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I’m kind of disappointed that the generative AI hype has overshadowed how many non-generative tasks are basically “solved”, especially in vision. Human level o

I’m kind of disappointed that the generative AI hype has overshadowed how many non-generative tasks are basically “solved”, especially in vision. Human level object recognition can easily be trained up for custom use cases. Image segmentation is amazing. I can take a photo of a document and it’s accurately OCRd. 10-15 years ago that would be unfathomable. I think current LLMs would give AI a much better reputation if they focused on non generative applications. Sentiment analysis, translation, named entity extraction, etc. these were all problems that data folks have been wrestling with that could very well be seen as “solved” and a big win for AI that businesses would b able onconfidently integrate into their workflows, but instead they went with the generative route and we have to deal with hallucinations and slop

lynx97 1y ago
Ahh, I wanted to list translation as another "byproduct". That totally feels like solved now. However, while OCR done by vision models feels neat, I personally dont feel like it changed anything for me. I have been using KNFB Reader and later Seeing AI, and both have sufficiently solved the "OCR a document you just photographed" use case for me. They even aid the picture taking process by letting me know that a particular edge of the document is not visible. Besides, I still don't understand the actual potential for hallucinations when doing OCR through vision models fully. I have a feeling there are a number of corner cases which will lead to hallucinations. The tendency to fill in things that might fit but aren't there is rather concerning. Talking about spelling errors and numerical data.