5 ms·

You post amounts to: in order to be smarter I need to increase my smartness. Great insight.

by FpUser 3y ago

lazide 3y ago
I think it's more subtly misleading - to be smarter, I need more knowledge. But knowledge != smart, knowledge == informed, or educated. And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers. Which arguably isn't about being smart, and is only tangentially about less or more (external) knowledge really. It's about self-knowledge. Going down the first path is about knowing everything (in the form of facts, usually). Which hey, maybe? Going down the second path is about knowing oneself. Which hey, maybe? They are not the same.
- p1esk 3y ago
  Hallucinations are an interesting problem - in both humans and statistical models. If we asked an average person 500 years ago how the universe works, they would have confidently told you the earth is flat and it rests on a giant turtle (or something like that). And that there are very specific creatures - angels and demons who meddle in human affairs. And a whole a lot more which has no grounding in reality. How did we manage to reduce that type of hallucination?
  - ottaborra 3y ago
    by taking steps to verify everything that was said
  - Anotheroneagain 3y ago
    The expression "Urbi et orbi" goes back longer than that. It's a modern myth that they didn't know the world was a sphere.
- ebcode 3y ago
  >> And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers. I think the mistake lies in the belief that the LLM "knows" things. As humans, we have a strong tendency to anthropomorphize. And so, when we see something behave in a certain way, we imagine that thing to be doing the same thing that we do when we behave that way. I'm writing, and the machine is also writing, but what I'm doing when I write is very different from what the machine does when it writes. So the mistake is to say, or think, "I think when I write, so the machine must also think when it writes." We probably need to address the usage of the word "hallucination", and maybe realize that the LLM is always hallucinating. Not: "When it's right, it's right, but when it's wrong, it's hallucinating." It's more like, "Sweet! Some of these hallucinations are on point!"
  - p1esk 3y ago
    I think when I write, so the machine must also think when it writes. What is it exactly you do when you “think”? And how is it different from what LLM does? Not saying it’s not different, just asking.
    - Jensson 3y ago
      There are probably many, but the most glaring one is that LLMs has to write a word every time it thinks, meaning it can't solve a problem before it starts to write down the solution. That is an undeniable limitation of current architectures, it means that the way the LLM answers your question also matches its thinking process, meaning that you have to trigger a specific style of response if you want it to be smart with its answer.
      - p1esk 3y ago
        Ok, so how do humans solve a problem? Isn’t it also a sequential, step by step process, even if not expressed explicitly in words? What if instead of words a model would show you images to solve a problem? Would it change anything?

You post amounts to: in order to be smarter I need to increase my smartness. Great insight.

by FpUser 3y ago

You post amounts to: in order to be smarter I need to increase my smartness. Great insight.

I think it's more subtly misleading - to be smarter, I need more knowledge. But knowledge != smart, knowledge == informed, or educated. And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers. Which arguably isn't about being smart, and is only tangentially about less or more (external) knowledge really. It's about self-knowledge. Going down the first path is about knowing everything (in the form of facts, usually). Which hey, maybe? Going down the second path is about knowing oneself. Which hey, maybe? They are not the same.

p1esk 3y ago
Hallucinations are an interesting problem - in both humans and statistical models. If we asked an average person 500 years ago how the universe works, they would have confidently told you the earth is flat and it rests on a giant turtle (or something like that). And that there are very specific creatures - angels and demons who meddle in human affairs. And a whole a lot more which has no grounding in reality. How did we manage to reduce that type of hallucination?
- ottaborra 3y ago
  by taking steps to verify everything that was said
- Anotheroneagain 3y ago
  The expression "Urbi et orbi" goes back longer than that. It's a modern myth that they didn't know the world was a sphere.
ebcode 3y ago
>> And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers. I think the mistake lies in the belief that the LLM "knows" things. As humans, we have a strong tendency to anthropomorphize. And so, when we see something behave in a certain way, we imagine that thing to be doing the same thing that we do when we behave that way. I'm writing, and the machine is also writing, but what I'm doing when I write is very different from what the machine does when it writes. So the mistake is to say, or think, "I think when I write, so the machine must also think when it writes." We probably need to address the usage of the word "hallucination", and maybe realize that the LLM is always hallucinating. Not: "When it's right, it's right, but when it's wrong, it's hallucinating." It's more like, "Sweet! Some of these hallucinations are on point!"
- p1esk 3y ago
  I think when I write, so the machine must also think when it writes. What is it exactly you do when you “think”? And how is it different from what LLM does? Not saying it’s not different, just asking.
  - Jensson 3y ago
    There are probably many, but the most glaring one is that LLMs has to write a word every time it thinks, meaning it can't solve a problem before it starts to write down the solution. That is an undeniable limitation of current architectures, it means that the way the LLM answers your question also matches its thinking process, meaning that you have to trigger a specific style of response if you want it to be smart with its answer.
    - p1esk 3y ago
      Ok, so how do humans solve a problem? Isn’t it also a sequential, step by step process, even if not expressed explicitly in words? What if instead of words a model would show you images to solve a problem? Would it change anything?