Causal Physical World Foundation Model (WFM)
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A Causal Physical World Foundation Model (WFM) is a world model that can create multi-modal representations (to predict real-world outcomes).
- Context:
- It can typically construct Internal Representation of physical environments through multi-modal input processing.
- It can typically simulate Future State based on current observations and physics-based rules.
- It can typically predict Environmental Change using causal reasoning mechanisms and dynamical system models.
- It can typically integrate Sensory Input across multiple modalities including text data, visual data, and audio data.
- It can typically maintain Spatial Relationship between physical objects in virtual representations.
- ...
- It can often generate Action Plan for autonomous systems based on environmental predictions.
- It can often support Decision Making through outcome simulation and risk assessment.
- It can often adapt Internal Model based on environmental feedback and prediction errors.
- It can often enable Robotic Control through world state prediction and action consequence modeling.
- ...
- It can range from being a Simple World Foundation Model to being a Complex World Foundation Model, depending on its representational capacity.
- It can range from being a Domain-Specific World Foundation Model to being a General World Foundation Model, depending on its application scope.
- It can range from being a Deterministic World Foundation Model to being a Probabilistic World Foundation Model, depending on its uncertainty handling.
- ...
- It can have Model Component consisting of encoder functions, prediction functions, and state representations.
- It can compute Future Prediction through mathematical formulation where s(t+1) = Pred(h(t), s(t), z(t), a(t)).
- It can process Model Input including observations, current states, and potential actions.
- It can produce Model Output in the form of predicted states and expected outcomes.
- ...
- Examples:
- World Foundation Model Categories, such as:
- Domain-Specific World Foundation Models, such as:
- Multi-Modal World Foundation Models, such as:
- Physics-Based World Foundation Models, such as:
- Implementation Approaches, such as:
- Neural World Foundation Models, such as:
- Hybrid World Foundation Models, such as:
- ...
- World Foundation Model Categories, such as:
- Counter-Examples:
- Large Language Models, which focus on linguistic patterns rather than causal reasoning about physical world.
- Scientific Models, which represent specific phenomena without the general-purpose learning capacity.
- Control Theory Models, which optimize for specific engineering objectives rather than general world understanding.
- Statistical Prediction Models, which rely on correlations rather than causal mechanisms.
- Video Generation Models, which create visual content but lack true physical understanding.
- See: World Model, Foundation Model, Causal Model, Physical Simulation, Digital Twin, Predictive System.
References
2022
- (LeCun, 2022) ⇒ [[::Yann LeCun]]. ([[::2022]]). “A Path Towards Autonomous Machine Intelligence.”
- QUOTE: How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combine concepts such as configurable predictive world model, behavior-driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.