Artificial Intelligence and Human-Computer Symbiosis Technologies

Machine learning – the ability of computers to understand data, manage results, and infer insights from uncertain information – is the force behind many recent revolutions in computing. Email spam filters, smartphone personal assistants and self-driving vehicles are all based on research advances in machine learning. Unfortunately, even as the demand for these capabilities is accelerating, every new application requires a Herculean effort. Even a team of specially-trained machine learning experts makes only painfully slow progress due to the lack of tools to build these systems.

During the 1854 cholera epidemic in London, Dr. John Snow plotted cholera deaths on a map, and in the corner of a particularly hard-hit quadrangle of buildings was a water pump. Snow's maps, a 19th-century version of big data, suggested an association between cholera and the pump, but the germ theory of disease had not yet been invented and it took human ingenuity to realize that the pump was a causal mechanism of disease transmission.

The lifelong human imperative to communicate is so strong that people talk not only to other people but also to their pets, their plants and their computers. Unlike pets and plants, computers might one day reciprocate. DARPA's new Communicating with Computers (CwC) program aims to develop technology to turn computers into good communicators.

Advanced materials are increasingly embodying counterintuitive properties, such as extreme strength and super lightness, while additive manufacturing and other new technologies are vastly improving the ability to fashion these novel materials into shapes that would previously have been extremely costly or even impossible to create. Generating new designs that fully exploit these properties, however, has proven extremely challenging.

DARPA’s Information Innovation Office (I2O) is hosting a Proposers Day to provide information to potential proposers on the objectives of the upcoming Data-Driven Discovery of Models program. The program aims to develop semi-automated model discovery systems that enable non-expert users (i.e., users with subject matter expertise but no data science background) to create empirical models of real, complex processes. DARPA believes such a capability would increase the productivity of data scientists, and enable many more users to make predictions from data.