Now this is how[0] we get some of the most magical Star Trek technology that eludes us to this day, such as automatic doors. Because if you notice, they work much, much better than real-life ones, because they seem to be doing something like this:

  if(within 10 meters of door then) {
    if(Jev(
       [A] Intends to go through, expects doors to open
       [B] Approaches with no intent to pass
       [C] Passing by, loiters, or otherwise
       [D] Other
    ) == most definitely A) {
      // open doors, +/- identity/security/interlocks check
    } else {
      // ignore
    }
  }

Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer. The computer seems to be aware of the user and surrounding, and actively infers intent from context, to DWIM ("do what I mean") and when they mean it, instead of doing dumb things[1] on simple triggers.

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[0] - The direction, not final implementation - surely we can work out how to do it more efficiently than wrapping around final stage of LLM. But the point is, multimodal.

[1] - Obviously it's a fictional show, but in this, both Watsonian and Doylist explanations align near-perfectly: this is/portrays advanced technology, that Just Works and doesn't do stupid shit. Same intent recognition algorithm is there - fictionally in the computer, in reality in the minds of on-set technicians.

To do this you need video/motion understanding, the intent cannot be judged from still images or state descriptions.

We’ve had the tool to do this since mid 2025, V-JEPA2 [1], Yann Lecun’s last work at Meta.

It runs at several FPS on a macbook and can even be trained locally. Chaining it with Jev for decision-making would probably work great!

[1] https://ai.meta.com/research/vjepa/

> Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer.

Almost like the Star Trek mechanisms can infer perfect intent.

Like there’s a hidden script or something.

More seriously, I think there’s real value in an automatic door that behaves consistently rather than one that tries to infer messy human intent. Real life isn’t a TV show and there’s both ambiguity in how people behave and how they even intend to behave. It’s mostly not hard to understand how a proximity sensor door will function. Using a black-box classifier to improve that won’t necessarily make people like it more. And calling up to the cloud for every sensor event, ignoring privacy issues, adds weird latency and a huge failure mode during data center outages.

How does the model get context to decide ABCD ?