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> If you wanted a palace and someone built you the foundation, would you deride it as being nothing like a palace and in fact a step AWAY from a palace?

Depends on whether it's actually possible to build a palace on top of the foundation. If I know the foundation is going to crumble under the extra weight of the walls, then yes, it's a step backwards.

This is a fantastic achievement, and I think people who are saying it's nothing new are kind of being obstinate. But there is real debate over whether GPT is a foundation we can build on.

It's not as simple as just saying, "generate the code, and then we'll come up with ways to make sure the code is correct." Fundamentally, that might require us to generate the code differently than GPT does. GPT's foundation might crumble under the weight when we try to put walls on top of it.

This is particularly worrisome with GPT because it's still a very active area of research, so we don't know for sure that GPT's weaknesses aren't intrinsic to its design. We could end up devoting a ton of time to pushing GPT to its limits only to find out that the entire process has to be scrapped and that we'll need to start over from the beginning.

I think people have a tendency to see something new and either only see the capabilities or only see the weaknesses. There's been some really startlingly impressive things coming out of GPT-3, in particular the 'infinite' text adventure someone posted a while ago. But all of those projects have also had substantial weaknesses, and the weaknesses are forming a pattern across all of the projects. There are certain tendencies that GPT seems to universally have around recycling content, going off on weird asides -- stuff that should have proponents at least slightly worried, even while they rightly praise its advances.



We could end up devoting a ton of time to pushing GPT to its limits only to find out that the entire process has to be scrapped and that we'll need to start over from the beginning.

This is perfectly fine. In fact it's the only way forward currently. Unless you have some alternative which is more promising? GPT models are like large convolutional networks in 2012 - they were so much better than all existing CV approaches at the time that it didn't make any sense to keep working on those other approaches.


Totally agree with you! I'm definitely not arguing that GPT-3 is necessarily the foundation; I'm arguing that it's part of the research process by which we build the foundation.




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