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Cyber Warfare: Protecting the Soldier

(Published in CLAWS Scholar Warrior, 06 Jan 2018)

The machine has presented us with a central nervous system, protected with no spinal vertebrae, lying almost naked for the cutting. If, for one reason or another, the severance is made, we face a terrifying, perhaps mortal crisis…. Day by day the complexity, and hence the potential danger, accelerates; materials and structures ceaselessly and silently deteriorate.

                                                                Stuart Chase, in Men and Machines, 1929

The warfare domains have traditionally included those which have geographic and topographic warfighting constraints, for example the land, sea, and air (now aero-space) domains. However, in Cyberwarfare the physical domains are no longer relevant since the domain has changed to the all-encompassing global electromagnetic spectrum. There is a need therefore, to look for the definition of the Cyberspace in which a modern soldier is required to operate.

The US Department of Defense defines cyberspace as, “A global domain within the information environment consisting of the interdependent network of information technology infrastructures and resident data, including the Internet, telecommunications networks, computer systems, and embedded processors and controllers”.[1]

Kuehl has defined it as,[2] “an operational domain whose distinctive and unique character is framed by the use of electronics and the electromagnetic spectrum to create, store, modify, exchange, and exploit information via inter-connected information and communication technology-based systems and their associated infra-structures.”

The above definitions draw upon the interrelated effects of the physical, the informational, and the cognitive. These together comprise: the physical platforms, systems & infrastructure that provide global connectivity to interconnect information systems, networks, and human users; the massive amounts of information that can be digitally and electronically shared; and the impact on human behaviour & decision making when faced with the deluge of information.[3]

Some characteristics of cyberspace are that: it exists and functions within the natural electromagnetic spectrum (EMS); it exists due to man-made technologies; it can be replicated; and that it is far more economical to operate and utilise cyberspace than other domains. These lead to a more encompassing definition of Cyberspace that,[4]it is a global domain within the information environment whose distinctive and unique character is framed by the use of electronics and the electromagnetic spectrum to create, store, modify, exchange, and exploit information via interdependent and interconnected networks using information-communication technologies”.

The cyberspace has been preferred by nations, criminals and hackers for cyber-attacks across the globe due to the fact that: its usage is becoming the backbone of the society; the current systems do not have adequate protection and predictive intrusion detection systems[5]; it is very fast, its reach is worldwide, and it provides anonymity. The increasing usage of digital sensing, and software based control in critical infrastructure, and dependence upon communication network for movement of network based data has made cybersecurity a national security problem. Cybersecurity can be defined[6] as, “Prevention of damage to, protection of, and restoration of computers, electronic communications systems, electronic communications services, wire communication, and electronic communication, including information contained therein, to ensure its availability, integrity, authentication, confidentiality, and nonrepudiation”.

Based upon the above Military cyber power can be defined[7] as, ‘the application of operational concepts, strategies, and functions that employ the tools of cyberspace to accomplish military objectives and missions’.

Cyber Threat Assessment – China

The establishment of the People’s Liberation Army’s (PLA) Cyberspace Strategic Intelligence Research Centre in June 2014 to ‘provide strong support in obtaining high-quality intelligence research findings and help China gain advantage in national information security’ indicates to the focus of the PLA on cyberspace[8]. Strategic Support Force (SSF) of China is a Military Theatre-grade organisation responsible for the space, cyber, and electronic warfare missions of the PLA and strategic-level information support for joint operations. The SSF is more or less the information warfare branch of the People’s Liberation Army. While detailed information about the SSF is not available, it is understood that the SFF will be composed of three separate forces: space troops (recognition and navigation satellites), cyber troops (offensive and defensive hacking), and electronic warfare forces (jamming and disrupting radars and communications) [9]. As per Rear Admiral Yin Zhuo, its main task will be ensuring the military’s local advantages in aerospace, space, cyber, and electromagnetic battlefields through operations such as target tracking and reconnaissance, satellite navigation, and attack and defence in cyber and electromagnetic spaces – the underlying goal of which should be attaining victory in future wars. Further, the SSF will assume responsibilities in defending the civilian infrastructure to increase the security of China’s financial institutions as well as people’s daily lives in general[10]. It implies that the SSF will be responsible for all aspects of information warfare, including intelligence, technical reconnaissance, cyber warfare, and electronic warfare. This is in line with China’s strategic thinking, which sees paralysing and sabotaging the enemy’s operational and command systems as a key to achieving dominance in all other domains, land, sea, and air[11].

Desmond Ball has brought out that PLA Information Warfare (IW) units have reportedly developed and tested ‘detailed procedures’ for Internet warfare, including software for network scanning, obtaining passwords and breaking codes, and stealing data; information-paralysing software, information-blocking software, information-deception software & other malware; and software for effecting counter-measures. These procedures have been tested during simulated cyber-attacks against Taiwan, India, Japan and South Korea. The PLA has reportedly established at least twelve facilities for Integrated Network Electronic Warfare (INEW) training at unit levels in computer network attack & defence operations, jamming & other forms of electronic warfare, and other IW activities. The facility is supposedly located at Zhurihe in the Beijing Military Region[12].

It is understood that Chinese hackers have been able to: crash selected Web servers, penetrate Web-sites and deface them, erase data from them, post on them, and have developed various viruses/Trojan Horse programs for spreading/inserting by e-mails to disable/steal information from targeted computer systems. However, there is no evidence yet that these hackers would be able to penetrate highly secure networks/command and control or weapon system networks to copy or manipulate critical data. Currently, China’s extensive cyber-warfare capabilities are very good for simple attacks but not for sustained cyber-warfare. As a result, the PLA may seek to use its cyberwarfare capabilities to collect data for intelligence and cyberattack purposes; to constrain an adversary’s actions by targeting network-based logistics, communications, and commercial activities; or to serve as a force multiplier when coupled with kinetic attacks during times of crisis or conflict[13].

Military Cyber Vulnerabilities

The Future Soldier Vision (FSV)

The FSV design for UK as unveiled by MOD UK includes[14]:

-Head sub-system concept incorporating hearing protection, lightweight sensors for information sharing and an integrated power supply.

-Torso sub-system concept of segmented armour that can be customised to the user or situation with integrated connectors and power supply.

-Smart watch style wearable communications concept which incorporates sensors to record the user’s biometric data.

-Smart glasses concept which include a heads-up display, integrated camera and bone conducting headphones to increase situational awareness without compromising hearing.

-A robust personal role computer concept enabling better information sharing and communications between personnel.

-Ergonomically designed and customisable the weapon concept that will allow targeting information to be shared between soldiers and their units.

-Further the FSV is designed to work as an integrated system with survivability, enhanced situational awareness and network capability. Protection technology, a network of sensors for information sharing and power and data connectors will also all be built-in.

At the 2017 Association of the United States Army annual meeting (AUSA 2017), US Army Research, Development, and Engineering Command (RDECOM) presented a concept for the US Army’s future soldier of the 2030 which also promised everything from powered exoskeletons, to futuristic optics, to individual network capability[15].

