Harnessing Complexity

The exponential growth of diverse science data represents an unprecedented opportunity to make substantial advances in complex science and engineering, such as discovery of novel materials or drugs. However, without tools to unify principles, results, models and other kinds of data into a single computational representation, it is difficult to relate data from any one scientific problem or area to the broader body of knowledge.

Uncertainty is sometimes unavoidable. But in the world of scientific computing and engineering, at least, what’s worse than uncertainty is being uncertain about how uncertain one is.

Whether designed to predict the spread of an epidemic, understand the potential impacts of climate change, or model the acoustical signature of a newly designed ship hull, computer simulations are an essential tool of scientific discovery. By using mathematical models that capture the complex physical phenomena of the real world, scientists and engineers can validate theories and explore system dynamics that are too costly to test experimentally and too complicated to analyze theoretically.

Robots can learn to recognize objects and patterns fairly well, but to interpret and be able to act on visual input is much more difficult.  Researchers at the University of Maryland, funded by DARPA’s Mathematics of Sensing, Exploitation and Execution (MSEE) program, recently developed a system that enabled robots to process visual data from a series of “how to” cooking videos on YouTube. Based on what was shown on a video, robots were able to recognize, grab and manipulate the correct kitchen utensil or object and perform the demonstrated task with high accuracy—without additional human input or programming.  

Conventional optical imaging systems today largely limit themselves to the measurement of light intensity, providing two-dimensional renderings of three-dimensional scenes and ignoring significant amounts of additional information that may be carried by captured light.