Defense Advanced Research Projects AgencyTagged Content List

Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

Showing 23 results for AI + Algorithms RSS
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.
It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so?
Machine learning (ML) systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. As these systems have progressed, deep neural networks (DNN) have emerged as the state of the art in ML models. DNN are capable of powering tasks like machine translation and speech or object recognition with a much higher degree of accuracy. However, training DNN requires massive amounts of labeled data–typically 109 or 1010 training examples. The process of amassing and labeling this mountain of information is costly and time consuming.
Over its 60-year history, DARPA has played a leading role in the creation and advancement of artificial intelligence (AI) technologies that have produced game-changing capabilities for the Department of Defense. Starting in the 1960s, DARPA research shaped the first wave of AI technologies, which focused on handcrafted knowledge, or rule-based systems capable of narrowly defined tasks. While a critical step forward for the field, these systems were fragile and limited.
Today’s machine learning systems are more advanced than ever, capable of automating increasingly complex tasks and serving as a critical tool for human operators. Despite recent advances, however, a critical component of Artificial Intelligence (AI) remains just out of reach – machine common sense. Defined as “the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate,” common sense forms a critical foundation for how humans interact with the world around them.