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Datasets of photos with emphasis on striking detail created before generative AI weren't tagged with AGI in mind, and I think that that wasn't the "first problem" of AGI says more about the difficulty of creating effective, quality tagging and metadata than anything else. For instance, SDXL produces very different results when you expand your prompt vocabulary even marginally. Pairing prompts with Hindu, Sikh, desi, Telegu, Brahmins, North/South, Kerala, country/city, etc provides detailed and diverse results, and that's all pretty generic. It also recognizes clothing styles and types, food, holidays and events, and it even generates recognizable background details and architectural styles with regional prompts. Also, to their example, "Jollof rice" beats prompting "Nigerian food" if you expect to see jollof rice. I plug this to artists who also teach, but this is a great way to show the tremendous value of the arts and art history. Start tagging for training, make better datasets, and license them. People think they're slick because they know how to prompt "cool picture, in the style of $artist", but most of the world doesn't know what filigree, sfumato, or Rococo are. Guess who does? Their art students. |