[qsort]

> Your response on definitions actually supports my point on the matter

I'm afraid I failed to understand your point, then. I don't have a problem with what you said there.

> using the word "intelligence" is misleading. I do not, however, think this misuse has any serious consequences

This is where I fundamentally differ. Its misuse implies a connection between a formal model (algorithm expressed in a computational model) and a philosophical concept (intelligence) that's dubious at best. On a conceptual level, this makes it harder to reason clearly about those fundamentally mathematical and abstract concepts, and on a concrete level, it misleads the public at large, implying that certain goals have been reached when that's plainly untrue. That's pretty "serious" in my book.

> Then that is an unfortunate choice of proxy, unless, perhaps, you intended to imply that it is a priori impossible for intelligence to be created by running x86 machine code.

Again, I'm afraid I don't understand your objection. It's widely accepted that all reasonable computational models are equivalent. Citing x86 was colorful language, it has clearly no bearing on the point at large. Machine Learning algorithms are clearly computable, which means they are expressible as Turing machines, terms of a classical untyped lambda calculus, Python scripts, C++ template metaprograms, or anything else. They are literally just programs.

> At least since Newton, mathematical models have proved very useful in discerning "truths about the world." Are we to just assume they will not work for the biological phenomena of intelligence and consciousness?

I certainly believe mathematical models to be useful, you would be hard pressed to say otherwise. The ontological status of scientific theories is however at the very least a debatable topic. One needs not believe Newtonian mechanics is ontologically true, it's a tenable position to claim it's just a model, and we accept that model because it's useful.

Specifically, one could easily argue that Newtonian mechanics is false, because, for example, it fails to accurately predict Mercury's orbit.

Similarly, one needs not believe ML is anything more than relatively simple math to find it useful.

[mannykannot]

> I'm afraid I failed to understand your point, then. I don't have a problem with what you said there.

You have to go back a couple of posts to see the point. There, you wrote "'Intelligence' is a word that, etymologically and semantically, is related to human or human-like capabilities. You wouldn't say that a leaf floating on a lake is swimming." As we are now agreed that definitions follow from knowledge and are modified in response to new knowledge, it would not be somehow wrong to extend the concept of intelligence to a certain class of machines, if it turns out to be useful and informative to do so.

> They are literally just programs.

I take it, then, that you don't agree with the sort-of Platonist view that algorithms have an existence independently of any implementation? I'm on the fence, myself, but lean towards the Platonist side.

Regardless, it follows from your position here that your original statement "What's happening in my brain is something we don't have full scientific knowledge of, but we know it's not x86 machine code" can be rewritten as "What's happening in my brain is something we don't have full scientific knowledge of, but we know it's not computable." - but while the former is true, the status of the latter is not yet decided, so they are not identical propositions.

> One needs not believe Newtonian mechanics is ontologically true, it's a tenable position to claim it's just a model, and we accept that model because it's useful.

One could say the same about specific ontologies - they are as subject to revision in the face of increasing knowledge as are both mathematical models and individual words - and if it turns out that a mathematical model of biological intelligence or consciousness is effective and useful, it would be tendentious to imagine an ontological line between that model and intelligence.

[qsort]

> it would not be somehow wrong to extend the concept of intelligence to a certain class of machines, if it turns out to be useful and informative to do so.

No objections, but it's a pretty big "if". You could restate my point as "there is no evidence it is in fact useful".

> I take it, then, that you don't agree with the sort-of Platonist view that algorithms have an existence independently of any implementation? I'm on the fence, myself, but lean towards the Platonist side.

I don't, my position is essentially formalist. While I believe most research mathematicians would side with me here, your position is absolutely valid; famously, Kurt Godel was a Platonist, as are many others. My only observation here is that even from a Platonist point of view you are not really rejecting formalism, at least in the sense that while you don't agree it's the only view, I find it impossible to argue that one can't view mathematical objects as formal constructs.

> "What's happening in my brain is something we don't have full scientific knowledge of, but we know it's not computable." - but while the former is true, the status of the latter is not yet decided, so they are not identical propositions.

No, I don't really hold that view. I probably worded that badly. My position is that the latter is unknown, and I would be content to accept that my brain is not fundamentally different than an algorithm if you showed me an algorithm that can effectively emulate my brain within an acceptable margin of error. Ironically, if it were possible to do that, it would be proof there is no "intelligence", only "computability", making the first entirely redundant.

> One could say the same about specific ontologies -

Again, this is really thin ice. I don't really know what to think about this because it's getting too abstract for my monkey brain, but it's certainly not obvious that a better model implies an ontological line between itself and reality. To put it bluntly, is a better model really "more true", i.e. qualitatively different from a worse one?

I'll take the seventh.

[magusdei]

Can you specify what you mean by "emulate my brain within an acceptable margin of error"? What would this mean as an actual experiment? Depending on your answer, I think we can actually test your implicit proposition that no such algorithm exists.

[qsort]

I'm not actually claiming no such algorithm exists, as I have already stated, but simply that such a thing is not known to exist.

