SHUBHANGI’S COLUMN:”Battle of A.I Fighter Jets: China Set to Challenge US Air Force In Aerial Warfare With Smart Air Combat AI” 

 

Pic Courtesy: Internet

 

Shubhangi Palve is a Defence & Aerospace journalist currently associated with EurAsian Times. Prior to this role, she worked as a staff writer at ET Prime, focusing on defence strategies and the defence sector from a financial perspective. She has more than 15 years of extensive experience in the media industry, spanning print, electronic, and online domains.

 

Her article on

“Battle of A.I Fighter Jets: China Set to Challenge US Air Force In Aerial Warfare With Smart Air Combat AI” 

was published on 20 May 2024 on “The EurAsian Times”.

 

(Besides the two quotes, the views of the author are her own)

 

“Battle of A.I Fighter Jets: China Set to Challenge US Air Force In Aerial Warfare With Smart Air Combat AI” 

 

Picture this: An unmanned combat air squadron launches into hostile skies, guided not by human pilots but by the cold calculus of artificial intelligence. With lightning speed, the AI war manager assesses threats, devises intricate battle plans, and unleashes a blistering onslaught of precision strikes against enemy strongholds. Each manoeuvre executes with machine perfection as the AI mastermind adapts seamlessly to the ever-shifting tides of aerial combat.

But hold on, this isn’t Hollywood fiction…

Welcome to the new age of hybrid airpower!

 

The Race for AI Supremacy Takes To the Skies

In the high-stakes game of military one-upmanship, a new battlefront has emerged – the fusion of Artificial Intelligence (AI) with aerial combat systems.

China claims to have seized a potential edge, developing an “intelligent air combat AI” capable of making split-second tactical decisions and explaining its reasoning to human partners using an intelligent discourse of data visualisations and natural language.

This shatters the long-standing “black box” quandary that has handcuffed militaries – the inability of inscrutable AI systems to articulate the rationale behind their choices. Chinese researchers claim that their ground-breaking AI can engage in intelligent discourse, using words, data visualisations, and charts to illuminate why it issues specific flight instructions.

The Profound implications? An AI co-pilot can forge an unprecedented hybrid of linguistics between the domains of machine logic and human contextual intellect. Moreover, the Chinese team audaciously boasts that this symbiotic melding of abilities can achieve a staggering near-100% win rate in simulated aerial combat scenarios.

Meanwhile, the United States still grapples with the opaqueness of current AI architectures, a situation that underscores the importance of transparency and explainability in AI-driven systems. The US Air Force Secretary recently experienced the limitations of a “still-learning” AI controlling his F-16 flight, and its decision-making processes during potential weapon deployments remain obfuscated.

“Warfare, in general, and air warfare, in particular, is undergoing a dramatic change rapidly due to advanced technologies. Among these technologies, those with the greatest impact include Quantum, AI, Hypersonics, Stealth, Nano, Miniaturization, and Robotics. AI has a big potential for warfare applications,” Air Marshal Anil Khosla (Retd.), Vice Chief of the Air Staff (VCAS) of the Indian Air Force, told the EurAsian Times.

 

General Dynamics X-62 VISTA US Skyborg

After recently receiving a new look and modifications at the Ogden Air Logistics Complex, the NF-16D, known as VISTA (Variable stability In-flight Test Aircraft), they departed Hill Air Force Base, Utah, on Jan 30, 2019. This aircraft is the only one of its kind in the world and is the flagship of the United States Air Force Test Pilot School. This F-16 has been highly modified, allowing pilots to change the aircraft flight characteristics and stability to mimic that of other aircraft. (U.S. Air Force photo by Alex R. Lloyd).

 

US Armament with AI

In a bold move, the US has embarked on an ambitious endeavour dubbed ‘Replicator,’ designed to rapidly bolster its capabilities in the face of escalating competition, particularly from the People’s Republic of China.

The heart of Replicator lies in swiftly deploying thousands of autonomous systems, harnessing the power of AI, robotics, and cutting-edge technology. With a staggering budget of US$1 billion allocated by the Department of Defence, the Replicator program aims to construct a formidable fleet of compact, weaponised autonomous vehicles.

The Pentagon is abuzz with over 800 active military AI projects, from streamlining processes and evaluating threats to enhancing battlefield decision-making. Notable initiatives include the innovative “Loyal Wingman” program and the deployment of swarm drones like the formidable V-BAT aerial drone.

