Being able to ask an AI to draw you a picture of a panda in a chef's hat seems like magic, but despite impressive improvements, text-to-image generators aren't yet ready for public use
that is trained on a large number of examples to categorise how images relate to text descriptions. When given a new text description, the neural network repeatedly generates images, altering them until they most closely match the text based on what it has learned.While the images presented by both firms are impressive, researchers have questioned whether the results are being cherry-picked to show the systems in the best light.
Despite this, the friendly competition between the big firms is likely to mean the technology continues to advance rapidly, as tools developed by one group can be incorporated into another’s future model. For example, diffusion models, where neural networks learn how to reverse the process of adding random pixels to an image in an effort to improve them, have shown promise in machine-learning models in the past year.
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