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Royal Society report on machine learning

27 June 2017

The Royal Society released a report in April 2017 entitled Machine learning: the power and promise of computers that learn by example. Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience.

Machine learning: the power and promise of computers that learn by example.

Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules. Recent years have seen significant advances in machine learning, which have raised its capabilities across a suite of applications. Increasing data availability has allowed machine learning systems to be trained on a large pool of examples, while increasing computer processing power has supported the analytical capabilities of these systems. Within the field itself there have also been algorithmic advances, which have given machine learning greater power. As a result of these advances, systems which only a few years ago performed at noticeably below-human levels can now outperform humans at some specific tasks. 

Dr Sabine Hauert was an active member of the working group which spent 18 months putting the report together. One of the benefits of the technology that was highlighted is its potential use in medicine, for example to improve diagnosis of cancer. In healthcare, machine learning could help provide more accurate diagnoses and more effective healthcare services, through advanced analysis that improves decision-making. One example of this function comes from breast cancer diagnoses, which typically include an assessment by pathologists of a tissue sample. A machine learning system trained on tissue images was able to achieve a higher accuracy than pathologists, by finding and utilising features of the image that were predictive but had not previously been used in the pathology assessments. In doing so, the system was able to help doctors more accurately assess a patient’s prognosis. Sabine wrote an article for BBC News, published 25 April 2017, which mentions the use of machine learning in cancer. 

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