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Linux May Be the Best Way to Avoid the AI Nightmare
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this post was submitted on 03 Jun 2024
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I wonder if some big AI heads will publish some "AI enhanced" Linux distros, that will also have other issues...
I expect canonical to do it to Ubuntu.
IBM's Watson Enhanced Red Hat
imagine if pop os does it first.
I'd actually be surprised.
I guess id be ok with an installable debian package for an end user controlled llama package with gui avatar interface overlay. Local learning data set storage plus ability to use API calls to injest info from other cloud based llm ai systems when the local dataset doesnt have a reliable answer.
Almost definitely.
There is a command line program called tesseract that does image to text generation. It produces plaintext from a picture of text. I didn't look into exactly how it works but iirc, image to text that's actually good and accurate needs ai shenanigans.
It's built by Google, but it's open source, and is probably the best optical character recognition by far. It's one pip/pipx installation away and I find it pretty useful on occasion. Same as WhisperAI by by OpenAI. Fully open source and one pip/pipx command away, probably close to the best audio transcription there is as well.
Not sure either count as AI, at least not AI chatbot kind of AI more like more simple algorithms, but they're great in the sense it's just another program but a very useful tool. Not some baked in copilot kind of deal
The algorithm is exactly the same as the chat bot, only the underlying data is different. Yes, they are all deep neural networks
I've actually just been working with OCR this week, trying to capture data off of the screen of a stupid proprietary Schneider device as that's the only way to get at it.
Long story short Tesseract stinks at this task.
The Chinese designed PaddleOCR seems significantly superior as it runs a more modern neural net and requires a lot less preprocessing. I would class it as more of a "full service AI" and not just a simple recognition system like Tesseract, it can correct for skew and do its own normalization and thresholding internally while Tesseract wants a perfect boolean raster fed to it.
Unfortunately, the barrier to entry is a lot higher due to trying to understand their text vomit website and the fact that it seems prone to random segfaulting.