Why general artificial intelligence will not be realized • Hacker News Board

Re: Why general artificial intelligence will not be realized

#351

Post by phonypc » Sat, Jul 11, 2020, 1:54 PM UTC

codr7 wrote: post #337

Strilanc wrote: post #194

Earlier quoted context omitted.

> The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.

But humans learn by themselves, from observing the world around them and drawing conclusions. Which is a major advantage and a core problem in strong AI. The only reason it works is that a lot of humans put a lot of effort into more or less figuring out exactly how to ride a bike. It doesn't generalize at all, teaching the same robot to ride a skateboard would mean doing it all over again.

>It doesn't generalize at all, teaching the same robot to ride a skateboard would mean doing it all over again.

This doesn't sound right, but I'm just a curious layman when it comes to AI.

I'm thinking AlphaGo vs. AlphaZero. Hypothetically couldn't the same relation exist between an AlphaBike and AlphaRide?

Re: Why general artificial intelligence will not be realized

#352

Post by glenstein » Sat, Jul 11, 2020, 2:04 PM UTC

visarga wrote: post #260

abetusk wrote: post #131

Here is, to me, the relevant line: "Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. " I have not read the rest of the article but in the introduction it's stated: "The article furt…

> So the main argument is that humans have intelligence that is impossible to express through reason or codification. In other words, humans have a literal soul, divorced from physical world, that cannot be expressed in our physical world thus making any endeavour to create artificial intelligence impossible. No, it just means there is no simple symbolic path that is human understandable towards human level intellige…

>there is no simple symbolic path that is human understandable towards human level intelligence

I imagine that, piece by piece, if we really wanted to, we could look at the 175 billion parameters in GPT-3, test which are 'active' when this poem is written, which are active when that imitation of copypasta is active, and through a torturous interrogation, find that one particular parameter, say, is for weighting the 0.001% likelihood that you would use an accented é during a particular rhetorical flourish in certain contexts. Perhaps another parameter codes a meta-meta-meta abstraction about how a meta-meta rule governs a meta-rule for how to use a sometimes-used linguistic rule.

And the totality of those 175 billion parameters could in principle be uncovered and described in ways that are satisfactory to humans. It would be tedious and unproductive, and akin to the project of archeologists patiently, tediously uncovering a dinosaur.

But the point is it would be practically difficult, not something forbidden as a matter of principle.

More importantly though, is that I don't think the supposed incomprehensibility to humans has relevance to anything. What's the argument supposed to be? Humans don't depend in any explicit, conscious way, on having conscious grasp of our own tacit knowledge. I don't know why I unconsciously shift my weight a certain way when going up stairs. This doesn't stop me from walking up stairs. And it doesn't stop us from making machines that could walk up stairs.

We need neural nets? Okay, sure, we need them. But we can run those on machines that, at the end of the day, are silicon and 0s and 1s, which are every bit the brute, formal systems that supposedly can't model intelligent things. Weren't we supposed to have encountered a barrier to what computers can do at some point in this thought exercise? Because it appears that what began as an extremely bold claim, that computers can't do X, Y, and Z, ends in a whimper, as a vague exhortation to appreciate that neural nets are in some sense structurally different from logic design. Nothing about that latter claim is making any bold statements about the limits of what computers can or can't do, which makes me feel like the argument forgot what it was supposed to be about halfway through.

Re: Why general artificial intelligence will not be realized

#353

Post by dragonwriter » Sat, Jul 11, 2020, 2:07 PM UTC

SiVal wrote: post #164

abetusk wrote: post #131

Here is, to me, the relevant line: "Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. " I have not read the rest of the article but in the introduction it's stated: "The article furt…

Tacit knowledge is knowledge that results from adapting to experience, like learning to tie shoelaces with practice, rather than something like finding the derivative of sin(x) by the usual mathematical proof method (reasoning step by formal step). Every deep learning system has tacit knowledge: it knows a chair when it sees one but can't explain how it knows. It just adapted its connections to training until it got…

> But AGI isn't that.

We haven't got much of a clue about what AGI is or isn't because we don't have much of a clue about GI is or isn't without the A.

Re: Why general artificial intelligence will not be realized

#354

Post by flogic » Sat, Jul 11, 2020, 2:12 PM UTC

juliangamble wrote: post #250

PaulDavisThe1st wrote: post #167

This is possibly the worst article I have ever seen in Nature. It is barely at the level of an undergraduate essay on AI (or AGI or ANI, as the author prefers). A hand-waving argument about "computers not being in the world" that relies on not bothering to define "computers", "being in" or "the world". Convenient avoidance of almost every more subtle anti-Searle/anti-Dreyfus critic (notably Dennett). Almost wilfull n…

Not disagreeing with this. Does anyone have a reasonable article or book making this point (for or against) that is well-researched and argued?

The fundamental problem this argument has is that computers are in the world. Probably not to the degree required and certainly not up to AGI. But, self driving cars and Boston dynamics exist. The gap between "in the world" and not isn't some insurmountable barrier. It's an engineering problem that's been solved to varying degrees by engineers and hobbyists.

Re: Why general artificial intelligence will not be realized

#355

Post by glenstein » Sat, Jul 11, 2020, 2:23 PM UTC

Strilanc wrote: post #194

TheOtherHobbes wrote: post #181

Earlier quoted context omitted.

