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OK, thanks for clarifying. I feel that your description of neural nets' inner
workings is a bit idealised and I'm not convinced that we have seen any evidence
that they are as powerful in representing real-world phenomena as you suggest.
But that's a big discussion so let's leave this aside for a moment. I can agree that a neural net can learn a model that can predict the behaviour
of a system, to some extent, within some margin of error. That's not enough for me to see neural net models as (scientific) "laws". For
the sake of having a common definition of what a scientific law is, I'm going
with what wikipedia describes as a scientific law: a statement that describes or
predicts some set of natural phenomena, according to some observations
(paraphrasing from: https://en.wikipedia.org/wiki/Scientific_law). Sorry for not
introducing this definition earlier on. If you disagree with it, then that's my
bad for not estabilishing common terminology beforhand. In that sense, neural net models are not scientific laws because, while they can
predict (but not describe) they are not "statements". Rather they are systems.
They have behaviour and their behaviour may match that of some target system,
like the weather say. But like a simulation of the economy, or an armillary
sphere are not, themselves "laws", even though they are possibly based on
"laws", a neural net's model can't be said to be a "law", even if it's based on
observations and even if it has an internal structure that makes its behaviour
consistent with some (known or unknown) law. There is also the matter of usability: neural net models are, as we know, "black
boxes" that can't be inspected or queried, except by asking them to analyse some
data. While useful, that's not a "law", because it does not help us understand
the systems they model. If this sounds like a semantic quibble, it isn't. To me
anyway it doesn't make sense to base scientific knowledge on a bunch of
inscrutable black boxes. Scientific laws and scientific theories are not black
boxes. As an aside, neural nets fall short of what Donald Michie (father of AI in the
UK) called "ultra-strong machine learning" [1]. That's the property fo a machine
learning system that improves not only its own performance, but that of its
user, also. Current techniques aren't even close to that. ____________________ [1] Machine Learning: the next five years, Donald Michie, 1988 https://dl.acm.org/doi/10.5555/3108771.3108781 |