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If you asked GPT to write a program that multiplies 32 and 64 it could do it. Since the thing is a computer, why can’t it answer queries by writing and executi

If you asked GPT to write a program that multiplies 32 and 64 it could do it. Since the thing is a computer, why can’t it answer queries by writing and executing a program? Does a transformer-based AI have to be 100% transformer?

samus 3y ago
This is simply not how transformer models work. Perhaps overly simplified, it maps an input string to an output string by pushing it through a neural network. But enabling it to access databases, APIs and to write and execute programs on accellerators would indeed significantly increase its capabilities.
spywaregorilla 3y ago
Of course that's ok. That's also how brains work, delegating certain tasks to specific specialized parts and processes within the brain. But it's no longer a token prediction model.
- criddell 3y ago
  If GPT transforms are Turing-complete[1], then why couldn't the ability to write and execute a program be part of a prediction model? [1]: https://aclanthology.org/2020.conll-1.37.pdf https://aclanthology.org/2020.conll-1.37.pdf
  - spywaregorilla 3y ago
    Because the optimization logic of tweaking the gpt transforms via training data and nudging weights and biases to achieve token prediction is not a path to utilizing the full potential of the gpt transforms.
    - criddell 3y ago
      I don’t see why not. I haven’t tried this, but I think I could describe a very basic logical CPU to it and have it execute opcodes and tell me the CPU state at each step (like Knuth’s MIX). Couldn’t I ask it to write programs and execute programs for this computer when doing so would help it give better answers?
      - spywaregorilla 3y ago
        Highly unlikely to work in the near future. LLMs are bad state machines. But even if it did, you are basically describing programming in a contrived language, not the llm learning to do math. You can explicitly teach a model to follow an algorithm. But that's not the same as the algorithm being "learned" during training and "invoked" when asked what's 2 + 2?

samus 3y ago

This is simply not how transformer models work. Perhaps overly simplified, it maps an input string to an output string by pushing it through a neural network. But enabling it to access databases, APIs and to write and execute programs on accellerators would indeed significantly increase its capabilities.

spywaregorilla 3y ago

Of course that's ok. That's also how brains work, delegating certain tasks to specific specialized parts and processes within the brain. But it's no longer a token prediction model.

criddell 3y ago
If GPT transforms are Turing-complete[1], then why couldn't the ability to write and execute a program be part of a prediction model? [1]: https://aclanthology.org/2020.conll-1.37.pdf https://aclanthology.org/2020.conll-1.37.pdf
- spywaregorilla 3y ago
  Because the optimization logic of tweaking the gpt transforms via training data and nudging weights and biases to achieve token prediction is not a path to utilizing the full potential of the gpt transforms.
  - criddell 3y ago
    I don’t see why not. I haven’t tried this, but I think I could describe a very basic logical CPU to it and have it execute opcodes and tell me the CPU state at each step (like Knuth’s MIX). Couldn’t I ask it to write programs and execute programs for this computer when doing so would help it give better answers?
    - spywaregorilla 3y ago
      Highly unlikely to work in the near future. LLMs are bad state machines. But even if it did, you are basically describing programming in a contrived language, not the llm learning to do math. You can explicitly teach a model to follow an algorithm. But that's not the same as the algorithm being "learned" during training and "invoked" when asked what's 2 + 2?