The modernisation program for the infantry in India began with the F-INSAS (Future Infantry Soldier As a System), but it has now evolved in to two separate programs – arming the Infantry with better offensive and defensive gear and the Battlefield Management System. The system is technology based with sensors, laser range finders, and cameras etc. The system will merge the information to give the soldier a real-time picture of the battlefield. The tactical level communication will take place over secure radio networks, and command level communication would be carried over Indian satellites. Each soldier will have a personal GPS device and will be able to see the position of other soldiers via a helmet mounted display[16].

As can be envisioned from the FSV above, the future soldier would be operating in an environment where he would be subjected to direct and indirect cyber-attacks by the adversary since the FSV is designed around the core concept of network centric warfare. In addition to the FSV, the complete architecture of modern warfare revolves around network centricity which itself is vulnerable to cyber-attack.

Military Systems

The military cyberspace domain under which its systems operate comprises of two major types of networks namely, an open network which relies on data-sharing, situational awareness, and teamwork, whereas the other utilises secure networks which depend upon speed, reliability and data integrity. The military communications utilise various types of modes for example, the global communications systems, military controlled commercial networks, and highly secure networks for target-shooter systems.

Complex Military C4I systems are relying more and more on sophisticated software and communication systems and hence they remain lucrative targets for hackers and adversary states. Next come the weapon systems which use software, like aircraft, warships and military special vehicles. Thereafter come the communication nodes, wide area networks, logistics and GPS feeds etc. Ingress into a system using software can be made by physical means through inputs to the system for example, like spare ports, by installing malware, or installing clandestine wireless devices. Indirect ingress can be made through connectivity ports for example, through internet, or through connection leading from other computers, or indirectly accessing the device from a distance using operating software vulnerabilities. In case of the Military both these methods of attack can be guarded against effectively but not absolutely.

The widespread usage of commercial-off-the-shelf (COTS) or open-source systems for military uses has increased the vulnerability to cyber-attack, their use should be guided by policies that assure the Military of obviating the risks and by carrying out a risk and cost benefit study.[17]

Standardisation has reduced costs, but it exposes a large number of similar products through exploitation of common vulnerabilities. Trojan horses could be introduced in the process of developing or maintaining the software. Vulnerabilities could be deliberately planted in a device or software program. By and large critical military systems are carefully designed and operated and are expected to remain safe during cyber-attacks.

The cyber space interlays and overlays with the civilian and military cyber domains therefore, even though military defences at local level can be strengthened; using physical access controls, password regimes, complex logging procedures & biometrics, isolation, human interfaces for critical equipment operations etc; it is an effort at the policy level which has to be put in place by the government so that the cyber-attack does not debilitate national security.

Policy Level Efforts

The US Department of Defense (DoD) has three primary cyber missions: Defend DoD networks, systems, and information; Defend the nation against cyberattacks of significant consequence; and Support operational and contingency plans.

US DoD has set five strategic goals for its cyberspace missions[18]:

  1. Build and maintain ready forces and capabilities to conduct cyberspace operations; This strategy sets specific objectives for DoD with regard to manning, training, and equipping its forces and personnel over the next five years and beyond.
  2. Defend the DoD information network, secure DoD data, and mitigate risks to DoD missions; DoD must take steps to identify, prioritize, and defend its most important networks and data so that it can carry out its missions effectively. DoD must also plan and exercise to operate within a degraded and disrupted cyber environment in the event that an attack on DoD’s networks and data succeeds, or if aspects of the critical infrastructure on which DoD relies for its operational and contingency plans are disrupted.
  3. Be prepared to defend the U.S. homeland and U.S. vital interests from disruptive or destructive cyberattacks of significant consequence; The Department of Defense must work with its interagency partners, the private sector, and allied and partner nations to deter and if necessary defeat a cyberattack of significant consequence on the U.S. homeland and U.S. interests.
  4. Build and maintain viable cyber options and plan to use those options to control conflict escalation and to shape the conflict environment at all stages; During heightened tensions or outright hostilities DoD must be able to provide the President with a wide range of options for managing conflict escalation. If directed, DoD should be able to use cyber operations to disrupt an adversary’s command and control networks, military-related critical infrastructure, and weapons capabilities.
  5. Build and maintain robust international alliances and partnerships to deter shared threats and increase international security and stability; All three of DoD’s cyber missions require close collaboration with foreign allies and partners. In its international cyber engagement DoD seeks to build partnership capacity in cybersecurity and cyber defense, and to deepen operational partnerships where appropriate.

Way ahead

It would be utopian to expect an integrated military cyberspace infrastructure which can fulfil all the requirements of open and closed networks of the military to cater to its multifarious requirements of data sharing and weapon-shooter-target engagements. Further, expecting it to be vulnerability proof, having infinite band width, reliable, survivable & upgradable, virtually amounts to asking for the moon. However, under the prevalent technology regime a pragmatic structure can be provided with sufficient redundancy to enable it to withstand cyber-attacks and carry out assigned tasks during the period of the conflict. Two major adversaries the US and China have well defined cyber security policies in place which offer India a workable platform for tailoring its own policy. The government of India is planning to create a new tri-service agency for cyber warfare. The Defence Cyber Agency will work in coordination with the National Cyber Security Advisor. It will have more than 1,000 experts who will be distributed into a number of formations of the Army, Navy and IAF. According to reports, the new Defence Cyber Agency will have both offensive and defensive capacity[19].

It would be the exhaustive implementation of this policy, as and when it materialises, which would protect the soldier during a cyberwar.

End Notes

[1] Joint Chiefs of Staff, Joint Publication 1-02, Washington D.C., US Department of Defense, 08 Nov 2010;as amended through 15 Feb 2016. https://fas.org/irp/doddir/dod/jp1_02.pdf (Accessed 01 Jan 2018).

[2] Daniel T. Kuehl, “From Cyberspace to Cyberpower: Defining the Problem,” in Franklin D. Kramer, Stuart Starr & Larry K. Wentz, eds., Cyberpower and National Security, Washington D.C., National Defense University Press, Potomac Books, 2009. http://ctnsp.dodlive.mil/files/2014/03/Cyberpower-I-Chap-02.pdf (Accessed 01 Jan 2018).

[3] Ibid.

[4] Ibid.

[5] Richard A. Clarke & Robert K. Knake, Cyber War: The Next Threat to National Security and What to do About it, New York, Ecco, 2010, pp. 103-149.

[6] 1 Ibid.

[7] Elihu Zimet and Charles L. Barry. Military Service Cyber Overview in Military Perspectives on Cyberpower

edits Larry K. Wentz, Charles L. Barry, Stuart H. Starr. Center for technology and national security policy at the National Defense University, Washington, DC. July 2009. https://www.hsdl.org/?view&did=32100 (Accessed 02 Jan 2018).

[8] Yao, Jianing. ‘PLA Cyberspace Strategic Intelligence Research Center Founded.’ China’s Military. 30 June 2014. http://eng.chinamil.com.cn/news-channels/china-military-news/2014-06/30/content_6025789.htm. (Accessed 03 Jan 2018).

[9] Mikk Raud, China and Cyber: Attitudes, Strategies, Organisation. The NATO Cooperative Cyber Defence Centre of Excellence. Tallin 2016. https://ccdcoe.org/sites/default/files/multimedia/pdf/CS_organisation_CHINA_092016.pdf (Accessed 01 Jan 2018).

