[Editor’s Note: Mad Scientist Laboratory welcomes back returning guest blogger and proclaimed Mad Scientist Mr. Samuel Bendett with today’s post, addressing Russia’s commitment to mass produce independent ground combat robotic systems. Simon Briggs, professor of interdisciplinary arts at the University of Edinburgh,predicts that “in 2030 AI will be in routine use to fight wars and kill people, far more effectively than humans can currently kill.” Mr. Bendett’s post below addresses the status of current operationally tested and fielded Russian Unmanned Ground Vehicle (UGV) capabilities, and their pivot to acquire systems able to “independently recognize targets, use weapons, and interact in groups and swarms.” (Note: Some of the embedded links in this post are best accessed using non-DoD networks.)]
Over the past several years, the Russian military has invested heavily in the design, production, and testing of unmanned combat systems. In March 2018, Russian Defense Minister Sergei Shoigu said that mass production of combat robots for the Russian army could begin as early as that year. Now, the Ministry of Defense (MOD) is moving ahead with creating plans for such systems to act independently on the battlefield.
According to theRussian state media (TASS), Russian military robotic complexes (RBCs) will be able to independently recognize targets, use weapons, and interact in groups and swarms. Such plans were stated in the article by the staff of the 3rd Central Scientific Research Institute of the Russian Federation’s MOD.
Russia has already tested several Unmanned Ground Vehicles (UGVs) in combat. Its Uran-6, Scarab, and Sphera demining UGVs were rated well by the Russian engineering forces, and there areplans to start acquisition of such vehicles. However, these systems were designed to have their operators close by. When it came to a UGV that was originally built for operator remoteness in potential combat, things got more complicated.
Russia’s Uran-9 combat UGV experienced a large number of failures when tested in Syria, among them transportation, communication, firing, and situational awareness. The lessons from Uran-9 testssupposedly prompted the Russian military to consider placing more emphasis on using such UGVs as one-off attack vehicles against adversary hard points and stationary targets.
Nonetheless, the aforementioned TASS article analyzes the general requirements for unmanned military systems employed by Russian ground forces. Among them is the ability to solve tasks in different combat conditions during day and night, under enemy fire, electronic and informational counteraction, in conditions of radiation, chemical contamination, and electromagnetic attack – as well as requirements such as modularity and multifunctionality. The article also points out “the [systems’] ability to independently perform tasks in conditions of ambiguity” – implying the use of Artificial Intelligence.
To achieve these requirements, the creation of an “intelligent decision-making system” is proposed, which will also supervise the use of weapons. “The way out of this situation is the intensification of research on increasing the autonomy of the RBCs and the introduction of intelligent decision-making systems at the control stages, including group, autonomous movement and use of equipment for its intended purpose, including weapons, into military robotics,” the article says.
The TASS articlestates that in the near future, the MOD is planning to initiate work aimed at providing technical support for solving this problem set. This research will include domestic laser scanning devices for geographical positioning, the development of methods and equipment for determining the permeability of the soil on which the UGV operates, the development of methods for controlling the military robot in “unstable communications,” and the development of methods for analyzing combat environments such as recognizing scenes, images, and targets.
Successfully employing UGVs in combat requires complicated systems, something that the aforementioned initiatives will seek to address. This work will probably rely on Russia’s Syrian experience, as well as on the current projects and upgrades to Moscow’s growing fleet of combat UGVs. On 24 January 2018, the Kalashnikov Design Bureau that oversees the completion of Uran-9 workadmitted that this UGV has been accepted into military service. Although few details were given, the statement did include the fact that this vehicle will be further “refined” based on lessons learned during its Syria deployment, and that the Uran-9 presents “good scientific and technical groundwork for further products.” The extent of upgrades to that vehicle was not given – however,numerous failures in Syrian trials imply that there is lots of work ahead for this project. The statement also indicates that the Uran-9 may be a test-bed for further UGV development, an interesting fact considering the country’s already diverse collection of combat UGVs
Today, the Russian military is testing and evaluating several systems, such as Nerekhtaand Soratnik. The latter was alsosupposedly tested in “near-combat” conditions, presumably in Syria or elsewhere. The MOD has been testing smaller Platforma-Mand large Vikhr combat UGVs, along with other unmanned vehicles. Yet the defining characteristic for these machines so far has been the fact that they were all remote-operated by soldiers, often in near proximity to the machine itself. Endowing these UGVs with more independent decision–making in the “fog of war” via an intelligent command and control system may exponentially increase their combat effectiveness — assuming that such systems can function as planned.
… and watch Zvezda Broadcasting‘svideo, showing a Vikhr unmanned, tele-operated BMP-3 maneuvering and shooting its 7.62mm MG, 30mm cannon, and automatic grenade launcher on a test range.
Automated lethality is but one of the many Future Operational Environment trends that the U.S. Army’s Mad Scientist Initiative is tracking. Mad Scientist seeks to crowdsource your visions of future combat with our Science Fiction Writing Contest 2019. Our deadline for submission is1 APRIL 2019, so please review the contest details and associated release formhere, get those creative writing juices flowing, and send us your visions of combat in 2030! Selected submissions may be chosen for publication or a possible future speaking opportunity.
Samuel Bendett is a Researcher at CNA and a Fellow in Russia Studies at the American Foreign Policy Council. He is also a proud Mad Scientist.
[Editor’s Note: At the Mad Scientist Learning in 2050 Conference with Georgetown University’s Center for Security Studies in Washington, DC, Leading scientists, innovators, and scholars gathered to discuss how humans will receive, process, and integrate information in the future. The convergence of technology, the speed of change, the generational differences of new Recruits, and the uncertainty of the Future Operational Environment will dramatically alter the way Soldiers and Leaders learn in 2050. One clear signal generated from this conference is that learning in the future will be personalized, continuous, and accelerated.]
“The principal consequence of individual differences is that every general law of teaching has to be applied with consideration of the particular person.” – E.L. Thorndike (1906)
The world is becoming increasingly personalized, and individual choice and preference drives much of daily life, from commerce, to transportation, to entertainment. For example, your Amazon account today can keep your payment information on file (one click away), suggest new products based on your purchase history, and allow you to shop from anywhere and ship to any place, all while tracking your purchase every step of the way, including providing photographic proof of delivery. Online retailers, personal transportation services, and streaming content providers track and maintain an unprecedented amount of specific individual information to deliver a detailed and personalized experience for the consumer.
There is an opportunity to improve the effectiveness in targeted areas of learning – skills training, foundational learning, and functional training, for example – if learning institutions and organizations, as well as learners, follow the path of personalization set by commerce, transportation, and entertainment.1 This necessitates an institutional shift in the way we educate Soldiers. Instead of training being administered based on rank or pre-determined schedule, it is conducted based on need, temporally optimized for maximum absorption and retention, in a style that matches the learner, and implemented on the battlefield, if needed.
An important facet of personalized learning is personal attention to the learner. Tutors have been used in education for 60,000 years.2 However, they always have been limited to how many educators could devote their attention to one student. With advancements in AI, intelligent tutors could reduce the cost and manpower requirements associated with one-on-one instructor to student ratios. Research indicates that students who have access to tutors as opposed to exclusive classroom instruction were more effective learners as seen in the chart below. In other words, the average tutored student performed better than 98 percent of the students in the traditional classroom.3 What was a problem of scale in the past – cost, manpower, time – can be alleviated in the future through the use of AI-enabled ubiquitous intelligent tutors.
Another aspect of personalized learning is the diminishing importance of geo-location. Education, in general, has traditionally been executed in a “brick and mortar” setting. The students, learners, or trainees physically travel to the location of the teacher, expert, or trainer in order for knowledge to be imparted. Historically, this was the only viable option. However, a hyper-connected world with enabling technologies likevirtual and augmented reality; high-bandwidth networks with low latency; high fidelity modeling, simulations, and video; and universal interfaces reduces or eliminates the necessity for physical co-location. This allows Soldiers to attend courses hosted virtually anywhere, participate in combined arms and Joint exercises globally, and experience a variety of austere and otherwise inaccessible environments through virtual and augmented reality.4
Based on these trends and emerging opportunities to increase efficiency, the Army may have to re-evaluate its educational and training frameworks and traditional operational practices to adjust for more individualized and personalized learning styles. When personalized learning is optimized, Soldiers could become more lethal, specially skilled, and decisive along a shorter timeline, using lesser budget resources, and with reduced manpower.
Continuous learning, or the process of repeatedly engaging in activities designed to learn new information or skills, is a natural process that will remain necessary for Soldiers and Leaders in 2050. The future workforce will define and drive when, where, and how learning takes place. Continuous learning has the advantage of allowing humans to learn from past mistakes and understand biases by “working the problem” – assessing and fixing biases, actively changing behavior to offset biases, moving on to decision-making, and then returning to work the problem again for further solutions. Learners must be given the chance to fail, and failure must be built in to the continuous learning process so that the learner not only arrives at the solution organically, but practices critical thinking and evaluation skills.5
There are costs and caveats to successful continuous learning. After a skill is learned, it must be continually practiced and maintained. Amy Titus explained how skills perish after 3-5 years unless they are updated to meet present needs and circumstances. In an environment of rapidly changing technology and situational dynamics, keeping skills up to date must be a conscious and nonstop process. One of the major obstacles to continuous learning is that learning is work and requires a measure of self-motivation to execute. Learners only effectively learn if they are curious, so learning to pass a class or check a box does not yield the same result as genuine interest in the subject.6 New approaches such as gamification and experiential learning can help mitigate some of these limitations.
The concept of accelerated learning, or using a compressed timeline and various approaches, methodologies, or technological means to maximize learning, opens up several questions: what kinds of technologies accelerate learning, and how does technology accelerate learning? Technologies useful for accelerated learning include the immersive reality spectrum – virtual reality/augmented reality (mixed reality) and haptic feedback – as well as wearables, neural stimulation, and brain mapping. These technologies and devices enable the individualization and personalization of learning. Individualization allows the learner to identify their strengths and weaknesses in learning, retaining, and applying information and provides a program structured to capitalize on his/her naturally favored learning style to maximize the amount and depth of information presented in the most time and cost-effective manner.
Digital learning platforms are important tools for the tracking of a Soldier’s progress. This tool not only delivers individualized progress reports to superiors and instructors, but also allows the learner to remain up to date regardless of their physical location. Intelligent tutors may be integrated into a digital learning platform, providing real-time, individual feedback and suggesting areas for improvement or those in need of increased attention. Intelligent tutors and other technologies utilized in the accelerated learning process, such as augmented reality, can be readily adapted to a variety of situations conforming to the needs of a specific unit or mission.
Besides external methods of accelerated learning, there are also biological techniques to increase the speed and accuracy of learning new skills. DARPA scientist Dr. Tristan McClure-Begley introduced Targeted Neuroplasticity Training (TNT), whereby the peripheral nervous system is artificially stimulated resulting in the rapid acquisition of a specific skill. Soldiers can learn movements and retain that muscle memory faster than the time it would take to complete many sets of repetitions by pairing nerve stimulation with the performance of a physical action.
Accelerated learning does not guarantee positive outcomes. There is a high initial startup cost to producing mixed, augmented, and virtual reality training programs, and these programs require massive amounts of data and inputs for the most realistic product.7 There are questions about the longevity and quality of retention when learning is delivered through accelerated means. About 40 percent of information that humans receive is forgotten after 20 minutes and another 40 percent is lost after 30 days if it is not reinforced.8
Most learners attribute mastery of a skill to practical application and not formal training programs.9 TNT attempts to mitigate this factor by allowing for multiple physical repetitions to be administered quickly. But this technique must be correctly administered, or psychological and physiological pairing may not occur correctly or occur between the wrong stimuli, creating maladaptive plasticity, which is training the wrong behavior.
