2025/07/10 by Eunsu Baek, Keondo Park, Baek, Eunsu +9
Computer Science · Environmental Science · #Adaptation (eye) #Adaptive system #Air Quality Monitoring and Forecasting #Complex adaptive system #Domain Adaptation and Few-Shot Learning #Generalization #Limiting #Mobile Crowdsensing and Crowdsourcing #Paradigm shift #Scale (ratio) #Sustainability
paper · pdf · doi:10.48550/arxiv.2507.07820
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs significant environmental, economic, and ethical costs, limiting sustainability and equitable access. Inspired by biological sensory systems, where adaptation occurs dynamically at the input (e.g., adjusting pupil size, refocusing vision)--we advocate for adaptive sensing as a necessary and foundational shift. Adaptive sensing proactively modulates sensor parameters (e.g., exposure, sensitivity, multimodal configurations) at the input level, significantly mitigating covariate shifts and improving efficiency. Empirical evidence from recent studies demonstrates that adaptive sensing enables small models (e.g., EfficientNet-B0) to surpass substantially larger models (e.g., OpenCLIP-H) trained with significantly more data and compute. We (i) outline a roadmap for broadly integrating adaptive sensing into real-world applications spanning humanoid, healthcare, autonomous systems, agriculture, and environmental monitoring, (ii) critically assess technical and ethical integration challenges, and (iii) propose targeted research directions, such as standardized benchmarks, real-time adaptive algorithms, multimodal integration, and privacy-preserving methods. Collectively, these efforts aim to transition the AI community toward sustainable, robust, and equitable artificial intelligence systems.