• Home
  • Technology
  • Gaming
  • Entertainment
  • World & Business
  • Science
  • Sports
  • AI
HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • Home
    • Technology
    • Gaming
    • Entertainment
    • World & Business
    • Science
    • Sports
    • AI
    AI

    World Mechanics seeks two founding research scientists for physical AI

    World Mechanics says the SF Bay Area roles will help shape a research agenda that makes interpretability and safety priorities from the earliest stages of pretraining.

    KD
    SJ
    2 Sources, ,

    TLDR

    World Mechanics says it is recruiting two founding research scientists focused on interpretability, training dynamics and physical AI. The lab aims to use physical-world “ground truth” to develop models that capture causal dynamics and are inherently interpretable and controllable. Its intended applications include robotics, autonomous driving, manufacturing and scientific discovery.

    Combined views

    22.8K

    2 Sources, first seen 16d ago

    Combined views

    22.8K

    2 Sources, first seen 16d ago

    387 likes
    Today's Rank

    —

    Not ranked yet

    Today's Rank

    —

    Not ranked yet

    16d ago
    first seen 16d ago
    387 likes
    18 comments
    113 saves
    30 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    18 comments
    113 saves
    30 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    2 Sources

    @soniajoseph_We’re building the founding research team at World Mechanics, a frontier neolab for interpretable models of the physical world. Building on what we’ve learned from our LLM predecessors, physical AI gives us a rare (and historically time-sensitive!!) opportunity to make interpretability and safety first-class objectives from the earliest stages of pretraining. The physical world gives us ground truth that language often does not. We can use this physical "ground truth" to develop interpretability and pretraining methods that capture causal dynamics of the physical directly-- building inherently interpretable and controllable foundation models for robotics, autonomous driving, manufacturing, and scientific discovery. We’re looking for two Founding Research Scientists in interpretability, training dynamics, and physical AI to join us in the SF Bay Area and build this research agenda with us.
    @CSProfKGDRT @soniajoseph_: We’re building the founding research team at World Mechanics, a frontier neolab for interpretable models of the physical…

    2 Sources

    @soniajoseph_We’re building the founding research team at World Mechanics, a frontier neolab for interpretable models of the physical world. Building on what we’ve learned from our LLM predecessors, physical AI gives us a rare (and historically time-sensitive!!) opportunity to make interpretability and safety first-class objectives from the earliest stages of pretraining. The physical world gives us ground truth that language often does not. We can use this physical "ground truth" to develop interpretability and pretraining methods that capture causal dynamics of the physical directly-- building inherently interpretable and controllable foundation models for robotics, autonomous driving, manufacturing, and scientific discovery. We’re looking for two Founding Research Scientists in interpretability, training dynamics, and physical AI to join us in the SF Bay Area and build this research agenda with us.
    @CSProfKGDRT @soniajoseph_: We’re building the founding research team at World Mechanics, a frontier neolab for interpretable models of the physical…