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I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here? If LLMs were successfully dep

I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here? If LLMs were successfully deployed in a production environment is the day I bite my tongue. What about you? I'm not even sure that LLMs are even capable of solving standard bugs see: [1]. Hallucination seems to be a significant hurdle and any time spent validating the fixes of an LLM is wasted when it could be spent tackling the bug head on. The amount of energy spent espousing garbage requires an order of magnitude more effort to invalidate. [1]. https://daniel.haxx.se/blog/2024/01/02/the-i-in-llm-stands-for-intelligence/ https://daniel.haxx.se/blog/2024/01/02/the-i-in-llm-stands-f...

exe34 2y ago
> If LLMs were successfully deployed in a production environment is the day I bite my tongue. This shows so much faith in management!
- Infinity315 2y ago
  Alright, fine. Maybe you don't have faith in management, but perhaps you do have faith in the open market and capitalism. Feel free to point out any error in my logic: There are huge financial incentives--tens if not hundreds of billions of dollars--for developing an LLM which can solve novel bugs. So surely there exists AI companies developing an LLM capable of doing so. If an LLM capable of solving novel bugs exists, AI companies would rush to showing it off to capture tonnes of VC money. AI companies could show off their fancy bug-fixing LLM by closing issues on public Github repos using said LLMs. No such mythical LLM exists. We are thus left with two choices: 1. My logic is flawed or there is an alternative possibility I haven't considered. 2. The LLM capable of doing what OP asserts doesn't exist and can't be made, despite their assertion that it is trivial to fine tune and put into application.
  - dambi0 2y ago
    Even LLMs can see the false dichotomy here
    - Infinity315 2y ago
      Then it should be trivial to point out the error. So do it.
      - dambi0 2y ago
        Perhaps there are more lucrative applications where LLMs can be applied
        Infinity315 2y ago
        Maybe. But in the list of lucrative applications I think bug-fixing is near the top. I think it's lucrative enough to attract at least a decent chunk of engineering talent.
        thwarted 2y ago
        Agreed. There have been many assessments of what bugs cost, and the assessments are often very high, and that's the reason the industry has, for decades, been working towards having _fewer_ bugs.
  - exe34 2y ago
    My point was that management has a history of rolling out shiny things to production and then having egg on their face. See Microsoft's racist bot, Google's AI making up stuff in their adverts, etc. Your original wager was that it would be in production, not that it would work.
    - kzs0 2y ago
      [dead]
    - Infinity315 2y ago
      Wrong. My original wager would be a successful deployment, but sure that could be interpreted as a weasel word. What I mean by successful is that the LLM can generate accurate (>80%) incident response reports and propose correct fixes. I'm fairly certain anyone literate could have read the rest of my comment and parse out what a successful deployment means.
  - bryanrasmussen 2y ago
    Your logic seems sort of like the inverse of Augustine's proof for God.
    - Infinity315 2y ago
      It's a funny thing, because the inverse is falsifiable (testable) whereas the positive version is not. The inverse proof (I would say a hypothesis) is simply application of the scientific method. There is a way to disprove that the statement: "There is no god" by simply showing a counterfactual god. There is however no way to disprove the statement: "There is a god." Likewise, there is a way to disprove the statement: "LLMs cannot be successfully used for X application." By showing that LLMs have been used in X application. Again, there is no way to disprove the statement: "LLMs can (eventually) be used in X application." The meat of my question was meant to demonstrate a failure to apply the scientific method.
      - bryanrasmussen 2y ago
        >There is a way to disprove that the statement: "There is no god" by simply showing a counterfactual god. that both parties to the argument agree is a god. >Likewise, there is a way to disprove the statement: "LLMs cannot be successfully used for X application." By showing that LLMs have been used in X application. Again there the point of argumentation will be the word "successfully", the LLM would have to be such an overwhelming success at what it is trying to do that one cannot weasel out of it with "successfully".
  - reissbaker 2y ago
    The base technology capable of this has only been broadly available for about a month — prior to Llama-3.1-70b being released on July 23rd, you couldn't finetune any GPT-4 class models that had long context support (OpenAI only allowed fine-tuning their GPT-3.5-Turbo model until last week), and you'd need long context for the incident data — so I think "proof by inexistence" is pretty weak here. The first personal computer shipped in 1974, but it took five years until the development of Visicalc for spreadsheets to appear, despite the huge business value. I wouldn't expect most use cases for LLMs to appear within a month of them being made available. To answer your question more directly, I would go with option 1.
    - Infinity315 2y ago
      It's not proof by inexistence, it's simply application of the scientific method--only the most successful method to date. It seems like your assumptions are unfalsifiable. As computer scientists, I believe it's important that our hypotheses are testable. If a hypothesis is unfalsifiable, then the hypothesis is no better than theology and should be discarded. What's stopping you and other VCs just pouring endless money into an idea that won't work?
      - reissbaker 2y ago
        The scientific method generally involves experiments, as opposed to claiming something won't work because of the "logic" that if it worked, someone would have done it already. This particular hypothesis is obviously a testable one: someone could simply follow the proposed steps from the hypothesis (e.g. finetune a model on their incident response data), and see if it works. This is in fact how essentially all machine learning research is done: coming up with a proposal and trying it out. If it works, great! You've probably contributed something new to machine learning research. If not, oh well, try and figure out why your experiment failed, and if you have a good alternative approach try that instead. Your variant of the "scientific method" would've meant we never discovered electricity, or invented airplanes, or really anything else, because why bother trying? If it worked someone else would've done it.
        2y ago

exe34 2y ago

> If LLMs were successfully deployed in a production environment is the day I bite my tongue. This shows so much faith in management!

