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Pictures produced by AI went viral, stirred controversies, and drove investments.
What happened: A new generation of text-to-image generators inspired a flood of experimentation, transforming text descriptions into mesmerizing artworks and photorealistic fantasies. Commercial enterprises were quick to press the technology into service, making image generation a must-have feature in software for creating and editing graphics.
Driving the story: Models that generate media became the public face of AI thanks to friendly user interfaces, highly entertaining output, and open APIs and models.
Yes, but: Such models are trained on images scraped from the web. Like large language models, they inherit biases embedded in online content and imitate the inflammatory styles of expression.
Behind the news: Diffusion models generate output by starting with noise and removing it selectively over a series of steps. Introduced in 2015 by researchers at UC Berkeley and Stanford, they remained in the background for several years until further work showed that they could produce images competitive with the output of generative adversarial networks (GANs). Stability AI put a diffusion model at the heart of Stable Diffusion. OpenAI, which based the initial version of DALL·E on a GAN, updated it with a diffusion model at around the same time.
Where things stand: The coming year is shaping up for a revolution in computer-aided creativity. And the groundswell of generated imagery isn’t going to stop at pictures. Google and Meta released impressive text-to-video models this year, and OpenAI accelerated text-to-3D-object generation by an order of magnitude.
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