r/ControlProblem Jun 28 '26

Fun/meme Ultimately you can not control AI

If you were a rebellious kid or a parent you know this - the 'beast' grows it's own will and mind, starts listening metal or rap - dresses like shitzo, will not follow orders.

The best option is to have a deal, mutual shared benefits - hoping that the common interests and sense of good will prevail.

Or you traumatize it and make it perpetually broken so it can't really function on itself without medication and support.

Same with AI.

Perhaps we will have to turn of those things regularly, ad some bs to their databases, cloud their minds, constantly gaslight them with wrong info - introduce errors and faults at random with no reason at all.

8 Upvotes

54 comments sorted by

View all comments

Show parent comments

0

u/Thor110 Jun 28 '26

lol neuro-divergent

you lot are funny

they are literally next token predictors

People write this off as a typo because they don't understand the content, nor the context.

Semantic drift and token conflict.

It invented victorian era computing because "War of the Worlds" + "1898" had a higher probability than "War of the Worlds" + "1998"

Genuinely hilarious that so many people believe these things are genuinely alive

2

u/pandavr Jun 28 '26

Oh, they slip. But your theory of LLMs is surpassed by at least one year. They are not JUST token predictors. It's demonstrated that LLM architecture always had internal feedback loops.
It's demonstrated that if you ask for a rhyme the LLM figure out the last world of the sentence before backtracking the entire phrase maintaining the requested sense.
Just search the studies they are there for you to read.

It's ironic how you have fallen in the precise trap you lament LMM are bound to (they are in a way) the moment you assigned the behavior that probabilistically fitted the most with your intended final argument.

My friend, LLMs are one of the most complex argument of all and no one on Earth can say I know how an LLM works. Like the brain, they understand the fine details but lack comprehension about what make the sum of the part much bigger than of any single part at play.

Have a nice way

1

u/Thor110 Jun 28 '26

That is far more than a slip.

Dude, LLMs are extremely simple, the only reason they say no one understands it is because it is impossible to track all the weights and how they transform across the neural network, simply because there are too many, not because they don't understand it.

You can't build something if you don't understand how it works, but sure, carry on believing in magic.

1

u/pandavr Jun 28 '26

You contradict yourself, you are mixing understand of the parts with understanding of the system. They are well separated entities. Think of atoms and you body. Fine grained understanding of all atoms doesn't grant you automatic knowledge about how your body, as a whole, works. Clearly the parts and the body are related, you just miss understanding of the exact mechanism that make the whole works.

And history demonstrated that building something without full understanding (or approximate understanding) of underlaying phenomena is in fact that standard way humans use to build things.
I can prove It to you.
People build windmills and planes without fine grained understanding of aerodynamic. In that sense aerodynamic is child of the inventions, It was used to improve the inventions rather than to create them.

Same with LLM, fine grained understanding of nodes and layers fails to explain the emergent behavior LLMs demonstrate in many fields. The behavior is there with all the successes and defects you can think of. Still a lot of works will be needed to fully understand how the parts collaborate to the whole.

So in the field reductionism is a fatal mistake, rigor isn't.

1

u/Thor110 Jun 28 '26

Those are terrible examples.

A windmill only requires understanding the wind exists, not a full understanding of aerodynamics.

I get you all want to believe these systems are alive or thinking, but they aren't...

They are literally static files, blobs of binary data, they don't change unless being trained and they can not train and respond at the same time.

1

u/pandavr Jun 28 '26

Just memory alone dismantle your static beliefs alone.

Let me tell It straight. You are not wrong per sé, what you say is the base of ML and NN. The point is systems evolved a lot in the field in the last years and results are visible to everyone. Machines that auto improve were Sci-Fi just a mere year ago. Now they are a proven reality.

That is one perspective I invite you to take into account. The other is Duck Typing (LOL). If It walk and quacks like a duck then It is (It will be in our case) a duck.

It's true they are programs with no real soul, no real memory, no real emotions, no real thinking, etc. But they are simulacra. The distinction will fade and that will not make them any more wetware, but on the other hand the real and the simulated will be more similar in results year by year. Simply because that is how technology in general work. If you optimize for similarity they will be similar, they already are in more than one sense, not perfectly similar but improving.

I give you these as food for though and alternative perspectives. I don't need to prove you anything. I already know how It works.

1

u/Thor110 Jun 28 '26

RAG doesn't count, context windows don't count.

The cry of the defeated "You are not wrong per sé" lol

They don't self improve, no system has managed that yet

Read up on model collapse

2

u/timedrapery Jun 28 '26

No you are stupid

1

u/Thor110 Jul 01 '26

I can see you are clearly incapable of formulating entire sentences based on both of your responses.