The flurry of images generated by artificial intelligence feels like the product of a thoroughly modern tool. In fact, computers have been at the easel for decades. Discover how they are refining their craft
DALL-E-generated images of Donald Trump being handcuffed by police went viral online. And image generators are improving fast. How do they work—and how are they refining their craft?models are a type of deep learning, a software technique that uses layers of interconnected nodes that loosely mimic the structure of the human brain. The models behind image-generators are trained on enormous datasets:, the largest publicly available one, contains 5.85bn tagged images.
Let’s say you ask a generator app to create a picture of a hippopotamus. A model that has learned which types of pixel arrangement correlate to the word “hippopotamus” should be able to sample from its latent space to create a realistic image of the mammal.
The responses to complicated prompts can be erratic, particularly if the prompt is not clearly phrased or the scene it describes is not well represented in the training dataset. Even seemingly simple fare can trip models up. Human hands are often depicted with missing or extra fingers, or proportions that appear to bend the rules of physics. Because hands are usually less prominent than faces in photographs, there are smaller datasets formodels to hone their technique on.
Developers can help models to learn from their mistakes by refining the datasets that they are learning from or by tweaking algorithms. Midjourney was recently updated to improve the way it generates hands. Rapid improvements mean that telling an
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