> In the very simplest example, there is no AI that understands natural language.
Come on, now - understanding natural language is pretty much the 0 yard line when it comes to AGI, the fact that it's not solved now doesn't tell us anything about how far away it is.
And I'd be on the lookout for massive advances in NLP over the next couple of years; there have been enormous leaps in 2018 alone when it comes to how good we are at understanding text (better applications of transformer models, high quality pre-trained base models, etc.), and now that there have been a few high-profile successes we're likely to see that field evolve just like computer vision has, even though I grant that it's a much harder problem in general.
Mainstream physicists, neuroscientists, and ML researchers are all more or less united in their view that Penrose is really overstepping the valid application of the arguments that he's using when he talks about this stuff. He really really wants quantum mechanics to be an important part of the intelligence/consciousness debates, so when he sees an indication that it could be relevant, he jumps to the conclusion that not only is it relevant, it is of paramount importance.
...because even if you end up at a hospital where your coverage is great, they often have doctors or providers that won't accept your insurance, and there's just about no way to know this beforehand.
There are plenty of places in CT where you can get a decent 2BR for $1100, be walking distance from a Metro North station, and have less than a 2 hr commute.
It may have been a nice story as viewed by some of us privacy-conscious people (I remember rooting really hard for it, and a couple of my most shy family members still use it), but it wasn't a viable strategy when trying to steal significant (>10%) market share from an incumbent, especially when the target audience was the general public.
G+ positioned itself against Facebook sort of like DuckDuckGo went against Google: we made the same product, but fixed X!!!, where X is some gripe about the incumbent's product that only a small percentage of the product's potential userbase cares about (privacy, in both of these cases).
That was (and is!) a fantastic strategy for DDG, for whom a fraction of a percent of all search traffic counts as massive, life-changing success. Google is not DDG, though. G+ would have needed a much larger share of the social networking market to be considered a win for Google, and the initial differentiation was not anywhere near clear enough to get there against a rival as strong as Facebook.
I also agree about the UI mess, and all that, of course, it was not a great product to use out of the gate.
I like this metaphor, it accurately captures a lot of what managing is about, though it's worth mentioning that the greatest difficulties are never, ever technical, they're always about people and feelings, which can surprise a lot of new managers.
This article outlines some great first things to look for, but something important is missing: sometimes, after trying everything, it turns out that your team just has some truly terrible, morale-sucking people on it, and they need to be fired before things will pick up. That's sort of the "file a compiler bug" option of last resort, but there really are a lot of toxic people like that out there, especially amongst programmers, and as a manager you'll need to be able to pick them out and deal with them. That's almost the most important part of your job.
Come on, now - understanding natural language is pretty much the 0 yard line when it comes to AGI, the fact that it's not solved now doesn't tell us anything about how far away it is.
And I'd be on the lookout for massive advances in NLP over the next couple of years; there have been enormous leaps in 2018 alone when it comes to how good we are at understanding text (better applications of transformer models, high quality pre-trained base models, etc.), and now that there have been a few high-profile successes we're likely to see that field evolve just like computer vision has, even though I grant that it's a much harder problem in general.