
#60 – Jaime Sevilla on Trends in Machine Learning
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Is There a Bottleneck in Machine Learning?
The main question is just being how much data can we practically use given the constraints we have on the amount of compute we have access to. Once access to compute scales fast enough, then you really could feasibly use more than an entire internet's worth of data. And at that point, um, access to just more data becomes the bottleneck because it's hard to get more performance out of a data set which doesn't don't grow.
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