An increased emphasis on continuous and accelerated learning could present the Army with an opportunity to have Soldiers that are lifelong learners capable of quickly picking up emerging required skills and knowledge. However, this focus would need to account for peak learner interest and long-term viability.
[Editor’s Note: On 8-9 August 2018, the U.S. Army Training and Doctrine Command (TRADOC) co-hosted the Mad Scientist Learning in 2050 Conference with Georgetown University’s Center for Security Studies in Washington, DC. Leading scientists, innovators, and scholars from academia, industry, and the government gathered to address future learning techniques and technologies that are critical in preparing for Army operations in the mid-21st century against adversaries in rapidly evolving battlespaces. One finding from this conference is that tomorrow’s Soldiers will learn differently from earlier generations, given the technological innovations that will have surrounded them from birth through their high school graduation. To effectively engage these “New Humans” and prepare them for combat on future battlefields, the Army must discard old paradigms of learning that no longer resonate (e.g., those desiccated lectures delivered via interminable PowerPoint presentations) and embrace more effective means of instruction.]
The recruit of 2050 will be born in 2032 and will be fundamentally different from the generations born before them. Marc Prensky, educational writer and speaker who coined the term digital native, asserts this “New Human” will stand in stark contrast to the “Old Human” in the ways they assimilate information and approach learning.1 Where humans today are born into a world with ubiquitous internet, hyper-connectivity, and the Internet of Things, each of these elements are generally external to the human. By 2032, these technologies likely will have converged and will be embedded or integrated into the individual with connectivity literally on the tips of their fingers. The challenge for the Army will be to recognize the implications of this momentous shift and alter its learning methodologies, approach to training, and educational paradigm to account for these digital natives.
These New Humans will be accustomed to the use of artificial intelligence (AI) to augment and supplement decision-making in their everyday lives. AI will be responsible for keeping them on schedule, suggesting options for what and when to eat, delivering relevant news and information, and serving as an on-demand embedded expert. The Old Human learned to use these technologies and adapted their learning style to accommodate them, while the New Human will be born into them and their learning style will be a result of them. In 2018, 94% of Americans aged 18-29 owned some kind of smartphone.2 Compare that to 73% ownership for ages 50-64 and 46% for age 65 and above and it becomes clear that there is a strong disconnect between the age groups in terms of employing technology. Both of the leading software developers for smartphones include a built-in artificially intelligent digital assistant, and at the end of 2017, nearly half of all U.S. adults used a digital voice assistant in some way.3 Based on these trends, there likely will be in the future an even greater technological wedge between New Humans and Old Humans.
New Humans will be information assimilators, where Old Humans were information gatherers. The techniques to acquire and gather information have evolved swiftly since the advent of the printing press, from user-intensive methods such as manual research, to a reduction in user involvement through Internet search engines. Now, narrow AI using natural language processing is transitioning to AI-enabled predictive learning. Through these AI-enabled virtual entities, New Humans will carry targeted, predictive, and continuous learning assistants with them. These assistants will observe, listen, and process everything of relevance to the learner and then deliver them information as necessary.
There is an abundance of research on the stark contrast between the three generations currently in the workforce: Baby Boomers, Generation X, and Millennials.4, 5 There will be similar fundamental differences between Old Humans and New Humans and their learning styles. The New Human likely will value experiential learning over traditional classroom learning.6 The convergence of mixed reality and advanced, high fidelity modeling and simulation will provide New Humans with immersive, experiential learning. For example, Soldiers learning military history and battlefield tactics will be able to experience it ubiquitously, observing how each facet of the battlefield affects the whole in real-time as opposed to reading about it sequentially. Soldiers in training could stand next to an avatar of General Patton and experience him explaining his command decisions firsthand.
There is an opportunity for the Army to adapt its education and training to these growing differences. The Army could—and eventually will need—to recruit, train, and develop New Humans by altering its current structure and recruitment programs. It will become imperative to conduct training with new tools, materials, and technologies that will allow Soldiers to become information assimilators. Additionally, the incorporation of experiential learning techniques will entice Soldiers’ learning. There is an opportunity for the Army to pave the way and train its Soldiers with cutting edge technology rather than trying to belatedly catch up to what is publicly available.
Evolution in Learning Technologies
If you enjoyed this post, please also watch Elliott Masie‘s video presentation onDynamic Readiness and MarkPrensky‘s presentation on The Future of Learning from of the Mad Scientist Learning in 2050 Conference …
[Editor’s Note: Mad Scientist welcomes back returning guest blogger Dr. Nir Buras with today’s post. We’ve found crowdsourcing (i.e., the gathering of ideas, thoughts, and concepts from a widespread variety of interested individuals) to be a very effective tool in enabling us to diversify our thoughts and challenge our assumptions. Dr. Buras’ post takes the results from one such crowdsourcing exercise and extrapolates three future urban scenarios. Given The Army Vision‘s clarion call to “Focus training on high-intensity conflict, with emphasis on operating in dense urban terrain,” our readers would do well to consider how the Army would operate in each of Dr. Buras’ posited future scenarios…]
Thechallenges of the 21st century have been forecast and are well-known. In many ways we are already experiencing the future now. But predictions are hard to validate. A way around that is turning to slightly older predictions to illuminate the magnitude of the issues and the reality of their propositions.1Futurists William E. Halal and Michael Marien’s predictions of 2011 have aged enough to be useful. In an improved version of theDelphi method, they iteratively built consensus among participants. Halal and Marien balanced the individual sense of over sixty well-qualified experts and thinkers representing a range of technologies with facilitated feedback from the others. They translated their implicit or tacit know how to make qualified quantitative empirical predictions.2
From their research we can transpose three future urban scenarios: TheHigh-Tech City, The Feral City, and Muddling Through.
The High-Tech City
The High-Tech City scenario is based primarily on futurist Jim Dator’s high-tech predictions. It envisions the continued growth of a technologically progressive, upwardly mobile, internationally dominant, science-guided, rich, leisure-filled, abundant, and liberal society. Widespread understanding of what works largely avoids energy shortages, climate change, and global conflict.3
The high-tech, digital megacity is envisaged as a Dubai on steroids. It is hyper-connected and energy-efficient, powered by self-sustaining, renewable resources and nuclear energy.4
Connected by subways and skyways, with skyscraping vertical gardens, the cities are ringed by elaborately managed green spaces and ecosystems. The city’s 50 to 150-story megastructures, “cities-in-buildings,” incorporate apartments, offices, schools and grocery stores, hospitals and shopping centers, sports facilities and cultural centers, gardens, and running tracks. Alongside them rise vertical farms housing animals and crops. The rooftop garden of the 2015 filmHigh Rise depicts how aerial terraces up high provide a sense of suburban living in the high-tech city.5
On land, zero-emission driverless traffic zips about on intelligent highways. High-speed trains glide silently by. After dark, spider bots and snake drones automatically inspect and repair buildings and infrastructure.6
In the air, helicopters, drones, and flying cars zoom around. Small drones, mimicking insects and birds, and programmable nano-chips, some as small as “smart” dust, swarm over the city into any object or shape on command. To avoid surface traffic, inconvenience, and crime, wealthier residents fly everywhere.7
Dominated by centralized government and private sector bureaucracies wielding AI, these self-constructing robotic “cyburgs” have massive technology, robotics, and nanotechnology embedded in every aspect of their life, powered by mammoth fusion energy plants.8
Every unit of every component is embedded with at least one flea-size chip. Connected into a single worldwide digital network,trillions of sensors monitor countless parameters for the cityand everything in it. The ruling AI, commanded directly by individual minds, autonomously creates, edits, and implements software, simultaneously processing feedback from a global network of sensors.9
The High-Tech City is not a new concept. It goes back to Jules Verne, H. G. Wells, and Fritz Lang, who most inspired its urban look in the 1927 film Metropolis. The extrapolated growth of technology has long been the basis for predictions. But professional futurists surprisingly agree that a High-Tech Jetsons scenario has only a 0%-5% probability of being realized.10
Poignantly, the early predictors transmitted a message that the stressful lifestyle of the High-Tech City contradicts the intention of freedom from drudge. Moreover, the High-Tech megacities’ appetite for minerals may lay waste to whole ecosystems. Much of the earth may become a feral wilderness. Massive, centralized AI Internet clouds and distribution systems give a false sense of cultural robustness. People become redundant and democracy meaningless. The world may fail to react to accelerated global crises, with disastrous consequences. The paradoxical obsolescence of high-tech could slide humanity into a new Dark Age.11
The Feral City
Futurists disturbingly describe a Decline to Disaster scenario as five times more likely to happen than the high-tech one. From Tainter’s theory of collapse and Jane Jacobs’s Dark Age Ahead we learn that the cycles of urban problem-solving lead to more problems and ultimately failures. If Murphy’s Law kicks in, futurists predict a 60% chance that large parts of the world may be plunged into an Armageddon-type techno-dystopian scenario, typified by the films Mad Max (1979) and Blade Runner (1982).12
Apocalyptic feral cities, once vital components in national economies, are routinely imagined as vast, sprawling urban environments defined by blighted buildings. An immense petri dish of both ancient and new diseases, rule of law has long been replaced by gang anarchy and the only security available in them is attained through brute power.13
Neat suburban areas were long ago stripped for their raw materials. Daily life in feral cities is characterized by a ubiquitous specter of murder, bloodshed, and war, of the militarization of young men, and the constant threat of rape to females. Urban enclaves are separated by wild zones, fragmented habitats consisting of wild nature and subsistence agriculture. With minimal or no sanitation facilities, a complete absence of environmental controls, and massive populations, feral cities suffer from extreme air pollution from vehicles and the use of open fires and coal for cooking and heating. In effect toxic-waste dumps, these cities pollute vast stretches of land, poisoning coastal waters, watersheds, and river systems throughout their hinterlands.14
Pollution is exported outside the enclaves, where the practices of the desperately poor, and the extraction of resources for the wealthy, induce extreme environmental deterioration. Rivers flow with human waste and leached chemicals from mining, contaminating much of the soil on their banks.15
Globally connected, a feral city might possess a modicum of commercial linkages, and some of its inhabitants might have access to advanced communication and computing. In some areas, agriculture might forcefully cultivate high-yield, GMO, and biomass crops. But secure long-distance travel nearly disappears, undertaken mostly by the super-rich and otherwise powerful.16
