3 ms·

Definitely needs faster and cheaper models. Fast and cheap models could replace software in tons of situations. Imagine a vending machine or a mobile game or a

Definitely needs faster and cheaper models. Fast and cheap models could replace software in tons of situations. Imagine a vending machine or a mobile game or a word processor where basically all logic is implemented as a prompt to an llm. It would serve as the ultimate high level programming language.

janalsncm 1y ago
I think natural language to code is the right abstraction. Easy enough barrier to entry but still debuggable. Debugging why an LLM randomly gives you Mountain Dew instead of Sprite if you have a southern accent sounds like a nightmare.
- geysersam 1y ago
  I'm not sure it would be that hard to debug. Make sure you can reproduce the llm state (by storing the random seed for the session, or something like that) and then ask it "why did you just now give that customer mountain dew when they ordered sprite?"
  - otabdeveloper4 1y ago
    > and then ask it "why did you just now give that customer mountain dew when they ordered sprite?" Worse than useless for debugging. An LLM can't think and doesn't have capabilities for self-reflection. It will just generate a plausible stream of tokens in reply that may or may not correspond to the real reason why.
    - geysersam 1y ago
      Of course a llm can't think. But that doesn't mean it can't answer simple questions about the output that was produced. Just try it out with chatgpt when you have time. Even if it's not perfectly accurate it's still useful for debugging. Just think about it as a human employee. Can they always say why they did what they did? Often, but not always. Sometimes you will have to work to figure out the misunderstanding.
      - otabdeveloper4 1y ago
        > it's still useful for debugging How so? What the LLM says is whatever is more likely given the context. It has no relation to the underlying reality whatsoever.
        geysersam 1y ago
        Not sure what you mean by "relation to the underlying reality". The explanation is likely to be correlated with the underlying reason for the answer. For example, here is a simple query: > I put my bike in the bike stand, I removed my bike from the bike stand and biked to the beach, then I biked home, where is my bike. Answer only with the answer and no explanation > Chatgpt: At home > Why did you answer that? > I answered that your bike is at home because the last action you described was biking home, which implies you took the bike with you and ended your journey there. Therefore, the bike would logically be at home now. Do you doubt that the answer would change if I changed the query to make the final destination be "the park" instead of "home"? If you don't doubt that, what do you mean that the answer doesn't correspond to the underlying reality? The reality is the answer depends on the final destination mentioned, and that's also the explanation given by the LLM, clearly the reality and the answers are related.
        janalsncm 1y ago
        You need to find an example of the LLM making a mistake. In your example, ChatGPT answered correctly. There are many examples online of LLMs answering basic questions incorrectly, and then the person asking the LLM why it did so. The LLM response is usually nonsense. Then there is the question of what you would do with its response. It’s not like code where you can go in and update the logic. There are billions of floating point numbers. If you actually wanted to update the weights you’ll quickly find yourself fine-tuning the monstrosity. Orders of magnitude more work than updating an “if” statement.
        geysersam 1y ago
        I don't think llms always can give correct explanations for their answers. That's a misunderstanding. > Then there is the question of what you would do with its response. I Sure but that's a separate question. I'd say the first course of action would be to edit the prompt. If you have to resort to fine tuning I'd say the approach has failed and the tool was insufficient for the task.
  - janalsncm 1y ago
    Why not just store the state in the code and debug as usual, perhaps with LLM assistance? At least that’s tractable.
suddenlybananas 1y ago
Why on earth would you implement a vending machine using an LLM?
- K0balt 1y ago
  The same reason we make the butter dish suffer from existential angst.
- jacob019 1y ago
  Because it's easy and cheap. Like how many products use a Raspberry Pi or ESP32 when an ATtiny would do.
  - kadushka 1y ago
    How in the world is this easy and cheap? Are you planning to run this LLM inside the vending machine? Or are you planning to send those prompts to a remote LLM somewhere?
    - geysersam 1y ago
      The premise here is that the model runs fast and cheap. With the current state of the technology running a vending machine using an LLM is of course absurd. The point is that accuracy is not the only dimension that brings qualitative change to the kind of applications that LLMs are useful for.
      - kadushka 1y ago
        Running a vending machine using an LLM is absurd not because we can't run LLMs fast or cheap enough - it's because LLMs are not reliable, and we don't know yet how to make them more reliable. Our best LLM - o3 - doubled the previous model (o1) hallucination rate. OpenAI says it hallucinated a wrong answer 33% of the time in benchmarks. Do you want a vending machine that screws up 33% of the time? Today, the accuracy of LLMs is by far a bigger concern (and a harder problem to solve) than its speed. If someone releases a model which is 10x slower than o3, but is 20% better in terms of accuracy, reliability, or some other metric of its output quality, I'd switch to it in a heartbeat (and I'd be ready to pay more for it). I can't wait until o3-pro is released.