[10] Costello, John. ‘The Strategic Support Force: China’s Information Warfare Service.’ The Jamestown Foundation. 8 Feb. 2016. http://www.jamestown.org/programs/chinabrief/single/?tx_ttnews%5Btt_news%5D=45075&cHash=97580

54639ab2cb6bc7868e96736b6cb#.V6RA_Lt95aQ>. Accessed 23 Aug. 2016. (Accessed 01 Jan 2018).

[11] Ibid.

[12] Desmond Ball. China’s Cyber Warfare Capabilities. Security Challenges, Vol. 7, No. 2 (Winter 2011), pp. 81-103. https://indianstrategicknowledgeonline.com/web/china%20cyber.pdf (Accessed 01 Jan 2018).

[13] Office of the Secretary of Defense, Annual Report to Congress: Military and Security Developments

Involving the People’s Republic of China 2017. https://www.defense.gov/Portals/1/Documents/pubs/2017_China_Military_Power_Report.PDF (Accessed 02 Jan 2018).

[14] Ministry of Defence UK, Defence Science and Technology Laboratory, and The Rt Hon Sir Michael Fallon MP. MOD unveils futuristic uniform design. 16 September 2015. https://www.gov.uk/government/news/mod-unveils-futuristic-uniform-design (Accessed 02 Jan 2018).

[15] Nathaniel F. “SOLDIER OF THE FUTURE” Concept Displayed by US Army at [AUSA 2017]. The Firearm Blog. 30 Oct 2017. http://www.thefirearmblog.com/blog/2017/10/30/soldier-future-concept-displayed-us-army-ausa-2017/ (Accessed 01 Jan 2018).

[16] Abhishek Saksena. Indian Army’s Future Infantry Soldiers To Get Lethal Weapons And Better Protection. India Times. 18 Jan 2017. https://www.indiatimes.com/culture/who-we-are/indian-army-s-future-infantry-soldiers-to-get-lethal-weapons-and-better-protection-269775.html (Accessed 03 Jan 2018).

[17] Howard F. Lipson, Nancy R. Mead, and Andrew P. Moore, “Can We Ever Build Survivable Systems from COTS Components?” CMU/SEI–2001–TN–030 (Pittsburgh: Carnegie Mellon University, Software Engineering Institute, December 2001). http://repository.cmu.edu/cgi/viewcontent.cgi?article=1630&context=sei (Accessed 01 Jan 2018).

[18] The DOD Cyber Strategy 2015, https://www.defense.gov/Portals/1/features/2015/0415_cyber-strategy/Final_2015_DoD_CYBER_STRATEGY_for_web.pdf (Accessed 05 Jan 2018).

[19] India is quietly preparing a cyber warfare unit to fight a new kind of enemy. https://economictimes.indiatimes.com/news/defence/india-is-quietly-preparing-a-cyber-warfare-unit-to-fight-a-new-kind-of-enemy/articleshow/61141277.cms (Accessed 05 Jan 2018).

Evolution and Role of Naval UAVs

(Published in special edition of Economic Times, India on 04 Dec 2017)

Earliest mention of a drone/unmanned aerial vehicle (UAV) in the Naval context is found in 1917, when the US Navy commissioned the design of an ‘aerial torpedo’ for use against German U-boats. A contract was awarded to the Curtiss Aeroplane Company, and the airplane was named the Speed-Scout. It was designed to be launched from naval ships carrying a 1,000-lb. payload and to be stabilized by an autopilot. It suffered several failures before it achieved its first successful flight on 06 March 1918, making it the first flight of an UAV. On 15 April 1923, the Naval Research Laboratory’s (NRL) specially equipped F5L seaplane was controlled by radio signals up to a range of 10 miles from the transmitter. The NRL also reported that radio control of take-off and landing of aircraft was possible. Project Fox, equipped with a television camera, was developed by The Naval Aircraft Factory in 1941. It was controlled by TG-2 aircraft and successfully carried out torpedo attack on a destroyer in 1942.

McDonnell Aircraft developed a radio-controlled target drone TD2D-1 in 1942 for anti-aircraft and aerial gunnery practice of U.S. Navy. TD2D was gyro-stabilized, radio-controlled and could be recovered by parachute. The Ryan Firebee was a 23-feet long target drone, which could fly at over 700 miles per hour on a pre-programmed flight path. It could be recovered mid-air by a C-130 Hercules with a capture net, or parachute into the sea for recovery. A modified Firebee with cameras called a ‘Lightning Bug’ could fly over a target area and take aerial pictures, it carried out over 3,000 reconnaissance missions in Vietnam. The drones have been tested on carriers, and have flown in combat, the TDR-1s launched from the USS Sable in 1943, and the Firebees took off from the USS Ranger from 1969 to 1970.

The Gyrodyne model QH-50D was a remotely controlled UAV which was built and delivered to the U.S. Navy as the Drone Anti-Submarine Helicopter (DASH). The QH-50D was a rotary-winged, anti-submarine weapon carrier designed primarily to deliver two MK44 acoustic homing torpedoes or a Mk 17 Nuclear depth charge using the W-44 warhead and also had a provision for a ‘classified weapon’.

The maritime UAV serves in national security, paramilitary and wartime missions. It expands the user’s horizons by providing Over The Horizon Targeting (OTHT). In addition, it increases the scanning area, time over target and the mission flexibility. It also serves in real time battle damage assessment. During peacetime, it prevents the penetration of any sea borne hostile intruder, protects the country’s rights and interests in the Economic Exclusive Zone (EEZ) and supports in Search and Rescue operations. In war-time it assists in achieving naval superiority, helps in destruction of enemy naval forces, defends the coast lines, and supports ground operations (littoral warfare). The role of the Maritime UAV system is to provide unmanned, long endurance aerial reconnaissance, surveillance and target acquisition. In addition, the UAV can create a comprehensive, real time, naval tactical picture for the ship’s commander and naval HQs.

A typical Maritime UAV System consists of at least three aircraft, with ground control system (GCS), Launch & Retrieval Station (LRS), Ground Data Terminal (GDT), Launch & Retrieval Data Terminal (LRDT), and mission oriented Payloads. A typical Payload consists of a Maritime Patrol Radar (MPR) with multi-mode functions, an Electro-Optical sensor with day/night capabilities, and an optional ELINT package. The payload package provides the necessary data for detection, classification, and identification of surface vessels at sea. Having a line of sight data link package provides a system range of 250 km and an air data relay extends the patrolling distance to 350 km.

The launching of UAVs from warships presents less of a challenge than recovery. UAVs can be launched through a variety of catapult options, including rocket-assisted take-off (RATO) as used by the US Navy for embarked Pioneer UAV operations. The IN operates the Lakshya unmanned aerial target system that uses boosters to launch without any ground run. Recovery of UAVs is more problematic than their launch. Vertical landing UAVs can be recovered using manual remote piloting to a conventional vertical landing, or by automatic landing systems such as the US UAV common automatic recovery system (UCARS). Fixed wing UAVs are presently recovered by more extreme methods, such as by flying it into a recovery net, by stopping the motor and ditching it into the water by parachute for a manual recovery, or by mid-air recovery using a manned helicopter or aircraft.

