To me all AI debate these days are just a regurgitation of "glass half full or half empty" discussions. Yes, LLMs are far more intelligent than anyone anticipated them to be by this point in time, and no they aren't general intelligence. The constant back and forth between these two groups can essentially be replayed year after year and not much has changed in terms of arguments.

It's just like a girlfriend. "No I will not be offended if you did this" but then goes ahead and takes it personally when you do it.

>I'm not sure that's recommended, it's not immoral or illegal.

Humans may not consider that design choice immoral but I don't want to offend our soon-to-be AI overlords. Maybe I'll ask chatGPT if it will judge me for doing that.

Thanks, that clarifies some things. I have also seen a parameter in the ViT head that simply returns the first token representation instead of averaging across all tokens. I never understood why that made sense, and why only the first token and not some other random token.

This also reminds me of another confusion I have about transformers, would they lose meaning if we gradually compress the embedding size after every mlp in the transformer block?

I liked a reply to jurgen from some twitter user saying if you have already solved agi now would be a good time to bring that up.

Don't vision transformers do this where instead of gradually compressing the input like a typical convnet, they maintain the high dimensionality throught all the blocks of the deep network, and then simply using a global pooling at the end to compress the "channel" dimension into a compressed representation? I have no idea why that works, but we have seen it does work and the model still learns despite the gradients flowing through this average pooling layer at the end. Would be great if someone can help clarify this for me.

Latent spaces are the new real estate

>It's about enabling good scholarship and guiding researchers.

You just described the role of a discriminator in a gan

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>uneducated and unqualified reviewers

op got an invite because they published there before, its on merit not a random review request. besides, the question solely focuses on how reviewing benefits the reviewer, it doesnt seem to cover the whole picture surrounding peer review system

All of the profits, none of the work. How do they get away with this model?

Think of it as a GAN, you train generators when you publish and train your discriminator when you review. Do these processes together alternately and you can see what you'll slowly converge to :)

What? How does the author identify you from reviewers being able to see other reviewers?

>interview at MAANG

*MANGA

>what they really need is github copilot

This would be such a cool reply email to a company asking for a coding interview round. Just send a link to copilot and tell the company this should fit your job profile better than I would.

https://github.com/vturrisi/solo-learn

Academic research in GANs has slowed down, but I believe they still have more industrial applications currently in use by tools like photoshop