You can add location information to your Tweets, such as your city or precise location, from the web and via third-party applications. You always have the option to delete your Tweet location history. Learn more
Of course, if you can amass a sufficiently dense sampling of situations within a sufficiently narrow domain, you can always train a machine model -- but it will break down as soon as it encounters anything it has never seen before. That's modern deep learning.
And for many high-value real-world tasks, that's just about every day. Consider self-driving cars, or domestic robotics. You can't enumerate the set of possible situations a driver might ever encounter -- billions of miles are not nearly enough.
You can't even enumerate the set of possible kitchens a robot might operate in. If you want to ever be able to deploy a L5 self-driven system or a human-level domestic robot, you have to figure out how to implement broad cognitive abilities -- beyond task-specific skills.
I promise you they have not. "Self-driving" is not a binary classification. That's what the L categories try to capture. Fchollet is talking about c3po level flexibility. Not deep nets that can recognize pedestrians and obey traffic laws.
Humans make mistakes too, they also have accidents when given a situation they haven't faced, self driving cars don't need to be perfect for all possible situations, they need to be better and more reliable than humans.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.
