A word strings together form a sentence which carries meaning yes, that is language. And the order of those words will affect the meaning too, as in any language. LLM then will reflect those statistically significant words together as a feature in higher dimensional space. Now, LLM themselves don't understand it nor can it reason from those feature. But it can find a distance in those spaces. And if for example a lot of similar kanji and the translation appear enough times, LLM will make the said kanji and translation closer together in the feature space.

The more token size, the more context and more precise the feature will be. You should understand that LLM will not look at a single kanji in isolation, rather it can read from the whole page or book. So a single kanji may be statistically paired with the word "king" or whatever, but with context from the previous token it can become another word. And again, if we know the literary art in advance, we could use the different model for the type of language that is usually used for that. You can have a shonen manga translator for example, or a web novel about isekai models. Both will give the best result for their respective types of art.

I am not saying it will give 100% correct results, but neither does human translation as it will always be a lossy process. But you do need to understand that statistical models aren't inherently bad at embedding different meanings for the same word. "Ruler" in isolation will be statistically likely to be an object used to measure or a person in charge of a country depending on the model used. But "male ruler" will have a significantly different location in the feature space for the same LLM for the former, or closer for the latter case.

This is actually one of the best use cases of LLM. Indeed there is culture and nuance that may be lost in translation, but so does every other translation. And most of the time, if we know the literary art being translated ahead of time, we can predict a higher use of more nuanced language and adjust accordingly or skim it by a human.

After all, most "AI" is basically feature embedding in higher dimensions. A different language that refers to the same concept should appear close to each other in those dimensions.

Emulation time it is!

So modern math is proven to be incomplete and we cannot prove that it is consistent either. Those 2 words, incomplete and consistent have a very technical meaning here.

The first is that there is a statement in modern mathematics, which is true, but cannot be proven. And even if we expand it, there will always be such a statement. Hence, incomplete.

And the second, we cannot have a system that proves everything as that system will be inconsistent. Basically if a system can prove everything, then we can easily prove 1=1 AND 1 ≠ 1. If both are proven, then we lose meaning since there is no "truth". But a consistent system cannot prove its self consistency. Ergo, with modern math, we cannot know if math is consistent.

Now, the problem lies in that we use math to model our perceived reality. It means there is a limit to human knowledge, or put simply, there will be something in the universe that we may never know the answer to.

My favorite is the busy beaver function. There exist, at a certain number, that our modern math cannot make any meaningful statement about the function. Here is a great video about it. (youtube link warning). But you can also look at veritasium video for more in depth explanations.

Our current understanding is not enough to state that with confidence. We used to be so confident with classical mechanics and even claims that physics is almost complete. God knows how long our current probabilistic model will last before we find another better model. It may be probabilistic, or it may not.

Gödel Incompleteness Theorem and the boundary of our understanding of the universe.

It is worse in HW prototyping where sometimes loose wire is all over the place

I was curious and then trying to edit the contrast, sure enough. This is 3×

~~It means it isn't there. I don't see the hue either. Which leaves, screen issue.~~

Edit: It seems like a contrast issue? I took the screenshot and modified it contrast value -3×

Punch cards are gonna be back baby

Imma upvote just to spite you then

Can't have cache latency if there is no cache!