5 ms·

we can know certain things about things that are smarter than us. perhaps even smarter than a post singularity intelligence, for instance, we are "computation

by _qfi9 4y ago

we can know certain things about things that are smarter than us. perhaps even smarter than a post singularity intelligence, for instance, we are "computationally" equivalent to a turing machine: a tape with symbols on it and a head that reads and writes symbols to the tape according to rules. the natural generalization of a turing machine to infinity would be a hyper turing machine: an infinitely wide tape and infinitely many heads. if one were to employ some kind of godel numbering esque scheme, most (the overwhelming majority) of the symbols the hyper turing machine used in it's operation/had in it's rule lookup table would correspond to transcendental numbers, e.g. pi or e (cuz their cardinality is larger than natural numbers). The alphabet our math uses is pretty much all finite. What percent of the axioms and theorems that you know are infinitely long? Shouldn't the overwhelming majority of axioms and theorems be infinitely long (and in particular map onto an uncountable set? (e.g. transcendental numbers). So a hyper turing machine would be a supra-mathematical entity. But you can pretty much use math to deduce that.

geysersam 4y ago
> we are "computationally" equivalent to a turing machine: Why do you think that? We know of modes of computation different from Turing machines, quantum computers. There's nothing saying there might not be yet other kinds of computers currently undiscovered.
- zzzzzzzza 4y ago
  actually i think we are more limited than a turing machine since even the default turing machine has a (single) infinite tape/running time. But there is a quantum turing machine/quantum lambda calculus as well. just has (probably?) a faster running time on a subset of algorithms. but essentially turing machine/quantum turing machine have the same kinds of inpus/outputs or domain/range, whereas a hyper turing machine has a bigger domain/range.
  - geysersam 4y ago
    I don't understand what makes you think humans operate anything like Turing machines to begin with. The only way I see to reach this conclusion is to assume Turing machines are all that exist, and the conclusion follows.
    - kbelder 4y ago
      Physics (as far as we know) is calculable and can be simulated with a Turing machine. Humans reside within the domain of physics. Basically, either humans operate like Turing machines, or we're magic.
- edanm 4y ago
  > We know of modes of computation different from Turing machines, quantum computers. That's wrong. Quantum computers are Turing complete just like any other form of computation we've found / invented. You can't do something on a quantum computer that you can't do on any computer, albeit in some cases you can get a speed-up by using a quantum computer.
maemre 4y ago
I am nerd-sniped with the computability claims here. The last paragraph sums up what I think about what you wrote more broadly. That description of a "hyper Turing machine" is pretty boring and ridiculous, such a machine trivially solves any computational problem we have because you can embed the problem into the transition function. Here is a sketch of how to do it: Let's call the heads on each tape 0, 1, ...; and let the input be on tape 0. Then the following is a machine that _decides_ a given language: 1. read the input and place the first symbol to tape 1, the second table to tape 2, ... . This takes a single step! 2. Read the whole input by reading a single symbol from each tape, and accept it if it is in the language. This can be done because the transition function can map each string to accept or reject state directly now. Now, here is the kicker: there are uncountably many "hyper" Turing machines but only countably many strings, so almost all of these machines cannot be described, and there cannot be a universal "hyper" Turing machine. So, I don't think they are that interesting. Note that the alphabet here is still finite, the infinity is handled by having infinitely many tapes (this is equivalent to going through the trouble of building up something like Goedel encoding). Moreover, I didn't specify the number of tapes here, but countably infinitely many tapes are enough, so you don't need to build an "uncountably-wide" tape. The point I'm trying to make is that if you let a Turing machine-like thing to have countably infinite descriptions, then the description may as well be "look up the solution" so it gets boring from that point on. We need only countably infinite descriptions because a language is countably infinite (because it is a subset of the set of all strings). If one tries to do some shenanigans like making the languages also have higher cardinalities, well just pump up the cardinality of the "hyper" Turing machine to match and you'd end up with the same proofs with slight changes. > The alphabet our math uses is pretty much all finite. What percent of the axioms and theorems that you know are infinitely long? Shouldn't the overwhelming majority of axioms and theorems be infinitely long (and in particular map onto an uncountable set? (e.g. transcendental numbers). As for this, we _want_ all of that (and proofs) to be finite. That gives a lot of nice structure we can work with when dealing with first-order logic and proving stuff like compactness and (in)completeness. All of our axioms and theorems are finite (assuming using FOL), we just have countably infinitely many of them (building countably infinitely many theorems is trivial, as for axioms: axiom schemas are just a countably infinite collection of axioms). This was all to set the record straight when it comes to computability theory. If you want tamer examples of hypercomputation, there is a lot of work on oracle machines and Turing machines that can take infinitely many steps. I think those would be better examples for the "we can reason about 'supra-mathematical' entities using plain old math." claim you are making. Although, I am not buying into this because these are all mathematical entities. We do mathematics because it is an interesting endeavor, and computability is important only in that it helps us understand the math we are doing (can we prove all "true" statements? can we devise an algorithm to solve X?), anything beyond that is still math, and a lot of the notions here (Turing machine as a proxy for algorithm, using first order logic, picking a particular set of axioms) are arbitrary choices that work well so that we can do math with a foundation that won't make us lose much sleep.
gwern 4y ago
The Turing machine argument (which Scott Aaronson has also made) for superintelligence being comprehensible is pointless because you wouldn't have time to run the TM to completion (look up how many FLOPS it takes to run even merely a GPT-3 model for a conversation and estimate how long it'd take you to do that by hand), the Chinese Room argument applies (only the TM understands, you, a mere component, do not), and humans are too error-prone and backslide too much to achieve certain things. Why can't you teach the dumbest kid in your school calculus if he's equivalent to a Turing machine? Because he forgets too fast to learn anything on net! The further up in difficulty you get, the more time gets spent on review and relearning before any new material can be broached. I've tutored low-performing kids, and you can almost see them forgetting material as it slips away from them after a brief period of comprehension, fading like a dream upon awakening, leaving only frustration and dissatisfaction. Even more extreme example: my mother worked in special ed and told me about how each year higher you go with the kids, the more you have to review; especially in contrast to the mainstreamed kids. Unsurprisingly, this asymptotes at a low level. Perhaps the most extreme example are 'click' or threshold things: you can talk about dynamic programming to someone all you want, but if it hasn't clicked, it isn't there; many Ravens matrix style problems are like that; and Piagetian developmental stages are famous for that - if you are a kid who doesn't get that volume of water is conserved from a rectangular glass to a square glass, you don't get it.

