How is that structurally different from how a human answers a question? We repeat an answer we “know” if possible, assemble something from fragments of knowledge if not, and just make something up from basically nothing if needed. The main difference I see is a small degree of self reflection, the ability to estimate how ‘good or bad’ the answer likely is, and frankly plenty of humans are terrible at that too.
I would argue that a decent portion of humans are usually ok with admitting they don’t know something
Unless they are in a situation where they will be punished for not knowing
My favorite doctor claimed he didn’t know something and at first I was thinking
“Man that’s weird” but then I thought about all the times I’ve personally had or heard stories of doctors that bullshited their way into something like how I couldn’t possibly be diagnosed with ADHD at 18
I dare say that if you ask a human “Why should I not stick my hand in a fire?” their process for answering the question is going to be very different from an LLM.
ETA: Also, working in software development, I’ll tell ya… Most of the time, when people ask me a question, it’s the wrong question and they just didn’t know to ask a different question instead. LLMs don’t handle that scenario.
I’ve tried asking ChatGPT “How do I get the relative path from a string that might be either an absolute URI or a relative path?” It spat out 15 lines of code for doing it manually. I ain’t gonna throw that maintenance burden into my codebase. So I clarified: “I want a library that does this in a single line.” And it found one.
An LLM can be a handy tool, but you have to remember that it’s also a plagiarizing, shameless bullshitter of a monkey paw.
“Most of the time, when people ask me a question, it’s the wrong question and they just didn’t know to ask a different question instead.”
“I’ve tried asking ChatGPT “How do I get the relative path from a string that might be either an absolute URI or a relative path?” It spat out 15 lines of code for doing it manually. I ain’t gonna throw that maintenance burden into my codebase. So I clarified: “I want a library that does this in a single line.” And it found one.”
You see the irony right? I genuinely can’t fathom your intent when telling this story, but it is an absolutely stellar example.
You can’t give a good answer when people don’t ask the right questions. ChatGPT answers are only as good as the prompts. As far as being a “plagiarizing, shameless bullshitter of a monkey paw” I still don’t think it’s all that different from the results you get from people. If you ask a coworker the same question you asked chatGPT, you’re probably going to get a line copied from a Google search that may or may not work.
You see the irony right? I genuinely can’t fathom your intent when telling this story, but it is an absolutely stellar example.
Yes, I did mean for it to be an example.
And yes, I do think that correctly framing a question is crucial whether you’re dealing with a person or an LLM. But I was elaborating on whether a person’s process of answering a question is fundamentally similar to an LLM’s process. And this is one way that it’s noticeably different. A person will size up who is asking, what they’re asking, and how they’re asking it… and consider whether they should actually answer the exact question that was asked or suggest a better question instead.
You can certainly work around it, as the asker, but it does require deliberate disambiguation. I think programmers are used to doing that, so it may feel like not that big of a deal, but if you start paying attention to how often people are tossing around half-formed questions or statements and just expecting the recipient to fill in the gaps… It’s basically 100% of the time.
We’re fundamentally social creatures first, and intelligent creatures second. (Or third, or not at all, depending.) We think better as groups. If you give 10 individuals a set of difficult questions, they’ll bomb almost all of them. If you give the questions to a group of 10, they’ll get almost all of them right. (There’s several You Are Not So Smart episodes on this, but the main one is 111.)
Asking a question to an LLM is just completely different from asking a person. We’re not optimized for correctly filling out scantron sheets as individuals, we’re optimized for brainstorming ideas and pruning them as a group.
Yeah, and a car uses more energy than me. It still goes faster. What’s your point? The debate isn’t input vs output. It’s only about output(the ability of the AI).
How is that structurally different from how a human answers a question? We repeat an answer we “know” if possible, assemble something from fragments of knowledge if not, and just make something up from basically nothing if needed. The main difference I see is a small degree of self reflection, the ability to estimate how ‘good or bad’ the answer likely is, and frankly plenty of humans are terrible at that too.
I would argue that a decent portion of humans are usually ok with admitting they don’t know something
Unless they are in a situation where they will be punished for not knowing
My favorite doctor claimed he didn’t know something and at first I was thinking “Man that’s weird” but then I thought about all the times I’ve personally had or heard stories of doctors that bullshited their way into something like how I couldn’t possibly be diagnosed with ADHD at 18
I dare say that if you ask a human “Why should I not stick my hand in a fire?” their process for answering the question is going to be very different from an LLM.
ETA: Also, working in software development, I’ll tell ya… Most of the time, when people ask me a question, it’s the wrong question and they just didn’t know to ask a different question instead. LLMs don’t handle that scenario.
I’ve tried asking ChatGPT “How do I get the relative path from a string that might be either an absolute URI or a relative path?” It spat out 15 lines of code for doing it manually. I ain’t gonna throw that maintenance burden into my codebase. So I clarified: “I want a library that does this in a single line.” And it found one.
An LLM can be a handy tool, but you have to remember that it’s also a plagiarizing, shameless bullshitter of a monkey paw.
“Most of the time, when people ask me a question, it’s the wrong question and they just didn’t know to ask a different question instead.”
“I’ve tried asking ChatGPT “How do I get the relative path from a string that might be either an absolute URI or a relative path?” It spat out 15 lines of code for doing it manually. I ain’t gonna throw that maintenance burden into my codebase. So I clarified: “I want a library that does this in a single line.” And it found one.”
You see the irony right? I genuinely can’t fathom your intent when telling this story, but it is an absolutely stellar example.
You can’t give a good answer when people don’t ask the right questions. ChatGPT answers are only as good as the prompts. As far as being a “plagiarizing, shameless bullshitter of a monkey paw” I still don’t think it’s all that different from the results you get from people. If you ask a coworker the same question you asked chatGPT, you’re probably going to get a line copied from a Google search that may or may not work.
Yes, I did mean for it to be an example.
And yes, I do think that correctly framing a question is crucial whether you’re dealing with a person or an LLM. But I was elaborating on whether a person’s process of answering a question is fundamentally similar to an LLM’s process. And this is one way that it’s noticeably different. A person will size up who is asking, what they’re asking, and how they’re asking it… and consider whether they should actually answer the exact question that was asked or suggest a better question instead.
You can certainly work around it, as the asker, but it does require deliberate disambiguation. I think programmers are used to doing that, so it may feel like not that big of a deal, but if you start paying attention to how often people are tossing around half-formed questions or statements and just expecting the recipient to fill in the gaps… It’s basically 100% of the time.
We’re fundamentally social creatures first, and intelligent creatures second. (Or third, or not at all, depending.) We think better as groups. If you give 10 individuals a set of difficult questions, they’ll bomb almost all of them. If you give the questions to a group of 10, they’ll get almost all of them right. (There’s several You Are Not So Smart episodes on this, but the main one is 111.)
Asking a question to an LLM is just completely different from asking a person. We’re not optimized for correctly filling out scantron sheets as individuals, we’re optimized for brainstorming ideas and pruning them as a group.
If you fed that information into one I bet you would get different answers.
That is information that isn’t available to it generally.
A human brain can do that for 20 watt of power. chatGPT uses up to 20 megawatt.
Yeah, and a car uses more energy than me. It still goes faster. What’s your point? The debate isn’t input vs output. It’s only about output(the ability of the AI).