3 ms·

Its insane really, anyone who has worked with LLMs for a bit and has an idea of how they work shouldn't think its going to lead to "AGI". Hopefully some big pl

by stuaxo 1y ago

Its insane really, anyone who has worked with LLMs for a bit and has an idea of how they work shouldn't think its going to lead to "AGI". Hopefully some big players, like FB bankrupt themselves.

guardian5x 1y ago
Just scaling them up might not leat to "AGI", but they can still lead to AGI as a bridge.
blackhaz 1y ago
Is it widely accepted that LLMs won't lead to AGI? I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly: 1) LLMs as simple "next token predictors" so they just mimicry thinking: But can it be argued that current models operate on layers of multiple depth and are able to actually understand by building concepts and making connections on abstract levels? Also, don't we all mimicry? 2) Grounding problem: Yes, models build their world models on text data, but we have models operating on non-textual data already, so this appears to be a technical obstacle rather than fundamental. 3) Lack of World Model. But can anyone really claim they have a coherent model of reality? There are flat-earthers, yet I still wouldn't deny them having AGI. People hallucinate and make mistakes all the time. I'd argue hallucinations is in fact the sign of an emerging intelligence. 4) Fixed learning data sets. Looks like this is now being actively solved with self-improving models? I just couldn't find a strong argument supporting this claim. What am I missing?
- globnomulous 1y ago
  Why on earth would you copy and paste an LLM's output into a comment? What does that accomplish or provide that just a simply stated argument doesn't accomplish more succinctly? If you don't know something, simply don't comment on it -- or ask a question.
  - blackhaz 1y ago
    None of the above is AI.
    - globnomulous 1y ago
      > I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly: This line means, and literally says, that everything that follows is a summary or direct quotation from an LLM's output. There's a more charitable but unintuitive interpretation, in which "commenting on them briefly" is intended to mean "I will comment on them briefly:". But this isn't a natural interpretation. It's one I could be expected to reach only after seeing your statement that 'none of the above is AI.' But even this more charitable interpretation actually contradicts your claim that it's not AI. So now I'm even less sure I know what you meant to communicate. Either I'm missing something really obvious or the writing doesn't communicate what you intended.
- welferkj 1y ago
  Fur future reference, pasting llm slop feels exactly as patronizing as back when people pasted links to google searches in response to questions they considered beneath their dignity to answer. Except in this case, no-one asked to begin with.
amelius 1y ago
The only way we'll have AGI is if people get dumber. Using modern tech like LLMs makes people dumber. Ergo, we might see AGI sooner than expected.
menaerus 1y ago
> ... and has an idea of how they work shouldn't think its going to lead to "AGI" Not sure what level of understanding are you referring to but having learned and researched about the pretty much all LLM internals I think this has led me exactly to the opposite line of thinking. To me it's unbelievable what we have today.
IanCal 1y ago
Tbh I find this view odd, and I wonder what people view as agi now. It used to be that we had extremely narrow pieces of AI and I remember being on a research project about architectures and just very basic “what’s going on?” was advanced. Understanding that someone asked a question, that would be solved by getting a book and being able to then go and navigate to the place the book was likely to be was fancy. Most systems could solve literally one type of problem. They weren’t just bad at other things they were fundamentally incapable of anything but an extremely narrow use case. I can throw wide ranging problems at things like gpt5 and get what seem like dramatically better answers than if I asked a random person. The amount of common sense is so far beyond what we had it’s hard to express. It used to be always pointed out that the things we had were below basic insect level. Now I have something that can research a charity, find grants and make coherent arguments for them, read matrix specs and debug error messages, and understand sarcasm. To me, it’s clear that agi is here. But then what I always pictured from it may be very different to you. What’s your image of it?
- adwn 1y ago
  I think the discrepancy between different views on the matter mainly stems from the fact that state-of-the-art LLMs are better (sometimes extremely better) at some tasks, and worse (sometimes extremely worse) at other tasks, compared to average humans. For example, they're better at retrieving information from huge amounts of unstructured data. But they're also terrible at learning: any "experience" which falls out of the context window is lost forever, and the model can't learn from its mistakes. To actually make it learn something requires very many examples and a lot of compute, whereas a human can permanently learn from a single example.
  - andsoitis 1y ago
    > human can permanently learn from a single example This, to me at least, seems like an important ingredient to satisfying a practical definition / implementation of AGI. Another might be curiosity, and I think perhaps also agency.
- AlienRobot 1y ago
  Nobody is saying that LLM's don't work like magic. I know how neural networks work and they still feel like voodoo to me. What we are saying is that LLM's can't become AGI. I don't know what AGI will look like, but it won't look like an LLM. There is a difference between being able to melt iron and being able to melt tungsten.
thaawyy33432434 1y ago
Recently I realized that US are very close to a centrally planned economy. Meta wasted 50B on metaverse, which like how much Texas spends on healthcare. Now the "AI" investments seems dubious. You could fund 1000+ projects with this kinds of money. This is not an effective capital allocation.
meowface 1y ago
This is not and has not been the consensus opinion. If you're not an AI researcher you shouldn't write as if you've set your confidence parameter to 0.95. Of course it might be the case, but it's not a thing that should be expressed with such confidence.
janalsncm 1y ago
I think AI research is like anything else really. The smartest people are heads down working on their problems. The people going on podcasts are less connected to day to day work. It’s also pretty useless to talk about whether something is AGI without defining intelligence in the first place.
foobarian 1y ago
I think something like we saw in the show "Devs" is much more likely, although what the developers did with it in the show was bonkers unrealistic. But some kind of big enough quantum device basically.

