5 ms·

This gcc script that I created below is just as "deterministic" as an LLM. It produces the same result every time. Doesn't make it useful though. echo '#!/

This gcc script that I created below is just as "deterministic" as an LLM. It produces the same result every time. Doesn't make it useful though. echo '#!/usr/bin/env bash' > gcc echo 'cat <<EOF' >> gcc openssl rand -base64 100 >> gcc echo 'EOF' >> gcc chmod +x gcc Also, how transformers work is not a spec of the LLM that anyone can use to learn how LLM produces code. It's no gcc source code.

soulofmischief 2y ago
You claimed they weren't deterministic, I have shown that they can be. I'm not sure what your point is. And it is incorrect to base your analysis of future transformer performance on current transformer performance. There is a lot of ongoing research in this area and we have seen continual progress.
- soraminazuki 2y ago
  I reiterate: > This is assuming by "deterministic," you mean the same thing I said about programming language implementations being "controllable, reproducible, and well-defined." If you mean it produces random but same results for the same inputs, then you haven't made any meaningful points. "Determinism" is a word that you brought up in response to my comment, which I charitably interpreted to mean the same thing I was originally talking about. Also, it's 100% correct to analyze things based on its fundamental properties. It's absurd to criticize people for assuming 2 + 2 = 4 because "continual progress" might make it 5 in the future.
  - soulofmischief 2y ago
    What are these fundamental properties you speak of? 8 years ago this was all a pipe dream. Are you claiming to know what the next 8 years of transformer development will look like?
    - soraminazuki 2y ago
      That LLMs are by definition models of human speech and have no cognitive capabilities. There is no sound logic behind what LLMs spit out, and will stay that way because it merely mimics its training data. No amount of vague future transformers will transform away how the underlying technology works. But let's say we have something more than an LLM, that still wouldn't make natural languages a good replacement for programming languages. This is because natural languages are, as the article mentions, imprecise. It just isn't a good tool. And no, transformers can't change how languages work. It can only "recontextualize," or as some people might call it, "hallucinate."
      - soulofmischief 2y ago
        Citation needed. Modern transformers are much, much more than just speech models. Precisely define "cognitive capabilities", and provide proof as to why neural models cannot ever mimic these cognitive capabilities. > But let's say we have something more than an LLM We do. Modern multi-modal transformers. > This is because natural languages are, as the article mentions, imprecise Two different programmers can take a well-enough defined spec and produce two separate code bases that may (but not must) differ in implementation, while still having the exact same interfaces and testable behavior. > And no, transformers can't change how languages work. It can only "recontextualize," or as some people might call it, "hallucinate." You don't understand recontextualization if you think it means hallucination. Or vice versa. Hallucination is about returning incorrect or false data. Recontextualization is akin to decompression, and can be lossy or "effectively" lossless (within a probabilistic framework; again, the interfaces and behavior just need to match)
        soraminazuki 2y ago
        The burden of proof is on the one making extraordinary claims. There has been no indication from any credible source that LLMs are able to think for itself. Human brains are still a mystery. I don't know why you can so confidently claim that neural models can mimic what humanity knows so little about. > Two different programmers can take a well-enough defined spec and produce two separate code bases that may (but not must) differ in implementation, while still having the exact same interfaces and testable behavior. Imagine doing that without a rigid and concise way of expressing your intentions. Or trying again and again in vain to get the LLM produce the software that you want. Or debugging it. Software development will become chaotic and lot less fun in that hypothetical future.

soulofmischief 2y ago

You claimed they weren't deterministic, I have shown that they can be. I'm not sure what your point is. And it is incorrect to base your analysis of future transformer performance on current transformer performance. There is a lot of ongoing research in this area and we have seen continual progress.

soraminazuki 2y ago
I reiterate: > This is assuming by "deterministic," you mean the same thing I said about programming language implementations being "controllable, reproducible, and well-defined." If you mean it produces random but same results for the same inputs, then you haven't made any meaningful points. "Determinism" is a word that you brought up in response to my comment, which I charitably interpreted to mean the same thing I was originally talking about. Also, it's 100% correct to analyze things based on its fundamental properties. It's absurd to criticize people for assuming 2 + 2 = 4 because "continual progress" might make it 5 in the future.
- soulofmischief 2y ago
  What are these fundamental properties you speak of? 8 years ago this was all a pipe dream. Are you claiming to know what the next 8 years of transformer development will look like?
  - soraminazuki 2y ago
    That LLMs are by definition models of human speech and have no cognitive capabilities. There is no sound logic behind what LLMs spit out, and will stay that way because it merely mimics its training data. No amount of vague future transformers will transform away how the underlying technology works. But let's say we have something more than an LLM, that still wouldn't make natural languages a good replacement for programming languages. This is because natural languages are, as the article mentions, imprecise. It just isn't a good tool. And no, transformers can't change how languages work. It can only "recontextualize," or as some people might call it, "hallucinate."
    - soulofmischief 2y ago
      Citation needed. Modern transformers are much, much more than just speech models. Precisely define "cognitive capabilities", and provide proof as to why neural models cannot ever mimic these cognitive capabilities. > But let's say we have something more than an LLM We do. Modern multi-modal transformers. > This is because natural languages are, as the article mentions, imprecise Two different programmers can take a well-enough defined spec and produce two separate code bases that may (but not must) differ in implementation, while still having the exact same interfaces and testable behavior. > And no, transformers can't change how languages work. It can only "recontextualize," or as some people might call it, "hallucinate." You don't understand recontextualization if you think it means hallucination. Or vice versa. Hallucination is about returning incorrect or false data. Recontextualization is akin to decompression, and can be lossy or "effectively" lossless (within a probabilistic framework; again, the interfaces and behavior just need to match)
      - soraminazuki 2y ago
        The burden of proof is on the one making extraordinary claims. There has been no indication from any credible source that LLMs are able to think for itself. Human brains are still a mystery. I don't know why you can so confidently claim that neural models can mimic what humanity knows so little about. > Two different programmers can take a well-enough defined spec and produce two separate code bases that may (but not must) differ in implementation, while still having the exact same interfaces and testable behavior. Imagine doing that without a rigid and concise way of expressing your intentions. Or trying again and again in vain to get the LLM produce the software that you want. Or debugging it. Software development will become chaotic and lot less fun in that hypothetical future.