I’m simply very curious about the subject, it’s super important :)! Given that, I’m also frustrated with what seems like a popular lack of critical thought and curiosity on the specifics.
In these comments, when I’ve talked about an intelligence I can distinguish, I’ve been talking about human / animal intelligence. AGI implies an intelligence independent of that, so I’m asking about the specifics there - what are we calling intelligence if not “what humans do”?
If we are calling it just that, then I’d argue everything I know about how these models do things is very different from what I know of how humans approach the specific tasks the models are built against. And I’ve read that that’s intentional. So, even with that sort of definition I don’t see how it follows that these approaches are on any linear path to AGI (maybe nonlinear if we learn limits and such from mistakes).
I’ve since read more of the article (it’s long, huh?) I like the framework they use from Roitblat in Section 2 - and again, don’t see how LLMs and such are on the road to fulfilling those criteria.
The person you originally replied to and I disagree that we have any real, practical definition. I can recognize what humans and to an extent animals do as intelligent, but haven’t seen a definition that separates that intelligent behavior from them. I have never seen anything that’s been called ai do something I could call intelligent in that animal-like sense (though some have been impressive in the same way Google / page rank was impressive when it first came out)
So, I don’t see why rubbing these statistical model sticks should suddenly burst into intelligence, but I’m open to seeing convincing reasoning on that at some point. I wouldn’t invest time or energy in the meantime and like that original poster, think it’s kinda insane to if my goal was to see human-like intelligence emerge outside of humans
That’s a good start, but doesn’t help us since even the most basic programs can fit that definition and are not what we mean when we say AGI. I have yet to see anything approaching a useful definition of intelligence across several discussions of impressive new language, and other statistical models - it feels like we should have that before talking about making it real!
With fire, even “when things turn from themselves to ashes and produce heat” (which I imagine a prehistoric child could come up with) distinguishes fire usefully from most other phenomena in the world
But we can hardly define intelligence, let alone “entirely understand” it. A child could give a good , practical definition of fire and manipulate it skillfully thousands of years ago. Not so much us grown adults wrt intelligence today.
Grog could define fire in a very real way even back then, which is how he knew he’d so easily created it . It is hard for us to even know intelligence (the one we mean in AGI) when we see it, much less create it, no?
But either way, is knowing the electron knowing electricity? There are so many properties of it that can be known and manipulated without that insight- and indeed they built up that understanding to reach practical engineering and use of electricity. That’s what I think is being gotten at wrt intelligence.
“Knowing” something isn’t necessarily about being aware of its smaller parts.
What are other examples of things we engineer before knowing what they are? I’m honestly having trouble thinking of any.
And I think it’s a shame we haven’t considered what the limits are on “intelligence” very much. Knowing them has been immeasurably valuable in software engineering and algorithms, for one.
In this case, it’s not so much the mechanisms of pharma we wouldn’t know, but what it is we’re even trying to cure - not having a definition of the disease or it’s symptoms and yet trying to engineer a cure would be pretty insane.
In these comments, when I’ve talked about an intelligence I can distinguish, I’ve been talking about human / animal intelligence. AGI implies an intelligence independent of that, so I’m asking about the specifics there - what are we calling intelligence if not “what humans do”?
If we are calling it just that, then I’d argue everything I know about how these models do things is very different from what I know of how humans approach the specific tasks the models are built against. And I’ve read that that’s intentional. So, even with that sort of definition I don’t see how it follows that these approaches are on any linear path to AGI (maybe nonlinear if we learn limits and such from mistakes).
I’ve since read more of the article (it’s long, huh?) I like the framework they use from Roitblat in Section 2 - and again, don’t see how LLMs and such are on the road to fulfilling those criteria.