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The Artificial Intelligence Race: U.S. China and Russia

Ecatarina Garcia

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Artificial intelligence (AI), a subset of machine learning, has the potential to drastically impact a nation’s national security in various ways. Coined as the next space race, the race for AI dominance is both intense and necessary for nations to remain primary in an evolving global environment. As technology develops so does the amount of virtual information and the ability to operate at optimal levels when taking advantage of this data. Furthermore, the proper use and implementation of AI can facilitate a nation in the achievement of information, economic, and military superiority – all ingredients to maintaining a prominent place on the global stage. According to Paul Scharre, “AI today is a very powerful technology. Many people compare it to a new industrial revolution in its capacity to change things. It is poised to change not only the way we think about productivity but also elements of national power.”AI is not only the future for economic and commercial power, but also has various military applications with regard to national security for each and every aspiring global power.

While the U.S. is the birthplace of AI, other states have taken a serious approach to research and development considering the potential global gains. Three of the world’s biggest players, U.S., Russia, and China, are entrenched in non-kinetic battle to out-pace the other in AI development and implementation. Moreover, due to the considerable advantages artificial intelligence can provide it is now a race between these players to master AI and integrate this capability into military applications in order to assert power and influence globally. As AI becomes more ubiquitous, it is no longer a next-generation design of science fiction. Its potential to provide strategic advantage is clear. Thus, to capitalize on this potential strategic advantage, the U.S. is seeking to develop a deliberate strategy to position itself as the permanent top-tier of AI implementation.

Problem

The current AI reality is near-peer competitors are leading or closing the gap with the U.S. Of note, Allen and Husain indicate the problem is exacerbated by a lack of AI in the national agenda, diminishing funds for science and technology funding, and the public availability of AI research. The U.S. has enjoyed a technological edge that, at times, enabled military superiority against near-peers. However, there is argument that the U.S. is losing grasp of that advantage. As Flournoy and Lyons indicate, China and Russia are investing massively in research and development efforts to produce technologies and capabilities “specifically designed to blunt U.S. strengths and exploit U.S. vulnerabilities.”

The technological capabilities once unique to the U.S. are now proliferated across both nation-states and other non-state actors. As Allen and Chan indicate, “initially, technological progress will deliver the greatest advantages to large, well-funded, and technologically sophisticated militaries. As prices fall, states with budget-constrained and less technologically-advanced militaries will adopt the technology, as will non-state actors.” As an example, the American use of unmanned aerial vehicles in Iraq and Afghanistan provided a technological advantage in the battle space. But as prices for this technology drop, non-state actors like the Islamic State is making noteworthy use of remotely-controlled aerial drones in its military operations. While the aforementioned is part of the issue, more concerning is the fact that the Department of Defense (DoD) and U.S. defense industry are no longer the epicenter for the development of next-generation advancements. Rather, the most innovative development is occurring more with private commercial companies. Unlike China and Russia, the U.S. government cannot completely direct the activities of industry for purely governmental/military purposes. This has certainly been a major factor in closing the gap in the AI race.

Furthermore, the U.S. is falling short to China in the quantity of studies produced regarding AI, deep-learning, and big data. For example, the number of AI-related papers submitted to the International Joint Conferences on Artificial Intelligence (IJCAI) in 2017 indicated China totaled a majority 37 percent, whereas the U.S. took third position at only 18 percent. While quantity is not everything (U.S. researchers were awarded the most awards at IJCAI 2017, for example), China’s industry innovations were formally marked as “astonishing.”For these reasons, there are various strategic challenges the U.S. must seek to overcome to maintain its lead in the AI race.

Perspectives

Each of the three nations have taken divergent perspectives on how to approach and define this problem. However, one common theme among them is the understanding of AI’s importance as an instrument of international competitiveness as well as a matter of national security. Sadler writes, “failure to adapt and lead in this new reality risks the U.S. ability to effectively respond and control the future battlefield.” However, the U.S. can longer “spend its way ahead of these challenges.” The U.S. has developed what is termed the third offset, which Louth and Taylor defined as a policy shift that is a radical strategy to reform the way the U.S. delivers defense capabilities to meet the perceived challenges of a fundamentally changed threat environment. The continuous development and improvement of AI requires a comprehensive plan and partnership with industry and academia. To cage this issue two DOD-directed studies, the Defense Science Board Summer Study on Autonomy and the Long-Range Research and Development Planning Program, highlighted five critical areas for improvement: (1) autonomous deep-learning systems,(2) human-machine collaboration, (3) assisted human operations, (4) advanced human-machine combat teaming, and (5) network-enabled semi-autonomous weapons.

