[tlb]

If you connect A and B as the input to a linear neural net, and train against C, it'll very quickly arrive at weights of [-1, +1] and be able to correctly predict C given A and B. Whether or not it represents it in notation humans are familiar with, it has learned it for the practical purpose of being able to compute the function.

[goatlover]

But how would a neural network know to connect data for mass with data for the measured speed of light? Why would a neural network be looking for an equation for energy conversion in the first place? If you just provide tons of raw data from instruments, what does that mean? What do yo do with it?

Sure, a human can clean the data and put it into a format that gives meaningful results. But if we're just talking about an AI learning from raw data with no supervision, where does it even start?

[mannykannot]

As someone who finds this interesting but does not know enough to take a position, I think a bigger question would be how does it come up with the abstract concept of energy?

I am aware that Alpha Go Zero came up with various strategic abstractions of the game that are recognized by competent players, and some novel ones, but I do not know where this program and its self-play training stands in the dichotomy of this debate.

[goatlover]

Go has a clear objective that you can train for. What would be the objective in coming up with a physics law from raw data? What would it even be training for? That sounds like asking whether DL could create a new board game from a bunch of data on human behavior.

> think a bigger question would be how does it come up with the abstract concept of energy?

I don't see how it could, and that's kind of how Kant argued against empiricism. You can't derive a conceptual understanding of the world from raw data. There's nothing in raw data to structure or make sense of it without some way to interpret the data. Even calling it data is an interpretive act (as opposed to noise).