I do not see any evidence of this happening in the article. Also, OpenAI claims to have checked for contamination in every benchmark, so I don't see what the author's are trying to show here.

The complexity of the input wouldn't change in this case since it's just a screen grab of the display. Just that you'd need to do inference at a certain frame rate to be able to detect the cursor, which isn't that cheap with GPT-4. Now, I'm not sure what the latency or cost would be, I'd need to get access to the API to answer it.

It woulde be quite expensive to do tho. You have to do inference very fast with multiple images of your screen, don't know if it is even feasible.

Thanks for the input. I have seen some papers claiming SOTA in image segmentation using diffusion so I am also curious to see how they perform.

I have another question, if you don't mind. How difficult would it be to extend the code for image-to-image translation so that it works on 3D data (64x64x64 for example)?

Great work! Would the image translation work for (binary) image segmentation?

I would also like to know from anyone who might have a clue, can RLHF offer any significant boost to machine translation to offer better language-to-language translation?

What's used more in practice?

It already is trained on that. For a version only trained on scientific papers check Galactica.

I would take the best checkpoints (aka when the validation loss starts diverging from the training loss). Not the same number of steps because it can happen that the networks don't converge to a minima at the same time, some may be stuck somewhere for longer.

Do 5 or 10 fold cross validation in this case. Often used when there is not a lot of data.

I thought that it was possible to load SD using around 1 GB of VRAM right?

There are thousands of people now going through Schmidhuber's old papers looking for the new revolution in ML

Meta as a company might be shit, but their AI research is incredible.

I mean this is just speculation but I don't think that the spam rn is happening because Twitter is free. It's because it's profitable and as long as it's profitable it will happen. The only way I think Twitter can reduce spam by asking for user verification.

ML also has the benefit of scaling well. If you build a ML system to detect spam, I wouldn't say there's much difference in development costs if you 10x the user base and I do not see Twitter not having such a ML system. The model isn't going to be 1/10 as cheaper to train and the size of the engineering team isn't going to be 1/10 too.

As per ML in general, I doubt we are going to see a decline, all these layoffs in big tech and ML teams are basically the only ones that are still hiring over the board. I think that's it is pretty clear that investment in ML saves money by now, it can automate processes for a fraction of the cost. My experience is only in research tho, so I'd like to hear your thoughts on this.

He says unless you have to use ML, don't do it. I see no other way to detect spam for example other than ML.