Nvidia Cosmos 3

It can be used to generate synthetic data to train physical AI for robots, cars, drones, etc. The world can be simulated from first person perspective to generate training data without sending robots to peoples homes.

Most of the examples they've chosen seem.. not good? What an odd mix of bad game engine and AI slop. I can't imagine that this stuff makes good training data for real-world applications.

  This release unifies those capabilities with a Mixture-of-Transformers (MoT) architecture built around two towers. 
  Reasoner tower: A vision-language model (VLM) ... This serves as the ‘brain’ that reasons about the world before any generation happens.
  Generator tower: Generates future observations and action sequences. This tower uses a diffusion-based process to generate physics-aware video and action outputs that are conditioned on the reasoner tower’s understanding.

This sort of approach (and others i've seen like it) always appeal to my inner engineer, trying to optimize and balance tradeoffs between model architectures and combine two things to yield the best of both worlds

But based on my understanding of the Bitter Lesson (http://www.incompleteideas.net/IncIdeas/BitterLesson.html), this is precisely the wrong approach in the long term. I'm linking the actual text of the bitter lesson because I think it's misunderstood (or I just don't agree with how i've seen it used in discourse). Specifically:

  The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach. 

This architecture feels specifically like "trying to build knowlege into the agent that will help in the short term" but will plateau long term. That's not to say that there won't be some interesting learnings or things built on top of it, but I doubt that there's a lot of juice to squeeze with this kind of approach IMO.

This feels like the opposite to me? The MoT architecture looks like the ideal that the Bitter Lesson alludes to - just take all of your data in all of your formats (audio, image, text, action, video) and dump it all into a single shared latent space. Then let the model sort things out, with just enough structure to handle the different requirements/output formats needed (e.g. autoregressive stuff for sequence modeling/prediction, diffusion stuff for generation).

I have the GPU but no robot. What’s the minimum viable robot needed to play with this?