It’s because they’re hitting model size constraints. There’s only so much memory bandwidth you can get between racks or even rack spaces and memory bandwidth is the constraint for nearly every ml thing.
Expect a reversal once a more memory dense component hits.
There’s no reason to think that the architecture itself can scale indefinitely. It might very well be that LLMs have some hard constraints on the scope of the problems they’re capable of solving.
Of course, that’s what I’m saying. Physical constraints of hardware mean there’s a limit to how much further (read: larger in terms of working memory footprint, because that’s how they’re getting “better” and better “frontier” models) development can continue until a more dense component comes along.
I meant that simple making models bigger might not actually make them more capable. So even if you had unlimited hardware to play with, you might have to find a different approach.
You could create a way to measure the idea of capability that would bear that out but from a pure discrete mathematics perspective, no, you only get better with a larger memory footprint.
There’s a lot of ways to make that faster or make that behave like a process running on a bigger memory footprint, but ultimately that’s the constraint.
And companies competing in the field of ai can’t justify the expense of cutting down their gigantic model to only know how to identify wood because that has a known and limited impact. They already said they’re shooting for unlimited immeasurable impact on the scale of replacing all human labor and got massive funding for it.
It doesn’t matter if it’s easier to do one backflip, you asked me to triple dog dare you to do a million backflips. Well… we’re waiting!
Again, there is no reason to think that you can just keep making the model bigger and keep getting improved capability that way. In fact, we already know that’s not the case because simply making them bigger stopped being the focus. The real breakthrough is going to come from better algorithms.
It’s because they’re hitting model size constraints. There’s only so much memory bandwidth you can get between racks or even rack spaces and memory bandwidth is the constraint for nearly every ml thing.
Expect a reversal once a more memory dense component hits.
There’s no reason to think that the architecture itself can scale indefinitely. It might very well be that LLMs have some hard constraints on the scope of the problems they’re capable of solving.
Of course, that’s what I’m saying. Physical constraints of hardware mean there’s a limit to how much further (read: larger in terms of working memory footprint, because that’s how they’re getting “better” and better “frontier” models) development can continue until a more dense component comes along.
Every singularity a sigmoid.
I meant that simple making models bigger might not actually make them more capable. So even if you had unlimited hardware to play with, you might have to find a different approach.
You could create a way to measure the idea of capability that would bear that out but from a pure discrete mathematics perspective, no, you only get better with a larger memory footprint.
There’s a lot of ways to make that faster or make that behave like a process running on a bigger memory footprint, but ultimately that’s the constraint.
And companies competing in the field of ai can’t justify the expense of cutting down their gigantic model to only know how to identify wood because that has a known and limited impact. They already said they’re shooting for unlimited immeasurable impact on the scale of replacing all human labor and got massive funding for it.
It doesn’t matter if it’s easier to do one backflip, you asked me to triple dog dare you to do a million backflips. Well… we’re waiting!
Again, there is no reason to think that you can just keep making the model bigger and keep getting improved capability that way. In fact, we already know that’s not the case because simply making them bigger stopped being the focus. The real breakthrough is going to come from better algorithms.