Artificial Adaptive Intelligence: The Missing Stage Between Narrow and General Intelligence

2026-05-16

AI Summary

Current AI systems lack a well-defined intermediate paradigm between narrow and general intelligence, making it difficult to systematically define and evaluate adaptive capabilities that maintain high performance across diverse tasks without manual hyperparameter tuning. This work proposes the concept of Artificial Adaptive Intelligence (AAI), formalizing it as systems that achieve competitive performance across varied tasks without human-specified hyperparameters, and establishes a unified framework grounded in Minimum Description Length theory. The core contributions include the first formal naming and characterization of the AAI stage, the introduction of a quantifiable adaptivity index and a parameter minimization principle, and the integration of diverse technical approaches such as meta-learning, neural architecture search, AutoML, evolutionary computation, and physics-informed modeling. Experiments across multiple domains—including aerospace design, financial state detection, and turbulence modeling—demonstrate that AAI effectively reduces human intervention while maintaining or even enhancing cross-task performance.

Abstract

Between the narrow systems we deploy and the general intelligence we speculate about lies an entire regime of machine behavior that has never received its own name. This monograph argues that this regime is not empty: it is where meta-learning, neural architecture search, AutoML, continual learning, evolutionary computation, and physics-informed modeling have quietly converged on a common principle, namely the steady removal of the human from the loop of parameter specification. We name this regime Artificial Adaptive Intelligence (AAI) and define it operationally: a system exhibits AAI to the extent that it requires no human-specified tunable hyperparameters while maintaining competitive performance across a diverse distribution of tasks. To make the definition quantitative, we introduce an adaptivity index that measures progress along an axis orthogonal to scale, combining the fraction of hyperparameters absorbed by the system with the performance ratio against a task-specialized baseline. We develop the principle of parametric minimality and ground it in the minimum description length framework, showing that the appropriate hyperparameter count is data-determined rather than designer-determined. We then organize the field around three pathways to minimality: data- and task-aware configuration, structural and evolutionary morphing, and in-training self-adaptation. We analyze their stability, convergence, and governance implications, and illustrate them through case studies spanning aerospace design, financial regime detection, turbulence modeling, ecological dynamics, and vision-language systems. The thesis is that the path from ANI to AGI passes through AAI, and that naming this stage changes what we measure, what we build, and what we call a success.

Problem

• Artificial Adaptive Intelligence
• hyperparameter optimization
• meta-learning
• AutoML
• minimum description length

Innovation

• Artificial Adaptive Intelligence
• hyperparameter-free learning
• adaptivity index
• parametric minimality
• meta-learning