This type of thinking is a step in the right direction, but both the ML research and the AI alignment communities already have some quite detailed predictions about the actual mechanisms of language modeling -> intelligence:
Essentially, in this Twitter language, the LM learns Guy Typology to next-token-predict text on the internet, including Guy Who Does Maths, Guy Who Knows Facts About The Roman Empire, and a zillion others. If we had an infinite amount of text produced from any single Guy, the low-loss limit of training would model what that Guy says perfectly.
And each Guy is only a view on the world model; by learning views from many different viewpoints, the LM develops a world model consistent with those views. Sort of like neural radiance fields intuition.
There are of course architectural constraints: functionalities require more parameters as they get more complex, and a priori the optimization landscape might be difficult.. But adaptive gradient methods on transformers seem to work well so far.
This type of thinking is a step in the right direction, but both the ML research and the AI alignment communities already have some quite detailed predictions about the actual mechanisms of language modeling -> intelligence:
https://www.alignmentforum.org/posts/vJFdjigzmcXMhNTsx/simulators
https://twitter.com/jacobandreas/status/1600118539263741952 (paper submitted in May 2022)
Essentially, in this Twitter language, the LM learns Guy Typology to next-token-predict text on the internet, including Guy Who Does Maths, Guy Who Knows Facts About The Roman Empire, and a zillion others. If we had an infinite amount of text produced from any single Guy, the low-loss limit of training would model what that Guy says perfectly.
And each Guy is only a view on the world model; by learning views from many different viewpoints, the LM develops a world model consistent with those views. Sort of like neural radiance fields intuition.
There are of course architectural constraints: functionalities require more parameters as they get more complex, and a priori the optimization landscape might be difficult.. But adaptive gradient methods on transformers seem to work well so far.
Great article thank you.