I know a company that burned $100k in tokens after they they let like 50 worker bees using general AI for OCR, simply converting images and PDFs to text.
They didn’t bother to create a skill, or teach the AI how to reuse a shared script so every request resulted in it writing a new python project, pulling libraries, using a frontier model rather than offloading a dumb one etc.
Basically find a business process that happens often and let em at it inefficiently, it’ll happily chew through the budget.
There has been some serious leaps in terms of quality. It couldn’t read human writing or half the fonts for that matter like 5 years ago, let alone 20.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone…
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
I know a company that burned $100k in tokens after they they let like 50 worker bees using general AI for OCR, simply converting images and PDFs to text.
They didn’t bother to create a skill, or teach the AI how to reuse a shared script so every request resulted in it writing a new python project, pulling libraries, using a frontier model rather than offloading a dumb one etc.
Basically find a business process that happens often and let em at it inefficiently, it’ll happily chew through the budget.
Thats pretty much what people freaked out about llms doing at my work and all they use it for. I’m here like…we have had OCR for over 20 years.
People are duuumb.
There has been some serious leaps in terms of quality. It couldn’t read human writing or half the fonts for that matter like 5 years ago, let alone 20.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
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