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> I figure it's hard to argue that that is not at least somewhat intelligent? The fact that this technology can be very useful doesn't imply that it's intellig

by imiric 1y ago

> I figure it's hard to argue that that is not at least somewhat intelligent? The fact that this technology can be very useful doesn't imply that it's intelligent. My argument is about the language used to describe it, not about its abilities. The breakthroughs we've had is because there is a lot of utility from finding patterns in data which humans aren't very good at. Many of our problems can be boiled down to this task. So when we have vast amounts of data and compute at our disposal, we can be easily impressed by results that seem impossible for humans. But this is not intelligence. The machine has no semantic understanding of what the data represents. The algorithm is optimized for generating specific permutations of tokens that match something it previously saw and was rewarded for. Again, very useful, but there's no thinking or reasoning there. The model doesn't have an understanding of why the wolf can't be close to the goat, or how a cabbage tastes. It's trained on enough data and algorithmic tricks that its responses can fool us into thinking it does, but this is just an illusion of intelligence. This is why we need to constantly feed it more tricks so that it doesn't fumble with basic questions like how many "R"s are in "strawberry", or that it doesn't generate racially diverse but historically inaccurate images.

tim333 1y ago
I imagine if you asked the LLM why the wolf can't be close to the goat it would give a reasonable answer. I realise it does it by using permutation of tokens but I think you have to judge intelligence by the results rather than the mechanism otherwise you could argue humans can't be intelligent because they are just a bunch of neurons that find patterns.
- Jensson 1y ago
  We have had programs that can give good answers to some hard questions for a very long time now. Watson won jeapordy already 2011, but it still wasn't very good at replacing humans. So that isn't a good way to judge intelligence, computers are so fast and have so much data that you can make programs to answer just about anything pretty well, LLM is able to do that but more automatic. But it still doesn't automate the logical parts yet, just the lookup of knowledge, we don't know how to train large logic models, just large language models.
  - eMPee584 1y ago
    LLMs are not the only model type though? There's a plethora of architectures and combinations being researched.. And even transformers start to be able to do cool sh1t on knowledge graphs, also interesting is progress on autoregressive physics PDE (partial differential equations) models.. and can't be too long until some providers of actual biological neural nets show up on openrouter (probably a lot less energy and capital intense to scale up brain goo in tanks compared to gigawatt GPU clusters).. combine that zoo of "AI" specimen using M2M, MCP etc. and the line between mock and "true"intelligence will blur, escalating our feable species into ASI territory.. good luck to us.
    - Jensson 1y ago
      > There's a plethora of architectures and combinations being researched There were plethora of architectures and combinations being researched before LLM, still took a very long time to find LLM architecture. > the line between mock and "true"intelligence will blur Yes, I think this will happen at some point. The question is how long it will take, not if it will happen. The only thing that can stop this is if intermediate AI is good enough to give every human a comfortable life but still isn't good enough to think on its own. Its easy to imagine such an AI being developed, imagine a model that can learn to mimic humans at any task, but still cannot update itself without losing those skills and becoming worse. Such an AI could be trained to perform every job on earth as long as we don't care about progress. If such an AI is developed, and we don't quickly solve the remaining problems to get an AI to be able to progress science on its own, its likely our progress entirely stalls there as humans will no longer have a reason to go to school to advance science.
      - tim333 1y ago
        >The only thing that can stop this is if intermediate AI is good enough... Not going to happen due to competition. As soon as one company has a good one their rivals will develop a better one.
- swat535 1y ago
  > you have to judge intelligence by the results rather than the mechanism This would be the exact opposite conclusion of the Chinese room: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room I think you'd need to offer a stronger counter argument than the one you presented here.
  - tim333 1y ago
    Actually I think the Chinese room fits my idea. It's a silly thought experiment that would never work in practice. If you tried to make one you would judge it unintelligent because it wouldn't work. Or at least in the way Searle implied - he basically proposed a look up table.
grugagag 1y ago
I keep on trying this wolf cabbage goat problem with various permutations, let’s say just a wolf and a cabbage, no goat mentioned. At some step the got materializes in the answer. I tell it there is no goat and yet it answers again and the goat is there.
BriggyDwiggs42 1y ago
This approach to defining “true” intelligence seems flawed to me because of examples in biology where semantic understanding is in no way relevant to function. A slime mold solving a maze doesn’t even have a brain, yet it solves a problem to get food. There’s no knowing that it does that, no complex signal processing, no self-perception of purpose, but nevertheless it gets the food it needs. My response to that isn’t to say the slime mold has no intelligence, it’s to widen the definition of intelligence to include the mold. In other words, intelligence is something one does rather than has; it’s not the form but the function of the thing. Certainly LLMs lack anything in any way resembling human intelligence, they even lack brains, but they demonstrate a capacity to solve problems I don’t think is unreasonable to label intelligent behavior. You can put them in some mazes and LLMs will happen to solve them.
- tim333 1y ago
  >LLMs lack anything in any way resembling human intelligence I think intelligence has many aspects from moulds solving mazes to chess etc. I find LLMs resemble very much human rapid language responses where you say something without thinking about it first. They are not very good at thinking though. And hopeless if you were to say hook one to a robot and tell it to fix your plumbing.
- imiric 1y ago
  While it's debatable whether slime molds showcase intelligence, there's a substantial difference between its behavior and modern AI systems. The organism was never trained to traverse a maze. It simply behaves in the same way as it would in its natural habitat, seeking out food in this case, which we interpret as "solving" a human-made problem. In order to get an AI system to do the same we would have to "train" it on large amounts of data that specifically included maze solving. This training wouldn't carry over any other type of problem, for which we would also need to specifically train it on. When you consider how humans and other animals learn, knowledge is carried over. I.e. if we learn how to solve a maze on paper, we can carry this knowledge over to solve a hedge maze. It's a contrived example, but you get the idea. When we learn, we build out a web of ideas in our minds which we can later use while thinking to solve other types of problems, or the same problems in different ways. This is a sign of intelligence that modern AI systems simply don't have. They're showing an illusion of intelligence, which as I've said before, can still be very useful.
hackinthebochs 1y ago
>The machine has no semantic understanding of what the data represents. How do you define "semantic understanding" in a way that doesn't ultimately boil down to saying they don't have phenomenal consciousness? Any functional concept of semantic understanding is captured to some degree by LLMs. Typically when we attribute understanding to some entity, we recognize some substantial abilities in the entity in relation to that which is being understood. Specifically, the subject recognizes relevant entities and their relationships, various causal dependences, and so on. This ability goes beyond rote memorization, it has a counterfactual quality in that the subject can infer facts or descriptions in different but related cases beyond the subject's explicit knowledge. But LLMs excel at this. >feed it more tricks so that it doesn't fumble with basic questions like how many "R"s are in "strawberry" This failure mode has nothing to do with LLMs lacking intelligence and everything to do with how tokens are represented. They do not see individual characters, but sub-word chunks. It's like expecting a human to count the pixels in an image it sees on a computer screen. While not impossible, it's unnatural to how we process images and therefore error-prone.
- TheOtherHobbes 1y ago
  You don't need phenomenal consciousness. You need consistency. LLMs are not consistent. This is unarguable. They will produce a string of text that says they have solved a problem and/or done a thing when neither is true. And sometimes they will do it over and over, even when corrected. Your last paragraph admits this. Tokenisation on its own simply cannot represent reality accurately and reliably. It can be tweaked so that specific problems can appear solved, but true AI would be based on a reliable general strategy which solves entire classes of problems without needing this kind of tweaking. It's clear we're nowhere close to that.
  - hackinthebochs 1y ago
    Consistency is a strange criteria seeing as humans aren't very consistent either. Intelligent beings can make mistakes.
    - 1y ago
rafaelero 1y ago
2025 and we are still discussing if LLM's are intelligent or not, gee.

