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Of course art is much more than randomly generated aesthetics, but I still think this project can be insightful, if not useful. Sure, it's not telling us what objectively makes great art, but I don't believe anything can.

But if statistically significant, it might tell us something more fundamental about the pure aesthetics assigned by people to patterns. It's more like the bouba/kiki experiment.

As I said elsewhere in this thread, I think this probably just approximates a description of the neurobiology of vision, from the retina to the occipital cortex. For example, newborns prefer high contrast images, but I presume no-one would consider them to have any understanding of art. The same for things like red / green preferences, which are likely to have their roots in the red / green cones in the retina. I'd argue it's not just useless, but lacks any meaningful insight into art.

I agree it's not particularly useful as far as art goes, but I think it can be insightful as far as visual aesthetics go.

The mistake to me is in implicating art, instead of just aesthetics. But Art Vote is a lot more catchy than Neurobiology Of Visual Aesthetics From Retina To Occipital Cortex Vote.

Accurate or not, it's a fun project.

It's sorely obvious that the creator of this project was a computer scientist, or someone who thinks in an overly technical manner rather than an artist, who thinks more about emotions and feel.

Is it?

https://qcpages.qc.cuny.edu/~chanusa/about/art.html

This page just confirms the above poster's hunch. A professor of mathematics who teaches one "Mathematical Design 128" class.

As linked on that page, he is an artist as well: https://christopherhanusa.com/

I don't know how "technical" or "emotional" that is, but it's certainly art.

I had a very different reaction. I had the sense this was an art world person (student or otherwise) who wasn’t technical enough to make it interesting. Many computer generated art projects are much more dynamic and produce much more passable art (https://ml.berkeley.edu/blog/posts/clip-art/). That said, I think I would have to agree with other comments here, I don’t think any deep conclusions could be drawn from these results. For starters the artworks are really repetitive and i would guess it biases respondents quickly toward the negative.

> I studied photography and sculpture, and initially the only thing that made any difference to whether I enjoyed a piece of art was interpretation by someone who's seen a lot of art. There were only two or three people who could do this effectively for me. Eventually, after talking to them a lot and seeing a lot of art, and thinking about it deeply, you start to develop your own tastes, and realise what kinds of things speak to you.

I think that might be specific to you, or at least not how everyone appreciate art. My enjoyment of a piece of art is directly linked to how I feel when I see it/think about it. I can enjoy something without explanations and not enjoy something with explanations. I don't think I'm in the minority here.

> If statistically significant results come out of this, I'd argue that any information it generates is going to be extremely shallow. It's like a kind of statistical fetish.

They will be, but not because of the nature of the project, but because of the implementation. There are only a few variations of a few basic things.

> This project seems under the misapprehension that there's some kind of "perennial philosophy" of art that can be uncovered by asking enough people about randomly generated images.

I'm not sure this is a misapprehension. The project itself is limited, but there are information to learn with randomly generated images. If people like art A and not art B, you could generate a graduation of A to B and see if people like art close to A (which could mean they like art "like" A), or if they only like A (which could mean that A has something specific).

>>> like a kind of statistical fetish

Live stats at web scale is a really fun problem to have. Un-optimized, using public cloud infra, round-trip latency for collecting data, calculating statistics, and render/viz to the user, it's shockingly slow! For crowd training Art GANs, latency probably doesn't matter, but seeing the live stats displayed is still magical to me. Imagine how fun ArtVote would be just using the public collection of images from MoMA, seeing the crowd evaluate a new work live ;)

I sort of love projects like this (i.e. the attempts to automate humanity itself by trying to naively quantify experiential subjectivity) exactly because they force the issue. Each silly example of "oh look, a machine can paint, make music, write stories" gives us an opportunity to say: that's not the point.