Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

Extracting information and insights from massive datasets; "big data"; "data mining"

Showing 43 results for Data + Programs RSS
Social media, sensor feeds, and scientific studies generate large amounts of valuable data. However, understanding the relationships among this data can be challenging. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks produced from these data sources and draw conclusions from the observed patterns.
Rapid comprehension of world events is essential for informing U.S. national security - a task that becomes more difficult as the amount of unstructured, multimedia information grows exponentially. Humans make sense of events by organizing them into narrative structures that occur frequently. These structures are abstracted into schemas, which are organized units of knowledge that represent a pattern of memory used in human cognition.
| AI | Analytics | Data |
In supervised machine learning (ML), the ML system learns by example to recognize things, such as objects in images or speech. Humans provide these examples to ML systems during their training in the form of labeled data. With enough labeled data, we can generally build accurate pattern recognition models.
| AI | Algorithms | Data |
The U.S. Government operates globally and frequently encounters so-called “low-resource” languages for which no automated human language technology capability exists. Historically, development of technology for automated exploitation of foreign language materials has required protracted effort and a large data investment. Current methods can require multiple years and tens of millions of dollars per language—mostly to construct translated or transcribed corpora.
Machine common sense has long been a critical—but missing—component of AI. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. The MCS program seeks to create the computing foundations needed to develop machine commonsense services to enable AI applications to understand new situations, monitor the reasonableness of their actions, communicate more effectively with people, and transfer learning to new domains.