A border is something where the territorial boundary of a sovereign country ends and what begins is a bigger responsibility of protecting its boundary from any external threat, which can be defined in a broad term as border security. Different countries have different types of borders, including land borders, coastal borders and aerial borders (or airspace). Securing these different types of borders at all times makes border security a challenging task.
This analyst insight will highlight the pain points and opportunities in the smart border management market and how the traditional analog borders can be transformed into digital borders with the help of technology.
The dynamics of border security change with every country, as every country has different types of terrains, a different type of threat perception and different types of borders.
The terrain can be anything from plains, marsh, mountains, deserts, creeks, riverine, dense forests, deltas, etc. The more types of terrains a country has on its borders, the bigger the problem would be to secure its borders.
Threat perception can be anything from arms and drugs smuggling to illegal immigrants, to cross-border terrorism, to illegal occupation of its boundaries by neighbors.
Types of borders can be anything from fenced to unfenced borders, to friendly borders or hostile borders.
Every border is different and needs different, tailor-made solutions to protect them.
We are living in an era where technology is driving everything and is changing so fast that it has nullified all the traditional wisdom of securing borders. Today, hybrid warfare is possible, wherein cyberattacks, satellite attacks, and drone attacks are the reality and terrorist organizations are using them globally. We have seen an attack on the Saudi Aramco facility, wherein using drone-based loitering ammunition hampered the overall global supply chain. If a nation does not adapt to these technological changes, sooner or later, enemies will find a way to enter its borders, and the effects can be catastrophic.
So a nation is required to have 360-degree protection to form a smart, comprehensive border management system that is digital and can cope with these ever-changing global security scenarios.
First of all:
They need to secure their maritime borders, land borders and airspace using different technologies – perimeter security sensors, radars/sonars, C4ISR systems, digital intelligence, predictive analysis tools, etc. – for security from any kind of outside intrusions/attacks.
They need a strong intelligence collection mechanism at the borders so that information on any upcoming threat can be gathered beforehand and preventive measures can be taken. Different tools and systems should be deployed for SIGINT, COMINT, ELINT, and IMIMT.
They also need to secure themselves from any kind of airborne attacks and should have systems to detect and neutralize not only bigger aerial vehicles and missiles but also for UAVs flying on/near their airspace.
A strong response mechanism is needed to respond to any intrusion events, which can include autonomous UAVs/UGVs/remote weapon stations, to act as a force multiplier and can help ground forces in effectively disseminating the threats without endangering forces that are physically protecting the borders.
It should have a reliable communication system (wired and wireless) in place with a strong encryption mechanism (an overlap of 256-bit encryption and proprietary algorithms) and their exclusive waveforms so that nobody can hack into their mission-critical communication.
Second, they need to secure their ports of entry – airports, seaports, land ports:
At these ports, they should have robust security mechanisms (which should be fast as well as effective) for identity check, immigration, baggage screening, physical security, etc.
For coastal borders and seaports, artificial intelligence and machine learning-based maritime analytics can play a bigger role by taking information from centralized systems like AIS, GIS, etc., and can inform the authorities in advance about any suspicious vessels/ships/ boats before they even enter national waters.
Third, countries need to have a strong national cybersecurity system in place that can help detect threats and vulnerabilities in the system and suggest ways to overcome them.
By adopting the above-mentioned measures and technologies, nations can transform their existing analog borders into digital borders, wherein every suspicious activity gets detected, recorded and presents a holistic picture of the overall security scenario to concerned officials for better decision-making.
