I’m just saying the models used in medical imaging and protein folding, to use your examples, are not LLMs, but very different dedicated medical imaging models or protein folding models. Still falls under the overarching umbrella of AI/ML, but not really any more relevant to language models. LLMs also aren’t a very relevant tool for big data or statistical analysis. Even with an agent harness, they could be used to write scripts that a human could also write, but couldn’t directly do any sort of reliable statistical analysis.
Oh OK I wasn’t aware of that. Actually I wondered as I was writing my comment how an LLM would compare to more tightly fitted model.
Well there goes my one example. Is there anything they’re legitimately good for? I’ll admit they seem good at finding “fuzzy” connections, and their text and image generation capabilities are impressive even if they aren’t my cup of tea—mostly because of the externalities.
I’m just saying the models used in medical imaging and protein folding, to use your examples, are not LLMs, but very different dedicated medical imaging models or protein folding models. Still falls under the overarching umbrella of AI/ML, but not really any more relevant to language models. LLMs also aren’t a very relevant tool for big data or statistical analysis. Even with an agent harness, they could be used to write scripts that a human could also write, but couldn’t directly do any sort of reliable statistical analysis.
Oh OK I wasn’t aware of that. Actually I wondered as I was writing my comment how an LLM would compare to more tightly fitted model.
Well there goes my one example. Is there anything they’re legitimately good for? I’ll admit they seem good at finding “fuzzy” connections, and their text and image generation capabilities are impressive even if they aren’t my cup of tea—mostly because of the externalities.