If AI behaves identically to me but our internals are different, does that mean I can learn everything about myself from studying it? If so, the input->output pipeline is the only thing that matters, and we can disregard internal mechanisms. Black boxes are all you need to learn everything about the universe, and observing how the output changes for every input is enough to replicate the functions and behaviours of any object in the world. Does this sound correct? If not, then clearly it is important to point out that the algorithm is doing Y and not X.
There’s no sense in which my computer is doing matrix multiplication but isn’t recognising dogs.
At the level of internal mechanism, the computer is doing neither, it’s just varying transistor voltages.
If you admit a computer can be multiplying matrices, or sorting integers, or scheduling events, etc — then you’ve already appealed to the X-Y Criterion.
NN recognising dogs vs cats as part of an image net classifier that would class a piece of paper with ‘dog’ written on as a dog
GPT-4 able to describe an image of a dog/cat in great detail
Computer doing matrix multiplication.
The range of cases in which the equivalence between the what the computer is doing, and our high level description is doing holds increases as we do down this list, and depending on what cases are salient, it becomes more or less explanatory to say that the algorithm is doing task X.
My claim is that the deep metaphysical distinction is between “the computer is changing transistor voltages” and “the computer is multiplying matrices”, not between “the computer is multiplying matrices” and “the computer is recognising dogs”.
Once we move to a language game in which “the computer is multiplying matrices” is appropriate, then we are appealing to something like the X-Y Criterion for assessing these claims.
The sentences are more true the tighter the abstraction is —
The machine does X with greater probability.
The machine does X within a larger range of environments.
The machine has fewer side effects.
The machine is more robust to adversarial inputs.
Etc
But SOTA image classifiers are better at recognising dogs than humans are, so I’m quite happy to say “this machine recognises dogs”. Sure, you can generate adversarial inputs, but you could probably do that to a human brain as well if you had an upload.
The philosophical leap from voltages to matrices, i.e. allowing that a physical system could ever be ‘doing’ high level description X. This is a bit weird at first but also clearly true as soon you start treating X as having a specific meaning in the world as opposed to just being a thing that occurs in human mind space.
The empirical claim that this high level description X fits what the computer is doing.
I think the pushback to the post is best framed in terms of which frame is best for talking to people who deny that it’s ‘really doing X’. In terms of rhetorical strategy and good quality debate, I think the correct tactic is to try and have the first point mutually acknowledged in the most sympathetic case, and try to have a more productive conversation about the extent of the correlation, while I think aggressive statements of ‘it’s always actually doing X if it looks like its doing X’ are probably unhelpful and become a bit of a scissor. (memetics over usefulness har har!)
I think the issue is that what people often mean by. “computing matrix multiplication” is something like what youve described here, but when (at least sometimes, as you’ve so elegantly talked about in other posts, vibes and context really matter!) talk about “recognizing dogs” they are referring not only to the input output transformation of the task (or even the physical transformation of world states) but also the process by which the dog is recognized, which includes lots of internal human abstractions moving about in a particular way in the brains of people, which may or may not be recapitulated in an artificial classification system.
To some degree it’s a semantic issue. I will grant you that there is a way of talking about “recognizing dogs” that reduces it to the input/output mapping, but there is another way in which this doesn’t work. The reason it makes sense for human beings to have these two different notions of performing a task is because we really care about theory of mind, and social settings, and figuring out what other people are thinking (and not just the state of their muscles or whatever dictates their output).
Although for precisions sake, maybe they should really have different words associated with them, though I’m not sure what the words should be exactly. Maybe something like “solving a task” vs. “understanding a task” though I don’t really like that.
Actually my thinking can go the other way to. I think there actually is a sense in which the computer is not doing matrix multiplication, and its really only the system of computer+human that is able to do it, and the human is doing A LOT of work here. I recognize this is not the sense people usually mean when they talk about computers doing matrix multiplication, but again, I think there are two senses of performing a computation even though people use the same words.