Futurists backcasting from 2050 say that the current urbanization of violence and war are harbingers of the feral city scenario. But feral cities have long been present. The Warsaw Ghetto in World War Two was among them, as were the Los Angeles’ Watts neighborhood in the 1960s and 1990s; Mogadishu in 2003, and Gaza repeatedly.17
Conflict and crime changed once charming, peaceful Aleppo, Bamako, Caracas, Erbil, Mosul, Tripoli, and Salvador into feral cities. Medieval San Gimignano was one. Spectacularly, from 1889 to 1994 the ghastly spaces of Hong Kong’s singular urban phenomenon, theWalled City of Kowloon, provided a living example.18
The good news is that futurists tend to believe in a 65%-85% probability of a Muddling Through scenario. Despite interlinked, cascading catastrophes, they suggest that technologies may gain some on the problems. Somehow securing a sustainable world for 9 billion people by 2050, they suggest the world will be massively changed, yet somehow livable.19
Lending credibility to the Muddling Through scenario is that it blends numerous hypotheses. It predicts that people living in rural communities will tend the land scientifically. Its technological salvation hypothesis posits that science will come to the rescue. Its free market hypothesis assumes that commerce will drive technological advancements.20
It pictures a “conserver” society tinged by Marxism, a neo-puritan “ecotopia,” colored by both the high-tech and feral scenarios. Tropical diseases, corruption, capitalism, socialism, inequality, and war are not eradicated. But nationalism, tribalism, and xenophobia are reduced after global traumas. Though measurably poorer, most people will still have a reasonable level of wellbeing.21 According to the Muddling Through scenario, large cities retract and densify around their old centers and waterfronts. Largely self-sufficient, small towns and cities survive amid the ruins of suburban sprawl, separated by resurgent forests and fields. Shopping malls, office towers and office parks, town dumps, tract homes, and abandoned steel and glass buildings are stripped for their recyclables. Unsalvageable downtowns in some cases go feral.22
A mix of high and low tech fosters digital communication with those at a distance. There would be drip irrigation, hydroponic farming, aquaculture, and grey water recycling, overlaid with artificial intelligence, biotechnology and biomimicry, nuclear power, geoengineering, and oil from algae.23
In some places, rail links are maintained, but cars are a rarity, and transportation is greatly reduced. Collapsed or dismantled freeways and bridges return to the forest or desert. While flying still exists, it is rarer. But expanded virtual mobility offering “holodeck” experiences subsumes tourism. Cosmopolitanism happens on the porch with an iPad.24
Surprisingly, the Muddling Through scenario ends up with urban fabric similar in properties tohomeostatic planning had it been done intentionally. Work is a short walk from home. Corner stores pop up, as do rudimentary cafés, bistros, and other gathering places. Forty percent of the food is produced in or around cities on small farms. Wildlife returns to course freely. Groups of travelers move on surviving “high roads.” Communities meet at large sports venues situated in the countryside between them.25
Sea level rise is met with river and sea walls. At their base, vast new coral beds and kelp forests grow over the skeletons of submerged districts and towns. In a matter of years, rivers and seas build new beaches. Their flood plains are populated with new plants. Smaller scale trade waterfronts are reactivated for shipping, and some ships are even powered by sail. Cities occupying harbors, rivers, and railroad junctions reconnect to distant supply chains, mostly for non-quotidian (i.e., luxury) goods.26
Learning from Rome to Understand Detroit
Rome’s deterioration from a third century city of more than 1,000,000 people started long before it was acknowledged. An unnoticed population drop to 800,000 was characterized by ever larger buildings of decreasing beauty and craft, including the huge Baths of Diocletian (298-306 CE). Anticipating barbarian invasion, Rome’s walls were built (271-275 CE). It was ransacked twice (410 and 455 CE).27
But as if in a dream, 5th century life of the diminishing but still substantial population continued as normal. Invading Goths maintained Rome’s Senate, taxes, and cops. But administrative and military infrastructure vaporized. An unraveling education system led to the rise of illiteracy. Noble families began using mob politics, economic and social linkages broke down, travel and transportation became unsafe, and manufacturing collapsed.28
By 500 CE, Rome had less than 100,000 people. Systematic agriculture disappeared, and much land returned to forest. The Pope and nobility pillaged abandoned public buildings for their materials. The expansive city was reduced to small groups of inhabited buildings, interspersed among large areas of abandoned ruins and overgrown vegetation. In the 12th and 13th centuries the population of Rome was possibly as few as 20,000 people.29
The long journey from first cities, to Ancient Greece, Rome, and the Middle Ages, through Paris, Washington, and Shanghai, helps us understand how our cities might end up. Holding Rome up to the mirrors of history reads like backcasting Rome’s decline and survival in a Muddling Through scenario from today’s view. Halal predicted that muddling would start about 2023 to 2027 and that if we weren’t muddling by then, collapse would set in by 2029.30
Detroit started muddling in 1968. New York proved to be a fragile city during blackouts, as did Dubai in its 2009 financial crisis. Since the 1970s, most of America’sten “dead cities,” many formerly among its largest and most vibrant, came disturbingly close to being feral. The overlapping invisibilities of heavily armed warlords and brutal police, make the favelas of Medellin and Rio de Janeiro virtually feral.31
Today we are at a tipping point. We can wait for the collapse of systems to reach homeostasis or attain it intentionally by applying Classic Planning principles.32
If you enjoyed this post, please also see Dr. Buras’ other posts:
Nir Buras is a PhD architect and planner with over 30 years of in-depth experience in strategic planning, architecture, and transportation design, as well as teaching and lecturing. His planning, design and construction experience includes East Side Access at Grand Central Terminal, New York; International Terminal D, Dallas-Fort-Worth; the Washington DC Dulles Metro line; work on the US Capitol and the Senate and House Office Buildings in Washington. Projects he has worked on have been published in the New York Times, the Washington Post, local newspapers, and trade magazines. Buras, whose original degree was Architect and Town planner, learned his first lesson in urbanism while planning military bases in the Negev Desert in Israel. Engaged in numerous projects since then, Buras has watched first-hand how urban planning impacted architecture. After the last decade of applying in practice the classical method that Buras learned in post-doctoral studies, his book, *The Art of Classic Planning* (Harvard University Press, 2019), presents the urban design and planning method of Classic Planning as a path forward for homeostatic, durable urbanism.
1 Population growth, clean water, compromised resilience of infrastructures, drug-resistant microbes, pandemics, possible famine, authoritarian regimes, social breakdowns, terrestrial cataclysms, terrorist mischief, nuclear mishaps, perhaps major war, inequity, education and healthcare collapse, climate change, ecological devastation, biodiversity loss, ocean acidification, world confusion, institutional gridlock, failures of leadership, failure to cooperate. Sources include: Glenn, Jerome C., Theodore J. Gordon, Elizabeth Florescu, 2013-14 State of the Future Millennium Project: Global Futures Studies and Research, Millennium-project.org (website), Washington, DC, 2014; Cutter, S. L. et al., Urban Systems, Infrastructure, and Vulnerability, in Climate Change Impacts in the United States: The Third National Climate Assessment, in Melillo, J. M. et al., (eds.), U.S. Global Change Research Program, 2014, Ch. 11, pp. 282-296; Kaminski, Frank, A review of James Kunstler’s The Long Emergency 10 years later, Mud City Press (website), Eugene, OR, 9 March 2015; Urban, Mark C., Accelerating extinction risk from climate change, Science Magazine, Vol. 348, Issue 6234, 1 May 2015, pp. 571-573; Kunstler, J.H., Clusterfuck Nation: A Glimpse into the Future, Kunstler.com (website), 2001b; US Geological Survey, Materials Flow and Sustainability, Fact Sheet FS-068-98, June 1998; Klare, M. T., The Race for What’s Left, Metropolitan Books, New York, 2012; Drielsma, Johannes A. et al., Mineral resources in life cycle impact assessment – defining the path forward, International Journal of Life Cycle Assessment, 21 (1), 2016, pp. 85-105; Meinert, Lawrence D. et al., Mineral Resources: Reserves, Peak Production and the Future, Resources 5(14), 2016; OECD World Nuclear Agency and International Atomic Energy Agency, 2004; Tahil, William, The Trouble with Lithium Implications of Future PHEV Production for Lithium Demand, Meridian International Research, 2007; Turner, Graham, Cathy Alexander, Limits to Growth was right. New research shows we’re nearing collapse, Guardian, Manchester, 1 September 2014; Kelemen, Peter, quoted in Cho, Renee, Rare Earth Metals: Will We Have Enough?, in State of the Planet, News from the Earth Institute, Earth Institute, Columbia University, September 19, 2012; Griffiths, Sarah, The end of the world as we know it? CO2 levels to reach a ‘tipping point’ on 6 June – and Earth may never recover, expert warns, Daily Mail, London, 12 May 2016; van der Werf, G.R. et al., CO2 emissions from forest loss, Nature Geoscience, Volume 2, November 2009, pp. 737–738; Global Deforestation, Global Change Program, University of Michigan, January 4, 2006; Arnell, Nigel, Future worlds: a narrative description of a plausible world following climate change, Met Office, London, 2012; The End, Scientific American, Special Issue, Sept 2010; Dator, Jim, Memo on mini-scenarios for the pacific island region, 3, November, 1981b, quoted in Bezold, Clement, Jim Dator’sAlternative Futures and the Path to IAF’s Aspirational Futures, Journal of Futures Studies, 14(2), November 2009, pp. 123 – 134.
2 Halal, William, Through the megacrisis: the passage to global maturity, Foresight Journal, VOL. 15 NO. 5, 2013a, pp. 392-404; Halal, William E., and Michael Marien, Global MegaCrisis Four Scenarios, Two Perspectives, The Futurist, Vol. 45, No. 3, May-June 2011; Halal, William E., Forecasting the technology revolution: Results and learnings from the TechCast project, Technological Forecasting and Social Change, 80.8, 2013b, pp. 1635-1643; TechCast Project, George Washington University, TechCast.org (website), Washington, DC, N.D.; National Research Council, Persistent Forecasting of Disruptive Technologies—Report 2, The National Academies Press, Washington, DC,2010. Halal, William E., Technology’s Promise: Expert Knowledge on the Transformation of Business and Society, Palgrave Macmillan, London, 2008; Halal et al., The GW Forecast of Emerging Technologies, Technology Forecasting & Social Change, Vol. 59, 1998, pp. 89-110. The name was inspired by the oracle at Delphi (8th century BCE to 390 CE). The modern Delphi Method helps uncover data, and collect and distill the judgments of experts using rounds of questionnaires, interspersed with feedback. Each round is developed based on the results of the previous, until the research question is answered, a consensus is reached, a theoretical saturation is achieved, or sufficient information was exchanged. Linstone, Harold A., & Murray Turoff (eds.), The Delphi method: Techniques and applications, Addinson-Wesley, London, 1975; Halal, William E., Business Strategy for the Technology Revolution: Competing at the Edge of Creative Destruction, Journal of Knowledge Economics, Springer Science+Business Media, New York, September 2012. The author consolidated both of Halal and Marien muddling scenarios into one. The uncertainty of each particular forecast element was about 20% – 30 %.
4 Chan, Tony, in Reubold, Todd, Envision 2050: The Future of Cities, Ensia.com (website), 16 June, 2014; Kunstler, James Howard, Back to the Future, Orion Magazine, June 23, 2011. Urry, John et al., Living in the City, Foresight, Government Office for Science, London, 2014; Hoff, Mary, Envision 2050: The Future of Transportation, Ensia.com (website), 31 March, 2014.
5 Kaku, Michio, The World in 2100, New York Post, New York, 20 March 2011. Tonn, Bruce E., LeCorbusier Meets the Jetsons in Anytown U.S.A. in the Year 2050: Glimpses of the Future, Planning Forum, Community and, Regional Planning, Volume 8, School of Architecture, The University of Texas, Austin, 2002; Urry et al., 2014.
6 Kaku, 2011; Hon, 2016. Rubbish bins will send alarms when they are about full. Talking garbage bins will reward people with poems, aphorisms, and songs for placing street rubbish in the bin. Heinonen, 2013.
