A prevailing sentiment online is that GPT-4 still does not understand what it talks about. We can argue semantics over what “understanding” truly means. I think it’s useful, at least today, to draw the line at whether GPT-4 has succesfully modeled parts of the world. Is it just picking words and connecting them with correct grammar? Or does the token selection actually reflect parts of the physical world? One of the most remarkable things I’ve heard about GPT-4 comes from an episode of This American Life titled “Greetings, People of Earth”.
  • @BitSound
    0•1 year ago

    Your concept of a chair is an abstract thought representation of a chair. An LLM has vectors that combine or decompose in some way to turn into the word “chair,” but are not a concept of a chair or an abstract representation of a chair. It is simply vectors and weights, unrelated to anything that actually exists.

    Just so incredibly wrong. Fortunately, I’ll have save myself time arguing with such a misunderstanding. GPT-4 is here to help:

    This reads like a misunderstanding of how LLMs (like GPT) work. Saying an LLM’s understanding is “simply vectors and weights” is like saying our brain’s understanding is just “neurons and synapses”. Both systems are trying to capture patterns in data. The LLM does have a representation of a chair, but it’s in its own encoded form, much like our neurons have encoded representations of concepts. Oversimplifying and saying it’s unrelated to anything that actually exists misses the point of how pattern recognition and information encoding works in both machines and humans.

    • VeraticusEnglish
      0•1 year ago

      Are you kidding me? I sourced GPT4 itself disagreeing with you that it is intelligent and you told me it’s lying. And here you are, using it to try to reinforce your point? Are you for real or is this some kind of complicated game?

        • VeraticusEnglish
          -1•1 year ago

          Here, let’s ask GPT4 itself since you’ve decided it’s suddenly an okay source:

          Your statement is correct in asserting that the vector representation in a language model is not an abstract representation. It’s purely a mathematical construct. However, saying it’s “unrelated to anything that actually exists” might be an overstatement. These vectors do capture statistical patterns in human language, which are reflections of human thought and culture. They’re just not capable of the deep, nuanced understanding that comes from human experience.

          I accept it’s an overstatement. But it is neither “incredibly wrong,” nor is it thought. (Or intelligence.)

          • @[email protected]
            1•1 year ago

            I’d just like to step in here and mention that asking an LLM is probably not a good proof (and this is directed at both of you). Its understanding of AI is from before it was trained, so it is wildly out of date at this point given how much has happened in the space since.

            • VeraticusEnglish
              1•1 year ago

              GPT4 has knowledge of its own training since it was trained in 2022.

              • @[email protected]
                1•1 year ago

                Care to provide some proof of that? They did update their system prompt to include a few things like it is now GPT4 (it used to always say GPT3). Other than that, I don’t think it knows anything. But in general, I was more talking about developments in AI since it was trained which it certainly does not know.

          • @BitSound
            1•1 year ago

            So you admit that you were wrong?

            • VeraticusEnglish
              -1•1 year ago

              I was in this case – but the overall point I made is still correct. If winning this minor battle is what you were seeking, congratulations. You are no closer to understanding the truth of this or what we were actually talking about. Not that that was either your point or within your capabilities.