[HN Gopher] Content moderation and fraud detection - patterns in...
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Content moderation and fraud detection - patterns in industry
Author : mr-ai
Score : 36 points
Date : 2023-03-07 10:50 UTC (1 days ago)
(HTM) web link (eugeneyan.com)
(TXT) w3m dump (eugeneyan.com)
| swyx wrote:
| > A typical approach is to generate synthetic data based on
| existing ground truth. DoorDash did this via random text
| augmentation... During model training, they had a ratio of 100
| synthetic labels to 1 actual label.
|
| i'm sure this is well understood by practitioners but this isn't
| intuitive to me. when you synthetically generate data, you are
| resampling an observed distribution, but you're not materially
| going to get out something different than what you put in. (just
| read the linked cloudflare post - ok so we are generating
| negative examples and there's a lot more negative than positive -
| but what if we accidentally generate positives?)
|
| whats a math/statistics intuition for why does generating
| synthetic data from existing data, to a 100 to 1 ratio, work so
| well?
| ted_dunning wrote:
| Paraphrasing slightly:
|
| > when you synthetically generate data ... you're not
| materially going to get > out something different than what you
| put in
|
| Well, yes and no.
|
| If I give you five points and tell you that they are actually
| samples from a normal distribution, you could easily generate
| lots of samples with the same mean and variance.
|
| If you were to then compute the mean and variance of this new
| and "improved" dataset, you would get results very nearly the
| same as the mean and variance of the original data. It would be
| fair to note at this point that the expanded dataset has no
| information that the original had.
|
| On the other hand, if you generate 10,000 synthetic points in
| the same way and compute the 99th percentile of these points,
| you will get a more interesting result than if you simply
| computed the 99th percentile of the original points.
|
| This makes it seem that the augmented dataset has better
| information in it than the original sample.
|
| In fact, the augmented dataset is only better because our
| estimation algorithm is deficient. A better algorithm would
| look at the mean and variance and find the 99th percentile of
| the corresponding normal distribution. This improved algorithm
| would get nearly the same result with the original or the
| augmented data.
|
| In this simple example, we can see and remedy the defect in our
| estimation algorithm, but with most machine learning methods it
| is much, much harder to remedy the algorithms' appetite for
| data. Augmentation is, however, a nice and simple alternative.
|
| A more complex example that isn't as trivial can be seen with
| the problem of finding a linear classifier for sets of points
| in the plane. If we don't have a lot of samples, we will often
| have the problem that there are an infinite number of solutions
| that will perfectly separate the sets in the training data. We
| would like a better answer, however, that is likely to work
| well on data we haven't seen yet.
|
| We have several choices of learning algorithms. One of the
| simplest is logistic regression, but that often doesn't
| converge well if the training set is small. We could add
| regularization and use fancier algorithms like ridge regression
| or LASSO or support vector methods to get a better result.
|
| OR
|
| We could just add samples that are "pretty near" each of our
| training examples. With a sufficiently sloppy definition of
| pretty near or with enough added samples, even the simplest
| algorithm will give us nice results. If the samples we add are
| normally distributed around the data, we get something like
| ridge regression. If the distance of the new samples from the
| originals are exponentially distributed, we get LASSO.
|
| If I were stuck on a desert island and had to implement
| regularization for linear classifiers, data augmentation is a
| pretty straightforward way to do it. For more complicated
| models or more complicated data, it rapidly becomes a really
| good approach.
| nighthawk454 wrote:
| It's more of a regularization method. It can generate a lot of
| "additional" data that is mostly known to have the same label,
| which can prevent the model from overfitting to the 1 instance
| you actually have.
|
| It's most successful with images - where it's really easy to
| generate significant modifications that are definitely still
| semantically the same. (Crop, translate, blur, desaturate, ...)
| How many true labels would you need to learn to be invariant to
| all that?
| swyx wrote:
| huge, huge fan of Eugene's blog. he is almost singlehandedly
| documenting the industry SOTA of dozens of ML efforts in
| practice, doing the hard work of collating, comparing and
| contrasting between published posts from various companies. Work
| that you'd have to do yourself if you were tasked to do something
| like this, but now you have an authoritative, well
| researched/argued source. For free. (I mean i guess you could
| also pay him for extra consulting)
|
| I think every sub-industry needs a Eugene.
| speedgoose wrote:
| > For example, "Is this nudity?" is more objective than "Is this
| adult content?"
|
| I know it's only an example, but it's annoying that nudity is
| such a problem on internet. It's easy to block all nudity but it
| prevents a lot of non adult content.
| shakna wrote:
| This is one that annoys me.
|
| There's Ancient Greek style statuary all over my town, penis,
| vagina and all. It's out in public and very few people care,
| because it's art, and in appropriate places like the public
| gardens. Hiding it away, would be an overreaction.
|
| Yet, somehow, seeing naked art, on a private device is somehow
| a terrible thing.
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