I'm not an expert in neurosciences so I can only give an informal description. Let's also remove "me" from the equation, let's talk about a randomly chosen human H. We know for a fact that there is nothing physically special about human brains, they are just ordinary organic matter. This matter forms a system subject to the laws of physics. With enough computational power and scientific knowledge (we have neither as of now, AFAIK), we could write a program for a quantum Turing machine that runs a 1:1 simulation of H's brain in software. Any quantum program can be emulated by a Turing machine equipped with sufficient random numbers with at most an exponential slowdown, making this program computable in exactly the classical sense.

My questions are (1) is it possible, even in principle, to make a program of this kind? (2) Would such a program be sufficiently predictive (with any statistical notion of that concept you prefer) of H's behavior?

If there exists a program that satisfies both (1) and (2), then I'm content with the notion that I am, myself, not significantly different from such a program.

[magusdei]

Hmm, this still doesn't quite answer my question at the level of concreteness I was looking for. But thank you for clarifying.

The thing is, you can only define predictive accuracy relative to some experimental design. Otherwise you can always claim that there is some unknown, unperformed experiment where the predictions of the model and your actual behaviour would diverge to a greater degree than is permissible by your accuracy threshold, no matter how many successful experiments have already been done in constrained conditions.

Imagine a task where you have to classify images as being of dogs or non-dogs. We can already train a model that can almost perfectly predict the choices you would make during the runs of such an experiment. But we obviously wouldn't call such a model a "model of your brain"!

My question is this: what would be a sufficient experimental design or empirical criterion to decide that some program is a model of you? The loosest criterion I could imagine would be something like "can successfully deceive your loved ones into believing they are you in a single text chat of unbounded duration with some extremely high success rate." Recent advances in NLP lead me to believe that we'll be able to reach at least this level of fidelity quite soon.

[mannykannot]

To be fair, qsort is not insisting on seeing an algorithm that is metaphysically identical with a human mind, nor claiming that such an algorithm would be a p-zombie, devoid of subjective experiences, which are both positions that you might find from dualist philosophers.

[mannykannot]

> You could restate my point as "there is no evidence it is in fact useful"

And if you had originally stated your point that way, I would probably pointed out that there is equally no evidence that it will not be useful, if it turns out to be the case.

> ...but it's certainly not obvious that a better model implies an ontological line between itself and reality.

Clearly, I failed to get my point across, to the point where I cannot guess where this question is coming from. Let's see if I can be clearer...

My position on the definitions of words is that they are contingent on our knowledge and that new knowledge can change our definitions (I gave 'energy' as an example, and you appear to have accepted this point a couple of posts back.)

I take the same view of ontologies; the categories we see are contingent on what we know and may change as our knowledge increases. This should not be surprising, given that ontologies are specific cases of words with meanings that nominally pertain to how the world is. There is no implication here that a better model implies an ontological line between itself and reality; rather, the point here is effectively a "so what" reply to your statement, "it's a tenable position to claim [Newtonian mechanics] is just a model, and we accept that model because it's useful." Mutatis mutandis, as they say, and consequently, there is no justification for holding on to old ontologies if new facts suggest a better alternative, any more than there is for models or theories.

> To put it bluntly, is a better model really "more true", i.e. qualitatively different from a worse one?

Did you mean to write that, especially given that, in your previous post, you offered an argument for the proposition that "Newtonian mechanics is false, because, for example, it fails to accurately predict Mercury's orbit." If there is a relevant point here, I think it is that "more true" models are qualitatively better (and quantitatively better, also.)

> Ironically, if it were possible to [show an algorithm that can effectively emulate your brain within an acceptable margin of error], it would be proof there is no "intelligence", only "computability", making the first entirely redundant.

How so? If "intelligence" is a useful concept now (and your objection to "artificial intelligence" seems to be predicated on it being so), when we do not know if the mind is computationally modelable, why would this usefulness necessarily vanish if this turns out to be the case?

> I'll take the seventh.

? - I'm not familiar with this expression.

[qsort]

> And if you had originally stated your point that way, I would probably pointed out that there is equally no evidence that it will not be useful, if it turns out to be the case.

No objections, but isn't it a bit weird to argue that we should do that just in case it might be useful someday? We'll deal with it when it comes up.

> Clearly, I failed to get my point across

I'm sorry, I'm pretty sure there's an argument but I just don't get it. I'm not really following the train of thought anymore.

>> I'll take the seventh. >? - I'm not familiar with this expression.

Play on words: https://en.wikipedia.org/wiki/Fifth_Amendment_to_the_United_...

https://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus

[mannykannot]

> No objections, but isn't it a bit weird to argue that we should do that just in case it might be useful someday?

That is not an argument that we should do that just in case it might be useful someday, it is just a response to the quoted statement. As you know, I am not in favor of the current usage of "intelligence" in AI, and the only thing we differ on in that regard is whether it matters much.

> I'm sorry, I'm pretty sure there's an argument but I just don't get it.

It is an argument that ontologies are not privileged, canonical or fixed ways of representing the world; they have to conform to current knowledge as it evolves, or be replaced, and they are only interesting if they can "earn their keep" by being useful. Consequently, I do not think your argument from ontology, that this abusage of "intelligence" is a big deal, is definitive.