When machine learning matters

I joined Spotify in 2008 to focus on machine learning and music recommendations. It’s easy to forget, but Spotify’s key differentiator back then was the low-latency playback. People would say that it felt like they had the music on their own hard drive. (The other key differentiator was licensing – until early 2009 Spotify basically just had all kinds of weird stuff that employees had uploaded. In 2009 after a crazy amount of negotiation the music labels agreed to try it out as an experiment. But I’m getting off topic now.)

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Subway waiting math

Why does it suck to wait for things? In a previous post I analyzed a NYC subway dataset and found that at some point, quite early, it’s worth just giving up.

This isn’t a proof that the subway doesn’t run on time – in fact it might actually proves that the subway runs really well. The numbers indicate that it’s not worth waiting after 10 minutes, but it’s a rare event and usually involves something extraordinary like a multi-hour delay. You should roughly give up after some point related to the normal train frequency, and 10 minutes is not a lot at all. Conversely if the trains ran hourly, it probably would had been worth waiting an hour or more. My analysis gave me a lot of respect for the job MTA is doing.

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Dollar cost averaging

(I accidentally published an unfinished draft of this post a few days ago – sorry about that).

There’s a lot of sources preaching the benefits of dollar cost averaging, or the practice of investing a fixed amount of money regularly. The alleged benefit is that when the price goes up, well, then your stake is worth more, but if the price goes down, then you get more shares for the same amount of money. According to Wikipedia, it “minimises downside risk”, about.com says it “drastically reduces market risk”, and an article on Nasdaq.com claims that it’s a “smart investment strategy”.

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NYC subway math

Apparently MTA (the company running the NYC subway) has a real-time API. My fascination for the subway takes autistic proportions and so obviously I had to analyze some of the data. The documentation is somewhat terrible, but here’s some relevant code for how to use the API:

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