by stuaxo 1y ago

Its insane really, anyone who has worked with LLMs for a bit and has an idea of how they work shouldn't think its going to lead to "AGI". Hopefully some big players, like FB bankrupt themselves.

guardian5x 1y ago

Just scaling them up might not leat to "AGI", but they can still lead to AGI as a bridge.

blackhaz 1y ago

Is it widely accepted that LLMs won't lead to AGI? I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly: 1) LLMs as simple "next token predictors" so they just mimicry thinking: But can it be argued that current models operate on layers of multiple depth and are able to actually understand by building concepts and making connections on abstract levels? Also, don't we all mimicry? 2) Grounding problem: Yes, models build their world models on text data, but we have models operating on non-textual data already, so this appears to be a technical obstacle rather than fundamental. 3) Lack of World Model. But can anyone really claim they have a coherent model of reality? There are flat-earthers, yet I still wouldn't deny them having AGI. People hallucinate and make mistakes all the time. I'd argue hallucinations is in fact the sign of an emerging intelligence. 4) Fixed learning data sets. Looks like this is now being actively solved with self-improving models? I just couldn't find a strong argument supporting this claim. What am I missing?

globnomulous 1y ago
Why on earth would you copy and paste an LLM's output into a comment? What does that accomplish or provide that just a simply stated argument doesn't accomplish more succinctly? If you don't know something, simply don't comment on it -- or ask a question.
- blackhaz 1y ago
  None of the above is AI.
  - globnomulous 1y ago
    > I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly: This line means, and literally says, that everything that follows is a summary or direct quotation from an LLM's output. There's a more charitable but unintuitive interpretation, in which "commenting on them briefly" is intended to mean "I will comment on them briefly:". But this isn't a natural interpretation. It's one I could be expected to reach only after seeing your statement that 'none of the above is AI.' But even this more charitable interpretation actually contradicts your claim that it's not AI. So now I'm even less sure I know what you meant to communicate. Either I'm missing something really obvious or the writing doesn't communicate what you intended.
welferkj 1y ago
Fur future reference, pasting llm slop feels exactly as patronizing as back when people pasted links to google searches in response to questions they considered beneath their dignity to answer. Except in this case, no-one asked to begin with.

amelius 1y ago

The only way we'll have AGI is if people get dumber. Using modern tech like LLMs makes people dumber. Ergo, we might see AGI sooner than expected.

menaerus 1y ago

> ... and has an idea of how they work shouldn't think its going to lead to "AGI" Not sure what level of understanding are you referring to but having learned and researched about the pretty much all LLM internals I think this has led me exactly to the opposite line of thinking. To me it's unbelievable what we have today.

IanCal 1y ago

Tbh I find this view odd, and I wonder what people view as agi now. It used to be that we had extremely narrow pieces of AI and I remember being on a research project about architectures and just very basic “what’s going on?” was advanced. Understanding that someone asked a question, that would be solved by getting a book and being able to then go and navigate to the place the book was likely to be was fancy. Most systems could solve literally one type of problem. They weren’t just bad at other things they were fundamentally incapable of anything but an extremely narrow use case. I can throw wide ranging problems at things like gpt5 and get what seem like dramatically better answers than if I asked a random person. The amount of common sense is so far beyond what we had it’s hard to express. It used to be always pointed out that the things we had were below basic insect level. Now I have something that can research a charity, find grants and make coherent arguments for them, read matrix specs and debug error messages, and understand sarcasm. To me, it’s clear that agi is here. But then what I always pictured from it may be very different to you. What’s your image of it?

adwn 1y ago
I think the discrepancy between different views on the matter mainly stems from the fact that state-of-the-art LLMs are better (sometimes extremely better) at some tasks, and worse (sometimes extremely worse) at other tasks, compared to average humans. For example, they're better at retrieving information from huge amounts of unstructured data. But they're also terrible at learning: any "experience" which falls out of the context window is lost forever, and the model can't learn from its mistakes. To actually make it learn something requires very many examples and a lot of compute, whereas a human can permanently learn from a single example.
- andsoitis 1y ago
  > human can permanently learn from a single example This, to me at least, seems like an important ingredient to satisfying a practical definition / implementation of AGI. Another might be curiosity, and I think perhaps also agency.
AlienRobot 1y ago
Nobody is saying that LLM's don't work like magic. I know how neural networks work and they still feel like voodoo to me. What we are saying is that LLM's can't become AGI. I don't know what AGI will look like, but it won't look like an LLM. There is a difference between being able to melt iron and being able to melt tungsten.

thaawyy33432434 1y ago

Recently I realized that US are very close to a centrally planned economy. Meta wasted 50B on metaverse, which like how much Texas spends on healthcare. Now the "AI" investments seems dubious. You could fund 1000+ projects with this kinds of money. This is not an effective capital allocation.

meowface 1y ago

This is not and has not been the consensus opinion. If you're not an AI researcher you shouldn't write as if you've set your confidence parameter to 0.95. Of course it might be the case, but it's not a thing that should be expressed with such confidence.

janalsncm 1y ago

I think AI research is like anything else really. The smartest people are heads down working on their problems. The people going on podcasts are less connected to day to day work. It’s also pretty useless to talk about whether something is AGI without defining intelligence in the first place.

foobarian 1y ago

I think something like we saw in the show "Devs" is much more likely, although what the developers did with it in the show was bonkers unrealistic. But some kind of big enough quantum device basically.