Jay

Karma: 388

Jay 16 Nov 2024 0:28 UTC
1 point
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in reply to: Noosphere89’s comment on: Quick look: applications of chaos theory
The ancients considered everything to be the work of spirits. The medievals considered the cosmos to be a kingdom. Early moderns likened the universe to a machine. Every age has its dominant metaphors. All of them are oversimplifications of a more complex truth.

Jay 16 Nov 2024 0:20 UTC
1 point
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in reply to: quetzal_rainbow’s comment on: Quick look: applications of chaos theory
Suppose you had an identical twin with identical genes and, until very recently, an identical history. From the perspective of anyone else, you’re similar enough to be interchangeable with each other. But from your perspective, the twin would be a different person.
The brain is you, full stop. It isn’t running a computer program; its hardware and software are inseparable and developed together over the course of your life. In other words, the hardware/software distinction doesn’t apply to brains.

Jay 14 Nov 2024 11:25 UTC
1 point
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in reply to: Noosphere89’s comment on: Quick look: applications of chaos theory
To call something an “uploaded brain” is to make two claims. First, that it is a (stable) mind. Second, that it is in some important sense equivalent to a particular meat brain (e.g., that its output is the same as the meat brain, or that its experiences are the same as the meat brain’s). The sorts of methods you’re talking about to stabilize the mind help with the first claim, but not with the second.
I’ve always struggled to make sense of the idea of brain uploading because it seems to rely on some sort of dualism. As a materialist, it seems obvious to me that a brain is a brain, a program that replicates the brain’s output is a program (and will perform its task more or less well but probably not perfectly), and the two are not the same.

Jay 19 Oct 2024 20:30 UTC
4 points
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on: Struggling like a Shadowmoth
What if there would never be someone I trusted who could tell me I was Good Enough, that things were in some sense Okay?
The internalized feeling that you’re not okay is a huge part of what motivates you to become better. If you lost it, you would be much more likely to become complacent and stagnate. Both inner peace and relentless drive are profoundly valuable, but they are mutually exclusive.

Jay 22 Aug 2024 22:52 UTC
3 points
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in reply to: Ben’s comment on: Quick look: applications of chaos theory
I certainly agree that brains are complicated.
I think part of the difference is that I’m considering the uploading process; it seems to me that you’re skipping past it, which amounts to assuming it works perfectly.
Consider the upload of Bob the volunteer. The idea that software = Bob is based on the idea that Bob’s connectome of roughly 100 trillion synapses is accurately captured by the upload process. It seems fairly obvious to me that this process will not capture every single synapse with no errors (at least in early versions). It will miss a percentage and probably also invent some that meat-Bob doesn’t have.
This raises the question of how good a copy is good enough. If brains are chaotic, and I would expect them to be, even small error rates would have large consequences for the output of the simulation. In short, I would expect that for semi-realistic upload accuracy (whatever that means in this context), simulated Bob wouldn’t think or behave much like actual Bob.

Jay 21 Aug 2024 21:53 UTC
1 point
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in reply to: Ben’s comment on: Quick look: applications of chaos theory
Surely both (1) and (2) are true, each to a certain extent.
Are the random thermal fluctuations pushing me around somehow better than the equally random measurement errors pushing my soft-copy around?
It depends. We know from experience how meat brains change over time. We have no idea how software brains change over time; it surely depends on the details of the technology used. The changes might be comparable, but they might be bizarre. The longer you run the program, the more extreme the changes are likely to be.
I can’t rule it out either. Nor can I rule it in. It’s conceivable, but there are enough issues that I’m highly skeptical.

Jay 21 Aug 2024 11:20 UTC
−1 points
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in reply to: Elizabeth’s comment on: Quick look: applications of chaos theory
Let’s try again. Chaotic systems usually don’t do exactly what you want them to, and they almost never do the right thing 1000 times in a row. If you model a system using ordinary modeling techniques, chaos theory can tell you whether the system is going to be finicky and unreliable (in a specific way). This saves you the trouble of actually building a system that won’t work reliably. Basically, it marks off certain areas of solution space as not viable.
Also, there’s Lavarand. It turns out that lava lamps are chaotic.

Jay 21 Aug 2024 0:41 UTC
2 points
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in reply to: Jay’s comment on: Quick look: applications of chaos theory
That wasn’t well phrased. Oops.

Jay 21 Aug 2024 0:40 UTC
1 point
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in reply to: Hudjefa’s comment on: Quick look: applications of chaos theory
Any physical system has a finite amount of mass and energy that limit its possible behaviors. If you take the log of (one variable of) the system, its full range of behaviors will use fewer numbers, but that’s all that will happen. For example, the wind is usually between 0.001 m/s (quite still) and 100 m/s (unprecedented hurricane). If you take the base-10 log, it’s usually between −3 and 2. A change of 2 can mean a change from .001 to .1 m/s (quite still to barely noticeable breeze) or a change from 1 m/s to 100 m/s (modest breeze to everything’s gone). For lots of common phenomena, log scales are too imprecise to be useful.
Chaotic systems can’t be predicted in detail, but physics and common sense still apply. Chaotic weather is just ordinary weather.

Jay 20 Aug 2024 11:17 UTC
4 points
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in reply to: Hudjefa’s comment on: Quick look: applications of chaos theory
That’s the point. Nobody thought such tiny variations would matter. The fact that they can matter, a lot, was the discovery that led to chaos theory.

