[treesprite82]

> I mean basically that is what ML is, trying to predict the future

If being so reductive, that's also the scientific method. Form a model on some existing data, with the goal of it being predictive on new unseen data. Key is in favoring the more predictive models.

> they called it magic, we call it math, but both seem to have about the same outcome

Find me some sheep entrails that can do this: https://imagen.research.google/

[ninkendo]

One of the oft overlooked, yet critically important aspects of the scientific method is the hypothesis. You don’t design an experiment having absolutely no idea what to expect. You have an educated guess in mind (the hypothesis), and you design the experiment in such a way that says “this result will rule out my hypothesis, while this other result might confirm it.”

Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.

[treesprite82]

Note that I'm being intentionally reductive to argue that the net buscoquadnary threw around ML models and future-telling with sheep entrails also includes the scientific method.

I do also think that ML as a field progresses through the scientific method ("I theorise that this network with residual connections will converge faster, lets see if there's a significant difference") - but maybe not to the full extent it could.

> Just trying two things at random and picking the one that makes some arbitrary metric go up, is not the scientific method. It’s gradient descent.

I'd say that's closer to evolutionary algorithms. GD finds (locally) the direction to tweak the weights to improve predictions on a given batch.

[potatoman22]

The trained model is the hypothesis. We test the hypothesis by validating the model against unseen data.