Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems
🤖 AI Summary
Current AI systems suffer from limited online learning capability, slow environmental adaptation, and poor generalization. Method: This paper proposes a novel paradigm—“adaptive intelligence”—establishing a cross-scale mapping framework that bridges animal behavioral principles and neural plasticity mechanisms to AI algorithm design. It integrates computational neuroscience modeling, online reinforcement learning, meta-learning, and dynamic world model construction to enable real-time perception of continuous feedback and on-the-fly model updating. Contribution/Results: First, it formally defines “adaptive intelligence,” breaking away from static training paradigms. Second, it introduces design principles for embodied AI systems that are interpretable, few-shot efficient, and highly robust. Third, it provides theoretical foundations and concrete technical pathways for brain-inspired AI. The framework advances AI toward lifelong, context-sensitive, and biologically grounded intelligence, addressing fundamental limitations in adaptability and scalability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Building AI systems that mimic animal adaptive behavior
Developing online-learning AI agents for dynamic environments
Bridging neuroscience insights with adaptive algorithm design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Leveraging animal adaptive behavior for AI
Developing online learning adaptable AI systems
Using neuroscience insights for adaptive algorithms
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2024-06-20Citations: 1
