You are describing LLMs, yes. But not what I’m describing.
I’m talking about machine finding syllogisms and checking their correctness. This can’t be rock solid because of interpretation of the statement in natural language with its fuzzy semantics, but everything after that can be made rock solid. While in LLMs even it isn’t.
That’s what I’m talking about.
Humans make mistakes, but not such as LLM-generated texts contain.
I mean that one can build a reasoning machine which an LLM isn’t.
I’m not describing LLMs. LLMs are completely irrelevant, and my examples had nothing to do with LLMs.
Formal logic requires propositions be Boolean in nature. They’re true, or they’re false.
That’s not the real world. There are no booleans in the real world. In the real world, everything, down to the fundamental particles, is inherently probabilistic.
Our “certainty” is at most 99. a lot of 9s. It’s never 100%. You can’t say “the New York Times said X”, and “the New York Times is perfectly reliable”, so “X must be true”. It’s “given that the NYT said X and the NYT has a history of reporting facts with reasonably high accuracy, the probability X is true is…”. If they get caught being shady, the estimates of previous information learned from them is retroactively changed. But there is no “proof”, because there is no certainty anywhere in the chain. The world and human understanding of it has to be Bayesian. Again, down to the Uncertainty Principle about low level particles. Uncertainty is fundamental to reality. There is no certainty.
It doesn’t require being certain of the information we’re building it on. Only of existence of such categories.
Naturally people in Antiquity and Middle Ages who used symbolic logic were even less certain of the actual truths and lies in the world than we are.
It allows the truth to be subjective, but not the logical constructions. This is a very important trait both then and now.
The difference between the filter and the data going through it.
Of course you can’t just feed all the data of all the PoVs and similar cases on something, integrate it into a model and expect your PoV to not clash with its output.
It’s philosophically the same as why using dialectics is bad for science.
A syllogism is a tool for theoretical reasoning that doesn’t actually apply in the real world, because it relies on Boolean possibility spaces. There is never an “all articles by X are correct”, and there is no theoretical possibility that “all articles by X are correct” in the real world. The connections in the real world are literally always probabilistic. In every case. Every time.
You can’t use formal logic for any real world use case because there are no valid starting assumptions. The only thing logic can ever prove is internal consistency, not fact.
The only thing logic can ever prove is internal consistency, not fact.
Yes, and being able to build structures with internal consistency would be an advantage.
Nobody says you can prevent any “AI” oracle from saying things that aren’t true.
But a tool which would generate a tree of possible logical conclusions from something given in language and then divided into statements on objects with statistical dependencies could be useful.
You are describing LLMs, yes. But not what I’m describing.
I’m talking about machine finding syllogisms and checking their correctness. This can’t be rock solid because of interpretation of the statement in natural language with its fuzzy semantics, but everything after that can be made rock solid. While in LLMs even it isn’t.
That’s what I’m talking about.
Humans make mistakes, but not such as LLM-generated texts contain.
I mean that one can build a reasoning machine which an LLM isn’t.
I’m not describing LLMs. LLMs are completely irrelevant, and my examples had nothing to do with LLMs.
Formal logic requires propositions be Boolean in nature. They’re true, or they’re false.
That’s not the real world. There are no booleans in the real world. In the real world, everything, down to the fundamental particles, is inherently probabilistic.
Our “certainty” is at most 99. a lot of 9s. It’s never 100%. You can’t say “the New York Times said X”, and “the New York Times is perfectly reliable”, so “X must be true”. It’s “given that the NYT said X and the NYT has a history of reporting facts with reasonably high accuracy, the probability X is true is…”. If they get caught being shady, the estimates of previous information learned from them is retroactively changed. But there is no “proof”, because there is no certainty anywhere in the chain. The world and human understanding of it has to be Bayesian. Again, down to the Uncertainty Principle about low level particles. Uncertainty is fundamental to reality. There is no certainty.
Why are you writing this to me?
Do you know what a syllogism is?
It doesn’t require being certain of the information we’re building it on. Only of existence of such categories.
Naturally people in Antiquity and Middle Ages who used symbolic logic were even less certain of the actual truths and lies in the world than we are.
It allows the truth to be subjective, but not the logical constructions. This is a very important trait both then and now.
The difference between the filter and the data going through it.
Of course you can’t just feed all the data of all the PoVs and similar cases on something, integrate it into a model and expect your PoV to not clash with its output.
It’s philosophically the same as why using dialectics is bad for science.
A syllogism is a tool for theoretical reasoning that doesn’t actually apply in the real world, because it relies on Boolean possibility spaces. There is never an “all articles by X are correct”, and there is no theoretical possibility that “all articles by X are correct” in the real world. The connections in the real world are literally always probabilistic. In every case. Every time.
You can’t use formal logic for any real world use case because there are no valid starting assumptions. The only thing logic can ever prove is internal consistency, not fact.
Yes, and being able to build structures with internal consistency would be an advantage.
Nobody says you can prevent any “AI” oracle from saying things that aren’t true.
But a tool which would generate a tree of possible logical conclusions from something given in language and then divided into statements on objects with statistical dependencies could be useful.