Category Archives: Algemeen

Rethinking the fundamental objective of our urban future and their destinies.

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Humanity is making tremendous progress. It’s the best time ever to be alive. Why does no one know it?

Cities are a dense network of interconnected systems of increasing complexity, all of which use feedback information to exist in dynamic equilibrium. A new era of innovation for our urban future. A moment of recognition/realization

The city as the general form of human settlement, as the ecological niche of our species, belongs to the world of antifragility. The city as a system has proven through history to be capable to adapt, self-organize, improve and take advantage of the unpredictable, in short to prosper in disorder. Not only does the city exist for ten thousands years, not only is our culture predominantly a product of city, but also the majority of the human population lives in cities and the trends are that of a further consistent and rapid increase of urban population.

RETHINKING …… cities accelerate time – space compression associated with globalization and expand consequences and interactions of interrelated phenomena, creating new configurations, redefying concepts of cities as major engines of global economy and redefying boundaries and cooperation. Converges of mega poles and emerge of mega regions expand the edges of urban socio ecological dynamics beyond the individual city boundaries and accelerate changes in multiple scales.

Cities are under constant pressure to adapt and this makes our build environment fragile. This pressure is applied by stressors that lead to change in areas such as economy, technology, society, ecology etc. and cannot be immediately neutralized by standard design, engineering and architectural concepts. Solutions that are being put forward today must therefore also be considered with a view to their -shortening- expiry date. Cities futures must be able to react to change by deploying different strategies. During the conception phase, the challenges of dealing with uncertainties and acknowledging the unknown are fundamental for development strategies.

These development strategies and concepts are not static, there is no absolute definition of a city, no end point, but rather a process, or series of steps, by which cities become more liveable and resilient and, hence, able to respond quicker to new challenges, shocks and continuous stress. The current urban and natural systems are seldom capable of dealing with sudden shocks, which are bound to occur at an increasing rate.

This are elements that recognize the important fact that cities are complex adaptive, hybrid systems -as systems of systems (natural, social, economical, geographical, ecological,…)- have always had the capability to adapt and to improve, due to external and internal stressors, due to the variety and plurality of needs and desires of their inhabitants, users, social and economic subjects, using available technologies and information.

THE REAL-TIME CITY IS REAL! As layers of networks and intelligent technology blanket urban space, new approaches to the study of cities are emerging. The way we describe and understand cities is being radically transformed—as are the tools we use to design them.

The view of the city as an ecosystem has changed the way we look at urban change and design. More recently, the city is seen as a complex adaptive hybrid system and this implies that adaptation, resilience and anti-fragility of the city can be discussed as core characteristics. A development dependent on accurate predictions is fragile, since such forecasting on complex system is, strictly speaking, impossible. This is in particular true for social systems, which are twice complex: besides their objective complexity due to numerous non-linear interactions among its constitutive elements, they also contain agents capable of choice and—within limits—free to chose.

But a development that does not tend towards a future and does not aim at producing a future, is a contradiction in terms. And it is not unreasonable to hold that a community wants and ought to think about its future, at least within an imaginable time horizon of three to four generations, and to try to avoid undesirable futures. In order to adapt to new circumstances urban systems need to become agile. Rather than only responding to change by coping with it, urban environments can actually become stronger than before through their response to stresses and unpredicted events (shocks). This concept is called anti-fragility. Anti-fragility is defined as a convex response to a stressor or source of harm (for some range of variation), leading to a positive sensitivity to increase in volatility (or variability, stress, dispersion of outcomes, or uncertainty, what is grouped under the designation disorder cluster, and offers interesting opportunities.

However, most of current urban futures and developments are based on a technological paradigm in which the quantification of elements such as housing, jobs and parking spaces, standards, and regulations seem more important than achieving resilience and anti-fragility strategies. These kinds of urbanism are strongly single issue driven and linear simple system-based. Recent developments, with a focus on data, often deepen the technological paradigm, hence adding vulnerability to urban systems. In this sense, not only does it appear inappropriate to call a city smart, especially in the case of the smartness of things and not people, but it also appears to us insufficient to promote the mere resilience and anti-fragility of cities.

A complex, adaptive and anti-fragile system such as city cannot only limit itself to absorb or ward off blows: it ought to do more than just adapt, it needs to evolve, transform: redundancies, duplications, plasticity, exaptation’s, are all elements of an evolution which has enabled the city to survive and thrive, and to evermore become the ecological niche of the human species.

A future avenue to increase the strength of the city is to create anti-fragile environments, which grow under influence of external impacts. Applying stressors in the conception of antifragile strategies necessitates a systemic view of the built environment. The entire built environment consists not only of constructional and technical systems, but also includes living space with complex spatial, social and economic interaction and its comprehension calls for a systemic approach. A systemic view includes an understanding of the environment that assumes interacting systems with dynamic relationships to everyday reality.

The different subsystems in the city, such as the transportation system, the energy system, and others, are increasingly seen as complex systems as such, but these feed in to the complexity of the city as a whole. The adaptivity of the city can be influenced through strategic design interventions supporting self organisation. Adaptation, anti-fragility requires creating space to adapt, hence this concept advocates redundancy (‘space for the unknown’) in the urban realm.

The complexity of the entire city is difficult -but not impossible- to grasp, as the interrelations, dependencies, and connectivity between all subsystems in the city are complex by nature. The metabolism and design of flows and modelling by Urban System Engineering made it possible to discuss the sustainability and anti-fragility of the city as a whole, and close the cycles and dynamic interactions of energy, water, materials, social, cultural, economic, ecological and technology.

