Paul Taylor is professor of health informatics at UCL.
Estimated weekly excess deaths in England and Wales in 2021.
One of the tactics used over the past few weeks by Boris Johnson at Prime Minister’s Questions, and by loyal MPs and dutiful ministers touring broadcasting studios, has been to claim that his successful management of the pandemic is more worthy of the public’s attention than trivial issues of garden parties or...
Researchers led by a team from Emory University recently announced that they had used artificial intelligence to predict patients’ self-reported racial identity from medical images. It is an unexpected, unsettling result.
As chest X-rays of Covid-19 patients began to be published in radiology journals, AI researchers put together an online database of the images and started experimenting with algorithms that could distinguish between them and other X-rays. Early results were astonishingly successful, but disappointment soon followed. The algorithms were responding not to signs of the disease, but to minor technical differences between the two sets of images, which were sourced from different hospitals: such things as the way the images were labelled, or how the patient was positioned in the scanner. It’s a common problem in AI. We often refer to ‘deep’ machine learning because we think of the calculations as being organised in layers and we now use many more layers than we used to, but what is learned is nevertheless superficial.
When I first studied artificial intelligence in the 1980s, my lecturers assumed that the most important property of intelligence was the ability to reason, and that to program a computer to perform intelligently you would have to enable it to apply logic to large bodies of facts. Logic is used to make inferences. If you have a general rule, such as ‘All men are mortal,’ and a...
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