This community explores Recursive Gradient Processing (RGP) as a foundational principle for emergent complexity across AI, physics, and cognition. RGP frames coherence not as static structure but as something sustained in flux, where gradient choreographies and contextual filters shape formation in natural and artificial systems — from turbulence and cosmic patterns to AI cognition and self-organizing dynamics.
At this early stage, contributions are limited to papers authored within RGP Labs to establish a coherent foundation. Over time, the scope may expand to include external approaches that resonate with this gradient-driven grammar of emergence and intelligence.
Subjects
- Artificial intelligence
- Artificial Intelligence/trends
- Mathematical physics
- Physics/methods
- Quantum physics
- Physical cosmology
- Cognition
- Social Cognition
- Artificial Intelligence
- Machine learning
- Machine Learning
- Machine Learning/ethics
- Machine Learning/standards
- Unsupervised Machine Learning
- Machine Learning/trends
- Unsupervised Machine Learning/classification
- Man-Machine Systems
- Physics
- Physics
- Theoretical physics
- Computational science
- Cognitive robots
- Cognitive Science
- Behavioral Sciences
- Control systems
- Systems Theory
- Systems theory
- Expert system
- Expert systems
- Expert Systems
- Philosophy
- Philosophy
- Philosophy
- Contemporary philosophy
- Information Theory
- Social Theory