These methodologies, including emergism and anti-fragilism, focus on the city as a responsive system, in which self-organisation and the adaptive capacities of complex systems determine urban processes. Emergism takes complexity as the input for the design of cities . Self-organisation and emergence are key concepts, and are used to design interventions in the system to achieve certain changes. These concepts are common in nature and can be used in designing future cities and landscapes that are more adaptable and anti-fragile. They exist in nature, between humans and nature, between humans and humans, between human-created structures and people in cities, societies, states, supply chains, social services, the globalized world. The create patterns of mutually supportive and reinforcing properties creating feedback-loops of communication in relational interdependency. We need to understand which patterns are creating a sense of liveness in our cities, the conditions for aliveness in natural settings, architectural, geographical space and social as well as economic systems.

Modelling by Urban System Engineering acts as a ‘facilitator’ of the process of change, intervening at specific structures, places or times to initiate a change in the system. Approaches such as eco-acupuncture and Swarm Intelligence (collective intelligence as a more flexible way of thinking about how to plan, design for and respond to challenges , based on the behavior of swarms in nature), aim to design interventions in an existing urban system to transform to become more resilient and anti-fragile; ‘fluid’ and capable of responding to different paces of change that might occur in the city: fast, slow, or sudden.

To support the processes which make cities resilient and antifragile is the fundamental objective of our urban future and of their destinies.

From Urban Complexity to predictive models.

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Over the past few decades an extensive literature has been published on the study of complex physical, biological and social systems. As complex systems tend to be generic and pervasive they differ from complicated  systems that are distinctive and specialized. Complex systems have some  generally accepted properties. Their structure spans several scales, constituents are interdependent and interact in  nonlinear ways. These interactions give rise to novel and emergent dynamics. The  combination of structure and emergence is viewed as self-organization.

Complex Systems Properties.

  • Many (relatively simple) components
  • Nonlinear interactions (including feedback loops)
  • No centralized control
  • Emergent behavior
  • Evolution and adaptation
  • Complexity arises from variables and processes that operate over different scales in space and time. Boundaries in time and space in non-equilibrium systems which separate alternative stable states

Ecosystems and social systems are complex adaptive systems: complex because they have many parts and many connections between the parts; adaptive because their feedback structure gives them the ability to change in ways that promote survival in a fluctuating environment.

How can we understand human – ecosystem interaction when social systems and ecosystems are so overwhelmingly complex? The answer lies in emergent properties: the distinctive features and behavior that ‘emerge’ from the way that complex adaptive systems are organized. Once aware of emergent properties, it is easier to ‘see’ what is really happening. Emergent properties are cornerstones for comprehending human – ecosystem interactions in ways that provide insights for sustainable development.

Emergent properties are the most often observed real world phenomenon in a complex system. Emergent properties are patterns and regularities arising through interactions  among smaller or simpler entities in a system that themselves do not exhibit such  properties. In biological systems interactions at lower levels emerge as objects expressing  their properties at a higher level. Emergent properties tend to arise  as new objects from one scale to another. Emergent properties are a key generic property  of complex adaptive economic system; it is what makes economies become complex.

A city is not just a large scale artificial built environment composed of smaller scale artifacts such as buildings, roads, bridges … each of which is composed of still smaller artifacts and so on. And, artifacts are essentially simple systems. They might be very complicated such as super computers but essentially they are simple system. So what is it that makes the simple system and artifact ‘city’ a complex system?

 Cities as dually complex systems

Complex systems studies range from detailed studies of specific systems, to studies of the mechanisms by which patterns of collective behaviors arise, to general studies of the principles of description and representation of complex systems. These studies are designed to enable us to understand and modify complex systems, design new ones for new functions or create contexts in which they self-organize to serve our needs without direct design or specification. The need for applications to biological, cognitive, social, information, and other engineered systems is apparent.

A city is to be understood as a human – ecosystem interaction, as dual complex adaptive systems because they are composed of material components and human components. As a set of material components alone, the city is an artifact and as such a simple system; as a set of human components – the urban agents – the city is a complex system. It is the urban agents that by means of their interaction – among themselves, with the city’s material components and with the environment – transform the artifact city into the complex artificial system city. In order for a complex -biological- system to survive and evolve there must be interplay between competition  and  co-operation  at  different  scales. Complex biological/social systems are called adaptive systems because they can  adapt to a changing environment. A small subset of adaptive complex systems are self reproducing and experience birth, growth and death.

As a dual complex adaptive system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. Furthermore, because of its size, the city is a large-scale collective and complex artifact that on the one hand interacts with its environment, while on the other it is an environment for the millions of people that live and act in cities.

Artifacts are not just the outcome of human interaction; rather they are also the media of interaction. The process involves internal representations in the form of ideas, intentions, memories thoughts that originate and reside in the mind/brain of urban agents, and, on the other, external representations, that is to say artifacts such as texts, cities, buildings or roads that reside in the world.

They interact by means of the externally represented artifacts; roads, bridges, parks … buildings, neighborhoods and whole cities and metropoles.

The city is a dual complex adaptive system also in the sense that the city as a whole is a complex system and each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities.

Urgent need for a qualitative and quantitative understanding of cities.

Human civilization, its various parts, including its technology, and its environmental context, are all complex. The increasing complexity suggests that there will be a growing need for widespread understanding of complex systems as a counter point to the increasing specialization of professions and professional knowledge. The insights of complex systems research and its methodologies may become pervasive in guiding what we build, how we build it, and how we use and live with it. Possibly the most visible outcome of these developments will be an improved ability of human beings aided by technology to address complex global social and ecological/ environmental problems, third world development, poverty in developed countries, war and natural disasters.

These dual complex systems are dynamic and far from equilibrium. It is not possible to plan for an optimal future state.

For this we have to develop the ability to capture and represent specific systems, rather than just accumulate data about them. In this context: to describe relationships, know key behaviors, recognize relevance of properties to function, and to simulate dynamics and response. Furthermore to understand the interplay of behaviors at multiple scales, and between the system and its environment, connected or integrated within and across levels.

