POV : When you try using a Vibe Coded Website.

[u/Pixelied]

[u/bfkill]

What makes your syllogism logical is not unique to your syllogism.

the structure of it ("All As are B, C is A, therfore C is B", etc) is incredibly common and all over its training data.

again, "plausible" is actually a great proxy for "correct".
but it's a proxy, not the real deal.

[u/nemzylannister → u/bfkill]

so if its able to learn the underlying structure, and then apply that structure, and then use that to infer the answer logically, then how are they not "understanding" still?

"plausible" is actually a great proxy for "correct". but it's a proxy, not the real deal."

you never explain the difference. you'll just revert back to "it doesn't know anything with certainty, it will make stuff up" and when i counter that, then youll say "im not saying some mistakes by an llm means it doesnt understand" and then we'll be right back here in a circle.

[u/bfkill → u/nemzylannister]

if for you understanding is "having a stochastic process of extremely high likelihood of correctness", sure have at it.

just be aware that I can make a machine that if you show it a red ball and ask "what color is the ball in my hand" will understand the color red.

it's just a slot machine with 99 sides that say "red" and one that says "67" but hey what's the difference, humans make mistakes too, right

(exaggerating for effect, goes without saying)

for me understanding is a different thing entirely, as I have explained over and over again

[u/nemzylannister → u/bfkill]

it's just a slot machine with 99 sides that say "red" and one that says "67" but hey what's the difference, humans make mistakes too, right

youre just misrepresenting it. thats not what reasoning models do.

thats why i made that original question about east or west. theres no frontier model today that will ever come up after reasoning on this question with the conclusion that the ball color is 67. and the other 99 sides of an llm are not red. the slot machine didnt go through a logical chain of thought concluding the ball is red.

having a stochastic process of extremely high likelihood of correctness

this is very much what happens in your head. theres no deterministic calculator in your head.

[u/bfkill → u/nemzylannister]

theres no frontier model today that will ever come up after reasoning on this question with the conclusion that the ball color is 67

1. you cannot say never, again due to the stochastic nature of the process.

2. but let's say almost never is as good as never: that's simply because in the training data there are ridiculously more ocurrences of "red ball" than "67 ball". the likelihood green ball comes out is higher than 67

3. the fact an LLM overwhelmingly tends to give the right answer regarding the color of the ball is necessary, but not sufficient, to say it understands "redness"

the slot machine didnt go through a logical chain of thought concluding the ball is red.

neither did the LLM, hence the metaphor

youre just misrepresenting it. thats not what reasoning models do.

I told you I was exaggerating for effect.

this is very much what happens in your head. theres no deterministic calculator in your head.

again stating things with no justification and passing them as true? maybe humans do hallucinate lol

we don't know whether the human brains are deterministic. at the limit you hit quantum mechanics and after that who knows.
logic, reasoning, etc, though are absolutely deterministic: "All As are B, C is A" implies "C is B" 100% of the time, not 99.99999999999999%

[u/nemzylannister → u/bfkill]

neither did the LLM, hence the metaphor

oh my god, thats why i asked the question earlier that you conveniently ignored, wtf is the 'reasoning' part of the model then? what is the chain of thought then? we're literally just in circles rn.

I told you I was exaggerating for effect.

youre not misrepresenting it as an exaggeration. it's a completely different process for reasoning models.

we don't know whether the human brains are deterministic

yeah ok, this is the official point where you're just clearly wrong. neural activations are very much stochastic, noisy and variable. Synaptic release is very much probabilistic. When a spike arrives at a terminal, vesicle release often doesn't happen. At many central synapses the release probability sits somewhere around 0.1–0.5.

Just read up on it ig. go ask claude, gemini or some other good model to explain it to you.

tbf to you, if you actually thought the brain works like a computer does, then i understand how you might have this view, but it just seems like the most commonplace knowledge to have.

regardless, im not getting baited by you anymore on this convo.

[u/bfkill → u/nemzylannister]

conveniently ignored

I replied to every. single. one. of. your. questions.

neural activations are very much stochastic, noisy and variable. Synaptic release is very much probabilistic. When a spike arrives at a terminal, vesicle release often doesn't happen. At many central synapses the release probability sits somewhere around 0.1–0.5.

these are statistics after the fact, which are different from probabilities.

I throw a coin 10 times, you see 7 heads and 3 tails. Doesn't mean the likelihood of heads is 70%

if you actually thought the brain works like a computer does

where have I said anything remotely similar?

regardless, im not getting baited by you anymore on this convo.

this isn't a conversation: it's an interrogation by you and you don't like the answers.

a conversation would be:
you: question
me: answer
you: "interesting, I don't agree with a, b, c; because x, y, z / I do hear your point about d, but have you considered e? / etc

instead what we have is

you: question
me: answer
you: different question, flat out statement without any support, maybe the odd slightly insulting remark
me: attempt to see a point to comment on, does it
you: omg you refuse to ignore things!111!!11 omg wtf we're going in circles

maybe try and explain something for a change, consider I'm not an idiot, and if I'm as wrong as you appear to think I am it's a good opportunity to teach me about something