An ambient computing system comprising: one or more sensors configured to generate sensor signals; a controller; and a plurality of processing components including a low-power processing component (132, 136), a machine learning engine, and multiple other processing components (138, 140, 150), wherein the ambient computing system is configured to perform operations comprising: remaining in a monitoring power state until the controller receives an interrupt indicating presence of one or more sensor signals, transitioning from the monitoring power state to a processing power state upon the controller receiving the interrupt, wherein transitioning to the processing power state causes the ambient computing system to activate the low-power processing component, determining (330, 340), by the low-power processing component using the one or more sensor signals, that the machine learning engine should perform an inference pass using the one or more sensor signals, in response, performing the inference pass by the machine learning engine using the one or more sensor signals as input to generate an output that represents which of the multiple other processing components of the ambient computing system should be activated to further process the one or more sensor signals, activating the other processing components represented by the output of the machine learning engine, and processing the one or more sensor signals by the other processing components represented by the output of the machine learning engine. one or more sensors configured to generate sensor signals; a controller; and a plurality of processing components including a low-power processing component (132, 136), a machine learning engine, and multiple other processing components (138, 140, 150), wherein the ambient computing system is configured to perform operations comprising: remaining in a monitoring power state until the controller receives an interrupt indicating presence of one or more sensor signals, transitioning from the monitoring power state to a processing power state upon the controller receiving the interrupt, wherein transitioning to the processing power state causes the ambient computing system to activate the low-power processing component, determining (330, 340), by the low-power processing component using the one or more sensor signals, that the machine learning engine should perform an inference pass using the one or more sensor signals, in response, performing the inference pass by the machine learning engine using the one or more sensor signals as input to generate an output that represents which of the multiple other processing components of the ambient computing system should be activated to further process the one or more sensor signals, activating the other processing components represented by the output of the machine learning engine, and processing the one or more sensor signals by the other processing components represented by the output of the machine learning engine. remaining in a monitoring power state until the controller receives an interrupt indicating presence of one or more sensor signals, transitioning from the monitoring power state to a processing power state upon the controller receiving the interrupt, wherein transitioning to the processing power state causes the ambient computing system to activate the low-power processing component, determining (330, 340), by the low-power processing component using the one or more sensor signals, that the machine learning engine should perform an inference pass using the one or more sensor signals, in response, performing the inference pass by the machine learning engine using the one or more sensor signals as input to generate an output that represents which of the multiple other processing components of the ambient computing system should be activated to further process the one or more sensor signals, activating the other processing components represented by the output of the machine learning engine, and processing the one or more sensor signals by the other processing components represented by the output of the machine learning engine.