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You are making a lot of sense to me. Thanks for the link to that essay, too. The point about programming-language nonambiguity is a good one. After I started u

You are making a lot of sense to me. Thanks for the link to that essay, too. The point about programming-language nonambiguity is a good one. After I started using ChatGPT a year ago, it took me a while to realize that I didn’t have to be careful about my spelling, capitalization, punctuation, etc. It turned out to be good at interpreting the intention of sloppily written prompts. And it never pointed out or complained about my mistakes, either—another difference from humans.

palata 3y ago
> And it never pointed out or complained about my mistakes, either—another difference from humans. Yeah that is a big difference indeed: a human has some notion of confidence about what they know. Not every human expresses it the same way: a scientist will say "I am pretty sure that climate change is a consequence of human activity", meaning "well, maybe we all live in the Matrix and gravity is an illusion, but from what we know, we are pretty sure". On the other end of the spectrum, some will say "I know for a fact that God exists, because it is written in a very old book". But both have a consistent notion of confidence about what they (think they) know and what they don't know. An LLM does not have that. An LLM is not critical. It cannot conclude stuff like "I have to take into account that this text was written in a period of war and the author is not neutral, so there is probably a bias". It can generate text that pretends it does, but it remains a generated text, it is not a critical analysis. Also it cannot be offended by something you write, even though it could generate text that pretends it is. So yeah, it can probably be engineered to never/always point out or complain about mistakes, with the caveat that it does not have an "idea" of what a mistake is. It just generates text that pretends it does. > It turned out to be good at interpreting the intention of sloppily written prompts. In the context of a conversation with a human (you), who is very good at navigating ambiguous communications. But if you tried to describe an algorithm with an ambiguous natural language, you may quickly have problems. And errors accumulate, so it gets worse when the task becomes more complex. Which is an interesting thought: it may make simple tasks simpler, but it's not at all a given that it scales.

palata 3y ago

> And it never pointed out or complained about my mistakes, either—another difference from humans. Yeah that is a big difference indeed: a human has some notion of confidence about what they know. Not every human expresses it the same way: a scientist will say "I am pretty sure that climate change is a consequence of human activity", meaning "well, maybe we all live in the Matrix and gravity is an illusion, but from what we know, we are pretty sure". On the other end of the spectrum, some will say "I know for a fact that God exists, because it is written in a very old book". But both have a consistent notion of confidence about what they (think they) know and what they don't know. An LLM does not have that. An LLM is not critical. It cannot conclude stuff like "I have to take into account that this text was written in a period of war and the author is not neutral, so there is probably a bias". It can generate text that pretends it does, but it remains a generated text, it is not a critical analysis. Also it cannot be offended by something you write, even though it could generate text that pretends it is. So yeah, it can probably be engineered to never/always point out or complain about mistakes, with the caveat that it does not have an "idea" of what a mistake is. It just generates text that pretends it does. > It turned out to be good at interpreting the intention of sloppily written prompts. In the context of a conversation with a human (you), who is very good at navigating ambiguous communications. But if you tried to describe an algorithm with an ambiguous natural language, you may quickly have problems. And errors accumulate, so it gets worse when the task becomes more complex. Which is an interesting thought: it may make simple tasks simpler, but it's not at all a given that it scales.