LLMs are so much more than you are assuming… text, images, code are merely abstractions to represent reality. Accurate prediction requires no less than usefully generalizable models and deep understanding of the actual processes in the world that produced those representations.
I know they can provide creative new solutions to totally novel problems from firsthand experience… instead of assuming what they should be able to do, I experimented to see what they can actually do.
Focusing on the simple mechanics of training and prediction is to miss the forest for the trees. It’s as absurd as saying how can living things have any intelligence? They’re just bags of chemicals oxidizing carbon. True but irrelevant- it misses the deeper fact that solving almost any problem deeply requires understanding and modeling all of the connected problems, and so on, until you’ve pretty much encompassed everything.
Ultimately it doesn’t even matter what problem you’re training for- all predictive systems will converge on general intelligence as you keep improving predictive accuracy.
I know they can provide creative new solutions to totally novel problems from firsthand experience… instead of assuming what they should be able to do, I experimented to see what they can actually do.
Focusing on the simple mechanics of training and prediction is to miss the forest for the trees. It’s as absurd as saying how can living things have any intelligence? They’re just bags of chemicals oxidizing carbon. True but irrelevant- it misses the deeper fact that solving almost any problem deeply requires understanding and modeling all of the connected problems, and so on, until you’ve pretty much encompassed everything.
Ultimately it doesn’t even matter what problem you’re training for- all predictive systems will converge on general intelligence as you keep improving predictive accuracy.