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A comment on Are deep neural networks dramatically overfitted? (2019). Parent comment

Re:Are deep neural networks dramatically overfitted? (2019) by logicchains on Monday April 05, 2021 @07:37AM (#26697722)
>Like trying to learn from images instead of the 3d world. Humans learn to distinguish between objects in a 3-dimensional space using sight and interaction. This learning generalizably transfers to recognition in 2 dimensions.

If we use human "comprehension" as a reference point, then the relevant point of comparison should be the understanding a human can develop given the same inputs.

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Re:Are deep neural networks dramatically overfitted? (2019) by wongarsu on Monday April 05, 2021 @10:19AM (#26699288)
Sure, but how do you measure that? How do we figure out how much understanding a human can develop from only ever seeing 2d pictures, without any movement or interaction with a 3d world?

Most ML problems are things humans are quite good at and have a lot of context to draw from.

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Re:Are deep neural networks dramatically overfitted? (2019) by fractionalhare on Monday April 05, 2021 @07:40AM (#26697742)
Sure. But again, practically speaking, that isn't the reality of how we learn. The commenter wasn't refuting Kolmogorov complexity. They're just saying it's an extremely limited way of viewing the problem. Useful sure, but insufficient.
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