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I enjoyed reading some of their results. I see some methodological issues here, however:
1) The test makes you distinguish between real words and a set of words they've made up in some way. As others have pointed out, some of them are pretty obvious non-words. I would expect the results to change depending on the method used to generate non-words.
2) Measuring the performance of a binary classification is a well studied problem with many metrics and approaches to quantifying performance (http://en.m.wikipedia.org/wiki/Binary_classification#Evaluat...). Subtracting the false positive rate from the true positive rate is not among them. The final score is not a consistent estimator of the fraction of words in their corpus you know.
My favorite explanation of this (posed instead as the Locomotive problem) is in Allen Downey's "Think Bayes," pp.22
It's online too, and worth reading!
An interesting bit on the computational complexity of solving this problem (with a slightly different scoring function):
http://cstheory.stackexchange.com/questions/1854/is-finding-...
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I think they've done something quite clever by (I infer) getting people to join up when they receive money. Venmo puts up an unnecessary wall by requiring that the payee sign up before they can be paid.
There are some orthogonal advantages and disadvantages though. On the upside, you'll reduce CO2 and other pollutants from inside your house -- it can get quite stuffy when you can't ventilate due to poor outdoor air quality. A downside is that it may also bring in air at a different temperature or humidity than you'd like (though that can also be controlled to some extent with a HRV/ERV.)