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

TL;DR

This paper introduces Artificial Adaptive Intelligence (AAI), a new regime of machine behavior characterized by autonomous hyperparameter tuning and adaptability, bridging narrow AI and general intelligence.

Contribution

It defines AAI, proposes an adaptivity index, and organizes the field around three pathways to minimality, advancing understanding of autonomous, adaptable AI systems.

Findings

Introduces the concept of AAI as a distinct regime of machine intelligence.

Proposes an adaptivity index to measure progress towards AAI.

Analyzes three pathways to minimality: data-aware configuration, structural morphing, and self-adaptation.

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.