Describing and understand system behavior and the relations and boundaries between subsystems, and what are the relevant parameters for description or for affecting the behavior of the system. And by this dealing with complexity, with strategies that relate the complexity of the challenge to the complexity of the system that must respond to them.

In dual complex systems it is possible to predict most preferable futures -the desired system conditions- to some degree and for some time-scales. It is important to know what qualitative structure could emerge and discuss the merits and demerits of these, since these are the choices open to the system at present. Without models that can explore the possible future structures and morphologies of the system, planning and interventions can have no predictable outcomes.

Understanding complex systems does not mean that we can predict their behavior exactly, it is not just about massive databases, or massive simulations, even though these are important tools of research in complex systems. Understanding complexity is neither about prediction or lack of predictability, but rather a qualitative and quantitative knowledge of how well we can predict, and only within this constraint, what the prediction is.

Under construction

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In my lectures on robotics, AI and bioscience we not only focus on the ‘science’, but lately we deliberate on the range, growth and convergence of emerging technologies that unlock solutions to the most intractable problems, fueling new industries, and enabling massive disruption. The  convergence of artificial intelligence, robotics, AR/VR, synthetic biology, etc. are discussed and debated  and we are painting the implications and potential impact of these technologies across a wide range of disciplines, economy and industries. A multi-disciplinary picture of the future driven by these exponential technologies, as we are at an intersection in time where globalization, entrepreneurship, innovation and sciences are blending.

One of the latest discussion was on how these developments could generate new models of an innovation-driven inclusive economy characterized by a range of new technologies that fuse physical, digital and biological worlds. Embracing entrepreneurship to seize opportunities of this new development paradigm by nurturing an environment that encourages new ideas, tolerates mistakes and supports new businesses.

We are still in the midst of Industry 4.0, where manufacturing has taken on the label of ‘smart’ through the integration of the IoT, AI, cyber-physical systems, cloud and cognitive computing. The basic principle behind this industrial revolution is that by chaining machines, intelligent devices and systems manufacturers are creating smart networks throughout the value chain (from materials to production) that can control each other.

As mentioned above, the scientific and technological advancements continue to grow at an incredible speed—so much that we already see the next step on the horizon, one which will bring an increased human touch back to manufacturing. Consumers high-demand of individualization in the products they buy, preferring a degree of ‘hands-on’ personalization and customization with their products. Therefore, where at the moment technology is at the forefront of manufacturing, we will see an increased collaboration between humans and intelligent systems. The merge between the high-speed intelligent systems and machines with the cognitive, critical thinking skills of humans. Estimates vary, but artificial intelligence and automation will probably affect about half of jobs within the next two decades. It is striking that all jobs have aspects that are routine, repetitive and ripe for machine learning. The key question is whether the new, emerging jobs are ones in which humans have a comparative advantage over machines, or if they will require human skills as a complement. The only certainty is that most workers will have to adjust. Their ability to work and contribute to society will depend on that adjustment being successful.

This accelerating pace of change and the widespread disruption enabled by technology feels extremely uncomfortable. Our brains have been hard-wired to think linearly for hundreds of thousands of years. Learning to think and anticipate on the forthcoming future is incredibly hard…but critical. That is one thing. We also need to meaningfully evolve policy, ethics, law, economic and social structures, etc.

The discussion continue.

AI in the city

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By definition, complex systems have many agents, but what makes it different from simply a large or complicated system is that the behavior of the system is not solely determined by the behavior of the agents, but also of the relationships between the agents. It has suites of interactivity, feedback loops and tipping points and emergent properties. These interdependencies increase the overall value (emergent value), may decrease the required investments. Complexity is the result of the interactions among the various agents, it can not be simplified without losing its essence.

As a complex system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. The city in this respect is a complex evolving system. The city is a dual complex system as each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities. The city is an hybrid, co-evolving system.

Understanding these complex hybrid co-evolving systems takes intricate study, testing agents and their relation to each other in nuanced detail. Cities are comprehensive complex systems and many models have been developed that mimic its behavior. We can quite accurately replicate past behavior, but there are many aspects, feedback systems, emergence in urban systems that we don’t understand.

As post-industrial societies change into information and knowledge societies, gradually arizes the digital city of ubiquitous computers that are so prevalent that they are invisible, effectively melding into the background while having a profound effect in our everyday lives. Although the digital city is often referred to as a smart city – a label widely adopted by marketing departments of corporations and cities alike – its scope is much greater. Beyond a reductionist view of discrete solutions centered on digital technologies aiming at improving urban efficiencies, the digital city encompasses a deeper evolution: transforming them into systems capable of dynamically mediating the interactions between humans and their environments.

Improvements and convergences in machine learning and neurosciences combined with the availability of massive datasets and the ubiquity of high-performance scalable computing are propelling us into a new age of Artificial Intelligence (AI). The ability of advanced machine-learning algorithms to mine the growing stocks and flows of data related to the planning and operations of complex systems at the micro or macro levels is likely to trigger a wave of optimization across domains.

The impact that artificial intelligence (AI) has and will continue to have on our cities and the way we live and work in them over the next couple of decades, can be tremendous. AI presents a complex set of considerations for cities. As with any new technology, the possibilities are exciting but also raise deep policy (and philosophical) questions about their impact across several areas of urban life. But can artificial intelligence also support us to observe, understand, measure the interaction between agents, view this complex evolving urban system holistically, encourage anticipation of surprises and thresholds, encourage reflection and adaptive management?

Artificial Intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between agents and let us understand the levers across the system we need to activate to enact change. AI can provide an virtual nervous model that employs a number of purpose-built AI programs and machine-learning algorithms to process the vast amounts of incoming data. A model that reflects the real social-physical-ecological city committed to support a new form of urban modelling that is capable of self-correction, predictive analytics and decision support.









