arXiv:2606.11988v1 Announce Type: new Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling. In this paper, we offer a machine learning perspective on uncertainty modeling for dynamical systems, which has been studied much less so far. In particular, we ask: what uncertainties do we need for dynamical systems? We discuss sources of uncertainty, clarify their nature (aleatoric or epistemic), and consider how the obj
This publication addresses a foundational machine learning challenge — rigorously quantifying uncertainty in dynamical systems — which is gaining urgency as AI models are deployed in real-world, safety-critical applications.
Improved uncertainty quantification for dynamical systems is crucial for developing robust, reliable AI, particularly for autonomous agents and intelligent control systems that operate in complex, unpredictable environments.
The ability to distinguish between different types of uncertainty (aleatoric vs. epistemic) in dynamic AI applications will lead to more nuanced model behavior and ultimately safer deployment.
- · AI safety researchers
- · Autonomous vehicle developers
- · Robotics industry
- · Control systems engineers
- · Developers of brittle or unexplainable AI systems
Better understanding of AI model limitations in dynamic scenarios leading to more cautious deployment.
Accelerated development of more reliable and trustworthy AI for complex physical and operational systems.
Broader public and regulatory acceptance of AI in high-stakes applications, enabled by clearer uncertainty bounds.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG