Much like the nuclear arms race of the 20th century, Artificial Intelligence (AI) arms race poses the new challenge in 21st century. Autonomous operations by aerial drones, robots and naval systems are fast replacing the manned platforms in every role. Aerial systems which cover large distances at very high speeds will be the key beneficiaries of AI. AI shall provide enhanced air warfare capabilities such as target identification, designation and tracking, optimised attack manoeuvre and autonomous combat engagements. AI will greatly support the transition from fighter jets to Unmanned Combat Air Vehicles (UCAV).
As USA and China compete over AI-enabled military hardware, AI technologies are being incorporated into autonomous drones, new generation missiles with autonomous targeting capabilities and autonomous robotic submarines. AI could offer advantage to new players. The Government of India’s Niti Aayog came out with a paper on ‘National Strategy for Artificial Intelligence’ in June 2018. According to sources, India ranks third in the world in terms of high-quality research publications related to AI. AI will support decision-making to destruction of adversary military capabilities through the disruptive use of AI. Conscious of its force multiplier effects, the Indian Armed Forces are evolving a roadmap for inducting AI technologies.
What is Artificial Intelligence?
The development of computer systems to perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making and translation between languages is termed as AI. Intelligent machines that can interpret complex data; perceive the environment and take appropriate actions using learning and problem solving techniques, are termed to have AI. As it is in humans, AI processes include perception, reasoning, knowledge, planning, learning, statistical analysis, computation and finally manipulates output. It has evolved using expertise in fields such as computer sciences, mathematics, psychology, neuroscience, among others. AI applications already exist in industrial machines, automotive industry, surgery and aviation.
As it has done so for most other technologies, US DoD allotted huge funds in the 1960s for AI. In the early 1980s, AI got a boost with the commercial success of expert systems. Late 1990s, AI began to be used for logistics, data mining and medical diagnosis. ‘Deep Blue’ became the first computer chess-playing system to beat a reigning world chess champion, Gary Kasparov in May 1997. By mid-2010s, machine learning applications became common across the world. IBM’s Question Answering System defeated the two greatest quiz champions by a significant margin. Microsoft’s development of a Skype system that can automatically translate from one language to another, and Facebook’s system that can describe images to blind people have become possible due AI.
The Huge Data Analysis
A significant amount of collected data never actually gets analysed as the analytical capacity of AI is low. AI will help connect billions of individuals and machines in a nano second. When Elon Musk’s Tesla vehicle hits a pothole, it takes into account where the pothole is and its size and the data is then transferred through high-speed networks for other electric cars to avoid should they take the same road. Similar exercise needs to be done by connecting all space, air, surface and sub-surface platforms to pass relevant information about own and adversary positions and actions.
The United States Department of Defense has set up the Joint Artificial Intelligence Center (JAIC) that will help the US military to ‘preserve and expand the military advantage’ and aggressively pursue AI applications while ensuring commitment to military ethics and AI safety. Projects are in partnership with the industry, academia and allies and will monitor all individual service and defense agency AI efforts. Defense Advanced Research Projects Agency (DARPA) has been tasked to “develop the next wave of AI technologies”. The Advanced Targeting and Lethality Automated System (ATLAS) programme will use AI and machine learning to give combat vehicles autonomous targeting capabilities. DARPA’s ‘OFFSET’ programme aims at using swarms comprising more than 250 Unmanned Aircraft Systems (UAS) to accomplish diverse missions in complex urban environments. Meanwhile, the US Armed Forces have set up AI task forces to identify “specific skill sets” to operate in an AI-supported environment and against any adversary.
The Chinese government too is pursuing an aggressive policy for the development of AI. Along with its ‘Made in China 2025’ initiative is the ‘New Generation Artificial Intelligence Development Plan’. China has emerged as the ‘most attractive country for AI investment and financing’. President Xi Jinping has called for China to follow “the road of military-civil fusion-style innovation”, wherein military innovation is integrated into China’s national strategy. The People’s Liberation Army (PLA) relies heavily on the larger private AI research organisations. There are a growing number of collaborations between defence and academic institutions in China. China’s Ziyan’s Blowfish A2 killer drone autonomously performs more complex combat missions, including fixed-point detection, fixed-range reconnaissance and targeted precision strikes. The US is visibly concerned about losing its military edge to China in the field of AI.
The Russian Foundation for Advanced Research Projects, the Russian equivalent of DARPA, opened the national centre for the ‘Development of Technology and Basic Elements of Robotics’ in 2015. There is increased cooperation between military and civilian scientists in developing AI. Russia is developing an autonomous drone, which will be able to take off, accomplish its mission and land without human interference, though weapons use will require human approval. A new city named Era, devoted entirely to military innovation, is currently under construction. In 2017, Kalashnikov – Russia’s largest gun manufacturer – announced that it had developed a fully automated combat module based on neural-network technologies that enable it to identify targets and take decisions.
