More people who aren’t there

Remember that website full of photos of fake faces? Well, Dr Julian Koplin from the University of Melbourne has been combining those AI generated portraits with AI generated text, and now there’s a whole city of them.

Humans of an unreal city
These stories were composed by Open AI’s GPT-2 language model and AllenAI’s Grover news generator, which were given various prompts and asked to elaborate. My favourite results are recorded here – some lightly edited, many entirely intact. The accompanying photos were generated by the AI at This Person Does Not Exist. They are not real humans, but you can look into their eyes nonetheless.

As he explains in this commentary on the ethics of the project, some of the results are convincingly human.

more-people-who-arent-there

The very human language of AI
AI can tell stories about oceans and drowning, about dinners shared with friends, about childhood trauma and loveless marriages. They can write about the glare and heat of the sun without ever having seen light or felt heat. It seems so human. At the same time, the weirdness of some AI-generated text shows that they ‘understand’ the world very differently to us.

I’m worried less about the machines becoming sentient and taking over, with their AI generated art and poetry, and more about the dangers these tools pose when in the hands of ill-intentioned humans.

Meanwhile.

100,000 free AI-generated headshots put stock photo companies on notice
It’s getting easier and easier to use AI to generate convincing-looking, yet entirely fake, pictures of people. Now, one company wants to find a use for these photos, by offering a resource of 100,000 AI-generated faces to anyone that can use them — royalty free. Many of the images look fake but others are difficult to distinguish from images licensed by stock photo companies. […]

Zhabinskiy is keen to emphasize that the AI used to generate these images was trained using data shot in-house, rather than using stock media or scraping photographs from the internet. “Such an approach requires thousands of hours of labor, but in the end, it will certainly be worth it!” exclaims an Icons8 blog post. Ivan Braun, the founder of Icons8, says that in total the team took 29,000 pictures of 69 models over the course of three years which it used to train its algorithm.

There are valid concerns about technology that’s able to generate convincing-looking fakes like these at scale. This project is trying to create images that make life easier for designers, but the software could one day be used for all sorts of malicious activity.

Author: Terry Madeley

Works with student data and enjoys reading about art, data, education and technology.

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