Some good use cases of ML are crowdsourced computing projects like protein folding (folding@home) and exoplanet hunting (TESS) by manually reviewing starlight spectral plots. Pure pattern recognition, no genuine intelligence there.
Also OCR, speech-to-text transcription, and generally any sort of “find and point out/parse patterns in this huge amount of noisy data” task. It’s never foolproof, but it has massively improved all of those. The problem is just the way LLMs are being misused to try to make a worse search engine and then pretend that this shitty, extremely unreliable chatbot can replace workers. Also the insane costs of trying to roll out the infrastructure for them to just continue being awful on ever greater scales and the knock-on effects that’s having on everything even remotely related.
Some good use cases of ML are crowdsourced computing projects like protein folding (folding@home) and exoplanet hunting (TESS) by manually reviewing starlight spectral plots. Pure pattern recognition, no genuine intelligence there.
Also OCR, speech-to-text transcription, and generally any sort of “find and point out/parse patterns in this huge amount of noisy data” task. It’s never foolproof, but it has massively improved all of those. The problem is just the way LLMs are being misused to try to make a worse search engine and then pretend that this shitty, extremely unreliable chatbot can replace workers. Also the insane costs of trying to roll out the infrastructure for them to just continue being awful on ever greater scales and the knock-on effects that’s having on everything even remotely related.