7 ms·

GPTs Are Maxed Out

32 points by pseudolus 2y ago

caekislove 2y ago
[flagged]
- deleted 2y ago
  [deleted]
deleted 2y ago
[deleted]
robertlagrant 2y ago
> and certainly not enough to satisfy users’ growing appetite for AGI There is no AGI. No one is creating Minds from Iain M Banks books. It's the world's best autocomplete. It's if you look at the Earth once from space, and "recreate" it by making a giant disc that looks like that view of the Earth. Nothing behind it is like intelligence as we would think of it. AI is going to do some amazing things still. AGI is just an unbelievably high bar.
- glassounds 2y ago
  That's part of the problem though: It's not an intelligence, artificial or otherwise, yet we still call it "AI", giving people the impression that LLMs are almost AGI or a step on the way while a true artificial intelligence would be a completely different animal. A parrot can produce human sounding speech and associate certain prompts with desired responses, but there's no internal process that actually understands the meaning of either the prompt or the response (other than a shallow connection of sounds to a specific thing, rather than understanding human language). You'll never be able to train a parrot well enough to have that process in the same way a human does. Our current terminology would be like calling parrots "artificial humans". LLMs are still great tools for various tasks, they just shouldn't be categorized as AI.
  - bryanlarsen 2y ago
    Both parrots and AI's have intelligence, just not very much of it. AGI is generally defined as super-human intelligence, and neither parrots nor AI's have that.
    - glassounds 2y ago
      I'd argue that LLMs don't have intelligence, they have the ability to match a correct response to a prompt but that's not a product of any internal process that's like what we call intelligence (unless that's used to refer to smart programming / probabilistic models / machine learning). Parrots are closer but I used them for the example since they also mimic instead of understand.
  - danielbln 2y ago
    Could you describe a system that would be worthy of the moniker "AI"?
    - glassounds 2y ago
      I'm sure a better philosophical definition exists but off the top of my head: An intelligence should be able to actually understand its input / output (rather than appear to understand), be able to reason about itself recursively*, be able to learn and generally have state, be able to have an internal thought process without being prompted**. Some emotion isn't strictly a requirement but likely a desirable quality for human interaction. * "Gödel, Escher, Bach: an Eternal Golden Braid" and "I am a Strange Loop" are meant to cover this but I've only started the latter ** A sci-fi book series called Old Man's War features a race of aliens that are intelligent but not sentient due to not having an internal thought process and self awareness, which does raise the question of whether it's possible to have one without the other.
      - yarg 2y ago
        You don't know what artificial means.
- fsflover 2y ago
  See also: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room
- randomdata 2y ago
  > AGI is just an unbelievably high bar. Technically it is an impossible bar as it, like AI, is a forever moving target. When chess was still considered AI, the Turing Test was considered the bar for AGI. Now passing the Turing Test is considered AI and chess is basic computing. Soon GPT and the like will just be basic computing and the AGI bar you can image now will become the next AI, and AGI whatever next forward-looking goal there is at that point. Lather, rinse, repeat.
  - robertlagrant 2y ago
    > the Turing Test was considered the bar for AGI By some measures, PARRY[0] passed this test in 1972. It's a bad test for general intelligence, although I'm not convinced it was meant to be that. [0] https://en.wikipedia.org/wiki/PARRY https://en.wikipedia.org/wiki/PARRY
    - spwa4 2y ago
      One way to look at it is that we've already passed the point where humans can't reliably pass the most famous AGI test ... Ironically the Turing test. After all, the only way for an algorithm to pass it, is for a human to fail that same test. The Turing test is now considered too easy to be a real test of AI. In other words, these days we think that an algorithm that can't beat a human at "being human" is pretty bad.
      - robertlagrant 2y ago
        > In other words, these days we think that an algorithm that can't beat a human at "being human" is pretty bad. No, that's a bad summary. The Turing test is not a great test of being human.
        spwa4 2y ago
        I think it's the perfect way to answer what it is to be human. It's reversing the question. Moving the question from some abstract weird philosophical or absurd religious argument into a concrete test. It defines what an answer to "can you create a human?" must be. Can an algorithm convince a panel of humans that it is an (to the panel unknown) human, given text communication and 30 minutes? For me, that's the definition of 30 minutes of being human. And yes, it illustrates why being human for 60 minutes, never mind a year, is a more difficult problem than 30 minutes. But it's the same a real human would perform. Would there be extra value in dropping these requirements? Dropping the text-only requirement, do it for a week or two, and maybe even imitate a human known to the panel? Yes, absolutely.
throwup238 2y ago