MoD AI Initiatives
India’s Ministry of Defence (MoD) has initiated the process of preparing Indian defence forces for use of AI. A multi-stakeholder task force on strategic implementation of AI for national security and defence with members from the government, defence services, academia, industry, Defence Research and Development Organisation (DRDO), Defence Public Sector Undertakings (DPSUs), National Cyber Security Coordinator (NCSC), Indian Space Research Organisation (ISRO), Bhabha Atomic Research Centre (BARC) and start-ups was constituted in February, 2018. It submitted its report in June. The MoD implemented the recommendations by providing an institutional framework for policy implementation, issuing guidelines to the defence organisations and laying out a vision for capacity-building. In February 2019, a high-level Defence AI Council (DAIC) under the chairmanship of the Minister of Defence, was assigned with the task of providing strategic direction towards the adoption of AI in defence. It also envisions the formation of a Defence AI Project Agency (DAIPA) as the central executive body.
The government was studying a task force report by a group led by Natarajan Chandrasekharan, Chairman Tata Sons, which had recommended the use of technology in aviation, naval, land, cyber, nuclear and biological warfare. This, he said, had a potential to provide military superiority through both defensive and offensive actions.
Each Service Headquarter (SHQ) has been provided with funds for AI specific application developments. DRDO’s Centre of Artificial Intelligence and Robotics (CAIR), has a comprehensive library for AI-based algorithms and data mining toolboxes that can potentially be used for image/video recognition and swarming.
AI in Military Aviation
Precise application of kinetic power has been the hallmark of aerial warfare. To achieve maximum effect and minimise collateral effects, requires instant intelligence. UAS are the preferred ISR platforms. AI supports terabytes of data to convert into actionable intelligence in near real time. AI and deep learning is being used for missions on smarter and fully autonomous platforms and also for automating the process of testing and analysis. AI will help access, analyse and process abundance of data from aircraft sensors, weapons and satellites. Let computers do what computers are good at and let humans do what humans are good at, is the approach.
AI Project ‘Maven’ used machine learning to recognise objects from moving or still imagery, to develop targets for drone strikes. Predator drones loiter autonomously taking out targets with pinpoint accuracy. ‘Man in the loop’ may remain for some time. In integrated manned-unmanned missions, US Army Apache crew have been successfully controlling flight path and sensor loads of unmanned systems in the air. Launch, flight at supersonic speed and recovery of an F-16 Falcon without a pilot in the cockpit as far back as in 2013, shows possibilities of manned and AI-enabled unmanned fighter formations coordinating and sharing seamlessly various tasks in execution of a complex mission.
AI in Aviation Design Support and Flight Safety
The AI-supported Design of Aircraft, or AIDA, is used to help designers create conceptual designs of aircraft. NASA’s Dryden Flight Centre has created software that could enable a damaged aircraft to continue flight till safe landing. The software compensates for all the damaged components by relying on the undamaged components. AI would make it possible to cope with unfamiliar situations such as serious emergencies, sudden turbulence, engine failures, loss of critical flight data, reducing pilot workload and fatigue. Technologies that help drones avoid terrain, obstacles, traffic and weather or self-diagnose a mechanical problem and returns to base using AI are already in use. Beyond generating alerts, AI could advise on new flight plans dynamically generated with weather data, fuel consumption rate and other parameters. AI-supported integrated vehicle health management system can automatically and periodically run maintenance scenarios and check systems status. It can detect inconsistent patterns and trigger repair at the first signs of weakness before something is broken.
Current cockpit instrumentation is for pilot interpretation. Most errors are at reading or interpretation stage. Using technology and sensors, the aerial platform knows exactly where it is in all three dimensions all the time. Interpretation errors can thus be removed. AI is expected to deliver ultra-safe, next-generation airliners as well as military aircraft and hopefully, finally eliminate Human Error (HE) in aviation accidents. Keep the pilot in the process of flight, yet take the pilot error out of the process, will be the approach.
AI in Flight Operations
Fighter jets are already using AI to provide pilot guidance for best interception profile, weapon guidance and trajectory, flight departure prevention and to perform Intelligence, Surveillance, and Reconnaissance (ISR) missions among others. AI has been increasingly used in the F-35 Lightening II aircraft. They are being used extensively in unmanned swarms and mixed manned and unmanned formations. Unmanned helicopters are already landing autonomously on fast moving rocking ships. The new next-generation bomber B-21 Raider could be optionally manned using AI. Russians have been using unmanned MiG-21s as aerial targets for decades through partial autonomous control. The F-16 has an unmanned variant. The ground control of flight path, sensor payload and weapons of autonomous UAS are using AI. US Navy X-47B UCAV has completed multiple aircraft carrier catapult launches, arrestor landings, and touch-and-go landings with only human supervision. X-47B has successfully conducted autonomous aerial refuelling.