8 Heinonen, 2013. The prefix cy*, an abbreviation of cybernetics, relates to computers and virtual reality. The suffix *burg means city, fortified town. Urrutia, Orlando, Eco-Cybernetic City of the Future, Pacebutler.com (website), 12 February 2010; Tonn, 2002.
9 Shepard, M., Sentient City: Ubiquitous Computing, Architecture, And The Future of Urban Space. MIT Press, Cambridge, 2011; Kurzweil, Ray, The Singularity is Near, Penguin Group, New York, 2005. Some futurists predict that the energy required to keep a “global brain” operating may so deplete energy that it will bankrupt society and cause total collapse. Heinonen, 2013. The terms smart city, intelligent city, and digital city are sometimes synonymous, but the digital or intelligent city is considered heavily technological. Heinonen, 2013; Giffinger, Rudolf et al., Smart cities – Ranking of European medium-sized cities. Centre of Regional Science, Vienna UT, October 2007; Kaku, 2011; Vermesan, Ovidiu and Friess, Peter, Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, River Publishers, Aalborg DK, 2013; Cooper, G., Using Technology to Improve Society, The Guardian, Manchester, 2010; Heinonen, 2013. Typical smart city programs utilize traffic data visualization, smart grids, smart water and e-government solutions, The Internet, smartphones, inexpensive sensors, and mobile devices. Amsterdam, Dubai, Cairo, Edinburg, Malaga, and Yokohama have smart city schemes. Webb, Molly et al., Information Marketplaces: The New Economics of Cities, The Climate Group, ARUP, Accenture and The University of Nottingham, 2011.
10 Dator, 2002; Bezold, 2009. The Jetsons originally ran a single season in 1962-63. It was revived but not resuscitated in 1985. The term Jetsons today stands for “unlikely, faraway futurism.” Novak, Matt, 50 Years of the Jetsons: Why The Show Still Matters, Smithsonian.Com, 19 September 2012.
11 Perrow, Charles, Normal Accidents: Living with High-Risk Technologies, Basic Books, New York, 1984. By adding complexity, including conventional engineering warnings, precautions, and safeguards, systems failure not only becomes inevitable, but it may help create new categories of accidents, such as those of Bhopal, the Challenger disaster, Chernobyl, and Fukushima. Deconcentrating high-risk populations, corporate power, and critical infrastructures is suggested. Perrow, Charles, The Next Catastrophe: Reducing Our Vulnerabilities to Natural, Industrial, and Terrorist Disasters, Princeton University Press, Princeton, 2011; Turner, 2014; Jacobs, Jane, Dark Age Ahead, Random House, New York, 2004, p.24.
12 Jacobs, 2004; Dirda, Michael, A living urban legend on the sorry way we live now, Washington Post, Washington DC, 6 June, 2004; Dator, 2002; Bezold, 2009; Dator, James, Alternative futures & the futures of law, in Dator, James & Clement Bezold (eds.), Judging the future, University of Hawaii Press, Honolulu, 1981. pp.1-17; Halal, 2013b.
13 The term feral city was coined in Norton, Richard J., Feral Cities, Naval War College Review, Vol. LVI, No. 4, Autumn 2003. See also Brunn, Stanley D. et al., Cities of the World: World Regional Urban Development, Rowman & Littlefield, Lanham, MD, 2003, pp. 5–14, chap. 1.
15 Urry, J., Offshoring. Polity, Cambridge, 2014; Gallopin, G., A. Hammond, P. Raskin, R. Swart, Branch Points, Global Scenario Group, Stockholm Environment Institute, Stockholm, 1997, p. 34. Norton, 2003.
17Backcasting is future hindsight. Kilcullen, David, Out of the Mountains: The Coming Age of the Urban Guerrilla, Oxford University Press, Oxford, 2013.
18Heterotopia, in Foucault, Michel, The Order of Things, Vintage Books, New York, 1971; Foucault, M., Of Other Spaces, Diacritics 16, 1986, pp. 22-27. Girard, Greg, and Ian Lambot, City of Darkness: Life in Kowloon Walled City, Watermark, Chiddingfold, 1993, 2007, 2014; Tan, Aaron Hee-Hung, Kowloon Walled City: Heterotopia in a Space of Disappearance (Master’s Thesis), Harvard University, Cambridge, MA, 1993; Sinn, Elizabeth, Kowloon Walled City: Its Origin and Early History (PDF). Journal of the Hong Kong Branch of the Royal Asiatic Society, 27, 1987, pp. 30–31; Harter, Seth, Hong Kong’s Dirty Little Secret: Clearing the Walled City of Kowloon, Journal of Urban History 27, 1, 2000, pp. 92-113; Grau, Lester W. and Geoffrey Demarest, Diehard Buildings: Control Architecture a Challenge for the Urban Warrior, Military Review, Combined Arms Center, Fort Leavenworth, Kansas, September / October 2003; Kunstler, James Howard, A Reflection on Cities of the Future, Energy Bulletin, Post Carbon Institute, 28 September, 2006; ArenaNet Art Director Daniel Dociu wins Spectrum 14 gold medal!, Guild Wars.com (website), 9 March 2007. Authors, game designers, and filmmakers used the Walled City to convey a sense of feral urbanization. It was the setting for Jean-Claude Van Damme’s 1988 film Bloodsport; Jackie Chan’s 1993 film Crime Story was partly filmed there during among genuine scenes of building demolition; and the video game Shadowrun: Hong Kong features a futuristic Walled City. Today the location of the former Kowloon Walled City is occupied by a park modelled on early Qing Dynasty Jiangnan gardens.
19 Halal, 2013a; Wright, Austin Tappan, Islandia, Farrar & Rinehart, New York, Toronto, 1942; Tonn, Bruce E., Anytown U.S.A. in the Year 2050: Glimpses of the Future, Planning Forum, Community and, Regional Planning, Volume 8, School of Architecture, The University of Texas, Austin, 2002; Porritt, Jonathon, The World We Made: Alex McKay’s Story from 2050, Phaidon Press, London, 2013. World Made by Hand novels by James Howard Kunstler: World Made By Hand, Grove Press, New York, 2008; The Witch of Hebron, Atlantic Monthly Press, 2010; A History of the Future, Atlantic Monthly, 2014; The Harrows of Spring, Atlantic Monthly Press, 2016
21 Dator, 2002; Bezold, 2009; Dator & Bezold, 1981; Dator, 1981a; Dator, 1981b; Dator, James, The Unholy Trinity, Plus One (Preface), Journal of Futures Studies, University of Hawaii, 13(3), February 2009, pp. 33 – 48; McDonough, William & Michael Braungart, Cradle to Cradle: Remaking the Way We Make Things, Macmillan, New York, 2002; Porritt, 2013; Urry et al., 2014.
22 Wright, 1942; Kunstler, 2011; Givens, Mark, Bring It On Home: An Interview with James Howard Kunstler, Urban Landscapes and Environmental Psychology, Mung Being (website), Issue 11, N.D., p. 30; Kunstler, World Made by Hand series.
23 Tonn, 2002; Mollison, B. C. Permaculture: A Designer’s Manual. Tagari Publications, Tyalgum, Australia, 1988; Holmgren, D. and B. Mollison, Permaculture One, Transworld Publishers, Melbourne, 1978; Holmgren, D., Permaculture: Principles and Pathways beyond Sustainability, Holmgren Design Services, Hepburn, Victoria, Australia, 2002; Holmgren, David, Future Scenarios: How Communities Can Adopt to Peak Oil and Climate Change, Chelsea Green Publishing White River Junction, Vermont, 2009; Walker, L., Eco-Village at Ithaca: Pioneering a Sustainable Culture, New Society Publishers, Gabriola Island, 2005; Hopkins, R., The Transition Handbook: From Oil Dependency to Local Resilience, Green Books, Totnes, Devon, 2008; Urry et al., 2014; Porritt, 2013.
24 Urry et al., 2014; Porritt, 2013; Caletrío, Javier, “The world we made. Alex McKay’s story from 2050” by Jonathon Porritt (review), Mobile Lives Forum, forumviesmobiles.org (website), 21 May 2015.
27 Krautheimer, Richard, Rome: Profile of A City, 312-1308, Princeton University Press, Princeton, 1980.
28 Palmer, Ada, The Shape of Rome, exurbe.com (website), Chicago, 15 August 2013.
29 Procopius of Caesarea, (c.490/507- c.560s) Procopius, Dewing, H B., and Glanville Downey (trans), Procopius, Harvard University Press, Cambridge, MA, 2000.On the Wars in eight books(Polemon or De bellis) waspublished 552, with an addition in 554; Storey, Glenn R., The population of ancient Rome, Antiquity, December 1, 199; Wickham, Chris, Medieval Rome: Stability and Crisis of a City, 900-1150, Oxford Studies in Medieval European History, Oxford University Press, New York, Oxford, 2015. Population numbers are uncertain well into the Renaissance. Krautheimer, 1980.
30 Porritt, 2013; Alexander, Samuel, Resilience through Simplification: Revisiting Tainter’s Theory of Collapse, Simplicity Institute Report, Melbourne (?), 2012b; Palmer, 2013: Halal, 2013a, 2013b.
31 America’s “Ten Dead Cities” in 2010: Buffalo; Flint; Hartford; Cleveland; New Orleans; Detroit; Albany; Atlantic City; Allentown, and Galveston. McIntyre, Douglas A., America’s Ten Dead Cities: From Detroit to New Orleans, 24/7 Wall Street (website), 23 August, 2010; Gibson, Campbell, Population of The 100 Largest Cities And Other Urban Places In The United States: 1790 To 1990, Population Division, U.S. Bureau of the Census, Washington, DC, June 1998. See also “America’s 150 forgotten cities.” Hoyt, Lorlene and André Leroux, Voices from Forgotten Cities Innovative Revitalization Coalitions in America’s Older Small Cities, MIT, Cambridge, MA, 2007; Manaugh, Geoff, Cities Gone Wild, Bldgblog.com (website), 1 December 2009.
32 Buras, Nir, The Art of Classic Planning for Beautiful and Enduring Communities, Harvard University Press, Cambridge, 2019.
[Editor’s Note: As stated previously here in the Mad Scientist Laboratory, the nature of war remains inherently humanistic in the Future Operational Environment. Today’s post by guest blogger COL James K. Greer (USA-Ret.) calls on us to stop envisioning Artificial Intelligence (AI) as a separate and distinct end state (oftentimes in competition with humanity) and to instead focus on preparing for future connected competitions and wars.]
Thepossibilities and challenges for future security, military operations, and warfare associated with advancements in AI are proposed and discussed with ever-increasing frequency, both within formal defense establishments and informally among national security professionals and stakeholders. One is confronted with a myriad of alternative futures, including everything from a humanity-killing variation of Terminator’s SkyNet to uncontrolled warfare ala WarGames to Deep Learning used to enhance existing military processes and operations. And of courselegal and ethical issues surrounding the military use of AI abound.
Yet in most discussions of the military applications of AI and its use in warfare, we have a blind spot in our thinking about technological progress toward the future. That blind spot is that we think about AI largely as disconnected from humans and the human brain. Rather than thinking about AI-enabled systems as connected to humans, we think about them as parallel processes. We talk abouthuman-in-the loop or human-on-the-loop largely in terms of the control over autonomous systems, rather than comprehensive connection to and interaction with those systems.
