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”.
  • Veraticus@lib.lgbtEnglish
    1·
    8 months ago

    The Anthropic one is saying they think they have a way to figure it out, but it hasn’t been tested on large models. This is their last paragraph:

    Again, all your quotes indicate that what they’ve figured out is a way to inspect the interior state of models and transform the vector space into something humans can understand without analyzing the output.

    I think your confusion is you believe that because we don’t know what the vector space is on the inside, we don’t know how AI works. But we actually do know how it accomplishes what it accomplishes. Simply because its interior is a black box doesn’t mean we don’t understand how we built that black box, or how it operates and functions.

    For an overview of how many different kinds of LLMs function, here’s a good paper: https://arxiv.org/pdf/2307.06435.pdf You’ll note that nowhere is there any confusion about the process of how they generate input or produce output. It is all extremely well-understood. You are correct that we cannot interrogate their internals, but that is also not what I mean, at least, when I say that we can understand them and how they work.

    I also can’t inspect the electrons moving through my computer’s CPU. Does that mean we don’t understand how computers work? Is there intelligence in there?

    I think you’re maybe having a hard time with using numbers to represent concepts. While a lot less abstract, we do this all the time in geometry. ((0, 0), (10, 0), (10, 10), (0, 10), (0, 0)) What’s that? It’s a square. Word vectors work differently but have the same outcome (albeit in a more abstract way).

    No, that is not my main objection. It is your anthropomorphization of data and LLMs – your claim that they “have intelligence.” From your initial post:

    But also, can you define what intelligence is? Are you sure it isn’t whatever LLMs are doing under the hood, deep in hidden layers?

    I think you’re getting caught up in trying to define what intelligence is; but I am simply stating what it is not. It is not a complex statistical model with no self-awareness, no semantic understanding, no ability to learn, no emotional or ethical dimensionality, no qualia…

    ((0, 0), (10, 0), (10, 10), (0, 10), (0, 0)) is a square to humans. This is the crux of the problem: it is not a “square” to a computer because a “square” is a human classification. Your thoughts about squares are not just more robust than GPT’s, they are a different kind of thing altogether. For GPT, a square is a token that it has been trained to use in a context-appropriate manner with no idea of what it represents. It lacks semantic understanding of squares. As do all computers.

    If you’re saying that intelligence and understanding is limited to the human mind, then please point to some non-religious literature that backs up your assertion.

    I’m disappointed that you’re asking me to prove a negative. The burden of proof is on you to show that GPT4 is actually intelligent. I don’t believe intelligence and understanding are for humans only; animals clearly show it too. But GPT4 does not.

    • SirGolan@lemmy.sdf.org
      1·
      8 months ago

      Simply because its interior is a black box doesn’t mean we don’t understand how we built that black box, or how it operates and functions.

      Wait a sec. I think we’re saying the same thing here. I guess depending on what you mean by how it operates and functions. I’ve said multiple times we understand the math and the code. We understand how values propagate through it because again, that’s all the math and code people wrote. What we don’t understand is how it uses that math and code to actually do thinks that seem intelligent (putting aside the point of whether it is or is not intelligent). If that’s what you’re arguing then great, we’re on the same page!

      I also can’t inspect the electrons moving through my computer’s CPU. Does that mean we don’t understand how computers work? Is there intelligence in there?

      Well, I don’t have the equipment to look at electrons either (I don’t think that tech exists), but I can take a logic probe and get some information that I could probably understand, or someone who designs CPUs could look at the gates and whatever and tell you what they did and how they relate to whatever higher level operations. You’re bringing up something completely different here. Computers are not a black box at all. LLMs are-- you just said that yourself.

      No, that is not my main objection. It is your anthropomorphization of data and LLMs

      I’m not anthropomorphisizing them. What are you talking about? I keep saying they don’t work like human brains. I just said I don’t think they’re sentient or conscious. I said they don’t have agency.

      I think you’re getting caught up in trying to define what intelligence is; but I am simply stating what it is not.

      How do you know what it’s not if we can’t define what it is?

      It is not a complex statistical model with no self-awareness, no semantic understanding, no ability to learn, no emotional or ethical dimensionality, no qualia…

      Jury’s still out on whether human brains are complex statistical models. I mean (from here)…

      Our brains have learned, through evolution and experience, the statistical properties of our natural environments and exploit this knowledge when performing perceptual tasks.

      I don’t make any claim to understanding neuroscience, and I don’t think that article is saying for sure we know that.

      Anyway, in-context learning is a thing for LLMs. Maybe one day we’ll figure out how to have them adjust their weights after training, but that’s not happening now (well people are experimenting with it).

      New research is showing they do have semantic understanding.

      They don’t by themselves have self-awareness, but a software framework built up around them can generally do that to some extent.

      They do understand emotions and ethics. Someone built a fun GPTrolley web site a while ago. I think it died pretty quickly because it was too expensive for them, but it had GPT 3(?) answering Trolley Problem questions. It did (in my memory of it) like to save any “AGI” on one track over humans, which was amusing. They don’t have emotions, no. Does something have to have emotions to be intelligent?

      And no, I’ve said all along they aren’t conscious, so no qualia. Again, is that required for intelligence?

      This is the crux of the problem: it is not a “square” to a computer because a “square” is a human classification. Your thoughts about squares are not just more robust than GPT’s, they are a different kind of thing altogether. For GPT, a square is a token that it has been trained to use in a context-appropriate manner with no idea of what it represents. It lacks semantic understanding of squares. As do all computers.

      No. A square to GPTs is not just a token. It’s associated with some meaning. I’m not going to re-hash embedding and word vectors and whatever since I feel like I’ve explained that to death.

      If you’re saying that intelligence and understanding is limited to the human mind, then please point to some non-religious literature that backs up your assertion.

      I’m disappointed that you’re asking me to prove a negative.

      I’m literally not. “Intelligence is limited to the human mind” is not a negative.

      The burden of proof is on you to show that GPT4 is actually intelligent. I don’t believe intelligence and understanding are for humans only; animals clearly show it too. But GPT4 does not.

      I feel like I’ve laid out my argument for that mostly through the Microsoft and Max Tegmark papers. Are you saying intelligence is only the domain of biological life?

      Here’s a question-- are you conflating “intelligence” with “general intelligence” like AGI? I find a lot of people think “AI” means “AGI.” It doesn’t help that some people do say those things interchangeably. I was just reading a recent argument between Yann LeCun and Yoshua Bengio and they were both totally doing that. Anyway, I don’t at all believe GPT4 is AGI or that LLMs could even be AGI.

      For an overview of how many different kinds of LLMs function, here’s a good paper: https://arxiv.org/pdf/2307.06435.pdf

      Looks like a great paper-- I hadn’t seen it yet. I know how LLMs are constructed (generally-- while I could go and write some code for a multi-layer neural network with back propagation without looking anything up, I couldn’t do that for an LLM without looking at a diagram of the layers or whatnot).