Similar to the U.S., Russian leadership has stated the importance of AI on the modern battlefield. Russian President Vladimir Putin commented, “Whoever becomes the leader in this sphere (AI) will become the ruler of the world.” Not merely rhetoric, Russia’s Chief of General Staff, General Valery Gerasimov, also predicted “a future battlefield populated with learning machines.” As a result of the Russian-Georgian war, Russia developed a comprehensive military modernization plan. Of note, a main staple in the 2008 modernization plan was the development of autonomous military technology and weapon systems. According to Renz, “The achievements of the 2008 modernization program have been well-documented and were demonstrated during the conflicts in Ukraine and Syria.”

China, understanding the global impact of this issue, has dedicated research, money, and education to a comprehensive state-sponsored plan.  China’s State Council published a document in July of 2017 entitled, “New Generation Artificial Intelligence Development Plan.” It laid out a plan that takes a top-down approach to explicitly mapout the nation’s development of AI, including goals reaching all the way to 2030.  Chinese leadership also highlights this priority as they indicate the necessity for AI development:

AI has become a new focus of international competition. AI is a strategic technology that will lead in the future; the world’s major developed countries are taking the development of AI as a major strategy to enhance national competitiveness and protect national security; intensifying the introduction of plans and strategies for this core technology, top talent, standards and regulations, etc.; and trying to seize the initiative in the new round of international science and technology competition. (China’s State Council 2017).

The plan addresses everything from building basic AI theory to partnerships with industry to fostering educational programs and building an AI-savvy society.

Recommendations

Recommendations to foster the U.S.’s AI advancement include focusing efforts on further proliferating Science, Technology, Engineering and Math (STEM)programs to develop the next generation of developers. This is similar to China’s AI development plan which calls to “accelerate the training and gathering of high-end AI talent.” This lofty goal creates sub-steps, one of which is to construct an AI academic discipline. While there are STEM programs in the U.S., according to the U.S. Department of Education, “The United States is falling behind internationally, ranking 29th in math and 22nd in science among industrialized nations.” To maintain the top position in AI, the U.S. must continue to develop and attract the top engineers and scientists. This requires both a deliberate plan for academic programs as well as funding and incentives to develop and maintain these programs across U.S. institutions. Perhaps most importantly, the United States needs to figure out a strategy to entice more top American students to invest their time and attention to this proposed new discipline. Chinese and Russian students easily outpace American students in this area, especially in terms of pure numbers.

Additionally, the U.S. must research and capitalize on the dual-use capabilities of AI. Leading companies such as Google and IBM have made enormous headway in the development of algorithms and machine-learning. The Department of Defense should levy these commercial advances to determine relevant defense applications. However, part of this partnership with industry must also consider the inherent national security risks that AI development can present, thus introducing a regulatory role for commercial AI development. Thus, the role of the U.S. government with AI industry cannot be merely as a consumer, but also as a regulatory agent. The dangerous risk, of course, is this effort to honor the principles of ethical and transparent development will not be mirrored in the competitor nations of Russia and China.

Due to the population of China and lax data protection laws, the U.S. has to develop innovative ways to overcome this challenge in terms of machine-learning and artificial intelligence. China’s large population creates a larger pool of people to develop as engineers as well as generates a massive volume of data to glean from its internet users. Part of this solution is investment. A White House report on AI indicated, “the entire U.S. government spent roughly $1.1 billion on unclassified AI research and development in 2015, while annual U.S. government spending on mathematics and computer science R&D is $3 billion.” If the U.S. government considers AI an instrument of national security, then it requires financial backing comparable to other fifth-generation weapon systems. Furthermore, innovative programs such as the DOD’s Project Maven must become a mainstay.

Project Maven, a pilot program implemented in April 2017, was mandated to produce algorithms to combat big data and provide machine-learning to eliminate the manual human burden of watching full-motion video feeds. The project was expected to provide algorithms to the battlefield by December of 2018 and required partnership with four unnamed startup companies. The U.S. must implement more programs like this that incite partnership with industry to develop or re-design current technology for military applications. To maintain its technological advantage far into the future the U.S. must facilitate expansive STEM programs, seek to capitalize on the dual-use of some AI technologies, provide fiscal support for AI research and development, and implement expansive, innovative partnership programs between industry and the defense sector. Unfortunately, at the moment, all of these aspects are being engaged and invested in only partially. Meanwhile, countries like Russia and China seem to be more successful in developing their own versions, unencumbered by ‘obstacles’ like democracy, the rule of law, and the unfettered free-market competition. The AI Race is upon us. And the future seems to be a wild one indeed.

