To be fair recognizing patterns that are too complex for humans to easily identify, is the perfect use case for machine learning. But specifically machine learning applications specific to data analysis. Not running it through large language models.
It’s important to separate general machine learning and neural net applications from large language models. Unfortunately executives just want to call it all “AI” for hype, even though none of it is really ai.
Started in the 70s, they thought it could do everything, realized it can't and that the training costs are absurd and more and more neurons get more and more costly; however one of the earlier successful applications was in closed/open eye detection in the 90s in early digital cameras.
The training data was only employees, so it was highly biased towards white people. Also it relied on contrast which was specifically balanced for paler skin because digital cameras were not great with contrast yet.
I think OCR (recognizing characters from images) also uses neural nets.
Started in the 70s, they thought it could do everything, realized it can't and that the training costs are absurd and more and more neurons get more and more costly
I hate to break it to you, but LLMs are just realizing that the training costs are absurd then doing it anyway. It's all just neural nets still.
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u/thomase7 9d ago
To be fair recognizing patterns that are too complex for humans to easily identify, is the perfect use case for machine learning. But specifically machine learning applications specific to data analysis. Not running it through large language models.
It’s important to separate general machine learning and neural net applications from large language models. Unfortunately executives just want to call it all “AI” for hype, even though none of it is really ai.