4 ms·

> as a reminder, there are people who, with varying degrees of certainty, think their loved ones have been replaced by actors, as well as people who think they'

> as a reminder, there are people who, with varying degrees of certainty, think their loved ones have been replaced by actors, as well as people who think they're actually the god of the world around them, for it is just their imagination. None of that is any proof at all that LLMs or computers in general can reason. "some humans are dumb, so LLMs are smart" is not a valid argument here.

fragmede 2y ago
how do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. That there's no qualia behind that, that doesn't really reason or think or breath or love, we have to go back and ask what is reasoning. there are some definitions for reasoning that LLMs can pass, there are some they can't. If they're able to outperform dumb humans whom we assume do reason, why does that not mean that LLMs have some capacity to reason?
- vhantz 2y ago
  > how do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. Not true. A LLM might give you that answer x% of the time, x being a number less than 100. However, any thinking person answering your question, will give you the same answer, no matter how many times you ask it. That's the fundamental difference between thinking and statistically mapping and reproducing the structure of human language.
  - sharemywin 2y ago
    I'm pretty sure if you set the temp to 0 it will product the exact same output every time. its the sampling that produces the output variation.
  - perching_aix 2y ago
    > any thinking person answering your question, will give you the same answer, no matter how many times you ask it Oh, will they? Will they really?!
    - vhantz 2y ago
      Yes 2 + 2 is always 4 if you're not a language model and know basic arithmetics
      - CamperBob2 2y ago
        Or if the language model answers the question by writing and running a Python script. Which is exactly what it can do. Never mind that the tendency to give the exact same answer to the same question over time is not the exhibition of reasoning power you seem to think it is. Have you actually asked some people to multiply 10-digit numbers in their heads? Did they always get the same result? No? Well, there goes that argument. We don't do anything that the LLMs don't do at this point, except adjust our weights (poorly) to move short-term context into long-term memory. Once that capability is added to the models -- which will happen soon enough, because why wouldn't it? -- where will the goalposts go next?
        vhantz 2y ago
        It's not about giving the same answer to the same question. It's about getting the right answer 100% of the time, in some very specific domains. If you know, understand and are able to use the basic rules of arithmetic, 2 + 2 only has one answer. If you know, understand and are able to use the basic rules of formal logic, the same premises will lead you to the same conclusion. Two trivial cases for any reasoning person . Two cases that illustrate how fundamentally different LLMs text generation is to reasoning. Two cases that illustrate some of the challenges that need to be solved to bring AI models closer the fiction so many on this site are desperately taking them to be. Of course those who don't care about improving those systems also don't care about understanding their limits, which is unsurprisingly the case for a lot of people on this website.
perching_aix 2y ago
> None of that is any proof at all that LLMs or computers in general can reason. It was never meant to be a proof that LLMs or computers in general can reason [or rather, that they can reason generalistically]. Instead, it was a demonstration of how their argument looks when mapped to other situations, illustrating that it isn't really putting anything to the table in the ways of, proofs, evidences, definitions, or logic / arguments, nor does it enable others to do so. > some humans are dumb, so LLMs are smart That kind of reading loses the point just about completely, so it shouldn't be surprising you can't find an argument in there. The point was and is that simply pointing at an AI model and saying "nah it's cappin" is awfully lacking, and is suspiciously similar to how certain people with certain mental conditions view their world. It is not insightful, nor reasonable. It's just an assertion of disbelief, following a - as you can hopefully agree - dubious logic that cannot be disproven or substantially argued against, as it was never designed to enable that in the first place.

fragmede 2y ago

how do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. That there's no qualia behind that, that doesn't really reason or think or breath or love, we have to go back and ask what is reasoning. there are some definitions for reasoning that LLMs can pass, there are some they can't. If they're able to outperform dumb humans whom we assume do reason, why does that not mean that LLMs have some capacity to reason?

vhantz 2y ago
> how do we test for reasoning? if A -> B and B -> C, then something that can reason could conclude A -> C. If I give A -> B and B -> C to an LLM, and ask it about the relationship between A and C, it'll tell me about the transitive property of implication, graph theory, transitivity. Not true. A LLM might give you that answer x% of the time, x being a number less than 100. However, any thinking person answering your question, will give you the same answer, no matter how many times you ask it. That's the fundamental difference between thinking and statistically mapping and reproducing the structure of human language.
- sharemywin 2y ago
  I'm pretty sure if you set the temp to 0 it will product the exact same output every time. its the sampling that produces the output variation.
- perching_aix 2y ago
  > any thinking person answering your question, will give you the same answer, no matter how many times you ask it Oh, will they? Will they really?!
  - vhantz 2y ago
    Yes 2 + 2 is always 4 if you're not a language model and know basic arithmetics
    - CamperBob2 2y ago
      Or if the language model answers the question by writing and running a Python script. Which is exactly what it can do. Never mind that the tendency to give the exact same answer to the same question over time is not the exhibition of reasoning power you seem to think it is. Have you actually asked some people to multiply 10-digit numbers in their heads? Did they always get the same result? No? Well, there goes that argument. We don't do anything that the LLMs don't do at this point, except adjust our weights (poorly) to move short-term context into long-term memory. Once that capability is added to the models -- which will happen soon enough, because why wouldn't it? -- where will the goalposts go next?
      - vhantz 2y ago
        It's not about giving the same answer to the same question. It's about getting the right answer 100% of the time, in some very specific domains. If you know, understand and are able to use the basic rules of arithmetic, 2 + 2 only has one answer. If you know, understand and are able to use the basic rules of formal logic, the same premises will lead you to the same conclusion. Two trivial cases for any reasoning person . Two cases that illustrate how fundamentally different LLMs text generation is to reasoning. Two cases that illustrate some of the challenges that need to be solved to bring AI models closer the fiction so many on this site are desperately taking them to be. Of course those who don't care about improving those systems also don't care about understanding their limits, which is unsurprisingly the case for a lot of people on this website.

perching_aix 2y ago

> None of that is any proof at all that LLMs or computers in general can reason. It was never meant to be a proof that LLMs or computers in general can reason [or rather, that they can reason generalistically]. Instead, it was a demonstration of how their argument looks when mapped to other situations, illustrating that it isn't really putting anything to the table in the ways of, proofs, evidences, definitions, or logic / arguments, nor does it enable others to do so. > some humans are dumb, so LLMs are smart That kind of reading loses the point just about completely, so it shouldn't be surprising you can't find an argument in there. The point was and is that simply pointing at an AI model and saying "nah it's cappin" is awfully lacking, and is suspiciously similar to how certain people with certain mental conditions view their world. It is not insightful, nor reasonable. It's just an assertion of disbelief, following a - as you can hopefully agree - dubious logic that cannot be disproven or substantially argued against, as it was never designed to enable that in the first place.