But even while significant progress is being made in the development of AI, almost no attention is paid to the military implications of advances in human connectivity. Experiments have already been conductedconnecting the human brain directly to the internet, which of course connects the human mind not only to the Internet of Things (IoT), but potentially to every computer and AI device in the world. Such connections will be enabled by a chip in the brain that provides connectivity while enabling humans to perform all normal functions, including all those associated with warfare (as envisioned by John Scalzi’s BrainPal in “Old Man’s War”).
Moreover, experiments inconnecting human brains to each other are ongoing. Brain-to-brain connectivity has occurred in a controlled setting enabled by an internet connection. And, in experiments conducted to date, the brain of one human can be used to direct the weapons firing of another human, demonstrating applicability to future warfare. While experimentation in brain-to-internet and brain-to-brain connectivity is not as advanced as the development of AI, it is easy to see that the potential benefits, desirability, and frankly, market forces are likely to accelerate the human side of connectivity development past the AI side.
So, when contemplating the future of human activity, of which warfare is unfortunately a central component, we cannot and must not think of AI development and human development as separate, but rather as interconnected. Future warfare will be connected warfare, with implications we must now begin to consider. How would such connected warfare be conducted? How would mission command be exercised between man and machine? What are the leadership implications of the human leader’s brain being connected to those of their subordinates? How will humans manage information for decision-making without being completely overloaded and paralyzed by overwhelming amounts of data? What are the moral, ethical, and legal implications of connected humans in combat, as well as responsibility for the actions of machines to which they are connected? These and thousands of other questions and implications related to policy and operation must be considered.
The power of AI resides not just in that of the individual computer, but in the connection of each computer to literally millions, if not billions, of sensors, servers, computers, and smart devices employing thousands, if not millions, of software programs and apps. The consensus is that at some point the computing and analytic power of AI will surpass that of the individual. And therein lies a major flaw in our thinking about the future. The power of AI may surpass that of a human being, but it won’t surpass the learning, thinking, and decision-making power of connected human beings. When a future human is connected to the internet, that human will have access to the computing power of all AI. But, when that same human is connected to several (in a platoon), or hundreds (on a ship) or thousands (in multiple headquarters) of other humans, then the power of AI will be exceeded by multiple orders of magnitude. The challenge of course is being able to think effectively under those circumstances, with your brain connected to all those sensors, computers, and other humans. This is whatRay Kurzwell terms “hybrid thinking.” Imagine how that is going to change every facet of human life, to include every aspect of warfare, and how everyone in our future defense establishment, uniformed or not, will have to be capable of hybrid thinking.
So, what will the military human bring to warfare that the AI-empowered computer won’t? Certainly, one of the major challenges with AI thus far has been its inability to demonstrate human intuition. AI can replicate some derivative tasks with intuition using what is now called “Artificial Intuition.” These tasks are primarily the intuitive decisions that result from experience: AI generates this experience through some large number of iterations, which is how Goggle’s AlphaGo was able to beat the human world Go champion. Still, this is only a small part of the capacity of humans in terms not only of intuition, but of “insight,” what we call the “light bulb moment”. Humans will also bring emotional intelligenceto connected warfare. Emotional intelligence, including aspects such as empathy, loyalty, and courage, are critical in the crucible of war and are not capabilities that machines can provide the Force, not today and perhaps not ever.
Warfare in the future is not going to be conducted by machines, no matter how far AI advances. Warfare will instead be connected human to human, human to internet, and internet to machine in complex, global networks. We cannot know today how such warfare will be conducted or what characteristics and capabilities of future forces will be necessary for victory. What we can do is cease developing AI as if it were something separate and distinct from, and often envisioned in competition with, humanity and instead focus our endeavors and investments in preparing for future connected competitions and wars.
If you enjoyed this post, please read the following Mad Scientist Laboratory blog posts:
… and watch Dr. Alexander Kott‘s presentation The Network is the Robot, presented at the Mad Scientist Robotics, Artificial Intelligence, & Autonomy: Visioning Multi Domain Battle in 2030-2050 Conference, at the Georgia Tech Research Institute, 8-9 March 2017, in Atlanta, Georgia.
COL James K. Greer (USA-Ret.) is the Defense Threat Reduction Agency (DTRA) and Joint Improvised Threat Defeat Organization (JIDO) Integrator at the Combined Arms Command. A former cavalry officer, he served thirty years in the US Army, commanding at all levels from platoon through Brigade. Jim served in operational units in CONUS, Germany, the Balkans and the Middle East. He served in US Army Training and Doctrine Command (TRADOC), primarily focused on leader, capabilities and doctrine development. He has significant concept development experience, co-writing concepts for Force XXI, Army After Next and Army Transformation. Jim was the Army representative to OSD-Net assessment 20XX Wargame Series developing concepts OSD and the Joint Staff. He is a former Director of the Army School of Advanced Military Studies (SAMS) and instructor in tactics at West Point. Jim is a veteran of six combat tours in Iraq, Afghanistan, and the Balkans, including serving as Chief of Staff of the Multi-National Security Transition Command – Iraq (MNSTC-I). Since leaving active duty, Jim has led the conduct of research for the Army Research Institute (ARI) and designed, developed and delivered instruction in leadership, strategic foresight, design, and strategic and operational planning. Dr. Greer holds a Doctorate in Education, with his dissertation subject as US Army leader self-development. A graduate of the United States Military Academy, he has a Master’s Degree in Education, with a concentration in Psychological Counseling: as well as Masters Degrees in National Security from the National War College and Operational Planning from the School of Advanced Military Studies.
[Editor’s Note: Mad Scientist Laboratory is pleased to publish the following post by repeat guest blogger Mr. Victor R. Morris, addressing the relationship of Artificial Intelligence (AI), Robotic and Autonomous systems (RAS), and Quantum Information Science (QIS) to Quantum Artificial Intelligence (QAI), and why we should pursue a parallel QAI strategy in order to predict alternative possibilities in a quantum multiverse. Prepare to have your consciousness expanded — Read on! (Note: Some of the embedded links in this post are best accessed using non-DoD networks.)]
The U.S. defense industry routinely analyzes emerging and potentially disruptive technological trends influencing long-term strategic competition. This post describes the greater defense community as public and private sectors responsible for national security and associated interests abroad. Interstate competition has implications for global order and disorder, according to the2018 National Defense Strategysummary.
The three defense industry trends identified in this post are:
Artificial Intelligence (AI),
Robotic and Autonomous systems (RAS), and
Quantum Information Science (QIS).
According to Paul Scharre‘s preface to Elsa Kania‘s paper onBattlefield Singularity, published by the Center for a New American Security (CNAS), “Artificial intelligence (AI) is fast heating up as a key area of strategic competition.” (N.B., both Mr. Scharre and Ms. Kania are proclaimed Mad Scientists whose works have previously graced this blog site). Furthermore, structured analysis identified interrelated aspects of these trends and the requirement for a multi-disciplinary strategy focused on Quantum Artificial Intelligence (QAI), anticipating the potential impact on global systems.
First, this post argues that AI, QIS, and RAS are components of a greater QAI ecosystem underpinned by the scientific notion of information discussed in detail later. Information does not measure what is known, ratherit measures the number of possible alternatives for something. CombiningAI and quantum computing applicationspotentially results in QAI, according to a variety of scientists and theorists in the field. Additionally, information is the nucleus or “quanta” of the entire QAI ecosystem. Understanding information is critical to understanding the natural world. Secondly, the post argues “keeping up with the Joneses” in AI is counterproductive and perpetuates misunderstanding of advancements and implications for the future.
The first section of this post briefly describes AI, Machine Learning (ML), RAS, QIS, and QAI, and their relationships with information. The second section describes theoretical interpretations of reality based on quantum mechanical properties.
Section 1 Overview AI, sometimes called machine intelligence, includes the machine learning field enabling autonomous or independent functions and activity. QIS and computing are the next evolution of classical computing with implications for machine learning, reasoning, and autonomous systems behavior. As mentioned above, information is a fundamental consideration for all of these fields and the ability to perform parallel probabilistic tasks. “Probabilistic” refers to probabilities indirectly associated with randomness.
Artificial Intelligence (AI) and Machine Learning (ML)
AI involves computer systems performing tasks normally requiring human intelligence. In computer science, AI is the study of intelligent agents or autonomous entitiesperceiving and acting upon their environment. AI is intelligence exhibited by machines, enabled by machine learning algorithms in simpler terms. Algorithms are rule sets defining sequences of operations.ML is a field of AI and set of statistical techniques associated with machines performing intellectual, human tasks. ML includes deep learning and is critical to AI because it involves Artificial Neural Networks (ANN) like the human brain, enabling learning from large quantities of data to improve predictions and data driven decisions. ANNs are a framework for ML algorithms working together to process complex data sets.
Robotic and Autonomous Systems (RAS) Robots are one type of AI entity, while others include cyber agents, decision aids, and virtual assistants. Amazon’s Alexa and Apple’s Siri are good examples of AI-enabled virtual assistants using ML to perform tasks. RAS are technologies grantedautonomy or level of independence to execute tasks in a prescribed environment in a military context. RAS examples include both land and air systems like explosive ordnance disposal robots and unmanned aerial vehicles commonly referred to as “drones.” Autonomous behavior is designed by humans through a combination ofsensors and advanced computing processes. Advanced computing involves both environmental navigation and software enabled decision-making. RAS independence is a progressive spectrum, ranging from remote control to full autonomy.
Quantum Information Science (QIS)
According to the September 2018 United States Government’sNational Strategic Overview for Quantum Information Science report, “Quantum information science (QIS) applies the best understanding of the sub-atomic world—quantum theory—to generate new knowledge and technologies.” Quantum theory, also called quantum mechanics, describes the smallest finite quantities, or “quanta,” making up thequantum fields composing the universe. QIS includes the quantum computing field using quantum mechanical properties to advance information processing, transmission, and measurement.For example, quantum computation uses the quantum analog of a bit, called a quantum bit, existing in multiple states due toquantum superposition. Superposition allows quantum systems the ability to simultaneously occupy different quantum states. This fundamental principle means qubits are described as a linear combination of 0 and 1 (composition of basis states), and not solely 0 or 1 as in classical computing before measurement.
Quantum Artificial Intelligence (QAI)
This section does not attempt to explain AI and QIS intersections in detail. Both areas are so extensive that unifying concepts are difficult to understand. This post sees QAI as a different element of the taxonomy and not a subset of classical AI. “Quantum physics is based on information theory and probability theory” according to Andreas Wichert, author of Principles of Quantum Artificial Intelligence. He presents both theories in his book, highlighting quantum physics’ relationship to AI through associative memory and Bayesian networks. Associate memory and Bayesian networks are applied later to QAI based on their access to information.
Section 2 Overview
This section outlines interpretations of information and quantum theories and AI intersections. Information is a finite measurement of possible alternatives existing in the multiverse. Quantum computing has the potential for reversible or time-invertible deep learning and associative memory based on quantum entanglement and superposition. Quantum AI has the potential to test the multiverse theory, because QAI networks process, transmit, and measure information across space-time.
Information takes many forms that differ from one another, like natural language, symbols, acoustic speech, and pictures. The scientific notion of information is more precise.Information theory, proposed by Claude E. Shannon, studies the quantification, storage, and communication of information. Once again, Information does not measure what is known, it measures the number of possible alternatives for something.
