andersource

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by andersource 4y ago
I fully agree with your perspective, and I think there's a lot of cargo-culting in that area that explains your observations. Sure, if you're a FAANG collecting massive amounts of data comes almost for free, and it makes sense to
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by andersource 4y ago
This article strongly resonates with me (thanks OP!). Models trained on huge datasets are truly very impressive, and it's easy to jump on the hype train of "BIG MODEL GO VROOM" and overlook the cool / useful things one c
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by andersource 4y ago
SEEKING WORK | Remote | Data Science Need to solve a nonstandard optimization problem? Could use an interactive visualization to explore complex data? I'd love to hear about it! https://andersource.dev/ hi@<username
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Lasers in Space: remote-friendly physical co-op game made in 24h for a hackathon(andersource.dev)
2 points by andersource 4y ago|0 comments
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by andersource 4y ago
In case you're asking about the parent comment (s/Differential/Differentiable), it means the title should be "differentiable CV" (instead of "differential CV"). "Differentiable" describes some co
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How to determine which of two sequences of coin flips is real and which is fake?(stats.stackexchange.com)
1 points by andersource 4y ago|0 comments
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by andersource 4y ago
Overall agree, although regarding > large enough AI models cannot be "race-blind" because if you remove race as a feature, they will be able to infer it anyways from proxy features In theory using a gradient reversal layer and
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by andersource 4y ago
> I'm kind of looking for a unified data storage with visualization options. That phrase reminded me of elasticsearch & kibana[0], though that may be an overkill (the elastic stack is a lot of work). Also seems like datadog has
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by andersource 4y ago
I agree in principle, although I think to get an effect size similar to what DK observed you'd need quite large noise. Which again comes back to the test reliability.
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by andersource 4y ago
Right. Just to be clear, "an effect like this" is (comparatively) unreliable tests, not some elusive statistical phenomena as implied by the original article. I'd have no issue if the author had called the article "the D
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by andersource 4y ago
Thanks John! Very interesting. What your simulation includes and the original article didn't (and I didn't touch at all in my article) is the statistical reliability of the tests they administered. Where you got a CC of -0.38 you
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by andersource 4y ago
Hey omnicognate, good to see you here, appreciated our previous discussion. What you're saying is that we need to verify the statistical reliability of the skill tests DK gave, and to some extent that we need to scrutinize the assumpti
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by andersource 4y ago
I don't think there's a definite one-size-fits-all answer to that, it will depend on what's most enjoyable to you, what jobs you'll be applying to etc. My personal recommendation (which is based on my n=1 subjective expe
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by andersource 4y ago
Mostly in reaction to this thread: https://news.ycombinator.com/item?id=31036800
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I can't let go of “The Dunning-Kruger Effect is Autocorrelation”(andersource.dev)
15 points by andersource 4y ago|3 comments
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by andersource 4y ago
"Null hypothesis" comes down to agreeing on a prior that is "reasonable". Most of the time, that indeed means not assuming dependencies, e.g. when testing the outcomes of a medical treatment. But that's not always t
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by andersource 4y ago
Sorry, but this doesn't make sense to me. There has to be a boundary - a person who got all the answers wrong can't underestimate their performance, and a person who got all the answers right can't overestimate their performa
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by andersource 4y ago
I strongly disagree, not necessarily with everything (e.g. I don't have access to the raw data from the DK experiment, don't know how well they performed all the analysis leading to the plot). But the plot itself is not inherently
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by andersource 4y ago
Thanks! No worries, I appreciate you writing this. > I can see the objection that a more convincing example might be to demonstrate the "false" DK effect in an example that does have some signal More than that - as it is, the a
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by andersource 4y ago
> 1. As mentioned in another comment, X and Y can't be independent and correlated at the same time And as I replied to that comment, "sorry, my bad - Y - X and X will be negatively correlated." > 2. The point of the art
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by andersource 4y ago
Right, sorry, my bad - X and Y - X will be negatively correlated
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by andersource 4y ago
Here's my main point of confusion - what does the random data experiment have to do with the DK results? As stated elsewhere DK has 2 claims: 1. Low-skilled people overestimate their performance and skilled people underestimate their p
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by andersource 4y ago
> The difference between bias and variance. But when you're bad at the skill and can't underestimate, they look the same. > That's the hypothesis that's being tested And evidence from DK supports it.
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by andersource 4y ago
> DK doesn't mean no correlation, it means inverse correlation. It's the correct analysis at the bottom that shows what no correlation actually looks like (at least no correlation in tend, there is heteroskedasticity). Not sure
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by andersource 4y ago
> But I think the author’s right that obviously nothing psychological is happening here. There’s the psychological effect of no one being able to assess themselves, but the fact that unskilled people overestimate themselves in this world
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by andersource 4y ago
The one reproduced in the article doesn't show the density of points, so it's hard to conclude anything from it. Figure 4 from the Nuhfer et al. paper does seem, to me at least, to support DK's conclusions.
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by andersource 4y ago
> The null hypothesis for Dunning-Kruger isn't "people of all skill levels are good at estimating their performance" it's "people of all skill levels have equal bias" in estimating their performance" OK
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by andersource 4y ago
Not necessarily. If for example y =~ x, comparing x - y to x would yield zero correlation, which to me is what DK is all about - people _aren't_ as good as we would expect at estimating their own skill.
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by andersource 4y ago
This is my understanding as well.
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by andersource 4y ago
> The fact that the statistical artifact is seen in completely uncorrelated data is only shown as a demonstration that it is not itself evidence of the claimed effect I don't understand this part. "Completely uncorrelated data&

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