Based on the Frost & Sullivan analysis of the global border protection market, the industry is expected to grow to $168 billion by 2025, expanding at a CAGR of 7%. Below are the key technology investment opportunities for security companies:
Autonomous UAS (UAV and UGV) – To automatically respond to any threat
Counter UAS Systems – To detect and neutralize unmanned aerial threats
Remote Weapon Station – To guard the borders, without endangering lives
Software-defined Radios – For robust, futureproof, unhackable communication
Maritime Analytics – To detect and catch the suspicious vessels before entering national waters
Predictive Analytics – A strong, centralized and automated Cyber Threat Intelligence (CTI) platform that can detect probable cyberattacks and suggest ways to mitigate them
Integrated C4ISR System – To build a system of systems that can take information from various subsystems and show a holistic view of the overall security system
Every problem brings an opportunity to solve it. These problems of securing different types of borders in different countries, for different terrains, and to address different threats present a much bigger opportunity for security companies.
The World After COVID-19: Does Transparent Mean Healthy?
The insanity of despair and primaeval fear for one’s health (and today, no matter how ironic and paradoxical it sounds, this may be the state of mind that brings many of us together) will most likely give rise to a new global formation that will then become a global reality. It is still hard to say what it will be like exactly, but it is clear that the world will become more transparent. And I do not mean in the usual sense of anti-corruption measures, but rather in the original sense of the word – the world will become more “see-through.” Our temperature will be monitored. Smartphones with built-in sensors will collect precise data not only about our clicks and likes, but about our physical and possibly emotional state. The world and the people that live in it will undergo a number of changes once the current coronavirus pandemic is over, and many of those changes will be accompanied by a leap in technological development.
For many experts and scientists, the events that are unfolding today are reminiscent of what happened in 2003, when the SARS virus presented the first large-scale threat to human health of the new millennium. Unlike today’s unbidden crowned guest, SARS was not so virulent, yet it caused major concerns for a number of countries, particularly in East and Southeast Asia. Hence the deadly lessons learned in Singapore and partly in Taiwan, where the government has for two decades now been successfully using a system of mass surveillance of the everyday life of its citizens – a system that has received the approval of the people. This system is part of Singapore’s cybersecurity strategy and allows the physical condition of large masses of people to be monitored, thereby preventing diseases from spreading and escalating into epidemics. This, combined with their ability to enforce extremely strict quarantine measures and carry out mass testing instead of the selective testing currently practised in Europe and Russia, has allowed Singapore and Taiwan to contain the spread of the disease and prevent it from turning into an epidemic. Of course, their compact territories have certainly played a part here. Other countries, for instance, Israel and Russia, have already followed this example and approved a monitoring system that uses mobile data and geolocation in order to trace the movements of persons with confirmed infection. We have to assume that one of the first steps after the COVID-19 pandemic will be to embed this surveillance system even deeper into the public life. Most likely, this step will be met with approval instead of protests and street rallies.
I would not wish to speak for everyone, but it seems to me that the choice between health and privacy is a no-brainer. The pandemic will end, and what the world emerging from the pandemic will look like is an interesting question worthy of discussion. To quote the Deputy Minister of Health of Iran, who had COVID-19, we can note that the coronavirus came to us from a relatively safe country and, contrary to recent rumours, it does not only affect those of Asian heritage: quite the opposite, it is very democratic in its choice of victims, which is to say, it affects everyone.
Hence the question: by self-isolating, we are buying doctors and scientists time to find a cure to the virus and test vaccines, but what are we going to do in the event of a new pandemic? Here, humanity faces two choices. The first is to give free rein to nationalists who are already jubilant and triumphant over the failures of globalization and the inability of liberal democratic countries to shut down their borders to viruses and undesirable immigrants. The second is to move to a radically new formation where we will become even more mutually dependent and open to our societies and governments, because this will be a mandatory condition for moving about and doing business, and perhaps even starting a family. Personal secrets will become a thing of the past, a fairy tale we tell our grandchildren. In fact, the issue is far more serious, with multiple additions and ensuing consequences.
