5 ms·

Echoing many who now find themselves blindsided by the emergent abilities and rapid adoption of LLMs, the OP: * complains that we still lack a "comprehensive t

by cs702 3y ago

Echoing many who now find themselves blindsided by the emergent abilities and rapid adoption of LLMs, the OP: * complains that we still lack a "comprehensive theory to explain what intelligence is or how it emerges from first principles," * argues that deep neural nets like LLMs may not be capable of artificial general intelligence (AGI), and * contends that achieving AGI will require "new algorithmic paradigms." Rich Sutton wrote what I think is the perfect counter-argument to these points some years ago: "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation."[a] Go read the whole thing. --- [a] http://incompleteideas.net/IncIdeas/BitterLesson.html http://incompleteideas.net/IncIdeas/BitterLesson.html

majormajor 3y ago
I think there's likely to be a distinction here between "things that are learnable through computation" and things that aren't. A machine or algorithm that needed to evaluate various methods of planting seeds in soil, for instance, is going to be limited by hard time factors short of figuring out how to put in the "physics" of it from the current state of the art of human knowledge. And that pushes you up against the line of "tools that make it easy to interface with today and history's knowledge, art styles, etc" vs "generating new knowledge." The singularity would require the latter - there's a lot of talk around embodiment as a potential necessity there, but I think there's a certain difference too around experimentation and feedback. A perfect simulation of the universe would let you get around some of this - especially if you assume perfect or good-enough simulation of human behavior - but that's a LOT of compute. This gap between "what a human can do, but faster/cheaper/without getting tired" and "what a human couldn't even imagine" that is the "AI" dream (sometimes nightmare) that sci-fi planted in our heads.
- jacobr1 3y ago
  This is a good point, but the distinction isn't computational/human, it is something like deducible from current knowledge vs requires physical interactions/experiments with the world. You don't necessarily need humans for the latter. Robotics will enable a whole lot of physical interaction. We are pretty close to a fully-automatable definition wet-lab for example.
thegrim33 3y ago
I mean, your entire counter-argument is linking a single person's opinion piece. He says that in general more computation is "good" and that search/learning "seem" to scale with computation. That's about it. It doesn't refute the key ideas at all. He also gives the stereotypical horribly flawed trope about how "some people in the past didn't think computers could beat them in chess, and they were wrong, then some people thought computers couldn't beat them in go, and they were wrong, so now what they say about machine learning today must be wrong too". Which is a completely illogical line of reasoning. By that reasoning, I present this same argument: When cars were first invented some people said that they'd never be able to reach 50mph, and they were proven wrong, then some people said they'd never be able to reach 150mph, and they were proven wrong, and therefore anyone that doubts my claim that we'll have 1,500mph cars on our streets next year is obviously wrong, because look, some people in the past made bad predictions.
- hdufbdidhdj 3y ago
  nice! when do you plan on having a prototype of you 1500mph car? i would like to invest!
- eternalban 3y ago
  There is more there (which is implicit to that specific piece): http://incompleteideas.net/IncIdeas/DefinitionOfIntelligence.html http://incompleteideas.net/IncIdeas/DefinitionOfIntelligence... "John McCarthy long ago gave one of the best definitions: "Intelligence is the computational part of the ability to achieve goals in the world”. That is pretty straightforward and does not require a lot of explanation. It also allows for intelligence to be a matter of degree, and for intelligence to be of several varieties, which is as it should be. Thus a person, a thermostat, a chess-playing program, and a corporation all achieve goals to various degrees and in various senses. For those looking for some ultimate ‘true intelligence’, the lack of an absolute, binary definition is disappointing, but that is also as it should be." He then goes on and give a precise definition: "Intelligence is the computational part of the ability to achieve goals. A goal achieving system is one that is more usefully understood in terms of outcomes than in terms of mechanisms." When I first encountered ChatGPT, it prompted (as with many others) a reevaluation of my model of the mind. For whatever reason, intelligence was conflated with consciousness for me and the encounter was the catalyst of breaking free from that. Independently in short order I arrived at the notion of kinds and degrees of intelligence, as in the first quote. It now seems perfectly clear that intelligence, mind, and consciousness are 3 distinct things. At this point still holding the line regarding mind and consciousness, but it is clear that in the computation game, we will lose to purpose built machines.
fzeroracer 3y ago
That doesn't refute anything that the OP said at all. You just seem to be pasting that same quotation over multiple posts for disparate reasons.
progrus 3y ago
So there’s no speed of light limit, there’s no speed of clock limit, or else all useful algorithms are parallelizable? Sounds like bullshit.
- byby 3y ago