by imiric 1y ago

tim333 1y ago

I imagine if you asked the LLM why the wolf can't be close to the goat it would give a reasonable answer. I realise it does it by using permutation of tokens but I think you have to judge intelligence by the results rather than the mechanism otherwise you could argue humans can't be intelligent because they are just a bunch of neurons that find patterns.

Jensson 1y ago
We have had programs that can give good answers to some hard questions for a very long time now. Watson won jeapordy already 2011, but it still wasn't very good at replacing humans. So that isn't a good way to judge intelligence, computers are so fast and have so much data that you can make programs to answer just about anything pretty well, LLM is able to do that but more automatic. But it still doesn't automate the logical parts yet, just the lookup of knowledge, we don't know how to train large logic models, just large language models.
- eMPee584 1y ago
  LLMs are not the only model type though? There's a plethora of architectures and combinations being researched.. And even transformers start to be able to do cool sh1t on knowledge graphs, also interesting is progress on autoregressive physics PDE (partial differential equations) models.. and can't be too long until some providers of actual biological neural nets show up on openrouter (probably a lot less energy and capital intense to scale up brain goo in tanks compared to gigawatt GPU clusters).. combine that zoo of "AI" specimen using M2M, MCP etc. and the line between mock and "true"intelligence will blur, escalating our feable species into ASI territory.. good luck to us.
  - Jensson 1y ago
    > There's a plethora of architectures and combinations being researched There were plethora of architectures and combinations being researched before LLM, still took a very long time to find LLM architecture. > the line between mock and "true"intelligence will blur Yes, I think this will happen at some point. The question is how long it will take, not if it will happen. The only thing that can stop this is if intermediate AI is good enough to give every human a comfortable life but still isn't good enough to think on its own. Its easy to imagine such an AI being developed, imagine a model that can learn to mimic humans at any task, but still cannot update itself without losing those skills and becoming worse. Such an AI could be trained to perform every job on earth as long as we don't care about progress. If such an AI is developed, and we don't quickly solve the remaining problems to get an AI to be able to progress science on its own, its likely our progress entirely stalls there as humans will no longer have a reason to go to school to advance science.
    - tim333 1y ago
      >The only thing that can stop this is if intermediate AI is good enough... Not going to happen due to competition. As soon as one company has a good one their rivals will develop a better one.
swat535 1y ago
> you have to judge intelligence by the results rather than the mechanism This would be the exact opposite conclusion of the Chinese room: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room I think you'd need to offer a stronger counter argument than the one you presented here.
- tim333 1y ago
  Actually I think the Chinese room fits my idea. It's a silly thought experiment that would never work in practice. If you tried to make one you would judge it unintelligent because it wouldn't work. Or at least in the way Searle implied - he basically proposed a look up table.