Following the COVID-19 pandemic, consensus and mutual understanding between states will be relevant like never before, especially since the problems of disarmament, nuclear warheads, defence budgets propped up by taxpayer money, international sanctions, etc., that appeared and developed during the presidencies of Nikita Khrushchev and Ronald Reagan may finally move into the background. Instead, world leaders, especially given that most of them are at an age that makes them particularly vulnerable to the coronavirus, should start thinking about new plans for investing in healthcare, socioeconomic aspects of life and technological development, because those will be intrinsically linked with the other aspects of improving the state mentioned above. Will this represent a new social contract between the government, the public and the citizen? Probably. Will it represent a new pact between governments? One would hope so. Perhaps the coronavirus pandemic will break down the old world and give rise to the new one that so many expected to appear in the 1990s. But what was to await us back then was proxy wars and a confrontation through sanctions that split societies from within and raised barriers between states. Maybe this new world will be one where surveillance cameras and sensors will first prompt a feeling of relief and then become an integral part of the picture. Perhaps it will be a world where life without external surveillance and control will appear unsafe and unnatural.
From our partner RIAC
Future Goals in the AI Race: Explainable AI and Transfer Learning
Recent years have seen breakthroughs in neural network technology: computers can now beat any living person at the most complex game invented by humankind, as well as imitate human voices and faces (both real and non-existent) in a deceptively realistic manner. Is this a victory for artificial intelligence over human intelligence? And if not, what else do researchers and developers need to achieve to make the winners in the AI race the “kings of the world?”
Over the last 60 years, artificial intelligence (AI) has been the subject of much discussion among researchers representing different approaches and schools of thought. One of the crucial reasons for this is that there is no unified definition of what constitutes AI, with differences persisting even now. This means that any objective assessment of the current state and prospects of AI, and its crucial areas of research, in particular, will be intricately linked with the subjective philosophical views of researchers and the practical experience of developers.
In recent years, the term “general intelligence,” meaning the ability to solve cognitive problems in general terms, adapting to the environment through learning, minimizing risks and optimizing the losses in achieving goals, has gained currency among researchers and developers. This led to the concept of artificial general intelligence (AGI), potentially vested not in a human, but a cybernetic system of sufficient computational power. Many refer to this kind of intelligence as “strong AI,” as opposed to “weak AI,” which has become a mundane topic in recent years.
As applied AI technology has developed over the last 60 years, we can see how many practical applications – knowledge bases, expert systems, image recognition systems, prediction systems, tracking and control systems for various technological processes – are no longer viewed as examples of AI and have become part of “ordinary technology.” The bar for what constitutes AI rises accordingly, and today it is the hypothetical “general intelligence,” human-level intelligence or “strong AI,” that is assumed to be the “real thing” in most discussions. Technologies that are already being used are broken down into knowledge engineering, data science or specific areas of “narrow AI” that combine elements of different AI approaches with specialized humanities or mathematical disciplines, such as stock market or weather forecasting, speech and text recognition and language processing.
Different schools of research, each working within their own paradigms, also have differing interpretations of the spheres of application, goals, definitions and prospects of AI, and are often dismissive of alternative approaches. However, there has been a kind of synergistic convergence of various approaches in recent years, and researchers and developers are increasingly turning to hybrid models and methodologies, coming up with different combinations.
Since the dawn of AI, two approaches to AI have been the most popular. The first, “symbolic” approach, assumes that the roots of AI lie in philosophy, logic and mathematics and operate according to logical rules, sign and symbolic systems, interpreted in terms of the conscious human cognitive process. The second approach (biological in nature), referred to as connectionist, neural-network, neuromorphic, associative or subsymbolic, is based on reproducing the physical structures and processes of the human brain identified through neurophysiological research. The two approaches have evolved over 60 years, steadily becoming closer to each other. For instance, logical inference systems based on Boolean algebra have transformed into fuzzy logic or probabilistic programming, reproducing network architectures akin to neural networks that evolved within the neuromorphic approach. On the other hand, methods based on “artificial neural networks” are very far from reproducing the functions of actual biological neural networks and rely more on mathematical methods from linear algebra and tensor calculus.
