5 ms·

But I also fail catastrophically once a reasoning problem exceeds modest complexity.

monkeydust 11mo ago
But you recognise you are likely to fail and thus dont respond or redirect the problem to someone who has a greater likelihood of not failing.
- antonvs 11mo ago
  I’ve had models “redirect the problem to someone who has a greater likelihood of not failing”. Gemini in particular will do this when it runs into trouble. I don’t find all these claims that models are somehow worse than humans in such areas convincing. Yes, they’re worse in some respects. But when you’re talking about things related to failures and accuracy, they’re mostly superhuman. For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs?
  - ffsm8 11mo ago
    > For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs? Ez, just use codegen. Also the second part (not having bugs) is unlikely to be true for the LLM generated code, whereas traditional codegen will actually generate code with pretty much no bugs.
    - vidarh 11mo ago
      I have Claude reducing the number of bugs in my traditional codegen right now.
    - antonvs 11mo ago
      What's your point? Traditional codegen tools are inflexible in the extreme compared to what LLMs can do. The realistic comparison is between humans and LLMs, not LLMs and codegen tools.
      - ffsm8 11mo ago
        The point was that the listed argument of production tons of boilerplate code within a short period of time is a... Pointless metric to cite
  - pessimizer 11mo ago
    > I’ve had models “redirect the problem to someone who has a greater likelihood of not failing”. Gemini in particular will do this when it runs into trouble. I have too, and I sense that this is something that has been engineered in rather than coming up naturally. I like it very much and they should do it a lot more often. They're allergic to "I can't figure this out" but hearing "I can't figure this out" gives me the alert to help it over the hump. > But when you’re talking about things related to failures and accuracy, they’re mostly superhuman. Only if you consider speed to failure and inaccuracy. They're very much subhuman in output, but you can make them retry a lot in a short time, and refine what you're asking them each time to avoid the mistakes they're repeatedly making. But that's you doing the work.
- exe34 11mo ago
  If that were true, we would live in a utopia. People vote/legislate/govern/live/raise/teach/preach without ever learning to reason correctly.
davidhs 11mo ago
Do you? Don't you just halt and say this is too complex?
- p_v_doom 11mo ago
  Nope, audacity and Dunning-Krueger all the way, baby
- dspillett 11mo ago
  Some would consider that to be failing catastrophically. The task is certainly failed.
  - carlmr 11mo ago
    Halting is sometimes preferable to thrashing around and running in circles. I feel like if LLMs "knew" when they're out of their depth, they could be much more useful. The question is whether knowing when to stop can be meaningfully learned from examples with RL. From all we've seen the hallucination problem and this stopping problem all boil down to this problem that you could teach the model to say "I don't know" but if that's part of the training dataset it might just spit out "I don't know" to random questions, because it's a likely response in the realm of possible responses, instead of spitting out "I don't know" to not knowing. SocratesAI is still unsolved, and LLMs are probably not the path to get knowing that you know nothing.
    - ukuina 11mo ago
      > if LLMs "knew" when they're out of their depth, they could be much more useful. I used to think this, but no longer sure. Large-scale tasks just grind to a halt with more modern LLMs because of this perception of impassable complexity. And it's not that they need extensive planning, the LLM knows what needs to be done (it'll even tell you!), it's just more work than will fit within a "session" (arbitrary) and so it would rather refuse than get started. So you're now looking at TODOs, and hierarchical plans, and all this unnecessary pre-work even when the task scales horizontally very well (if it just jumped into it).
  - 11mo ago
AlecSchueler 11mo ago
I also fail catastrophically when trying to push nails through walls by I expect my hammer to do better.
- moffkalast 11mo ago
  I have one hammer and I expect it to work on every nail and screw. If it's not a general hammer, what good is it now?
  - arethuza 11mo ago
    You don't need a "general hammer" - they are old fashioned - you need a "general-purpose tool-building factory factory factory": https://www.danstroot.com/posts/2018-10-03-hammer-factories https://www.danstroot.com/posts/2018-10-03-hammer-factories
    - code_martial 11mo ago
      Reminds me of a 10 letter Greek word that starts with a k.
- hshdhdhehd 11mo ago
  Gold and shovels might be a more fitting analogy for AI
raddan 11mo ago
Yes, but you are not a computer. There is no point building another human. We have plenty of them.
- aoeusnth1 11mo ago
  Others would beg to disagree that we should be build a machine which can act as a human.

