Lemdro.id

-1 points

1 year ago

LOL

We understand less about how LLMs generate a single output than we do about the human brain. You clearly have no experience developing models.

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SuddenlyBlowGreen@lemmy.world

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2 points

1 year ago

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.

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[ - ]

3 points

1 year ago

You really don’t understand how these models work and you should learn about them before you make statements about them.

Machine learning models are, almost by definition, non-deterministic.

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SuddenlyBlowGreen@lemmy.world

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-1 points

1 year ago

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?

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[ - ]

3 points

1 year ago

Look, I understand why you think this. I thought this too when I was first beginning to learn machine learning and data science. But I’ve now been working with machine learning models including neural networks for nearly a decade, and the truth is that is nearly impossible to track the path of an input to a given output in machine learning models other than regression-based models and decision tree-based models.

There is an entire field of data science devoted to explaining how these models arrive at their conclusions. It’s called “explainable AI” or “xAI”, and I have a few papers that I’ve published in exploring the utility of them. The basic explanation for how they work is that we run hundreds of thousands of different models and then do statistical analysis to estimate why the models arrived at their conclusion. It isn’t an exact science, however.

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SuddenlyBlowGreen@lemmy.world

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0 points

1 year ago

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?

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[ - ]

2 points

1 year ago

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.

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.

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

Things that are reproducible with known inputs and outputs, allowing for all components to be studied and explained. 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.

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

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