Honest question: does the opinion of Gary Marcus still count? His criticism seems more philosophical than scientific. It's hard for me see what he builds or reasons to get to his conclusions.
I think this is a fair assessment but reason, and intelligence dont really have an established control or control group. If you build a test and say "Its not intelligent because it can't..." and someone goes out and add's that feature in is it suddenly now intelligent?
If we make a physics break through tomorrow is there any LLM that is going to retain that knowledge permanently as part of its core or will they all need to be re-trained? Can we make a model that is as smart as a 5th grader without shoving the whole corpus of human knowledge into it, folding it over twice and then training it back out?
The current crop of tech doesn't get us to AGI. And the focus to make it "better" is for the most part a fools errand. The real winners in this race are going to be those who hold the keys to optimization: short retraining times, smaller models (with less upfront data), optimized for lower performance systems.
I actually agree with this. Time and again, I can see that LLMs do not really understand my questions, let alone being able to perform logical deductions beyond in-distribution answers. What I’m really wondering is whether Marcus’s way of criticizing LLMs is valid.
I don't know but the standard reply to all of Gary Marcus' criticisms is that they don't count because it's Gary Marcus, which of course is a big honking ad-hominem.
I think this is a fair assessment but reason, and intelligence dont really have an established control or control group. If you build a test and say "Its not intelligent because it can't..." and someone goes out and add's that feature in is it suddenly now intelligent?
If we make a physics break through tomorrow is there any LLM that is going to retain that knowledge permanently as part of its core or will they all need to be re-trained? Can we make a model that is as smart as a 5th grader without shoving the whole corpus of human knowledge into it, folding it over twice and then training it back out?
The current crop of tech doesn't get us to AGI. And the focus to make it "better" is for the most part a fools errand. The real winners in this race are going to be those who hold the keys to optimization: short retraining times, smaller models (with less upfront data), optimized for lower performance systems.