But you recognise you are likely to fail and thus dont respond or redirect the problem to someone who has a greater likelihood of not failing.

antonvs 11mo ago
I’ve had models “redirect the problem to someone who has a greater likelihood of not failing”. Gemini in particular will do this when it runs into trouble. I don’t find all these claims that models are somehow worse than humans in such areas convincing. Yes, they’re worse in some respects. But when you’re talking about things related to failures and accuracy, they’re mostly superhuman. For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs?
- ffsm8 11mo ago
  > For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs? Ez, just use codegen. Also the second part (not having bugs) is unlikely to be true for the LLM generated code, whereas traditional codegen will actually generate code with pretty much no bugs.
  - vidarh 11mo ago
    I have Claude reducing the number of bugs in my traditional codegen right now.
  - antonvs 11mo ago
    What's your point? Traditional codegen tools are inflexible in the extreme compared to what LLMs can do. The realistic comparison is between humans and LLMs, not LLMs and codegen tools.
    - ffsm8 11mo ago
      The point was that the listed argument of production tons of boilerplate code within a short period of time is a... Pointless metric to cite
- pessimizer 11mo ago
  > I’ve had models “redirect the problem to someone who has a greater likelihood of not failing”. Gemini in particular will do this when it runs into trouble. I have too, and I sense that this is something that has been engineered in rather than coming up naturally. I like it very much and they should do it a lot more often. They're allergic to "I can't figure this out" but hearing "I can't figure this out" gives me the alert to help it over the hump. > But when you’re talking about things related to failures and accuracy, they’re mostly superhuman. Only if you consider speed to failure and inaccuracy. They're very much subhuman in output, but you can make them retry a lot in a short time, and refine what you're asking them each time to avoid the mistakes they're repeatedly making. But that's you doing the work.
exe34 11mo ago
If that were true, we would live in a utopia. People vote/legislate/govern/live/raise/teach/preach without ever learning to reason correctly.

davidhs 11mo ago

Do you? Don't you just halt and say this is too complex?

p_v_doom 11mo ago
Nope, audacity and Dunning-Krueger all the way, baby
dspillett 11mo ago
Some would consider that to be failing catastrophically. The task is certainly failed.
- carlmr 11mo ago
  Halting is sometimes preferable to thrashing around and running in circles. I feel like if LLMs "knew" when they're out of their depth, they could be much more useful. The question is whether knowing when to stop can be meaningfully learned from examples with RL. From all we've seen the hallucination problem and this stopping problem all boil down to this problem that you could teach the model to say "I don't know" but if that's part of the training dataset it might just spit out "I don't know" to random questions, because it's a likely response in the realm of possible responses, instead of spitting out "I don't know" to not knowing. SocratesAI is still unsolved, and LLMs are probably not the path to get knowing that you know nothing.
  - ukuina 11mo ago
    > if LLMs "knew" when they're out of their depth, they could be much more useful. I used to think this, but no longer sure. Large-scale tasks just grind to a halt with more modern LLMs because of this perception of impassable complexity. And it's not that they need extensive planning, the LLM knows what needs to be done (it'll even tell you!), it's just more work than will fit within a "session" (arbitrary) and so it would rather refuse than get started. So you're now looking at TODOs, and hierarchical plans, and all this unnecessary pre-work even when the task scales horizontally very well (if it just jumped into it).
- 11mo ago

AlecSchueler 11mo ago

I also fail catastrophically when trying to push nails through walls by I expect my hammer to do better.

moffkalast 11mo ago
I have one hammer and I expect it to work on every nail and screw. If it's not a general hammer, what good is it now?
- arethuza 11mo ago
  You don't need a "general hammer" - they are old fashioned - you need a "general-purpose tool-building factory factory factory": https://www.danstroot.com/posts/2018-10-03-hammer-factories https://www.danstroot.com/posts/2018-10-03-hammer-factories
  - code_martial 11mo ago
    Reminds me of a 10 letter Greek word that starts with a k.
hshdhdhehd 11mo ago
Gold and shovels might be a more fitting analogy for AI

raddan 11mo ago

Yes, but you are not a computer. There is no point building another human. We have plenty of them.

aoeusnth1 11mo ago
Others would beg to disagree that we should be build a machine which can act as a human.