“The current trend in air combat platforms involves AI-based unmanned aircraft collaborating with manned aircraft, harnessing both advantages. This strategy is dubbed the ‘Loyal Wingman Concept.’ I call it the ‘Mother Goose Concept.’ All sixth-generation platform programs are striving toward this objective,” remarked Air Marshal Anil Khosla.

In a ground-breaking demonstration of its capabilities, the US Naval Forces Central Command’s (NAVCENT) Task Force 59 recently showcased its prowess by executing a successful attack on a simulated enemy target using live rockets, all orchestrated by an unmanned vessel. Experimental submarines, tanks, and ships have already been outfitted with AI capabilities to navigate and engage targets autonomously.

Furthermore, the US military has openly acknowledged its utilisation of AI and machine learning algorithms to identify potential targets for airstrikes in conflict zones such as Iraq, Syria, and Yemen. These sophisticated algorithms, developed under Project Maven—a collaborative effort between Google and the Pentagon—are carefully supervised by human operators to ensure precision and ethical use in target selection processes.

 

China’s Investment in AI

While the world closely monitored China’s economic resurgence and geopolitical ambitions, a powerful undercurrent has been gathering force – a concerted national drive to harness artificial intelligence as a potent force multiplier across all war-fighting domains.

Beijing has supercharged investments in robotics, swarming technologies, artificial intelligence (AI), and machine learning’s myriad militant applications.

Their landmark 2017 “New Generation AI Development Plan” plainly prioritises unmanned combat systems, and other advanced military innovations take centre stage, reflecting China’s strategic prioritisation of AI technologies.

According to a report titled ‘AI Weapons in China’s Military Innovation’ by Global China, Chinese military experts and strategists from institutions like the PLA’s Academy of Military Science, National Defence University, and the National University of Defence Technology foresee a future where AI and intelligent weaponry will assume increasingly pivotal roles, potentially even tipping the scales in future conflicts.

 

China’s Challenges US

China is now challenging its long-standing US dominance in aerial combat platforms as it surges ahead in investment, research, and development (R&D) across several ground-breaking technologies.

While US technology has evolved and been proven over the years, Chinese advancements are claimed and not demonstrated or proven. Notwithstanding, these claims cannot be taken lightly, according to Anil Khosla.

Furthermore, Anil Khosla emphasises that maintaining a lead in the technological race revolves around the defence market. Securing a foothold in the defence market holds immense appeal for economic and strategic considerations. On the financial front, it serves as a vital revenue stream and contributes to job creation. Strategically, it reduces the dependency of importing nations on external sources.

As this AI arms race intensifies, extending beyond just aviation to permeate all domains of warfare, the nation that unlocks the secret of harmonising machine intelligence with human cognition could seize an extraordinary strategic advantage. The theatre may be the skies, but the stakes could hardly be higher.

 

Keeping the Atomic Finger off AI Trigger

Back in the Cold War days, all eyes were on the nuclear arms race, a chilling competition that morphed into today’s reality of mass destruction weapon systems on the battlefield.

Fast forward to now, and the numbers are staggering: a whopping 12,500 nuclear warheads, with Russia and the US dominating possession, claiming nearly 90% of this terrifying arsenal.

A recent report from the Arms Control Association reveals the extent of nuclear stockpiles: Russia leads with 5,889 warheads, trailed closely by the US with 5,244, and China with 410.

Moreover, beyond the five permanent Security Council members—US, China, France, Russia, and the UK—other nations recognised under the nuclear non-proliferation treaty as nuclear-capable include Israel, India, Pakistan, and North Korea.

In a recent statement, US State Department arms control official Paul Dean underscored the importance of human control over nuclear decisions, emphasising that the US has unequivocally committed to ensuring that only human beings have the authority to deploy nuclear weapons.

This sentiment is echoed by the UK and France, who have pledged to keep nuclear control firmly in human hands, shunning the involvement of AI. Furthermore, the US has urged China and Russia to follow suit, urging them to prioritise human oversight in utilising these potent weapons rather than entrusting such decisions to artificial intelligence.

 

The AI Conundrum

In conclusion, integrating AI into military systems represents a significant leap forward in modern warfare. As highlighted by Anil Khosla, within novel systems that amalgamate multiple sensors and weapon systems into a unified framework. These systems must sift through vast amounts of data for analysis.

The fusion of AI and quantum computing enables this process to occur rapidly. When combined with miniaturisation, one obtains an optimal system for airborne platforms—small and lightweight yet possessing high computing power and speed. Integrating these technologies would give decision-makers swift decision-making tools, such as decision support systems and ‘what if’ option tools.

