They learn in their own way, completely undirected by their programmers each AI develops its own understanding about what a horse is, completely untethered from the reality we understand. Over millions and billions of iterations – each taking a matter of seconds – they improve to the point where humans start struggling to tell the difference. But they learn the generator is rewarded if it fools the discriminator, and the discriminator is rewarded if it correctly picks the origin of an image. A "generator" AI then starts to create images, and a "discriminator" AI tries to guess if they're real images or AI creations.Īt first, they're evenly matched, both being absolutely terrible at their jobs.
The generative adversarial network, or GAN, first emerged in 2014, putting forth the idea of two AIs in competition with one another, both "trained" by being shown a huge number of real images, labeled to help the algorithms learn what they're looking at. The pace of progress in the field of AI-powered text-to-image generation is positively frightening.
The greatest artistic tool ever built, or a harbinger of doom for entire creative industries? OpenAI's second-generation DALL-E 2 system is slowly opening up to the public, and its text-based image generation and editing abilities are awe-inspiring.
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