It's almost like these are random token pulling machines, and not thinking, reasoning intelligences.

LLMs predict text based on what's in its static training data, as processed by a model ahead of time. It quite literally can't do reasoning. What the "chain-of-thought" technique is trying to do, is set up the context window, so that it can predict something like a reasoned response. The challenge is that if something similar to the reasoning it's trying to predict doesn't exist in its training data, then it will simply predict tokens as best it can, which might not be very useful. It's worth knowing that, because that's always where it falls down.

No thought in it.

You're lucky if it stumbles onto a pre-existing template that matches reality.

... how these things work? There seems to me a knowledge gap between the low level actual software implementation of the artificial neurons and the high level conceptual ideas of what the networks/models should be doing.

Researchers seemed to have copied a simplified version of what was earlier ideas of how the brain works (we know now it doesn't use back propagation) and then just applied a suck-it-and-see approach to improving these ANNs without really knowing how they do what they do. Or maybe I'm wrong, dunno.

It can take a while, but you can generally spot psychopaths. They have tells, and even if they're 99% truthful in their statements, you can pick up that you should never trust them.

The problem with LLMs is that they behave just like psychopaths, but they don't have any emotional valence - they will give you a correct answer with the exact same tone and confidence as they give you an incorrect answer.

You can never drop your guard when using an LLM.

Some of my employees have similar sophisticated reasoning abilities:

Me: Why did you do X ?

Employee: I don't know.

Aren't LLMs more like a fancy autocomplete than hard tested logic and math that companies like Wolfram has created? Just guessing but the human writings the LLMs are trained on probably are the source of the inconsistencies. If we honestly applied a teacher like grade to all the statements we make, aren't some of them inconsistent like we're seeing with the LLMs? Is the expectation of flawless computer logic from a LLM that exceeds its teachers, asking for too much?

I have low hopes for regulation of AI because its output is indistinguishable from the word salad produced by politicians.

the result will sometimes be lies.

Sir, I am shocked. Shocked, I say.

As long as they talk slick, sound authoritative and sycophantic, and more importantly cost less than human labor, companies will replace human by mediocre AI.

Because capitalism is a race to the bottom.

All this shit about "AI getting worse as it's tuned and tweaked" reminds me of when you start writing a program and it's shitty and so you go back and add code to it and to fix it, but it just makes it even worse and messy and tangled, so you add more code to it and of course it gets even worse until you realize that it's such a pile of steaming horseshit that NOTHING will ever fix it.

That seems to be the current state of AI. You have a poison milkshake, but if for some reason you're convinced that if you just add some more sugar, maybe it'll be okay.

Artificial Cognitive Dissonance is now a reality.

I'm not sure if this is the same article verbatim as the fully-paywalled Financial Times, but it is definitely the same topic.

[1]https://www.msn.com/en-gb/mone... [msn.com]

[1] https://www.msn.com/en-gb/money/topstories/the-struggle-to-get-inside-how-ai-models-really-work/ar-AA1Hi7wO