> Will anyone see the value in a chatbot that takes an hour to solve a problem? Yes! The biggest usability issue with almost all the AI products I’ve used has been the termination problem, especially with fixed price offerings like ChatGPT. The LLM doesn’t actually have any time to “think” (however you might want to interpret that) except for the attention mechanism that visits each token. I want to be able to query an llm, give it access to web search, and give it a time limit so that it keeps going, consuming sources a hundred pages deep, “thinking” and writing a report for me until it’s given a signal to terminate.
- jasfi 2y ago
  That's an agent, which makes use of an LLM. The LLM's architecture means its trained once-off.
- sottol 2y ago
  But the LLM doesn't "think" - it just spits out a sequence of tokens (forever, literally). The chat models have been fine-tuned to eventually emit an "end" token (when the training examples would have been answered) that the UI/backend interprets and cuts off the LLM to stop it from generating more output. But the model would gladly go on forever and devolve into more and more obscure output the further the original query moves out of the context window.
  - kelseyfrog 2y ago
    Sorry, but thinking is a social category. Debating it is pointless because it assumes the existence of social facts. Once you're aware of the existence of the meta category of social facts as reified ideas, all of them become unjustifiable. The line of argumentation is already dead.
    - BoxedEmpathy 2y ago
      If I understand currently, you're pointing out 'thinking' wasn't meant literally in your comment? Or did I miss?
Ukv 2y ago
> March 2022. Cognitive scientist Gary Marcus publishes a scathing article: “Deep Learning Is Hitting a Wall.” [...] In March 2022, no less! Was he out of his mind? That’s what the experts said back then—amid laughter and barely concealed scorn. Yet, with the evidence The Information has now presented, Marcus seems almost prophetic. Gary Marcus has been saying this since at least 2012: > Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012) To me it feels like having continually predicted rain throughout the longest dry-spell in history. Eventually you'll be right (assuming that we'll move to some other paradigm in due course), but it's hardly prophetic. I think that the challenges of intelligence and non-shallow reasoning will inherently involve fighting against diminishing returns (which is what the supposedly broken scaling laws predict) regardless of technique. Same as for computer graphics - doubling the compute doesn't double the perceptual quality, but that doesn't necessarily mean it's hitting a wall.
- thecleaner 2y ago
  Very poor F1 score if we're to bring ML into it.
goethes_kind 2y ago
With all the capital behind these LLM companies, I would be surprised if we don't see any architectural improvements that lead to better reasoning. Training bigger and bigger models is clearly not sustainable, so I'm sure all of them are working in this direction. Correct me if I'm wrong, but this is the first time that AI has so much financial capital behind it.
- heavyset_go 2y ago
  You could have poured billions into developing space flight in the 1800s, but it doesn't mean you'd get a working spaceship once the funds ran out. I suspect we're in a similar situation with AGI.
  - goethes_kind 2y ago
    We got so far with transformers and huge amounts of compute and huge amounts of data. If we can get an architecture that is able to extract a slightly higher order of reasoning it will have a cascading effect when we apply the same level of compute and data. I see lots of potential for progressive improvement in this direction. Problem is, it's quite expensive to develop and test new architectures, but that's were the financial capital comes in.
    - Jensson 2y ago
      Issue with AI is that most human tampering with the algorithms makes the end result worse, not better, it is really hard to get that tiny improvement.
    - jasfi 2y ago
      You can think of each of those as a bottleneck. The architecture (LLMs, transformers) was once the bottleneck, as was the amount of compute. From what I know the new bottleneck is the amount of quality data. Actually there was a breakthrough there too, because GPTs don't need supervised training.
thornewolf 2y ago
> The blind trust OpenAI and competitors like Google, Anthropic, or Meta put on the scaling laws—if you increase size, data, and compute you’ll get a better model—was unjustified. And how could it be otherwise! Scale was never a law of nature like gravity or evolution, but an observation of what was working at the time—just like Moore’s law, which today rests in peace, outmatched by the impenetrability of quantum mechanics and the geopolitical forces that menace Taiwan. This is a self-contradicting paragraph. It cites quantum mechanics understanding as breaking down Moore's law while ignoring the fact that it has implications on our so-called "laws" of physics in just the same way. I can imagine the author having originally written "physics" instead of "gravity" but needing to re-write the paragraph to squirm away from the contradiction they set up for themselves. I feel that this presentation is not intellectually honest.
deleted 2y ago
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deleted 2y ago
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qgin 2y ago
There is so much desire to hear this story —- that LLMs aren’t that cool and the hype is overblown —- that I’m just as skeptical of this as I am of AI companies promising an API for god. A lot of people really really really want this to fail.