The point isn't bike riding. Bike riding is an example of a class of problems, and your solution begs the question. The point is that humans and computers operate differently. The human approach is based on adaptive experiential heuristics. The computer approach is based on explicit formalism. (Even in neural networks, there's still a formal model. It's just made of weightings instead of logic paths.) The epistemolog…

> The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns. This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.

>Human-like is not the only way to approach doing or learning.

I would also say that we shouldn't agree that there's an irreducibly human-like way to do things that only belongs to humans. The things we think 'belong' to humans, such as our intrinsic bike-riding ability, may well turn out to be not intrinsic at all, and able to be modeled in all salient ways by a machine.

I think if we allow that distinction to be made, and proceed to argue that machines can learn in different ways, it allows a very strange human-essentiallism to go unchallenged.

Re: Why general artificial intelligence will not be realized

#356

Post by glenstein » Sat, Jul 11, 2020, 2:26 PM UTC

mullingitover wrote: post #24

This feels a lot like Douglas Hofstadter's elaborate explanation in Gödel, Escher, Bach: An Eternal Golden Braid of how computers will never beat the best human chess players.

And this is too bad coming out of DH, because in so many ways I think he's one of the best evangelists out there for understanding what computers can do, and for arguing against stuff such as Hubert Dreyfus' claims that computers can't do X.

Re: Why general artificial intelligence will not be realized

#357

Post by orbifold » Sat, Jul 11, 2020, 2:29 PM UTC

abetusk wrote: post #269

User23 wrote: post #244

Earlier quoted context omitted.

The Church-Turing thesis only states that the lambda calculus and Turing machines can compute the same functions. It has nothing to do with materialism or dualism and it certainly doesn’t state that the universe is a Turing machine.

The Church-Turing thesis is: "Every effectively calculable function is a computable function" [1] The definition is a little terse so we have to expand on what "effectively calculable function" and "computable function" mean. By a "computable function", we mean a Turing machine. The term "effectively calculable function" is a little unclear but one definition that I think is the closest to the intent is "it can be do…

In might be computable in principle but not in practice for a long time, similar to how it is possible to simulate all molecules of air in one cubic meter (neglecting quantum effects), but not actually feasible in practice. Any argument for computability all needs an argument why a "coarse graining" or "effective model" of the underlying physical system exists.

In the case of transistors that is because all that matters are binary stable states, which reliably abstract over the complicated device physics. In the case of biological cells and neurons in the brain it is much less obvious what the reliable abstraction is. Right now a lot points towards "it is just a bunch of linear algebra and lots of data", but especially when we come to things like memory, online and few shot learning, the answer becomes far less obvious.

Re: Why general artificial intelligence will not be realized

#358

Post by glenstein » Sat, Jul 11, 2020, 2:33 PM UTC

29athrowaway wrote: post #236

To better understand the brain, you have to not only talk about its cognitive functions, but all of it. Then you will understand what it is really for. Your body stays alive by keeping levels within certain range. There are many functions in your body that have as an objective controlling those levels and your brain controls them at different levels of automation. Like your heart rate, sweating, urine output, etc. Bu…

>But not everything can be automated, because some situations are more complex

This is where you lost me. You are very right that brains do all kinds of things to regulate the body that we don't usually think of as influencing thought. But we can't model those because they're too complex? I don't think environmental variables are too complex.

But (1) if those are important, we can model them too and (2) it may be that we don't need to model computer 'thinking' after the structure of human brains anyway to solve problems intelligently. And (3) the totality of things an AGI might be 'aware of', even without simulating biology, could very well mean that the 'intelligence' of a system is nestled in a complex web of variables that give it the ability to have the equivalent of our tacit knowledge. That's probably an informational question rather than a question of needing to simulate biology.

Re: Why general artificial intelligence will not be realized

#359

Post by glenstein » Sat, Jul 11, 2020, 2:37 PM UTC

dragonwriter wrote: post #234

analog31 wrote: post #227

Earlier quoted context omitted.

A friend of mine was the project manager for a project to develop one of the first color matching systems for paint stores. My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. I've gotten color matching done at Home Depot, to get paint for repairing my house. It's uncanny.

> My friend is colorblind. At least for him, the project was very much a matter of programming knowledge that he could not possess. Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships.

>Being unable to perceive color and color relationships does not equate to being unable to have knowledge of color and color relationships.

Yeah, I think that was their point. Whatever 'knowledge' we think we have with qualia is actually something that, functionally, we seem to be able to get along without and do just fine. Which makes you wonder what functional purpose we don't have by not having 'qualia'.

Re: Why general artificial intelligence will not be realized

#360

Post by mbrock » Sat, Jul 11, 2020, 2:43 PM UTC

glenstein wrote: post #345

mbrock wrote: post #315

Earlier quoted context omitted.

Hubert Dreyfus in his 1986 book "Mind Over Machine": > The digital computer, when programmed to operate by taking a problem apart into features and combining them step by step according to inference rules, operates as a machine—a logic machine. However, the computer is so versatile it can also be used to model a holistic system. Indeed, recently, as the problems confronting the AI approach remained unsolved for more…

I think that Dreyfus has unfortunately set back the cultural understanding of computers by decades, by confidently declaring certain tasks impossible for computers to do, because minds have "insight" or "tacit knowledge" or are "holistic", each of which functionally lets a mind be a ghost in the machine. A lot of the rhetorical momentum comes from pointing at the progress of technology at various stages in human hist…

He is talking about AI as presently conceived when writing. The quote I posted has him explicitly imagining what you say he could not imagine. His critique was in fact INFLUENTIAL for the currently successful approaches.