Owain_Evans

Owain_Evans's Comments

Neural nets as a model for how humans make and understand visual art
There's also lots of artistic concepts where the dependence on the medium is highly significant

Great examples. I agree the physical medium is really important in human art: see my Section 1.3.1.

It seems like it's not a surprise that NNs would be good at perspective compared to humans, since there's a cleaner inverse between the perceptive and the creation of perspective from the GAN's point of view than the human's (who has to use their hands to make it, rather than their inverted eyes).

I like the point about hands vs. "inverted eyes". At the same time, the GANs are trained on a huge number of photos, and these photos exhibit a perfect projection of a 3D scene onto a finite-size 2D array. The GAN's goal is to match these photos, not to match 3D scenes (which it doesn't know anything about). Humans invented perspective before having photos to work with. (They did have mirrors and primitive projection techniques.)

I think most humans have pretty good facility with creating and understanding 'stick figures' that comes from training on a history of communicating with other humans using stick figures, rather than simply generalizing from visual image recognition,

I agree that our facility with stick figures probably depends partly on the history of using stick figures. However, I think our general visual recognition abilities make us very flexible. For example, people can quickly master new styles of abstract depiction that differ from the XKCD style (say in a comic or a set of artworks). DeepMind has a cool recent paper where they learn abstract styles of depiction with no human imitation or labeling.

We might want to look for find concepts that are easier for humans than NNs; when I talk to people about ML-produced music, they often suggest that it's hard to capture the sort of dependencies that make for good music using current models (in the same way that current models have trouble making 'good art' that's more than style transfer or realistic faces or so on; it's unlikely that we could hook a NN up to a DeviantArt account and accept commissions and make money).

Currently humans play a major role in the interesting examples of neural art. Getting more artist-like autonomy is probably AI-complete, but I can imagine neural nets being more and more widely used in both visual art and music. I agree there’s great potential in neural music! (I suggest some experiments in my conclusion but there's tons more that could be tried).

Neural nets as a model for how humans make and understand visual art
You'd need a third and separate scheme to make Kandinskys, and then I'd just bring up another artist not covered yet.

Again, replicating all human art is probably AGI-complete. However, there are some promising strategies for generating non-representational art and I’d guess artists were (implicitly) using some of them. Here are some possible Sensory Optimization objectives:

1. Optimize the image to be a superstimulus for random sets of features in earlier layers (this was already discussed).

2. Use Style Transfer to constrain the low-level features in some way. This could aim at grid-like images (Mondrian, Kelly, Albers) or a limited set of textures (Richter). This is mentioned in Section 1.3.1.

3. If you want the image to evoke objects (without explicitly depicting them), then you could combine (1) and (2) with optimizing for some object labels (e.g. river, stairs, pole). This is simpler than your Kandinsky example but could still be effective.

4. In addition to (1) and (2), optimize the image for human emotion labels (having trained on a dataset with emotion labels for photos). To take a simplistic example: people will label photos with lots of green or blue (e.g. forest or sea or blue skies) as peaceful/calming, and so abstract art based on those colors would be labeled similarly. Red or muddy-gray colors would produce a different response. This extends beyond colors to visual textures, shapes, symmetry vs. disorder and so on. (Compare this Rothko to this one).

Maybe you could train an AI on patriotic paintings and then it could produce patriotic paintings, but I think only by working on theory of mind would an AI think to produce a patriotic painting without having seen one before.

I agree with your general point about the relevance of theory of mind. However, I think Sensory Optimization could generate patriotic paintings without training on them. Suppose you have a dataset that's richer than ImageNet and includes human emotion and sentiment labels/captions. Some photos will cause patriotic sentiments: e.g. photos of parades or parties on national celebrations, photos of a national sports team winning, photos of iconic buildings or natural wonders. So to create patriotic paintings, you would optimize for labels relating to patriotism. If there are emotional intensity ratings for photos, and patriotic scenes cause high intensity, then maybe you could get patriotic paintings by just optimizing for intensity. (Facebook has trained models on a huge image dataset with Instagram hashtags -- some of which relate to patriotic sentiment. Someone could run a version of this experiment today. However, I think it's a more interesting experiment if the photos are more like everyday human visual perception than carefully crafted/edited photos you'll find on Instagram.)

