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Machine Learning Is Shallow Compared to Math - Ryan Greenblatt

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Machine Learning Is Shallow Compared to Math - Ryan Greenblatt

Transcript

18 segments

0:00

I think ML is a very shallow [music]

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domain relative to math. I think in math

0:04

there's much more of a you find some

0:06

true deep abstraction. If you really

0:08

understand that thing, which is hard

0:09

[music] to understand, then you get

0:10

somewhere. Whereas I feel like the

0:11

things that are the equivalent of that

0:13

in ML are really like dumb

0:16

Like I'm like scaling laws. Like come on

0:17

guys, we can explain scaling laws really

0:19

quickly. And I think the like deepest

0:20

and most important concepts in [music]

0:22

math, for example, don't have the

0:23

property of like you can really

0:24

understand the underlying thing and why

0:26

it matters in a very short period of

0:27

time.

Interactive Summary

The speaker argues that Machine Learning lacks the deep, abstract complexity found in mathematics, characterizing ML concepts like scaling laws as relatively simplistic compared to the profound principles of mathematics.

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