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I see parallels with AGI/takeoff. "It's just 2 years away" every year. KK agues that the process is continuous, but AI optimists argue the inflection will be ab
by paulpauper 9mo ago
I see parallels with AGI/takeoff. "It's just 2 years away" every year. KK agues that the process is continuous, but AI optimists argue the inflection will be abrupt , like a step-function.
- CrazyStat 9mo agoThe premise of the singularity concept was always superhuman intelligence, so it’s not so much a parallel as a renaming of the same thing. > In Vinge’s analysis, at some point not too far away, innovations in computer power would enable us to design computers more intelligent than we are, and these smarter computers could design computers yet smarter than themselves, and so on, the loop of computers-making-newer-computers accelerating very quickly towards unimaginable levels of intelligence.
- d_silin 9mo agoWould never work in reality, you can't optimize algorithms beyond their computation complexity limits. You can't multiply matrix x matrix (or vector x matrix) faster than O(N^2). You can't iterate through array faster than O(N). Search & sort are sub- or near-linear, yes - but any realistic numerical simulations are O(N^3) or worse. Computational chemistry algorithms can be as hard as O(N^7). And that's all in P class, not even NP.
- dekhn 9mo agoWe don't need to optimize algorithms beyond their computational complexity limits to improve hardware.
- d_silin 9mo agoHardware is bound by even harder limits (transistor's gate thickness, speed of light, Amdahl's law, Landauer's limit and so on).
- dekhn 9mo agoBut that doesn't disprove the hypothesis that in principle you can have an effective self-improvement loop (my guess is that it would quickly turn into extremely limited gains that do not justify the expenditure).
- d_silin 9mo agoAny such "self-improvement loop" would have a natural ceiling, though. From both algorithmic complexity and hardware limits of underlying compute substrate. P.S. I am not arguing against, but rather agreeing with you.
- rbanffy 9mo agoThe natural ceiling is the amount of compute per unit of energy. At the point you can no longer improve energy efficiency, you can still add more energy to operate more compute capacity.
- d_silin 9mo agoWhich hints that truly superintelligent AIs will consume vast amount of energy to operate and matter to build.
- rbanffy 9mo agoAt some point they’ll hit the speed of light as a limit to how quickly it can propagate its internal state to itself - as the brain grows larger, the mind slows down or breaks apart into smaller units that can work faster before rejoining the bigger entity and propagating its new state. Must feel really strange.
- dekhn 9mo agoRealistically, the physical limits to computation are the speed of light and energy dissipation.
- razodactyl 9mo agoYou're measuring speed not intelligence. It's a different metric.
- d_silin 9mo agoIt is exactly the same metric. Intelligence is not magic, be it organic or LLM-based. You still need to go through the training set data to make the any useful extrapolations about the unknown inputs.
- direwolf20 9mo agohttps://en.wikipedia.org/wiki/Computational_complexity_of_matrix_multiplication https://en.wikipedia.org/wiki/Computational_complexity_of_ma... The n in this article is the size of each dimension of the matrix — N=n^2. Lowest known is O(N^1.175...). Most practical is O(N^1.403...). Naive is already O(N^1.5) which, you see, is less than O(N^2).
- d_silin 9mo agoWell, but still superlinear.