Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems

TL;DR

This paper explores how biological adaptive behavior can inspire the development of flexible, online-learning AI systems capable of rapid environmental adaptation, by reviewing neuroscience insights and proposing brain-inspired approaches.

Contribution

It synthesizes biological and AI research to propose new brain-inspired methods for creating more adaptive artificial intelligence.

Findings

Neuroscience studies reveal mechanisms of animal learning and adaptation.

AI progress parallels biological insights in adaptive behavior.

Brain-inspired algorithms show promise for flexible AI systems.

Abstract

Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop "adaptive intelligence," defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize, and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their world models. In this Perspective, I will review the behavioral and neural foundations of adaptive biological intelligence, the parallel progress in AI, and explore brain-inspired approaches for building more adaptive algorithms.