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We don't really have the proper vocabulary to talk about this. Well, we do, but C.S. Peirce's writings are still fairly unknown. In short, there are two fundame

by User23 2y ago

We don't really have the proper vocabulary to talk about this. Well, we do, but C.S. Peirce's writings are still fairly unknown. In short, there are two fundamentally distinct forms of reasoning. One is corollarial reasoning. This is the kind of reasoning that follows deductions that directly follow from the premises. This of course includes subsequent deductions that can be made from those deductions. Obviously computers are very good at this sort of thing. The other is theorematic reasoning. It deals with complexity and creativity. It involves introducing new hypotheses that are not present in the original premises or their corollaries. Computers are not so very good at this sort of thing. When people say AGI, what they are really talking about is an AI that is capable of theorematic reasoning. The most romanticized example of that of course being the AI that is capable of designing (not aiding humans in designing, that's corollarial!) new more capable AIs. All of the above is old hat to the AI winter era guys. But amusingly their reputations have been destroyed much the same as Peirce's was, by dissatisfied government bureaucrats. On the other hand, we did get SQL, which is a direct lineal descendent (as in teacher to teacher) from Peirce's work, so there's that.

godelski 2y ago
We don't have proper language, but certainly we've improved. Even since Peirce. You're right that many people are not well versed in the philosophical and logician discussions as to what reasoning is (and sadly this lack of literature review isn't always common in the ML community), but I'm not convinced Peirce solved it. I do like that there are many different categories of reasoning and subcategories. > All of the above is old hat to the AI winter era guys. But amusingly their reputations have been destroyed much the same as Peirce's was, by dissatisfied government bureaucrats. Yeah, this has been odd. Since a lot of their work has shown to be fruitful once scaled. I do think you need a combination of theory people + those more engineering oriented, but having too much of one is not a good thing. It seems like now we're overcorrecting and the community is trying to kick out the theorists. By saying things like "It's just linear algebra"[0] or "you don't need math"[1] or "they're black boxes". These are unfortunate because they encourage one to not look inside and try to remove the opaqueness. Or to dismiss those that do work on this and are bettering our understanding (sometimes even post hoc saying it was obvious). It is quite the confusing time. But I'd like to stop all the bullshit and try to actually make AGI. That does require a competition of ideas and not everyone just boarding the hype train or have no careers.... [0] You can assume anyone that says this doesn't know linear algebra [1] You don't need math to produce good models, but it sure does help you know why your models are wrong (and understanding the meta should make one understand my reference. If you don't, I'm not sure you're qualified for ML research. But that's not a definitive statement either).
- User23 2y ago
  > We don't have proper language, but certainly we've improved. Even since Peirce. You're right that many people are not well versed in the philosophical and logician discussions as to what reasoning is (and sadly this lack of literature review isn't always common in the ML community), but I'm not convinced Peirce solved it. I do like that there are many different categories of reasoning and subcategories. I'd love to hear more about this please, if you're inclined to share.
  - randcraw 2y ago
    I'm no expert, but I've been looking into the prospects and mechanisms of automated reasoning using LLMs recently and there's been a lot of work along those lines in the research literature that is pretty interesting, if not enlightening. It seems clear to me that LLMs are not yet capable of understanding simple implication much less full-blown causality. It's also not clear how limited LLMs' cognitive gains will be with so incomplete an understanding as they have of mechanisms behind the world's multitude of intents/goals, actions, and responses. The concepts of cause and effect are learned by every animal (to some degree) and long before language in humans. It forms the basis for all rational thought. Without understanding it natively, what is rationality? I foresee longstanding difficulties for LLMs evolving into truly rational beings until that comprehension is fully realized. And I see no sign of that happening, despite the promises made for o1 and other RL-based reasoners.
    - godelski 2y ago
      Yeah one of the tricky things about causality is that it's not unique. If you didn't record the history of the event then you only have a probabilistic notion of it since many different things and in different permutations could lead to the result you observed. This has led to people believing in multiple universes when it's not akin to there being multiple ways to sum numbers to ten.
dfilppi 2y ago
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by User23 2y ago

godelski 2y ago

We don't have proper language, but certainly we've improved. Even since Peirce. You're right that many people are not well versed in the philosophical and logician discussions as to what reasoning is (and sadly this lack of literature review isn't always common in the ML community), but I'm not convinced Peirce solved it. I do like that there are many different categories of reasoning and subcategories. > All of the above is old hat to the AI winter era guys. But amusingly their reputations have been destroyed much the same as Peirce's was, by dissatisfied government bureaucrats. Yeah, this has been odd. Since a lot of their work has shown to be fruitful once scaled. I do think you need a combination of theory people + those more engineering oriented, but having too much of one is not a good thing. It seems like now we're overcorrecting and the community is trying to kick out the theorists. By saying things like "It's just linear algebra"[0] or "you don't need math"[1] or "they're black boxes". These are unfortunate because they encourage one to not look inside and try to remove the opaqueness. Or to dismiss those that do work on this and are bettering our understanding (sometimes even post hoc saying it was obvious). It is quite the confusing time. But I'd like to stop all the bullshit and try to actually make AGI. That does require a competition of ideas and not everyone just boarding the hype train or have no careers.... [0] You can assume anyone that says this doesn't know linear algebra [1] You don't need math to produce good models, but it sure does help you know why your models are wrong (and understanding the meta should make one understand my reference. If you don't, I'm not sure you're qualified for ML research. But that's not a definitive statement either).

User23 2y ago
> We don't have proper language, but certainly we've improved. Even since Peirce. You're right that many people are not well versed in the philosophical and logician discussions as to what reasoning is (and sadly this lack of literature review isn't always common in the ML community), but I'm not convinced Peirce solved it. I do like that there are many different categories of reasoning and subcategories. I'd love to hear more about this please, if you're inclined to share.
- randcraw 2y ago
  I'm no expert, but I've been looking into the prospects and mechanisms of automated reasoning using LLMs recently and there's been a lot of work along those lines in the research literature that is pretty interesting, if not enlightening. It seems clear to me that LLMs are not yet capable of understanding simple implication much less full-blown causality. It's also not clear how limited LLMs' cognitive gains will be with so incomplete an understanding as they have of mechanisms behind the world's multitude of intents/goals, actions, and responses. The concepts of cause and effect are learned by every animal (to some degree) and long before language in humans. It forms the basis for all rational thought. Without understanding it natively, what is rationality? I foresee longstanding difficulties for LLMs evolving into truly rational beings until that comprehension is fully realized. And I see no sign of that happening, despite the promises made for o1 and other RL-based reasoners.
  - godelski 2y ago
    Yeah one of the tricky things about causality is that it's not unique. If you didn't record the history of the event then you only have a probabilistic notion of it since many different things and in different permutations could lead to the result you observed. This has led to people believing in multiple universes when it's not akin to there being multiple ways to sum numbers to ten.

dfilppi 2y ago

[dead]