by _qfi9 4y ago

geysersam 4y ago

> we are "computationally" equivalent to a turing machine: Why do you think that? We know of modes of computation different from Turing machines, quantum computers. There's nothing saying there might not be yet other kinds of computers currently undiscovered.

zzzzzzzza 4y ago
actually i think we are more limited than a turing machine since even the default turing machine has a (single) infinite tape/running time. But there is a quantum turing machine/quantum lambda calculus as well. just has (probably?) a faster running time on a subset of algorithms. but essentially turing machine/quantum turing machine have the same kinds of inpus/outputs or domain/range, whereas a hyper turing machine has a bigger domain/range.
- geysersam 4y ago
  I don't understand what makes you think humans operate anything like Turing machines to begin with. The only way I see to reach this conclusion is to assume Turing machines are all that exist, and the conclusion follows.
  - kbelder 4y ago
    Physics (as far as we know) is calculable and can be simulated with a Turing machine. Humans reside within the domain of physics. Basically, either humans operate like Turing machines, or we're magic.
edanm 4y ago
> We know of modes of computation different from Turing machines, quantum computers. That's wrong. Quantum computers are Turing complete just like any other form of computation we've found / invented. You can't do something on a quantum computer that you can't do on any computer, albeit in some cases you can get a speed-up by using a quantum computer.

