If someone wants to go as far as to claim that any computation is just curve-fitting then your statement is equivalent to Church–Turing thesis. There are no formal arguments against Church–Turing thesis.

From that perspective intelligence is indeed just a curve fitting.

https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis

I really enjoyed the "The Measure of Intelligence" by François Chollet. https://arxiv.org/abs/1911.01547

"We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power."

He argues that we should move towards evaluating "Intelligence as skill-acquisition efficiency".

I agree with him. We should move away from benchmarks that involve training and evaluating algorithms on the same datasets. This is indeed more or less "curve fitting". Instead we should focus on benchmarking how efficient algorithms are at solving tasks involving completely new datasets, preferably even unknown to the developers. For example, language model GPT-2 was trained to predict next word given some previous words. After that training GPT-2 was able to do things that were unrelated like question answering, translating etc. GPT-2 is of course doing that very badly, and requires GB of training data, but it is a step towards skill-acquisition efficiency and away from what everyone sees as curve-fitting.

We should benchmark models so that we select for these that are able to do solve tasks they were not build to solve.