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Statistical outputs are not as interesting as you think?

Like sure, but its still just a probabilistic outcome. Not one reasoned at any step.

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But you can’t?

It didn’t reason its way down a branch, it made coinflips all along the way. So its not actually thinking, just weighted coin flips. There’s no ‘why’ it chose a branch. Just the result of a coin flip.

You can’t ask a coin why it chose heads. You can unroll the physics of it all day long til the cow comes home. But it still doesn’t help you on the next one.

I mean sure, but to what end?

You can see where it went wrong. Not why, how

I’m not convinced of that you do. Nor is knowing that flip helpful. Because it’s non deterministic. An internal coin flip is not an input either.

No… No they’re not. Either that or nerual nets are even dumber than I thought. My understanding was emulating nodes of information like brains do. Thats not probabilities. But willing to be wrong if you can bring sources. The connection between points in probability doesn’t make sense. Thats a nonsense statement.

Thats also no how simulated annealing works. The temperature value is roughly the probability it will pick a different, less optimal step, in order to try and find better alternative paths. The temperature value is roughly the probability of trying something ‘random’. Not deterministic at all. The temperature value is lowered with time and progression. Its known the entire time, but its still a weighted coin flip that determines which path to take.

https://en.wikipedia.org/wiki/Simulated_annealing

For training maybe. But execution as far as i know are just a bunch of probabilities chucked into matrices. Back in my physcos days I could u derstand the math, but not today.

Also what needs citing, that nodes translate to debugable, reproducible outputs.

Because again, it’s all probablilties under the hood.

Its not neural nets, its staticics. There aren’t nodes to trace.

and fed the same inputs be a potentially more transparent machine, thinking or otherwise?

This is where you go off the rails. LLMs are probalistic, not deterministic. It will probably give the same output, but thats still not actually true. This is where hallucinations come from.