Speaking at a technology event in 2017, Mark Cuban, the billionaire owner of the Dallas Mavericks, predicted that the world’s first trillionaire will be whoever masters commercial applications for AI, the reason being that artificial intelligence could prove particularly lucrative when applied to traditionally ‘white collar’ industries like insurance, software development or accountancy. ‘I would rather be a philosophy major,’ Cuban said of those training to enter such professions today.
Attention-seeking? Perhaps. Hyperbolic? Absolutely. Wrong? Probably not, because from cardiac surgery to calculating taxes, historically well-paid professions are just as repetitive and subject to the division of labour, and therefore automation, as anything else.
Take the da Vinci surgery robot. In 2017, University College London announced that this relatively low-cost machine had saved the lives of around 500 men with prostate cancer. While the robot itself is not automated – it instead grants a human surgeon far higher levels of dexterity and precision – the paths to automating a range of its regular operations resemble the blueprint for a self-driving car: you give a powerful data processor huge amounts of information, machine learning and a scalpel. The first part allows algorithms to model and reproduce outcomes and work their way through highly repetitive tasks, while the second allows for immediate and smart responses to unexpected situations.
In medicine, you can see how that would be applied to pretty much anything – from eye examinations to treating prostate cancer or taking blood. In areas more dependent on pattern recognition, such as radiology, machines have even more of an advantage. Radiologists use medical images like X-rays, CT and PET scans, MRIs and ultrasounds to diagnose and treat patients. While the field has greatly improved patient care over the last few decades, it has contributed to escalating costs and is relatively labour intensive. That is, until now.
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