References

Allen, Greg, and Taniel Chan. “Artificial Intelligence and National Security.” Publication. Belfer Center for Science and International Affairs, Harvard University. July 2017. Accessed April 9, 2018. https://www.belfercenter.org/sites/default/files/files/publication/AI%20NatSec%20-%20final.pdf

Allen, John R., and Amir Husain. “The Next Space Race is Artificial Intelligence.” Foreign Policy. November 03, 2017. Accessed April 09, 2018. http://foreignpolicy.com/2017/11/03/the-next-space-race-is-artificial-intelligence-and-america-is-losing-to-china/.

China. State Council. Council Notice on the Issuance of the Next Generation Artificial Intelligence Development Plan. July 20, 2017. Translated by RogierCreemers, Graham Webster, Paul, Paul Triolo and Elsa Kania.

Doubleday, Justin. 2017. “Project Maven’ Sending First FMV Algorithms to Warfighters in December.” Inside the Pentagon’s Inside the Army 29 (44). Accessed April 1, 2018.https://search-proquest-com.ezproxy2.apus.edu/docview/1960494552?accountid=8289.

Flournoy, Michèle A., and Robert P. Lyons. “Sustaining and Enhancing the US Military’s Technology Edge.” Strategic Studies Quarterly 10, no. 2 (2016): 3-13. Accessed April 12, 2018. http://www.jstor.org/stable/26271502.

Gams, Matjaz. 2017. “Editor-in-chief’s Introduction to the Special Issue on “Superintelligence”, AI and an Overview of IJCAI 2017.” Accessed April 14, 2018. Informatica 41 (4): 383-386.

Louth, John, and Trevor Taylor. 2016. “The US Third Offset Strategy.” RUSI Journal 161 (3): 66-71. DOI: 10.1080/03071847.2016.1193360.

Sadler, Brent D. 2016. “Fast Followers, Learning Machines, and the Third Offset Strategy.” JFQ: Joint Force Quarterly no. 83: 13-18. Accessed April 13, 2018. Academic Search Premier, EBSCOhost.

Scharre, Paul, and SSQ. “Highlighting Artificial Intelligence: An Interview with Paul Scharre Director, Technology and National Security Program Center for a New American Security Conducted 26 September 2017.” Strategic Studies Quarterly 11, no. 4 (2017): 15-22. Accessed April 10, 2018.http://www.jstor.org/stable/26271632.

“Science, Technology, Engineering and Math: Education for Global Leadership.” Science, Technology, Engineering and Math: Education for Global Leadership. U.S. Department of Education. Accessed April 15, 2018. https://www.ed.gov/stem.

Ecatarina Garcia is an Instructor in the Air Force’s Intelligence Officer Course where she instructs initial skills training on topics such as Signals Intelligence, Cyber, Human Intelligence, Surveillance and Reconnaissance (ISR) foundations and applied ISR to the Air Force’s newest Intelligence Officer students. She has been a Network Fusion Analyst for the Air Force for 13 years and has supported various operations concerning Iraq, Afghanistan, Syria, Yemen, and Russia. She is currently a doctoral student in the American Military University’s inaugural Global Security and Strategic Intelligence doctoral program.

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Future Goals in the AI Race: Explainable AI and Transfer Learning

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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?”

Background

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.

Growth Potential?

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.

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How as strategist we can compete with the sentient Artificial intelligence?

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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

Self-improvement

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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What is more disruptive with the AI: Its dark potentials or our (anti-Intellectual) Ignorance?

Anis H. Bajrektarevic

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Throughout the most of human evolution both progress as well as its horizontal transmission was extremely slow, occasional and tedious a process. Well into the classic period of Alexander the Macedonian and his glorious Alexandrian library, the speed of our knowledge transfers – however moderate, analogue and conservative – was still always surpassing snaillike cycles of our breakthroughs.

When our sporadic breakthroughs finally turned to be faster than the velocity of their infrequent transmissions – that marked a point of our departure. Simply, our civilizations started to significantly differentiate from each other in their respective techno-agrarian, politico-military, ethno-religious and ideological, and economic setups. In the eve of grand discoveries, that very event transformed wars and famine from the low-impact and local, into the bigger and cross-continental.

Faster cycles of technological breakthroughs, patents and discoveries than their own transfers, primarily occurred on the Old continent. That occurrence, with all its reorganizational effects, radically reconfigured societies. It finally marked a birth of mighty European empires, their (liberal) schools and overall, lasting triumph of the western civilization.

