I just want to push back on Academic Machine Learning is a (low quality with no novelty) paper mill and devaluing researchers efforts.
To be clear ML research has “paper mill” problems but we should be careful that we don’t imply that there are only “rare successes”
There are many many amazing results published at ICLR, NeurIPs, ICML every year that are important developments that are not only research successes but also commercial and open source success stories. For example LoRA and DPO are two recent incredible development and these are not “rare” - this years ICLR had many promising results that will in turn be built on to produce the next “transformer” level development. Without this work there are no transformers.
Even transformers themselves were a contribution whose impact only became valuable through the work of many researches improving the architecture and finding applications (for example LLMs were not a given use case of transformers until additional researches put the work in to develop them)
I would not say it’s impossible… my lab is working on this (https://arxiv.org/abs/2405.14577) and though it’s far from mature - in theory some kind of resistance to downstream training isn’t impossible. I think under classical statistical learning theory you would predict it’s impossible with unlimited training data and budget for searching for models but we don’t have those same gaurentees with deep neural networks.
I don’t understand why very large neural networks can’t model causality in principal.
I also don’t understand the argument that even if NNs can model causality in principal they are unlikely to do so in practice (things I’ve heard: spurious correlations are easier to learn, the learning space is too large to expect causality to be learned from data, etc).
I also don’t understand why people aren’t convinced that LLM can demonstrate causal understanding in setting where they have been used for things like control like decision transformers… like what else is expected here?
My research involve applying Popper's epistemology to natural language processing.
So I am quite involved in this.
As far as I can tell, almost all of what Popper tried to do with quantification measures of information are exactly what you are talking about.
In particular Conjectures and Refutations covers this really extensively so I'd recommend reading or re-reading that. Though Logic of Scientific Probability covers an early form. David Miller's Critical Rationalism covers it well too and some of it's problems.
I.e:
His notion (shared with positivists like Carnap and others) that science is a set of logical statements. A collection of statements is a theory, a theory entails a set of predictions which is called the information content of the theory (sometimes I(c) or C(I) in his notation).
If the I(c) > I(c') where c' is a competing theory then it is said to have more explanitory power. I.e. it makes more predictions.
This is part of his defnition of what makes a good explanation and what david desutch calls "hard to vary".
The other main part of the definitition is about whether these statements reflect Truth in anyway.. that is covered by his notion of verisimiltude or truthlikeness which is quantified as the degree to which the information content of a theory I(c) can be corroborated.
Both of these are essentailly "The predictive strength of someone's Truth"
The problem you and many other have probably encountered is the information content of an explanation is *intractable* it's an open set of statements which cannot full by fleshed out. So instead we can never have a perfect quantification of whether my theories or your theories are better... there may indeed be statements entailed by flat earth theory that have yet to be discovered and could indeed be more corroborated and provide better information content than a non-flat earth theory! Popper revels in this fact and fully embraces it.
Beyond Popper though, we need to understand more of the dynamics of "predictive strength" - I am finding causality a great source of literature that for which I would recommend Judea Perl and the Book of Why among other things.
For Philosophy of Science in particular there are ton's of great articles on stanford encyclopedia of philosophy about Explanation that go into this in depth - in fact the positivists like Carnap wrote amazing things about this which I would recommend.