Date Tags links
A line drawing with the caption 'GARTER: someone who advertises or repairs dots in the sea. The drawing depicts a boat floating in the ocean next to a large disc, with a person on the boat holding a ladder descending to the disc. Other discs float in the water in the distance.
A version of the first image without the caption in which the outlines have been crudely coloured in with a graphics editor. Choppy waves and four fluffy white clouds have been added.
An image generated by the StableDiffusion system using the second image as a prompt. The image now has a pleasing brushtstroke texture and the boat seems to be a speedboat with two indistinct figures.
An image generated by the StableDiffusion system using a darkened and desaturated version of the second image as a prompt. This one is moodier and the red disc has a kind of yellow ruffled skirt and a yellow boss in the middle. The human figure in the boat is considerably larger and better defined, although their face is blurry

Ranjit commented on my drawing of Glossatory's definition of GARTER: "I find this one particularly evocative! Please do an oil painting next" so I colourised the original and fed it to StableDiffusion with different degrees of desaturation, and the prompt text "A man in a green boat holds a ladder to a red disc floating in a choppy sea under a blue sky with white fluffy clouds, yellow, green and purple discs in the distance, dramatic oil painting, winslow homer". I think the algorithm did pretty well.

I didn't post as many links this week because I was preoccupied helping to run the RSE Asia Australia Unconference - RSE referring to research software engineer, which is an attempt to carve out a professional identity which bridges people like myself, technical specialists who support research systems, and researchers who write and maintain software. It went really well and I plan on writing a longer post here in the next week or so.

It wasn't one of my daily links but this post about prompt injection attacks against GPT-3 is essential reading if you're interested in either machine learning or software engineering, and a great example of how the industry commits the same mistakes over and over again in different contexts.