Augmented Reality

José María Ariso, José María Ariso

• Publisher: De Gruyter
• Year: 2017
• ISBN:
  9783110495836
• Edition: 1
• Topic: Philosophy
• Subtopic: Epistemology in Philosophy

There is at present no publication specifically dedicated to analyzing the philosophical implications of augmented reality, especially regarding knowledge formation, which constitutes a fundamental trait of knowledge society. That is why this volume includes an analysis of the applications and implications of augmented reality. While applications cover diverse fields like psychopathology and education, implications concern issues as diverse as negative knowledge, group cognition, the internet of things, and ontological issues, among others. In this way, it is intended not only to generate answers, but also, to draw attention to new problems that arise with the diffusion of augmented reality. In order to contemplate these problems from diverse perspectives, the auhors are from a variety of fields - philosophy, computer sciencess, education, psychology, and many more. Accordingly, the volume offers varied and interesting contributions which are of interest to professionals from multiple disciplines.

Part1:Augmented Reality and Historical Issues

Klaus Mainzer

From Augmented Reality to the Internet of Things: Paradigm Shifts in Digital Innovation Dynamics

Abstract: In the past, “Augmented Reality” only meant that our real-world environment is extended by digital instruments which are equipped with sensor interfaces (sound, video, touching) to enhance men-machine communication. An even elder paradigm was “virtual reality” replacing the real world with a computationally simulated one. But, nowadays, exponential growth of computer capacities, Big Data and fast algorithms lead to a new paradigm with is called the “Internet of Things” (IoT) with dramatic change of our living world. Billions of objects (“things”) are equipped with trillions of sensors to communicate with one-another. This kind of machine-to-machine communication enables self-organizing IT-networks growing together with global technical and societal infrastructures: Smart cities, smart grids, smart mobility, and the industrial internet (Industry 4.0) are examples of cyberphysical systems. Reality is no longer only “augmented” by IT-technology. It is changed into a new kind of self-organizing superorganism controlled by fast Big Data algorithms as nervous system.

Keywords: Augmented Reality, Internet of Things, embodied robotics, cyberphysical systems, complex systems dynamics, paradigm shift, innovation dynamics, self-organization.

1.From AR Systems to Embodied Mind

1.1What are AR Systems?

Classical computers are isolated to their physical environment. They only need electrical power and instructions given by human operators on keyboards. Humans and animals communicate with their physical environment by sensors of hearing, seeing, and feeling. In the history of technology, human abilities were enforced and extended by technical instruments and machines. Augmented Reality continues this development with computer-generated sensory input such as sound, video, graphics, and any kind of sensitive signal (Azuma 1997, Metz 2012). Augmentation also means enhancement of human abilities.

Many Augmented Reality-Systems are already worn on the human body. Eyeglasses are enhanced by Augmented Reality displays with cameras. Head-mounted displays take images according to the user’s head movements. Tracking technologies incorporate digital cameras, optical sensors, accelerators, GPS, RFID and wireless sensors. Augmented Reality platforms are applied to medical, industrial, and military applications. A huge market of Augmented Reality is nowadays entertainment.

Augmented Reality came up with technical breakthroughs of processors, displays, sensors, and input devices. Modern sensor technology is more and more inspired by biological evolution. Natural organisms are full of sensors which support controlling and self-organization of motor processes. All kinds of sensations are realized by specialized sensor cells. Even cognitive abilities like consciousness depend on self-awareness by organic sensors. In general, a sensor can be defined as a converter of a system that receives an input signal from the system’s environment which is converted into an internal signal of the system. In the case of biological organisms, analog physical signals such as light, sound, temperature, pressure etc. are converted into digital neural pulses which are understood by the nervous systems. But signal converters are not only sensor cells, but also signal molecules such as hormones or neurotransmitters. Even proteins can be considered as signal converters of an organism to detect toxins, hostile or alien substances. Thus, in nature, we can distinguish a scaling hierarchy from molecular to cellular and organic sensors which make life as selforganizing systems possible.

