12 essays have been commissioned.  (5 received so far).

Publication slated for December 2019.

Edition of 12 boxsets + one artist proof.

2 sold already. Tiered pricing. (Ah, the wistful poet tries to break evn)

Here’s a funny screenshot from spell-checking process:

Ask an AI algorithm to break an AI-created poem that contains the text-string:  O0000OOOO … and it suggests: O0000 OOOO

Note: there will be lots of neologisms left in the RERITES — even after the spell-check — because the algorithms ask for them, create them, suggest, bind, blend, merge, and contort the arbitrary rigid temporary skin of syntax.

I’m tired. Even though the computer did a significant amount of labour, the process still entailed writing-editing (or what I refer to as “carving the text”) about 60 hours every month. 2 hours a day,  6-7 days a week, between 6:30-8:30am, in the quiet meditative emptiness before the internet awakes, with email closed, and the web only used for searching, I carved.

Carving the text occurred inside a Sublime Text editor. Usually a first rough pass was made then the entire manuscript was re-carved (re-edited) during the final days of each month. As can be seen here, in the final footage of writing the RERITES:

All 12 RERITES books will be published by Anteism in Fall/Winter 2018.

Here’s the poster:

For the workshop I used the Rerites March corpus

Numbered each verse.

Text:

generated-2018-04-16T22-00-37-Screencast-2018-04-16-222348_WORKSHOP

+ RISD corpus [contributed by RISD students]  (+Jacket2)

Text:

generated-2018-04-16T22-25-20_Screencast-2018-04-16-223448_RISD_class

Revived the old ferment.

Changed the process.

Returned:

TXT: generated-2018-03-31T17-16-02_Screencast 2018-03-31 19:12:08

$ cd '/home/jhave/Documents/Github/awd-lstm-lm-master'
$ source activate awd-py36
$ python generate_April-2018_nocntrl_RELOAD.py --cuda --words=333 --checkpoint="models/March-2018_16mb_QRNN_nhid1500_batch20_nlayers4_emsize400.pt" --model=QRNN --data='data/March-2018_16mb' --mint=.95 --maxt=1.05

It’s as simple as that. The muse fueled by a million lines will lose to the muse capable of ingesting and contemplating and comprehending a billion lines: diverse, diffusive ecstatic anamnesis.

I’ve been making videos of the writing purpose. Here’s the first one:

3xspeed didn’t seem fast enough for our temporal performance obsessed generation so i moved to 4x speed.

More here:

https://vimeo.com/album/5062019

Txt excerpt:

 I can even hear your violent debates. Of prayer,
 weep, Whatever you say, я recognise me. paulist. That's
 right no.
 laughing?

Complete txt output:

generated-2018-03-08T19-35-32_Screencast 2018-03-08 21:10:55

Corpus retuned to reflect tech and other torque

100selected-archiveorg.txt
 Jacket2_ALL.txt
 99 Terms You Need To Know When You’re New To Tech.txt jhavelikes_CLEANED-again.txt
 ABZ.txt
 jorie_graham.txt
 Aeon_autonomousrobots_science-and-metaphysics_enemies_biodiversity.txt
 literotica.txt
 capa.txt ndsu.edu-creativeWriting-323.txt
 Cathay by LiBai pg50155.txt neurotransmitter.txt
 celan.txt nyt_china-technology-censorship-borders-expansion.txt
 citizen-an-american-lyric-claudia-rankine.txt patchen.txt
 citylights.txt
 Patti Smith - all songs lyrics.txt
 deepmind.txt
 poetryFoundation_CLEANED_lower.txt
 evergreenreview_all_poem.html.txt
 rodneyJones.txt
 glossary-networking-terms.txt
 swear.txt
 glossary-neurological-terms.txt
 Teen Love Meets the Internet.txt
 glossary-posthuman-terms.txt
 tumblr_jhavelikes_de2107-feb2018.txt
 Grief.txt tumblr_jhavelikes_EDITED.txt
 harari_sapiens-summary_guardian-review.txt
 twoRiver.txt
 I Feel It Is My Duty to Speak Out_SallyOReilly.txt

Process left to run for 36 hours on Nvidia Titan X:

(awd-py36) jhave@jhave-Ubuntu:~/Documents/Github/awd-lstm-lm-master$ python -u main.py --epochs 500 --data data/March-2018_16mb --clip 0.25 --dropouti 0.4 --dropouth 0.2 --nhid 1500 --nlayers 4 --seed 4002 --model QRNN --wdrop 0.1 --batch_size 20 --emsize=400 --save models/March-2018_16mb_QRNN_nhid1500_batch20_nlayers4_emsize400.pt

$ python -u finetune.py --epochs 500 --data data/March-2018_16mb --clip 0.25 --dropouti 0.4 --dropouth 0.2 --nhid 1500 --nlayers 4 --seed 4002 --model QRNN --wdrop 0.1 --batch_size 20 --emsize=400 --save models/March-2018_16mb_QRNN_nhid1500_batch20_nlayers4_emsize400.pt

