In the future, computing will be much more integrated with our physical and social environment; computers will be capable of self-learning, and will need to be able to process and distribute massive volumes of data at an unimaginable scale.

The new interactions will require new theory, design tools, development paradigms, and run-time support to handle the challenges of distributed sensing, privacy, information distillation, control, robustness, system troubleshooting, energy, and sustainability, among others. The unprecedented amounts of data will require novel approaches and close interactions with application experts.

Three examples of approaches being pursued by CSL researchers include adaptive exploitation; utilization of tools from information theory, machine learning, game theory and optimal control, and signal processing to advance theoretical and practical aspects of information processing and decision-making in uncertain environments under resource and complexity constraints; and new bio-inspired (neurosynaptic) and communication-inspired (Shannon-inspired) computing platforms that are moving away from traditional computer architecture design.