Like natural sensors, the abilities of technical sensors are determined by their resolution of received signals. Very small signals must correspond to very high sensitivity of sensors. In engineering sciences, sensor technology started with macroscopic devices which are miniaturized to MEMS (microelectromechanical systems) and NEMS (nanoelectromechanical systems). Nanoelectronics follow Richard Feynman’s visionary statement “There’s plenty of Room at the Bottom”. According to Moore’s law, computational capacity is not only doubled in a period of 18 months: the computational instruments are simultaneously miniaturized with decreasing costs. But Moore’s law runs into limitations of miniaturization with nano and atomic scaling. Traditional semiconductor technology (CMOS) must be improved and overcome by integration of “More-than-Moore” technologies with HF, analog/mixed signals, biochips, and interactions with the environment by sensors and actuators.

1.2Augmented Reality and Embodied Behavior

Augmented Reality technology is closely adapted to the human body. It is an embodied technology. Therefore, the success of Augmented Reality-technology depends on our knowledge of the human body and its interaction with the physical environment. Even our feeling and mind cannot be separated from bodily experience. What do we know about the embodied mind?

Organisms are a subclass of information systems which can be found in nature, technology, and society (Mainzer 2016b). There are different examples of information systems – animals, primates and humans, populations and societies, computers and robots, and communication networks. They all are distinguished by different kinds and degrees of abilities, sometimes in interaction and in dependence of humans. But with increasing autonomy of agents and robots in self-organizing information and communication networks, we observe a technical development of abilities surpassing natural evolution of organisms and populations. There are increasing abilities to solve complex problems. Complexity degrees can be measured by the algorithmic tools of computational complexity, i.e. time or size of the procedures to solve a problem. The distinction of natural and artificial systems is only justified by the fact that “artificial” systems were once initiated by human technology. In the future, originally “artificial” systems may reproduce and organize themselves in an automated evolution (Mainzer 2003, 2010).

But, how can a robot prevent incomplete knowledge in an open environment? How can it distinguish between reality and its restricted perspective? Situated agents like human beings need no symbolic representations and updating. They look, talk, and interact bodily, for example, by pointing to things. Even rational acting in sudden situations does not depend on symbolic representations and logical inferences, but on bodily interactions with a situation (for example, looking, feeling, and reacting).

Thus, we distinguish formal and embodied acting in games with more or less similarity to real life: Chess is a formal game with complete representations, precisely defined states, board positions, and formal operations. Soccer is a non-formal game with skills depending on bodily interactions, without complete representations of situations and operations which are never exactly identical. According to the French philosopher Merleau-Ponty, intentional human skills do not need any symbolic representation, but they are trained, learnt, and embodied by the organism (Merleau-Ponty 1962, Dreyfus 1982). An athlete like a pole-vaulter cannot repeat her successful jump like a machine generating the same product. The embodied mind is no mystery. Modern biology, neural, and cognitive science give many insights into its origin during the evolution of life.

1.3Embodied Mind and Brain Dynamics

The coordination of the complex cellular and organic interactions in an organism needs a new kind of self-organizing controlling. Their development was made possible by the evolution of nervous systems that also enabled organisms to adapt to changing living conditions and to learn bodily from experiences with its environment. We call it the emergence of the embodied mind (Mainzer 2009). The hierarchy of anatomical organizations varies over different scales of magnitude, from molecular dimensions to that of the entire central nervous system (CNS). The research perspectives on these hierarchical levels may concern questions, for example, of how signals are integrated in dendrites, how neurons interact in a network, how networks interact in a system like vision, how systems interact in the CNS, or how the CNS interact with its environment.

In the complex systems approach, the microscopic level of interacting neurons can be modeled by coupled differential equations modelling the transmission of nerve impulses by each neuron. The Hodgekin-Huxley equation is an example of a nonlinear reaction diffusion equation of a travelling wave of action potentials which give a precise prediction of the speed and shape of the nerve impulse of electric voltage. In general, nerve impulses emerge as new dynamical entities like the concentric waves in chemical reactions or fluid patterns in nonequilibrium dynamics.