grugagag 1y ago

I keep on trying this wolf cabbage goat problem with various permutations, let’s say just a wolf and a cabbage, no goat mentioned. At some step the got materializes in the answer. I tell it there is no goat and yet it answers again and the goat is there.

BriggyDwiggs42 1y ago

This approach to defining “true” intelligence seems flawed to me because of examples in biology where semantic understanding is in no way relevant to function. A slime mold solving a maze doesn’t even have a brain, yet it solves a problem to get food. There’s no knowing that it does that, no complex signal processing, no self-perception of purpose, but nevertheless it gets the food it needs. My response to that isn’t to say the slime mold has no intelligence, it’s to widen the definition of intelligence to include the mold. In other words, intelligence is something one does rather than has; it’s not the form but the function of the thing. Certainly LLMs lack anything in any way resembling human intelligence, they even lack brains, but they demonstrate a capacity to solve problems I don’t think is unreasonable to label intelligent behavior. You can put them in some mazes and LLMs will happen to solve them.

tim333 1y ago
>LLMs lack anything in any way resembling human intelligence I think intelligence has many aspects from moulds solving mazes to chess etc. I find LLMs resemble very much human rapid language responses where you say something without thinking about it first. They are not very good at thinking though. And hopeless if you were to say hook one to a robot and tell it to fix your plumbing.
imiric 1y ago
While it's debatable whether slime molds showcase intelligence, there's a substantial difference between its behavior and modern AI systems. The organism was never trained to traverse a maze. It simply behaves in the same way as it would in its natural habitat, seeking out food in this case, which we interpret as "solving" a human-made problem. In order to get an AI system to do the same we would have to "train" it on large amounts of data that specifically included maze solving. This training wouldn't carry over any other type of problem, for which we would also need to specifically train it on. When you consider how humans and other animals learn, knowledge is carried over. I.e. if we learn how to solve a maze on paper, we can carry this knowledge over to solve a hedge maze. It's a contrived example, but you get the idea. When we learn, we build out a web of ideas in our minds which we can later use while thinking to solve other types of problems, or the same problems in different ways. This is a sign of intelligence that modern AI systems simply don't have. They're showing an illusion of intelligence, which as I've said before, can still be very useful.

hackinthebochs 1y ago

>The machine has no semantic understanding of what the data represents. How do you define "semantic understanding" in a way that doesn't ultimately boil down to saying they don't have phenomenal consciousness? Any functional concept of semantic understanding is captured to some degree by LLMs. Typically when we attribute understanding to some entity, we recognize some substantial abilities in the entity in relation to that which is being understood. Specifically, the subject recognizes relevant entities and their relationships, various causal dependences, and so on. This ability goes beyond rote memorization, it has a counterfactual quality in that the subject can infer facts or descriptions in different but related cases beyond the subject's explicit knowledge. But LLMs excel at this. >feed it more tricks so that it doesn't fumble with basic questions like how many "R"s are in "strawberry" This failure mode has nothing to do with LLMs lacking intelligence and everything to do with how tokens are represented. They do not see individual characters, but sub-word chunks. It's like expecting a human to count the pixels in an image it sees on a computer screen. While not impossible, it's unnatural to how we process images and therefore error-prone.

TheOtherHobbes 1y ago
You don't need phenomenal consciousness. You need consistency. LLMs are not consistent. This is unarguable. They will produce a string of text that says they have solved a problem and/or done a thing when neither is true. And sometimes they will do it over and over, even when corrected. Your last paragraph admits this. Tokenisation on its own simply cannot represent reality accurately and reliably. It can be tweaked so that specific problems can appear solved, but true AI would be based on a reliable general strategy which solves entire classes of problems without needing this kind of tweaking. It's clear we're nowhere close to that.
- hackinthebochs 1y ago
  Consistency is a strange criteria seeing as humans aren't very consistent either. Intelligent beings can make mistakes.
  - 1y ago

rafaelero 1y ago

2025 and we are still discussing if LLM's are intelligent or not, gee.