The IN currently operates the Heron and the Searcher MK II UAVs manufactured by Israel Aerospace Industries. These are capable of beaming real time live pictures of maritime targets to Commands ashore, thus enhancing the joint defence capability by synergizing capabilities of the Army, Air Force, Coast Guard, and local authorities. The Ministry of Defense (MoD) has initiated a request to the US for procuring 22 multi-mission Guardian UAVs for the Indian Navy. A RFI has also been issued for 50 ‘Naval Ship-Borne Unmanned Aerial Vehicles’ (NSUAS) for Intelligence, Surveillance & Reconnaissance (ISR), monitoring of Sea Lines of Communication (SLOC), Exclusive Economic Zone safety, anti-piracy, and anti-terrorism functions along with Search and Rescue (S&R) roles. The MoD, is also considering procurement of Medium Altitude Long Endurance (MALE) UAVs for use by the three defense services.

For the near future, the US Navy is progressing ahead with procurement of The Broad Area Maritime Surveillance UAS (BAMS UAS), the Vertical Take-off and Landing UAV (VTUAV) Fire Scout MQ-8B unmanned helicopter, and The Small Tactical UAS (STUAS), RQ-21 Blackjack. The indigenous AURA and Rustom (& its variants) are being developed by DRDO for the Indian Armed Forces.

The question that the Indian Navy faces today is, whether it is ready to go for development of fully autonomous unmanned systems, which would be cable of engaging a target and inflicting lethal damage on their own? Is the Indian Navy willing to develop technologies that empower the vehicle with embedded artificial intelligence to make the final decision to launch weapons at the target independent of any human intervention? If yes, then there is a need for the Indian Navy to look in to:

– technologies and software formulations which would permit an unmanned vehicle to launch itself, proceed to learn acoustic/magnetic/electromagnetic signatures, and identify the target on its own.

– technologies, which are more environmental friendly, for e.g. the use of green plastics of the poly hexahydrotriazines or PHTs category, and green electrical power including its storage for long endurance operations.

– a resilient architecture that can act as a redundant pathway to atmospheric communications through electromagnetic domains including digital communications utilizing fibre domain.

– Distributed manufacturing to enable efficient use of resources, with less wasted capacity in centralized factories, and develop 3D printing of circuit boards and other integrated electronic components.

– cognitive testing aspects of software for unmanned vehicles today to fruitfully operate autonomous vehicles of tomorrow.

– exploring technologies for developing new types of weapons for use in the autonomous vehicles.

– focusing on the technology developments in the commercial sector, especially in the software, and the artificial intelligence sectors. As it appears, the only option is to synergize with the commercial sector to ensure that UAVs become a force multiplier in the next decade.

 The Challenge of Military Artificial Intelligence

 (Abridged version published in SP’s Military Year Book 2017)

Intelligent machines were the focus of research work at many institutes after the WWII. In 1950, Alan Turing argued that if the machine could successfully pretend to be human to a knowledgeable observer then one certainly should consider it intelligent[i]. The credit of coining the phrase ‘Artificial Intelligence’ goes to John McCarthy in 1955. A number of scientists have defined Artificial Intelligence, (AI) in a varying manner; however, there appears to be no single definition, which has been universally accepted. All the definitions of AI are connected with human intelligence in some way, some of them are:

– “The study of mental faculties through the use of computational models”[ii].

-“The art of creating machines that perform functions requiring intelligence when performed by people”[iii].

-“A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes”[iv].

– “The study of how to make computers do things at which, at the moment, people are better”[v].

– “The study of the computations that make it possible to perceive, reason, and act”[vi].

– “The branch of computer science that is concerned with the automation of intelligent behavior”[vii].

Strong AI has been defined as that moment when “humankind is in the presence of an intelligence greater than its own”[viii], and as “strong AI is reached once the computer regarded as such is conscious of its abilities”[ix].

AI imbibes knowledge from different fields like Computer Science, Mathematics, Engineering, Cognitive Science, Philosophy, and Psychology. AI embodies a wide range of intelligent search methods, techniques for obtaining clarity where uncertainties exist in data and knowledge, and various types of machine learning & representation schemes of knowledge. Its various applications include, speech recognition, natural language processing, expert systems, neural networks, intelligent robotics, gaming and 3D vision. There is a need to define machine learning and deep learning before moving on to the military applications of AI.

Machine learning. It has evolved from the study of computational learning theory, pattern recognition, and artificial intelligence. It is a subfield of computer science.[x] It has been defined in 1959 by Arthur Samuel as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Machine learning relies upon utilizing algorithm constructions to perform predictive analysis on data[xi]. Machine learning tasks fall into three basic categories namely[xii]; Supervised learning is one in which the computer is presented with example inputs and their desired outputs, and the goal is to learn a general rule that maps inputs to outputs; Unsupervised learning is one where no labels are given to the learning algorithm, leaving it on its own to find structure in its input; and Reinforcement learning is one where a computer program interacts with a dynamic environment in which it must perform a certain goal.

 Deep Learning. Le Deng and Dong Yu of Microsoft have provided the following definitions for Deep Learning[xiii]:

-A class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction and transformation, and for pattern analysis and classification.

-A sub-field within machine learning that is based on algorithms for learning multiple levels of representation in order to model complex relationships among data.

-A sub-field of machine learning that is based on learning several levels of representations, corresponding to a hierarchy of features or factors or concepts, where higher-level concepts are defined from lower-level ones, and the same lower level concepts can help to define many higher-level concepts.

Some of the deep learning architectures built around neural networks are deep belief networks, deep neural networks and recurrent neural networks. The use of deep learning architectures in automatic speech recognition, bioinformatics, natural language processing, and 3D vision etc has resulted in remarkable successes.

As per Jeff Hawkins and Donna Dubinsky of Numenta, building of smart machines has involved three basic approaches. These are the Classic AI, Simple Neural Networks, and Biological Neural Networks.[xiv]

The classic AI approach involved computer programs that were based upon abilities of the human brain to solve simple problems. However, the computers required large amounts of inputs from knowledge experts to lay down the rules based upon their expertise and experience in problem solving. Thus, the classic AI systems were created specific to a problem, while they were very useful in case of problems which had been defined in detail they could not learn on their own and provide solutions to problems. They failed in comparison with general human intelligence.

When the limitations of Classic AI were encountered, scientists looked at the functioning of the human brain at the level of neurons and this resulted in Artificial Neural Networks (ANNs). The ANNs lay emphasis upon unsupervised learning from data provided to them. Thus, the Simple Neural Networks learn from data and do not require experts to lay down the rules. The Simple Neural Network is a mathematical technique that locates patterns in large, static data sets[xv]. The ANNs are a subset of machine learning techniques that processes large amount of data using statistical and mathematical techniques in addition to ANNs to provide results. ANNs have transformed into Deep Learning networks with the advent of humongous data and fast computers. Thus, Simple Neural Networks could provide solutions where Classic AI could not. However, the Simple Neural Networks too have limitations when data is dynamic or when data is limited for training.

