steve_bank
Diabetic retinopathy and poor eyesight. Typos ...
From an old CS course on theory of computation pre AI for a problem to be solvable it must be Turng Commutable. Meaning a Turing Machine which the modern PC is, without infinite memory. IO) it6 mus to be solvable.
Deci9deability in part is a fundamental problem in logic. The processor in a PC makes decisions based in logic, AND, OR, etc. Logic can not resolve a logical conundrum or paradox.
I ran into problems coding where you 'can't there there form here' because it is logically impossible.
I don't know much about AI algorithms but it is algorithmic. AI uses fuzzy logic, which makes decisions based on weighted inputs and weighted data. It is like a balance beam. There are no deterministic logic values to trigger a decision point.
In the balancee beam, seesaw, analogy any number of combinations of wights and positions on the seesaw can balance the seesaw.
In AI you could call that learned intuition.
Fuzzy logic processors predate AI and can learn or be taught from data.
And as we know AI like humans can make mistakes.
Deci9deability in part is a fundamental problem in logic. The processor in a PC makes decisions based in logic, AND, OR, etc. Logic can not resolve a logical conundrum or paradox.
I ran into problems coding where you 'can't there there form here' because it is logically impossible.
I don't know much about AI algorithms but it is algorithmic. AI uses fuzzy logic, which makes decisions based on weighted inputs and weighted data. It is like a balance beam. There are no deterministic logic values to trigger a decision point.
In the balancee beam, seesaw, analogy any number of combinations of wights and positions on the seesaw can balance the seesaw.
In AI you could call that learned intuition.
Fuzzy logic processors predate AI and can learn or be taught from data.
AI is not fuzzy logic rather, fuzzy logic is a technique used within AI to help systems make decisions based on vague or imprecise information, similar to how humans do. While traditional AI might rely on strict, binary logic (true or false), fuzzy logic allows for degrees of truth, which makes it useful for complex, real-world applications where data is not perfectly clear.
And as we know AI like humans can make mistakes.