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I find this article a bit odd, considering what the author is an expert in: generative imagery. It's the exact problem he discusses, the lack of a target that i

I find this article a bit odd, considering what the author is an expert in: generative imagery. It's the exact problem he discusses, the lack of a target that is measurable. Defining art is well known to be ineffable, yet it is often agreed upon. For thousands of years we've been trying to define what good art means. But you do not get good art by early stopping, you do not get it by injecting noise, you do not get it by regularization. All these do help and are essential to our modeling processes, but we are still quite far. We have better proxies than FID but they all have major problems and none even come close (even when combined). We've gotten very good at AI art but we've still got a long way to go. Everyone can take a photo, but not everyone is a photographer and it takes great skill and expertise to take such masterpieces. Yet there are masters of the craft. Sure, AI might be better than you at art but that doesn't mean it's close to a master. As unintuitive as this sounds. This is because skill isn't linear. The details start to dominate as you become an expert. A few things might be necessary to be good, but a million things need be considered in mastery. Because mastery is the art of subtly. But this article, it sounds like everything is a nail. We don't have the methods yet and my fear is that we don't want to look (there are of course many pursuing this course, but it is very unpopular and not well received. Scale is all you need is quite exciting, but lacking sufficient complexity, which even Sutton admits to be necessary). It's my fear that we get too caught up in excitement that we become blind to our limitations. Because it's knowing those limitations that is what gives us direction to improve upon. When every critique is seen as spoiling the fun of the party, we'll never be able to have anything better. I'm not trying to stop the party, in fact, I'm worried it'll stop.

mirekrusin 2y ago
I think he agrees more with you than you think. Evolution also picked it up as "satiation" - eating icecream feels good however you can't keep eating 1 per minute, same with pretty much everything. In art it means not hijacking everything for some local maximum.
- godelski 2y ago
  I think you're probably right tbh. But I do think this point could be stressed a bit more. Especially when we're talking about how easy it is to trick ourselves into thinking we're doing what's good enough.
ahartmetz 2y ago
> But this article, it sounds like everything is a nail In the process, acting somewhat like a generalization of the problem it describes: overly precise and narrow approaches to "improve" ineffable qualities. But the author seems to understand that - he comments on the absurdity of some direct transfers of ML methods to real world problems. I think he just added a bunch of not necessarily well solvable, but particularly suffering from "overfitting", example problems. It's a food for thought article, not a grand proposal.

mirekrusin 2y ago

I think he agrees more with you than you think. Evolution also picked it up as "satiation" - eating icecream feels good however you can't keep eating 1 per minute, same with pretty much everything. In art it means not hijacking everything for some local maximum.

godelski 2y ago
I think you're probably right tbh. But I do think this point could be stressed a bit more. Especially when we're talking about how easy it is to trick ourselves into thinking we're doing what's good enough.

ahartmetz 2y ago

> But this article, it sounds like everything is a nail In the process, acting somewhat like a generalization of the problem it describes: overly precise and narrow approaches to "improve" ineffable qualities. But the author seems to understand that - he comments on the absurdity of some direct transfers of ML methods to real world problems. I think he just added a bunch of not necessarily well solvable, but particularly suffering from "overfitting", example problems. It's a food for thought article, not a grand proposal.