BigMuffin69
joined 10 months ago
I'm getting a tramp stamp that says "Remember the Markov Monkey Fallacy"
And the number of angels that can dance on the head of a pin? 9/11
You know for a blog that's on its face about computational complexity, you'd think Scott would show a little more skepticism to the tech bro saying "all we need is 14 quintillion x compute to solve the Riemann hypothesis"
Big Yud: You try to explain how airplane fuel can melt a skyscraper, but your calculation doesn't include relativistic effects, and then the 9/11 conspiracy theorists spend the next 10 years talking about how you deny relativity.
Similarly: A paperclip maximizer is not "monomoniacally" "focused" on paperclips. We talked about a superintelligence that wanted 1 thing, because you get exactly the same results as from a superintelligence that wants paperclips and staples (2 things), or from a superintelligence that wants 100 things. The number of things It wants bears zero relevance to anything. It's just easier to explain the mechanics if you start with a superintelligence that wants 1 thing, because you can talk about how It evaluates "number of expected paperclips resulting from an action" instead of "expected paperclips * 2 + staples * 3 + giant mechanical clocks * 1000" and onward for a hundred other terms of Its utility function that all asymptote at different rates.
The only load-bearing idea is that none of the things It wants are galaxies full of fun-having sentient beings who care about each other. And the probability of 100 uncontrolled utility function components including one term for Fun are ~0, just like it would be for 10 components, 1 component, or 1000 components. 100 tries at having monkeys generate Shakespeare has ~0 probability of succeeding, just the same for all practical purposes as 1 try.
(If a googol monkeys are all generating using English letter-triplet probabilities in a Markov chain, their probability of generating Shakespeare is vastly higher but still effectively zero. Remember this Markov Monkey Fallacy anytime somebody talks about how LLMs are being trained on human text and therefore are much more likely up with human values; an improbable outcome can be rendered "much more likely" while still being not likely enough.)
An unaligned superintelligence is "monomaniacal" in only and exactly the same way that you monomaniacally focus on all that stuff you care about instead of organizing piles of dust specks into prime-numbered heaps. From the perspective of something that cares purely about prime dust heaps, you're monomaniacally focused on all that human stuff, and it can't talk you into caring about prime dust heaps instead. But that's not because you're so incredibly focused on your own thing to the exclusion of its thing, it's just, prime dust heaps are not inside the list of things you'd even consider. It doesn't matter, from their perspective, that you want a lot of stuff instead of just one thing. You want the human stuff, and the human stuff, simple or complicated, doesn't include making sure that dust heaps contain a prime number of dust specks.
Any time you hear somebody talking about the "monomaniacal" paperclip maximizer scenario, they have failed to understand what the problem was supposed to be; failed at imagining alien minds as entities in their own right rather than mutated humans; and failed at understanding how to work with simplified models that give the same results as complicated models
Tim cook is an absolute hustler
Not prying! Thankful to say, none of my coworkers have ever brought up ye olde basilisk, the closest anyone has ever gotten has been jokes about the LLMs taking over, but never too seriously.
No, I don't find the acasual robot god stuff too weird b.c. we already had Pascal's wager. But holy shit, people actually full throat believing it to the point that they are having panic attacks wtf. Like:
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Full human body simulation -> my brother-in-law is a computational chemist, they spend huge amounts of compute modeling simple few atom systems. To build a complete human simulation, you'd be computing every force interaction for approx ~ 10^28 atoms, like this is ludicrous.
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The chuckle fucks who are posing this are suggesting ok, once the robot god can sim you (which again, doubt), it's going to be able to use that simulation of you to model your decisions and optimize against you.
So we have an optimization problem like:
min_{x,y} f(x) s.t. y in argmin{ g(x,y),(x,y) in X*Y}
where x and f(x) would be the decision variables and obj function 🐍 is trying to minimize, and y and g(x,y) is the objective of me, the simulated human who has its own goals, (don't get turned to paperclips).
