Refined intelligence theory: artificial intelligence regarding complex dynamic objects

Abstract

A new round of scientific and technological change and industrial transformation is emerging. Based on deep learning and big data, a third achievement of artificial intelligence is represented by AlphaGo and other typical application scenarios. When dealing with complex dynamic objects, the traditional artificial intelligence based on statistical linear dynamic modeling experiences bottlenecks related to interpretability, generalization, and reproducibility. It is urgent that a new generation of artificial intelligence theory be established that is based on complexity and multi-scale analysis, which we refer to as refined intelligence. To deal with the non-linearity of complex systems, we constructed a systems learning theory of embedded domain knowledge and a mathematical physical mechanism that is accurate and intelligent at three performance levels: complex data perception, complex system refined construction, and complex intelligent behavior analysis. Specifically, we built a scientific data system with interpretability, including embedded spatiotemporal characteristics and mathematical laws, through complex data perception. We also built a multi-level, multi-scale, and interpretable artificial intelligence dynamic learning model that can deal with the nonlinear relationship of complex logic based on complex system-defined construction. We developed a new theory and method for interpreting and controlling the evolution of artificial-intelligence-oriented behavior and global dynamic analysis based on complex intelligent-behavior analysis. We applied the above refined intelligence theory to the proposed method for crowd intelligence with crowd entropy, and found that it can measure complexity and provide effective guidance regarding system stimulation and aggregation behavior.