Carlo Rovelli uses a dice example in his book Reality Is Not What It Seems: The Journey to Quantum Gravity to illustrate this point. If a dice is thrown, it can land on one of six sides. When we observe it fall on a number, we have an amount of information where N=6 because the possible alternatives are six. Instead of “N” (number of alternatives), scientists measure information in terms of quantity deemed “S” after Shannon. Rovelli also states information is finite in nature based on quantum mechanical properties. New or “relevant” information cancels out “irrelevant” information in a physical system, therefore systems can always obtain new information from other systems. *This point is important for later.
Measuring Possible Alternatives
The fundamental unit of classical information is a “bit.” The natural unit of information, or “nat,” is a unit of information orentropy. Information entropy is the average rate information is produced by a random source of data. Information entropy can be measured in bits, nats, or decimal digits, depending on the base logarithm defining it. Once again, a binary digit, characterized as 0 and 1, represents information in classical computing. A quantum bit, or “qubit,” is the basic unit of quantum information in the quantum world. A qubit can be acoherent superposition of both 0 and 1 eigenstates according to quantum mechanical properties. A qubit can also hold more information than a classical bit. Lastly, probability amplitudes are complex numbers. They are the probability of a qubit to appear in its basis states.
Quantum Machine Learning through Quantum Information
Quantum ANNs potentially enable deep learning from large quantities of qubits. Qubits are information, so they measure possible alternatives. Quantum ANNs are like Bayesian networks graphically modelling probabilistic relationships in this specific interpretation. The quantum nature of these networks expand access to reciprocal or correlated information.
An interpretation of reciprocal information is discussed through quantum mechanical properties and quantum many-worlds, also called“multiverse” theory in the last part of this post. This specific interpretation is multiverses are finite because information is. This is loosely based on Steven Hawking and Thomas Hertog’s April 2018 article,A smooth exit from internal inflation? where they state, “eternal inflation does not produce an infinite fractal-like multiverse, but is finite and reasonably smooth.”
Quantum Many Worlds
Quantum computing has the potential to allow reversible or time-invertible deep learning and associative memory, based on quantum entanglement and superposition. Qubits contain entangled relevant and irrelevant (anti-correlated probabilities) information across space-time. This concept ensures a retro-causality loop of finite information exchange. Quantum associative memory is the ability to learn and remember correlations between seemingly unrelated items. This is possible because all “items” are correlated through quantum phenomena. Relevant information in one world or universe (macro possible alternative) is simultaneously irrelevant information in the adjacent world because of quantum states and finite quantity of information in nature. Quantum information cannot be copied according to the no-cloning theorem. Conversely, it cannot be deleted based on a time reversed dual called the “no-deleting theorem.”
Information is the quanta of consciousness. It is a measurement of awareness following all possible trajectories through the quantum multiverse ensuring the feedback loop of finite information that is reality.
This specific interpretation is based on Hugh Everett’s relative state or many-worlds interpretation (MWI) and informationreality code concept. MWI states “allpossible alternate histories and futures are real, each representing an actual world” or universe. The reality code behaves similarly to classical coding. Coding theory is the application of information theory manifesting efficient and reliable data transmission in a non-deterministic manner (where meaning is relative). Information in a data set is characterized by its Shannon entropy.
Summary of Key Points (You made it!) • The QAI ecosystem is underpinned by the scientific notion of information • Information does not measure what is known, it measures the number of possible alternatives for something • Relevant information cancels out irrelevant information in a physical system, therefore systems can always obtain new information from other systems • A qubit can be a coherent superposition of both 0 and 1 eigenstates, according to quantum mechanical properties • Qubits contain entangled relevant and irrelevant information across the multiverse • MWI states all possible alternate histories and futures are real, each representing an “actual world” or universe • Multiverses are finite because information is • Information is the quanta of consciousness and measurement of awareness
One interpretation of AI iswhoever becomes the leader in this sphere will become the ruler of the world. This is one possible alternative for QAI. Another possible alternative is the validation the many-worlds theory, providing insight into observable world alternate histories and optimized futures because information is available to QAI agent networks. The predictive nature of classical AI to support global superpower decision-making may not happen as planned either. Predictions in the observable world exist in other worlds, so AI predicting the observable future is relative. For example, when a dice lands on the number 1 in the observable world, it lands on the other five alternatives in alternate worlds. Additionally, unknown events in the observable world are known elsewhere in the quantum multiverse and vice versa (alternate histories and futures). Physicist David Deutsch, a proponent of the MWI, believes MWI will be testable throughquantum computing. Based on this blog’s conjecture, developing a parallel QAI strategy is the first step in preparing for our changing understanding of the world.
If you enjoyed this mind-bending post, please see Mr. Morris’ previous guest blog posts:
[Editor’s Note: Mad Scientist Laboratory is pleased to publish today’s guest blog post by MAJ Vincent Dueñas, addressing how AI can mitigate a human commander’s cognitive biases and enhance his/her (and their staff’s) decision-making, freeing them to do what they do best — command, fight, and win on future battlefields!]
Humans are susceptible to cognitive biases and these biases sometimes result in catastrophic outcomes, particularly in the high stress environment of war-time decision-making. Artificial Intelligence (AI) offers the possibility of mitigating the susceptibility of negative outcomes in the commander’s decision-making process by enhancing the collective Emotional Intelligence (EI) of the commander and his/her staff. AI will continue to become more prevalent in combat and as such, should be integrated in a way that advances the EI capacity of our commanders. An interactive AI that feels like one is communicating with a staff officer, which has human-compatible principles, can support decision-making in high-stakes, time-critical situations with ambiguous or incomplete information.
Mission Command in the Army is the exercise of authority and direction by the commander using mission orders to enable disciplined initiative within the commander’s intent.i It requires an environment of mutual trust and shared understanding between the commander and his subordinates in order to understand, visualize, describe, and direct throughout the decision-making Operations Process and mass the effects of combat power.ii
The mission command philosophy necessitates improved EI. EI is defined as the capacity to be aware of, control, and express one’s emotions, and to handle interpersonal relationships judiciously and empathetically, at much quicker speeds in order seize the initiative in war.iii The more effective our commanders are at EI, the better they lead, fight, and win using all the tools available.
AI Staff Officer
To conceptualize how AI can enhance decision-making on the battlefields of the future, we must understand that AI today is advancing more quickly in narrow problem solving domains than in those that require broad understanding.iv This means that, for now, humans continue to retain the advantage in broad information assimilation. The advent of machine-learning algorithms that could be applied to autonomous lethal weapons systems has so far resulted in a general predilection towards ensuring humans remain in the decision-making loop with respect to all aspects of warfare.v, vi AI’s near-term niche will continue to advance rapidly in narrow domains and become a more useful interactive assistant capable of analyzing not only the systems it manages, but the very users themselves. AI could be used to provide detailed analysis and aggregated assessments for the commander at the key decision points that require a human-in-the-loop interface.
The Battalion is a good example organization to visualize this framework. A machine-learning software system could be connected into different staff systems to analyze data produced by the section as they execute their warfighting functions. This machine-learning software system would also assess the human-in-the-loop decisions against statistical outcomes and aggregate important data to support the commander’s assessments. Over time, this EI-based machine-learning software system could rank the quality of the staff officers’ judgements. The commander can then consider the value of the staff officers’ assessments against the officers’ track-record of reliability and the raw data provided by the staff sections’ systems. The Bridgewater financial firm employs this very type of human decision-making assessment algorithm in order to assess the “believability” of their employees’ judgements before making high-stakes, and sometimes time-critical, international financial decisions.vii Included in such a multi-layered machine-learning system applied to the battalion, there would also be an assessment made of the commander’s own reliability, to maximize objectivity.
Observations by the AI of multiple iterations of human behavioral patterns during simulations and real-world operations would improve its accuracy and enhance the trust between this type of AI system and its users. Commanders’ EI skills would be put front and center for scrutiny and could improve drastically by virtue of the weight of the responsibility of consciously knowing the cognitive bias shortcomings of the staff with quantifiable evidence, at any given time. This assisted decision-making AI framework would also consequently reinforce the commander’s intuition and decisions as it elevates the level of objectivity in decision-making.
The capacity to understand information broadly and conduct unsupervised learning remains the virtue of humans for the foreseeable future.viii The integration of AI into the battlefield should work towards enhancing the EI of the commander since it supports mission command and complements the human advantage in decision-making. Giving the AI the feel of a staff officer implies also providing it with a framework for how it might begin to understand the information it is receiving and the decisions being made by the commander.
Stuart Russell offers a construct of limitations that should be coded into AI in order to make it most useful to humanity and prevent conclusions that result in an AI turning on humanity. These three concepts are: 1) principle of altruism towards the human race (and not itself), 2) maximizing uncertainty by making it follow only human objectives, but not explaining what those are, and 3) making it learn by exposing it to everything and all types of humans.ix
Russell’s principles offer a human-compatible guide for AI to be useful within the human decision-making process, protecting humans from unintended consequences of the AI making decisions on its own. The integration of these principles in battlefield AI systems would provide the best chance of ensuring the AI serves as an assistant to the commander, enhancing his/her EI to make better decisions.
Making AI Work
The potential opportunities and pitfalls are abundant for the employment of AI in decision-making. Apart from the obvious danger of this type of system being hacked, the possibility of the AI machine-learning algorithms harboring biased coding inconsistent with the values of the unit employing it are real.
The commander’s primary goal is to achieve the mission. The future includes AI, and commanders will need to trust and integrate AI assessments into their natural decision-making process and make it part of their intuitive calculus. In this way, they will have ready access to objective analyses of their units’ potential biases, enhancing their own EI, and be able overcome them to accomplish their mission.
MAJ Vincent Dueñas is an Army Foreign Area Officer and has deployed as a cavalry and communications officer. His writing on national security issues, decision-making, and international affairs has been featured in Divergent Options, Small Wars Journal, and The Strategy Bridge. MAJ Dueñas is a member of the Military Writers Guild and a Term Member with the Council on Foreign Relations. The views reflected are his own and do not represent the opinion of the United States Government or any of its agencies.
i United States, Army, States, United. “ADRP 5-0 2012: The Operations Process.” ADRP 5-0 2012: The Operations Process, Headquarters, Dept. of the Army., 2012, pp. 1–1.
iii “Emotional Intelligence | Definition of Emotional Intelligence in English by Oxford Dictionaries.” Oxford Dictionaries | English, Oxford Dictionaries, 2018, en.oxforddictionaries.com/definition/emotional_intelligence.
[Editor’s Note: Mad Scientist Laboratory is pleased to present our November edition of “The Queue” – a monthly post listing the most compelling articles, books, podcasts, videos, and/or movies that the U.S. Army’s Training and Doctrine Command (TRADOC) Mad Scientist Initiative has come across during the previous month. In this anthology, we address how each of these works either informs or challenges our understanding of the Future Operational Environment (OE). We hope that you will add “The Queue” to your essential reading, listening, or watching each month!]
The United States Army’s concept of Multi-Domain Operations 2028describes Russia and China as strategic competitors working to synthesize emerging technologies, such as artificial intelligence, hypersonics, machine learning, nanotechnology, and robotics, with their analysis of military doctrine and operations. The Future OE’sEra of Contested Equality (i.e., 2035 through 2050) describes China’s ascent to a peer competitor and our primary pacing threat. The fuel for these innovations is research and development funding from the Chinese Government and businesses.
CSIS’s China Power Project recently published an assessment of the rise in China’s research and development funding. There are three key facts that demonstrate the remarkable increase in funding and planning that will continue to drive Chinese innovation. First, “China’s R&D expenditure witnessed an almost 30-fold increase from 1991 to 2015 – from $13 billion to $376 billion. Presently, China spends more on R&D than Japan, Germany, and South Korea combined, and only trails the United States in terms of gross expenditure. According to some estimates, China will overtake the US as the top R&D spender by 2020.”