City Intelligence

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Deep uncertainties veil our view of the future of our urban planet after 2050. Within this timeframe, the planet will face changes in migration, climate change, disruptions in financial systems, shifting energy supplies, and pandemics – these and more are complex enough on their own, let alone together.

The cities of the future will be huge,  dense and the statistics paint a bleak future.

The urban sphere is central to building sustainability for people, planet, and prosperity. Because of its importance, we need expanded and flexible scientific research and knowledge generation about urban systems. Creating this new urban knowledge requires interdisciplinary efforts for greater understanding of complex urbanization processes and their multi-scale interactions and feedbacks with the Earth system.

Cities are highly complex, socio-ecological and technical in nature and are difficult to analyze. By definition complex adaptive system are systems in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behavior. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.

We were largely unable to view this complex system holistically. Existing modeling and design tools invoke static ‘playbook’ concepts that don’t adequately represent the complexity with its constantly changing variables. Simple measures have proven to not work terribly well, the influences across the system are more powerful.

The understanding of adaptive and evolving complex systems and the ability to process relevant (!) amounts of data supported by artificial intelligence, with its ability to analyze scores of information from varied sources, can tease out the interactions between agents and let us understand the levers across the system we need to activate to enact change.

These programs will leverage rapid improvements in computing and neural networks in the coming decades. The challenges to design an intelligent analytic, predictive and prescriptive model to support decision making are certainly formidable. A model that will employ a number of purpose-built AI programs and machine-learning algorithms to process and monitor the vast amounts of incoming sensory data.

To keep such a system real time, sensors — whether cameras, acoustic networks, swarm drones and other wireless systems — will communicate information about the health and status of the city and its infrastructure. Geostationary and other satellites and orbital platforms will monitor the city’s atmosphere, pollution levels, weather systems, and local environment. Could the increasingly complex systems needed to manage the next generation of megacities become our first true artificial intelligence? The challenge of city intelligence is, to employ a biological analogy, more like genetic engineering than mechanical engineering, and part of the solution will require rewriting a city’s DNA

Dynamic systems such as these with novel AI and mathematical techniques generate capabilities to exploit and provide a deeper understanding of urban system component interactions and a unified view of the urban system behaviors. In fact it could be the first truly human-scale AI system capable of reactive and independent cognition.

Autonomous and Intelligent

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Although the burgeoning impact of robotics and autonomy has been evident for some time, we are now recognizing the rapidly accelerating emergence of robotics, artificial intelligence, and autonomy in our daily lives.

Fossil records demonstrate the sudden appearance – about 542 million years ago – of complex animals with mineralized skeletal remains. Some describe this Cambrian Explosion as the most significant event in Earth’s evolutionary history, one that irreversibly changed the biosphere and led to a stunning diversity of body forms and types. Today, the impacts of robotics, artificial intelligence and autonomy – together with their derivatives, e.g., machine learning — are about to induce a metaphorical Cambrian Explosion of transformative capabilities and applications. However this will confront us with unanticipated, high-order consequences from external factors principally outside our control.

The original Cambrian Explosion’s transformative evolutionary developments were triggered by a complex interplay of relatively small environmental changes, and the emergence of nervous networks, and the ability to move and interact with the world. A similarly complex interplay is clearly at work across the topics of robotics, artificial intelligence and autonomy as sensors, actuators, and processors get both cheaper and better. The fields of robotics, artificial intelligence and autonomy share a number of enabling technologies, research challenges, and future; it is difficult to discuss one in the absence of the others. Autonomous, for example, is a quality of a robotic system; autonomous swarms are typically multi-robotic configurations. Autonomous systems are inherently, and irreducibly, artificially intelligent robots.

Robotics, artificial intelligence, and autonomy – far from narrow topics – are closely linked to a broad range of enabling / adjunct technologies. Although much of the technology development will occur in the very fragmented labs and innovation centers, there is a need to view robotics, artificial intelligence and autonomy as a holistic, seamless system. Technology will alter our fundamental thinking about science because of the exponential convergence of key technologies including …

… Nanoscience and nanotechnology

… Biotechnology and Biomedicine

… Information Technology

… Cognitive Science and Neuroscience

… Quantum Science

Autonomy can evolve from solutions that are reactive, single platform, point solutions under minimal human control to solutions that are flexible, multi-modal, and goal-oriented featuring trusted man-machine collaboration, distributed autonomy and continuous learning.

  • Fully : Human Out of the Loop: no ability for human to intervene in real time
  • Supervised Autonomous: Human on the Loop: humans can intervene in real time
  • Semi-Autonomous: Human in the Loop: machines wait for human input before taking action. Augmented Teleoperation. A mode of operation wherein the human operator leverages video or other sensory feedback to directly control the actuators on a continuous basis
  • Non-Autonomous (Remote Control): human in the loop via remote controls; no autonomy in system.