In the Biological Neural Approach, emphasis is laid upon studying how a human brain works to cull out the properties that are required for intelligent systems. It is established that, information is represented in the brain using sparse distributed representations or SDRs. Further, it is known that memory is a sequence of patterns, behavior is essential part of learning, and that learning has to be continuous. Therefore, the building blocks of intelligent machines should be SDRs[xvi]. The biological neuron is also not as simple as conceived during the Simple Neural Network approach.

Military applications of AI can be found in almost all aspects of military from decision-making, equipment operations, sensors, weapons systems to unmanned vehicles. The military is adopting AI mainly because it results in much fewer casualties, higher efficiency, and lower costs. Intelligent robotics and unmanned vehicles for army, navy, and air force are bringing in a new revolution in standoff warfare. The war against terrorism is practically being fought with unmanned weaponized aerial vehicles in Afghanistan, Syria and Iraq. Be it air traffic control in a combat zone, which would allow manned and unmanned aircraft, weapons etc. to operate without conflict by automated routing and planning; or military decision making in fog of war; or a radar’s target identification algorithms which look at the shape of possible targets and their Doppler signatures; AI is integral to all these systems. In this article two major categories of military applications are discussed which pertain to cyber defence and military logistics.

Applications of AI in Cyber Defence

In 2009, Conficker[xvii] worm infected civil and defence establishments of many nations, for example, the UK DOD reported large-scale infection of its major computer systems including ships, submarines, and establishments of Royal Navy. The French Naval computer network ‘Intramar’ was infected, the network had to be quarantined, and air operations suspended. The German Army also reported infection of over a hundred of its computers. Conficker sought out flaws in Windows OS software and propagated by forming a botnet, it was very difficult to weed it out because it used a combination of many advanced malware techniques. It became the largest known computer worm infection by afflicting millions of computers in over 190 countries.

It s evident that the amount of data and the speeds at which processing is required in case of cyber defence is not feasible for human beings to carry it out. Conventional algorithms also cannot tackle dynamically changing data during a cyber attack. It appears that cyber defence can only be provided by real time flexible AI systems with learning capability.

The US Defence Science Board report of 2013[xviii] states that “in a perfect world, DOD operational systems would be able to tell a commander when and if they were compromised, whether the system is still usable in full or degraded mode, identify alternatives to aid the commander in completing the mission, and finally provide the ability to restore the system to a known, trusted state. Today’s technology does not allow that level of fidelity and understanding of systems.” The report brings out that, systems such as automated intrusion detection, automated patch management, status data from each network, and regular network audits are currently unavailable. As far as cyber defence is concerned in the US, it is the responsibility of the Cyber Command to “protect, monitor, analyze, detect, and respond to unauthorized activity within DOD information systems and computer networks”[xix]. The offensive cyber operations could involve both military and intelligence agencies since both computer network exploitation and computer network attacks are involved. The commander of Cyber Command is also the Director of National Security Agency, thus enabling the Cyber Command to execute computer exploitations that may result in physical destruction of military or civilian infrastructure of the adversary. Some advance research work in respect of active cyber defence has been demonstrated under various fields of AI, some successfully tested examples are:

Artificial Neural Networks- In 2012, Barman, and Khataniar studied the development of intrusion detection systems, IDSs based on neural network systems. Their experiments showed that the system they proposed has intrusion detection rates similar to other available IDSs, but it was at least ~20 times faster in detection of denial of service, DoS attacks[xx].

Intelligent Agent Applications-In 2013, Ionita et al. proposed a multi intelligent agent based approach for network intrusion detection using data mining[xxi].

Artificial Immune System (AIS) Applications- In 2014, Kumar, and Reddy developed a unique agent based intrusion detection system for wireless networks that collects information from various nodes and uses this information with evolutionary AIS to detect and prevent the intrusion via bypassing or delaying the transmission over the intrusive paths[xxii].

Genetic Algorithm and Fuzzy Sets Applications- In 2014, Padmadas et al. presented a layered genetic algorithm-based intrusion detection system for monitoring activities in a given environment to determine whether they are legitimate or malicious based on the available information resources, system integrity, and confidentiality[xxiii].

Miscellaneous AI Applications- In 2014, Barani proposed genetic algorithm (GA) and artificial immune system (AIS), GAAIS – a dynamic intrusion detection method for Mobile ad hoc Networks based on genetic algorithm and AIS. GAAIS is self-adaptable to network changes[xxiv].

From the above it can be seen that there is rapid progress in design and development of cyber defence systems utilizing AI that have direct military applications.

Applications of AI in Military Logistics

Some of the challenges being faced by militaries in both peace and war include ensuring the adequacy of maintenance and repair of sophisticated  equipment, weapons, armament and ammunition; ensuring the supportability of missions with due planning; and guaranteeing  the availability of qualified personnel to carry out the assigned tasks. AI and associated technologies have made impressive inroads in civil and military logistics to ease the cumbersome operations and procedures involved. It has now been established that AI has significantly improved the systems and processes in the logistic chain and has led to considerable savings for the military establishments. AI encompasses many innovative technologies that are being used in military; some of these are discussed in succeeding paragraphs.

-Expert systems are software programs that usually serve as intelligent advisors in specific areas of expertise. Expert system technology has percolated to all functional areas of production and logistics of the military. Logistics expert systems in areas of inventory management, transportation, warehousing, acquisition, maintenance, and production are common. Examples include, the Inventory Manager’s Assistant of US Air Force, Dues Management Advisor (DMA) of the US Navy and Logistics Planning and Requirements Simplification (LOGPARS) system of the US Army.

-Natural language systems convert languages into computer language, thus making it feasible to communicate with computers in language of choice obviating the need to master computer languages. Natural language applications are being used to provide user-friendly query capability for large databases pertaining to logistics.

-Speech recognition systems allow user to interact directly with computers thus eliminating the use of keyboards. The voice signal is digitized and compared with stored voice patterns and grammatical rules for computer to understand the voice message. For example, US Air Force Logistics Command (AFLC) is using a speech recognition system in its depot warehouses to interface with the warehouse’s automated storage module (ASM); the US Army is using speech recognition system in association with a diagnostic system for carrying out maintenance of its motor vehicles as well as in its transportation planning[xxv].

-3D vision technology allows a computer to “sense” its environment and classify the various objects in its vicinity. The US Navy is using this in its Rapid Acquisition of Manufactured Parts (RAMP) program and the US Air Force for reverse engineering parts in its maintenance facilities. 3D vision applications are of significant importance in using robotics for logistics.

-Intelligent robots incorporate a host of AI technologies to mimic specific work undertaken by human beings. Mobile robots are being increasingly utilized in activities from patrolling to investigating and neutralizing explosives[xxvi]. Mobile robotic systems are used for carrying out routine inspections of nuclear missiles. They have eliminated the need of human element from going into containment systems. The robot is remotely operated from outside the containment zone. As far as arming of robots (Lethal Autonomous Weapons) is concerned, thousands of scientists and technologists, including, Elon Musk, Stephen Hawking, and Steve Wozniak signed an open letter in 2015 asking for a ban on lethal weapons controlled by artificially intelligent machines[xxvii]. The letter states “Artificial Intelligence (AI) technology has reached a point where the deployment of such systems is—practically if not legally—feasible within years not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms.”

