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That last observation, about training vintage language models on images of the physical world, is, I think, a fascinating one.

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They added: "While no final decisions have been made, we appreciate this is an uncertain time for our colleagues at Uttoxeter and we are supporting them throughout the consultation process.

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Due to this more measured approach, error-diffusion dithering is even better at preserving details and can produce a more organic looking final image. However, the algorithm itself is inherently serial and not easily parallelised. Additionally, the propagation of error can cause small discrepancies in one part of the image to cascade into other distant areas. This is very obvious during animation, where pixels will appear to jitter between frames. It also makes files harder to compress.