  We hit a clock limit ages ago and switched to parallelism. I still remember the days there things were in mhz and we were going 90, 100, 200, 300, 500, 1ghz, 2, 3 ... and we'd be at hundreds of ghzs if we didn't hit a limit. Now we switched to cores.
agalunar 3y ago
I don't see how that at all counters point 1 or maybe even point 2. Although LLMs are incredible feats of engineering, they're useless scientifically. The hallmark of a good scientific theory is that it not only explains what's true but that it fails to predict what's false. There are constraints that all human languages obey [1]. Humans are incapable of learning languages that violate these constraints (i.e. we don't have hardware acceleration for them and are reduced to explicit symbolic manipulation). However, LLMs are just as capable of learning inhuman languages as human ones, so they tell us nothing about the nature of human intelligence, or at least our language capacity, which is our most distinguishing feature from every other species on earth. [1] This isn't an example of such a constraint, but it's fun example of human limitation: center embedding! We seem to be incapable of doing it more than once or twice. "A man that a woman that a child that a bird that I heard saw knows loves" is perfectly grammatical but nearly impossible to understand without seeing it in print, whereas we can right embed all day long: "a man who is loved by a woman who is known by a child who was seen by a bird that I heard".
- xcv123 3y ago
  > Although LLMs are incredible feats of engineering, they're useless scientifically https://blogs.nvidia.com/blog/2022/09/20/bionemo-large-language-models-drug-discovery/ https://blogs.nvidia.com/blog/2022/09/20/bionemo-large-langu...
  - agalunar 3y ago
    LLMs can be useful tools for conducting scientific research, in much the way that ordinary computer programs, or desk calculators, or slide rules are useful for conducting scientific research. I meant that (insofar as I am aware) they are not useful as models that we can study to understand the nature of human intelligence.
    - xcv123 3y ago
      > They are not useful as models that we can study to understand the nature of human intelligence Correct, but they were not explicitly designed to replicate human cognitive processes. That was never the goal. There is no pretense of them being a cognitive model that we can scientifically study.
kerkeslager 3y ago
> Echoing many who now find themselves blindsided by the emergent abilities and rapid adoption of LLMs, the OP I've said this over and over again: there are no emergent abilities. Before you leap to link me this paper, I'll link it myself: https://arxiv.org/abs/2206.07682 https://arxiv.org/abs/2206.07682 I read that paper. Did you? Did you understand it? Because if you had, you'd have seen that early on they define what they mean when they say "emergent abilities", and it's not what almost anyone else means when they say "emergent abilities". They're not claiming that the abilities of LLMs are anything more than the sum of their parts. "Emergent abilities" in that paper is an extraordinarily poor communication of the idea that larger models can do more than smaller models, which should be a surprise to no one. Stop spreading this nonsense. I'm not saying that LLMs aren't impressive. I'm just saying this breathless fantasy where they're doing totally unexpected and unexplained things that are beyond human understanding, is totally false. Since this is controversial and seemingly most HN folks can't hold a conversation with any nuance, if you don't use the word "shape" in your response to this post, I'm simply going to point out that you didn't read the post you're responding to, and therefore shouldn't be responding. If you stop reading at the first chance you see to correct something, go away--you're dragging down the level of the conversation.
- aquariusDue 3y ago
  I agree with you and hopefully once the hype dies down a year or two from now we will see how LLMs can actually shape the tech landscape (if at all significantly).
- albertzeyer 3y ago
  > the idea that larger models can do more than smaller models, which should be a surprise to no one. Actually this was quite a surprise to a lot of people, since the whole race to scaling up began, with GPT2 or so. It was totally not obvious that you can scale up the model (and also training) and it would improve the performance. Many (most?) people thought there would be some limit, and we were close to that limit with 100M-500M params or so. Then GPT2 came. And it was a surprise to a lot of people, that scaling up works so well. But then the question remained, is the limit reached now, or not, or is there any? The scaling laws appeared, and seemed to indicate that there really is no limit. Still, GPT3 and then GPT4 were still surprising to people, that it really got better and better. But the question still remains, is there a limit? If there is no limit, it means we can easily surpass human intelligence by just scaling up further. Maybe the limit is just always current technical hardware limitations and cost.
byby 3y ago
Except this doesn't make any sense. Moores law is beginning to fail size has a physical limit in that we can't get much smaller than an atom. The potential of AI on the other hand is clear. Look at your self. If someone as intelligent as a human can be physically realized then the possibility of building something as intelligent as a human exists by simple logic. It's easy to see the convoluted argument on your end. Why bet on something that is physically impossible to continue when you can bet on AI that is not only theoretically possible, but walking and talking versions of intelligence are all around us everyday.