GPTs Are Maxed Out

32 points by pseudolus 2y ago

caekislove 2y ago

[flagged]

deleted 2y ago
[deleted]

deleted 2y ago

[deleted]

robertlagrant 2y ago

> and certainly not enough to satisfy users’ growing appetite for AGI There is no AGI. No one is creating Minds from Iain M Banks books. It's the world's best autocomplete. It's if you look at the Earth once from space, and "recreate" it by making a giant disc that looks like that view of the Earth. Nothing behind it is like intelligence as we would think of it. AI is going to do some amazing things still. AGI is just an unbelievably high bar.

glassounds 2y ago
That's part of the problem though: It's not an intelligence, artificial or otherwise, yet we still call it "AI", giving people the impression that LLMs are almost AGI or a step on the way while a true artificial intelligence would be a completely different animal. A parrot can produce human sounding speech and associate certain prompts with desired responses, but there's no internal process that actually understands the meaning of either the prompt or the response (other than a shallow connection of sounds to a specific thing, rather than understanding human language). You'll never be able to train a parrot well enough to have that process in the same way a human does. Our current terminology would be like calling parrots "artificial humans". LLMs are still great tools for various tasks, they just shouldn't be categorized as AI.
- bryanlarsen 2y ago
  Both parrots and AI's have intelligence, just not very much of it. AGI is generally defined as super-human intelligence, and neither parrots nor AI's have that.
  - glassounds 2y ago
    I'd argue that LLMs don't have intelligence, they have the ability to match a correct response to a prompt but that's not a product of any internal process that's like what we call intelligence (unless that's used to refer to smart programming / probabilistic models / machine learning). Parrots are closer but I used them for the example since they also mimic instead of understand.
- danielbln 2y ago
  Could you describe a system that would be worthy of the moniker "AI"?
  - glassounds 2y ago
    I'm sure a better philosophical definition exists but off the top of my head: An intelligence should be able to actually understand its input / output (rather than appear to understand), be able to reason about itself recursively*, be able to learn and generally have state, be able to have an internal thought process without being prompted**. Some emotion isn't strictly a requirement but likely a desirable quality for human interaction. * "Gödel, Escher, Bach: an Eternal Golden Braid" and "I am a Strange Loop" are meant to cover this but I've only started the latter ** A sci-fi book series called Old Man's War features a race of aliens that are intelligent but not sentient due to not having an internal thought process and self awareness, which does raise the question of whether it's possible to have one without the other.
    - yarg 2y ago
      You don't know what artificial means.
fsflover 2y ago
See also: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room
randomdata 2y ago
> AGI is just an unbelievably high bar. Technically it is an impossible bar as it, like AI, is a forever moving target. When chess was still considered AI, the Turing Test was considered the bar for AGI. Now passing the Turing Test is considered AI and chess is basic computing. Soon GPT and the like will just be basic computing and the AGI bar you can image now will become the next AI, and AGI whatever next forward-looking goal there is at that point. Lather, rinse, repeat.
- robertlagrant 2y ago
  > the Turing Test was considered the bar for AGI By some measures, PARRY[0] passed this test in 1972. It's a bad test for general intelligence, although I'm not convinced it was meant to be that. [0] https://en.wikipedia.org/wiki/PARRY https://en.wikipedia.org/wiki/PARRY
  - spwa4 2y ago
    One way to look at it is that we've already passed the point where humans can't reliably pass the most famous AGI test ... Ironically the Turing test. After all, the only way for an algorithm to pass it, is for a human to fail that same test. The Turing test is now considered too easy to be a real test of AI. In other words, these days we think that an algorithm that can't beat a human at "being human" is pretty bad.
    - robertlagrant 2y ago
      > In other words, these days we think that an algorithm that can't beat a human at "being human" is pretty bad. No, that's a bad summary. The Turing test is not a great test of being human.
      - spwa4 2y ago
        I think it's the perfect way to answer what it is to be human. It's reversing the question. Moving the question from some abstract weird philosophical or absurd religious argument into a concrete test. It defines what an answer to "can you create a human?" must be. Can an algorithm convince a panel of humans that it is an (to the panel unknown) human, given text communication and 30 minutes? For me, that's the definition of 30 minutes of being human. And yes, it illustrates why being human for 60 minutes, never mind a year, is a more difficult problem than 30 minutes. But it's the same a real human would perform. Would there be extra value in dropping these requirements? Dropping the text-only requirement, do it for a week or two, and maybe even imitate a human known to the panel? Yes, absolutely.

