If there was an argument that proved such a thing, then it must distinguish between humans and 'artificial' intelligences. Can someone explain how they do so?
AGI is Mathematically Impossible 2: When Entropy Returns
231–240 of 437 posts
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#232FTA: > Strange, isn't it? The AI hasn’t crashed. It’s still running. As a human I answer a question because my time to do so is finite. Why can't we just ask an AI to give its best answer in due time ? As a human I can do that easily. Will my answer be optimal ? No of course, but every manager on earth do that all the time. We're all happy with approximate answers. (and I would add: approximation are sometimes based…
G. E. Moore (in his Principia Ethica, 1903) makes a very similar case to this relation to consequentialist ethics:
"The first difficulty in the way of establishing a probability that one course of action will give a better total result than another, lies in the fact that we have to take account of the effects of both throughout an infinite future. We have no certainty but that, if we do one action now, the Universe will, throughout all time, differ in some way from what it would have been, if we had done another; and, if there is such a permanent difference, it is certainly relevant to our calculation.
But it is quite certain that our causal knowledge is utterly insufficient to tell us what different effects will probably result from two different actions, except within a comparatively short space of time; we can certainly only pretend to calculate the effects of actions within what may be called an ‘immediate’ future. No one, when he proceeds upon what he considers a rational consideration of effects, would guide his choice by any forecast that went beyond a few centuries at most; and, in general, we consider that we have acted rationally, if we think we have secured a balance of good within a few years or months or days."
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#233Earlier quoted context omitted.
Thanks for this - Looking forward to reading the full paper. That said, the most obvious objection that comes to mind about the title is that … well, I feel that I’m generally intelligent, and therefore general intelligence of some sort is clearly not impossible. Can you give a short précis as to how you are distinguishing humans and the “A” in artificial?
Sure I can (and thanks for writing) Well, given the specific way you asked that question I confirm your self assertion - and am quite certain that your level of Artificiality converges to zero, which would make you a GI without A... - You stated to "feel" generally intelligent (A's don't feel and don't have an "I" that can feel) - Your nuanced, subtly ironic and self referential way of formulating clearly suggests th…
> You stated to "feel" generally intelligent (A's don't feel and don't have an "I" that can feel) - Your nuanced, subtly ironic and self referential way of formulating clearly suggests that you are not a purely algorithmic entity
This is completely unrelated to the proof in the link. You have to clearly explain what reasoning in your argument for “AGI is impossible” also implies human intelligence is possible. You can’t just jump to conclusions “you sound human therefore intelligence is possible”
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#234Earlier quoted context omitted.
could you explain for a layman
I'm not sure if this will help, but happy to elaborate further: The set of Turing computable functions is computationally equivalent to the lambda calculus, is computationally equivalent to the generally recursive functions. You don't need to understand those terms, only to know that these functions define the set of functions we believe to include all computable functions . (There are functions that we know to not b…
What program would a Turing machine run to spontaneously prove the incompleteness theorem?
Can you prove such a program may exist?
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#235I find the mathematics in this paper a little incoherent so it's hard to criticise it on those grounds - but on a charitable read, something that sticks out to me is the assumption that AGI is some fixed total computable function from the fixed decision domain to a policy.
AIs these days autonomously seek information themselves. Much like living things, they are recycling entropy and information to/from their environment (the internet) at runtime. The framing as a sterile, platonic algorithm is making less and less sense to me with time.
(obviously they differ from living things in lots of other ways, just an example)
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#236> Therefore the jhalting problem is to aply and the problem is not computable.
I'm not a pedantic person, but they didn't even perform the most basic spell check or proofreading. This greatly reduces my trust in this paper.
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#237I find the mathematics in this paper a little incoherent so it's hard to criticise it on those grounds - but on a charitable read, something that sticks out to me is the assumption that AGI is some fixed total computable function from the fixed decision domain to a policy. AIs these days autonomously seek information themselves. Much like living things, they are recycling entropy and information to/from their environ…
Ok - where do AIs put the information that they "seek" from the internet?
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#238Earlier quoted context omitted.
“Imagine little Albert asking his physics teacher in 1880: "Sir - for how long do I have to stay at high speed in order to look as grown up as my elder brother?"” Is that not the other way around? “…how long do I have to stay at high speed in order for my younger brother to look as grown up as myself?”
Indeed. One of my other thoughts here on the Relativity example was "That sets the bar high given most humans can't figure out special relativity even with all the explainers for Einstein's work". But I'm so used to AGI being conflated with ASI that it didn't seem worth it compared to the more fundamental errors.
Given rcxdude’s reply it appears I am one of those humans who can’t figure out special relativity (let alone general)
Wrt ‘AGI/ASI’, while they’re not the same, after reading Nick Bostrom (and more recently https://ai-2027.com) I hang towards AGI being a blib on the timeline towards ASI. Who knows.
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#239If there was an argument that proved such a thing, then it must distinguish between humans and 'artificial' intelligences. Can someone explain how they do so?
Seems like a provocative piece that stirs up some discussion, which is good. But I get what you're hinting at. Humans are GI and obviously exist. So it's trivially disproven by counter-example.
Re: AGI is Mathematically Impossible 2: When Entropy Returns
#240Earlier quoted context omitted.
The mathematical proof, as you describe it, sounds like the "No Free Lunch theorem". Humans also can't generalise to learning such things. As you note in 2.1, there is widespread disagreement on what "AGI" means. I note that you list several definitions which are essentially "is human equivalent". As humans can be reduced to physics, and physics can be expressed as a computer program, obviously any such definition ca…
1. I appreciate the comparison — but I’d argue this goes somewhat beyond the No Free Lunch theorem. NFL says: no optimizer performs best across all domains. But the core of this paper doesnt talk about performance variability, it’s about structural inaccessibility. Specifically, that some semanti spaces (e.g., heavy-tailed, frame-unstable, undecidable contexts) can’t be computed or resolved by any algorithmic policy…
> Specifically, that some semanti spaces (e.g., heavy-tailed, frame-unstable, undecidable contexts) can’t be computed or resolved by any algorithmic policy — no matter how clever or powerful. The model does not underperform here, the point is that the problem itself collapses the computational frame.
I see no proof this doesn’t apply to people