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> Seriously just watch. He's not actually going to be able to coherently define his "reasoning" in a way that can be tested. Google gives the following definit

> Seriously just watch. He's not actually going to be able to coherently define his "reasoning" in a way that can be tested. Google gives the following definition of the verb "reason": > think, understand, and form judgments by a process of logic. LLMs do not think, they do not understand, and they do not form judgments. They do not come to their own conclusions. They do not have the physical capability. They are statistical models, nothing more. > LLMs obviously reason and understand by any evaluation that can be carried out. Uh-huh. Sure, Jan.

famouswaffles 4y ago
>LLMs do not think, they do not understand, and they do not form judgments. They do not come to their own conclusions. They do not have the physical capability. They are statistical models, nothing more. "LLMs don't reason because they don't understand" is not the bastion of genius you think it is. It's a circular argument that relies on whatever bespoke interpretations you have cooked up. They don't form judgement or conclusions? Sure looks like they do. So what's the difference ? What is GPT-4 doing then when it correctly looks like it is reasoning and what's the difference between that and "real" understanding or reasoning. Such a huge difference I should be able to test for it. Don't understand how you can tell me what I'm seeing isn't real reasoning but fail to provide a way to empirically determine the difference. >Uh-huh. Sure, Jan. Yeah. https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196 Many other evaluations to carry out.
kqr 4y ago
This is the problem with non-operational definitions, because now we need to know how you define "think" and "understand" and "form judgments", to move on. Instead, could you operationally define "reason" in a way that a human is, say, 90 % likely to pass the test and GPT is 10 % likely to do?
- sanxiyn 4y ago
  Yes, François Chollet released ARC(Abstraction and Reasoning Corpus) benchmark for this in 2019, and the benchmark can be scored automatically. Humans solve 100% of tests and GPTs solve 0% of tests and GPTs made exactly zero progress from 2019 to 2022. https://twitter.com/fchollet/status/1631699463524986880 https://twitter.com/fchollet/status/1631699463524986880 https://github.com/fchollet/ARC https://github.com/fchollet/ARC
  - kqr 4y ago
    Wow, this is a much more interesting answer than I expected. Thank you!
    - famouswaffles 4y ago
      It is. Although I'd be careful with the conclusions. The problems are presented in a way that make it difficult to solve. The vision problems will require the equivalent of an artificial visual cortex, something we are seriously lacking in artificial intelligence at the moment. Image to text won't cut it here. For the text there could be tokenizer issues. LLMs don't really have any problem with abstract analogical reasoning https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196
  - freilanzer 4y ago
    This seems more like an issue with the tokenizers, no? This doesn't seem more difficult than other problems it solves.
    - famouswaffles 4y ago
      You're right that LLMs can solve abstract analogical reasoning problems https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196 Another issue is the vision side. The vast majority of multimodal models are working on essentially an image to text objective task. That won't cut it here. We need the equivalent of an artificial visual cortex. We don't have that yet
cjbprime 4y ago
New bar for people claiming LLMs can't reason: invent a specific, testable problem, representable in text, that many humans can solve and LLMs can't, and tell us what it is.
- Godel_unicode 4y ago
  … and then perform a careful search of books and the whole internet to be sure what you think is novel hasn’t been thoroughly debated somewhere on stackexchange.
  - cjbprime 4y ago
    If it's a stochastic parrot, then merely randomizing proper nouns and filler text should be enough to prevent its abstraction ability. If you're saying that we can't use a problem if any analog of that problem has ever been described, you seem to be arguing more strongly that it is a general intelligence than I am.
    - Godel_unicode 4y ago
      I’m saying that people in my circle have been asking what they think are novel questions and getting interesting answers, only to find out that very similar content exists on websites we know are in the training set. That’s not intelligence that’s computers having better memory than humans. Useful, certainly, but hardly skynet.
      - cjbprime 4y ago
        I don't think you're being clear about whether the questions were novel. If you discover your question was uncreative, surely e.g. some details, wording, facts, names, or numbers inside the question can be changed to defeat a model that is answering it from memory? If you're saying that it is not possible to change the details enough to avoid the model being able to answer that type of question, I think you are admitting that the model has learned a generalized ability to answer questions of that class, and is not actually using its memory to answer at all. I don't care about whether it learned that generalized ability from seeing examples of the question and answer, which it then deduced an algorithm for and generalized -- that's how most people learn most things.
sdenton4 4y ago
We're survival machines, nothing more... Complex behavior arises from simple systems all the time. You can't prove that these systems don't reason, no matter how loudly thou doth protest.
- Al0neStar 4y ago
  from the beginning of time people have been overestimating the complexity of things like the human brain and attributing it to magical things (like a creator) far beyond our comprehension but what seems to be happening now is that some people are underestimating it.

