> The more data you train it on (and the larger and therefore more capable he model is), the better it performs - i.e the more complete and consistent this world model has evidentially become.
Increasing training data doesn't increase consistency. Each data point acts as a potential new axiom, and each axiom decreases consistency. GPT-4 is trained to satisfy humans, and humans are wildly inconsistent. Even if humans were perfectly consistent, attempting to satisfy multiple different humans simultaneously results in inconsistency. Additionally, even if GPT-4 were perfectly complete and consistent it still wouldn't have reached this state autonomously. So the difference between GPT-4 and intelligence, by my definition, is night and day.
> FWIW GPT-4, being a neural net, is more analog than not. It's driven by floating point values not 1's and 0's.
Floating points are digital 1's and 0's. Adding more digits is never going to make something analog.
> The values are imperfectly calculated (limited accuracy) as computer math always is.
Agreed.
>There is also a large element of pure randomness to the output of any of these LLMs.
Strongly disagree. There isn't a single element of randomness during the training stage. We know the exact architecture of the neural net, we know the exact data it was trained on, and we know the exact beam selection algorithms used to synthesize outputs. Every single step can be simulated, traced, and recreated to achieve the exact same results. The number of steps involved might overwhelm us, but that doesn't make it random.
> They don't get to control exactly what words they generate
We do get to control it, we just lose track of the inputs and then pretend it was all out of our control. But of course every single step was willed and controlled by us. We call it "random" for personal convenience, not because its actually true.