The human work of tomorrow will not be based on competencies best-suited for machines, because creative work that is continuously changing cannot be replicated by machines or code. While machine learning may be powerful, connected human learning is novel, innovative, and inspired.
A good post on why learning how to learn is the only reasonable way to think about the future of work (and professional education). The upshot is that Communities of Practice are implicated in helping us adapt to working environments that are constantly changing, as will most likely continue to be the case.
However, I probably wouldn’t take the approach that it’s “us vs machines” because I don’t think that’s where we’re going to end up. I think it’s more likely that those who work closely with AI-based systems will outperform and replace those who don’t. In other words, we’re not competing with machines for our jobs; we’re competing with other people who use machines more effectively than we do.
Trying to be better than machines is not only difficult but our capitalist economy makes it pretty near impossible.
This is both true and a bit odd. No-one thinks they need to be able to do complex mathematics without calculators, and those who are better at using calculators can do more complex mathematics. Why is it such a big leap to realise that we don’t have to be better image classifiers than machines either? Let’s accept that diagnosis from CT will be performed by AI and focus on how that frees up physician time for other human- and patient-centred tasks. What will medical education look like when we’re teaching students that adapting while working with machines is the only way to stay relevant? I think that clinicians who graduate from medical schools who take this approach are more likely to be employed in the future.