> but I won't be impressed because all of those things are simple

When professionals in the field repeatedly say "X requires general intelligence" and yet time after time we solve them with shockingly simple techniques, the correct response is not to deride the AI for performing under expectations. It's to recognize that professionals have systematically overestimated the intrinsic difficulty of tasks, even really complex ones like photorealistic image synthesis, and propagate this belief through to everything else they say is hard.

I don't know what the next groundbreaking AI achievement is going to be, but I'd be happy to bet that the solution is going to be simpler than people expect. And while this doesn't necessarily generalize to everything, since there's a selection effect (though I suspect it's secondary), it does generalize to a lot of very important things.

Oh, you never ran into Hugo de Garis?

iirc (it was back in 1997 when I heard him talk), he was promising human level "artilects" about 2015, and super-human intelligence by 2025.

The problem that I have observed, is that the popular press always ignores the 100 experts that say, nope, not going to happen, for the one "visionary", who'll promise whatever they want to hear, as long as somebody else is paying for dinner.

One correction: the military absolutely does not have any more advanced technology than the private sector. Very much the opposite, they've been totally incompetent at figuring out how to build software, or even buying software from the traditional contractors, which is the motivation for eric schmid'ts defense innovation stuff and Google's project maven. Many of the statements of the current DoD secretary flat out admit this stuff (plus if you've had the misfortunate working for a traditional DoD contractor it's also laughably obvious).

I came to say roughly this but you’ve said it with more style.

Who is to say that in 15 years we won’t make difference-of-kind advancements in, say, natural language sythesis by virtue of a broad patchwork, piecewise sort of landscape made up of advancements in restaurant reservations, calendar items, customer service chatbots, etc.

Each solution region would be driven by incremental research and profitability constraints, and it’s fine to point at any one solution region and say, well that probably won’t generalize to something “AI complete” in natural language.

But as each bespoke arm of research gets more advanced, we could learn metaheuristics about selecting between them, or distilling common solution architectures.

People, even an informed academic in this article, still seem to appeal to some vague idea that “real” human level skill at a generic sythetic task like natural language has some mystical greater theoretical structure to it.

But time and again we see that the steady accumulation of a frankensteined patchwork of many approaches and many bespoke, situation-specific architectures can end up with uncanny, competitive results on tasks of import.

I’m not saying there’s anything about current fashionable methods that gives any guarantees, and of course there is always overblown hype, especially in corners of industry that try to use that hype for credential signalling and rent-seeking opportunities.

But it just seems short-sighted to come across as so certain that ugly patchworks of different siloed solutions could never be glued together or agglomerated to make difference-of-kind improvements. In fact, applied machine learning seems to a historical trail of exactly this type of patchwork.

Are there going to be cases when a whole new theoretical paradigm is needed to reach some performance goal? Yes, of course. And any sensible current practitioner would admit as much.

But this is a reason to throw the baby out with the bathwater?

The "simplicity" of tasks that mostly just require a lot of computing power was recognized over thirty years ago: https://en.wikipedia.org/wiki/Moravec%27s_paradox

> it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility - 1988