Yes, that’s what “neural networks” in computing are, a method of systematically making a machine that does novel pattern matching tricks without needing to understand how to make that pattern matching machine yourself. Which is cool, and has useful applications, and it starts to do some scary things when scaled up, but it’s hitting a clear limit of what just making its numbers bigger will do and all the grifters keep doubling down on just throwing more and more and more processing power at this dead end instead of admitting they were wrong and going back to rethink their fundamental approach (because the grifters don’t care and don’t know shit, and they’re the ones controlling everything).
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.
Yes, that’s what “neural networks” in computing are, a method of systematically making a machine that does novel pattern matching tricks without needing to understand how to make that pattern matching machine yourself. Which is cool, and has useful applications, and it starts to do some scary things when scaled up, but it’s hitting a clear limit of what just making its numbers bigger will do and all the grifters keep doubling down on just throwing more and more and more processing power at this dead end instead of admitting they were wrong and going back to rethink their fundamental approach (because the grifters don’t care and don’t know shit, and they’re the ones controlling everything).
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.