Second, globally businesses are funding the majority of the research and development activities. China is now following this trend with its “businesses financing 74.7 percent ($282 billion) of the country’s gross expenditure on R&D in 2015.” Tracking the origin of this funding is difficult with the Chinese government also operating a number of State Owned Entities. This could prove to be a strength for the Chinese Army’s access to commercial innovation.
Third, the Chinese government is funding cutting edge technologies where they are seeking to be global leaders. “Expenditures by the Chinese government stood at 16.2 percent of total R&D usage in 2015. This ratio is similar to that of advanced economies, such as the United States (11.2 percent). Government-driven expenditure has contributed to the development of the China National Space Administration. The Tiangong-2 space station and the “Micius’ quantum satellite – the first of its kind – are just two such examples.”
Success in the future OE relies on many key assumptions. One such assumption is that the innovation cycle has flipped. Where the DoD used to drive technological innovation in this country, we now see private industry (namely Silicon Valley) as the driving force with the Army consuming products and transitioning technology for military use. If this system is to work, as the assumption implies, the Army must be able to work easily with the country’s leading technology companies. Microsoft’s President Brad Smith stated recently that his company will “provide the U.S. military with access to the best technology … all the technology we create. Full stop.”
This is significant to the DoD for two reasons: It gives the DoD, and thus the Army, access to one of the leading technology developers in the world (with cloud computing and AI solutions), and it highlights that the assumptions we operate under are never guaranteed. Most recently, Google made the decision not to renew its contract with the DoD to provide AI support to Project Maven – a decision motivated, in part, by employee backlash.
Our near-peer competitors do not appear to be experiencing similar tensions or friction between their respective governments and private industry. China’s President Xi is leveraging private sector advances for military applications via a “whole of nation” strategy, leading China’sCentral Military-Civil Fusion Development Commissionto address priorities including intelligent unmanned systems, biology and cross-disciplinary technologies, and quantum technologies. Russia seeks to generate innovation by harnessing its defense industries with the nation’s military, civilian, and academic expertise at their Era Military Innovation Technoparkto concentrate on advances in “information and telecommunication systems, artificial intelligence, robotic complexes, supercomputers, technical vision and pattern recognition, information security, nanotechnology and nanomaterials, energy tech and technology life support cycle, as well as bioengineering, biosynthetic, and biosensor technologies.”
Microsoft openly declaring its willingness to work seamlessly with the DoD is a substantial step forward toward success in the new innovation cycle and success in the future OE.
This documentary film could have been a highly informative piece on the disruptive potential posed by robotics and autonomous systems in future warfare. While it presents a jumble of interesting anecdotes addressing the societal changes wrought by the increased prevalence of autonomous systems, it fails to deliver on its title. Indeed, robot lethality is only tangentially addressed in a few of the documentary’s storylines: the accidental death of a Volkswagen factory worker crushed by autonomous machinery; the first vehicular death of a driver engrossed by a Harry Potter movie while sitting behind the wheel of an autonomous-driving Tesla in Florida, and the use of a tele-operated device by the Dallas police to neutralize a mass shooter barricaded inside a building.
Instead, Mr. Pozdorovkin misleads his viewers by presenting a number creepy autonomy outliers (including a sad Chinese engineer who designed and then married his sexbot because of his inability to attract a living female mate given China’s disproportionately male population due to their former One-Child Policy); employing a sinister soundtrack and facial recognition special effects; and using a number of vapid androids (e.g., Japan’s Kodomoroid) to deliver contrived narrationhyping a future where the distinction between humanity and machines is blurred. Where are Siskel and Ebert when you need ’em?
The retail superpower Walmart is employing hundreds of robots in stores across the country, starting next month. These floor-scrubbing janitor robots will keep the stores’ floors immaculate using autonomous navigation that will be able to sense both people and obstacles.
The introduction of these autonomous cleaners will not be wholly disruptive to Walmart’s workforce operations, as they are only supplanting a task that is onerous for humans. But is this just the beginning? As humans’ comfort levels grow with the robots, will there then be an introduction of robot stocking, not unlike what is happening with Amazon? Will robots soon handle routine exchanges? And what of the displaced or under-employed workers resulting from this proliferation of autonomy, the widening economic gap between the haves and the have-nots, and the potential for social instability from neo-luddite movements in the Future OE? Additionally, as these robots become increasingly conspicuous throughout our everyday lives in retail, food service, and many other areas, nefarious actors could hijack them or subvert them for terroristic, criminal, or generally malevolent uses.
The introduction of floor-cleaning robots at Walmart has larger implications than one might think. Robots are being considered for all the dull, dirty, and dangerous tasks assigned to the Army and the larger Department of Defense. The autonomous technology behind robots in Walmart today could have implications for our Soldiers at their home stations or on the battlefield of the future, conducting refueling and resupply runs, battlefield recovery, medevac, and other logistical and sustainment tasks.
“Right now the most interesting science fiction is produced in all sorts of non-traditional places,” says Anindita Banerjee, Associate Professor at Cornell University, whose research focuses on global sci-fi. Sci-Fi andstory tellingenable us to break through our contemporary, mainstream echo chamber of parochialism to depict future technological possibilities and imagined worlds, political situations, and conflict. Unsurprisingly, different visions of the future imagining alternative realities are being written around the world – in China, Russia, and Africa. This rise of global science fiction challenges how we think about the evolution of the genre. Historically, our occidental bias led us to believe that sci-fi was spreading from Western centers out to the rest of the world, blinding us to the fact that other regions also have rich histories of sci-fi depicting future possibilities from their cultural perspectives. Chinese science fiction has boomed in recent years, with standout books like Cixin Liu’s The Three-Body Problem. Afrofuturism is also on the rise since the release of the blockbusterBlack Panther.
The Mad Scientist Initiative uses Crowdsourcing and Story Telling as two innovative tools to help us envision future possibilities and inform the OE through 2050. Strategic lessons learned from looking at the Future OE show us that the world of tomorrow will be far more challenging and dynamic. In ourFY17 Science Fiction Writing Contest, we asked our community of action to describe Warfare in 2030-2050. The stories submitted showed virtually every new technology is connected to and intersecting withother new technologies and advances. The future OE presents us with a combination of new technologies and societal changes that will intensify long-standing international rivalries, create new security dynamics, and foster instability as well as opportunities. Sci-fi transcends beyond a global reflection on resistance; non-Western science fiction also taps into a worldwide consciousness – helping it conquer audiences beyond their respective home markets.
A significant barrier to the modeling and simulation of dense urban environments has been the complexity of these areas in terms of building, vehicle, pedestrian, and foliage density.Megacitiesand their surrounding environments have such a massive concentration of entities that it has been a daunting task to re-create them digitally. Nvidia has recently developed a first-step solution to this ongoing problem. Using neural networks and generative models, the developers are able to train AI to create realistic urban environments based off of real-world video.
As Nvidia admits, “One of the main obstacles developers face when creating virtual worlds, whether for game development, telepresence, or other applications is that creating the content is expensive. This method allows artists and developers to create at a much lower cost, by using AI that learns from the real world.” This process could significantly compress the development timeline, and while it wouldn’t address the other dimensions of urban operations — those entities that are underground or inside buildings (multi-floor and multi-room) — it would allow the Army to divert and focus more resources in those areas. The Chief of Staff of the Army has madereadiness his #1 priority and stated, “In the future, I can say with very high degrees of confidence, the American Army is probably going to be fighting in urban areas,” and the Army “need[s] to man, organize, train and equip the force for operations in urban areas, highly dense urban areas.” 1 Nvidia’s solution could enable and empower the force to meet that goal.
If you read, watch, or listen to something this month that you think has the potential to inform or challenge our understanding of the Future OE, please forward it (along with a brief description of why its potential ramifications are noteworthy to the greater Mad Scientist Community of Action) to our attention at: firstname.lastname@example.org — we may select it for inclusion in our next edition of “The Queue”!
[Editor’s Note: As addressed in last week’s post, entitled The Human Targeting Solution: An AI Story, the incorporation of Artificial Intelligence (AI) as a warfighting capability has the potential to revolutionize combat, accelerating the future fight to machine speeds. That said, the advanced algorithms underpinning these AI combat multipliers remain dependent on the accuracy and currency of their data feeds. In the aforementioned post, the protagonist’s challenge in overriding the AI-prescribed optimal (yet flawed) targeting solution illustrates the inherent tension between human critical thinking and the benefits of AI.
Today’s guest blog post, submitted by MAJ Cynthia Dehne, expands upon this theme, addressing human critical thinking as the often neglected, yet essential skill required to successfully integrate and employ emergent technologies while simultaneously understanding their limitations on future battlefields. Warfare will remain an intrinsically human endeavor, the fusion of deliberate and calculating human intellect with ever more lethal technological advances. ]
The future character of war will be influenced by emerging technologies such as AI, robotics, computing, and synthetic biology. Cutting-edge technologies will become increasingly cheaper and readily available, introducing a wider range of actors on the battlefield. Moreover, nation-state actors are no longer the drivers of cutting-edge technology — militaries are leveraging the private sector who are leading research and development in emergent technologies. Proliferation of these cheap, accessible technologies will allow both peer competitors and non-state actors to wage serious threats in the future operational environment. Due to the abundance of new players on the battlefield combined with emerging technologies, future conflicts will be won by those who both possess “critical thinking” skills and can integrate technology seamlessly to inform decision-making in war instead of relying on technology to win war. Achieving success in the future eras of accelerated human progress and contested equality will require the U.S. Army to develop Soldiers who are adept at seamlessly employing technology on the battlefield while continuously exercising critical thinking skills.
The Foundation for Critical Thinkingdefines critical thinking as “the art of analyzing and evaluating thinking with a view to improve it.” 1 Furthermore, they assert that a well cultivated critical thinker can do the following: raise vital questions and problems and formulate them clearly and precisely; gather and assess relevant information, using abstract ideas to interpret it effectively; come to well-reasoned conclusions and solutions, testing them against relevant criteria and standards; think open-mindedly within alternative systems of thought, recognizing and assessing, as needed, their assumptions, implications, and practical consequences; and communicate effectively with others in figuring out solutions to complex problems.2
Many experts in education and psychology argue that critical thinking skills are declining. In 2017, Dr. Stephen Camarata wrote about the emerging crisis in critical thinking and college students’ struggles to tackle real world problem solving. He emphasized the essential need for critical thinking and asserted that “a young adult whose brain has been “wired’ to be innovative, think critically, and problem solve is at a tremendous competitive advantage in today’s increasingly complex and competitive world.”3 Although most government agencies, policy makers, and businesses deem critical thinking important, STEM fields continue to be prioritized. However, if creative thinking skills are not fused with STEM, then there will continue to be a decline in those equipped with well-rounded critical thinking abilities. In 2017, Mark Cuban opined during an interview with Bloomberg TV that the nature of work is changing and the future skill that will be more in-demand will be “creative thinking.” Specifically, he stated “I personally think there’s going to be a greater demand in 10 years for liberal arts majors than there were for programming majors and maybe even engineering.”4 Additionally, Forbes magazine published an article in 2018 declaring that “creativity is the skill of the future.”5
Employing future technologies effectively will be key to winning war, but it is only one aspect. During the Vietnam War, the U.S. relied heavily on technology but were defeated by an enemy who leveraged simple guerilla tactics combined with minimal military technology. Emerging technologies will be vital to inform decision-making, but will not negate battlefield friction. Carl von Clausewitz ascertained that although everything is simple in war, the simplest things become difficult and accumulate and create friction.6 Historically, a lack of information caused friction and uncertainty. However, complexity is a driver of friction in current warfare and will heavily influence future warfare. Complex, high-tech weapon systems will dominate the future battlefield and create added friction. Interdependent systems linking communications and warfighting functions will introduce more friction which will require highly skilled thinkers to navigate.
