I think it’s wrong to say that LLMs fundamentally cannot do that. I think LLMs do do that, they just do it poorly. So poorly compared to humans that it’s tempting to round their ability to do this down to zero. The difference between near-zero and zero is a really important difference though.
I have been working a lot with LLMs over the past couple years doing AI alignment research full-time, and I have the strong impression that LLMs do a worse job of concept generalization and transfer than humans. Worse, but still non-zero. They do some. This is why I believe that current 2023 LLMs aren’t so great at general reasoning, but that they’ve noticeably improved over ~2021 era LLMs. I think further development and scale of LLMs is a very inefficient way to AGI, but nevertheless will get us there if we don’t come up with a more efficient way first. And unfortunately, I suspect that there are specific algorithmic improvements available to be discovered which will greatly improve efficiency at this specific generalization skill.
It wasn’t my intention to respond to your comment specifically, but rather to add to the thread generally. But yes, I suppose since my comment was directed at Thane then it would make sense to place this as a response to his comment so that he receives the notification about it. I’m not too worried about this though, since neither Thane nor you are my intended recipients of my comment, but rather I speak to the general mass of readers who might come across this thread.
I think it’s wrong to say that LLMs fundamentally cannot do that. I think LLMs do do that, they just do it poorly. So poorly compared to humans that it’s tempting to round their ability to do this down to zero. The difference between near-zero and zero is a really important difference though.
I have been working a lot with LLMs over the past couple years doing AI alignment research full-time, and I have the strong impression that LLMs do a worse job of concept generalization and transfer than humans. Worse, but still non-zero. They do some. This is why I believe that current 2023 LLMs aren’t so great at general reasoning, but that they’ve noticeably improved over ~2021 era LLMs. I think further development and scale of LLMs is a very inefficient way to AGI, but nevertheless will get us there if we don’t come up with a more efficient way first. And unfortunately, I suspect that there are specific algorithmic improvements available to be discovered which will greatly improve efficiency at this specific generalization skill.
I think you responded to the wrong comment.
It wasn’t my intention to respond to your comment specifically, but rather to add to the thread generally. But yes, I suppose since my comment was directed at Thane then it would make sense to place this as a response to his comment so that he receives the notification about it. I’m not too worried about this though, since neither Thane nor you are my intended recipients of my comment, but rather I speak to the general mass of readers who might come across this thread.