Machine learning primitives in rustc (2018)

This seems fascinating, but how much is that bullshit? E.g the claimed performance numbers... Or many of the use cases... Indeed, many Heuristics are based on a prioris and would benefits from plasticity&#x2F;dynamicity à la PGO and from <i>classic</i> use of statistics to drive program optimizations decisions. I have big doubts that a neural network would be better but I would love to be proved wrong, this could be big. It&#x27;s from 1.5 years ago though.<p>Edit: this is the paper for replacing some major data structures with a neural network that take into input the key and output the position in memory. One big downside is that is has a margin of error which is very often unacceptable.<p>And I guess it would mostly work for static size (which are already O(1) structures not growables ones (otherwise it should be trained again? Baidu introduced continual learning with ernie 2 but the overhead must be so huge..)

As it is my system is so complicated that I can&#x27;t understand it. To litter a bunch of black boxes throughout removes any hope of really understanding the system. I think maybe we should strive for simpler systems in the first place, rather than try to optimize our already too-complex systems with a bunch of tiny function approximators that we don&#x27;t even really fully understand at an individual level.

I may be wrong, but my guess is that Jeff Dean’s presentation is what lead to Google’s efforts with Swift for Tensorflow.

Sounds like premature optimization.

This could use a (2018) in the title. The original discussion was started Dec 2017, and the last comment was made Jan 2018. The recent change was the discussion getting locked in Mar of this year.