I was thinking of how some things aren't art if they're normal sized, but if you make them really big, then they're art.

Again, I expect a richer training set would convey lots of this information. Humans would use different emotional/aesthetic labels on seeing unusually large natural objects (e.g. an abnormally large dog or man, a huge tree or waterfall).

For "limited," I imagined something like Dennett's example of the people on the bridge. The artist only has to paint little blobs, because they know how humans will interpret them.

Some artworks depend on idiosyncratic quirks of human visual cognition (e.g. optical illusions). It's probably hard for a neural net to predict how humans will respond to all such works (without training on other images that exploit the same quirk). This will limit the kind of art the Sensory Optimization model can generate. Still, this doesn't undermine my claim that artists are doing something like Sensory Optimization. For example, humans have a bias towards seeing faces in random objects -- pareidolia. By exploiting this, artists exploit an image that looks like two things at once. (The artist knows the illusion will work, because it works on his or her own visual system).

My impression is that DeepDream et al. have been trained to make visual art - by hyperparameter tuning (grad student descent).

I think this first blogpost on Deep Dream and the original paper on Style Transfer already were already very impressive. The regularization tweak for Deep Dream is very simple and quite different from what I mean by "training on visual art". (It's less surprising that a GAN trained on visual art can generate something that looks like visual art -- although it is surprising how well they can deal with stylized images.)

Neural nets as a model for how humans make and understand visual art

I agree there's great variety and intellectual sophistication in art. My paper argues that the Sensory Optimization model captures *some* (not all) key properties of visual art. The model is simple, easy to experiment with (e.g. generating art-like images), and captures a surprising amount. That said, there are probably simple computational models that could do better and I'd be excited to see concrete proposals.

The paper does touch on some of your concerns. Feature Visualization can generate non-representational images (Section 1.2). I suspect these images could be made more aesthetic and evocative by training on datasets with captions that include human emotional and aesthetic responses (Section 2.3), and the same goes for art that's strongly rooted in emotions (Section 2.3.3). Do you have examples in mind when you mention "human experience" and "embodiment" and "limited agents"? I don't really address art where the artist has different knowledge/understanding than the audience and that's an important topic for further work (Section 2.3.4 is related).

I agree that lots of art (including some painting) is "heavily linguistic, or social, or relies on ... thinking on the part of the audience". Having a computational model that can generate this kind of art is plausibly AGI-complete. Yet (as already noted) it's likely we can do better than my current model.

(In general, I’m optimistic about what neural nets can create by Sensory Optimization and related techniques. Current neural nets have zero experience of the physical act of painting or drawing. They have no understanding of how animals or humans move and act in the world or of human values or interests. Yet even with zero prior training on visual art they can make pretty impressive images by human lights. I think this was surprising to most people both in and outside deep learning. I'm curious whether this was surprising to you.)

Regarding your last paragraph, I want to make some clarifications. I don't express a view about whether Deep Dream makes art. I claim that by combining ideas from Deep Dream and Style Transfer with richer datasets we could create something close to a basic form of human visual art. I don't claim that the creative process for humans is like optimization by gradient descent. Instead, humans optimize by drawing on their general intelligence (e.g. hierarchical planning, analytical reasoning, etc.).

Semantic Stopsigns

Even so, you'd hope people would notice that on the particular puzzle of the First Cause, saying "God!" doesn't help. It doesn't make the paradox seem any less paradoxical even if true. How could anyone not notice this?

Thinking well is difficult, even for great philosophers. Hindsight bias might skew our judgment here.

"About two years later, I became convinced that there is no life after death, but I still believed in God, because the "First Cause" argument appeared to be irrefutable. At the age of eighteen, however, shortly before I went to Cambridge, I read Mill's Autobiography, where I found a sentence to the effect that his father taught him the question "Who made me?" cannot be answered, since it immediately suggests the further question "Who made God?" This led me to abandon the "First Cause" argument, and to become an atheist."

– Bertrand Russell, Autobiography of Bertrand Russell, Vol. 1, 1967.