janalsncm 1y ago

I think natural language to code is the right abstraction. Easy enough barrier to entry but still debuggable. Debugging why an LLM randomly gives you Mountain Dew instead of Sprite if you have a southern accent sounds like a nightmare.

geysersam 1y ago
I'm not sure it would be that hard to debug. Make sure you can reproduce the llm state (by storing the random seed for the session, or something like that) and then ask it "why did you just now give that customer mountain dew when they ordered sprite?"
- otabdeveloper4 1y ago
  > and then ask it "why did you just now give that customer mountain dew when they ordered sprite?" Worse than useless for debugging. An LLM can't think and doesn't have capabilities for self-reflection. It will just generate a plausible stream of tokens in reply that may or may not correspond to the real reason why.
  - geysersam 1y ago
    Of course a llm can't think. But that doesn't mean it can't answer simple questions about the output that was produced. Just try it out with chatgpt when you have time. Even if it's not perfectly accurate it's still useful for debugging. Just think about it as a human employee. Can they always say why they did what they did? Often, but not always. Sometimes you will have to work to figure out the misunderstanding.
    - otabdeveloper4 1y ago
      > it's still useful for debugging How so? What the LLM says is whatever is more likely given the context. It has no relation to the underlying reality whatsoever.
      - geysersam 1y ago
        Not sure what you mean by "relation to the underlying reality". The explanation is likely to be correlated with the underlying reason for the answer. For example, here is a simple query: > I put my bike in the bike stand, I removed my bike from the bike stand and biked to the beach, then I biked home, where is my bike. Answer only with the answer and no explanation > Chatgpt: At home > Why did you answer that? > I answered that your bike is at home because the last action you described was biking home, which implies you took the bike with you and ended your journey there. Therefore, the bike would logically be at home now. Do you doubt that the answer would change if I changed the query to make the final destination be "the park" instead of "home"? If you don't doubt that, what do you mean that the answer doesn't correspond to the underlying reality? The reality is the answer depends on the final destination mentioned, and that's also the explanation given by the LLM, clearly the reality and the answers are related.
        janalsncm 1y ago
        You need to find an example of the LLM making a mistake. In your example, ChatGPT answered correctly. There are many examples online of LLMs answering basic questions incorrectly, and then the person asking the LLM why it did so. The LLM response is usually nonsense. Then there is the question of what you would do with its response. It’s not like code where you can go in and update the logic. There are billions of floating point numbers. If you actually wanted to update the weights you’ll quickly find yourself fine-tuning the monstrosity. Orders of magnitude more work than updating an “if” statement.
        geysersam 1y ago
        I don't think llms always can give correct explanations for their answers. That's a misunderstanding. > Then there is the question of what you would do with its response. I Sure but that's a separate question. I'd say the first course of action would be to edit the prompt. If you have to resort to fine tuning I'd say the approach has failed and the tool was insufficient for the task.
- janalsncm 1y ago
  Why not just store the state in the code and debug as usual, perhaps with LLM assistance? At least that’s tractable.

suddenlybananas 1y ago

Why on earth would you implement a vending machine using an LLM?

K0balt 1y ago
The same reason we make the butter dish suffer from existential angst.
jacob019 1y ago
Because it's easy and cheap. Like how many products use a Raspberry Pi or ESP32 when an ATtiny would do.
- kadushka 1y ago
  How in the world is this easy and cheap? Are you planning to run this LLM inside the vending machine? Or are you planning to send those prompts to a remote LLM somewhere?
  - geysersam 1y ago
    The premise here is that the model runs fast and cheap. With the current state of the technology running a vending machine using an LLM is of course absurd. The point is that accuracy is not the only dimension that brings qualitative change to the kind of applications that LLMs are useful for.
    - kadushka 1y ago
      Running a vending machine using an LLM is absurd not because we can't run LLMs fast or cheap enough - it's because LLMs are not reliable, and we don't know yet how to make them more reliable. Our best LLM - o3 - doubled the previous model (o1) hallucination rate. OpenAI says it hallucinated a wrong answer 33% of the time in benchmarks. Do you want a vending machine that screws up 33% of the time? Today, the accuracy of LLMs is by far a bigger concern (and a harder problem to solve) than its speed. If someone releases a model which is 10x slower than o3, but is 20% better in terms of accuracy, reliability, or some other metric of its output quality, I'd switch to it in a heartbeat (and I'd be ready to pay more for it). I can't wait until o3-pro is released.