hi fediverse, I just posted transcripts of a few talks and lectures I've given over the past few years, mostly concerning the connections between machine learning, language and poetry: https://posts.decontextualize.com
(notes and summaries in individual posts below)
"Language models can only write poetry" is an attempt to categorize the outputs of language models (from Tzara's newspaper cutups to Markov chains to GPT-3) using speech act theory. Touches on the immersive fallacy, Jhave's ReRites, Janelle Shane's AI Weirdness, Kristeva, etc. https://posts.decontextualize.com/language-model
(excerpted from my Vector Institute / BMOLab "distinguished lecture")
"Rewordable versus the alphabet fetish" outlines how conventional spelling games (like Scrabble) are based on cryptography (via Poe's The Gold-Bug) and mystical alphabetical metaphysics—and how we attempted to circumvent those influences in Rewordable, a board game I co-designed a few years ago. includes a very adorable illustration I found of neoplatonist medieval philospher John Scotus Erigena https://posts.decontextualize.com/rewordable-versus/
(originally a talk at NYU Game Center's Practice conference)
I've got a workflow going now where I can create a presentation and a nicely formatted transcript (i.e., my speaker notes) from one file, and post it to the web straight from my notes app (Zettlr), so hopefully it's easier for me to do all this in the future
@aparrish Hi Allison, that url is wrong for some reason. I assume it's this one:
@wim_v12e yeah I copied them wrong apparently, guess I should have checked the links. both urls should work now!
@aparrish these are great! as an aside, following up on one of the references in this, do you know when people started refering to bigram (etc) models as "markov chains"? hayes' scientific american article doesn't use the term (he says "eddington monkey", which I much prefer)
@mewo2 that is a really good question and I don't know! Cramer in _Words Made Flesh_ dates it back to Theo Lutz?
@aparrish @mewo2 This 1961 paper is the earliest relevant reference I can find. This biography of the author, Hockett, claims that he was the person who pioneered application of Markov chains to language and grammar in 1955:
earliest generation of text by markov chain: claude shannon, 1948: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf (calls it a "markoff process")
earliest suggestion of doing this creatively: francois le lionnais, 1963
then by 1968, kawano can say "many computer-aided works of art have been experimentally produced with this method"
the lutz reference is interesting - cramer claims he used markov chains in 1959, but afaik he used a madlib/grammar? http://stuttgarter-schule.de/lutz_schule_en.htm
@darius @aparrish then the whole business seems to have been independently reinvented by bennett in 1977: https://www.jstor.org/stable/27848169 (no reference to markov or shannon, who you'd sort of assume a physicist who previously worked at bell labs would have heard of)
hayes picks it up from bennett, then kenner from hayes, and suddenly you have a second independent tradition
@darius @aparrish kenner and o'rourke (1984) do make reference to shannon, but none of the work from the 60s. kenner was a literary critic who wrote about modernist writers in france, so it's likely he was aware of the oulipo manifesto, but he probably didn't know what a markov chain was, so may not have understood what le lionnais was saying
more markov chain history
kawano had made visual work using markov chains from 63/64: https://direct.mit.edu/leon/article/52/1/75/46683/ and hiller composed music in 57: http://www.medienkunstnetz.de/works/illiac-suite/ but both were using manually constructed transition probabilities, not derived from a "text"
bense wrote about shannon's ideas in 1960, but it seems like only for analysis, not generation: https://monoskop.org/log/?p=16249
I'm now actually unsure if anyone wrote a markov chain text generation computer program pre-1977
more markov chain history
@mewo2 @aparrish Not sure if you found this survey paper, it's mostly about music, but it's nice and I like the broad conclusions and it amuses me (especially given Allison's teaching experience) that there is a big focus on "hey Markov can be approximately as good as AI if you do it right" in 1988
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