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What's the refinement process been like over the last five years? Many small problems or a few larger ones?

Damn. I bought rl.ai when I was a grad student but it's been laying fallow since I can't really blog about the stuff I'm working on right now. How does one go about selling their domain for millions of dollars?

A good idea and very cleanly implemented. I imagine that there's a ton of other possible applications that don't require much modification to the code. Thanks for sharing!

Who, whom?

Less tersely: this article is one in a long procession of journalists trying to exert control over tech. The opening example (Speech2Face, which they aver is transphobic) is inflammatory and utterly unrepresentative of the usual topics of AI conferences. The other references are far better, but the choice is revealing-- it's not so much an abstract concern about an unaccountable few exerting control from the shadows, but alarm that someone else might be muscling in on their territory.

This was great, actually. I don't program in Scala, but it was very interesting to hear about the difference between types as abstractions vs types as they are used.

For unfamiliar topics or when presented with uncommon insight, I believe rants, monologues, even diatribes are actually some of the best things to read.

Depends what you mean by "practical". Assuming you've done CV/ML stuff before, you could probably get something working pretty well over a weekend, and I think I could solve it completely with a bit more effort via 3D scanning + synthetic dataset generation... but unless you have cubic meters of lego to sort, doing it by hand would be faster, albeit less fun.

In the words of John von Neumann, there's a lot more that's known than is proved.

It's often frustratingly difficult to go from the known to the proven; still, just because something's not proved doesn't mean that scientists are ignoring it.

It would be nice if citing repositories were easier-- either for generating a reference for my own code or acknowledging when I've used someone else's code in my research.

There's tons of math and physics blogs that contain useful results that the author wanted to make available but didn't manage to incorporate into a paper. I wonder if there'd be any interest in a sort of GitHub for proofs? It could even use git, since (assuming consistency) isn't math just a DAG anyways (and therefore isomorphic to a neural net, as are all things).

> A theory is nothing but a way to turn (a finite number of) measurements into predictions.

That's a very nice way of putting it. Is there a particular book, lecture, or other resource you could recommend for this interpretation of theorizing?