> neural networks are algorithms

... are they?

Algorithms are predictable:

1. it's really difficult to produce true randomness in an algorithm, which is why there's a whole area of study surrounding the qualities of pseudo-randomness, specifically with regards to the generation of cryptographic material with most conclusions being that we should draw entropy from the environment (e.g. electrical noise as cloudflare does - the lava lamp is a distraction)

2. when algorithms are not predictable despite being mathematically sound, this is indicative of a problem nowadays called "numerical stability" which again spawned its own area of study.

CGoL shows how really simple rules (couple of bytes of data) can lead to extremely complex outcomes and you have the BB turing-golf aspect as proof of the worst case scenario.

And with current models being measured in hundreds of gigabytes, with frontier labs deprecating then removing the "temperature" parameter (which never worked to begin with, temp=0 did not result in reproducible results), where exactly is the confidence of this 10^10 prediction coming from?