Hidden traces of humanity: what AI images reveal about our world

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Hidden traces of humanity: what AI images reveal about our world
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The long read: As generative AI advances, it is easy to see it as yet another area where machines are taking over – but humans remain at the centre of AI art, just in ways we might not expect

As generative AI advances, it is easy to see it as yet another area where machines are taking over – but humans remain at the centre of AI art, just in ways we might not expect

Stable Diffusion generates four images in response to any prompt; I cheated slightly by just choosing my favourite to proceed. From the centre of the frame, a decently realistic tabby cat stared me down, green eyes glowing wide, mouth hanging open to display a salmon-pink tongue.

Clip Interrogator described this tableau as “a painting of a cat sitting on a toilet, PlayStation 2 gameplay still, in style of pop-art, by Ignacy Witkiewicz, the fool tarot, inspired by Phil Foglio, punkdrone, molecular gastronomy, app, bong, persona 5, text: roborock, destroy lonely, dog, ASCII, 1 8 2 4, tarot card design.” Destroy Lonely is not a command, I learned, but a trap artist from Atlanta. Roborock is a Roomba-like automated vacuum cleaner.

By 2015, algorithmic processes were able to form simple sentences or phrases to describe an image. Patterns of pixels identified as, say, “cat” or “cup” were matched with linguistic tags, which were then translated into automated image captions in natural language.

In contrast, ImageNet, a project initiated in 2006 by the computer scientist Fei-Fei Li, had the immodest aim of “map out the entire world of objects”. The dataset contains upward of 14m annotated images, organised into more than 100,000 “meaningful categories”. It also employed the labour of more than 25,000 workers via Mechanical Turk. While 100,000 is an astonishing number of categories, it’s extraordinarily small when you consider the visual complexity of the world.

Rather than fix the foundational issues in the datasets, these tools’ developers have attempted to obscure them through “debiasing”, or coding in safeguards to ensure diversity – which is how we get Gemini, Google’s recently rebranded genAI tool, producingAs text-to-image genAI tools grew increasingly sophisticated, the surrounding discourse grew increasingly alarmed: “Generative AI Is Changing Everything”; “Did Picture-Generating AI Just Make Artists Obsolete?”; “Can AI End Your Design...

The best path forward for labour protections might be to ensure that those already trained in crafting communicative, compelling images – illustrators, artists, photographers, photo editors – will be best at using these systems. genAI tools are built on natural language processing, the language that tends to result in the best outcomes reads as far from “natural”.

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