Seems like a constant in doomsday "science": making a model that bear ressemblance to reality as much as SimCity does, run it to get the expected conclusion, then extrapolate and conclude with newsworthy title that X or Y is true in reality.

Then why using a TUI editor at all? Each time nano was opened because of some default settings, I saw this whole bottom bar with shortcuts. It sure doesn't seems to be easy to learn either.

Then don't try to put discredit to the saying of an insider. GP is 100% right. I've been back after about ~5 years living abroad and the country is in an astonishing decline on every plan (educational, moral, economical, political, diplomatic, security, judicial, medical, etc. the list goes one). And most of it is stemming from the policies enforced in the last decades.

I read about a C++ package manager written by Microsoft few years ago, maybe it's now somewhat usable?

Just because the state of art evolved doesn't mean we have to erase history of the field. This is a ML algorithm so calling it AI is perfectly in line.

The fear you mention is built on the lack of understanding of what ML is. Showing that some AI has "dumb" yet useful implementations can help show the limits of this category of technology.

However it could be explained by the fact predictive typing on iOS is quite poor. Often a wrong letter in a word need to be corrected and the next predicted word (and it's conjugation; I'm speaking of French and English input) is often wrong. It may not be noticeable for someone who never used another keyboard, but as a former Windows Phone user the difference is very easy to see. The WP prediction was mind blowing and I could indeed write long sentences in French just by typing a few letter and using one of the prediction candidates.

Given the number of wannabes PhD in chemistry having peaceful religious beliefs that are entering in EU every year, this is really for the better.

So typical divisive BS gender study. I'll continue to work with coworkers from both sexes without taking offense in a disparity that isn't even proved to have influence on papers' quality and science output.