Jay 19 Aug 2024 22:50 UTC
−2 points
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in reply to: tailcalled’s comment on: Quick look: applications of chaos theory
Consider—A typical human brain has ~100 trillion synapses. Any attempt to map it would have some error rate. Is it still “you” if the error rate is .1%? 1%? 10%? Do positive vs. negative errors make a difference (i.e. missing connections vs. spurious connections)?
Is this a way to get new and exciting psychiatric disorders?
I don’t know the answers, or even how we’d try to figure out the answers, but I don’t want to spend eternity as this guy.

Jay 19 Aug 2024 22:39 UTC
−5 points
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in reply to: Elizabeth’s comment on: Quick look: applications of chaos theory
Hastings clearly has more experience with chaos theory than I do (I upvoted his comment). I’m hoping that my rather simplistic grasp of the field might result in a simpler-to-understand answer (that’s still basically correct).
Chaos theory is a branch of math; it characterizes models (equations). If your model is terrible, it can’t help you. What the theory tells you is how wildly your model will react to small perturbations.
AFAIK the only invention made possible by chaos theory is random number generators. If a system you’re modeling is extremely chaotic, you can use its output for random numbers with confidence that nobody will ever be able to model or replicate your system with sufficient precision to reproduce its output.

Jay 19 Aug 2024 21:58 UTC
2 points
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in reply to: faul_sname’s comment on: Quick look: applications of chaos theory
an exponential decrease in measurement error will only buy you a linear increase in how long that simulation is good for.
True, and in the real world attempts to measure with extreme precision eventually hit limits imposed by quantum mechanics. Quantum systems are unpredictable in a way that has nothing to do with chaos theory, but that cashes out to injecting tiny amounts of randomness in basically every physical system. In a chaotic system those tiny perturbations would eventually have macroscopic effects, even in the absence of any other sources of error.

Jay 18 Aug 2024 22:50 UTC
5 points
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in reply to: Elizabeth’s comment on: Quick look: applications of chaos theory
The seminal result for chaos theory came from weather modeling. An atmospheric model was migrated to a more powerful computer, but it didn’t give the same results as it had on the old computer. It turned out that, in the process of migration, the initial condition data had been rounded to the eighth decimal place. The tiny errors compounded into larger errors, and over the course of an in-model month the predictions completely diverged. An error in the eighth decimal place is roughly comparable to the flap of a butterfly’s wing, which led to the cliche about butterflies and hurricanes.
If you’re trying to model a system, and the results of your model are extremely sensitive to miniscule data errors (i.e. the system is chaotic), and there is no practical way to obtain extremely accurate data, then chaos theory limits the usefulness of the model. It may still have some value; using standard models and available data it’s possible to predict the weather rather accurately for a few days and semi-accurately for a few days more, but it may not be able to predict what you need.
This is one reason I’ve always been skeptical of the “uploaded brain” idea. My intuition is that inevitable minor errors in the model of the brain would cause the model to diverge from the source in a fairly short time.

Jay 25 Jul 2024 10:54 UTC
0 points
−4
in reply to: quila’s comment on: quila’s Shortform
I don’t want to live in a world where there’s only the final survivors of selection processes who shrug indifferently when asked why we don’t revive all the beings who were killed in the process which created the final survivors.
If you could revive all the victims of the selection process that brought us to the current state, all the crusaders and monarchists and vikings and Maoists and so, so many illiterate peasant farmers (on much too little land because you’ve got hundreds of generations of them at once, mostly with ideas that make Putin look like Sonia Sotomayor), would you? They’d probably make quite the mess. Bringing them back would probably restart the selection process and we probably wouldn’t be selected again. It just seems like a terrible idea to me.

Jay 21 Jul 2024 13:13 UTC
1 point
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in reply to: Linch’s comment on: Are the majority of your ancestors farmers or non-farmers?
In that case, it’s non-farmers by a good margin. Our ancestry goes back well over a billion years, mostly in species with short generation times. Farming goes back roughly ten thousand years in a single species with a ~25 year generation time.

Jay 19 Jul 2024 22:40 UTC
5 points
−2
in reply to: Raemon’s comment on: Optimistic Assumptions, Longterm Planning, and “Cope”
Almost everyone believes one and only one of the following statements:
1. We have, or can soon develop, the technology to power society with clean energy at a modern standard of living, or
2. Global warming is not a major problem (e.g. it won’t damage agricultural productivity to famine-inducing levels).
Logically, these statements have nothing to do with each other. Either, neither, or both could be correct (I suspect neither). They are, in the words of the post, questionable assumptions.
See also Beyond the Peak – Ecosophia and Germany’s economy struggles with an energy shock that’s exposing longtime flaws | AP News.

Jay 19 Jul 2024 10:52 UTC
6 points
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on: Optimistic Assumptions, Longterm Planning, and “Cope”
FYI, the green energy field is much, much worse in this regard than the AI field.

Jay 5 May 2024 13:15 UTC
3 points
1
in reply to: Jay’s comment on: Duct Tape security
I should have added—Determine whether this is a modeling problem or a manufacturing problem. If the model was sound but the physical screw was faulty, you’ll need an entirely different response.

Jay 27 Apr 2024 20:58 UTC
25 points
8
on: Duct Tape security
Actually ideal:
1. Reinforce that screw by the end of the day.
2. Fix the modeling error by the end of the week.
3. Develop a more robust modeling methodology over the next few months.
4. Brainstorm ideas to improve the institutional culture (without sacrificing flexibility, because you’re aware that these values require a tradeoff). Have a proposal ready for the next board meeting.