A few more examples:
It seems you are hitting against the expressive limits of Existential Positive First-Order Logic. It seems that they are exponentially less powerful than first order logic, in the following sense:
every existential positive first-order sentence can be transformed in an equivalent one in prenex normal form without an exponential blowup, thanks to the absence of universal quantifiers and negation symbols.
Bodirsky, Manuel, Miki Hermann, and Florian Richoux. "Complexity of existential positive first-order logic." Journal of Logic and Computation 23.4 (2013): 753-760.
It seems to me that chaos control and anti-control is another non-application.
[Handbook of Chaos Control: Schöll, Eckehard, Schuster, Heinz Georg](https://www.amazon.com/Handbook-Chaos-Control-Eckehard-Sch%C3%B6ll/dp/3527406050)
Do you have a citation for the claim that Gemini 1.0 Ultra trained for 1e26 FLOPs? I had searched all around but can't find any information on its compute cost.
This is not an answer to the broader question, but just regarding the "no Wikipedia page" thing.
I would like to write a Wikipedia page about Flux, but as it is, there is very little quality information about it. We have a lot of anecdotal information about how to use it, and a little academic description of it, but that's not enough.
Besides, it seems everyone who can write well in artificial intelligence wants to write their damned academic blog that is read by like 10 people a month and not Wikipedia, and Wikipedia accumulates a large amount of badly written stuff by amateurs.
As an example, see this page
https://en.wikipedia.org/wiki/Generative_adversarial_network
The "Applications" section is a typical example of how stupid and badly formatted it is. Everything above it I wrote myself. Everything below it I only did a light amount of editing. Before I went in to write all of that in 2022-07 (2022! Imagine that! GANs were famous since about 2018 and it waited until 2022 to get a decent Wikipedia page?), the entire page was crap like it: https://en.wikipedia.org/w/index.php?title=Generative_adversarial_network&oldid=1096565363
Similarly for the Transformer. https://en.wikipedia.org/w/index.php?title=Transformer_(deep_learning_architecture)&oldid=1095579622 I have only recently finished writing it. https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture) and then I tried applying for "Good Article" status, and got promptly rejected for not putting enough inline citations (do they really want me to put inline citations everywhere even if that means I just have to refer to the Attention is All You Need paper 30 times?) and too much primary literature and too much arXiv links (not a peer-reviewed source).
The RNN page is also terrible https://en.wikipedia.org/w/index.php?title=Recurrent_neural_network&oldid=1214097285 until I cleaned it up. There is still a large amount of crud but I put all of them in the lower half of the page, so that people know when to stop reading. I put them there just in case some annoyed editor reverts my edit for deleting their favorite section, and in case there is something valuable there (that I can't be bothered to figure out, because of how badly written it is).
The list of crud goes on and on. The Convolutional Neural Network page is still absolutely terrible. It has a negative amount of value, and I'm too tired to clean it up.
Sometimes there's an important model that's entirely neglected. Like the T5 model series. https://en.wikipedia.org/wiki/T5_(language_model) Why this model had to wait until me in 2024 to finally write it, I have no idea.
P.S.: The damned Transformer page gets someone (always a different one) writing in some Schmidhuber-propaganda. I remove it once a month. Why there are so many fans of Schmidhuber, I have no idea.
there's the Schmidhuber Scholarpedia articles in some cases, but aside from being outdated, it's, well, Schmidhuber.
I hate Schmimdhuber with a passion because I can smell everything he touches on Wikipedia and they are always terrible.
Sometimes when I read pages about AI, I see things that almost certainly came from him, or one of his fans. I struggle to speak of exactly what Schmidhuber's kind of writing gives, but perhaps this will suffice: "People never give the right credit to anything. Everything of importance is either published by my research group first but miscredited to someone later, or something like that. Deep Learning? It's done not by Hinton, but Amari, but not Amari, but by Ivanenkho. The more obscure the originator, the better, because it reveals how bad people are at credit assignment -- if they were better at it, the real originators would not have been so obscure."
For example, LSTM is actually originated by Schmidhuber... and actually, it's also credited to Schmidhuber (... or maybe Hochreiter?). But then GAN should be credited to Schmidhuber, and also Transformers. Currently he (or his fans) kept trying to put the phrase "internal spotlights of attention" into the Transformer page, and I kept removing it. He wanted the credit so much that he went for argument-by-punning, renaming "fast weight programmer" to "linear transformers", and to quote out of context "internal spotlights of attention" just to fortify the argument with a pun! I can do puns too! Rosenblatt (1962) even wrote about "back-propagating errors" in an MLP with a hidden layer. So what?
I actually took Schmidhuber's claim seriously and carefully rewrote of Ivanenkho's Group method of data handling, giving all the mathematical details, so that one may evaluate it for itself instead of Schmidhuber's claim. A few months later someone manually reverted everything I wrote! What does it read like according to a partisan of Ivanenkho?
The development of GMDH consists of a synthesis of ideas from different areas of science: the cybernetic concept of "black box" and the principle of successive genetic selection of pairwise features, Godel's incompleteness theorems and the Gabor's principle of "freedom of decisions choice", and the Beer's principle of external additions. GMDH is the original method for solving problems for structural-parametric identification of models for experimental data under uncertainty... Since 1989 the new algorithms (AC, OCC, PF) for non-parametric modeling of fuzzy objects and SLP for expert systems were developed and investigated. Present stage of GMDH development can be described as blossom out of deep learning neuronets and parallel inductive algorithms for multiprocessor computers.
Well excuse me, "Godel's incompleteness theorems"? "the original method"? Also, I thought "fuzzy" has stopped being fashionable since 1980s. I actually once tried to learn fuzzy logic and gave up after not seeing what is the big deal. It is filled with such pompous and self-important terminology, as if the lack of substance must be made up by the heights of spiritual exhortation. Why say "combined" when they could say "consists of a synthesis of ideas from different areas of science"?
As a side note, such turgid prose, filled with long noun-phrases is pretty common among the Soviets. I once read that this kind of massive noun-phrase had a political purpose, but I don't remember what it is.
Finally somepony noticed my efforts!
Concurring with the sentiment, I have realized that nothing I write is going to be as well-read as Wikipedia, so I have devoted myself to writing Wikipedia instead of trying to get a personal blog anymore.
I will comment on a few things:
Edit: I found it. It's from Yurchak, Alexei. "Soviet hegemony of form: Everything was forever, until it was no more." Comparative studies in society and history 45.3 (2003): 480-510.