by cs702 3y ago

majormajor 3y ago

I think there's likely to be a distinction here between "things that are learnable through computation" and things that aren't. A machine or algorithm that needed to evaluate various methods of planting seeds in soil, for instance, is going to be limited by hard time factors short of figuring out how to put in the "physics" of it from the current state of the art of human knowledge. And that pushes you up against the line of "tools that make it easy to interface with today and history's knowledge, art styles, etc" vs "generating new knowledge." The singularity would require the latter - there's a lot of talk around embodiment as a potential necessity there, but I think there's a certain difference too around experimentation and feedback. A perfect simulation of the universe would let you get around some of this - especially if you assume perfect or good-enough simulation of human behavior - but that's a LOT of compute. This gap between "what a human can do, but faster/cheaper/without getting tired" and "what a human couldn't even imagine" that is the "AI" dream (sometimes nightmare) that sci-fi planted in our heads.

jacobr1 3y ago
This is a good point, but the distinction isn't computational/human, it is something like deducible from current knowledge vs requires physical interactions/experiments with the world. You don't necessarily need humans for the latter. Robotics will enable a whole lot of physical interaction. We are pretty close to a fully-automatable definition wet-lab for example.

thegrim33 3y ago

I mean, your entire counter-argument is linking a single person's opinion piece. He says that in general more computation is "good" and that search/learning "seem" to scale with computation. That's about it. It doesn't refute the key ideas at all. He also gives the stereotypical horribly flawed trope about how "some people in the past didn't think computers could beat them in chess, and they were wrong, then some people thought computers couldn't beat them in go, and they were wrong, so now what they say about machine learning today must be wrong too". Which is a completely illogical line of reasoning. By that reasoning, I present this same argument: When cars were first invented some people said that they'd never be able to reach 50mph, and they were proven wrong, then some people said they'd never be able to reach 150mph, and they were proven wrong, and therefore anyone that doubts my claim that we'll have 1,500mph cars on our streets next year is obviously wrong, because look, some people in the past made bad predictions.

hdufbdidhdj 3y ago
nice! when do you plan on having a prototype of you 1500mph car? i would like to invest!
eternalban 3y ago
There is more there (which is implicit to that specific piece): http://incompleteideas.net/IncIdeas/DefinitionOfIntelligence.html http://incompleteideas.net/IncIdeas/DefinitionOfIntelligence... "John McCarthy long ago gave one of the best definitions: "Intelligence is the computational part of the ability to achieve goals in the world”. That is pretty straightforward and does not require a lot of explanation. It also allows for intelligence to be a matter of degree, and for intelligence to be of several varieties, which is as it should be. Thus a person, a thermostat, a chess-playing program, and a corporation all achieve goals to various degrees and in various senses. For those looking for some ultimate ‘true intelligence’, the lack of an absolute, binary definition is disappointing, but that is also as it should be." He then goes on and give a precise definition: "Intelligence is the computational part of the ability to achieve goals. A goal achieving system is one that is more usefully understood in terms of outcomes than in terms of mechanisms." When I first encountered ChatGPT, it prompted (as with many others) a reevaluation of my model of the mind. For whatever reason, intelligence was conflated with consciousness for me and the encounter was the catalyst of breaking free from that. Independently in short order I arrived at the notion of kinds and degrees of intelligence, as in the first quote. It now seems perfectly clear that intelligence, mind, and consciousness are 3 distinct things. At this point still holding the line regarding mind and consciousness, but it is clear that in the computation game, we will lose to purpose built machines.

fzeroracer 3y ago

That doesn't refute anything that the OP said at all. You just seem to be pasting that same quotation over multiple posts for disparate reasons.

progrus 3y ago

So there’s no speed of light limit, there’s no speed of clock limit, or else all useful algorithms are parallelizable? Sounds like bullshit.

byby 3y ago
We hit a clock limit ages ago and switched to parallelism. I still remember the days there things were in mhz and we were going 90, 100, 200, 300, 500, 1ghz, 2, 3 ... and we'd be at hundreds of ghzs if we didn't hit a limit. Now we switched to cores.

