How far can we scale up? Deep Learning's Diminishing Returns (Article Review)

#deeplearning #co2 #cost

Deep Learning has achieved impressive results in the last years, not least due to the massive increases in computational power and data that has gone into these models. Scaling up currently promises to be a reliable way to create more performant systems, but how far can we go? This article explores the limits of exponential scaling in AI, and what people are doing to get around this problem

OUTLINE:

0:00 - Intro & Overview

1:00 - Deep Learning at its limits

3:10 - The cost of overparameterization

5:40 - Extrapolating power usage and CO2 emissions

10:45 - We cannot just continue scaling up

13:25 - Current solution attempts

15:25 - Aside: ImageNet V2

17:50 - Are symbolic methods the way out?

Paper: https://spectrum.ieee.org/deep-learni...

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