Are There “Holes” in Neural Networks?
In the last decade, it was the connectionist, or subsymbolic, approach that brought about explosive progress in applying machine learning methods to a wide range of tasks. Examples include both traditional statistical methodologies, like logistical regression, and more recent achievements in artificial neural network modelling, like deep learning and reinforcement learning. The most significant breakthrough of the last decade was brought about not so much by new ideas as by the accumulation of a critical mass of tagged datasets, the low cost of storing massive volumes of training samples and, most importantly, the sharp decline of computational costs, including the possibility of using specialized, relatively cheap hardware for neural network modelling. The breakthrough was brought about by a combination of these factors that made it possible to train and configure neural network algorithms to make a quantitative leap, as well as to provide a cost-effective solution to a broad range of applied problems relating to recognition, classification and prediction. The biggest successes here have been brought about by systems based on “deep learning” networks that build on the idea of the “perceptron” suggested 60 years ago by Frank Rosenblatt. However, achievements in the use of neural networks also uncovered a range of problems that cannot be solved using existing neural network methods.
First, any classic neural network model, whatever amount of data it is trained on and however precise it is in its predictions, is still a black box that does not provide any explanation of why a given decision was made, let alone disclose the structure and content of the knowledge it has acquired in the course of its training. This rules out the use of neural networks in contexts where explainability is required for legal or security reasons. For example, a decision to refuse a loan or to carry out a dangerous surgical procedure needs to be justified for legal purposes, and in the event that a neural network launches a missile at a civilian plane, the causes of this decision need to be identifiable if we want to correct it and prevent future occurrences.
Second, attempts to understand the nature of modern neural networks have demonstrated their weak ability to generalize. Neural networks remember isolated, often random, details of the samples they were exposed to during training and make decisions based on those details and not on a real general grasp of the object represented in the sample set. For instance, a neural network that was trained to recognize elephants and whales using sets of standard photos will see a stranded whale as an elephant and an elephant splashing around in the surf as a whale. Neural networks are good at remembering situations in similar contexts, but they lack the capacity to understand situations and cannot extrapolate the accumulated knowledge to situations in unusual settings.
Third, neural network models are random, fragmentary and opaque, which allows hackers to find ways of compromising applications based on these models by means of adversarial attacks. For example, a security system trained to identify people in a video stream can be confused when it sees a person in unusually colourful clothing. If this person is shoplifting, the system may not be able to distinguish them from shelves containing equally colourful items. While the brain structures underlying human vision are prone to so-called optical illusions, this problem acquires a more dramatic scale with modern neural networks: there are known cases where replacing an image with noise leads to the recognition of an object that is not there, or replacing one pixel in an image makes the network mistake the object for something else.
Fourth, the inadequacy of the information capacity and parameters of the neural network to the image of the world it is shown during training and operation can lead to the practical problem of catastrophic forgetting. This is seen when a system that had first been trained to identify situations in a set of contexts and then fine-tuned to recognize them in a new set of contexts may lose the ability to recognize them in the old set. For instance, a neural machine vision system initially trained to recognize pedestrians in an urban environment may be unable to identify dogs and cows in a rural setting, but additional training to recognize cows and dogs can make the model forget how to identify pedestrians, or start confusing them with small roadside trees.
The expert community sees a number of fundamental problems that need to be solved before a “general,” or “strong,” AI is possible. In particular, as demonstrated by the biggest annual AI conference held in Macao, “explainable AI” and “transfer learning” are simply necessary in some cases, such as defence, security, healthcare and finance. Many leading researchers also think that mastering these two areas will be the key to creating a “general,” or “strong,” AI.
