Well, not surprised of course! Anyway, perhaps I'm just a simple man, ... besides searching the web, I think that's the best AI type feature for the consumer yet!
I'm surprised at this "Apple not AI" narrative. I've had Google Maps & Waze on my iPhone for years and neither of them seamlessly turned on "where did I park my car" feature. I just turned my iPhone on one morning and it just told me. Now, mind you, this may not be a fancy deep learning model. It could be some simple linear model, maybe a tree model, hell, maybe it's hardcoded rules? But despite these things Apple delivered a great feature which is in the realm of what I would consider "AI enabled feature".
Mind you, I don't have an Android device, so maybe Google Maps does this automatically for you on Andriod. It doesn't do it on the iPhone, at least not automatically (aka. I don't bother to look it up, which is kind of the point).
Perhaps what isn't said is that Mu-nan understood he was no good to his family and decided it would be best for him to leave. I wouldn't conclude that the actions are self-absorbed... only that that is a perspective, but ultimately not the only one.
> It's learning the meaning of words, and the relationships between them.
It's learning a meaning, not the meaning. It's just a probabilistic model for the occurrence of a word based on the words that surround it. This should not serve as a base for the rest of your claims.
Anyway -- the improvements gained by multi-modal systems essentially disprove your thesis. Which is a good news! We're making progress.
I actually make private Facebook wall posts to myself. It's great, easy to see and scroll through any time, and I can also add my friends individually (or a list of them) at a time when it's appropriate to share. The interface is great, I can comment on my own thoughts... I know this won't get any fans in this crowd but I think it's great.
How about taking into account how consumption of this content has changed? This kind of stuff is great for mobile consumption, which, as far as I understand, is growing at a rapid pace.
Sure. I don't have anything to link on the spot but this was/is/has been foreseeable for some time. Although it's all very cool and shiny - most business applications of machine learning remain squarely in the territory of classic algos like GLM & forests (random, boosted trees etc. etc.). As a fun note, advances like these highlight that data scientists etc. will not be beaten by more complex automated methods, but simply by speed. Much like the filing system that 'runs' whatever you're using to see these words (https://www.youtube.com/watch?v=EKWGGDXe5MA).
Edit: to elaborate... single model training runs are possible to do quite fast now, but knowing how to tune hyper parameters remains the 'voodoo' of the field. But the best hyper params are also possible to discover through brute force: try every combination you can! Today, you can use various heuristics to improve this process, but either way, being able to train whatever X times faster just means we can search hyper parameter space that much faster. The robots are coming :)
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