Saved by sari
Historical analogies for large language models
Instead of good writers, ok writers, and people who can't write, there will just be good writers and people who can't write.
Is that so bad? Isn't it common for skills to disappear when technology makes them obsolete? There aren't many blacksmiths left, and it doesn't seem to be a problem.
Yes, it's bad. The reason is something I mentioned earlier: w... See more
Is that so bad? Isn't it common for skills to disappear when technology makes them obsolete? There aren't many blacksmiths left, and it doesn't seem to be a problem.
Yes, it's bad. The reason is something I mentioned earlier: w... See more
Paul Graham • Writes and Write-Nots
Is that so bad? Isn't it common for skills to disappear when technology makes them obsolete? There aren't many blacksmiths left, and it doesn't seem to be a problem.
Yes, it's bad. The reason is something I mentioned earlier: writing is thinking. In fact there's a kind of thinking that can only be done by writing. You can't make this point better th
... See morePaul Graham • Writes and Write-Nots
This is one major reason I write; to continue to think, to process things. Sure, I use AI considerably in a lot of areas, like summarizing things, pulling out key points or to-dos, I even have it evaluate what I write, fix grammar, test the content to see if it is unique, cohesive, or compelling.
But, I don’t want to offload all of my thinking to AI and stop thinking. Similar to Paul’s point in the article, we used to get strong by working, now we workout to gain strength because working, in most cases, doesn’t provide this benefit for us.
I want to think and to think well. One of my favorite books this year is, “Clear Thinking” by Shane Parrish. I have invested a lot of time and energy into learning improve my thinking. It would be a shame to outsource it to token prediction algorithms.
We’re far from replacing humans for many tasks now being delegated to large language models. That doesn’t mean LLMs can’t be helpful; it just means they’re being used wrong. The key is applying them to tasks they’re well-suited to, such as analyzing, synthesizing, and manipulating data, and not for making important decisions on behalf of people.
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Reconsidering the Role of AI: Valuing Process Over Output
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