llms right now work like pre cnn computer vision based on MLP's. By this i mean brute force of a model not really built for the task, and lacking a task specific inductive bias, being made work with unfathomable volumes of data and sheer brute scale.

if you look at the damage being done in the name of making this work for nlp, a forseeable situation, then you might also understand why most who could have done this sooner, never did so for fear of repeating mistakes we should be learning from, which we get for free if we just heed history.

LLMs require massive datasets and massively parallel algorithms for processing them.

Nobody knew that LLM's were an option. The architecture was basically waiting there for someone to say, "do that, but turn it up to 11," if I understand right.

Technically text generation was there way before self driving (e.g markov chain generators are there from the 1990s).

The hard thing was to make it sound smart though.

Why would self driving be easier? Good text generation implies some general level of intelligence, while driving is more specialized.

> How come self-driving came years before LLMs

Did it? Outside of very limited testing zones, self driving _still_ doesn't really exist

There’s lots of ways of attacking the problem sufficiently to get to 95% and we’ve spent decades on object recognition

a) Self driving was far from completely solved before LLMs.

b) Text generation essentially means passing the Turing test, which for a long time was the bar for general intelligence. Locomotion does not necessarily require general intelligence.