HEY ARTISTS and general people who are looking for creative opportunities -->
i'm sharing what might be my life's work, which is a spreadsheet of almost 400 places that hold regular open calls for residencies, shows, and funding
y'all deserve this and we gotta take care of each other. apply to things and get that support:
one thing about torchlight 2 is that if it's even vaguely close to bed time, it puts me to sleep almost immediately and i'll be jolted awake when it switches to the "resurrect here for 234 gold" screen. then again i'm playing on normal difficulty right now so dozing off while holding the regular attack isn't a bad overall strategy
also just had to teach it "variational", "autoencoders", "divinatory", "ontologies" and it's like, firefox textarea spellcheck, you really don't know my life
https://www.skynettoday.com/editorials/ai-coverage-best-practices.html this is really really good. I think I'd add: "Don't use words like 'yet' or phrases like 'the current state' to imply that General AI is the natural and inescapable teleology of AI research"
just realized that if I finish this project I'm going to become one of those people that needs to put that diagram of GAN architecture into their slides. I'm going to find myself explaining how GANs work to someone at a party, dear god
definitely bit off more than I could chew when it comes to making something that I feel is conceptually sound with this. the instant temptation is to go full "alien artifact" (and include GAN-generated body horror imagery or whatever), or at least make page layouts that resemble those of typical novels. but then the project feels like it's "about" layout, or "about" books as artifacts, which aren't topics that I personally care to spend time making arguments about at the moment
retraining with a serif font instantly makes it seem more ancient manuscript-ey
prototype page layout, sampling each word at random (with the fully trained model, or at least as fully as I care to train it)
another change I made was having it train on bitmaps of random words weighted by the frequency of the words in a reference corpus (i.e. in this case spaCy's unigram probabilities). the idea was that this would help it learn higher-frequency letter combinations and generate words that mostly replicate the "look" of English in use (rather than words in a word list). the drawback is that it looks like half the latent space is trying to spell out "the"
after a few thousand batches of training at a usable resolution on an actual GPU. recognizably "words" now—I wonder if increasing the depth of the model (or the kernel size of the convolutions?) would help it actually learn longer-distance dependencies...
latent space interpolation on a lower resolution version of this model after just 100 batches or so, using matplotlib's default colors because it looks vaguely metroid prime-y
procrastinating... with quantified-self text analysis
most common 25 words with counts in the URLs of my open tabs
i was on a podcast recently! it was a fun conversation (mostly about poetry and programming) https://corecursive.com/beautiful-and-useless-coding-with-allison-parrish/
Poet, programmer, game designer, computational creativity researcher. Assistant Arts Professor at NYU ITP. she/her
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