famouswaffles 4y ago

>LLMs do not think, they do not understand, and they do not form judgments. They do not come to their own conclusions. They do not have the physical capability. They are statistical models, nothing more. "LLMs don't reason because they don't understand" is not the bastion of genius you think it is. It's a circular argument that relies on whatever bespoke interpretations you have cooked up. They don't form judgement or conclusions? Sure looks like they do. So what's the difference ? What is GPT-4 doing then when it correctly looks like it is reasoning and what's the difference between that and "real" understanding or reasoning. Such a huge difference I should be able to test for it. Don't understand how you can tell me what I'm seeing isn't real reasoning but fail to provide a way to empirically determine the difference. >Uh-huh. Sure, Jan. Yeah. https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196 Many other evaluations to carry out.

kqr 4y ago

This is the problem with non-operational definitions, because now we need to know how you define "think" and "understand" and "form judgments", to move on. Instead, could you operationally define "reason" in a way that a human is, say, 90 % likely to pass the test and GPT is 10 % likely to do?

sanxiyn 4y ago
Yes, François Chollet released ARC(Abstraction and Reasoning Corpus) benchmark for this in 2019, and the benchmark can be scored automatically. Humans solve 100% of tests and GPTs solve 0% of tests and GPTs made exactly zero progress from 2019 to 2022. https://twitter.com/fchollet/status/1631699463524986880 https://twitter.com/fchollet/status/1631699463524986880 https://github.com/fchollet/ARC https://github.com/fchollet/ARC
- kqr 4y ago
  Wow, this is a much more interesting answer than I expected. Thank you!
  - famouswaffles 4y ago
    It is. Although I'd be careful with the conclusions. The problems are presented in a way that make it difficult to solve. The vision problems will require the equivalent of an artificial visual cortex, something we are seriously lacking in artificial intelligence at the moment. Image to text won't cut it here. For the text there could be tokenizer issues. LLMs don't really have any problem with abstract analogical reasoning https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196
- freilanzer 4y ago
  This seems more like an issue with the tokenizers, no? This doesn't seem more difficult than other problems it solves.
  - famouswaffles 4y ago
    You're right that LLMs can solve abstract analogical reasoning problems https://arxiv.org/abs/2212.09196 https://arxiv.org/abs/2212.09196 Another issue is the vision side. The vast majority of multimodal models are working on essentially an image to text objective task. That won't cut it here. We need the equivalent of an artificial visual cortex. We don't have that yet

cjbprime 4y ago

New bar for people claiming LLMs can't reason: invent a specific, testable problem, representable in text, that many humans can solve and LLMs can't, and tell us what it is.

Godel_unicode 4y ago
… and then perform a careful search of books and the whole internet to be sure what you think is novel hasn’t been thoroughly debated somewhere on stackexchange.
- cjbprime 4y ago
  If it's a stochastic parrot, then merely randomizing proper nouns and filler text should be enough to prevent its abstraction ability. If you're saying that we can't use a problem if any analog of that problem has ever been described, you seem to be arguing more strongly that it is a general intelligence than I am.
  - Godel_unicode 4y ago
    I’m saying that people in my circle have been asking what they think are novel questions and getting interesting answers, only to find out that very similar content exists on websites we know are in the training set. That’s not intelligence that’s computers having better memory than humans. Useful, certainly, but hardly skynet.
    - cjbprime 4y ago
      I don't think you're being clear about whether the questions were novel. If you discover your question was uncreative, surely e.g. some details, wording, facts, names, or numbers inside the question can be changed to defeat a model that is answering it from memory? If you're saying that it is not possible to change the details enough to avoid the model being able to answer that type of question, I think you are admitting that the model has learned a generalized ability to answer questions of that class, and is not actually using its memory to answer at all. I don't care about whether it learned that generalized ability from seeing examples of the question and answer, which it then deduced an algorithm for and generalized -- that's how most people learn most things.

sdenton4 4y ago

We're survival machines, nothing more... Complex behavior arises from simple systems all the time. You can't prove that these systems don't reason, no matter how loudly thou doth protest.

Al0neStar 4y ago
from the beginning of time people have been overestimating the complexity of things like the human brain and attributing it to magical things (like a creator) far beyond our comprehension but what seems to be happening now is that some people are underestimating it.