Electronic Intelligence (ELINT), often known as E-Intelligence, is intelligence obtained through electronic sensors. Other than personal communications, ELINT intelligence is usually obtained. The goal is usually to determine a target's capabilities, such as radar placement. Active or passive sensors can be employed to collect data. A provided signal is analyzed and contrasted to collected data for recognized signal types. The information may be stored if the signal type is detected; it can be classed as new if no match is found. ELINT collects and categorizes data. In a military setting (and others that have adopted the usage, such as a business), intelligence helps an organization make decisions that can provide them a strategic advantage over the competition. The term "intel" is frequently shortened. The two main subfields of signals intelligence (SIGINT) are ELINT and Communications Intelligence (COMINT). The US Department of Defense specifies the terminologies, and intelligence communities use the categories of data reviewed worldwide.

"Most professional scientists aim to be the first to publish their findings, because it is through dissemination that the work realises its value." So wrote mathematician James Ellis in 1987. By contrast, he went on, "the fullest value of cryptography is realised by minimising the information available to potential adversaries." Ellis, like Alan Turing, and so many of the driving forces in the development of computers and the Internet, worked in government signals intelligence, or SIGINT. Today, this covers COMINT (harvested from communications such as phone calls) and ELINT (from electronic emissions, such as radar and other electromagnetic radiation).

Mercury Systems, Inc. today unveiled the EnterpriseSeries RES AI rugged rackmount server line, bringing High Performance Computing (HPC) capabilities to aerospace, defense and other mission-critical applications at the edge. "The proliferation of sensors, ever-growing data loads and the evolution of complex deep learning neural networks continues to increase computational demands, driving the need for supercomputing infrastructure closer to the edge," said Scott Orton, Vice President and General Manager of Mercury's Trusted Mission Solutions group. "Through close collaboration with technology leaders such as NVIDIA and Intel, we've developed reliable parallel computing systems that accelerate demanding artificial intelligence (AI), signal intelligence (SIGINT), and sensor fusion applications where it's needed the most." Why it Matters: Evolving compute-intensive AI, virtualization, big data analytics, SIGINT, autonomous vehicle, Electronic Warfare (EW) and sensor fusion applications require data center supercomputing capabilities closer to the source of data origin. Delivering HPC capabilities to the edge presents challenges as every application has its own security, performance, footprint, budget and reliability requirements.

The intelligence community's (IC) stock in trade has always been knowing what nobody else knows. Now it's looking to tap into new technology to expand its ability to forecast geopolitical events in several ways, including finding out what everybody knows. The Intelligence Advanced Research Projects Activity (IARPA), the IC's lead research arm, is looking to bring artificial intelligence to bear in a number of intelligence-gathering areas. This includes two projects at opposite ends of the spy game: at one end, intercepting and interpreting classified communications, and at the other, finding a way to read the tea leaves in massive amounts of public information. IARPA recently launched a competition to support its Mercury program, which focuses on the more familiar (to the public anyway) element of spycraft known as signals intelligence.