Infinity315 2y ago
Alright, fine. Maybe you don't have faith in management, but perhaps you do have faith in the open market and capitalism. Feel free to point out any error in my logic: There are huge financial incentives--tens if not hundreds of billions of dollars--for developing an LLM which can solve novel bugs. So surely there exists AI companies developing an LLM capable of doing so. If an LLM capable of solving novel bugs exists, AI companies would rush to showing it off to capture tonnes of VC money. AI companies could show off their fancy bug-fixing LLM by closing issues on public Github repos using said LLMs. No such mythical LLM exists. We are thus left with two choices: 1. My logic is flawed or there is an alternative possibility I haven't considered. 2. The LLM capable of doing what OP asserts doesn't exist and can't be made, despite their assertion that it is trivial to fine tune and put into application.
- dambi0 2y ago
  Even LLMs can see the false dichotomy here
  - Infinity315 2y ago
    Then it should be trivial to point out the error. So do it.
    - dambi0 2y ago
      Perhaps there are more lucrative applications where LLMs can be applied
      - Infinity315 2y ago
        Maybe. But in the list of lucrative applications I think bug-fixing is near the top. I think it's lucrative enough to attract at least a decent chunk of engineering talent.
        thwarted 2y ago
        Agreed. There have been many assessments of what bugs cost, and the assessments are often very high, and that's the reason the industry has, for decades, been working towards having _fewer_ bugs.
- exe34 2y ago
  My point was that management has a history of rolling out shiny things to production and then having egg on their face. See Microsoft's racist bot, Google's AI making up stuff in their adverts, etc. Your original wager was that it would be in production, not that it would work.
  - kzs0 2y ago
    [dead]
  - Infinity315 2y ago
    Wrong. My original wager would be a successful deployment, but sure that could be interpreted as a weasel word. What I mean by successful is that the LLM can generate accurate (>80%) incident response reports and propose correct fixes. I'm fairly certain anyone literate could have read the rest of my comment and parse out what a successful deployment means.
- bryanrasmussen 2y ago
  Your logic seems sort of like the inverse of Augustine's proof for God.
  - Infinity315 2y ago
    It's a funny thing, because the inverse is falsifiable (testable) whereas the positive version is not. The inverse proof (I would say a hypothesis) is simply application of the scientific method. There is a way to disprove that the statement: "There is no god" by simply showing a counterfactual god. There is however no way to disprove the statement: "There is a god." Likewise, there is a way to disprove the statement: "LLMs cannot be successfully used for X application." By showing that LLMs have been used in X application. Again, there is no way to disprove the statement: "LLMs can (eventually) be used in X application." The meat of my question was meant to demonstrate a failure to apply the scientific method.
    - bryanrasmussen 2y ago
      >There is a way to disprove that the statement: "There is no god" by simply showing a counterfactual god. that both parties to the argument agree is a god. >Likewise, there is a way to disprove the statement: "LLMs cannot be successfully used for X application." By showing that LLMs have been used in X application. Again there the point of argumentation will be the word "successfully", the LLM would have to be such an overwhelming success at what it is trying to do that one cannot weasel out of it with "successfully".
- reissbaker 2y ago
  The base technology capable of this has only been broadly available for about a month — prior to Llama-3.1-70b being released on July 23rd, you couldn't finetune any GPT-4 class models that had long context support (OpenAI only allowed fine-tuning their GPT-3.5-Turbo model until last week), and you'd need long context for the incident data — so I think "proof by inexistence" is pretty weak here. The first personal computer shipped in 1974, but it took five years until the development of Visicalc for spreadsheets to appear, despite the huge business value. I wouldn't expect most use cases for LLMs to appear within a month of them being made available. To answer your question more directly, I would go with option 1.
  - Infinity315 2y ago
    It's not proof by inexistence, it's simply application of the scientific method--only the most successful method to date. It seems like your assumptions are unfalsifiable. As computer scientists, I believe it's important that our hypotheses are testable. If a hypothesis is unfalsifiable, then the hypothesis is no better than theology and should be discarded. What's stopping you and other VCs just pouring endless money into an idea that won't work?
    - reissbaker 2y ago
      The scientific method generally involves experiments, as opposed to claiming something won't work because of the "logic" that if it worked, someone would have done it already. This particular hypothesis is obviously a testable one: someone could simply follow the proposed steps from the hypothesis (e.g. finetune a model on their incident response data), and see if it works. This is in fact how essentially all machine learning research is done: coming up with a proposal and trying it out. If it works, great! You've probably contributed something new to machine learning research. If not, oh well, try and figure out why your experiment failed, and if you have a good alternative approach try that instead. Your variant of the "scientific method" would've meant we never discovered electricity, or invented airplanes, or really anything else, because why bother trying? If it worked someone else would've done it.
      - 2y ago