arXiv:2407.06312v2 Announce Type: replace-cross Abstract: Many systems resist analytical modeling, making data-driven inference of dynamics important. Yet data-driven methods can fail to converge or generalize, leaving open a central question: When can system behavior be learned reliably from data, and when is such learning impossible? We answer this question using adversarial dynamical systems to identify the boundary between accessible and inaccessible regimes. In Koopman operator learning, a leading framework for representing nonlinear dynamics through linear spectral objects, we design opt
The proliferation of AI and data-driven systems necessitates a deeper understanding of their fundamental limitations, especially as these systems are applied to complex, non-linear domains.
This research provides a theoretical framework to identify the boundaries of what data-driven learning can reliably achieve, offering critical insights for the development and deployment of robust AI systems.
The ability to characterize when data-driven learning succeeds or fails allows for more informed allocation of resources and realistic expectations for AI in complex dynamic environments.
- · AI researchers
- · Developers of mission-critical AI systems
- · Industries relying on complex system modeling
- · Overly optimistic AI development projects
- · Systems attempting to learn inherently inscrutable dynamics
This research will lead to more robust and reliable AI models, particularly in dynamic, real-world applications.
Understanding these boundaries could accelerate AI development by focusing efforts on learnable problems and developing new techniques for inherently difficult ones.
The identified limitations may drive innovation in hybrid modeling, combining analytical approaches with data-driven methods where pure learning is insufficient.
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