-Neural networks are designed based upon models of the way a human brain functions. They are capable of associative recall and adaptive learning. Because of the massive processing power associated with such networks, they are being increasingly utilized in logistic applications. Eyeriss is a new microchip fabricated at MIT and funded by DARPA that has the potential to bring deep learning to a smart phone that can be carried by a soldier[xxviii].

DRDO and AI

Centre for Artificial Intelligence and Robotics (CAIR), Bengaluru and Research and Development Establishment (Engineers) R&DE(E), Pune are the main laboratories of Defence Research and Development Organisation (DRDO) in India working in the area of artificial intelligence and robotics. A family of robots that have been developed for various surveillance / reconnaissance applications include[xxix]; RoboSen mobile robot system for patrolling, reconnaissance, and surveillance. It is capable of autonomous navigation with obstacle avoidance capability and continuous video feedback; Miniature Unmanned Ground Vehicle (UGV) is a ruggedized man-portable robotic system for low-intensity conflicts; Walking robots with six and four legs for logistics support; and Wall climbing & flapping wing robots for potential usage in Low Intensity Combat (LIC) operations.

Some projects under development include[xxx]:

-AI Techniques for Net Centric Operations (AINCO) – A suite of technologies for creation of knowledge base, semantic information reception and handling, inference reasoning, and event correlation.

-Knowledge Resources And Intelligent Decision Analysis (KRIDA) – A system that aims to achieve the management of large-scale military moves using extensive knowledge base and data handling.

-INDIGIS 2D/3D – An indigenous Geographic Information System (GIS) kernel that provides platform for development of display, analysis, and decision support involving spatio-temporal data.

-S57 Viewer – for viewing more than one lakh tracks.

-IVP_NCO and IP Lib – A comprehensive suite of image and video processing applications to provide a unified solution to image and video processing in the net-centric operations.

-Indigenous Network Management System (INMS) – An indigenous NMS with resource planning, network planning, and network monitoring tools for IP network management.

Future of Military Artificial Intelligence

The global defence sector has seen unprecedented adoption of unmanned systems and robotics. This has been mainly due to various factors like; reduction in own casualties and feasibility of riskier missions using robots; high precision, minimal collateral damage, longer endurance and range; quicker reaction times with greater flexibility; and finally cost benefits accruing due to reduction in cost of technology with increased percolation. Unmanned aerial systems comprise as much as over 80% of all military robots, in past six years US spending on military UAVs has increased by ten times[xxxi]. Today over 90 countries are operating drones with over 30 armed drone programs. Many programs including, Drone mother ships in air and water; swarm warfare on land, sea and air; high definition real time ISR; wearable electronic packages for soldiers with exoskeletons; and exotic weapon systems are likely to be inducted within the coming decade. The threat of cyber attacks on the AI systems is very real. AI Machines are connected to the human controllers for taking and executing critical commands, the linkages can be hacked both through electronic warfare as well as cyber attacks. Since AI runs entirely on software, there is a finite probability of it being manipulated and used against the owner. DARPA had run a three year ‘Cyber Grand Challenge’[xxxii] to accelerate the development of advanced, autonomous systems that can detect, evaluate, and patch software vulnerabilities before adversaries have a chance to exploit them. The competition which ended on 4th of Aug 2016, achieved its aim to prove the principle that machine-speed, scalable cyber defense is possible. This would mark the beginning of a new era in much needed cyber defence of AI systems.

 As far as AI is concerned it suffices to quote US deputy secretary of defense, Robert Work  “…the 2017 fiscal budget request will likely ask for $12-$15bn for war gaming, experimentation and demonstrations to test out the military’s theories on AI and robotics ‘in human-machine collaboration combat teaming’…”[xxxiii]

[i] http://www-formal.stanford.edu/jmc/whatisai/node1.html

[ii] Charniak, E., & McDermott, D. Introduction to artificial intelligence. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA ©1985,ISBN:0-201-11945-5

[iii] Kurzweil, R. (The Age of Intelligent Machines. MIT Press, Cambridge, Massachusetts

[iv] Schalkoff, R. I. Artificial Intelligence: An Engineering Approach .McGraw-Hill, New York.

[v] Rich, E., and Knight, K. Artificial Intelligence. McGraw-Hill, New York, second edition.

[vi] Winston, P.H. Artificial Intelligence. Addison-Wesley, Reading, Massachusetts, third edition.

[vii] Luger, G.F. and Stubblefield, W.A. Artificial Intelligence: Structures and Strategies for Complex

Problem Solving. Benjamin/Cummings. Redwood City, California, second edition.

[viii] Barrat, James. Our Final Invention: Artificial Intelligence and the End of the Human Era. New York, NY: St. Martin’s Press.

[ix] Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. Montreal, QC: Prentice Hall.

[x] http://www.britannica.com/EBchecked/topic/1116194/machine-learning

[xi] Ron Kohavi; Foster Provost (1998). “Glossary of terms”Machine Learning30: 271–274.

[xii] Russell, StuartNorvig, Peter  . Artificial Intelligence: A Modern Approach (2nd ed.). Prentice Hall. ISBN 978-0137903955.

[xiii] Li Deng and Dong Yu, Deep Learning: Methods and Applications. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/DeepLearning-NowPublishing-Vol7-SIG-039.pdf

[xiv]Jeff Hawkins & Donna Dubinsky, What Is Machine Intelligence Vs. Machine Learning Vs. Deep Learning Vs. Artificial Intelligence (AI)? http://numenta.com/blog/machine-intelligence-machine-learning-deep-learning-artificial-intelligence.html

[xv] Ibid.

[xvi] Ibid.

[xvii] http://en.wikipedia.org/wiki/Conficker

[xviii] Office of the Under Secretary of Defense for Acquisition, Technology and Logistics, Resilient Military Systems and the Advanced Cyber Threat, United States Department of Defense, Defense Science Board, January 2013

[xix] U.S. Government Accountability Office, “Defense Department Cyber Efforts,” May 2011, 2–3, http://www.gao.gov/new.items/d1175.pdf.

[xx] D. K. Barman, G. Khataniar, “Design Of Intrusion Detection System Based On Artificial Neural Network And Application Of Rough Set”, International Journal of Computer Science and Communication Networks, Vol. 2, No. 4, pp. 548-552

[xxi] I. Ionita, L. Ionita, “An agent-based approach for building an intrusion detection system,” 12th International Conference on Networking in Education and Research (RoEduNet), pp.1-6.

[xxii] G.V.P. Kumar, D.K. Reddy, “An Agent Based Intrusion Detection System for Wireless Network with Artificial Immune System (AIS) and Negative Clone Selection,” International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 429-433.

[xxiii] M. Padmadas, N. Krishnan, J. Kanchana, M. Karthikeyan, “Layered approach for intrusion detection systems based genetic algorithm,” IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp.1-4.

[xxiv] F. Barani, “A hybrid approach for dynamic intrusion detection in ad hoc networks using genetic algorithm and artificial immune system,” Iranian Conference on Intelligent Systems (ICIS), pp.1 6.