Explainable AI allows for human beings (the user of the AI system) to understand the reasons why a system makes decisions and approve them if they are correct, or rework or fine-tune the system if they are not. This can be achieved by presenting data in an appropriate (explainable) manner or by using methods that allow this knowledge to be extracted with regard to specific precedents or the subject area as a whole. In a broader sense, explainable AI also refers to the capacity of a system to store, or at least present its knowledge in a human-understandable and human-verifiable form. The latter can be crucial when the cost of an error is too high for it only to be explainable post factum. And here we come to the possibility of extracting knowledge from the system, either to verify it or to feed it into another system.
Transfer learning is the possibility of transferring knowledge between different AI systems, as well as between man and machine so that the knowledge possessed by a human expert or accumulated by an individual system can be fed into a different system for use and fine-tuning. Theoretically speaking, this is necessary because the transfer of knowledge is only fundamentally possible when universal laws and rules can be abstracted from the system’s individual experience. Practically speaking, it is the prerequisite for making AI applications that will not learn by trial and error or through the use of a “training set,” but can be initialized with a base of expert-derived knowledge and rules – when the cost of an error is too high or when the training sample is too small.
How to Get the Best of Both Worlds?
There is currently no consensus on how to make an artificial general intelligence that is capable of solving the abovementioned problems or is based on technologies that could solve them.
One of the most promising approaches is probabilistic programming, which is a modern development of symbolic AI. In probabilistic programming, knowledge takes the form of algorithms and source, and target data is not represented by values of variables but by a probabilistic distribution of all possible values. Alexei Potapov, a leading Russian expert on artificial general intelligence, thinks that this area is now in a state that deep learning technology was in about ten years ago, so we can expect breakthroughs in the coming years.
Another promising “symbolic” area is Evgenii Vityaev’s semantic probabilistic modelling, which makes it possible to build explainable predictive models based on information represented as semantic networks with probabilistic inference based on Pyotr Anokhin’s theory of functional systems.
One of the most widely discussed ways to achieve this is through so-called neuro-symbolic integration – an attempt to get the best of both worlds by combining the learning capabilities of subsymbolic deep neural networks (which have already proven their worth) with the explainability of symbolic probabilistic modelling and programming (which hold significant promise). In addition to the technological considerations mentioned above, this area merits close attention from a cognitive psychology standpoint. As viewed by Daniel Kahneman, human thought can be construed as the interaction of two distinct but complementary systems: System 1 thinking is fast, unconscious, intuitive, unexplainable thinking, whereas System 2 thinking is slow, conscious, logical and explainable. System 1 provides for the effective performance of run-of-the-mill tasks and the recognition of familiar situations. In contrast, System 2 processes new information and makes sure we can adapt to new conditions by controlling and adapting the learning process of the first system. Systems of the first kind, as represented by neural networks, are already reaching Gartner’s so-called plateau of productivity in a variety of applications. But working applications based on systems of the second kind – not to mention hybrid neuro-symbolic systems which the most prominent industry players have only started to explore – have yet to be created.
This year, Russian researchers, entrepreneurs and government officials who are interested in developing artificial general intelligence have a unique opportunity to attend the first AGI-2020 international conference in St. Petersburg in late June 2020, where they can learn about all the latest developments in the field from the world’s leading experts.
From our partner RIAC
How as strategist we can compete with the sentient Artificial intelligence?
Universe is made up of humans, stars, galaxies, milky ways, black holes other objects linked and connected with each other. Everything in the universe has its level of mechanisms and complexities. Humans are very complex creatures man-made objects are more complex and difficult to understand. With the passage of time human beings are more evolved and become more advanced technologically. Human inventions are reached to that level of advancement, which initiates a competition between machines and humans, itself. Humans are the most intelligent mortals on the earth but now human are being challenged by the intelligence (artificial intelligence), which was invented as helping hand for humans to increase efficiency. Here it is important to question that whether human’s intelligence was not enough to survive in the fast growing technological world? Or the man-made intelligence has reached to its peak so that humans come in competition with machines and human intelligence is challenged by the artificial intelligence? If there is competition, then how strategists could compete with artificial intelligence? To answer these questions we first need to know what artificial intelligence actually is.
