>Like, the same AIs that can cure cancer can also create highly dangerous bioweapons or nanotechnology.

A good example of this:

https://www.nature.com/articles/s42256-022-00465-9

>I remember as recently as 2015 at ICLR/ICML/NIPS you’d get side-eye for even bringing up AGI.

You still do, imo rightfully so

Anything that scales sub-quadraticaly?

Anything "big-data"

>However there is a small Chance they acted in good faith and did Not see that the randomness in the GAN wont do anything.

Why is the default assumption malice?

Especially if the only benefit would be a workshop paper

>IMO reviewers at these journals/conferences need to be more mindful of this kind of plagiarism/low-effort submission.

Workshops in general have a very low bar, this surely wouldn't have been published in the main track.

Other than that I don't really see the point of this rant.

Yes there are a lot of bad papers, there are a lot of bad papers even in the main tracks, you just kind of get used to it.

It feels a lot like hitting down a well. Maybe these are some undergraduates doing their first research project and it's more about learning the methodologies and writing rather than very novel approaches.

Maybe try different regions?

>Running Linux on your desktop/laptop makes it significantly easier to run projects on the cloud

Just as a note, this can easily be done in a docker consider on windows as well.

Maybe we should consider adding more mods?

They're more theory focused imo, especially aistats

This being a conference, you can probably just fix it for the camera-ready version after acceptance

>DALL-E 2 also applies diffusion in latent space

Not really in the important part. Dalle2 uses diffusion in clip-"latent"-space and then conditions the pixel-diffusion model on the result.

However they still do a full diffusion pass in pixel-space, which is more complex than doing it in latent space, as LDMs do.

Exactly, the entire point of Latent Diffusion Models was to make them smaller and faster

>If you want to run GPU-accelerated neuroevolution in Brax or IsaacGym, then keeping everything on GPU is absolutely relevant

Do you have evidence for that?

I would assume that running brax rollouts for example would take 100x as long as the actual cmaes

Isn't SoundCloud basically danbooru for music?

There might not be a nicely accessible dataset yet, but that probably won't stop major players

Copyright issues never stopped research in the past, so why would it be different for music?

This sounds interesting, any previous papers you would recommend?

>i found that text to video problem is being actively researched and may not require as much compute as bare language models

There are always opportunities for research with little compute, usually this means your research has to avoid training new models, or at least avoid training from scratch.

However, text to video models are typically very compute extensive

>Set aside a validation set

Important: Ensure the duplicates are not shared between validation and train data

Oh that sounds very useful, you don't randomly happen to know a coded example of that?

The fact that big bench includes kanji-ascii art classification task is pretty funny.

But i guess if you want to have over a hundred tasks in a benchmark you run out of ideas at some point

Well, unless you actually train it and show some results, you're just putting out claims without much basis.

Don't get me wrong, it's cool in a way that the neural network provides code for another neutral network, but if it doesn't work properly it's not really helpful.

Works pretty well for what? You didn't say what this network is supposed to be used for

That's just the most basic VAE/AE possible, I can't imagine it works very well for anything.

You could get a better result via Google in the same amount of time

Apparently their parties also had strippers, but you had to know Russians to get in.

This is all just second hand though

There is a list of people who decided to share the paper and reviews despite rejection:

https://openreview.net/group?id=NeurIPS.cc/2022/Conference#rejected-papers-opted-in-public