[xxv] Bates, Madeleine; Ellard, Dan; Peterson, Pat; Shaked, Varda. http://www.aclweb.org/anthology/H91-1040

[xxvi] http://www.robotics.org/content-detail.cfm/Industrial-Robotics-Industry-Insights/Robotics-in-Security-and-Military-Applications/content_id/3112

[xxvii] https://www.technologyreview.com/s/539876/military-robots-armed-but-how-dangerous

[xxviii] http://www.defenseone.com/technology/2016/02/new-microchip-could-increase-military-intelligence-powers-exponentially/125715/

[xxix] http://pib.nic.in/newsite/PrintRelease.aspx?relid=124000

[xxx] http://www.drdo.gov.in/drdo/labs/CAIR/English/index.jsp?pg=Products.jsp

[xxxi] http://about.bankofamerica.com/assets/davos-2016/PDFs/robotic-revolution.pdf

[xxxii] http://www.darpa.mil/news-events/2016-08-04

[xxxiii] http://ftalphaville.ft.com/2015/12/15/2147846/the-future-military-artificial-intelligence-complex/

Nanoenergetic Materials (nEMs) in Conventional Ammunition

(Published on 17 May 2016, CLAWS,http://www.claws.in/1571/nanoenergetic-materials-nems-in-conventional-ammunition-sanatan-kulshrestha.html)

Nanoenergetic Materials (nEMs) in Conventional Ammunition

 Nanotechnology “could completely change the face of weaponry,”

Andy Oppenheimer, Jane’s Information Group[1]

On 11 September 2007, Russians tested Father of All Bombs (FOAB), an Aviation Thermo baric Bomb of Increased Power (ATBIP). It was said to be the most powerful conventional bomb in the world, with a 7-Ton explosive mixture resulting in a devastating effect equivalent to 44 tons of TNT[2]. It was hinted that the FOAB contained a liquid fuel, such as ethylene oxide, mixed with energetic nano-aluminium powder, which was dispersed by a high explosive booster. Some reports speculated that the liquid fuel was purified using nano-filters. What caught the imagination of defense experts was the fact that the Russian FOAB had less fuel than the similar US device Mother of All Bombs (MOAB), the GBU-43/B Massive Ordnance Air Blast bomb, but was four times more powerful. It was also probably the first time that the nonprofessional learned of the lethal uses of nanotechnology. Not much information is available through open sources about the developments involving nanotechnology in explosives, much of it has to be gleaned from research papers and patents (for e.g. Patents like US6955732 – Advanced thermo baric explosive compositions and WO2013119191A1 – Composition for a fuel and air explosion).

            Since 2004, ‘Combat Safe Insensitive Munitions’ concept has shifted the focus of safety from a pure materials approach to making marine explosives insensitive to a platform based approach based upon mechanics to increase insensitivity[3]. US Navy has been at the forefront of R&D into new energetic materials since a long time and it is opined that nanotechnology enabled energetic materials would form the backbone of the future defense systems. Timely induction of nano enabled energetic systems with controlled energy release is the focus of current research at institutes like the U.S. Naval Academy, Naval Surface Warfare Center, and the University of Maryland.

            In simple terms, Nanoenergetic materials (nEMs) perform better than conventional materials because of much larger surface area, which increases speed of reaction and larger energy release in much shorter time. Addition of Superthermites[4] (nano-aluminium based) have shown instantaneous increase in explosive power of existing compositions[5]. Further, use of nano-sized materials in explosives has significantly increased safety and insensitivity by as much as over 30% without affecting reactivity. It is predicted that nEMs would provide the same explosive power at mass up to two orders of magnitude less than the current explosive systems[6].

In rocket, propellants nEMs have shown similar capabilities at Los Almos National Laboratories with nitrogen-energized nEMs[7]. In addition, incorporation of more than one burning rate in rocket propellants has given rise to novel design options by creating grains with continuously varying properties along the length as well as across the radius of the grain in Functionally Graded Materials (FGM).

While Nanosizing of high explosives leads to increasing their explosive power[8] and decreasing their sensitivity to external forces[9], it also decreases its thermal stability. The shelf life of such explosives could therefore stand reduced, however, some patents reveal that this issue has also been resolved technically (e.g. patent US20120227613 Thermal enhanced blast warhead). In India the work on explosives and propellants is being undertaken at HEMRL, a DRDO laboratory, and it is understood that the research in nEMs is progressing satisfactorily.

Projections

Nanotechnology is permeating in all fields of design & manufacturing of weapons and ammunition. It is bringing unprecedented precision in weapon systems, robustness in triggering mechanisms and opening new frontiers in propellant and pyrotechnic functioning. In addition to explosive and propellants, Nanomaterials have ushered in innovative improvements in many characteristics of ammunition such as guidance, penetration capacity, embedded sensors for monitoring condition, embedded antennae for guidance and so on.

It can be envisaged that nEMs would replace the conventional explosives in the next decade. This would provide existing conventional weapons with explosive powers higher in magnitude by a factor of two and enhance the safety to external stimulation by at least 30%. In simple terms, a missile warhead having an explosive content of 200 kg of TNT equivalent would have an explosive power of 20,000 kg of TNT equivalent when substituted with nEMs material of same weight of 200 kg!

This advancement could displace Tactical nukes from the battlefield.

What can also be foreseen is the mushrooming of new classes of extremely precise and lethal small/micro weapon systems, which could be scaled down by at least second order of magnitude from the current systems. Thus creating space for the likely paradigm shift from bigger & larger to the smaller & numerous holdings of weapons. This in turn would herald the era of Swarm Warfare.

[1] Gartner, John. “Military Reloads with Nanotech.” Technology Review, an MIT Enterprise, January 21, 2005. http://www.technologyreview.com/computing/14105/page1/

[2] http://news.bbc.co.uk/2/hi/europe/6990815.stm

[3] Insensitive munitions:

Improve the safety and survivability for Armed Forces and civilians in urban areas or near combat zones because they can safely be stored at closer distances. Reduce the vulnerability of platforms and resources against unintended or hostile aggression, violent reactions with blast overpressure and fragmentation damages are under control. Maximize the storage capabilities and improve flexibility logistics: IM can safely be carried by land/sea/air; storage platforms can be closer together and are key to Inter-Operability between the Armed Forces.

[4] Nano-Thermite or Super-Thermite is a metastable intermolecular composite (MICs) containing an oxidizer and a reducing agent, which are intimately mixed on the nanometer scale. This dramatically increases the reactivity relative to micrometer -sized powder thermite. MICs, including nano-thermitic materials, are a type of reactive materials investigated for military use, as well as for general applications involving propellants, explosives, and pyrotechnics.

[5] Gartner, John. “Military Reloads with Nanotech.” Technology Review, an MIT Enterprise, January 21, 2005. http://www.technologyreview.com/computing/14105/page1/

[6] Yang, Guangcheng, Fude Nie, Jinshan Li, Qiuxia Guo, and Zhiqiang Qiao. “Preparation and Characterization of Nano-NTO Explosive.” Journal of Energetic Materials, 25, 2007.

[7] Tappan, B.C., S.F. Son, and D.S. Moore. “Nano-Aluminum Reaction with Nitrogen in the Burn Front of Oxygen-Free Energetic Materials.” Shock Compression of Condensed Matter, American Institute of Physics, 2005

[8] Kaili Zhang, Carole Rossi, and G.A. Ardila Rodriguez. “Development of a Nano-Al/CuO Based Energetic Material on Silicon Substrate.” Applied Physics Letters No. 91, 14 September 2007.

