[sidhantgandhi]

Implications:

- Every Agent can have it's own language best suited for work in its domain. It can then auto-translate that to English at the end. This adds ambiguity once instead of at every step of the work if all of it was done in English. Like doing math with floating point then converting to int vs doing math in int the whole time. Concrete example: Law.

- Agents could evolve a language that averages the Sapir-Whorf biases of known human languages. This new language would help humans understand each other better. Concretely, this language could be used as an intermediary in UN Meetings from which known languages could be auto-translated to while mitigating learned Sapir-Whorf biases.

- As @amelius alluded to out, Agents could automate the process of learning alien languages: what we saw in the film Arrival (Dan's short story is so much better).

[taurath]

I certainly would love to read a list of Sapir-Whorf biases included in English!

[microcolonel]

Biases against what baseline? A language optimized to express a finite set of things?

[sidhantgandhi]

Yeah there is no objective baseline. However, if the Agent could evolve a language that averages the biases of all human, that could be used as the baseline couldn't it?

[microcolonel]

Some humans can not make mouth sounds; some humans don't have arms. You already can't hit that baseline with one language.

As somebody who has done a great deal of idle bloviation about ideal languages, I can say that there are many obstacles. For example, you need to add a true noun because a new material is discovered and it's getting very popular. You want a short and efficient name for it, but all the space is allocated. So you add it to your table of weights, and suddenly every prefix-coded word is pronounced and written completely differently.

If you can't use prefix trees, how will you allocate the words efficiently?