eh, the exaggeration aside…the trend lately IS extremely worrying.
especially the treatment of protesters.
that said I’m pretty sure that’s got little to do with this situation…tend to agree with the others ITT: “libertarian” and “free speech” is seldom a good combination of words…
i know it’s not the point of the thread, but FYI:
bunker busters can’t destroy cities. not practically, anyways.
they’re hyper-specialized munitions that get dropped from high up in order to pick up a lot of gravitational momentum to then penetrate deep underground and through multiple layers of concrete.
they’re long, extremely resilient, stupidly heavy rods with relatively small warheads.
they’re the "rods from god"s little cousins, essentially.
although there’s some speculation that some kinds of modern concrete might be strong enough to withstand even multiple direct hits…
the kind of munitions that could cause enough destruction to level a city are M.O.A.B.s, for conventional arms, or straight-up nuclear bombs.
…or you could use a LOT of smaller bombs, like the russians in ukraine, or the israelis in gaza.
but bunker busters, while having an objectively badass name, are not actually that scary a weapon, unless you’re sitting in an actual bunker; they’re more of a precision weapon!
no, none of those are what i mean, that’s way too specific to be useful.
a system exhibits intelligence when it can use existing insights to build entirely new insights.
a popular example is that no current “AI” can extrapolate from basic mathematical stipulations to more advanced ones.
(there’s tons of example you could put here, but this is the one i like)
here’s the example:
teach an LLM/DNN/etc. basic addition, subtraction, multiplication, and division.
give it some arbitrary, but large, number of problems to solve.
it will eventually encounter a division that isn’t possible, but is not a divide-by-zero (which should be covered by the rules it was given).
then it will either:
…but what it will definitely NEVER do, is simply create a placeholder for that operation and give it a name: square root (or whatever ot calls it, that part isn’t important).
it simply can’t, because that would be a new insight, and that’s something these systems aren’t capable of.
a human (or a lot of them) would encounter these impossible divisions and eventually see a pattern in them and draw the proper conclusion: that this is a new bit of math that was just discovered! with new rules, and new applications!
even if it takes a hundred years and scores of them, humans will always, eventually, figure it out.
…but what we currently call “artificial intelligence” will simply never understand that. the machine won’t do that, no matter how many machines you throw at the problem.
because it’s not a matter of quantity, but of quality.
and that qualitative difference is intelligence!
(note: solving this particular math problem is a first step. it’s unlikely that it will immediately lead to an AGI, but it is an excellent proof-of-concept)
this is also why LLMs aren’t really getting any better; it’s a structural problem that can’t be solved with bigger data sets.
it’s a fundamental design flaw we haven’t yet solved.
current "AI"s are probably a part of the solution, but they are, definitely, not THE solution.
we’ve come closer to an AI, but we’re not there.
when it can come up with a solution it hasn’t seen before.
that’s the threshold.
that’s the threshold for creative problem solving, which isn’t all there is to intelligence, but i think it’s fair to say it’s the most crucial part for a machine intelligence.
generally, yes, but it’s a couple more now;
point being: it’s a clear trend!
it’s slow, yes, but it seems to be picking up steam!
the idea is being seriously discussed at basically all state institutions.
and more importantly: the reason for this trend is clearly data security. which states actually care about. so there’s a very clear and easy to understand incentive, which makes it politically palatable.
we’ll have to see, but the trend seems to be heading in the right direction!