'World Models,' an old idea in AI, mount a comeback

I'm sure neural networks are a great tool here, but I don't know how the training would proceed effectively off "mere data"; too much of the data we have is incomplete, inaccurate, or outright fantasy or misinformation or out of the ordinary.

I could see this being the domain of fleets of robots, many different styles, compositions, materials, etc. Send ten robots in to survey a room - drones, crawlers, dogs, rollers, etc - they'll bang against things, knock things off shelves, illuminate corners, etc. The aggregate of their observations is the useful output, kinda like networked toddlers.

And yeah, unfortunately, sometimes this means you just need to send a swarm of robots to attack a city bus... or a bank... to "learn how things work." Or an internment camp. Don't get upset, guy, we're building a world model.

Anybody wanna give me VC money to work on this?

When you're training an AI, that "mere data" adds up. Random error averages out, getting closer to zero with every data point. Systematic error leaks information about the system that keeps making the error.

A Harry Potter book doesn't ruin an AI's world model by contaminating reality with fantasy. It gives it valuable data points on human culture and imagination and fiction tropes and commercially successful creative works. All of which is a part of the broader "reality" the AI is trying to grasp the shape of as it learns from the vast unstructured dataset.

The AI learns nothing from Harry Potter other than the statistical likelihood of one token appearing after another.

The AI is trying to grasp nothing.