A really frustrating partial presentation, given an apparent lack of testing with a spread of efforts for each model.
Given that there's no reason to believe that Fable's xhigh is comparable to GPT-sol's xhigh, or Opus xhigh, for that matter, it would be far more useful to see the effort level where these tasks no longer achieved their goals.
Please forgive my naivety, but are world models (once they are in a consumer-ready form) expected to outperform any currently existing LLM on these sorts of tasks (i.e. of the physical world)?
From what I have seen "world models" are more like video models. The idea is: from a video clip, predict the next frame, like LLMs predict the next word/token from a bit of text.
The idea is that these models should be able to internalize the laws of physics and properties of objects just like LLMs do with grammar rules. To make training more efficient, frames from game engines with hardcoded physics are used. The application is mostly robotics, including autonomous vehicles.
This is a bit different, the idea is to write a physics engine, it means numbers: like for an airplane model, calculate the climb rate. The application is mostly science and engineering.
Given that there's no reason to believe that Fable's xhigh is comparable to GPT-sol's xhigh, or Opus xhigh, for that matter, it would be far more useful to see the effort level where these tasks no longer achieved their goals.
The idea is that these models should be able to internalize the laws of physics and properties of objects just like LLMs do with grammar rules. To make training more efficient, frames from game engines with hardcoded physics are used. The application is mostly robotics, including autonomous vehicles.
This is a bit different, the idea is to write a physics engine, it means numbers: like for an airplane model, calculate the climb rate. The application is mostly science and engineering.