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>because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers. There is no evidence, basically none

>because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers. There is no evidence, basically none whatsoever that general "perfect logical reasoning" is a thing that actually exists in the real world. None. No animal we've observed does it. Humans certainly don't do it. The only realm this idea actually works is Fiction. and this was not like for a lack of trying. Some of the greatest minds worked on this for decades and some people still don't seem to get it. Logic doesn't scale. They break at real world relationships. Logic systems are that guy in the stands yelling that he could've made the shot, while he's not even on the field.

YeezyMode 3y ago
Would you say that this in itself is due to how incomplete human reasoning is in the first place? That as a result, our ideas of logic and what perfect logic looks like are bound to fail? Or are you saying that the purest mathematical representation of logic cannot scale to a point where they can model and predict real world relationships successfully?
- famouswaffles 3y ago
  The second. Mathematical logic thrives on precision, clear definitions, and unambiguous axioms, but real-world systems are often marked by vagueness, uncertainty, and dynamic change. Gödel’s Incompleteness Theorems also demonstrates that in any sufficiently powerful mathematical system, there are true statements that cannot be proven within the system. This implies that no matter how refined a logical system you devise, it will invariably be incomplete or inconsistent when grappling with real-world phenomena.
  - deleted 3y ago
    [deleted]
  - YeGoblynQueenne 3y ago
    Gödel didn't say anything about real world phenomena. He was talking about formal languages and mathematics.
    - famouswaffles 3y ago
      Of course. But if you truly cannot model every true statement in any formally devised system, then you are by definition going to have to reject valid rules that your logic cannot verify if you intend your system to perfectly logical.
      - YeGoblynQueenne 3y ago
        I believe that's right but only in a deductive setting, and as long as there's a requirement for soundness. Inductive and abductive logical inference are not sound and they are very useful for real-world decision-making. But that is a developing field and there are still many unknowns there.
YeGoblynQueenne 3y ago
That's a common take but it doesn't really hold any water: computers are logic machines and all of Computer Science is based on logic; and it works just fine. Besides which, you may not hear about them in the news but pretty much all the classical, symbolic- and logic-based approaches of Good, Old-Fashioned AI are still going strong and are doing very well thank you in tasks in which statistical machine learning approaches underperform. To give a few examples: automated planning and scheduling (used e.g. by NASA in its autonomous guidance systems for its spaceships and rovers), program verification and model checking (the latter has transformed the semiconductor industry and led to several recent Turing awards), SAT-solving and constraint satisfaction (where recent algorithmic advances have made it possible to solve many instances of NP-complete decision problems in polynomial time), adversarial search (AlphaGo and friends aren't going anywhere without Monte Carlo Tree Search), program synthesis (you can generate code with LLMs, but good luck if you want it to work correctly), automated theorem proving, heuristic search, rule learning, etc etc. To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades. You may not read about them in the news and they're not even considered "AI" by many, but that's because they work and work very well, and the "AI Effect" takes hold [1]. Even poor old expert systems are the de facto standard for expressing business logic in the software industry. I guess. Informally, of course. _________________ [1] https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect
- famouswaffles 3y ago
  >computers are logic machines and all of Computer Science is based on logic; and it works just fine. Not what I mean. Logic is part of the real world. Logic is not the real world. The idea that you can use this small subset of the world to model the whole thing is what is incredibly suspect. No one has demonstrated this and there is no real reason to believe it can. >To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades Logic is good at what logic does. Please don't take this to mean me calling logic useless. It's not that statistical machine learning has not made progress. But you won't beat logic on problems with clear definitions and unambiguous axioms. That is very cool but that is clearly not all of reality.

YeezyMode 3y ago

Would you say that this in itself is due to how incomplete human reasoning is in the first place? That as a result, our ideas of logic and what perfect logic looks like are bound to fail? Or are you saying that the purest mathematical representation of logic cannot scale to a point where they can model and predict real world relationships successfully?

famouswaffles 3y ago
The second. Mathematical logic thrives on precision, clear definitions, and unambiguous axioms, but real-world systems are often marked by vagueness, uncertainty, and dynamic change. Gödel’s Incompleteness Theorems also demonstrates that in any sufficiently powerful mathematical system, there are true statements that cannot be proven within the system. This implies that no matter how refined a logical system you devise, it will invariably be incomplete or inconsistent when grappling with real-world phenomena.
- deleted 3y ago
  [deleted]
- YeGoblynQueenne 3y ago
  Gödel didn't say anything about real world phenomena. He was talking about formal languages and mathematics.
  - famouswaffles 3y ago
    Of course. But if you truly cannot model every true statement in any formally devised system, then you are by definition going to have to reject valid rules that your logic cannot verify if you intend your system to perfectly logical.
    - YeGoblynQueenne 3y ago
      I believe that's right but only in a deductive setting, and as long as there's a requirement for soundness. Inductive and abductive logical inference are not sound and they are very useful for real-world decision-making. But that is a developing field and there are still many unknowns there.

YeGoblynQueenne 3y ago

That's a common take but it doesn't really hold any water: computers are logic machines and all of Computer Science is based on logic; and it works just fine. Besides which, you may not hear about them in the news but pretty much all the classical, symbolic- and logic-based approaches of Good, Old-Fashioned AI are still going strong and are doing very well thank you in tasks in which statistical machine learning approaches underperform. To give a few examples: automated planning and scheduling (used e.g. by NASA in its autonomous guidance systems for its spaceships and rovers), program verification and model checking (the latter has transformed the semiconductor industry and led to several recent Turing awards), SAT-solving and constraint satisfaction (where recent algorithmic advances have made it possible to solve many instances of NP-complete decision problems in polynomial time), adversarial search (AlphaGo and friends aren't going anywhere without Monte Carlo Tree Search), program synthesis (you can generate code with LLMs, but good luck if you want it to work correctly), automated theorem proving, heuristic search, rule learning, etc etc. To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades. You may not read about them in the news and they're not even considered "AI" by many, but that's because they work and work very well, and the "AI Effect" takes hold [1]. Even poor old expert systems are the de facto standard for expressing business logic in the software industry. I guess. Informally, of course. _________________ [1] https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect

famouswaffles 3y ago
>computers are logic machines and all of Computer Science is based on logic; and it works just fine. Not what I mean. Logic is part of the real world. Logic is not the real world. The idea that you can use this small subset of the world to model the whole thing is what is incredibly suspect. No one has demonstrated this and there is no real reason to believe it can. >To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades Logic is good at what logic does. Please don't take this to mean me calling logic useless. It's not that statistical machine learning has not made progress. But you won't beat logic on problems with clear definitions and unambiguous axioms. That is very cool but that is clearly not all of reality.