Autopilots, Full Authority Digital Engine Control (FADEC) and load-shedding electrical systems are all using AI to take decisions. Comparing current engine signature to a database of millions of hours of past engine data, is predicting and warning of impending failure. A DARPA project called Aircrew Labor In-cockpit Automation System (ALIAS) aims to create a full replacement for a human co-pilot. Initially, it will be a mechanical system that manipulates the controls of an airplane like a human does. The next step will be real decision-making tasks.
Predicting weather is another place where AI will be of interest to aviation. AI deciphers the weather radar image and other data to make realistic predictions. It analyses fuel consumption rate and winds and suggest flight route. For regulation and certification of AI, Federal Aviation Administration (FAA) is looking at new capabilities. The pilots would also need to be re-skilled to understand AI-related failures.
AI in Air Combat
In air combat where milliseconds matter, AI shows opportunities. The US Air Force Research Laboratory (AFRL) created ‘ALPHA’, an AI agent. During flight simulator tests, it consistently beat experienced combat pilots in a variety of air-to-air combat scenarios. ALPHA will further be developed to increase autonomous capabilities for mixed combat teams of manned and unmanned fighters to operate in a highly contested environment.
Flight Workload Sharing
AI helps the aircraft itself become a co-operative partner in the aircraft control, flight management and decision-making process by monitoring every phase of the flight and ensuring compliance and flight safety. In fly-by-wire fighters, the computer takes over to prevent exceeding of load factor and other flight parameters. AI compares stored manufacturer’s exact flight manual performance and handling criteria with current flight status. It also takes into account, the digitally transferred external situational data and criteria. It then monitors the flight against where it should be especially all areas where pilot error could affect flight safety. The pilot, of course, still has overall command, but the aircraft shares responsibility and AI has primary authority for maintaining 100 percent error-free speeds and altitudes, stall-safe angle-of-attack and CFIT-safe trajectory. Even in case of pilot incapacitation, AI could take full control, divert and land autonomously, taking instructions from air traffic control.
AI Support Decision-Making
Future intelligence, surveillance, target acquisition and reconnaissance systems will generate even larger amounts of (near) real-time data that will be virtually impossible to process without automated support. For pilots to effectively orchestrate actions in such environments, they need situational understanding and decision-support on possible courses of action, their effects and consequences. AI can enhance decision-making. Observe-Orient-Decide-Act (OODA) loop embracing military action decision-making processes will use AI for each OODA step. AI supports harvesting data, fuses it into a unified view, provides condensed, unified situational view usable by humans, and helps in decision making and action.
The Manned-Unmanned Teaming (MUM-T) concept is a critical capability for future military operations in all domains. Testing and implementing is on for the safety of pilots, situational awareness, decision-making and mission effectiveness. Apache helicopter pilots are controlling unmanned MQ-1C Grey Eagles. The ‘Loyal Wingman’ model is where a manned command aircraft pairs with an unmanned aircraft serving as a wingman or scout. In 2015, an unmanned F-16 was paired with a manned F-16 in formation flight. In 2017, the pilotless F-16 broke off from the formation, attacked simulated targets on the ground, manoeuvered in flight in response to mock threats and re-joined the formation.
The USAF foresees pairing a manned F-35 with such an unmanned wingman. ‘Flocking’ will be the next step. Many unmanned aircraft in a flock will execute the Commander’s intent, while the command aircraft no longer exercises direct control over any aircraft in the flock. However, the command aircraft, if it wishes, could command discrete effects from individual aircraft. ‘Swarming’ is more complex than flocking where an operator cannot know the position or individual actions of any discrete swarm element, and must command the swarm as a group. In turn, the swarm elements will complete the bulk of the combat work. In October 2016, the US DoD demonstrated a swarm of 103 ‘Perdix’ autonomous micro-drones ejected from a fighter aircraft. The swarm successfully showed collective decision-making, adaptive formation flying, and self-healing abilities. Swarms act as expendable decoys to spoof enemy air defences by pretending to be much larger targets. In an even more spectacular display, China set a world record when a formation of 1,180 drones performed flawlessly forming in unison Chinese symbols and ideograms.
Aircraft Simulators and Diagnostics
Airplane simulators are already using AI, interpreting data taken from real flights. AI also helps come up with the best scenarios for simulated aerial warfare. AI supports pilots during air combat with best attack solutions and manoeuver guidance. The Interactive Fault Diagnosis and Isolation System (IFDIS) uses collated information from documents for expert advice. The system allows the regular workers to communicate with it, and avoids mistakes and miscalculations. AI-enabled sensors forecast when a component would fail and help extract most value before necessity of replacement. Sensors employed in ‘The Automatic Logistics Information’ System of F-35, monitor and transmit health status of crucial aircraft systems to appropriate agencies on a globally distributed network – thus enabling timely intervention and minimising downtime of aircraft.
