Technology
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
view the rest of the comments
Hey! Just asking you because I'm not sure where else to direct this energy at the moment.
I spent a while trying to understand the argument this paper was making, and for the most part I think I've got it. But there's a kind of obvious, knee-jerk rebuttal to throw at it, seen elsewhere under this post, even:
If producing an AGI is intractable, why does the human meat-brain exist?
Evolution "may be thought of" as a process that samples a distribution of situation-behaviors, though that distribution is entirely abstract. And the decision process for whether the "AI" it produces matches this distribution of successful behaviors is yada yada darwinism. The answer we care about, because this is the inspiration I imagine AI engineers took from evolution in the first place, is whether evolution can (not inevitably, just can) produce an AGI (us) in reasonable time (it did).
The question is, where does this line of thinking fail?
Going by the proof, it should either be:
I'm not sure how to formalize any of this, though.
The thought that we could "encode all of biological evolution into a program of at most size K" did made me laugh.
Ah, but here we have to get pedantic a little bit: producing an AGI through current known methods is intractable.
The human brain is extremely complex and we still don't fully know how it works. We don't know if the way we learn is really analogous to how these AIs learn. We don't really know if the way we think is analogous to how computers "think".
There's also another argument to be made, that an AGI that matches the currently agreed upon definition is impossible. And I mean that in the broadest sense, e.g. humans don't fit the definition either. If that's true, then an AI could perhaps be trained in a tractable amount of time, but this would upend our understanding of human consciousness (perhaps justifyingly so). Maybe we're overestimating how special we are.
And then there's the argument that you already mentioned: it is intractable, but 60 million years, spread over trillions of creatures is long enough. That also suggests that AGI is really hard, and that creating one really isn't "around the corner" as some enthusiasts claim. For any practical AGI we'd have to finish training in maybe a couple years, not millions of years.
And maybe we develop some quantum computing breakthrough that gets us where we need to be. Who knows?
I didn't quite understand this at first. I think I was going to say something about the paper leaving the method ambiguous, thus implicating all methods yet unknown, etc, whatever. But yeah, this divide between solvable and "unsolvable" shifts if we ever break NP-hard and have to define some new NP-super-hard category. This does feel like the piece I was missing. Or a piece, anyway.
I did think about this, and the only reason I reject it is that "human-like or -level" matches our complexity by definition, and we already have a behavior set for a fairly large n. This doesn't have to mean that we aren't still below some curve, of course, but I do struggle to imagine how our own complexity wouldn't still be too large to solve, AGI or not.
Anyway, the main reason I'm replying again at all is just to make sure I thanked you for getting back to me, haha. This was definitely helpful.
That's a great line of thought. Take an algorithm of "simulate a human brain". Obviously that would break the paper's argument, so you'd have to find why it doesn't apply here to take the paper's claims at face value.