True, but the same can be said of many things; e.g. biology just looks like P(reproduction|environment), the economy just looks like P(profit|markets), etc.
There can still be rich structure inside, and useful abstractions to describe them.
It’s possible that I’ve just fallen too far under the influence of Deutsch and Marletto, but as someone who has worked on systems like this I’m rather skeptical that one of these things is going to break the gridlock between quantum theory and general relatively any time soon.
There’s no reason why one couldn’t principle but I’ve babysat enough big ML systems that I tend to think in terms of “how do we keep this thing from shitting itself” rather than “damn this thing is going to win a Fields medal if I turn my back on it”.
The question is, though, if the expertise and intuition developed during, say, running XGboost classifiers at scale in AdTech really of much relevance when thinking about large transformer models trained with a self-supervised objective and RLHF?
If you try to study this in depth, these models can do something the usual "datascience"-tier ones commonly cannot: https://arxiv.org/abs/2205.10343 https://moultano.wordpress.com/2020/10/18/why-deep-learning-...
By the standards of AdTech, latent space stuff was pretty unproven when I left the game a few years ago.
Low-rank approximations were in some sense implicit in that, but were not at that time an explicit goal.
If I were doing an AdTech system from scratch I’d most likely reach for the kind of recommender systems that you’re alluding to sooner.
Sounds like you know your stuff :)
The fact that we observe what looks like emergent structures such as "understanding", "knowledge" or "reasoning" is fantastic, but it is not in any way incompatible with a P(text|internet) model simply "mimicking" humans.
I agree that exploring the inner workings of these emergent features is interesting in it's own right, but all that glitters is not gold.
Sayings that it just looks like P(text|internet) is a tautology, it's a text predictor trained on the internet. This doesn't tell you anything about why phenomena like the above occur, or why it occurs only in large language models and only in some of them.
But it’s one thing to regurgitate a causal structure that’s been said over and over and quite another to propose a novel and falsifiable hypothesis.
Telling a novel-ish just so story is well within the reach of a precocious 8 year old, and that’s selling the bright year old short.
Let’s see one of things propose a novel hypothesis before we get our drawers moist.
I understand that it’s very easy to ascribe all kinds of qualities to these things, but when the corpus is the Internet, the log likelihood of it sounding like a person is not so different from the corpus sounding like a person.
These things are impressive enough without any magical thinking.