4 ms·

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 finetun

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.
  - Infinity315 2y ago
    > 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. Are you saying that if someone finetunes a current SOTA LLM with incident response data and demonstrates that it doesn't work that you'll say that LLMs are infeasible for this application? That would invalidate the hypothesis: "X application can be done on current LLMs." Such a test could never invalidate the the hypothesis: "X application can (eventually) be done on LLMs." If it's the former hypothesis you were asserting, then yes I agree that it is testable, but I'm fairly confident you were asserting the latter. Earlier I had asked you: "I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here?" And you have yet to provide a response.
    - reissbaker 2y ago
      It would invalidate that particular approach, much as a failed attempt at creating a lightbulb would invalidate that particular approach, but would not disprove the lightbulb entirely. Proving that LLMs can never do this would require extremely rigorous theoretical evaluation that even top ML labs are currently unable to do, given the problem of interpretability. In general proving a negative is typically harder than a positive, since a single experiment succeeding proves a positive, but a single experiment failing does not prove a negative; generally science does not demand that scientists attempt to prove a negative when running experiments, or else nearly every drug trial, for example, would be impossible to perform. Complaining that you have staked out a very difficult to defend position — that it's impossible for LLMs to generate good incident reports — does not mean your ideological opponents, who have simpler positions, must do your proof work for you.
      - Infinity315 2y ago
        Are you not making the positive claim that LLMs can (eventually) generate good incident reports? Please refer to this: https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy) https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy) Just to make sure we both understand what burden of proof is: Suppose two people are having a debate over whether or not a teapot exists in the orbit of Jupiter which is impossible to observe via telescope. Where does the burden of proof lie? Just to reiterate plainly: Does the burden of proof lie on the person making empirically impossible to falsify claim or the person making the empirically possible to falsify claim? Which of the following two claims is impossible to empirically falsify? 1. "LLMs can eventually be used to produce good incident reports." 2. "LLMs can never eventually be used to produce good incident reports."

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.
- Infinity315 2y ago
  > 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. Are you saying that if someone finetunes a current SOTA LLM with incident response data and demonstrates that it doesn't work that you'll say that LLMs are infeasible for this application? That would invalidate the hypothesis: "X application can be done on current LLMs." Such a test could never invalidate the the hypothesis: "X application can (eventually) be done on LLMs." If it's the former hypothesis you were asserting, then yes I agree that it is testable, but I'm fairly confident you were asserting the latter. Earlier I had asked you: "I think we're at an epistemic impasse here. At what point would/could you be convinced that LLMs are incapable or unsuited here?" And you have yet to provide a response.
  - reissbaker 2y ago
    It would invalidate that particular approach, much as a failed attempt at creating a lightbulb would invalidate that particular approach, but would not disprove the lightbulb entirely. Proving that LLMs can never do this would require extremely rigorous theoretical evaluation that even top ML labs are currently unable to do, given the problem of interpretability. In general proving a negative is typically harder than a positive, since a single experiment succeeding proves a positive, but a single experiment failing does not prove a negative; generally science does not demand that scientists attempt to prove a negative when running experiments, or else nearly every drug trial, for example, would be impossible to perform. Complaining that you have staked out a very difficult to defend position — that it's impossible for LLMs to generate good incident reports — does not mean your ideological opponents, who have simpler positions, must do your proof work for you.
    - Infinity315 2y ago
      Are you not making the positive claim that LLMs can (eventually) generate good incident reports? Please refer to this: https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy) https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy) Just to make sure we both understand what burden of proof is: Suppose two people are having a debate over whether or not a teapot exists in the orbit of Jupiter which is impossible to observe via telescope. Where does the burden of proof lie? Just to reiterate plainly: Does the burden of proof lie on the person making empirically impossible to falsify claim or the person making the empirically possible to falsify claim? Which of the following two claims is impossible to empirically falsify? 1. "LLMs can eventually be used to produce good incident reports." 2. "LLMs can never eventually be used to produce good incident reports."