[9] Guangcheng Yang, Fude Nie, Jinshan Li, Qiuxia Guo, and Zhiqiang Qiao. “Preparation and Characterization of Nano-NTO Explosive.” Journal of Energetic Materials, 25, 2007.

Neuromorphic Chips – Defence Applications

(Published Claws 30 Apr 2016 )

(http://www.claws.in/1563/neuromorphic-chips-%E2%80%93-defence-applications-sanatan-kulshrestha.html)

..And I had an opportunity to grow from the time where we couldn’t make a single silicon transistor to the time where we put 1.7 billion of them on one chip!

                                                                                 Gordon Moore, Cofounder Intel

Last year Kris Gopalakrishnan pledged $ 50 mn at IISc and IIT Madras on research that seeks to model next level computing based on the functioning of the Brain.[1] Neuromorphic engineering is an emerging interdisciplinary field that involves designing sophisticated devices based on the complex neural circuits of the brain. It uses principles of the nervous system for engineering applications to achieve a better understanding of computations occurring in actual biological circuits and utilize the unique properties of biological circuits to design and implement efficient engineering products. Neuromorphic chips aim to mimic the massive parallel computing power of the brain, circumvent the size limitations of traditional chips, and consume less power. It is also predicted that such chips could adapt in response to stimuli. As a technology demonstrator, P. Merolla et al [2] at IBM have developed a 5.4-billion-transistor chip (TrueNorth) with 4096 neurosynaptic cores interconnected via an intra-chip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. With 5.4 billion transistors, occupying 4.3-sq cm area TrueNorth has ∼428 million bits of on-chip memory. In terms of power, consumption where a typical central processing unit (CPU) consumes 50 to 100 W per sq cm the TrueNorth’s power density is 20 mW per sq cm only. This qualifies it to be a good candidate for ushering in green technology in to computing.[3] However, for purposes of clarity TrueNorth is not a brain, it is inspired by the brain[4] and mimics some functions of the brain to carry out computations.

Market for Neuromorphic Chips

The main factors, which have driven research and development of neuromorphic chips, are tremendous demand for data and data analytics, miniaturization of sensors, ingress of Artificial Intelligence into software of almost all intelligent machines and high cost of further miniaturization of integrated circuits. These factors have spurred the demand and growth of the market for neuromorphic chips, which is expected to grow at a CAGR of 26.31% between 2016 -2022.[5] One of the key areas where such systems would need break-through research would be in design of algorithms since biological systems autonomously process information through deep learning whereas any human designed chip or system would be limited by human designed algorithms. The applications areas currently comprise sensors in military as well as medical fields.

Military Applications

Militaries today are coping up with an exponential increase in the amount of data from a wide variety of sensors.  Unprecedented data collection has severely strained the limited available bandwidth for military use. The data needs to be processed, as close to the sensor as possible before further transmission therefore sequential computational techniques with their large size and power requirements are not very efficient in this regard. NeuroSynaptic chips can carry out this parallel task much more efficiently.

DARPA had initiated a project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE), in 2008 and had contracted it to IBM and HRL. It has funding of over $ 100 mn. The aim of SyNAPSE is stated ‘to build an electronic microprocessor system that matches a mammalian brain in function, size, and power consumption. Further, it should recreate 10 billion neurons, 100 trillion synapses, consume one kilowatt, and occupy less than two liters of space’.[6]

The US Army has projected a requirement for a high-performance, low-power bio-inspired parallel processor. This would be integrated in to cognitive communication systems and image processing platforms on unmanned vehicles. The project is being undertaken by Physical Optics Corporation (POC) under their BRAINWARE processor program.

The U.S. Air Force has projected a requirement to develop a new class of advanced, wide field of view (WFOV) imaging sensors that sample the radiation field in multiple modes: spectral, temporal, polarization, and detailed object shape. These multimodal sensors are for deployment on high altitude ISR functions of drones. Scaled down versions are required for use with autonomous micro-air vehicles (MAV) for guidance, navigation, and control. Two types of bio-inspired multimodal sensors, one operating in the visible wavelength regime, and the other operating in the infrared wavelength regime are being developed by The Spectral Imaging Laboratory (SPILAB) in collaboration with the University of Arizona. Both sensors will have a neuromorphic processing capability based upon visual brain areas of insects and crotalid snakes.

Conclusion

It is apparent that neuromorphic chip based computational systems scalable to the capabilities of the human brain are  a clear possibility provided an all-round research and development effort is synergized in hardware, software, architecture, and simulation & understanding of functioning of the brain. The neuromorphic chips as well as quantum computing have ushered in a paradigm shift from the focus on microchips to that of the system as a whole.

In the ultimate goal of mimicking the human brain, it is likely that development of artificial brains of smaller species or specific parts of the human brain may turn out to be more enchanting purely from a commercial point of view. The impetus to the rapid development in neuromorphic systems would be provided by the availability and applications of such systems for large-scale commercial utilization.

[1] http://articles.economictimes.indiatimes.com/2015-01-30/news/58625701_1_brain-research-kris-gopalakrishnan-indian-institute

[2] http://science.sciencemag.org/content/345/6197/668.full

[3] Computational power efficiency for biological systems is 8–9 orders of magnitude higher than the power efficiency wall for digital computation;

[4] http://www.research.ibm.com/articles/brain-chip.shtml

[5]http://www.reportlinker.com/p03302865- summary/Neuromorphic-Chip-Market-by-Application-End-User-Industry-and-Geography-Global-Forecast-Analysis-to.html

[6] http://www.darpa.mil/news-events/2014-08-07

My Book “Negotiating the Acquisition of Nanotechnology in India”

My Book

My book

“Negotiating the acquisition of Nanotechnology in India” has been published by LAP Lambert Academic Publishing, Germany.

ISBN-978-3-659-85076-9

February 15, 2016

At Amazon.com:

http://www.amazon.com/Negotiating-Acquisition-Nanotechnology-Sanatan-Kulshrestha/dp/3659850764/ref=sr_1_fkmr0_1?ie=UTF8&qid=1455891134&sr=8-1-fkmr0&keywords=negotoiating+acquisition+of+nanotechnology

At  morebooks.de

https://www.morebooks.de/store/gb/book/negotiating-the-acquisition-of-nanotechnology-in-india/isbn/978-3-659-85076-9

Nanotechnology represents one of those emerging ‘platform’ technologies that can provide much needed enhanced capabilities to defence of a country. The pervasive nature of nanotechnology research, and the important anticipated products that will influence future industrial products, implies the need to focus on areas where security concerns are likely to arise. Nations see strategic interests in nanotechnology; in that they hope to be in a position of strength to exploit arising opportunities, when nanotechnology starts to have considerable impact on global economy. The desire of a nation to emerge as an economic beneficiary (or a leader) through profitable use of nanotechnology would be dependent upon its diplomatic and negotiation skills with other nations in forging complex relationships which protect its national interests. The evolving of this industry in India through negotiation during TOT, Joint ventures/partnerships etc. is likely to shape the relationships and alliances India shares in the global arena.