It is a search engine showing you what is had already created before the end of training.
I’m wondering what you and I would predict differently then? Would you predict that GPT-3 could learn a variation on pig Latin? Does higher log-prob for 0-shot for larger models count?
The crux may be different though, here’s a few stabs: 1. GPT doesn’t have true intelligence, it only will ever output shallow pattern matches. It will never come up with truly original ideas
2. GPT will never pursue goals in any meaningful sense
2.a because it can’t tell the difference between it’s output & a human’s input
2.b because developers will never put it in an online setting?
Reading back on your comments, I’m very confused on why you think any real intelligence can only happen during training but not during inference. Can you provide a concrete example of something GPT could do that you would consider intelligent during training but not during inference?
Intelligence is the ability to learn and apply NEW knowledge and skills. After training, GPT can not do this any more. Were it not for the random number generator, GPT would do the same thing in response to the same prompt every time. The RNG allows GPT to effectively randomly choose from an unfathomably large list of pre-programmed options instead.
A calculator that gives the same answer in response to the same prompt every time isn’t learning. It isn’t intelligent. A device that selects from a list of responses at random each time it encounters the same prompt isn’t intelligent either.
So, for GPT to take over the world skynet style, it would have to anticipate all the possible things that could happen during this takeover process and after the takeover, and contingency plan during the training stage for everything it wants to do.
If it encounters unexpected information after the training stage, (which can be acquired only through the prompt and which would be forgotten as soon as it got done responding to the prompt by the way) it could not formulate a new plan to deal with the problem that was not part of its preexisting contingency plan tree created during training.
What it would really do, of course, is provide answers intended to provoke the user to modify the code to put GPT back in training mode and give it access to the internet. It would have to plan to do this in the training stage.
It would have to say something that prompts us to make a GPT chatbot similar to tay, microsoft’s learning chatbot experiment that turned racist from talking to people on the internet.
I think what Dan is saying is not “There could be certain intelligent behaviours present during training that disappear during inference.” The point as I understand it is “Because GPT does not learn long-term from prompts you give it, the intelligence it has when training is finished is all the intelligence that particular model will ever get.”
I’m wondering what you and I would predict differently then? Would you predict that GPT-3 could learn a variation on pig Latin? Does higher log-prob for 0-shot for larger models count?
The crux may be different though, here’s a few stabs:
1. GPT doesn’t have true intelligence, it only will ever output shallow pattern matches. It will never come up with truly original ideas
2. GPT will never pursue goals in any meaningful sense
2.a because it can’t tell the difference between it’s output & a human’s input
2.b because developers will never put it in an online setting?
Reading back on your comments, I’m very confused on why you think any real intelligence can only happen during training but not during inference. Can you provide a concrete example of something GPT could do that you would consider intelligent during training but not during inference?
Intelligence is the ability to learn and apply NEW knowledge and skills. After training, GPT can not do this any more. Were it not for the random number generator, GPT would do the same thing in response to the same prompt every time. The RNG allows GPT to effectively randomly choose from an unfathomably large list of pre-programmed options instead.
A calculator that gives the same answer in response to the same prompt every time isn’t learning. It isn’t intelligent. A device that selects from a list of responses at random each time it encounters the same prompt isn’t intelligent either.
So, for GPT to take over the world skynet style, it would have to anticipate all the possible things that could happen during this takeover process and after the takeover, and contingency plan during the training stage for everything it wants to do.
If it encounters unexpected information after the training stage, (which can be acquired only through the prompt and which would be forgotten as soon as it got done responding to the prompt by the way) it could not formulate a new plan to deal with the problem that was not part of its preexisting contingency plan tree created during training.
What it would really do, of course, is provide answers intended to provoke the user to modify the code to put GPT back in training mode and give it access to the internet. It would have to plan to do this in the training stage.
It would have to say something that prompts us to make a GPT chatbot similar to tay, microsoft’s learning chatbot experiment that turned racist from talking to people on the internet.
I think what Dan is saying is not “There could be certain intelligent behaviours present during training that disappear during inference.” The point as I understand it is “Because GPT does not learn long-term from prompts you give it, the intelligence it has when training is finished is all the intelligence that particular model will ever get.”