetes Association released a new set of clinical standards that, for the first time, included the use of an FDA-approved autonomous artificial intelligence (AI) platform for detecting diabetic retinopathy in people with diabetes.
At the end of February physIQ and the US Department of Veteran’s Affairs published data from a clinical trial demonstrating the use of physIQ’s machine-learning algorithm to predict when an individual patient with previously diagnosed heart failure will likely require a readmission to hospital.
Cloud research tools provide a platform for new avenues of scientific research by providing fast access to bare-metal resources.
New processors and GPU technology continue to demand more power than the previous generations, combining this with increasingly dense architectures requires cooling solutions such as liquid or immersion cooling.
Tate Cantrell, CTO, Verne Global, comments on the cost of the AI computing revolution
European researchers are working on a new programming environment for exascale computing that aims to make using these systems more efficient and programmers more productive.
