[deeeeplearning]

>The industry is stagnated for exactly the reasons brought up: we don't know how to squeeze out the last mile problem because NNs are EFFING HARD and research is very math heavy: e.g., it cannot be hacked by a Zuck-type into a half-assed product overnight, it needs to be carefully researched for years. This makes programmers sad, because by nature we love to brute force trial-and error our code, and homey don't play that game with machine learning.

Uh what? You can literally finetune a Fast.ai model overnight to be borderline SOTA on whatever problem you have data for. 0 Math involved, isn't that exactly a hacker's wet dream?

[causalmodels]

Fast.ai works pretty well when you're working on standard tasks but starts to fall apart when you want to do something more exotic.

[deeeeplearning]

I doubt there's much in CV for instance that couldn't be achieved easily with fast.ai. You don't need to be doing exotic things to build a product.

[causalmodels]

Never said you need an exotic model to build a product nor that you couldn't do exotic things in fast.ai. Fast.ai is just a leaky abstraction.

[zwaps]

And yet, it might fail in actual application.

If every DGP could be captured by fast-tuning a sophisticated enough model, science probably would be solved even before DL.

[deeeeplearning]

We're talking about building useable products not solving science...

[zwaps]

My point being that the reason many products end up not usable, ref. accounts in this thread, is the same reason why science isn’t solved and doing ML correctly isn’t easy.