$ python pointer.py --lambdasm 0.1279 --theta 0.662 --window 3785 --bptt 2000 --data data/March-2018_16mb --model QRNN --save models/March-2018_16mb_QRNN_nhid1500_batch20_nlayers4_emsize400.pt

$ python generate_March-2018_nocntrl.py --cuda --words=444 --checkpoint="models/March-2018_16mb_QRNN_nhid1500_batch20_nlayers4_emsize400.pt" --model=QRNN --data='data/March-2018_16mb' --mint=0.75 --maxt=1.25

Training QRNN using awd-lstm exited manually after 252 epochs:

-----------------------------------------------------------------------------------------
| end of epoch 252 | time: 515.44s | valid loss 4.79 | valid ppl 120.25
-----------------------------------------------------------------------------------------
Saving Averaged!
| epoch 253 | 200/ 1568 batches | lr 30.00 | ms/batch 307.76 | loss 4.29 | ppl 73.12
^C-----------------------------------------------------------------------------------------
Exiting from training early
=========================================================================================
| End of training | test loss 3.99 | test ppl 54.11

Complete training terminal output: TRAINING__Mid March Corpus | Screencast 2018-03-08 21:10:55

So as if there was any need: here is more.

Here’s the code:

jhave@jhave-Ubuntu:~$ cd '/home/jhave/Documents/Github/awd-lstm-lm-master' 
jhave@jhave-Ubuntu:~/Documents/Github/awd-lstm-lm-master$ source activate awd-py36
jhave@jhave-Ubuntu:~/Documents/Github/awd-lstm-lm-master$ python generate_March-2018_PARAGRAPHS.py --cuda --words=333 --checkpoint="models/SUBEST4+JACKET2+LYRICS_QRNN-PBT_Dec11_FineTune+Pointer.pt" --model=QRNN --data='data/dec_rerites_SUBEST4+JACKET2+LYRICS' --mint=0.95 --maxt=1.25

Here’s a video:

Here’s an excerpt:

Things, media, le openness, and tenacity.
Consigned to vivian for winked. Our mother
On a boat, a heaven. Mother no envied by also.
Papa you never suffered from the played. Cruz:
And, @ to a man that is rapaciousness, just
Another kind of fact, to say that again, i
Was medicine.’ some apartment. Now i brothers’
Often, the defeated of redemption her hair.
She was born, and months an wheelchair — old
Story, and her one—that makes it difficult
To get their basic writing in language and
Sons. Blood onto initial meadows she spoke
To the touch, and all found her a gray rearrangement
Collector referenda gone vita. I didn’t even
Want to save her with some recordings. For
They have dug for his life in crotch. Power

 

Here’s ALL poems in txt format:

TXT FILE: generated-2018-02-25T13-40-12_Screencast 2018-02-25 16:35:12

Here’s wht they will be processed into:

This will be used as the source for March RERITES

I am now utterly convinced of the impossibility of neural nets ever producing coherent contextually-sensitive verse, yet as language play, and a window into the inherent biases within humans, it is is impeccably intriguing and occasionally nourishing.

Text: Screencast 2018-02-04 18:52:12_PARAGRAPHS.txt.tar 

(awd-py36) jhave@jhave-Ubuntu:~/Documents/Github/awd-lstm-lm-master$ python generate_Feb4-2018_PARAGRAPHS.py –cuda –words=1111 –checkpoint=”models/SUBEST4+JACKET2+LYRICS_QRNN-PBT_Dec11_FineTune+Pointer.pt” –model=QRNN –data=’data/dec_rerites_SUBEST4+JACKET2+LYRICS’ –mint=0.75 –maxt=1

Averaged Stochastic Gradient Descent
with Weight Dropped QRNN
Poetry Generation

Trained on 197,923 lines of poetry & pop lyrics.

Library: PyTorch
Mode: QRNN

Embedding size: 400
Hidden Layers: 1550
Batch size: 20
Epoch: 478
Loss: 3.62
Perplexity: 37.16

Temperature range: 0.75 to 1.0

 You disagree and have you to dream. We
 Are the bravest asleep open in the new undead

Text: Screencast 2018-02-03 19:25:34

May the poem sit in the centre of the page like a stone.
May its margins clear space in the mind.
May consistent structure
deliver semblance of an archetype.
This must be poetry;
it is shaped like a poem.

Text: generated-2017-12-24T16-29-55_Screencast 2017-12-24 18:17:36

Text : generated-2017-12-24T13-59-27_Screencast 2017-12-24 14:25:22

Text : generated-2017-12-23T15-24-44_Screencast 2017-12-23 16:36:56

Text :  generated-2017-12-22T16-32-23_Screencast 2017-12-22 18:01:22