Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what's critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of ?brain processing power?. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes.