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?
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.
Don't think there is much rationale behind all these models. It's more like P(would buy a vacuum | bought a vacuum) > P(would buy X | bought a vacuum) where X is a single product. Now P(would buy a vacuum | bought a vacuum) < sum(P (would buy X | bought a vacuum)) for X that is not a vacuum, but what would be the recommendation? Hey, you bought a vacuum, come back and buy some non-vacuum stuff?
For most recommendation UIs, you would need a hero item that make people want to click on. It might turn out that another vacuum is probably the best item for some people to click on, and go on to buy other stuff once they are on the site.
Be frugal. Don't buy a house unless you have at least 6 months of payments in the bank. Don't buy a car if you need to take a loan. Save as much you can. Build and maintain a strong network outside work (family/friends/professional contacts).
Every time I got into a difficult situation at work, I take a deep breath and tell myself: "Don't worry, give it your best shot to resolve this. And if that's not good enough, you know you can walk out that door and take a break for some time". It's been working well for me.
The original word2vec, written in C, could utilize all the cores available. It's actually refreshing to read that code, a true one person shop, hard engineering code :)
That's very childish and lame for Steve Huffman to say "Ellen wasn't the first Reddit engineer, so she probably lacked the expertise to do it, and even if she did, she was smart enough to not".
That's exactly the point. No one has ever offered such a document, so say what you want about Trump, he's taking a step towards transparency and accountability.