I don't think that's a problem. I live in Spain and speak Spanish daily with real people, many of them my friends. They'll correct me if needed, they often do. Though most are my own mistakes.

Don't forget people give wrong answers too. But people aren't available 24/7 to help me.

Anyone learning a new language massively benefits from being able to speak with native speakers.

That being said, LLMs are better at languages and translation tasks than any pretty much anything else. If you need vocabulary help or have difficulty with grammar they're incredibly helpful (vs Googling and hoping someone had the same issue and posted about it on Reddit).

I mean, if you can afford a native speaker tutor that is the superior choice. But, for the average person, an LLM is a massive improvement over trying to learn via YouTube or apps.

Completely agree, those summaries are incredibly bad. I was recently looking for some information in Gemini meeting notes and just couldn't find it, even though I was sure it had been talked about. Then I read the transcript itself and realised that the artificial unintelligence had simply left out all the most important bits.

The iPhone models are really bad. They aren’t representative of the usefulness of bigger ones, and it’s inexplicably stupid that Apple doesn’t like people pick their own API as an alternative.

You can disagree, but I find it helpful to decide whether I'm going to read a lengthy article or not. Also if AI picks up on a bunch of biased phrasing or any of a dozen other signs of poor journalism, I can go into reading something (if I even bother to at that point) with an eye toward the problems in an article. Sometimes that helps when an article is trying to lead you down a certain path of thinking.

I find I'm better at picking out the facts from the bias if I'm forewarned.

iPhone notification summaries were made with GPT3.5 I believe (maybe even the -turbo version).

It doesn't use reasoning and so when using very short outputs it can produce wild variations since there are not a lot of previous tokens in order to direct the LLM into the appropriate direction in kv-space and so you're more at the whims of temperature setting (randomly selecting the next token from a SOFTMAX'd list which was output from the LLM).

You can take those same messages and plug them into a good model and get much higher quality results. But good models are expensive and Apple is, for some reason, going for the budget option.