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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

Biomolecular machines are widely present in nature, especially in complex living organisms, and are involved in important biological processes. Inspired by nature and increasingly mature synthetic technologies, a series of artificial molecular machines (AMMs) that exhibit similar processes and functions as biomolecular counterparts have been developed, and to date, some dramatic achievements have been obtained. Herein, the use of AMMs in smart systems and materials with controllable regulations and interesting functions are summarized, presenting the specific micro‐ to macroscale applications in solid surface modification, transmembrane transport, smart catalysts, liquid crystals, artificial molecular muscles, and stimuli‐responsive polymers. The challenges of developing novel complex AMMs with intelligent functions are discussed, and some potential solutions are proposed.

Many biophysical and biochemical properties of pathogenic tissues are different from those of healthy tissues. These disease symptom‐associated properties include pH, reduction–oxidation conditions, enzyme generation and expression, blood glucose concentration, mechanical stiffness and strain, and temperature. Autonomous drug release that uses one or more of these disease symptom‐associated properties as the release trigger minimizes the delay in treatments and allows for the release of medications with precisely controlled amounts and with desired spatiotemporal patterns. Herein, a comprehensive assessment of current research progress on autonomous drug release systems (ADRS) is provided. The representative symptoms‐associated properties that can be potentially used as the endogenous stimuli are introduced. The autonomous drug systems that utilize these symptom‐associated signals as the endogenous stimulation signals are discussed, followed by the discussion of current challenges and opportunities in this field, as well as possible future directions in ADRS.