At least in my bubble, AI discussions have been going in predictable ways.. and getting grounded in the same places.
First, we try to build the (proverbial) foundation: What is AI? What is intelligence? Is it general? Lots of places to get stuck here. Can machines understand meaning? Is general intelligence statistical. Can it be?
No real way of settling these, so we have poor foundations.
Then we ask: What can it do? When? What will it do? Why? Can it automate driving? Other stuff? How big a deal is this economically? Will all cars be taxis?
At this point, foundations crack. We're trying to predict the economic side-effects of a technology, its viability, timelines, regulation... and we're building these predictions on very abstract foundations. Obviously, it's all too squishy so we end up nowhere.
Anyway, the effects of technology are very hard to predict... especially recent ones. Computerisation of offices has not measurably increased productivity since the 80s/90s[1], for example. A PC landed on every desk. Many more people work at a desk than before. What predictions would we have made in the 90s, when this was starting to look inevitable.
[1]David Graeber, Tyler Cowen & others highlight this point. It's hard to define or measure, so most economists don't. But within wide margins of error, it does not seem to
>Computerisation of offices has not measurably increased productivity since the 80s/90s[1],
As far as I can tell. It isn't AI, or whatever technology that is not getting productivities increases in business. When was the last time you saw a CRM / ERP replacement that had any productivities increase? It seems there is a very clear divider in between all Technology Companies vs Other non-tech company. It is that No one knows how to best integrate the two to maximise potentials. Only the one which have their feet on both side ( Amazon ) seems to understand it.
I think it runs deeper than "can't figure out how to X."
We're very good at figuring out productivities when dealing with a factory or somesuch. A transport authority or facebook "business headquarters" doesn't interplay with technology in the same ways.
First, we try to build the (proverbial) foundation: What is AI? What is intelligence? Is it general? Lots of places to get stuck here. Can machines understand meaning? Is general intelligence statistical. Can it be?
No real way of settling these, so we have poor foundations.
Then we ask: What can it do? When? What will it do? Why? Can it automate driving? Other stuff? How big a deal is this economically? Will all cars be taxis?
At this point, foundations crack. We're trying to predict the economic side-effects of a technology, its viability, timelines, regulation... and we're building these predictions on very abstract foundations. Obviously, it's all too squishy so we end up nowhere.
Anyway, the effects of technology are very hard to predict... especially recent ones. Computerisation of offices has not measurably increased productivity since the 80s/90s[1], for example. A PC landed on every desk. Many more people work at a desk than before. What predictions would we have made in the 90s, when this was starting to look inevitable.
[1]David Graeber, Tyler Cowen & others highlight this point. It's hard to define or measure, so most economists don't. But within wide margins of error, it does not seem to