2 ms·

That just shows that parroting will solve a number of problems that require reasoning, that is "reasoning" as we think of it can be reduced to a statistical pro

by all2 3y ago

That just shows that parroting will solve a number of problems that require reasoning, that is "reasoning" as we think of it can be reduced to a statistical process. Kids parrot their parents before they understand the meaning of what they are doing or saying. Meaning arises from a much more complex process than seeing/repeating. I think the same will be true with LLMs. True reasoning capabilities will be another revolution entirely. --- This just occurred to me; LLM's have a cargo cult level understanding of anything they've been trained on. Correct answers are actually statistical flukes -- purposeful because that's how we trained the models -- but not actually significant in terms of reasoning.

a_wild_dandan 3y ago
How do we test this alleged distinction between "true reasoning" and statistical parroting? What experiments can we perform on SotA models to make this idea falsifiable? Commonplace hypotheses like these give me strong "No True Scotsman" vibes, but I'm often wrong. Let's agree on a method, and I'll test it.
- Art9681 3y ago
  There is none. No one understands what intelligence really means or self-awareness. Which is why they feel threatened by anything that challenges their self proclaimed uniquely human trait. The goal posts WILL move a lot more before this debate is settled. Inevitably the success rate and accuracy of LLM responses is going to get better and better, quite possibly to the point where it is indistinguishable from a human. But it wont matter, because some humans will redefine intelligence to be something else so their place at the top of the intelligence pyramid remains in place.
- all2 3y ago
  You can coax LLM's into reasoning. I recall someone on here posting a link to prompts that instruct the LLM to reason through a request, and this improves its output significantly. I think what our models are missing is recursion on themselves. You and I can be self-referential, and we are capable of meta thought. We are also capable of "internal dialogue" where we speak and reason internally. The LLMs at present lack even state or memory. Arguably those aren't necessary for "reasoning" capabilities. I wonder how far off "stochastic parrot" is from what we do naturally. I have an image in my head of how we think using associated words/concepts/pictures for learning and I can't imagine it is too different from statistically associated concepts/words. --- This is sort of scatterbrained, and I apologize. I don't have enough time to write a more concise response.

by all2 3y ago

a_wild_dandan 3y ago

How do we test this alleged distinction between "true reasoning" and statistical parroting? What experiments can we perform on SotA models to make this idea falsifiable? Commonplace hypotheses like these give me strong "No True Scotsman" vibes, but I'm often wrong. Let's agree on a method, and I'll test it.

Art9681 3y ago
There is none. No one understands what intelligence really means or self-awareness. Which is why they feel threatened by anything that challenges their self proclaimed uniquely human trait. The goal posts WILL move a lot more before this debate is settled. Inevitably the success rate and accuracy of LLM responses is going to get better and better, quite possibly to the point where it is indistinguishable from a human. But it wont matter, because some humans will redefine intelligence to be something else so their place at the top of the intelligence pyramid remains in place.
all2 3y ago
You can coax LLM's into reasoning. I recall someone on here posting a link to prompts that instruct the LLM to reason through a request, and this improves its output significantly. I think what our models are missing is recursion on themselves. You and I can be self-referential, and we are capable of meta thought. We are also capable of "internal dialogue" where we speak and reason internally. The LLMs at present lack even state or memory. Arguably those aren't necessary for "reasoning" capabilities. I wonder how far off "stochastic parrot" is from what we do naturally. I have an image in my head of how we think using associated words/concepts/pictures for learning and I can't imagine it is too different from statistically associated concepts/words. --- This is sort of scatterbrained, and I apologize. I don't have enough time to write a more concise response.