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Research fields come in two flavors: those where your career progresses when you actually get something right (reality-grounded), and those where it's enough to create the appearance of getting something right (belief-grounded). Deep learning research is somewhere in between
As a result, the deception factor in deep learning papers is often quite high
Compared to what? Mathematics? Perhaps... Philosophy? Definitely not. I think that in even objective looking areas like mathematics, people just publish a lot of inconsequential random stuff. You still need to convince people that your research is "interesting enough".
The fact that not all truths (even mathematical ones) are equal is amazing.
Imo, it's gotten quite a lot worse in recent years. I think the fundamental issue is that we've outgrown our problem framings. When you're near human performance on a task of human mimicry, it's hard to say what progress looks like.
I think CLIP did an awesome job combatting some of these issues. They really took it upon themselves to look beyond simple classification targets and had excellent results. Still a long way to go though.
Testing of ideas helps in research but some people are afraid of it. However,research requires putting some skin in the game and is not about ego but about truly understanding how things work.A well reported failure of an idea help us gain better insight than a non tested one.
As always interesting views and good points. Thanks!
you mean like emotion recognition technology and the perversion that digital facial recognition is doing to humans all over the world?
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