Ok so... a computer? Like how is this any different from the analogue between an actual physical, finite computer and a Turing machine?
> No matter how big computationally the AI is, the test was always trying to deal with something new, outside its comfort zone. Preferably as far outside it as possible. To do that successfully requires general problem solving and abstraction skills.
None of this has any material meaning. I'd love to see someone define what "general problem solving" and "abstraction skills" are—we can write those off as simple terms only because these concepts are how our brains already work. This doesn't mean we have any analytical understanding of these concepts. For one, there are already many algorithms that "recognize patterns" but we don't call that "abstraction skill", we call that an algorithm, or maybe a heuristic. So if you think this term is stably meaningful, what is your analytic understanding of "abstraction skill"? What are we abstracting? With what thing? With what processing? Where? In what context? You might as well say "well once we figure out language the rest of AI will be easy". Well, let me introduce you to the linguistic turn.[0] If we figure out how to process language semantically the way we ourselves do literally all the dominos fall into place. I suspect this won't happen in any of our lifetimes, at least not in any generally affordable way. At the end of the day humans mostly parameterize human data for computers and describe how to interact with it; the current forms of AI parameterize themselves based on data we feed it in the way we tell it to parameterize it, a form of parameterization itself. It is not any more interesting than composition. Language is the ultimate key to parameterization (and obviously algorithm writing but I'm much less interested in that as most humans are bad at writing algorithms of any complexity).
> You cannot precompute most of the answer to a truly open question.
Sure, but isn't that why we have all sorts of algorithms for searching for answers? Again, we don't refer to this as "intelligence", this is just "search". Or maybe you use "dynamic programming", or a "constraint solving algorithm". Whatever these techniques are called in traditional textbooks, we've really just ramped up uses of these algorithms combined with extremely complicated heuristics.... that are still heuristics.
I'm really pushing hard against this term because it really, really, really, really feels like a marketing term for suckers. If it were meaningful, the bounds on the term were probably discovered centuries, if not millennia, ago. The concept of "ai" seems to have been rewritten to mean "hot new algorithm that can be over marketed to imply far more functionality than we technically advertise we offer". I don't understand how the non-suits take the concept seriously. "AI" analytics companies are going to make a shitload of money off these rubes.
What would ACTUALLY BE USEFUL rather than pimping your software as intelligent would be establishing some kind of test to figure out whether or not we've hit AGI. Oh wait, we already have, and nobody's even come close.
And this WHOLE TIME we could have just used "machine learning"—which is both meaningful and an appropriate metaphor for the concept—but Spielberg never directed a movie named "M.L.".
No matter how big computationally the AI is, the test was always trying to deal with something new, outside its comfort zone. Preferably as far outside it as possible. To do that successfully requires general problem solving and abstraction skills.
Of course one can do better or worse at that, including humans.
You cannot precompute most of the answer to a truly open question.