You're totally misunderstanding the context of that statement. The problem of classifying an image as a certain animal is related to the problem of generating a synthetic picture of a certain animal. But classifying an image of as a certain animal is totally unrelated to generating a natural-language description of "information about how to distinguish different species". In any case, we know empirically that these LLM-generated descriptions are highly unreliable.
I like how all of the currently running attempts have been equipped with automatic navigation assistance, i.e. a pathfinding algorithm from the 60s. And that's the only part of the whole thing that actually works.
The multiple authors thing is certainly a joke, it's a reference to the (widely accepted among scholars) theory that the Torah was compiled from multiple sources with different authors.
I'm not sure what you mean by your last sentence. All of the actual improvements to omega were invented by humans; computers have still not made a contribution to this.
Yes - on the theoretical side, they do have an actual improvement, which is a non-asymptotic reduction in the number of multiplications required for the product of two 4x4 matrices over an arbitrary noncommutative ring. You are correct that the implied improvement to omega is moot since theoretical algorithms have long since reduced the exponent beyond that of Strassen's algorithm.
From a practical side, almost all applications use some version of the naive O(n^3) algorithm, since the asymptotically better ones tend to be slower in practice. However, occasionally Strassen's algorithm has been implemented and used - it is still reasonably simple after all. There is possibly some practical value to the 48-multiplications result then, in that it could replace uses of Strassen's algorithm.
I think this theorem is worthless for practical purposes. They essentially define the "AI vs learning" problem in such general terms that I'm not clear on whether it's well-defined. In any case it is not a serious CS paper. I also really don't believe that NP-hardness is the right tool to measure the difficulty of machine learning problems.
honestly the only important difference between them is that emacs's default keybindings can and will give you a repetitive stress injury (ask me how i know...)
When people compile compilers do they actually specialize a compiler to itself (as in definition 3 in the paper) as one of the steps? That's super interesting if so, I had no idea. My only knowledge of bootstrapping compilers is simple sequences of compilers that work on increasing fragments of the language, culminating with the final optimizing compiler being able to compile itself (just once).
I've been using Anki, it works great but requires you to supply the discipline and willingness to learn yourself, which might not be possible for kids.
Writing "My Immortal" in 2006 when nothing quite like it had ever been written before, is a (possibly unintentional) stroke of genius. Writing "My Immortal" after it's already been written is worthless.
You're totally misunderstanding the context of that statement. The problem of classifying an image as a certain animal is related to the problem of generating a synthetic picture of a certain animal. But classifying an image of as a certain animal is totally unrelated to generating a natural-language description of "information about how to distinguish different species". In any case, we know empirically that these LLM-generated descriptions are highly unreliable.