zlacker

>>RobinH+(OP)
This does feel a bit like under grad introduction to statistical analysis and surprising anyone felt the need to explain these things. But I also suspect most AI people out there now a days have limited math skills so maybe it’s helpful?

>>fnordp+Are
As an ML researcher who started in physics (this seems common among physics/math turned ML people. Which Evan is included), I cannot tell you how bad is it... One year at CVPR when diffusion models hit the scenes I was asking what people's covariance was (I had overestimated the model complexity), and the most common answer I got was "how do I calculate that?" People do not understand things like what "pdf" means. People at top schools! I've been told I'm "gatekeeping" for saying that you should learn math (I say "you don't need math to build good models, but you do to understand why they're wrong"). Not that you need to, but should. (I guess this explains why Mission Impossible Language Models won best paper...)

I swear, the big reason models are black boxes are because we _want_ them to be. There's clear anti-sentiment mentality against people doing theory and the result of this shows. I remember not too long ago Yi Tay (under @agihippo but main is @YiTayML) said "fuck theorists". I guess it's not a surprise Deep Mind recently hired him after that "get good" stuff.

Also, I'd like to point out, the author uses "we" but the paper only has one author on it. So may I suggest adding their cat as a coauthor? [0]

[0] https://en.wikipedia.org/wiki/F._D._C._Willard

>>godels+5Ke
The front matter in Vladimir Vapnik’s book “Statistical Learning Theory” (first edition published 1995) has this quote:

*

During the last few years at various computer science conferences, I heard reiteration of the following claim:

“Complex theories do not work; simple algorithms do.”

One of the goals of this book is to show that, at least in the problems of statistical inference, this is not true. I would like to demonstrate that in the area of science a good old principle is valid:

“Nothing is more practical than a good theory.”

*

It’s seen in page xii of the front matter at: https://link.springer.com/content/pdf/bfm:978-1-4757-3264-1/...

Vladimir was a friend during this time, and I think about this quote a lot with regards to ML tinkering.