Peter's principle and whatnot, but I think there is something deeper. The manager positions are designed, by definition of the word, to manage, and the top goal is to extract values from workers. Managers (and product managers in tech companies) are encouraged to create a `healthy` tension with line workers (software engineers included) in work estimation and commitments. This is supposed to make the work challenging enough, but not so demanding that burn out the workers. The best managers can do that by providing the intellectual challenges and motivational goals. Most resort to processes and plain OKRs though (reflected in the worst ever software tool, JIRA. Also any line manager who's got any clue, meaning who can provide technical/business directions, would be quickly promoted to directors (where they are supposed to direct :-).
Protip for frontline managers: The percentage of time you spend on JIRA is negatively correlated to the chance of being promoted to the director level.
I am of the opinion that `true AI` is the science/engineering of understanding and replicating human intelligence. Why are we able to come up with abstract concepts from the surrounding physical environments? Why do we look at the stars and wonder what they are (and why)? How are we able to communicate with one another through pictures, words, writings, snapchat. Is that something special about our brains, our collective society, or something else, that enables such remarkable different behaviors from other any animal on earth? I don't know which direction we can start to go down to answer these questions, but collecting good data sets is probably as good as anything. Maybe we'll get the `quantity` of smarter specialized systems first, and once we get the `quantity`, maybe the `quality` will follow?
I agree. I think the fields of "computational cognitive science" and developmental psychology are the ones to look into to make progress towards the "hard fundamental problems". Some of the leading labs working on this are MIT CBMM (https://cbmm.mit.edu/, they have a nice youtube channel) and Berkeley Cocosci (https://cocosci.berkeley.edu/index.php).
Google Brain/DeepMind are also pushing some of those ideas. They must be, since they aggressively poach all the top researchers from those labs...
Ng approach is different: he wants a world powered by Deep Learning, so his goal is to make applied deep learning thrive. His strategy to do that: give those data-hungry models even more data, which is completely reasonable.
Those two approaches - fundamental research and applied deep learning - are often referred to as AI, causing much confusion.
Most of that CP did, at least where I grew up, was not advertisement but slogans. Like "in God we trust" in US. Nobody really takes it as an ad for God.
Could you quote a couple of them saying so? Because it doesn't match what I know about evangelicals, they don't exactly consider God something you can buy.
The AirBnB story about `literally a month from being homeless` is total BS. Both Chesky and Gebbia worked for a few years before starting the company and the other guy went to Harvard.
You can read the comments (and the linked papers) first. This is an advanced algorithm that could take days (or weeks) to fully internalize the details. One can't expect to just read the code and build a mental model of the program in one parse, no matter how expressive the variable names are.
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