agalunar 3y ago

I don't see how that at all counters point 1 or maybe even point 2. Although LLMs are incredible feats of engineering, they're useless scientifically. The hallmark of a good scientific theory is that it not only explains what's true but that it fails to predict what's false. There are constraints that all human languages obey [1]. Humans are incapable of learning languages that violate these constraints (i.e. we don't have hardware acceleration for them and are reduced to explicit symbolic manipulation). However, LLMs are just as capable of learning inhuman languages as human ones, so they tell us nothing about the nature of human intelligence, or at least our language capacity, which is our most distinguishing feature from every other species on earth. [1] This isn't an example of such a constraint, but it's fun example of human limitation: center embedding! We seem to be incapable of doing it more than once or twice. "A man that a woman that a child that a bird that I heard saw knows loves" is perfectly grammatical but nearly impossible to understand without seeing it in print, whereas we can right embed all day long: "a man who is loved by a woman who is known by a child who was seen by a bird that I heard".

xcv123 3y ago
> Although LLMs are incredible feats of engineering, they're useless scientifically https://blogs.nvidia.com/blog/2022/09/20/bionemo-large-language-models-drug-discovery/ https://blogs.nvidia.com/blog/2022/09/20/bionemo-large-langu...
- agalunar 3y ago
  LLMs can be useful tools for conducting scientific research, in much the way that ordinary computer programs, or desk calculators, or slide rules are useful for conducting scientific research. I meant that (insofar as I am aware) they are not useful as models that we can study to understand the nature of human intelligence.
  - xcv123 3y ago
    > They are not useful as models that we can study to understand the nature of human intelligence Correct, but they were not explicitly designed to replicate human cognitive processes. That was never the goal. There is no pretense of them being a cognitive model that we can scientifically study.

kerkeslager 3y ago

> Echoing many who now find themselves blindsided by the emergent abilities and rapid adoption of LLMs, the OP I've said this over and over again: there are no emergent abilities. Before you leap to link me this paper, I'll link it myself: https://arxiv.org/abs/2206.07682 https://arxiv.org/abs/2206.07682 I read that paper. Did you? Did you understand it? Because if you had, you'd have seen that early on they define what they mean when they say "emergent abilities", and it's not what almost anyone else means when they say "emergent abilities". They're not claiming that the abilities of LLMs are anything more than the sum of their parts. "Emergent abilities" in that paper is an extraordinarily poor communication of the idea that larger models can do more than smaller models, which should be a surprise to no one. Stop spreading this nonsense. I'm not saying that LLMs aren't impressive. I'm just saying this breathless fantasy where they're doing totally unexpected and unexplained things that are beyond human understanding, is totally false. Since this is controversial and seemingly most HN folks can't hold a conversation with any nuance, if you don't use the word "shape" in your response to this post, I'm simply going to point out that you didn't read the post you're responding to, and therefore shouldn't be responding. If you stop reading at the first chance you see to correct something, go away--you're dragging down the level of the conversation.

aquariusDue 3y ago
I agree with you and hopefully once the hype dies down a year or two from now we will see how LLMs can actually shape the tech landscape (if at all significantly).
albertzeyer 3y ago
> the idea that larger models can do more than smaller models, which should be a surprise to no one. Actually this was quite a surprise to a lot of people, since the whole race to scaling up began, with GPT2 or so. It was totally not obvious that you can scale up the model (and also training) and it would improve the performance. Many (most?) people thought there would be some limit, and we were close to that limit with 100M-500M params or so. Then GPT2 came. And it was a surprise to a lot of people, that scaling up works so well. But then the question remained, is the limit reached now, or not, or is there any? The scaling laws appeared, and seemed to indicate that there really is no limit. Still, GPT3 and then GPT4 were still surprising to people, that it really got better and better. But the question still remains, is there a limit? If there is no limit, it means we can easily surpass human intelligence by just scaling up further. Maybe the limit is just always current technical hardware limitations and cost.

byby 3y ago

Except this doesn't make any sense. Moores law is beginning to fail size has a physical limit in that we can't get much smaller than an atom. The potential of AI on the other hand is clear. Look at your self. If someone as intelligent as a human can be physically realized then the possibility of building something as intelligent as a human exists by simple logic. It's easy to see the convoluted argument on your end. Why bet on something that is physically impossible to continue when you can bet on AI that is not only theoretically possible, but walking and talking versions of intelligence are all around us everyday.