327 karma · joined September 5, 2010
But it's North American birds, not sure if you specifically want to cover birds that show up in Finland or if that was just your inspiration.
There are a couple apps that do what you're talking about, but they're both North American birds only as well.
http://www.birdsnap.com/ (I was involved in this one)
They deserve shit for lying.
FP / (FP + TN)
Where FP is number of false positives, and TN is number of true negatives. So it's a third option:
- Out of 1000 actually negative samples, 50 were tested as positive.
So in the case of 1000 samples, 949 correctly testing as negative, 50 incorrectly testing as positive, and 1 correctly testing as positive, the false positive rate is 50 / 999.
The article seems to quietly conflate the fastest 15% of drivers ignoring the limit with "most drivers" ignoring the limit. Which is odd. However, even under the _archon_ model, increasing the limit would lead to a reduction in speed variance.
> ... we lie, on average, three times during a routine ten-minute conversation with a stranger or casual acquaintance. Hardly anyone refrains from lying altogether, and some people report lying up to twelve times within that time span.
It sounds like there's a large variance; maybe the author is on the "twelve times" side of the distribution. Or maybe I'm naive. I can't imagine telling someone I like a restaurant just because he does, or giving a false compliment without a concrete reason.
(I'm American.)
The paper includes a quantitative evaluation, in which they take a set of 30 distractor face images not used elsewhere in the study, and for each of them determine whether the reconstructed face is more similar to it, or to the original face the subject was looking at. On average, the reconstructed face is closer to the correct face than the distractor 62.5% of the time.
So it's better than random, and I think it's pretty cool work, but the quality of the reconstructions is pretty terrible, considering that a randomly chosen distractor will usually be very different from the test face (~half the time opposite sex, frequently different race, different age, etc). For comparison, it would be interesting to evaluate some simple, obviously terrible reconstructions by the same metric. For example, we could "reconstruct" the face as a image that is a single, solid color, the average RGB value of the pixels in the original face. Another "reconstruction" that it seems would very likely do better under this evaluation metric is something like the "race-gender-age" of perp descriptions in the news ("white male in his thirties").