I think we should be able to detect this with a smart data analysis program that is able to do SQL queries on the database that holds the LW voting records.

"may return to it if some people with app-building experience and an interest in this raise their hands"

I would be interested. :)

Here is a couple of Android games I did: https://play.google.com/store/apps/developer?id=Ricardo+Magalh%C3%A3es+Cruz

It's something simple done over a weekend for fun. I am a machine learning researcher.

I would be interested - but I would like to know more about the project and the people involved...

Cheers :)

Why is regulation ungood?

Because all regulation does is redistribute power between fallible humans.

I am missing a step in your argument. Why is redistributing power between fallible humans ungood? I mean, surely some humans are more fallible than others, some have more information than others, some have incentives to be fallible in particularly harmful ways, etc.

(I am not arguing in favour of any particular bit of regulation; I just don't see that "regulation is bad because it just redistributes things between fallible humans" makes any more sense than "trade is bad because it just redistributes things between fallible humans".)

Here is one reason, but it's up for debate:

Deep learning courses rush through logistic regression and usually just mention SVMs. Arguably it's important for understanding deep learning to take the time to really, deeply understand how these linear models work, both theoretically and practically, both on synthetic data and on high dimensional real life data.

More generally, there are a lot of machine learning concepts that deep learning courses don't have enough time to introduce properly, so they just mention them, and you might get a mistaken impression about their relative importance.

Another related thing: right now machine learning competitions are dominated by gradient boosting. Deep learning, not really. This says nothing about starting with deep learning or not, but a good argument against stopping at deep learning.

Most people are bad at understanding. As students they usually prefer to memorize things, because it is a strategy that works best in short term. When they grow up and become teachers, they recite things to students and expect them to memorize it.

In math, in addition to memorizing facts verbally, there is also a lot of procedural knowledge (solving equations). This is probably one of the reasons most people hate math. But even the procedural knowledge can be taught in the memorizing way; only the verbal memory is replaced by the muscle memory.

Understanding is a step yet beyond procedural knowledge. Most people don't get there; even most teachers don't.

And being able to explain stuff to beginners -- that's the ultimate art. It requires not only having a good understanding of the topic, but also being able to untangle it to a linear thread that can be gradually fed to a human and will allow them to build a proper model of the topic. This requires also an understanding of humans, and an understanding of understanding.

So why aren't most math textbooks better? I guess it's either because there are not enough good mathematicians who also happen to be good at explaining to beginners... or maybe the market for textbooks that teach understanding simply is not big enough.

If you want to learn a specific topic, maybe you could ask about it on LW.