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This probably isn’t what you meant, but if the magic is on the input side, then everything interesting about interacting with GPT is being provided by the human

by dahart 2y ago

This probably isn’t what you meant, but if the magic is on the input side, then everything interesting about interacting with GPT is being provided by the human and not GPT. We don’t have any strong evidence that GPT “understands” its input in general. We absolutely have examples of GPT failing to understand some inputs (and not knowing it, and insisting on bogus output). And we know for a fact that it was designed and built to produce statistically plausible output. GPT is a mechanical device designed by humans to pass the Turing test. We’ve designed and built something that is exceptionally good at making humans believe it is smarter than it is.

CamperBob2 2y ago
We’ve designed and built something that is exceptionally good at making humans believe it is smarter than it is. Yep, and even ELIZA could do that, to some extent. But at some point you'll need to define what "understanding" means, and explain why an LLM isn't doing it.
- dahart 2y ago
  Totally, and that’s a fair point. I don’t know what understanding means, not enough to prove an LLM can’t, anyway, and I think nobody has a good enough definition yet to satisfy this crowd. But I think we can make progress with nothing more than the dictionary definition of “understand”, which is the ability to perceive and interpret. I think we can probably agree that a rock doesn’t understand. And we can probably also agree that a random number generator doesn’t understand. The problem with @FeepingCreature’s argument is that the quality of the response does matter. The ability for a machine that’s specifically designed to wait for input and then provide an output, to then provide a low quality response, doesn’t demonstrate any more intelligence than a bicycle… right? I don’t know where the line is between my random writer Markov chain text generator from college and today’s LLMs. I’m told transformers are fundamentally the same and just have an adaptive window size. More training data then is the primary difference. So then we are saying Excel’s least-squares function fitter does not understand, unless the function has a billion data points? Or, if there’s a line, what does it look like and where is it?

by dahart 2y ago

CamperBob2 2y ago

We’ve designed and built something that is exceptionally good at making humans believe it is smarter than it is. Yep, and even ELIZA could do that, to some extent. But at some point you'll need to define what "understanding" means, and explain why an LLM isn't doing it.

dahart 2y ago
Totally, and that’s a fair point. I don’t know what understanding means, not enough to prove an LLM can’t, anyway, and I think nobody has a good enough definition yet to satisfy this crowd. But I think we can make progress with nothing more than the dictionary definition of “understand”, which is the ability to perceive and interpret. I think we can probably agree that a rock doesn’t understand. And we can probably also agree that a random number generator doesn’t understand. The problem with @FeepingCreature’s argument is that the quality of the response does matter. The ability for a machine that’s specifically designed to wait for input and then provide an output, to then provide a low quality response, doesn’t demonstrate any more intelligence than a bicycle… right? I don’t know where the line is between my random writer Markov chain text generator from college and today’s LLMs. I’m told transformers are fundamentally the same and just have an adaptive window size. More training data then is the primary difference. So then we are saying Excel’s least-squares function fitter does not understand, unless the function has a billion data points? Or, if there’s a line, what does it look like and where is it?