Artificial intelligence can spot when correlation does mean causation

A new Artificial Intelligence (AI) has allowed AI researchers, for the first time, to demonstrate a useful and reliable way of sifting through masses of correlating data to spot when correlation means causation. By fusing old, overlapping and incomplete datasets this new method, inspired by quantum cryptography, paves the way for researchers to glean the results of medical trials that would otherwise be too expensive, difficult or unethical to run. The research is being published at the prestigious and peer-reviewed Association for Advancement of Artificial Intelligence (AAAI) conference in New York. Dr Saurabh Johri, Chief Science Officer at Babylon, said: "Until now, we have been limited to piecing together answers from studies that needed to capture all the data really neatly. But when we've seen a correlation between obesity and low vitamin D in one study, and obesity and heart failure in another, we have not been able to say whether vitamin D has a causal role in heart failure without doing another, hugely expensive clinical trial. Now we can put the pieces of the jigsaw together."