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> Experts regard ChatGPT-3 and Google’s LaMDA as equally unimpressive. Investors don't seem to understand this What is "ChatGPT-3" and which experts exactly re

by xg15 4y ago

> Experts regard ChatGPT-3 and Google’s LaMDA as equally unimpressive. Investors don't seem to understand this What is "ChatGPT-3" and which experts exactly regard LLMs as unimpressive? MY feeling so far is more that laymen are less impressed of them than experts - because for people not from the field, the fact that they produce so much bullshit seems to trump all other aspects. Whereas I think for people who have kept track of AI developments a bit more, the fact just how coherent the bullshit is is still extremely impressive. We've had random text generators for decades, starting with markov chains way back. Up until a few years ago, you were amazed if those things produced gramatically correct sentences - not even dreaming of producing any kind of coherent meaning. In the potentially more difficult field of understanding text, there are a million small subfields, but nothing that could parse arbitrary english text and extract actionable, semantic meaning, in the face of idioms, figures of speech, complicated coreferences, etc. Compare this to today, where you can give an LLM a written instruction in freeform and it will just execute that instruction - the answer may be glaringly incorrect, but it's exactly the kind of answer that fits your question. In the field of understanding text, this is a progress which had seemed largely impossible before.

bonsaibilly 4y ago
> MY feeling so far is more that laymen are less impressed of them than experts - because for people not from the field, the fact that they produce so much bullshit seems to trump all other aspects. The NYT literally just published an embarrassingly credulous account of how the Bing AI wanted to seduce the author away from his wife and commit various acts of violence, that fully took all of the interactions at face value. The non-experts are imagining full-on sentience where the experts correctly recognize mere word association shenanigans. Your feelings, in short, are ass backwards.
- fullshark 4y ago
  That author was playing dumb for the good of his story, in particular to help it go viral. This is what news media is in 2023.
- anonzzzies 4y ago
  > The non-experts are imagining full-on sentience where the experts correctly recognize mere word association shenanigans. Your feelings, in short, are ass backwards. The current model (which is an early version; computers are new on earth and exist only for a statistical error length of time on humanity scale, on earth scale, let's not talk universe scale) is already vastly superior than many humans I know in almost every way (outside manual dexterity and some other fringe stuff you can easily fix with external systems, like we humans do). We have no good definition for what sentience is either; maybe our brain is word association shenanigans; connect up 2 chatgpts and call one 'inner voice' and the other 'external voice'. It will start claiming sentience in no time flat; the same as you. Why are you right? It's a feeling yeah?
  - bonsaibilly 4y ago
    > We have no good definition for what sentience is either; maybe our brain is word association shenanigans; This is just stupid. We've pumped more English through GPT-3 than any existing English-speaking human has absorbed in their entire lifetime, and what we've ended up with is something that very very clearly has so utterly failed to generalize even the most basic level of understanding of basic concepts that if you ask it to count the number of letters in a word it will cheerfully pump out the wrong answer ("there are thirteen letters in the word 'twelve'") because its dataset correlates the two words with one another and it has learned precisely sweet fuck all about what it means to count, something a child's brain picks up with exposure to many orders of magnitude fewer language examples. To imagine your brain is just an LLM is to mistake your reflection for another person in the room. Utterly daft. Get off the LLM hype train and start looking at these things objectively and critically. They're nowhere near what you're imagining.
    - fiso64 4y ago
      >if you ask it to count the number of letters in a word it will cheerfully pump out the wrong answer ("there are thirteen letters in the word 'twelve'") Not to be too blunt, but you seem to be talking out of your ass. I'm tired of the overly dismissive comments here on hackernews by people who didn't bother to do the bare minimum of research. LLMs do not work with individual characters, they use tokens (i.e multiple characters, or sometimes entire words).
BaseballPhysics 4y ago
> In the field of understanding text, this is a progress which had seemed largely impossible before. ... LLMs don't understand text. They encode statistical probabilities of relationships between strings of characters. Those are not the same thing. That's the entire damn point of this article! Honestly, for someone taking pot shots at "laymen", this is a profound thing to misunderstand.
- xg15 4y ago
  Ok, on the risk of sounding exactly like one of the "sentience" guys, but "statistical probabilities of relationships between strings of characters" is a non-answer that makes the whole thing sound simpler than it is without actually providing any explanation how it works. What kind of statistical probabilities exactly? Right now, research is still ongoing what kind of knowledge is actually captured inside the models, but there are some hints that higher-level, "semantic" knowledge might emerge during training: https://thegradient.pub/othello/ https://thegradient.pub/othello/
  - deleted 4y ago
    [deleted]
  - tysam_and 4y ago
    Both are happening, I believe. The LLM absorbs the manifold (high-dimensional shape) of the data. The manifold contains the underlying concepts of grammatical structure, abstract thought, and inductive reasoning, which the LLM (within reasonable capacity and structural capability) captures because it is a more efficient representation of the underlying data. Transformers are simple and contain the correct building blocks to efficiently capture these representations during training. This allows for out-of domain generalization. This is most, to my knowledge, of what happens in most deep learning processes and is likely 90% of the main content that one needs to know to understand neural networks at their core. I think it's pretty basic but it often gets buried in unconscionable mathematical symbols and fancy-speak to be of use to anyone reading.
    - 4y ago
dylan604 4y ago
>the fact just how coherent the bullshit is is still extremely impressive great, so it's a politician? I really hate that this is being heralded as a triumph. Someone being very confident in their wrongness does not make it right. This is something we've been dealing with for some time, but it seems to be orders of magnitude stronger than say 20 years ago. Now we're celebrating how our AI systems are amazing at this. I just want to hang my head in shame at what we've come to accept.
- xg15 4y ago
  Yeah, I agree with you here. I find it impressive on a scientific level and as a demonstration of what is possible. Whether any of that is actually beneficial for society or if we're just opening pandora's box is a wholly different question.

