To be fair an argument can be made for the Lego block one, using a novel combination of existing technologies to get better results is how nearly all innovation happens in machine learning.
Proving a thing that’s only known empirically is extremely valuable, too. We’ve an enormous amount of evidence that the Riemann hypothesis is correct - we can produce an infinite amount of points on the line, in fact - but proving it is a different matter.
Especially in ML too. It’s currently easier to integrate multiple small specialised models than to train a big model for every use case. If I understand correctly, that was one of the main motivations for Anthropic developing the Model Context Protocol, including interacting with LLMs from front-end clients.
To be fair an argument can be made for the Lego block one, using a novel combination of existing technologies to get better results is how nearly all innovation happens in machine learning.
Proving a thing that’s only known empirically is extremely valuable, too. We’ve an enormous amount of evidence that the Riemann hypothesis is correct - we can produce an infinite amount of points on the line, in fact - but proving it is a different matter.
And for the kid challenging the 0.1% result, that’s about as close to pure scientific method as you can get.
Especially in ML too. It’s currently easier to integrate multiple small specialised models than to train a big model for every use case. If I understand correctly, that was one of the main motivations for Anthropic developing the Model Context Protocol, including interacting with LLMs from front-end clients.