A future that features ever more advanced human-robot-AI collaboration, a collaboration that in turn will accelerate the development of improved robotics and Artificial General Intelligence through rapid machine learning, adaptive controls, rapid algorithm development, and custom motion control systems. Novel mechanisms and high performance actuators will emerge as new construction paradigms are merging component design to generate compact multi-function systems that are both highly capable and energy efficient. Human-robotic system interaction will include conversational assistants, intent and emotion recognition, augmented reality, self-aware explainable systems, and multi-modal communications. Intelligent networks will develop to distributed, interconnected entities that have goals, sense their environment, identify constraints and threats, and plan and execute adaptive actions through the leverage of autonomy, artificial intelligence and robotics

Clearly humans and these technologies are destined to co-evolve. No aspect of our current existence will remain untouched. Humans will be augmented in many ways: physically, via exoskeletons, perceptionally, via direct sensor inputs, genetically, via AI-enabled gene-editing technologies such as CRISPR, and cognitively via AI.  Human reality will be a blended one in which physical and digital environments, media and interactions are woven together in a seamless integration of the virtual and the physical. As daunting – and worrisome – as these technological developments might seem, there will be an equally daunting challenge in the co-evolution between man and machine: the co-evolution of trust. We can expect autonomy to evolve from solutions that are reactive, single platform, point solutions under minimal human control to solutions that are flexible, multi-modal, and goal-oriented featuring trusted man-machine collaboration, distributed autonomy and continuous learning. Trusted man-machine collaboration will require validation of system competence, a process that will take our legacy test and verification procedures far beyond their current limitations. Humans will expect autonomy to be nonetheless directable, and will expect autonomous systems to be able to explain the logic for their behavior, regardless of the complexity of the deep neural networks that motivate it. These technologies in turn must be able to adapt to user abilities and preferences, and attain some level of human awareness (e.g., cognitive, physiological, emotional state, situational knowledge, intent recognition). Another consideration is how we should treat intelligent machines. Do humans have to empathize with robotic systems, especially when the systems resemble humans or pets?



How high are the stakes?

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Humanity is at the beginning of a technological revolution that is evolving at a much faster pace than earlier ones, and that is so far reaching it is destined to generate transformations we can only begin to imagine. Emerging technologies will change what have seemed to be the fundamental constants of human nature: in fact, they already are and, as a result, it now seems possible to drastically improve human memory, cognitive processes, and physical and intellectual capacities—even to the extent of extending our life expectancy to such a degree that it may actually change our concept of mortality. Offering absolutely new perspectives for the human species, including a move towards a post-human era in which people, with enormously augmented capacities, will coexist with artificial intelligences that surpass human intelligence and will be able to reproduce autonomously, generating even more intelligent offspring—a situation known as the singularity. Another possibility that is increasingly close to hand is the expansion of human, or post-human, life beyond our planet, as well as contact with other intelligences in different parts of the universe. The possibilities are enormous, but the questions they raise for humankind are equally significant.

Possibilities for all, or for some?

Many technologies currently deployed and being developed deserve more careful attention because they have the potential to (re)construct humans and society on an unprecedented scale and scope.

Conceiving of vulnerability as a precondition of being and becoming human – as an ontological given – bound by the fact that we are relational beings, exposed to one another. We are exposed, that is, by virtue of being finite, dependent and limited; and that exposure and vulnerability are what constitutes us as moral beings. Does (our) life requires need to undergo a final upgrade, to be master of its own destiny, finally fully free from its evolutionary shackles?

The fact is, if we’re being truly logical and expecting historical patterns to continue, we should conclude that much, more will change in the coming decades than we intuitively expect. Logic also suggests that if the most advanced species on a planet keeps making larger and larger leaps forward at an ever-faster rate, at some point, they’ll make a leap so great that it completely alters life as they know it and the perception they have of what it means to be a human—kind of like how evolution kept making great leaps toward intelligence until finally it made such a large leap to the human being that it completely altered what it meant for any creature to live on planet Earth.

Technology is seen as a continuation of human evolution. By way of consequence, a deep symbiosis between human and machine up to the emergence of post-human entities might occur. The distinction between human enhancement and technological innovation will fade and lead to a modification of the paradigm of hybridization technological innovation.

Finally, while there are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AI’s caliber. There are three major AI caliber categories:

AI Caliber 1) Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak AI, Artificial Narrow Intelligence is AI that specializes in one area. There’s AI that can beat the world chess champion in chess, but that’s the only thing it does. Ask it to figure out a better way to store data on a hard drive, and it’ll look at you blankly.

AI Caliber 2) Artificial General Intelligence (AGI): Sometimes referred to as Strong AI, or Human-Level AI, Artificial General Intelligence refers to a computer that is as smart as a human across the boarda machine that can perform any intellectual task that a human being can. Creating AGI is a much harder task than creating ANI, and we’re yet to do it. Professor Linda Gottfredson describes intelligence as a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. AGI would be able to do all of those things as easily as you can.

AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills. Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter—across the board. ASI is the reason the topic of AI is such a spicy meatball and why the words “immortality” and “extinction” will both appear in these posts multiple times.

As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a road that will change many things in our lives.

In any case the proliferation of intelligent artifacts, systems, and devices that are context-aware and self-adjusting creates a paradigm change. Priority seems to develop intelligent technologies that improve health, comfort, and security. More tailored to meet individuated demands and market requirements. In this perspective the premises and the main concepts of transhumanism can be easily identified: human nature is the subject of innovation and transformations. Promoting a certain pragmatism concerning exponential technologies linked to solving stringent human problems. On the other hand, it maintains the transhumanist view on innovation when it emphasizes human enhancement. This becomes visible in the idea of human enhancement and in the artificial intelligence research.

After 13.8 billion years of cosmic evolution, development has accelerated dramatically here on Earth: Life 1.0 arrived about 4 billion years ago, Life 2.0 (we humans) arrived about a hundred millennia ago, and many AI researchers think that Life 3.0 may arrive during the coming century, perhaps even during our lifetime, spawned by progress in AI. What will happen, and what will this mean for us?

A change in different directions, a new consciousness, a new worldview. It will continue to be daunting. But it is doubtful that this will require a Brave New World of centralized global moral enhancement schemes: instead, managing our emerging biomedical enhancement abilities begins with the tedious real world tasks of learning to live with human difference and meet human needs.