throwup238 2y ago

> Will anyone see the value in a chatbot that takes an hour to solve a problem? Yes! The biggest usability issue with almost all the AI products I’ve used has been the termination problem, especially with fixed price offerings like ChatGPT. The LLM doesn’t actually have any time to “think” (however you might want to interpret that) except for the attention mechanism that visits each token. I want to be able to query an llm, give it access to web search, and give it a time limit so that it keeps going, consuming sources a hundred pages deep, “thinking” and writing a report for me until it’s given a signal to terminate.

jasfi 2y ago
That's an agent, which makes use of an LLM. The LLM's architecture means its trained once-off.
sottol 2y ago
But the LLM doesn't "think" - it just spits out a sequence of tokens (forever, literally). The chat models have been fine-tuned to eventually emit an "end" token (when the training examples would have been answered) that the UI/backend interprets and cuts off the LLM to stop it from generating more output. But the model would gladly go on forever and devolve into more and more obscure output the further the original query moves out of the context window.
- kelseyfrog 2y ago
  Sorry, but thinking is a social category. Debating it is pointless because it assumes the existence of social facts. Once you're aware of the existence of the meta category of social facts as reified ideas, all of them become unjustifiable. The line of argumentation is already dead.
  - BoxedEmpathy 2y ago
    If I understand currently, you're pointing out 'thinking' wasn't meant literally in your comment? Or did I miss?

Ukv 2y ago

> March 2022. Cognitive scientist Gary Marcus publishes a scathing article: “Deep Learning Is Hitting a Wall.” [...] In March 2022, no less! Was he out of his mind? That’s what the experts said back then—amid laughter and barely concealed scorn. Yet, with the evidence The Information has now presented, Marcus seems almost prophetic. Gary Marcus has been saying this since at least 2012: > Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012) To me it feels like having continually predicted rain throughout the longest dry-spell in history. Eventually you'll be right (assuming that we'll move to some other paradigm in due course), but it's hardly prophetic. I think that the challenges of intelligence and non-shallow reasoning will inherently involve fighting against diminishing returns (which is what the supposedly broken scaling laws predict) regardless of technique. Same as for computer graphics - doubling the compute doesn't double the perceptual quality, but that doesn't necessarily mean it's hitting a wall.

thecleaner 2y ago
Very poor F1 score if we're to bring ML into it.

goethes_kind 2y ago

With all the capital behind these LLM companies, I would be surprised if we don't see any architectural improvements that lead to better reasoning. Training bigger and bigger models is clearly not sustainable, so I'm sure all of them are working in this direction. Correct me if I'm wrong, but this is the first time that AI has so much financial capital behind it.

heavyset_go 2y ago
You could have poured billions into developing space flight in the 1800s, but it doesn't mean you'd get a working spaceship once the funds ran out. I suspect we're in a similar situation with AGI.
- goethes_kind 2y ago
  We got so far with transformers and huge amounts of compute and huge amounts of data. If we can get an architecture that is able to extract a slightly higher order of reasoning it will have a cascading effect when we apply the same level of compute and data. I see lots of potential for progressive improvement in this direction. Problem is, it's quite expensive to develop and test new architectures, but that's were the financial capital comes in.
  - Jensson 2y ago
    Issue with AI is that most human tampering with the algorithms makes the end result worse, not better, it is really hard to get that tiny improvement.
  - jasfi 2y ago
    You can think of each of those as a bottleneck. The architecture (LLMs, transformers) was once the bottleneck, as was the amount of compute. From what I know the new bottleneck is the amount of quality data. Actually there was a breakthrough there too, because GPTs don't need supervised training.

thornewolf 2y ago

> The blind trust OpenAI and competitors like Google, Anthropic, or Meta put on the scaling laws—if you increase size, data, and compute you’ll get a better model—was unjustified. And how could it be otherwise! Scale was never a law of nature like gravity or evolution, but an observation of what was working at the time—just like Moore’s law, which today rests in peace, outmatched by the impenetrability of quantum mechanics and the geopolitical forces that menace Taiwan. This is a self-contradicting paragraph. It cites quantum mechanics understanding as breaking down Moore's law while ignoring the fact that it has implications on our so-called "laws" of physics in just the same way. I can imagine the author having originally written "physics" instead of "gravity" but needing to re-write the paragraph to squirm away from the contradiction they set up for themselves. I feel that this presentation is not intellectually honest.

deleted 2y ago

[deleted]

deleted 2y ago

[deleted]

qgin 2y ago

There is so much desire to hear this story —- that LLMs aren’t that cool and the hype is overblown —- that I’m just as skeptical of this as I am of AI companies promising an API for god. A lot of people really really really want this to fail.