The newly publishedU.S. Army in Multi-Domain Operations 2028 concept “describes how Army forces fight across all domains, the electromagnetic spectrum (EMS), and the information environment and at echelon“7to “enable the Joint Force to compete with China and Russia below armed conflict, penetrate and dis-integrate their anti-access and area denial systems and ultimately defeat them in armed conflict and consolidate gains, and then return to competition.”8 Even with technological advances and intelligence improvement, elements of friction will be present in future wars. Both great armies and asymmetric threats have vulnerabilities, due to small things in terms of friction that morph into larger issues capable of crippling a fighting force. Therefore, success in future war is dependent on military commanders that understand these elements and how to overcome friction. Future technologies must be fused with critical thinking to mitigate friction and achieve strategic success. The U.S. Army must simultaneously emphasize integrating critical thinking in doctrine and exercises when training Soldiers on new technologies.
Soldiers should be creative, innovative thinkers; the Army must foster critically thinking as an essential skill. The Insight Assessment emphasizes that “weakness in critical thinking skill results in loss of opportunities, of financial resources, of relationships, and even loss of life. There is probably no other attribute more worthy of measure than critical thinking skills.”9 Gaining and maintaining competitive advantage over adversaries in a complex, fluid future operational environment requires Soldiers to be both skilled in technology and experts in critical thinking.
If you enjoyed this post, please also see:
– Mr. Chris Taylor’s presentation onProblem Solving in the Wild, from the Mad Scientist Learning in 2050 Conference at Georgetown University, 8-9 August 2018;
and the following Mad Scientist Laboratory blog posts:
MAJ Cynthia Dehne is in the U.S. Army Reserve, assigned to the TRADOC G-2 and has operational experience in Afghanistan, Iraq, Kuwait, and Qatar. She is a graduate of the U.S. Army Command and General Staff College and holds masters degrees in International Relations and in Diplomacy and International Commerce.
1 Paul, Richard, and Elder, Linda. Critical Thinking Concepts and Tools. Dillon Beach, CA: Foundation for Critical Thinking, 2016, p. 2.
2 Paul, R., and Elder, L. Foundation for Critical Thinking. Dillon Beach, CA: Foundation for Critical Thinking, 2016, p. 2.
[Editor’s Note: Mad Scientist Laboratory is pleased to present our October edition of “The Queue” – a monthly post listing the most compelling articles, books, podcasts, videos, and/or movies that the U.S. Army’s Training and Doctrine Command (TRADOC) Mad Scientist Initiative has come across during the past month. In this anthology, we address how each of these works either informs or challenges our understanding of the Future Operational Environment. We hope that you will add “The Queue” to your essential reading, listening, or watching each month!]
This innovative Table of Disruptive Technologies, derived from Chemistry’s familiar Periodic Table, lists 100 technological innovations organized into a two-dimensional table, with the x-axis representing Time (Sooner to Later) and the y-axis representing the Potential for Socio-Economic Disruption (Low to High). These technologies are organized into three time horizons, with Current (Horizon 1 – Green) happening now, Near Future (Horizon 2 – Yellow) occurring in 10-20 years, and Distant Future (Horizon 3 – Fuchsia) occurring 20+ years out. The outermost band of Ghost Technologies (Grey) represents fringe science and technologies that, while highly improbable, still remain within the realm of the possible and thus are “worth watching.” In addition to the time horizons, each of these technologies has been assigned a number corresponding to an example listed to the right of the Table; and a two letter code corresponding to five broad themes: DE – Data Ecosystems, SP – Smart Planet, EA – Extreme Automation, HA – Human Augmentation, and MI – Human Machine Interactions. Regular readers of the Mad Scientist Laboratory will find many of thesePotential Game Changers familiar, albeit assigned to far more conservative time horizons (e.g., our community of action believes Swarm Robotics [Sr, number 38], Quantum Safe Cryptography [Qs, number 77], and Battlefield Robots [Br, number 84] will all be upon us well before 2038). That said, we find this Table to be a useful tool in exploring future possibilities and will add it to our “basic load” of disruptive technology references, joining the annualGartner Hype Cycle of Emerging Technologies.
Tim Berners-Lee, who created the World Wide Web in 1989, has said recently that he thinks his original vision is being distorted due to concerns about privacy, access, and fake news. Berners-Lee envisioned the web as a place that is free, open, and constructive, and for most of his invention’s life, he believed that to be true. However, he now feels that the web has undergone a change for the worse. He believes the World Wide Web should be a protected basic human right. In order to accomplish this, he has created the “Contract for the Web” which contains his principles to protect web access and privacy. Berners-Lee’s “World Wide Web Foundation estimates that 1.5 billion… people live in a country with no comprehensive law on personal data protection. The contract requires governments to treat privacy as a fundamental human right, an idea increasingly backed by big tech leaders like Apple CEO Tim Cook and Microsoft CEO Satya Nadella.” This idea for a free and open web stands in contrast to recent news about China and Russia potentially branching off from the main internet and forming their own filtered and censoredAlternative Internet, orAlternet, with tightly controlled access. Berners-Lee’s contract aims at unifying all users under one over-arching rule of law, but without China and Russia, we will likely have a splintered and non-uniform Web that sees only an increase in fake news, manipulation, privacy concerns, and lack of access.
The Future Operational Environment’s “Era of Contested Equality” (i.e., 2035 through 2050) will be marked by significant breakthroughs in technology and convergences, resulting in revolutionary changes. Under President Xi Jinping‘s leadership, China is becoming a major engine of global innovation, second only to the United States. China’s national strategy of “innovation-driven development” places innovation at the forefront of economic and military development.
Early innovation successes in artificial intelligence, sensors, robotics, and biometrics are being fielded to better control the Chinese population. Many of these capabilities will be tech inserted into Chinese command and control functions and intelligence, security, and reconnaissance networks redefining the timeless competition offinders vs. hiders. These breakthroughs represent homegrown Chinese innovation and are taking place now.
A recent example is the employment of ‘gait recognition’ software capable of identifying people by how they walk. Watrix, a Chinese technology startup, is selling the software to police services in Beijing and Shanghai as a further push to develop an artificial intelligence and data drive surveillance network. Watrix reports the capability can identify people up to 165 feet away without a view of their faces. This capability also fills in the sensor gap where high-resolution imagery is required for facial recognition software.
Tricking the brain can be fairly low tech, according to Dr. Alexis Mauger, senior lecturer at the University of Kent’s School of Sport and Exercise Sciences. Research has shown that students who participated in a Virtual Reality-based exercise were able to withstand pain a full minute longer on average than their control group counterparts. Dr. Mauger hypothesized that this may be due to a lack of visual cues normally associated with strenuous exercise. In the case of the specific research, participants were asked to hold a dumbbell out in front of them for as long as they could. The VR group didn’t see their forearms shake with exhaustion or their hands flush with color as blood rushed to their aching biceps; that is, they didn’t see the stimuli that could be perceived as signals of pain and exertion. These results could have significant and direct impact onArmy training. While experiencing pain and learning through negative outcomes is essential in certain training scenarios, VR could be used to train Soldiers past where they would normally be physically able to train. This could not only save the Army time and money but also provide a boost to exercises as every bit of effectiveness normally left at the margins could now be acquired.
Presently, there are two predominant techniques for machine learning: machines analyzing large sets of data from which they extrapolate patterns and apply them to analogous scenarios; and giving the machine a dynamic environment in which it is rewarded for positive outcomes and penalized for negative ones, facilitating learning through trial and error.
In programmed curiosity, the machine is innately motivated to “explore for exploration’s sake.” The example used to illustrate the concept of learning through curiosity details a machine learning project called “OpenAI” which is learning to win a video game in which the reward is not only staying alive but also exploring all areas of the level. This method has yielded better results than the data-heavy and time-consuming traditional methods. Applying this methodology for machine learning inmilitary training scenarios would reduce the human labor required to identify and program every possible outcome because the computer finds new ones on its own, reducing the time between development and implementation of a program. This approach is also more “humanistic,” as it allows the computer leeway to explore its virtual surroundings and discover new avenues like people do. By training AI in this way, the military can more realistically model various scenarios for training and strategic purposes.
A European Union plan to tax internet firms like Google and Facebook on their turnover is on the verge of collapsing. As the plan must be agreed to by all 28 EU countries (a tall order given that it is opposed by a number of them), the EU is announcing national initiatives instead. The proposal calls for EU states to charge a 3 percent levy on the digital revenues of large firms. The plan aims at changing tax rules that have let some of the world’s biggest companies pay unusually low rates of corporate tax on their earnings. These firms, mostly from the U.S., are accused of averting tax by routing their profits to the bloc’s low-tax states.
This is not just about taxation. This is about the issue of citizenship itself. What does it mean for virtual nations – cyber communities which have gained power, influence, or capital comparable to that of a nation-state – that fall outside the traditional rule of law? The legal framework of virtual citizenship turn upside down and globalize the logic of the special economic zone — a geographical space of exception, where the usual rules of state and finance do not apply. How will these entities be taxed or declare revenue?
Currently, for the online world, geography and physical infrastructure remain crucial to control and management. What happens when it is democratized, virtualized, and then control and management change? Google and Facebook still build data centers in Scandinavia and the Pacific Northwest, which are close to cheap hydroelectric power and natural cooling. When looked at in terms of who the citizen is, population movement, and stateless populations, what will the “new normal” be?
In this article, subtitled “Are we designing inequality into our genes?” Ms. Hercher echoes what proclaimed Mad ScientistHank Greely briefed at the Bio Convergence and Soldier 2050 Conference last March – advances in human genetics will be applied initially in order to have healthier babies via the genetic sequencing and the testing of embryos. Embryo editing will enable us to tailor / modify embryos to design traits, initially to treat diseases, but this will also provide us with the tools to enhance humans genetically. Ms. Hercher warns us that “If the use of pre-implantation testing grows and we don’t address the disparities in who can access these treatments, we risk creating a society where some groups, because of culture or geography or poverty, bear a greater burden of genetic disease.” A valid concern, to be sure — but who will ensure fair access to these treatments? A new Government agency? And if so, how long after ceding this authority to the Government would we see politically-expedient changes enacted, justified for the betterment of society and potentially perverting its original intent? The possibilities need not be as horrific as Aldous Huxley’s Brave New World, populated with castes of Deltas and Epsilon-minus semi-morons. It is not inconceivable that enhanced combat performance via genetic manipulation could follow, resulting in a permanent caste of warfighters, distinct genetically from their fellow citizens, with the associated societal implications.
If you read, watch, or listen to something this month that you think has the potential to inform or challenge our understanding of the Future Operational Environment, please forward it (along with a brief description of why its potential ramifications are noteworthy to the greater Mad Scientist Community of Action) to our attention at: email@example.com — we may select it for inclusion in our next edition of “The Queue”!