I'd imagine Tesla's and Waymo's AI are at the forefront of spatial cognition... this is what has made me hesitant to dismiss the AI hype as a bubble. Once spatial cognition is solved to the extent that language has been solved, a range of applications currently unavailable will drive a tidal wave of compute demand. Beyond self driving, think fully autonomous drone swarms... Militaries around the world certainly are and they're salivating.
Help me understand - my mental model of how auto AI work is that they're using neural nets to process visual information and output a decision on where to move in relation to objects in the world around them. Yes they are moving in a constrained 2D space but is that not fundamentally what animals do?
What you're describing is what's known as an "end to end" model that takes in image pixels and outputs steering and throttle commands. What happens in an AV is that a bunch of ML models produce input for software written by human engineers, and so the output doesn't come from an entirely ML system, it's a mix of engineered and trained components for various identifiable tasks (perception, planning, prediction, controls).
Tesla's problems with their multi camera non-Lidar system is precisely because they don't have any spacial cognition.
100% agree but not just military. Self-driving vehicles will become the norm, robots to mow the lawn, clean the house, eventually humanoids that can interact like LLMs and be functional robots that help out around the house.
Spatial cognition really means "autonomous robot," and nobody thinks Tesla or Waymo have the most advanced robots.
The automotive AIs are narrow pseudo-spatial models that are good at extracting spatial features from the environment to feed fairly simple non-spatial models. They don't really reason spatially in the same sense that an animal does. A tremendous amount of human cognitive effort goes into updating the maps that these systems rely on.