SkyKnit: How an AI Took Over an Adult Knitting Community - The Atlantic

23 bookmarks. First posted by audrey march 2018.

“Of course, that is what makes neural-network-inspired creativity so beguiling. The computers don’t understand the limitations of our fields, so they often create or ask the impossible. And in so doing, they might just reveal some new way of making or thinking, acting as a bridge into the future of these art forms.”
computers  culture  internet
may 2018 by leereamsnyder
SkyKnit: When knitters teamed up with a neural network & via…
april 2018 by tamberg
Prodded by a knitter on the knitting forum Ravelry, Shane trained a type of neural network on a series of over 500 sets of knitting instructions. Then, she generated new instructions, which members of the Ravelry community have actually attempted to knit. “The knitting project has been a particularly fun one so far just because it ended up being a dialogue between this computer program and these knitters that went over my head in a lot of ways,” Shane told me. “The computer would spit out a whole bunch of instructions that I couldn’t read and the knitters would say, this is the funniest thing I’ve ever read.”
april 2018 by spectrevision
Ribald knitters teamed up with a neural-network creator to generate new types of tentacled, cozy shapes.
culturaldigital
march 2018 by chrisunitt
Alexis C. Madrigal on how Janelle Shane set machine learning to work on existing knitting patterns to create new ones:
<p>here’s the first 4 rows from one set of instructions that the neural net generated and named “fishcock.”

fishcock

row 1 (rs): *k3, k2tog, [yo] twice, ssk, repeat from * to last st, k1.
row 2: p1, *p2tog, yo, p2, repeat from * to last st, k1.
row 3: *[p1, k1] twice, repeat from * to last st, p1.
row 4: *p2, k1, p3, k1, repeat from * to last 2 sts, p2.

The network was able to deduce the concept of numbered rows, solely from the texts basically being composed of rows. The system was able to produce patterns that were just on the edge of knittability. But they required substantial “debugging,” as Shane put it.

One user, bevbh, described some of the errors as like “code that won’t compile.” For example, bevbh gave this scenario: “If you are knitting along and have 30 stitches in the row and the next row only gives you instructions for 25 stitches, you have to improvise what to do with your remaining five stitches.”

But many of the instructions that were generated were flawed in complicated ways. They required the test knitters to apply a lot of human skill and intelligence. For example, here is the user BellaG, narrating her interpretation of the fishcock instructions, which I would say is just on the edge of understandability, if you’re not a knitter:

“There’s not a number of stitches that will work for all rows, so I started with 15 (the repeat done twice, plus the end stitch). Rows two, four, five, and seven didn’t have enough stitches, so I just worked the pattern until I got to the end stitch and worked that as written,” she posted to the forum. “Double yarn-overs can’t be just knit or just purled on the recovery rows; you have to knit one and purl the other, so I did that when I got to the double yarn-overs on rows two and six."

<img src="https://cdn.theatlantic.com/assets/media/img/posts/2018/03/fishcock/f0ba27c58.jpg" width="100%" /><br /><em>Fishcock: this is what it looks like</em>

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Ai  machinelearning  knitting
march 2018 by charlesarthur
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march 2018 by sbutts
RT : I never thought I'd see the day that wrote about LSG but here we are