Eh, you don’t need a gun necessarily to help out, see Desmond Doss.

And the UK goverment really REALLY wants to have backdoors put into every kind on encryption.

Well, I’m sure the two aren’t related.

So some drivers are not installed like I said

No, you said:

First of all they’re going to have to release a distro which actually has, shock horror, proprietary drivers installed on it, because your average user isn’t going to understand how to install them.

You’re moving the goalposts.

Not really, because there’s still some processes in the human brain we don’t understand.

For example, you can list the steps and processes for every step an AI makes. You have to, in order to code and run it.

But you can’t list every step or process taking place in cases of sudden savant syndrome, for example.

This is exactly correct, except you’re also not accounting for the insane amount of computational power that would be necessary to backtrack a single output of a single model. This is why it is a black box. It simply is not possible on a meaningful level.

It’s not practical, but it can be done. We simple don’t have the time or inclination to do it.

It’s like like saying we don’t understand how an internal combustion engine works. Every explosion is a bit different, it pushes the pistons a bit less or more, it leaves a bit more or less residue in different places. We can’t backtrack and check every cycle and every part on a meaningful level, but we understand of it works, and we could do it if we wanted to. It’s just not practical.

As an example from my field: if you damage the dorsolateral prefrontal cortex in a fully grown adult, they will have the impulse control of a three-year old. We know this because we have observed damage to this area in multiple individuals, and can measure the effects based on the severity of that damage.

Okay so explain how sudden savant syndrome works. Step by step, biochemical process by biochemical process.

In contrast, if you provide the same billion-parameter neural network identical inputs, you will not receive identical outputs.

If you take the same model, put it in a VM, give it an input, get an output and the restore the VM to the exact same state before and ensure there’s no randomness, the model will give you same output.

Again, we have the input, we have the math and code that make it work, we have the weights, we have everything.

Would it take a lot of time to backtrack and check why we got a given output to an input? Yes, maybe an inordinate amount of time. But it can be done. It’s only black box because nobody has the time (likely years to decades) to wade through the layers of a finished model to check every node and weight.

The whole thing at its core is mathematics. It’s a series of steps, that can be listed and reviewed each step of the way if we wanted. It’s just that if would take too much time.

If what you said were true, we couldn’t reproduce models. And since we can…

It isn’t an exact science, however.

So if math and computer science isn’t an exact science, what is?

We know the input, we can set the model to save the weight in checkpoints during training and can view them any time, and we can see weights of the finished model, and we can see the code.

If what you said about LLMs being completely black box were true, we wouldn’t be able to reproduce models, and each model would be unique.

But we can control every step of the training process, and we can reproduce not just the finished model, but the model at every single step during training.

We created the math, we created the training sets, we created the code and we can see and modify the weights and any other property of the model.

What exactly do we not understand?

Well, given how we’re the ones that developed the models, they are deterministic as we know and can save and reproduce the random weights they are given during training, and we can use a debugger to step through every single step the models makes in learning and “thinking”, yes, we understand them.

We can not however, do that for the human brain.

Well, there still a shit ton we don’t understand about human.

We do, however, understand everything about machine learning.

Unfortunately for you, the world doesn’t revolce around you. And since people who do have these problems and want to solve them outnumber you, they’re going to post about it.

Good for you. But others do.

Ah yes, the old head-in-the-sand strategy.

Can’t think of a time completely ignoring huge problems didn’t work out well.

You could schedule a task at 1 PM, then generate a random number between 0-60 inside that task, wait that many minutes, then launch the actual task.

The clokwerk crate.

deleted by creator

deleted by creator

deleted by creator

deleted by creator

deleted by creator

deleted by creator