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When your success is determined by objective reality -- user adoption, economic viability -- you're incentivized to deliver. When your success is determined by convincing people that your ideas are right and important, you're incentivized to deceive...
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
you're right, but outside the scientific community it's the same with any emerging tech.
I'm glad @ykilcher sorts through the mess for the rest of us :)
Sometimes what "gets something right" isn't obvious to the community immediately, right? "Appearance" and "reality" blend in unexpected ways :)
Sorry, but in this case, generalization is wrong. It's researchers without proper scientific ethics that is in between, not the field itself. The scientific method works, also in Deep Learning. If it were otherwise, I would leave the field. We just need better ethics education.
Ethics is good. Nevertheless, who watches the watchers?
To improve quality, do you think having better benchmarks would help? ( i.e. objective benchmarks closer to real-world use cases )
#NLProc #BigData #MachineLearning #AI #DataScience #Edge #EdgeComputing #NLP #ml #DataAnalytics #HPC #inference #medtech #HealthIT #inference
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