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Joined 1 year ago
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Cake day: June 22nd, 2023

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  • 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.


  • 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.