maemre 4y ago

I am nerd-sniped with the computability claims here. The last paragraph sums up what I think about what you wrote more broadly. That description of a "hyper Turing machine" is pretty boring and ridiculous, such a machine trivially solves any computational problem we have because you can embed the problem into the transition function. Here is a sketch of how to do it: Let's call the heads on each tape 0, 1, ...; and let the input be on tape 0. Then the following is a machine that _decides_ a given language: 1. read the input and place the first symbol to tape 1, the second table to tape 2, ... . This takes a single step! 2. Read the whole input by reading a single symbol from each tape, and accept it if it is in the language. This can be done because the transition function can map each string to accept or reject state directly now. Now, here is the kicker: there are uncountably many "hyper" Turing machines but only countably many strings, so almost all of these machines cannot be described, and there cannot be a universal "hyper" Turing machine. So, I don't think they are that interesting. Note that the alphabet here is still finite, the infinity is handled by having infinitely many tapes (this is equivalent to going through the trouble of building up something like Goedel encoding). Moreover, I didn't specify the number of tapes here, but countably infinitely many tapes are enough, so you don't need to build an "uncountably-wide" tape. The point I'm trying to make is that if you let a Turing machine-like thing to have countably infinite descriptions, then the description may as well be "look up the solution" so it gets boring from that point on. We need only countably infinite descriptions because a language is countably infinite (because it is a subset of the set of all strings). If one tries to do some shenanigans like making the languages also have higher cardinalities, well just pump up the cardinality of the "hyper" Turing machine to match and you'd end up with the same proofs with slight changes. > The alphabet our math uses is pretty much all finite. What percent of the axioms and theorems that you know are infinitely long? Shouldn't the overwhelming majority of axioms and theorems be infinitely long (and in particular map onto an uncountable set? (e.g. transcendental numbers). As for this, we _want_ all of that (and proofs) to be finite. That gives a lot of nice structure we can work with when dealing with first-order logic and proving stuff like compactness and (in)completeness. All of our axioms and theorems are finite (assuming using FOL), we just have countably infinitely many of them (building countably infinitely many theorems is trivial, as for axioms: axiom schemas are just a countably infinite collection of axioms). This was all to set the record straight when it comes to computability theory. If you want tamer examples of hypercomputation, there is a lot of work on oracle machines and Turing machines that can take infinitely many steps. I think those would be better examples for the "we can reason about 'supra-mathematical' entities using plain old math." claim you are making. Although, I am not buying into this because these are all mathematical entities. We do mathematics because it is an interesting endeavor, and computability is important only in that it helps us understand the math we are doing (can we prove all "true" statements? can we devise an algorithm to solve X?), anything beyond that is still math, and a lot of the notions here (Turing machine as a proxy for algorithm, using first order logic, picking a particular set of axioms) are arbitrary choices that work well so that we can do math with a foundation that won't make us lose much sleep.

gwern 4y ago

The Turing machine argument (which Scott Aaronson has also made) for superintelligence being comprehensible is pointless because you wouldn't have time to run the TM to completion (look up how many FLOPS it takes to run even merely a GPT-3 model for a conversation and estimate how long it'd take you to do that by hand), the Chinese Room argument applies (only the TM understands, you, a mere component, do not), and humans are too error-prone and backslide too much to achieve certain things. Why can't you teach the dumbest kid in your school calculus if he's equivalent to a Turing machine? Because he forgets too fast to learn anything on net! The further up in difficulty you get, the more time gets spent on review and relearning before any new material can be broached. I've tutored low-performing kids, and you can almost see them forgetting material as it slips away from them after a brief period of comprehension, fading like a dream upon awakening, leaving only frustration and dissatisfaction. Even more extreme example: my mother worked in special ed and told me about how each year higher you go with the kids, the more you have to review; especially in contrast to the mainstreamed kids. Unsurprisingly, this asymptotes at a low level. Perhaps the most extreme example are 'click' or threshold things: you can talk about dynamic programming to someone all you want, but if it hasn't clicked, it isn't there; many Ravens matrix style problems are like that; and Piagetian developmental stages are famous for that - if you are a kid who doesn't get that volume of water is conserved from a rectangular glass to a square glass, you don't get it.