World Models
13 days ago
- #AI
- #Machine Learning
- #World Models
- Major AI labs are converging on the development of World Models, with key figures like Yann LeCun and Ilya Sutskever highlighting their importance.
- World Models predict the next state or observation, understanding causal laws in environments like games, codebases, or markets, unlike current models that focus on token prediction.
- Examples of existing World Models include recommendation engines, algorithmic trading systems, and weather models, which predict state transitions rather than just patterns.
- Adversarial domains like business and finance require World Models to adapt to reactive environments, where static models fail.
- Language understanding enhances World Models by allowing them to process and predict outcomes from textual data like earnings calls or internal memos.
- Value functions are critical in World Models, estimating future rewards and enabling efficient multi-step planning by pruning bad trajectories early.
- The feedback loop in World Models creates a competitive advantage, as continuous updates from real-world outcomes improve model accuracy over time.
- Current LLMs lack the ability to predict real-world outcomes, as they are trained on imitation rather than causality, limiting their effectiveness in dynamic environments.
- The development of World Models is driven by diminishing returns in next-token prediction, advancements in video models as physics simulators, and insights from interpretability research.
- The first company to build reliable World Models for high-value domains will gain a significant edge, as these models improve through deployment and real-world feedback.