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You can't say the entire curve is over-fitted. The space is vast and the training data is sparse so given the amount it has to cover, for sure segments of the c

You can't say the entire curve is over-fitted. The space is vast and the training data is sparse so given the amount it has to cover, for sure segments of the curve must be over-fitted and other segments of the curve must be more generalized. Additionally you can't say that just because artificial neural nets have a property of "over-fitting" doesn't mean that humans themselves can't be in a state where they are themselves over-fitted. So right now given the emergent properties of LLMs we can't fully delineate the concept of human understanding away from what the AI is doing.

simonh 4y ago
I did not say the entire curve is over fitted, I said that in some circumstances with highly sophisticated models you get over fitting. Humans over fit in different ways. A human who saw security code that introduced a buffer overflow bug that made it vulnerable to attack might make the mistake of implementing new code in a similar way and introducing a similar bug. The human isn’t deliberately introducing bugs, they didn’t spot the bug. When an LLM over fits the point is it does spot the bug. Because the input training programs define the goal, introducing bugs like that becomes one of the goals for the LLM. This is a consequence of the different way LLMs encode knowledge. More technically, it’s the different ways humans and LLMs infer goals, which is an important aspect of it. Anyway this has become a long thread, much appreciated. I’ll just summarise by saying it seems like there are many radically different and perhaps infinite possible ways question response could be implemented. Just because the surface responses these things produce in some ways seem analogous to the responses humans provide in many cases really shouldn’t be taken as evidence they perform them the same way we do. Especially given their neural architecture is radically different from ours. So how about we say we’ll keep open minds about the future development of this technology.

simonh 4y ago

I did not say the entire curve is over fitted, I said that in some circumstances with highly sophisticated models you get over fitting. Humans over fit in different ways. A human who saw security code that introduced a buffer overflow bug that made it vulnerable to attack might make the mistake of implementing new code in a similar way and introducing a similar bug. The human isn’t deliberately introducing bugs, they didn’t spot the bug. When an LLM over fits the point is it does spot the bug. Because the input training programs define the goal, introducing bugs like that becomes one of the goals for the LLM. This is a consequence of the different way LLMs encode knowledge. More technically, it’s the different ways humans and LLMs infer goals, which is an important aspect of it. Anyway this has become a long thread, much appreciated. I’ll just summarise by saying it seems like there are many radically different and perhaps infinite possible ways question response could be implemented. Just because the surface responses these things produce in some ways seem analogous to the responses humans provide in many cases really shouldn’t be taken as evidence they perform them the same way we do. Especially given their neural architecture is radically different from ours. So how about we say we’ll keep open minds about the future development of this technology.