Glyn Britton made a good point in his piece about how AI and automation may actually free us up to be more human by enabling people to stop doing work that can actually be better done by machines.
'…what if we turn the narrative around? Look at AI and automation as a opportunity to repair, strengthen and deepen the exclusively human disciplines of empathy, intuition, judgement and creativity…'
It reminded me that I'd read last year (in one of the few empirically supported articles on the subject of whether automation actually does replace human jobs, which uses a study by Harvard economist James Bessen) that there is only one of the 270 occupations that are listed in the 1950 US Census that has since been completely eliminated by automation: the lift operator.
Instead (as a McKinsey analysis also quoted shows), whilst few jobs are likely to disappear completely it's likely that automation will replace at least some work in almost all job areas. So as with most things the answer to how automation might progress lies in the subtleties. One thought that might inform this is considering the different types of problem that we may typically have to solve in a job. Some areas of work are repeatable, relatively simple and unchanging – these are obvious candidates for automation since they are the domain of efficiency and best practice. Other areas may be characterised by more complicated problems that involve more variables – there is likely to ultimately be a significant role for automation here too, although since this is the domain of expertise it's likely to require more sophisticated automation involving machine learning perhaps, and it may be a time before these areas are fully automated. Complex problems however, have many variables but these many variables are also changing all the time, which presents learning challenges that potentially require the most sophisticated AI to solve.
There is, I realise, a fourth type of problem – so-called 'wicked problems'. These are not only complex, with multiple changing variables, but some of these variables may be characterised by contradictory, incomplete or hard to define information and the problem itself may be continuously evolving, thereby potentially enabling a better outcome but denying a complete resolution. It's likely that automation can bring scaled assistance of some kind to solving every kind of problem area, but the nuance of this is in how human and machine are blended, and I do wonder if the real value for those exclusively human disciplines that Glyn talks about will increasingly be in those complex and wicked challenges that characterise so much of our human existence.
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