[w4]

It is readily understandable if you are fluent in the jargon surrounding state of the art LLMs and deep learning. It’s completely inscrutable if you aren’t. The article is also very high level and disconnected from specifics. You can skip to FAIR’s paper and code (linked at the article’s end) for specifics: https://github.com/facebookresearch/vjepa2

If I had to guess, it seems likely that there will be a serious cultural disconnect as 20-something deep learning researchers increasingly move into robotics, not unlike the cultural disconnect that happened in natural language processing in the 2010s and early 20s. Probably lots of interesting developments, and also lots of youngsters excitedly reinventing things that were solved decades ago.

[godelski]

  > if you are fluent in the jargon surrounding state of the art LLMs and deep learning

It is definitely not following that jargon. Maybe it follows the tech influencer blog post jargon but I can definitively say it doesn't follow jargon used in research. Which, they are summarizing a research paper. Consequently they misinterpret things and use weird phrases like "actionable physics," which is self referential. "A" physics model is necessarily actionable. It is required to be a counterfactual model. While I can understand the rephrasing to clarify to a more general audience that's a completely different thing than "being fluent in SOTA work." It's literally the opposite...

Also, it definitely doesn't help that they remove all capitalization except in nouns.