Good example of this is number of submissions to neurips/icml/iclr. In 2017 that curve was exponential.

There are also rings of reviewer fraud going on where groups of people in these niche areas all get assigned their own papers and recommend acceptance and in many cases the AC is part of this as well. Am not saying this is common but it is occurring.

It feels as if every layer of society is in maximum extraction mode and this is just a single example. No one is spending time to carefully and deeply review a paper because they care and they feel on principal that’s the right thing to do. People did used to do this.

Comments URL: https://news.ycombinator.com/item?id=45270148

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Also the core of the argument is wrong, ai is clearly displacing jobs this is happening today.

And then ultimately if you believe we have democracies in the west it means we are all individually culpable as well. It’s just a line of logic that becomes extremely distressing and so there’s a huge, natural and probably healthy bias away from thinking like that.

Then what if their memory is so good, they repeat entire sections verbatim when asked. Does that violate it? I’d say it’s grey.

But that’s a very specific case - reproducing large chunks of owned work is something that can be quite easily detected and prevented and I’m almost certain the frontier labs are already going this.

So I think it’s just very not clear - the reality is this is a novel situation, the job of the courts is now to basically decide what’s allowed and what’s not. But the rational shouldn’t be ‘this can’t be fair use it’s just compression’. Because it’s clearly something fundamentally different and existing laws just aren’t applicable imo

I don’t think it’s hopeless though, I actually think RL is very close to working because what it lacked this whole time was a reliable world model/forward dynamics function (because then you don’t have to explore, you can plan). And now we’ve got that.

On the ‘we need to do rl loop rather than a generative model’ point - I’d say this is the consensus position today!

Like a lot of the symbolic/embodied people, the issue is they don’t have a deep understanding of how the big models work or are trained, so they come to weird conclusions. Like things that aren’t wrong but make you go ‘ok.. but what you trying to say’.

E.g ‘Instead of pre-supposing structure in individual modalities, we should design a setting in which modality-specific processing emerges naturally.’ Seems to lack the understanding that a vision transformer is completely identical for a standard transformer except for the tokenization which is just embedding a grid of patches and adding positional embeddings. Transformers are so general, what he’s asking us to do is exactly what everyone is already doing. Everything is early fusion now too.

“The overall promise of scale maximalism is that a Frankenstein AGI can be sewed together using general models of narrow domains.” No one is suggesting this.. everyone wants to do it end to end, and also thinks that’s the most likely thing to work. Some suggestions like lecuns jepa’s do suggest to induce some structure in the arch, but still the driving force there is to allow gradients to flow everywhere.

For a lot of the other conclusions, the statements are literally almost equivalent to ‘to build agi, we need to first understand how to build agi’. Zero actionable information content.

This is obviously an extremely high level simplification, but that's the core of it.

This is obviously not true at this point except for the most loose definition of interpolation.

>don't rely on things like differentiability.

I've never heard lecun say we need to move away from gradient descent. The opposite actually.

I don't understand why the argument continues to be we will have a rich ecosystem of base open source models; unlike opensource ai which is individuals donating time, opensource ai requires someone to donate very large amounts of capital.

People don't want to admit this because of the massive concentration of power that becomes clear after you accept this.