On one hand, thinking about our species, it seems like we’ll have one and only one shot to get this right. The first Artifical Super Intelligence we birth will also probably be the last—and given how buggy most 1.0 products are, that’s pretty terrifying. On the other hand, Nick Bostrom points out the big advantage in our corner: we get to make the first move here. It’s in our power to do this with enough caution and foresight that we give ourselves a strong chance of success. And how high are the stakes?


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Cities must rethink themselves in the context of planetary change. From a planetary perspective, the emergence and rapid expansion of cities across the globe may represent another turning point in the life of our planet. Our knowledge of the complex nature of the urban system is still insufficient and we need a deep understanding of what is going on in our cities. Cities are being transformed so fast and the transformations are but a surface change hiding processes touching virtually everything from (among others) social networks to physical and functional changes which go unnoticed. The sophistication we discern in the city is similar to that in many natural and man-made systems which we might call complex, and any attempt to imagine it would fail if it is not based on an explanation which is valid for all these systems. From here rises the core of research which relies on a thorough investigation of complexity theory in an attempt to understand cities as complex systems.

We tend to view our built and natural environments as opposing forces. This perceived incompatibility has more to do with lack of understanding urban dynamics than universal law. Far from being static, nature is the process of moving from few to many connections. In this successional process, each stage builds upon the previous stage and creates the conditions necessary for the next. In this way, ecological systems diverge, diversify, as new connections grow, the system expands, new possibilities emerge and the systems evolves over time.


Earth’s atmosphere emerged from the metabolic process of vast numbers of single-celled algae and bacteria living in the seas billions of years ago. These organisms transformed the environment into a place where human life could develop. The evolution of life has completely changed the characteristics of the planet. Can humans now change the course of Earth’s evolution? Can the way we build cities determine the probability of crossing thresholds that will trigger non-linear, abrupt change on a planetary scale? What role do cities play in the evolution of Earth?

To function, life on Earth depends on the close cooperation of multiple elements, the properties of complex networks that supply resources, process waste, and regulate the system’s functioning at various scales of biological organization and evolving hierarchical fractal-like branching. Other characteristics of evolvable systems are flexibility (i.e. phenotypic plasticity), and novelty. The internal plasticity and flexibility of living systems, whose functioning is controlled by dynamic relations rather that rigid mechanical structures, gives rise to a number of characteristic properties that can be seen as different aspects of the same dynamic principle-the principle of self-organization. A living organism is a self- organizing system, which means that its order in structure and function is not imposed by the environment but is established by the system itself. Self-organizing systems exhibit a certain degree of autonomy; for example, they tend to establish their size according to internal principles of organization, independent of environmental influences. This does not mean that living systems are isolated from their environment; on the contrary, they interact with it continually, but this interaction does not determine their organization. The two principal dynamic phenomena of self-organization are 1) self- renewal-the ability of living systems continuously to renew and recycle their components while maintaining the integrity of their overall structure-and 2) self- transcendence-the ability to reach out creatively beyond physical and mental boundaries in the processes of learning, development, and evolution. Each level of biological organization from molecules to ecosystems has characteristic behaviors which emerge at that level. These emergent properties, function synergistically at each level of organization to give that level a life of its own which is greater than the sum of its parts. The capacity for innovation is an essential precondition for any system to function. If systems lack the capacity for innovation and novelty, they may become over-connected and dynamically locked, unable to adapt. To be resilient and evolve, they must create new structures and undergo dynamic change. Differentiation, modularity, and cross-scale interactions of organizational structures have been described as key characteristics of systems that are capable of simultaneously adapting and innovating.


What is the state of a social system? What are its stability domains? Social-system state is everything about a society at a particular place and time – culture, knowledge, economy, technology, perceptions, values and social organization. It is constantly fluctuating in some ways, while remaining more or less the same in other ways. Negative feedback loops keep social systems within stability domains imposed by particular cultural, political and economic systems while processes such as cultural evolution gradually change the shape of the domains. Social systems sometimes experience major switches from one stability domain to another. Social systems have complex system cycles in them self that range in scale from a small part of society to entire nations. Just as the scale varies, the time period of a cycle can vary from a few months to years or centuries. Human cultures also evolve. The mutations for cultures are new ideas. New ideas survive if they fit with the rest of the culture and prove useful. Whether or not an idea survives can depend on the situation. A new idea may survive successfully in one particular culture at one particular time and place, but the same idea may fail to survive in a different culture at a different time and place because it does not fit. Human cultural evolution can be much faster than biological evolution because cultural mutations are not random events like biological mutations. Cultural mutations are ideas that people develop to solve problems, so cultural mutations frequently fit the culture well enough, and function well enough, to survive and become part of the culture.

Social systems are autopoietic systems which use communication as their characteristic form of autopoietic generation. Policies can change dramatically during social system cycles. Policies are well developed and often rigid during equilibrium. During dissolution, people question existing policies and reject them as inadequate. New policies, even radically new frameworks, are formulated during reorganization.

The arrival of an intelligent responsive environment ads another factor, especially one containing autonomously mobile agents, requires changes which must affect people. This will inevitably bring the needs of the digital agent into conflict with the needs of the individual or society, just as cars require roads and regulations which gives them effective rights over people in some circumstances. Ambient intelligence systems may also require changes in human behaviour. Given the expected complexity and scale of a city’s ambient intelligence, there are likely to be many contexts in which we cannot distinguish between human-originated and self-determined needs of digital actants. It is likely that some needs will arise via a combination of human-originated and self-determined needs. It is likely that many human goals will be accomplished via methodologies which were self-determined by autonomous systems


To understand coevolution of human-natural-technological systems will require advancement in the evolution and social theories that explain how complex societies and cooperation have evolved. These coupled human-natural-technological systems are not governed only by either natural selection or human ingenuity alone, but by hybrid processes and mechanisms. It is their hybrid nature that makes them unstable and at the same time able to innovate. This novelty of hybrid systems is key to reorganization and renewal. A high degree of nonequilibrium is absolutely necessary for urban self-organization; living organisms are open systems that continually operate far from equilibrium. Fluctuations play a central role in the dynamics of self-maintenance. The hybrid urban system (like any complex adaptive evolving system) can be described in terms of interdependent variables, each of which can vary over a wide range between an upper and a lower limit. All variables oscillate between these limits, so that the system is in a state of continual fluctuation, even when there is no disturbance. Such a state is known as homeostasis. It is a state of dynamic, transactional balance in which there is great flexibility; in other words, the system has a large number of options for interacting with its environment.

