Apple still do plenty of AI, no? Eg the whole horse-detection thing. Maybe it’s something more specific that you dislike?
E.g., if I open the share menu, whatever contacts and apps Google guesses are always wrong, and a simple list of whoever I've last shared with would be better.
I also don't want any "smart" contextual behavior (widgets changing out throughout the day, etc.). The phone simply doesn't have enough context to know what I want from it at any given moment. I'd rather just tell it how to behave using my own judgement.
A decent AI system would include a heavy bias toward recent. Good "AI" should mostly feel like dumb heuristics. I think a good example is directions on my iPhone: when it's plugged into a car it always suggests driving directions. When there's something in my calendar soon it suggests going there. If there's not and I'm not at home it often suggests going home (it's right more than half the time). After that, it just offers a list of buttons for places I've told it about: home, work, school.
I'd be annoyed if it tried to do something more clever (like suggesting the post office because I went there on this day last year). I think a key element is that I have an immediate and intuitive idea of why its suggestion is a reasonable one.
It even finds text inside images.
Am sure Android has these features but I don't know if they work offline.
I believe the iPhone automatically classifies photos based on who or what show up in them. Users can contribute to train classifiers, but the iPhone already works out of the box.
The iPhone also creates theme-based photo albums from the photos you took. I recall it creating Christmas photo books, photo books featuring a pair of people, persons and pets, etc.
This might be low-key AI, but it's the useful kind.
> It even finds text inside images.
I'm not sure OCR counts as AI. But yeah, the iPhone indeed does that too. We can take a photo of a telephone number or even a credit card and automatically fill in those numbers.
I worked on a similar feature in the past for an unrelated project, but that was not AI though. Marketing changes though.
This is a great example of the AI effect where people would call something AI when it was a daunting research problem but give it another label once it’s working:
https://en.wikipedia.org/wiki/AI_effect
In this case, Apple’s modern OCR is a complex neural network system which I think most people would class as an AI tool. It’s notably better than traditional approaches which were optimized for business documents.
I'm not sure I agree, primarily because "better" is subjective.
A pipeline with template matching is extremely effective at extracting fixed form text in a standard layout, such as telephone numbers or credit cards, and computationally cheap as well.
But I presume a drop-in black box model which isn't bound to a low computational budget, can output plenty of false negatives and false positives, and can run on a single pipeline might be preferable at least from a product management point of view.
Also, neural networks looks good on resume while template matching doesn't. Just like statician/image analyst looks lukewarm but AI engineer looks superb.
Isn’t Tesseract also neural network-based?