alt Hacker NewsThere is indeed a huge amount of useless jargon and IMO deliberate obscurantism in deep learning research.
Sometimes it is to defensively ward off critics, sometimes it is to woo the naive and ignorant, and sometimes it is because the basic ideas are really just incredibly simple and based on simple mathematical intuitions or hunches.
In the latter case, papers and authors appeal to some obscure or overly-complicated technical theory (which usually doesn't actually apply at all to current deep models as they are actually implemented, e.g. infinite-width networks and other such purely theoretical abstractions) to cover up the fact that they really just had a lucky intuitive guess (and likely that twenty other intuitive guesses just didn't pan out). This appeal to theory is often self-deception as much as it is appealing to academic reviewers.
I think this article is a bit of Nick wooing himself and others here... I'll wait for his supposedly forthcoming paper to see if there is any real meat in it.
I would even say "Arbitrary packing" was a weak conclusion to begin with.
'Arbitrary' was the wrong word, projections encode information.
The more charitable 'random' is a null hypothesis. It is formally impossible to prove. It is uncomputable.
So it is not surprising to see someone reject the null.
The fact that it was given any credence suggests to me deep problems with interpretability or ai safety in general.
Take the headline here: "interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building."
I deny even the existence of alignment. In my view, the field of 'alignment' works on a problem that does not exist.
Alignment is a reference to something that does not exist in the model: social norms. It will never be intrinsic to the model. You will not find it there.
'Neurons' are not primitives either, they are not intrinsic to the model. 'Neuron' is a human-constructed category. It is easy to just make up intrinsic properties of neurons and get away with it.