However, it is crucial to acknowledge AI’s inherent limitations, particularly in its current state. While AI excels at executing mundane tasks and analysing data patterns, its ability to make nuanced decisions remains questionable. This raises ethical and practical concerns, especially concerning lethal autonomous weapons (LAWs) equipped with AI.

The proliferation of LAWs, empowered by AI, sparks heated debates among experts, touching upon legality, ethics, and the potential for unintended consequences. While AI-enhanced drones may enhance military capabilities, they also introduce new risks and challenges that must be carefully considered.

As we navigate this AI conundrum, it is imperative to approach the integration of AI into military systems with caution and foresight. By striking a balance between technological advancement and ethical considerations, we can harness the potential of AI to enhance military capabilities while mitigating risks and safeguarding human interests. We can responsibly navigate AI’s complexities in modern warfare through thoughtful deliberation and collaboration.

 

My Comments on the subject:-

1. Warfare in general and air warfare in particular is undergoing a dramatic change rapidly due to advanced technologies.

2. Technologies with maximum effect are Quantum, AI, Hypersonics, Stealth, Nano, Miniaturisation, Robotics, etc.

3. AI has a big potential for warfare applications.

4. Firstly in unmanned autonomous platforms.

5. Unmanned platforms (Drones in airwarfare) are changing the air warfare in a revolutionary manner.

6. Second potential is in new systems which have multiple sensors and weapon systems integrated together. These systems have to analyse a large volume of data. AI and quantum computing combination can do that at a rapid rate. Couple them with miniaturisation and one gets an ideal system for Airborne platform (Small, light, high computing power and high computing speed).

7. The combination of these technologies would would provide the decision makers with quick decision making tools like decision support systems and what if option tools.

8. USA has been dominating the skies with creation of aerial combat platforms with advanced technology.

9. Now China is challenging their monopoly in this field as China is ahead in investment and R&D in some of these path breaking technologies.

10. USA is trying to retain it’s leadership position, while China is trying to catch up or race ahead.

11. USA technology has evolved and proven over the years. Chinese advancements are claimed and not demonstrated or proven. Not withstanding, these claims cannot be taken lightly.

12. Another reason for staying ahead in the technology race is the defence market. Capturing the defence market is highly desirable due to economic reasons (revenue source and job creation) and Strategic reasons (Dependency of importing countries).

13. The current trend in the air combat platforms is for AI based unmanned aircraft to work along with manned aircraft, reaping the benefits of both. It is called “Loyal Wingman Concept”. I call it mother goose Concept. All sixth generation platform programs are working towards it.

14. The trend of air warfare is towards “No contact warfare”, i.e. with long range vectors and unmanned aerial platforms.

15. In future the air wars would be fought by AI based unmanned platforms with smart weapons with minimal human intervention. – Scary thought.

 

Link to the Article at EurAsian Times:-

Battle Of A.I Fighter Jets: China Set To ‘Challenge’ US Air Force In Aerial Warfare With “Smart Air Combat AI”

Suggestions and value additions are most welcome.

 

618
Default rating

Please give a thumbs up if you  like The Post?

 

For regular updates, please register here:-

Subscribe

 

 

References and credits

To all the online sites and channels.

 

Disclaimer:

Information and data included in the blog are for educational & non-commercial purposes only and have been carefully adapted, excerpted, or edited from reliable and accurate sources. All copyrighted material belongs to respective owners and is provided only for wider dissemination.

 

 

ARTIFICIAL INTELLIGENCE: CAN DO, CAN NOT DO, MAKE IT DO

 

“The way that we are working and engaging is being fundamentally transformed by artificial intelligence.”

– Navveen Balani,

CTO/Co-Founder of Cittin Data Labs

 

Introduction

 

AI is quickly shifting from a cutting-edge IT application reserved for high-tech start-ups and large enterprises to an everyday tool for everyone.

 

Some of the current uses:-

 

Finance:  To automate complex data tasks such as scanning millions of transactions to identify fraud, as well as predicting market trends and managing risks.

 

Legal Profession: To summarise vast quantities of case law and draft contracts and documents.

 

Medical: To analyse medical images and make accurate diagnoses.

 

Designing: To use generative design to prototype products and structures, as well as predictive maintenance, to improve the efficiency of repairs and maintenance.

 

Sales and marketing: Across the spectrum, from identifying business opportunities and segmenting customers to creating content and tailoring personalised promotional materials.

 

As AI becomes more powerful and more widely accessible, more and more of us will find ourselves using it in our working lives.