Simulation for Military Training and Exercise
Computer-generated forces can support more complex and realistic exercise scenarios and manage same level learning with lesser human resource requirement. Air forces, including the IAF, have demonstrated reliable connectivity and beneficial training opportunities between multiple types of aircrew simulators and training centres. Data exchange between existing Command and Control, Communication, Computers and Intelligence (C4I) systems and simulators, the potential to switch from real units and systems, to robotics and autonomous systems, for simulation is now possible.
Filling the AI Gaps
The system must know when AI is falling short. AI is well-equipped for about 80 to 95 percent of the tasks, but the remaining tasks need to be addressed as well. AI leaders at Google, Amazon and the like, have figured out that when it comes to mission critical applications, you need a combination of AI and human judgment in order to close the gap. Google Maps was built by using Google’s AI to find the streets and intersections in imagery, but then Google’s ‘Team Ground Truth’ (human IQ) had to fill in the gap on tricky one-way streets and construction zones. When it comes to AI systems for complex environment, human judgment is needed to cover the last mile.
The Skeptics – Alternative View
Some consider AI as a threat to human jobs. Others consider AI a danger to humanity if it progresses unabatedly and may one day threaten human existence. There are also ethical and morality issues. Mostly discussions have been binary: human intelligence vs. AI More realistic phrase is “extended intelligence” to signify how AI is used to augment human decision-making rather than replace it. Some operators are questioning the ‘if-then-else’ approach that led to the catastrophic Airbus AF 447 crash over the Atlantic. The autopilot had disengaged in flight because of inconsistent airspeed data input from blocked pitot tubes, exactly as it was programmed to do by its human designers. Dumb programming could be a disaster. But AI is meant to cater to design and input failures by simultaneously taking alternative inputs from other data sources and sensors. Like pilots, the jobs of railroad crew, truck drivers, maritime crew, surgeons, diagnosticians, general management, lawyers, accountants and even government employees could be threatened by AI. Automation in the cockpit has already reduced the pilot to a flight systems manager. There is a need to develop legally binding instrument that ensure meaningful human control over autonomous ‘Killer Robots’ and other AI-based weapons systems.
The future combat environment is increasingly likely to be impacted by multiple disparate autonomous elements. That would bring uncertainty in the extended battle space. The required speed of response may force humans out of the OODA loop. However, AI will remain a key facilitator to comprehend the complexities of a rapidly evolving situation and suggesting necessary response. Moreover, military commanders need to ask themselves how much trust they want to place in what the AI-enabled autonomous system promises to be able to do. How much better is it with regard to persistence, precision, safety and reliability, as compared to the remote human operator?
AI is about ‘man-and-machine’, not ‘man-vs-machine’. While AI is required to handle myriad tasks with efficient decision making, it needs to interpret and adjust to human emotional state and give an appropriate response for those emotions. ‘Affective computing’ is the study and development of systems and devices that can recognise, interpret, process and simulate human affects. A motivation for the research is the ability to simulate empathy. Many researchers think that it all will eventually be incorporated by combining all the skills and exceeding human abilities. Anthropomorphic features like artificial consciousness or an artificial brain may be required.
Aviation is one of the most heavily regulated industries in the world due to safety reasons. Strict rules have helped the aviation industry provide the safest way of transport per mile travelled. Aviation incidents are few and far between and are getting rarer every year – ‘touchwood’. Some degree of automation has indeed helped get aviation to where it currently is. But human control and intervention have always been at the heart of it, from pilots to air traffic controllers. This is about to change. Air freight seems to be the obvious entry point for pilotless planes, just as driverless trucks are already disrupting the ground transportation industry. When a plane lands, a human alone does not decide how and which ‘gate’ it should arrive at.
The ever increasing processing power of silicon electronics and the ability to harvest massive amounts of data for computers to process, analyse and categorise is the single reason for AI to be possible. Amazon, Google, Facebook, IBM and Microsoft have established a non-profit partnership to formulate best practices of AI technologies, create awareness amongst the public and serve as a platform. Fully autonomous AI-controlled systems are unlikely to be flying passenger jets just yet. US DoD is looking into ‘autonomous wingmen’, the Russians and Chinese are looking into fully automated battlefields. Historically, regulation moves slower than technology, because ensuring safety requires lots of tests and certifications. AI remains one of the hottest buzzword and in military applications, aviation sector remains a key area. AI technology has become a crucial linchpin of the digital transformation taking place in military aviation.