I saw a lot of this for the first time during the LK-99 saga when the only active discussion on replication efforts was on r/singularity. For the past solid year or two before LK-99, all they'd been talking about were LLMs and other AI models. Most of them were utterly convinced (and betting actual money on prediction sites!) that we'd have a general AI in like two years and "the singularity" by the end of the decade.
At a certain point it hit me that the place was a fucking cult. That's when I stopped following the LK-99 story. This bunch of credulous rubes have taken a bunch of misinterpreted pop-science factoids and incoherently compiled them into a religion. I realized I can pretty safely disregard any hyped up piece of tech those people think will change the world.
While we're here, can I get an explanation on that one too? I think I'm having trouble separating the concept of algorithms from the concept of causality in that an algorithm is a set of steps to take one piece of data and turn it into another, and the world is more or less deterministic at the scale of humans. Just with the caveat that neither a complex enough algorithm nor any chaotic system can be predicted analytically.
I think I might understand it better with some examples of things that might look like algorithms but aren't.
Thanks for the help, but I think I'm still having some trouble understanding what that all means exactly. Could you elaborate on an example where thinking of something as an algorithm results in a clearly and demonstrably worse understanding of it?
Okay, I think I get it now. I see how one could really twist something like your evolution example every which way to make it look like an algorithm. Things like saying the process to crabs is prescribed by the environmental conditions selecting for crab like traits or whatever, but I can see how doing that is so overly broad as to be a useless way to analyze the situation.
One more thing: I don't know enough about algorithms to really say, but isn't it possible for an algorithm to produce wildly varying results from nearly identical inputs? Like how a double pendulum is analytically unpredictable. What's more, could the algorithmic nature of a system be entirely obscured as a result of it being composed of many associated algorithms linked input to output in a net, some of which may even be recursively linked? That looks to me like it could be a source of randomness and ambiguity in an algorithmic system that would be borderline impossible to sus out.
For the sake of argument, let’s be real generous with the terms “unambiguous”, “sequence”, “goal”, and “recognizable” and say everything is an algorithm if you squint hard enough.
when you soften these words, what you're left with is a heuristic - a method that occasionally does what you expect but that's underspecified. it's a decision procedure where the steps aren't totally clear or that sometimes arrives at unexpected results because it fails to capture the underlying model of reality at play.
I saw a lot of this for the first time during the LK-99 saga when the only active discussion on replication efforts was on r/singularity. For the past solid year or two before LK-99, all they'd been talking about were LLMs and other AI models. Most of them were utterly convinced (and betting actual money on prediction sites!) that we'd have a general AI in like two years and "the singularity" by the end of the decade.
At a certain point it hit me that the place was a fucking cult. That's when I stopped following the LK-99 story. This bunch of credulous rubes have taken a bunch of misinterpreted pop-science factoids and incoherently compiled them into a religion. I realized I can pretty safely disregard any hyped up piece of tech those people think will change the world.
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While we're here, can I get an explanation on that one too? I think I'm having trouble separating the concept of algorithms from the concept of causality in that an algorithm is a set of steps to take one piece of data and turn it into another, and the world is more or less deterministic at the scale of humans. Just with the caveat that neither a complex enough algorithm nor any chaotic system can be predicted analytically.
I think I might understand it better with some examples of things that might look like algorithms but aren't.
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Thanks for the help, but I think I'm still having some trouble understanding what that all means exactly. Could you elaborate on an example where thinking of something as an algorithm results in a clearly and demonstrably worse understanding of it?
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Okay, I think I get it now. I see how one could really twist something like your evolution example every which way to make it look like an algorithm. Things like saying the process to crabs is prescribed by the environmental conditions selecting for crab like traits or whatever, but I can see how doing that is so overly broad as to be a useless way to analyze the situation.
One more thing: I don't know enough about algorithms to really say, but isn't it possible for an algorithm to produce wildly varying results from nearly identical inputs? Like how a double pendulum is analytically unpredictable. What's more, could the algorithmic nature of a system be entirely obscured as a result of it being composed of many associated algorithms linked input to output in a net, some of which may even be recursively linked? That looks to me like it could be a source of randomness and ambiguity in an algorithmic system that would be borderline impossible to sus out.
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also, the word they actually mean is heuristic.
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when you soften these words, what you're left with is a heuristic - a method that occasionally does what you expect but that's underspecified. it's a decision procedure where the steps aren't totally clear or that sometimes arrives at unexpected results because it fails to capture the underlying model of reality at play.
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speaking of 4th dimensional processing, https://en.wikipedia.org/wiki/Holonomic_brain_theory is pretty interesting imo
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