Act

For the past few centuries, we lived fear but dreamt hope – all for the sake of modern times. From WWI to www. Is this modernity of internet age, with all the suddenly reviled breakthroughs and their instant transmission, now harboring us in a bay of fairness, harmony and overall reconciliation? Was and will our history ever be on holiday? Thus, has our world ever been more than an idea? Shall we stop short at the Kantian word – a moral definition of imagined future, or continue to the Hobbesian realities and grasp for an objective, geopolitical definition of our common tomorrow?

The Agrarian age inevitably brought up the question of economic redistribution. Industrial age culminated on the question of political participation. The AI (Quantum physics, Nanorobotics and Bioinformatics) brings a new, yet underreported challenge: Human (physical and mental) powers might – far and wide, and rather soon – become obsolete. If/when so, a question of human irrelevance is next to ask.

Why is the AI like no technology ever before? Why re-visiting and re-thing spirituality matters …

If you believe that the above is yet another philosophical melodrama, an anemically played alarmism, mind this:

We will soon have to redefine what we consider as a life itself.

Less than a month ago (January 2020), the successful trials have been completed. Border between organic and inorganic, intrinsic and artificial is downed forever. The AI has it now all-in: quantum physics (along with quantum computing), nanorobotics, bioinformatics and organic tissue tailoring. Synthesis of all that is usually referred as xenobots(sorts of living robots) – biodegradable symbiotic nanorobots that exclusively rely on evolutionary (self-navigable) algorithms. 

React

Although life is to be lived forward (with no backward looking), human retrospection is a biggest reservoir of insights. Of what makes us human.

Hence, what does our history of technology in relation to human development tell us so far?

Elaborating on a well-known argument of ‘defensive modernization’ of Fukuyama, it is evident that throughout the entire human history a technological drive was aimed to satisfy the security (and control) objective. It was rarely (if at all) driven by a desire to (gain a knowledge outside of convention, in order to) ease human existence, and to enhance human emancipation and liberation of societies at large. Thus, unless operationalized by the system, both intellectualism (human autonomy, mastery and purpose), and technological breakthroughs were traditionally felt and perceived as a threat. As a problem, not a solution. 

Ok. But what has brought us (under) the AI today?

It was our acceptance. Of course, manufactured.

All cyber-social networks and related search engines are far away from what they are portrayed to be: a decentralized but unified intelligence, attracted by gravity of quality rather than navigated by force of a specific locality. (These networks were not introduced to promote and emancipate other cultures but to maintain and further strengthen supremacy of the dominant one.)

In no way they correspond with a neuroplasticity of physics of our consciousness. They only offer an answer to our anxieties – in which the fear from free time is the largest, since free time coupled with silence is our gate to creativity and self-reflection. In fact, the cyber-tools of these data-sponges primarily serve the predictability, efficiency, calculability and control purpose, and only then they serve everything else – as to be e.g. user-friendly and en mass service attractive.

To observe the new corrosive dynamics of social phenomenology between manipulative fetishization (probability) and self-trivialization (possibility), the cyber-social platforms – these dustbins of human empathy in the muddy suburbs of consciousness – are particularly interesting.

This is how the human presence eliminating technologies have been introduced to and accepted by us.

Packed

How did we reflect – in our past – on new social dynamics created by the deployment of new technologies?

Aegean theater of the Antique Greece was the place of astonishing revelations and intellectual excellence – a remarkable density and proximity, not surpassed up to our age. All we know about science, philosophy, sports, arts, culture and entertainment, stars and earth has been postulated, explored and examined then and there. Simply, it was a time and place of triumph of human consciousness, pure reasoning and sparkling thought. However, neither Euclid, Anaximander, Heraclites, Hippocrates (both of Chios, and of Cos), Socrates, Archimedes, Ptolemy, Democritus, Plato, Pythagoras, Diogenes, Aristotle, Empedocles, Conon, Eratosthenes nor any of dozens of other brilliant ancient Greek minds did ever refer by a word, by a single sentence to something which was their everyday life, something they saw literally on every corner along their entire lives. It was an immoral, unjust, notoriously brutal and oppressive slavery system that powered the Antique state. (Slaves have not been even attributed as humans, but rather as the ‘phonic tools/tools able to speak’.) This myopia, this absence of critical reference on the obvious and omnipresent is a historic message – highly disturbing, self-telling and quite a warning. 

So, finally

Why is the AI like no technology ever before?

Ask google, you see that I am busy messaging right now!

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