In such a hybrid co-evolving system, is it possible to identify the real processes acting at the local scale interacting with transformations at the global scale? Yes, but it takes some effort… to convince people we can. To change our mind set. To jump.

What might remain uniquely human? And does this question even matter?

By | Algemeen | No Comments

We live at an extraordinary moment in the natural history of the planet and the cultural evolution of our species.  From a geological or paleontological perspective, humanity’s brief sojourn on this planet is as dramatic and significant as the invention of photosynthesis some two billion years ago.  This is because human evolution bypasses genetics and allows for intentional culturally-acquired adaptations and their cultural transmission between generations. As humans are about to embark upon large-scale genetic engineering of other species and ourselves, even as we have already engaged in large-scale environmental engineering, our biocultural evolution becomes literal. This new pattern of evolution now dominates all life on Earth and places the values and intentions of humans as the driving force in the future evolution of the planet.  Due to the global engagement with advanced technology, we are witness to a species-wise blurring of boundaries at the edge of the human. We also find ourselves in a digital age in which human identity is being transformed through networked technological intervention, a large part of our consciousness transferred to ‘smart’ external devices.

Many of the radical transformations in genetics, nanotechnology, biotechnology and related fields will be so disruptive that they can lead to a more frightening, unpredictable and chaotic world than ever before. An dystopian world of an unprecedented proliferation of quasi-human substitutes and surrogates, forming a spectrum of humanoids with fuzzy borders.

Extraordinary opportunities and challenges face humanity– and more broadly life on Earth. With ever more potent capabilities will come ever greater responsibilities. But humans are notoriously bad at making good decisions when faced with plenty. We have yet to understand how to cope. Hercules eventually rescued poor Prometheus from his torture.

In the past four decades, technology has fundamentally altered our lives: from the way we work, to how we communicate, to how we fight wars. These technologies have not been without controversy, and many have sparked intense debates, often polarized or embroiled in scientific ambiguities or dishonest demagoguery. Inevitably, the emerging technologies of the future will redefine our understanding of biology, the material world and will further extend into geopolitics and global balances of power.

Humanity is now at a crossroads that will determine its future path for centuries to come – survival or destruction, prosperity or collapse. A major change is coming, over unknown timescales but across every segment of society, and the people playing a part in that transition have a huge responsibility and opportunity to shape it for the best. Countless decisions must soon be made about how to address and navigate new forms of interaction with the future, in ways that transcend the borders between the physical, virtual, biological and digital. Discourse and decisions addressing:

  • the convergence of the NBIC (nano-, bio-, info-, cogno-) sciences;
  • ethics and aesthetics of human enhancement;
  • future of biological migration and transgressions;
  • emergence of systems and synthetic biology;
  • prospect of emotional and networked intelligence;
  • and ecosystem responsibility.

We’ve reached a point where advances in converging technologies are raising at least or even more ethical questions than practical ones. There aren’t any clear or easy answers to these questions, and it’s going to take a lot more time and thought to create frameworks or guidelines for both the appropriate and inappropriate uses of these potentially life-changing technologies. Undoubtedly the discourse presents entirely new ethical dilemmas, including some we may not yet be ready to negotiate. But it could offer, at least, solutions to inequalities that we find intractable today and remain committed to sustainable development, taking into account issues of inequality, human dignity and inclusiveness.

I remain confident that we are still in time and we can still prepare for the amazing yet uncertain future. What is definitely needed, among others things such as new skills, is initiating public discussions now.

It’s a smart city after all (4)

By | Algemeen | No Comments

The city is humanity’s greatest invention. An artificial ecosystem that enables millions of people to live in close proximity and to collaborate in the creation of new forms of value. While cities were invented many millennia ago, their economic importance has increased dramatically since the Industrial Revolution until they now account for the major fraction of the global economy. All human life is there and so the study of cities crosses boundaries among economics, finance, engineering, ecology, sociology, anthropology, and, well, almost all forms of knowledge. Yet, while we have great knowledge in each of these domains individually, we have little knowledge of how they come together in the overall system of systems that is a city. How does a city work?

At present, our urban theories provide a mixture of those that deal with the short term and the long term, with cities in equilibrium as well as out-of-equilibrium. There is, however, no sense in which there is an integrated scientific perspective making sense of how cities work. Central to this is the notion that cities are all about flows – about interactions and relationships – which underpin the patterns that we observe. There is now a clear imperative that suggests that as we observe and probe the city, and changing it, the models and analytics that we are using are changing the very systems that we are seeking to understand and manipulate. In the smart city, everything is contingent and changeable.

Applying this imperative to city development/management requires important recognition that a city is not a complicated system, but a complex one. The majority of the systems present in the world are nonlinear in nature. Nonlinear science has origins in ecology, mathematics, general systems theory, cybernetics, fractal geometry and meteorology. Nonlinear means that due to feedback or multiplicative effects between the components, the whole becomes something greater than the mere sum of its individual parts. Network centric cities borrowed concepts from nonlinear theory to describe a system prone to exponential changes. It describes certain nonlinear dynamical systems as having a sensitivity to changes in initial conditions. Cities are myriad of interactions among its inhabitants, its infrastructures and affordances, its natural environment, and its public, private, and civic organizations.


