Comment by m11a
8 days ago
> that's not what _humanity_ is about
I've not spent too long thinking on the following, so I'm prepared for someone to say I'm totally wrong, but:
I feel like the services economy can be broadly broken down into: pleasure, progress and chores. Pleasure being poetry/literature, movies, hospitality, etc; progress being the examples you gave like science/engineering, mathematics; and chore being things humans need to coordinate or satisfy an obligation (accountants, lawyers, salesmen).
In this case, if we assume AI can deal with things not in the grey zone, then it can deal with 'progress' and many 'chores', which are massive chunks of human output. There's not much grey zone to them. (Well, there is, but there are many correct solutions; equivalent pieces of code that are acceptable, multiple versions of a tax return, each claiming different deductions, that would fly by the IRS, etc)
I have considered this too. I frame it as problem solving. We are solving problems across all fields, from investing, to designing, construction, sales, entertainment, science, medicine, repair. What do you need when you are solving problems? You need to know the best action you can take in a situation. How is AI going to know all that? Some things are only tacicly known by key people, some things are guarded secrets (how do you make cutting edge chips, or innovative drugs?), some rely on experience that is not written down. Many of those problems have not even been fully explored, they are open field of trial and error.
AI progress depends not just on ideation speed, but on validation speed. And validation in some fields needs to pass through the physical world, which makes it expensive, slow, and rate limited. Hence I don't think AI can reach singularity. That would only be possible if validation was as easy to scale as ideation.
I'm not sure where construction and physical work goes into your categories. Process and chores maybe. But I think AI will struggle in the physical domain - validation is difficult and repeated experiments to train on are either too risky, too costly or potentially too damaging (i.e. in the real world failure is often not an option unlike software where test benches can allow controlled failure in a simulated env).
Neither, my categories only cover "services" (at least as Wikipedia would categorise things into this bracket: https://en.wikipedia.org/wiki/Service_economy).
I agree with you on construction and physical work.