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That's true for Craiyon, but Stable Diffusion came out today and looks like it may be able to beat DALL-E in terms of quality some of the time: https://github.com/huggingface/diffusers/releases/tag/v0.2.3
As someone who's used the models a fair bit (I work at OpenAI), I mentally sort applications into four buckets:
-generative use cases, where you give the model the kernel of an idea and then you curate its output (blog writing, code completion, etc.)
-extractive use cases, where you give the model some big piece of text, and then process it in some way (e.g., extract names and addresses, classify it, ask a question about the text)
-transformational use cases, where you need to fix/adjust a piece of text, or translate from one domain to another (e.g., sometimes I'll use GPT-3 for little tasks like copying and pasting a table from a presentation and then asking the model to translate it to markdown; saves me a visit to Google and finding some table generator website)
-comparisons, where you use embeddings to do search/clustering/recommendations over any set of strings (e.g., can combo nicely with the Q&A use case above, where you search over a knowledge base)
I started a repo here with some barebones examples of each: https://github.com/openai/openai-cookbook/
If you're looking for examples of commercial applications, OpenAI published two blog posts highlighting a few:
-GPT-3 use cases (2021): https://openai.com/blog/gpt-3-apps/
-Codex use cases (2022): https://openai.com/blog/codex-apps/