A complex system can be roughly understood as network of nodes, where the nodes themselves are interconnected to various degrees through single or multiple channels. This means that whatever happens in one node is transmitted through the network and is likely to impact other nodes to various degrees. The behavior of the system as a whole thus depends on the nodes, as well as the nature of the inter-linkages between them. The complexity of the system is influenced by a number of factors. These include the number of nodes, the number of inter-linkages, the nature of inter-linkages and the speed at which a stimulus or shock propagates to other nodes. Cities exhibit emergent characteristics that arise out of the interaction between its constituent parts.

Cities are highly complex, in fact they are complex and sociotechnical in nature. This means that, similarly to material and organic complex systems, cities exhibit the properties of natural complex systems and that many of the mathematical models developed to study natural complex systems also apply to cities. So what makes the city such a highly complex system? Cities differ from natural complex systems and suggest that, as a result, we have to include the cognitive, moral, emotional capabilities of urban agents in theorizing and simulating the dynamics of cities. In lieu of the more widespread complex adaptive system based on the influence of human interactions in cities, we can call urban systems complex evolving systems. Complex evolving systems work together to create new order and coherence, to sustain structure and to ensure its survival, particularly when its environment or social ecosystem is changing fast.

Cities exhibit a high number of complex evolving systems characteristics and are dual complex systems in four respects:

  • Cities are composed of material components and human components. As a set of material components alone, the city is an artifact and as such a simple system; as a set of human components – the urban agents – the city is a complex evolving system. It is the urban agents that by means of their interaction – among themselves, with the city’s material components and with the environment – transform the artifact city into the complex artificial evolving system city.
  • As a complex artificial evolving system the city emerges out of the interactional activities of its agents, but once it emerges, it affects the behavior of its agents and so on in circular causality. The city in this respect is a complex artificial environment. Furthermore, because of its size, the city is a large-scale collective and complex artifact that on the one hand interacts with its environment, while on the other it is an environment for the people that live and act in cities.
  • Artifacts are not just the outcome of human interaction; rather they are also the media of interaction. The process involves, on the one hand, internal representations in the form of ideas, intentions, memories thoughts that originate and reside in the mind/brain of urban agents, while on the other, external representations, that is to say interferences such as economics, politics, technology, biophysical boundaries etc reside in the world.
  • The city is a dual complex system also in the sense that the city as a whole is a complex system and each of its agents is also a complex system. The implication is that we have to include the cognitive capability of the urban agents in the dynamics of cities.

Among these are conceptual paradoxes. Many of these paradoxes take the form of the coexistence of properties that, in simpler contexts, appear to be incompatible. The essential role of understanding paradox in complex systems is to broaden our ability to conceive of the diversity of possibilities for our understanding of cities.


As mentioned emergent properties are a product of the interaction between the components within a system and typically cannot be deduced by reference to the properties of the parts. Thus, emergence typically produces novel phenomena that we could not have predicted until we ran the system and all the parts have interacted. We can fully analyze and understand how an individual change behaves, or what effects appear in isolation. But because cities are complex evolving systems where the behavior of the whole system is an emergent product of the interaction between its parts, we do not know what emergent behavior will arise from having many different algorithms interacting or different nodes coevolving within the whole system. The net result is an emergent phenomenon and we cannot deduce it from analyzing the parts in isolation.

Emergence leads to one of the key concepts within complexity theory, that of uncertainty. The fact that the future emerges is a key source of the fundamental uncertainty within complex systems. In this world of complexity, the future is not just unknown. It may well be in fact unknowable, and this fundamental uncertainty changes our whole approach to the future.


The arrival of an intelligent responsive urban environment, especially one containing autonomously mobile agents, requires changes which must affect people. This will inevitably bring the needs of the digital agent into conflict with the needs of the individual or society. Ambient intelligence systems may also require changes in human behaviour, raising the possibility that our environment will train us to suit its needs. Hence control of the digital actants within the smart city’s actor-network can be expected to grant influence and power over the human actants.

Given the expected complexity and scale of a city’s ambient intelligence, there are likely to be many contexts in which we cannot distinguish between human-originated and self-determined needs of digital actants. This integrated domain is a socio-technical system in which humans and digital devices co-mingle in a manner such that it becomes impossible (or even meaningless) to identify the origins of patterns within the system as being either human or digital. This integrated domain is autopoietic.


A smart city is a form of human society co-existing with an ambient digital environment, such that human perceptions, actions and intersubjectivity are unavoidably mediated and influenced by this ambient digital environment: the non-human domain of an autopoietic system of digital devices and networks based on the communicative triad  (three-stage process consisting of input, processing and output-. However, neither collective possesses strict boundaries against the other, but rather the two intermingle (hybrid systems which combine human and digital devices).

The close integration in the human society of a huge number range of devices, systems and deep integration of various ontological levels, from the individual nano-sensor to the global cloud, makes any model of the city more complex and inevitably incomplete.

The challenges in the development of smart cities are enormous. To deal with the smart city context, we need to handle this complexity. Much of the smart city rhetoric is phrased in terms of achieving a better quality of life for citizens, but the debate is strangely silent about questions of segregation, inequality and poverty, and tends to focus more on accessibility and economic opportunity. This is further magnified by the lack of discussion on the expectations and the human lived experience and effects of the deep fusion of digital cognitive processes with human deliberations.

Smart cities must prepare for change that will be revolutionary, rather than evolutionary, as they put in place next-generation systems that work in entirely new ways. In this urbanizing world, smart cities are gaining greater control over their development; those that become the most successful are those that have instrumented and interconnected core systems. The next wave of innovations will be from humans’ ability to connect to machines and the data that comes from these connections.