Recognising dogs by ML classification is different to recognising dogs using cells in your brain and eyes, and this makes using the word recognise for AI as though it were exactly identical to the human action of recognising things somewhat inappropriate. Sorting integers is similar, actually. But the difference is no one is confusing the computer sorting integers for the same process people use for sorting integers, it’s a much dumber concept so the word “sorting” is appropriate to use. On the other hand, when you invoke pop sci to say an AI is “recognising emotions’” then yes it can easily confuse people into thinking they are identical processes. No it’s not because one is sacred and the other is not, you’ve confused sacredness with varying degrees of complexity. It’s really just a matter of conveying the right information to readers based on what you assume they understand about computers. Or you could continue to say AI feels things and be no better than a pop sci opinion piece, it’s up to you.
Recognising dogs by ML classification is different to recognising dogs using cells in your brain and eyes
Yeah, and the way that you recognise dogs is different from the way that cats recognise dogs. Doesn’t seem to matter much.
as though it were exactly identical
Two processes don’t need to be exactly identical to do the same thing. My calculator adds numbers, and I add numbers. Yet my calculator isn’t the same as my brain.
when you invoke pop sci
Huh?
No it’s not because one is sacred and the other is not, you’ve confused sacredness with varying degrees of complexity.
What notion of complexity do you mean? People are quite happy to accept that computers can perform tasks with high k-complexity or t-complexity. It is mostly “sacred” things (in the Hansonian sense) that people are unwilling to accept.
or you could continue to say AI feels things
Nowhere in this article to I address AI sentience.
We could easily train an AI to be 70 percentile in recognising human emotions, but (as far as I know) no one has bothered to do this because there is ~ 0 tangible benefit so it wouldn’t justify the cost.
If AI behaves identically to me but our internals are different, does that mean I can learn everything about myself from studying it? If so, the input->output pipeline is the only thing that matters, and we can disregard internal mechanisms. Black boxes are all you need to learn everything about the universe, and observing how the output changes for every input is enough to replicate the functions and behaviours of any object in the world. Does this sound correct? If not, then clearly it is important to point out that the algorithm is doing Y and not X.
There’s no sense in which my computer is doing matrix multiplication but isn’t recognising dogs.
At the level of internal mechanism, the computer is doing neither, it’s just varying transistor voltages.
If you admit a computer can be multiplying matrices, or sorting integers, or scheduling events, etc — then you’ve already appealed to the X-Y Criterion.
Maybe worth thinking about this in terms of different examples:
NN detecting the presence of tanks just by the brightness of the image (possibly apocryphal—Gwern)
NN recognising dogs vs cats as part of an image net classifier that would class a piece of paper with ‘dog’ written on as a dog
GPT-4 able to describe an image of a dog/cat in great detail
Computer doing matrix multiplication.
The range of cases in which the equivalence between the what the computer is doing, and our high level description is doing holds increases as we do down this list, and depending on what cases are salient, it becomes more or less explanatory to say that the algorithm is doing task X.
Yeah, I broadly agree.
My claim is that the deep metaphysical distinction is between “the computer is changing transistor voltages” and “the computer is multiplying matrices”, not between “the computer is multiplying matrices” and “the computer is recognising dogs”.
Once we move to a language game in which “the computer is multiplying matrices” is appropriate, then we are appealing to something like the X-Y Criterion for assessing these claims.
The sentences are more true the tighter the abstraction is —
The machine does X with greater probability.
The machine does X within a larger range of environments.
The machine has fewer side effects.
The machine is more robust to adversarial inputs.
Etc
But SOTA image classifiers are better at recognising dogs than humans are, so I’m quite happy to say “this machine recognises dogs”. Sure, you can generate adversarial inputs, but you could probably do that to a human brain as well if you had an upload.
Hmm, yeah there’s clearly two major points:
The philosophical leap from voltages to matrices, i.e. allowing that a physical system could ever be ‘doing’ high level description X. This is a bit weird at first but also clearly true as soon you start treating X as having a specific meaning in the world as opposed to just being a thing that occurs in human mind space.
The empirical claim that this high level description X fits what the computer is doing.
