Assertion certainty 4/5 debate potential 3/5

Alex Wang: Synthetic data is nowhere close to impacting machine learning progress

Alex Wang · 20VC: Scale Founder Alex Wang on How To Hire Incredible Talent Before You Are A Hot Company, Why Beating Competition Is Not As Clear Cut As Investors Believe & Why AI Is Under-Hyped Today In Terms of Total Impact · Sep 6, 2019 · at 14:00

Alex Wang, founder and CEO of Scale AI, discusses why synthetic data generation platforms fail to deliver in practical machine learning applications.

0:00 / 0:46exact quote · 46.1s
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“It hasn't really worked in practice in most situations, particularly when it comes to visual data or even textual data. It really hasn't worked, and a lot of the reasons why it's sort of limited in terms of its overall impact is that at the end of the day, whenever you generate data, there's always some sort of weird artifacts or bias in the synthetic data that you generate. You can see this, like, When people generate deepfakes, there's always some weird artifacts in that data. And the thing is, machine learning models are really good at picking up those little points of bias in a way that they basically won't generalize very well. And so all that is to say, I think it's this very alluring idea. And if you talk about a high level, it's very exciting. But if you really dig in on like, where is the state of the technology and how impactful is it really? It's nowhere close to actually impact the curve of machine learning yet.”

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