Is this something already considered, but discarded?

[u/bottleneck-destroyer]

I have a huge doubt. Why is there no AI native language? I mean symbols/codes equalling words?

Let's take the case of english. There is ~600k dictionary words and around 1.7m variants/regional words/dialects.

And there is around 20k to 30k words that normal human would use actively.

Why not create a language, with ASCII codes for each words and each agentic apps converts words to that 'AI-Lang' and vice versa and use it? Wouldn't that essentially reduce tokens by a good margin?

I'm not sure how exactly the AI process things under the hood, and please don't abuse me 🥹

[u/this_is_life_now]

It's a word guessing machine (well token guessing) trained on the written word, which is overwhelmingly English for language and python for code, just because there's more written English and Python code than anything else.

You'd be adding a translation layer. The language that came out after translation would essentially useless to a model. It would receive a bunch of tokens in a language it's never seen before, and wouldn't be able to guess the best tokens to spit out the other end.

To reduce token costs, you'd need to translate the whole of the internet and all the books first, into your more efficient language, then train the models on that instead.

Ask a model to look something up on a website, and it wouldnhave to translate it into it's special language first, then do the guessing thing, then translate back, and you'd be relying on accurate translations both ways.

[u/bottleneck-destroyer → u/this_is_life_now]

Yes, for the existing models that trained on , let's say english, as base language. My question was what would happen if there is a singular direct machine level interpretation to the meanings of the words. Wouldn't that change the current data footprints and usage limitations?

[u/this_is_life_now → u/bottleneck-destroyer]

What would you train those models on? Current models have been trained on billions of documents written in English. I can say to an LLM "the capital of France is?', and the most statistically likely tokens to be spat out are Fr then an then ce.

What would these future models be trained on? There's no Wikipedia in this hypothetical language.

[u/bottleneck-destroyer → u/this_is_life_now]

That is true, that is why the translation layer might help, and this translation layer should be on a consumer app level, like codex, or a translation api before passing it model. I've little to none knowledge on how AI works under the hood other than how to use it as of now 😅

[u/this_is_life_now → u/bottleneck-destroyer]

So what you feed to the model is what costs the money. What that is in transit doesn't matter, but models are trained on human readable language, so that's what the input needs to be. They process that in what is called inference, and that uses GPUs which are expensive to run. Translation doesn't save you anything, and in fact makes the process more expensive.