What best advances AI world models for complex environments?

The answers emphasize learning from real or simulated environments—whether through observation, feedback, consequence, or interaction—as key to improving AI world models.

5 answers

Answers

  • Anastasis Germanidis

    Co-Founder & CTO · Runway

    Learned World Models

    Anastasis Germanidis says Learned World Models are the more scalable path, because the rules of the world don't have to be programmed and the model learns them from observation.

    Not Boring by Packy McCormick · Not Boring by Packy McCormick · Read

  • Ali Ansari

    Founder & CEO · Micro1

    Innate Sense of Consequence

    Ali Ansari points to an innate sense of consequence, saying that without it models can't fundamentally learn what they should from the consequences humans induce, while humans learn quickly from that innate feeling.

    Antler Global · Watch on YouTube

  • Sam Altman

    Chief Executive Officer · OpenAI

    Real‑World Deployment with Feedback

    Sam Altman says his approach is to put things out into the world and get feedback from real customers, seeing where they break and don't break, because that is how you make a good and safe product.

    David Senra · David Senra · Listen at 34:37

  • Sara Hooker

    Co-Founder · Adaption

    Continuous Interaction Learning

    Sara Hooker says the core need is models that interact and continuously learn from their environment, calling that efficiency of interaction and continuous learning what she thinks about a lot.

    AI Engineer · Watch on YouTube

  • Thomas Wolf

    Co-founder - Chief Science Officer · Hugging Face

    RL in Simulated Environments

    Thomas Wolf points to training models in full RL environments where they explore to achieve a single goal, describing this as an increasingly large part of model training.

    The MAD Podcast with Matt Turck · Watch on YouTube

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