Artificial intelligence was presented by John McCarthy in 1955; he characterized computerized reasoning in 1956 at Dartmouth Conference, the main counterfeit consciousness meeting that: Every fragment of learning or another element of insight can on a basic level be so unequivocally depicted that a machine can be made to empower it. An endeavor will be made to learn how to influence machines to exploit vernacular, mount deliberations and ideas, take care of sort of issues now held for people, and enhance themselves. There are seven main features of artificial intelligence as follows:-
“Simulating higher functions of brain
Programming a computer to use general language
Arrangement of hypothetical neurons in a manner so that they can form concept
Way to determine and measure problem complexity
Abstraction: it is defined as quality of dealing with ideas , not with events
Creativity and randomness”
Another definition is given by Elaine rich who expressed that counterfeit consciousness is tied in with making computer to do such thing which are presently being finished by human. He said that each computer is artificial intelligence framework. Jack Copland expressed that critical elements of artificial intelligence are speculation discovering that empowers the student to perform in the circumstance that are beforehand experienced. At that point its thinking, to reason is to make inference fittingly, critical thinking implied that by giving information it can finish up comes about lastly trickiness intends to break down a checked situation and investigating the highlights and connection between the articles and self-driving autos are its case.
Artificial intelligence is very common in the developed nations and developing nations are using artificial intelligence according to resources. Now question is that how artificial intelligence is being utilized in the above mentioned fields? Use of AI will be elaborated with help of phenomenon and examples of related fields for better understanding.
World is being more advanced and technologies are improving as well. In this situation states become conscious about their security. At this point states are involving AI approaches in their defense systems and some states are already using artificially integrated technologies. On 11 May 2017, Dan Coats, the executive of US National Intelligence, conveyed declaration to the US Congress on his yearly Worldwide Threat Assessment. In the openly discharged archive, he said that (AI) is progressing computational abilities that advantage the economy, yet those advances likewise empower new military capacities for our enemies’. In the meantime, the US Department of Defense (DOD) is taking a shot at such frameworks. Undertaking Maven, for example, otherwise called the Algorithmic Warfare Cross-Functional Team (AWCFT), is intended to quicken the incorporation of huge information, machine learning and AI into US military capacities. While the underlying focal point of AWCFT is on computer vision calculations for protest identification and characterization, it will unite all current calculation based-innovation activities related with US resistance knowledge. Command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) are achieving new statures of proficiency that empower information accumulation and preparing at exceptional scale and speed. At the point when the example acknowledgment calculations being produced in China, Russia, the UK, the US and somewhere else are combined with exact weapons frameworks, they will additionally expand the strategic preferred standpoint of unmanned elevated vehicles (UAVs) and other remotely worked stages. China’s resistance part has made achievements in UAV ‘swarming’ innovation, including an exhibition of 1,000 EHang UAVs flying in arrangement at the Guangzhou flying demonstration in February 2017. Potential situations could incorporate contending UAV swarms attempting to hinder each other’s C4ISR arrange, while at the same time drawing in dynamic targets.
Humans are the most intelligent creatures that created an artificial intelligence technology. The technology we human introduced is more intelligent than us and works fastest than humans. So here is big question marks that can humans compete with the artificial intelligence in near future. Now days it seems that AI is replacing humans in every field of life so what will be condition after decades or two. There is an alarming competition started between the human and AI. AI was called as demon by Tesla Elon Musk. A well physicist Stephen Hawking also stated that in future artificial intelligence could be proved as a bad omen for humanity. But signs of all this clear and we can clearly see the replacement of humans. We human are somehow losing the competition. But it is also clear that a creator can be destructor also. So as strategist we must have the counter strategies and second plans to overcome the competition. The edge human have over AI is the ability to think and we generate this in AI integrated techs so we must set the level for this. Otherwise this hazard could be a great threat in future and humanity could possibly be an extinct being.
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