Information intelligence technology will bring disruptive innovation to national defense technology

In recent years, the US Department of Defense's autonomous system has invested heavily, but information self-processing technology has not received enough attention. This is a difficult point of the independent technological revolution and should be the focus of the future. The future of autonomous technology development should move from platform-centric to data-centric, and data self-processing should first establish a robust and integrated PED architecture.

On November 28, 2016, a data analysis by Govini of the US National Defense magazine online magazine pointed out that the US Department of Defense is very confident in competing with more and more advanced competitors, and believes that through the combination of destructive and deadly weapons, such as super fast Missiles, drone bee colonies and self-killing robots can quickly surpass competitors. However, the autonomous weapons war is also facing the real challenge of military intelligence fragmentation.

Efficient super-intelligent war machines must be built with sophisticated artificial intelligence systems that allow them to collect, process and analyze large amounts of information in incredible ways to date. Some experts pointed out that although the development focus has been on autonomous spy planes, bombers and submarines, the performance of these systems will be severely limited if there is no data processing and analysis infrastructure to help with node links and provide reliable real-time intelligence.

Information architecture is a topic that is rarely discussed in the “Third Offset Strategy” and it is becoming the most difficult and challenging task. Govini's analyst of the analysis suggested that the US Department of Defense must conceive of a data architecture that meets the intelligence needs of the autonomous system. If you want to continue to use autonomous systems for large amounts of data collection, you must establish a corresponding autonomous process to achieve the actual value of the autonomous system. One of the first is to build a robust and integrated PED architecture (PED is the abbreviation for processing-processing, development-exploitaTIon and propagation-disseminaTIon) so that autonomous monitoring systems can realize their potential.

Data processing and analysis technology investment status and significance

Compared with the previous five-year average, the US Department of Defense's spending on data science and analysis in FY2016 increased by 39.4% to $1.1 billion, compared with an average annual growth rate of 39% in the previous five years. Real-time processing technology spending increased by 35.7% to $1.5 billion. These investments are seen as an important step in the transition from a platform-centric to a data-centric modernization strategy. For successful autonomous systems, the key is how the data they collect is consistent with previously collected data, that is, to achieve a more complete situational awareness. This is the true meaning of data science, analysis, machine learning and artificial intelligence supported by all independent technology investments.

With the application of the PED architecture, autonomous vehicles will be able to fully realize their potential. For the defense industry, the most important is that these architectures will open the way for the development of air, land and offshore platforms that can “cross-domain” share data.

US Department of Defense Autonomous System Investment Status and Trends

In 2011-2016, the US Department of Defense has invested nearly $60 billion in autonomous systems. The total number of drones is about 56.8%. If the military has an integrated information architecture, the US Department of Defense will be motivated to purchase more underwater and ground autonomous systems to create a global intelligent collection network. The current system tends to operate in isolation from each other, which leads to the decentralization of operational concepts and limited information sharing capabilities.

In recent years, the US military has slowed the purchase of traditional unmanned platforms and is looking for a new generation of systems. Currently, its focus has been on autonomous intelligent networks that receive data and convert it into data available for decision making. But at the same time, one aspect that the autonomy of the defense system is easily overlooked is the autonomy of data processing. That's why the DoD's investments are increasingly being directed toward cloud-based artificial intelligence, cloud computing, software and custom applications.

The reason the US Department of Defense continues to invest in specific service intelligence systems such as the Distributed Common Ground System (DCGS) is to achieve a "cross-domain solution." Each service is collecting the DCGS version for itself. Analysts believe that the autonomous PED architecture needs to be broader and more powerful than the DCGS itself. The third offset strategy should focus on enabling all of these autonomous platforms to seamlessly integrate and communicate with each other. Without a more cohesive plan to consolidate data, it makes no sense to invest more in the platform itself, and a broader, automated architecture must be implemented.

The US military's difficulties in connecting flue-type data sources have provided opportunities for Silicon Valley companies to develop solutions. The US Silicon Valley PalanTIr is developing the next generation of DCGS, but its application proposal has been rejected by the US Army. This is also an obstacle to the development of independent technology.

US Department of Defense data strategy

The ability of autonomous weapons to operate in complex theaters is at the heart of the third offset strategy. The idea is that these systems must be able to penetrate into the airspace or waters that the enemy is trying to defend. In this case, the unmanned system will be superior to the manned flight or navigation system, and this situation will require a more complex autonomous platform than currently available systems.

Edward Len, head of information systems at the US Department of Defense's Office of Information Deputy, said the hardest part of the third offset strategy was data. His office is responsible for the identification of the C4I (Command, Control, Communications, Computer, and Intelligence) architecture vision for the third offset strategy, and part of this vision is the data strategy.

One of the obstacles is the security barriers of government data to application developers or technical proficiency departments who may need to help commanders and analysts capture valuable intelligence in real time. Both creativity and innovation occur after data is available, and as it evolves, the US Department of Defense will implement a data center approach to innovate faster.

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