AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
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.
