*provided you speak English or Mandarin, the former preferably of a continental US variety
It's astonishing how bad things get again once you mix in an accent, local dialect (e.g. Swiss German) or a less frequently spoken language (like Croatian).
As for Chinese, it is also pretty amazing that you can visit a Chinese website, click "translate" in your browser's menu bar, and get a reasonably readable translated version.
I wonder if people just take too many things for granted.
Or internet search - they say quality of Google searches have been declining, nevertheless we had a pretty good run for the past 20 years or so with being able to find information on the internet. That is AI as well.
It won't for the foreseeable future. Not for technical reasons; it's just that other languages are usually not handled correctly because most companies think they can just use the exact same approach as in English and they're done.
Until they realise that non-English native speakers also use English words and abbreviations to some degree, both in IT-related contexts but also in everyday life. Now it doesn't just need to handle that one language but also English with an accent. If they're lucky it'll work reasonably well in most cases despite variations depending on the region.
Right now even keyboard completion suggestions struggle with mixing languages and become completely useless in some cases. As English words may be mixed in at any location (and in wildly different frequencies depending on the user) the software now has to guess the language for every single word. The results are not great.
> they say quality of Google searches have been declining, nevertheless we had a pretty good run for the past 20 years or so
As long as Google continues with blunders like showing wrong pictures of people in infoboxes they'll keep failing hard. Their amazing AI shows wrong pictures for serial killers, rape victims and more, which already led to consequences for those people. What makes it much worse is that when someone complains about such a case Google will just replace that picture with another wrong portrait - if they react at all. It would be helpful if those big tech companies would for once trust in human intelligence instead of throwing larger models at the problem.
Isn't that a bit like saying cars are crap because people die in accidents? Maybe there are just upsides and downsides to most new technologies, and if the upsides outweigh the downsides by far, people will go for it?
As for human intelligence, I am not convinced humans would necessarily fare better at such tasks. I mean they fall for the "same name, same person" fallacy.
Google was very successful with the latter for a long time before they started in on the former.
If you want better speech recognition for Swiss-German, then record and transcribe hundreds of thousands of hours or whatever level of parity you want to achieve with recognition.
It's not "astonishing" at all. Models won't generalize well unless they have sufficient data, so to achieve multi accent functionality, we need lots more high quality data. Or we need better architectures, so identifying where models fail and engineering a better architecture could be a breakthrough. The shortcomings are not surprising or mysterious at all, it's simply a function of the nature of these algorithms.
Addendum: don't overlook the incentives and biases of the people building said algorithms.
I don't think there's a single valid example of a biased or racist architecture as such. An algorithm can be seen as a particular use of an architecture, and like every human endeavor can be done well or badly.
The infamous tank detector neural network was biased towards clouds. Microsoft Tay was biased towards troll induced garbage. Neither bias says anything about the architectures underlying the implementation except that the tool was used poorly.
I think we should leave the discussions of incentive and bias at the level of particular implementation, as abstract architectures can't be generically adjusted or affected by biases or ethics or moral considerations. The selection of training data and intended functionality and particulars of a project are where biases and other considerations arrive on the scene. The ideas of transformer or other neural network models don't have any aspects where you can add in ethical considerations - they're fundamentally amoral abstractions.