 

Continue reading “ARTIFICIAL INTELLIGENCE: CAN DO, CAN NOT DO, MAKE IT DO”

ARTIFICIAL INTELLIGENCE: DIGITAL TWINS & SURROGATE MODELS

 

 

Defining DT

A digital twin is a digital representation that functions as a shadow/twin of a physical object or process.

The core idea behind Digital twins is to create a virtual model that incorporates all the necessary information about a physical ecosystem to solve a particular problem.

Digital twins are designed to model and simulate a process to understand it and predict its behaviour.

There is no standard definition of a Digital Twin but it can be defined as a bi-directional data link as well as a data processing entity that simulates, forecasts, and regulates a system in real-time and also transfers and stores data.

 

Components of Digital Twins

The basic idea of DT is quite straightforward, linking a physical object to a digital entity through a framework comprising at least the following components:

 

  1. Data Link
  2. Coupling (a two-way interface)
  3. Identifier
  4. Security
  5. Data Storage
  6. User Interface
  7. Simulation
  8. Analysis
  9. Artificial Intelligence
  10. Computation

 

Utility

Digital twin originated from engineering and is related to model-based systems engineering (MBSE) and surrogate modelling.

The usage of digital twins is now more mainstream in software development.

Once the system is modelled as a twin, various existing and new engineering problems can be modelled and simulated, such as predictive maintenance, anomaly detection, etc.

Digital twins can be combined with Augmented Reality and Virtual Reality to model physical processes. 

Digital Twins will have a big role in enhancing Model-based Design and simulation and will extend to AR (Augmented Reality) and VR (Virtual Reality).

Digital twins have a large scope in design and simulation.

This technology will have a significant impact over the next few years.

Surrogate model.

Definition. It is an engineering method used when an outcome of interest cannot be easily directly measured, so a model of the outcome is used instead. For example, in order to find the optimal aerofoil shape for an aircraft wing, an engineer simulates the airflow around the wing for different shape variables (length, curvature, material, ..).

 

For many real-world problems, a single simulation can take many hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and  what-if analysis become impossible since they require thousands or even millions of simulation evaluations.

One way of alleviating this burden is by constructing approximation models, known as  surrogate models,  metamodels or  emulators, that mimic the behavior of the simulation model as closely as possible while being computationally cheap(er) to evaluate.

Surrogate models are constructed using a data-driven, bottom-up approach. The exact, inner working of the simulation code is not assumed to be known (or even understood), solely the input-output behavior is important.

 

A model is constructed based on modeling the response of the simulator to a limited number of intelligently chosen data points. This approach is also known as behavioral modeling or black-box modeling, though the terminology is not always consistent.

Though using surrogate models in lieu of experiments and simulations in engineering design is more common, surrogate modelling may be used in many other areas of science where there are expensive experiments and/or function evaluations.

 

DT vis-à-vis SM

 

Digital Twin (DT) is a Physics based model, whereas, Surrogate Model (SM) is a Data based model.

DT is good in the parameter-space represented by physics equations, whereas, SM is good in the parameter-space represented by data, but not covering the space represented by equations

 

Some Interesting Terminologies

 

Artificial Intelligence (AI). A variety of machine learning and deep learning techniques are collectively referred to as AI.

Virtual Reality (VR). creates an immersive experience through VR devices like headsets and simulates a three-dimensional world. VR is used in instructional content and educational material for field workers, oil and gas, defence, aviation, etc.

Augmented Reality (AR). overlays digital information on a physical world. Typically, AR uses conventional devices like mobile phones.

Mixed Reality (MR). allows the manipulation of both physical and digital objects in an immersive world.

Model-based design. A set of technologies and techniques that help engineers and scientists to design and implement complex, dynamic, end-to-end systems using a set of virtual (digital) modelling technologies. Collectively, these technologies can simulate and model physical objects and processes in multiple industries.

Additive Manufacturing.  In the AM approach, first a digital 3D design is created from which the component is printed. The term Additive manufacturing (AM) is used to refer to how technologies like 3D printing are impacting manufacturing. Once the model is digitised, it can be optimised using topology optimization techniques.

 

Bottom Line

 Technology makes the imagination of today into reality of tomorrow.

Technology is a two edged sword – can be used in Civil & Military domain.

 

Suggestions and value additions are most welcome

 

For regular updates, please register here

Subscribe

 

 

References and credits

To all the online sites and channels.

 

Disclaimer:

Information and data included in the blog are for educational & non-commercial purposes only and have been carefully adapted, excerpted, or edited from sources deemed reliable and accurate. All copyrighted material belongs to respective owners and is provided only for purposes of wider dissemination.