This is a bilevel optimization problem, and it's very, very nasty to solve. Even in the nicest case possible, that somehow g,f, are convex functions and X,Y are all convex sets, (which is an insane ask considering y and g entails a complete human sim), this problem is provably NP-hard.
Basically, to build the acasual god, first you need a computer larger than the known universe, and this probably isn't sufficient.
Weird note: while I was in academia, I actually did do some work on training ANN to model the constraint that y is a minimizer of a follower problem by using an ANN to act as a proxy for g(x,*), and then encoding a representation of the trained network into a single level optimization problem... we got some nice results for some special low dim problems where we had lots of data🦍 🦍 🦍 🦍 🦍
David, please I was trying to have a nice day.
I got you homie
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The y-axis is absolute eye bleach. Also implying that an "AI researcher" has the effective compute of 10^6 smart high schoolers. What the fuck are these chodes smoking?
AH THE TSP MOVIE IS SO FUN :)
btw, as a shill for big MIP, I am compelled to share this site which has solutions for real world TSPs!
No, they never address this. And as someone who personally works on large scale optimization problems for a living, I do think it's difficult for the public to understand, that no, a 10000 IQ super machine will not be able to just "solve these problems" in a nano second like Yud thinks. And it's not like well, the super machine will just avoid having to solve them. No. NP hard problems are fucking everywhere. (Fun fact, for many problems of interest, even approximating the solution to a given accuracy is NP-hard, so heuristics don't even help.)
I've often found myself frustrated that more computer scientist who should know better simply do not address this point. If verifying solutions is exponentially easier than coming up with them for many difficult problems (all signs point to yes), and if a super intelligent entity actually did exist (I mean does a SAT solver count as a super intelligent entity?), it would probably be EASY to control, since it would have to spend eons and massive amounts of energy coming up with its WORLD_DOMINATION_PLAN.exe, but you wouldn't be able to hide a super computer doing this massive calculation, and someone running the machine seeing it output TURN ALL HUMANS INTO PAPER CLIPS, would say, 'ah, we are missing a constraint here, it thinks that this optimization problem is unbounded' <- this happens literally all the time in practice. Not the world domination part, but a poorly defined optimization problem that is unbounded. But again, it's easy to check that the solution is nonsense.
I know Francois Chollet (THE GOAT) has talked about how there are no unending exponentials and the faster growth the faster you hit constraints IRL (running out of data, running out of chips, running out of energy, etc... ) and I've definitely heard professional shit poster Pedro Domingos explicitly discuss how NP-hardness strongly implies EA/LW type thinking is straight up fantasy, but it's a short list of people who I can think of off the top of my head who have discussed this.
Edit: bizarrely, one person who I didn't mention who has gone down this line of thinking is Illya Sutskever; however, he has come to some frankly... uh... strange conclusions -> the only reason to explain the successful performance of ML is to conclude that they are Kolmogorov minimizers, i.e., by optimizing for loss over a training set, you are doing compression which done optimally is solving an undecidable problem. Nice theory. Definitely not motivated by bad sci-fi mysticism imbued with pure distilled hopium. But from my arm-chair psychologist POV, it seems he implicitly acknowledges for his fantasy to come true, he needs to escape the limitations of Turing Machines, so he has to somehow shoehorn a method for hyper computation into Turing Machines. Smh, this is the kind of behavior reserved for aging physicist, amirite lads? Yet in 2023, it seemed like the whole world was succumbing to this gas lighting. He was giving this lecture to auditoriums filled with tech bro shilling this line of thinking to thunderous applause. I have olde CS prof friends who were like, don't we literally have mountains of evidence this is straight up crazy talk? Like you can train an ANN to perform addition, and if you can look me straight in the eyes and say the absolute mess of weights that results looks anything like a Kolmogorov minimizer then I know you are trying to sell me a bag of shit.