5 ms·

I'll restate because both objections (which apparently skim instead of read) are missing the important point. Yes LLMs can run "what ifs" scenarios and build mo

by mythrwy 26d ago

I'll restate because both objections (which apparently skim instead of read) are missing the important point. Yes LLMs can run "what ifs" scenarios and build models. However LLMs deal entirely in symbols. 100%. Humans can "world build" aside from this and in fact are often at their best doing so. Did the first humans to use fire and some form of a wheel even have the capability to talk about it? Think about that.

willmarch 26d ago
Isn't thinking in images (visualizing non-verbally) also dealing entirely in symbols?
red75prime 26d ago
> However LLMs deal entirely in symbols. They use tokens as input/output encoding. They do 99.9999% of processing in a high-dimensional latent space.
- mythrwy 25d ago
  How exactly is this high dimensional latent space represented? Pixie dust and ethereal forces? Or floating point numbers? Where does it get the weights? Reddit? Do you hold your experience of "dogs" (for instance) as floating point numbers? The fur, the fear, the love, the wet mouths, the sounds and colors?
  - red75prime 24d ago
    Do you experience dogs as lots of action potentials traveling along axons and lots of neurons doing their thing in your brain? I don't know how it gets from the physical processes or the information processing to our first-hand experiences. So, I can't be sure that a bunch of high-dimensional vectors can't lead to experiences. Regardless, the claim "LLMs deal entirely in symbols" is wrong as a matter of fact.
    - mythrwy 24d ago
      Perhaps if you use a very restricted definition of symbols. If you consider symbols to be "anything that represents something" (which is the the sense I use the word in) it is fully the case. That you might not define floating point numbers to be "symbols" aside, the inputs and the outputs are symbols and the intent and purpose of the creation is strictly symbolic. It's right there in the name "Large Language Models". Language. Not direct experience, not emotion, not anything else. Language. i.e. symbolic representation. This does not cover the full spectrum of intelligence humans have, and it shows. And yes, the model can spin up Python parse the output and get mathematical intelligence but there is still a big gap. As I say, I see the holes. I'm just trying to figure out what it is I see and how to describe it. It's particularly difficult because we don't fully understand how human thinking works but I will say I believe human thinking is a lot more than informal statistical correlation.
      - red75prime 24d ago
        The latest LLMs (except Qwen and DeepSeek) are MLLMs (multimodal language models). Unless you count RGB values as symbols, they are dealing with more than symbols. Yes, there are functional gaps between MLLMs and humans. Their long-term memory is an external mechanism that can use RAG-like approaches, context compression or something like that. The models have problems managing those. The models can't do continual learning. Although there are promising directions (expert cloning in MoE models, and others). The only mode of learning available to a model while working on a task is in-context learning. This limits the models to concepts that they developed during autoregressive pretraining and the later stages of training. That is a model can't create new concepts as a result of working on a task (the model's maintainers could choose the task to be represented in the training data later though). But it's all about functionality. I guess you have the Leibniz's mill intuition. We can look at how those things work, and there are no experiences or intelligence in sight.
        mythrwy 24d ago
        It could be the mill intuition, but my thought is nothing along the lines of "computers can't have souls!" or the human mind is supernatural or anything of the sort. It's gaps in actual thinking or intelligence I notice. A diff between what I can see or understand and what the model sees or understands. Some are very big, and this in spite of the models having much more knowledge and (presumably) less error prone processing. My thought is that part of it has to do with inherent limitations of using symbolic representation for "thinking" and I suppose humans have other forms of thinking that occur outside of symbolic representation, and that is going to be hard to recreate digitally. This is my whole point and I'm not trying to win a debate here or prove "LLMs are useless". Just speculating.

by mythrwy 26d ago

willmarch 26d ago

Isn't thinking in images (visualizing non-verbally) also dealing entirely in symbols?

red75prime 26d ago

> However LLMs deal entirely in symbols. They use tokens as input/output encoding. They do 99.9999% of processing in a high-dimensional latent space.

mythrwy 25d ago
How exactly is this high dimensional latent space represented? Pixie dust and ethereal forces? Or floating point numbers? Where does it get the weights? Reddit? Do you hold your experience of "dogs" (for instance) as floating point numbers? The fur, the fear, the love, the wet mouths, the sounds and colors?
- red75prime 24d ago
  Do you experience dogs as lots of action potentials traveling along axons and lots of neurons doing their thing in your brain? I don't know how it gets from the physical processes or the information processing to our first-hand experiences. So, I can't be sure that a bunch of high-dimensional vectors can't lead to experiences. Regardless, the claim "LLMs deal entirely in symbols" is wrong as a matter of fact.
  - mythrwy 24d ago
    Perhaps if you use a very restricted definition of symbols. If you consider symbols to be "anything that represents something" (which is the the sense I use the word in) it is fully the case. That you might not define floating point numbers to be "symbols" aside, the inputs and the outputs are symbols and the intent and purpose of the creation is strictly symbolic. It's right there in the name "Large Language Models". Language. Not direct experience, not emotion, not anything else. Language. i.e. symbolic representation. This does not cover the full spectrum of intelligence humans have, and it shows. And yes, the model can spin up Python parse the output and get mathematical intelligence but there is still a big gap. As I say, I see the holes. I'm just trying to figure out what it is I see and how to describe it. It's particularly difficult because we don't fully understand how human thinking works but I will say I believe human thinking is a lot more than informal statistical correlation.
    - red75prime 24d ago
      The latest LLMs (except Qwen and DeepSeek) are MLLMs (multimodal language models). Unless you count RGB values as symbols, they are dealing with more than symbols. Yes, there are functional gaps between MLLMs and humans. Their long-term memory is an external mechanism that can use RAG-like approaches, context compression or something like that. The models have problems managing those. The models can't do continual learning. Although there are promising directions (expert cloning in MoE models, and others). The only mode of learning available to a model while working on a task is in-context learning. This limits the models to concepts that they developed during autoregressive pretraining and the later stages of training. That is a model can't create new concepts as a result of working on a task (the model's maintainers could choose the task to be represented in the training data later though). But it's all about functionality. I guess you have the Leibniz's mill intuition. We can look at how those things work, and there are no experiences or intelligence in sight.
      - mythrwy 24d ago
        It could be the mill intuition, but my thought is nothing along the lines of "computers can't have souls!" or the human mind is supernatural or anything of the sort. It's gaps in actual thinking or intelligence I notice. A diff between what I can see or understand and what the model sees or understands. Some are very big, and this in spite of the models having much more knowledge and (presumably) less error prone processing. My thought is that part of it has to do with inherent limitations of using symbolic representation for "thinking" and I suppose humans have other forms of thinking that occur outside of symbolic representation, and that is going to be hard to recreate digitally. This is my whole point and I'm not trying to win a debate here or prove "LLMs are useless". Just speculating.