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This reminds me of Moravec's paradox (https://en.wikipedia.org/wiki/Moravec%27s_paradox). Basically, tasks that seem hard for humans are easy for computers and vice versa.

Have a look at how driving a taxi has changed. (And I include the likes of Uber here.)

The hard part for humans used to be knowing all the roads in a city and selecting the best route quickly and reliably. Almost any adult can do the actual second-to-second driving reasonably competently in almost any city on the planet.

Now, we have outsourced the 'hard' part to Google Maps. But we are still far from a machine that drives in arbitrary locations on the globe. As far as I know, Waymo has the most mature system currently in development, but requires absurd amounts of precise mapping data for any location they want to drive in.

And let's not even talk about the even more 'trivial' task of chatting to the passengers.

> [...], but it doesn't seem to be very good at science/engineering (drug discovery/self driving cars/radiology).

Technology has already automated huge chunks for science and engineering. We just don't call any of the already solved chunks by the name of AI anymore.




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