I think the pushback to the post is best framed in terms of which frame is best for talking to people who deny that it’s ‘really doing X’. In terms of rhetorical strategy and good quality debate, I think the correct tactic is to try and have the first point mutually acknowledged in the most sympathetic case, and try to have a more productive conversation about the extent of the correlation, while I think aggressive statements of ‘it’s always actually doing X if it looks like its doing X’ are probably unhelpful and become a bit of a scissor. (memetics over usefulness har har!)
I think the issue is that what people often mean by. “computing matrix multiplication” is something like what youve described here, but when (at least sometimes, as you’ve so elegantly talked about in other posts, vibes and context really matter!) talk about “recognizing dogs” they are referring not only to the input output transformation of the task (or even the physical transformation of world states) but also the process by which the dog is recognized, which includes lots of internal human abstractions moving about in a particular way in the brains of people, which may or may not be recapitulated in an artificial classification system.
To some degree it’s a semantic issue. I will grant you that there is a way of talking about “recognizing dogs” that reduces it to the input/output mapping, but there is another way in which this doesn’t work. The reason it makes sense for human beings to have these two different notions of performing a task is because we really care about theory of mind, and social settings, and figuring out what other people are thinking (and not just the state of their muscles or whatever dictates their output).
Although for precisions sake, maybe they should really have different words associated with them, though I’m not sure what the words should be exactly. Maybe something like “solving a task” vs. “understanding a task” though I don’t really like that.
Actually my thinking can go the other way to. I think there actually is a sense in which the computer is not doing matrix multiplication, and its really only the system of computer+human that is able to do it, and the human is doing A LOT of work here. I recognize this is not the sense people usually mean when they talk about computers doing matrix multiplication, but again, I think there are two senses of performing a computation even though people use the same words.
Recognising dogs by ML classification is different to recognising dogs using cells in your brain and eyes, and this makes using the word recognise for AI as though it were exactly identical to the human action of recognising things somewhat inappropriate. Sorting integers is similar, actually. But the difference is no one is confusing the computer sorting integers for the same process people use for sorting integers, it’s a much dumber concept so the word “sorting” is appropriate to use. On the other hand, when you invoke pop sci to say an AI is “recognising emotions’” then yes it can easily confuse people into thinking they are identical processes. No it’s not because one is sacred and the other is not, you’ve confused sacredness with varying degrees of complexity. It’s really just a matter of conveying the right information to readers based on what you assume they understand about computers. Or you could continue to say AI feels things and be no better than a pop sci opinion piece, it’s up to you.
Yeah, and the way that you recognise dogs is different from the way that cats recognise dogs. Doesn’t seem to matter much.
Two processes don’t need to be exactly identical to do the same thing. My calculator adds numbers, and I add numbers. Yet my calculator isn’t the same as my brain.
Huh?
What notion of complexity do you mean? People are quite happy to accept that computers can perform tasks with high k-complexity or t-complexity. It is mostly “sacred” things (in the Hansonian sense) that people are unwilling to accept.
Nowhere in this article to I address AI sentience.
There are differences, but the major differences usually are quantitative, not binary changes.
The major differences are compute, energy, algorithms (sometimes), and currently memorylessness (Though PaLM-E might be changing this).
Can a AI recognize emotions right now? IDK, I haven’t heard of any results on it right now.
Can it learn to recognize emotions to X% accuracy? I’d say yes, but how useful that ability is depends highly on how accurate it can be.
There are differences, but the major differences usually are quantitative, not binary changes.
The major differences are compute, energy, algorithms (sometimes), and currently memorylessness (Though PaLM-E might be changing this).
Can a AI recognize emotions right now? IDK, I haven’t heard of any results on it right now.
Can it learn to recognize emotions to X% accuracy? I’d say yes, but how useful that ability is depends highly on how accurate it can be.
We could easily train an AI to be 70 percentile in recognising human emotions, but (as far as I know) no one has bothered to do this because there is ~ 0 tangible benefit so it wouldn’t justify the cost.
Efficiency/lifespan/memory window size.