Making Everything Awesome Again

Yeah, everything is boring. Streaming video, books, art—everything. It is the opposite of “everything is awesome” and, once again, it came about as a result of the internet attention economy. Or at least that is what Michelle Goldberg of the New York Times tells us, rounding up some thoughts from a literary critic as a first step and then jumping into some new social criticism that suggests the internet has ruined snobbery.

I was thinking back to the 1990s after I read the piece. I was a working computational linguist who dabbled in simulated evolution and spent time at Santa Fe Institute studying dreamy artificial life concepts. In my downtime I was in an experimental performance art group that detonated televisions and projected their explosions on dozens of televisions in a theater. I did algorithmic music composition using edge-of-chaos self-assembling systems. I read transgressive fiction and Behavioral and Brain Sciences for pleasure. I listened to Brian Eno and Jane Sibbery and Hole while reading Mondo 2000. My girlfriend and I danced until our necks ached at industrial/pop-crossover clubs and house parties. An early “tech nomad” visited us at one of our desert parties. Both in my Peace Corps service in Fiji and then traveling in Europe and Japan, I was without a cell phone, tablet, and only occasionally was able to touch email when at academic conferences where the hosts had kindly considered our unique culture. There was little on the internet—just a few pre-memes struggling for viability on USENET.

Everything was awesome.

But there was always a lingering doubt about the other cultural worlds that we were missing, from the rise of grunge to its plateau into industrial, and of the cultural behemoth cities on the coasts.… Read the rest

The Elusive in Art and Artificial Intelligence

Per caption.
Deep Dream (deepdreamgenerator.com) of my elusive inner Van Gogh.

How exactly deep learning models do what they do is at least elusive. Take image recognition as a task. We know that there are decision-making criteria inferred by the hidden layers of the networks. In Convolutional Neural Networks (CNNs), we have further knowledge that locally-receptive fields (or their simulated equivalent) provide a collection of filters that emphasize image features in different ways, from edge detection to rotation-invariant reductions prior to being subjected to a learned categorizer. Yet, the dividing lines between a chair and a small loveseat, or between two faces, is hidden within some non-linear equation composed of these field representations with weights tuned by exemplar presentation.

This elusiveness was at least part of the reason that neural networks and, generally, machine learning-based approaches have had a complicated position in AI research; if you can’t explain how they work, or even fairly characterize their failure modes, maybe we should work harder to understand the support for those decision criteria rather than just build black boxes to execute them?

So when groups use deep learning to produce visual artworks like the recently auctioned work sold by Christie’s for USD 432K, we can be reassured that the murky issue of aesthetics in art appreciation is at least paired with elusiveness in the production machine.

Or is it?

Let’s take Wittgenstein’s ideas about aesthetics as a perhaps slightly murky point of comparison. In Wittgenstein, we are almost always looking at what are effectively games played between and among people. In language, the rules are shared in a culture, a community, and even between individuals. These are semantic limits, dialogue considerations, standardized usages, linguistic pragmatics, expectations, allusions, and much more.… Read the rest