[rococode]

I think the academic side will start shifting towards research on efficiency and speed while companies will continue to push the cutting edge.

In the NLP space there's been a lot of work recently around reducing model sizes, since they've started to reach the point where model weights sometimes don't fit in the memory of most GPUs.

There's also projects like MarianNMT which completely abandon Python and write heavily optimized models with fast languages that can run quickly and accurately even without GPUs. I think we'll see a lot more of this, though of course there's a pretty big barrier in the sheer rarity of being good at both deep learning research and writing optimized low-level code.

[Nasrudith]

It would be a bit ironic for universities to compete on efficency and speed given those are two things companies optimize on. Not impossible of course, theory and encouragement to a bit more abstract could lead to providing that.

As for writing low level code, I thought that was something usually handled by the compiler or where even the advanced high performance for high price mostly tweaked the compiler after analyzing the output. Not my direct space so I speak with no authority.

[knodi123]

> I think the academic side will start shifting towards research on efficiency and speed

Constraints are the mother of creativity.

[bluenose69]

Julia is not hard for a Python programmer to pick up, and it can be very fast.