Impressive, but there's an easy formula for getting these systems to make mistakes. Just input a sentence with some kind of long distance dependency. For example, DeepL gets agreement right in English to Spanish translations when the two things that agree are close together:

    I like soup -> COMO sopa
    They eat soup -> COMEN sopa

Impressively, it can even get agreement correct across clause boundaries in many cases. But if you do wh-movement through two or more clauses, you're usually out of luck:

    Which boys does he say he believes eat soup?
    ->
    ¿Qué chicos dice que cree que COME sopa? [should be COMEN]

It doesn't really matter very much in practice if an MT system makes mistakes like this, but they are mistakes that you can rely on humans not to make systematically.

(outside edit window. first sentence should be 'I eat soup' not 'I like soup')

Hmm, that seems to have been fixed now. Here's a nastier case that shows a similar effect. (Nastier because the agreeing indirect object in Spanish is a subject in English.)

    Which boys does she know like cheese?
    ->
    ¿A qué chicos sabe que LE gusta el queso? [Should be LES]

AI researchers are certainly fully aware of such issues, and there have been various models trying to encode long term dependency information, e.g. LSTM.

DeepL is what I use for European language translations. I wish they added more languages and maybe a nice app like Google Translate. It blows every other translation service out of the water.

I can also confirm, it's very impressive. Not sure how their setup differs from Google Translate but I've read articles translated with it and only knew it was translated when it was written at the end of the article that it was using DeepL.

Word choice is sometimes very slightly off, but grammar appears to be flawless. As you say, very very impressive.

Wow! That's very good.