by xg15 4y ago

bonsaibilly 4y ago

> MY feeling so far is more that laymen are less impressed of them than experts - because for people not from the field, the fact that they produce so much bullshit seems to trump all other aspects. The NYT literally just published an embarrassingly credulous account of how the Bing AI wanted to seduce the author away from his wife and commit various acts of violence, that fully took all of the interactions at face value. The non-experts are imagining full-on sentience where the experts correctly recognize mere word association shenanigans. Your feelings, in short, are ass backwards.

fullshark 4y ago
That author was playing dumb for the good of his story, in particular to help it go viral. This is what news media is in 2023.
anonzzzies 4y ago
> The non-experts are imagining full-on sentience where the experts correctly recognize mere word association shenanigans. Your feelings, in short, are ass backwards. The current model (which is an early version; computers are new on earth and exist only for a statistical error length of time on humanity scale, on earth scale, let's not talk universe scale) is already vastly superior than many humans I know in almost every way (outside manual dexterity and some other fringe stuff you can easily fix with external systems, like we humans do). We have no good definition for what sentience is either; maybe our brain is word association shenanigans; connect up 2 chatgpts and call one 'inner voice' and the other 'external voice'. It will start claiming sentience in no time flat; the same as you. Why are you right? It's a feeling yeah?
- bonsaibilly 4y ago
  > We have no good definition for what sentience is either; maybe our brain is word association shenanigans; This is just stupid. We've pumped more English through GPT-3 than any existing English-speaking human has absorbed in their entire lifetime, and what we've ended up with is something that very very clearly has so utterly failed to generalize even the most basic level of understanding of basic concepts that if you ask it to count the number of letters in a word it will cheerfully pump out the wrong answer ("there are thirteen letters in the word 'twelve'") because its dataset correlates the two words with one another and it has learned precisely sweet fuck all about what it means to count, something a child's brain picks up with exposure to many orders of magnitude fewer language examples. To imagine your brain is just an LLM is to mistake your reflection for another person in the room. Utterly daft. Get off the LLM hype train and start looking at these things objectively and critically. They're nowhere near what you're imagining.
  - fiso64 4y ago
    >if you ask it to count the number of letters in a word it will cheerfully pump out the wrong answer ("there are thirteen letters in the word 'twelve'") Not to be too blunt, but you seem to be talking out of your ass. I'm tired of the overly dismissive comments here on hackernews by people who didn't bother to do the bare minimum of research. LLMs do not work with individual characters, they use tokens (i.e multiple characters, or sometimes entire words).

BaseballPhysics 4y ago

> In the field of understanding text, this is a progress which had seemed largely impossible before. ... LLMs don't understand text. They encode statistical probabilities of relationships between strings of characters. Those are not the same thing. That's the entire damn point of this article! Honestly, for someone taking pot shots at "laymen", this is a profound thing to misunderstand.

xg15 4y ago
Ok, on the risk of sounding exactly like one of the "sentience" guys, but "statistical probabilities of relationships between strings of characters" is a non-answer that makes the whole thing sound simpler than it is without actually providing any explanation how it works. What kind of statistical probabilities exactly? Right now, research is still ongoing what kind of knowledge is actually captured inside the models, but there are some hints that higher-level, "semantic" knowledge might emerge during training: https://thegradient.pub/othello/ https://thegradient.pub/othello/
- deleted 4y ago
  [deleted]
- tysam_and 4y ago
  Both are happening, I believe. The LLM absorbs the manifold (high-dimensional shape) of the data. The manifold contains the underlying concepts of grammatical structure, abstract thought, and inductive reasoning, which the LLM (within reasonable capacity and structural capability) captures because it is a more efficient representation of the underlying data. Transformers are simple and contain the correct building blocks to efficiently capture these representations during training. This allows for out-of domain generalization. This is most, to my knowledge, of what happens in most deep learning processes and is likely 90% of the main content that one needs to know to understand neural networks at their core. I think it's pretty basic but it often gets buried in unconscionable mathematical symbols and fancy-speak to be of use to anyone reading.
  - 4y ago

dylan604 4y ago

>the fact just how coherent the bullshit is is still extremely impressive great, so it's a politician? I really hate that this is being heralded as a triumph. Someone being very confident in their wrongness does not make it right. This is something we've been dealing with for some time, but it seems to be orders of magnitude stronger than say 20 years ago. Now we're celebrating how our AI systems are amazing at this. I just want to hang my head in shame at what we've come to accept.

xg15 4y ago
Yeah, I agree with you here. I find it impressive on a scientific level and as a demonstration of what is possible. Whether any of that is actually beneficial for society or if we're just opening pandora's box is a wholly different question.