But, local activity of a single nerve impulse is not sufficient to understand the complex brain dynamics and the emergence of cognitive and mental abilities. The neocortex with its more than 1011 neurons can be considered a huge nonlinear lattice, where any two points (neurons) can interact with neural impulses. How can we bridge the gap between the neurophysiology of local neural activities and the psychology of mental states? A single neuron can neither think nor feel, but only fire or not fire. They are the “atoms” of the complex neural dynamics.

In his famous book The organization of Behavior, Donald Hebb (1949) suggested that learning must be understood as a kind of self-organization in a complex brain model. As in the evolution of living organisms, the belief in organizing “demons” could be dropped and replaced by the self-organizing procedures of the self-organizing procedures of the complex systems approach. Historically, it was the first explicit statement of the physiological learning rule for synaptic modification. Hebb used the word “connectionism” in the context of a complex brain model. He introduced the concept of the Hebbian synapse where the connection between two neurons should be strengthened if both neurons fired at the same time (Hebb 1949, 50).

Hebb’s statement is not a mathematically precise model. But, later on, it was used to introduce Hebb-like rules tending to sharpen up a neuron’s predisposition “without a teacher” from outside. For example, a simple mathematical version of Hebb’s rule demands that the change ∆wBA of a weight wBA between a neuron A projecting to neuron B is proportional to the average firing rate vA of A and vB of B, i.e., ∆wBA = ε vBvA with constant ε. In 1949, the “Hebbian synapse” could only be a hypothetical entity. Nowadays, its neurophysiological existence is empirically confirmed.

On the macroscopic level, Hebb-like interacting neurons generate a cell assembly with a certain macrodynamics (Haken 1996). Mental activities are correlated with cell assemblies of synchronously firing cells. For example, a synchronously firing cell-assembly represents a plant perceptually which is not only the sum of its perceived pixels, but characterized by some typical macroscopic features like form, background or foreground. On the next level, cell assemblies of several perceptions interact in a complex scenario. In this case, each cell-assembly is a firing unit, generating a cell assembly of cell assemblies whose macrodynamics is characterized by some order parameters. The order parameters may represent similar properties of the perceived objects.

There is no “mother neuron” which can feel, think, or at least coordinate the appropriate neurons. The binding problem of pixels and features in a perception is explained by cell assemblies of synchronously firing neurons dominated by learnt attractors of brain dynamics. The binding problem asked: How can the perception of entire objects be conceived without decay into millions of unconnected pixels and signals of firing neurons? Wolf Singer (1994) and others could confirm Donald Hebb’s concept of synchronously firing neurons by observations and measurements.

In this way, we get a hierarchy of emerging levels of cognition, starting with the microdynamics of firing neurons representing a visual perception. On the following level, the observer becomes conscious of the perception. Then the cell assembly of perception is connected with the neural area that is responsible for states of consciousness. In a next step, a conscious perception can be the goal of planning activities. In this case, cell assemblies of cell assemblies are connected with neural areas in the planning cortex, and so on. Even high-level concepts like self-consciousness can be explained by self-reflections of self-reflections, connected with a personal memory which is represented in corresponding cell assemblies of the brain. Brain states emerge, persist for a small fraction of time, then disappear and are replaced by other states. It is the flexibility and creativeness of this process that makes a brain so successful in animals for their adaption to rapidly changing and unpredictable environments.

Cell assemblies behave like individual neurons. Thus, an assembly of randomly interconnected neurons has a threshold firing level for the onset of global activity. If this level is not attained, the assembly will not ignite, falling back to a quiescent state. If the threshold level is exceeded, firing activity of an assembly will rise rapidly to a maximum level. These two conditions ensure that assemblies of neurons can form assemblies of assemblies. Assemblies emerge from the nonlinear interactions of individual neurons. Assemblies of assemblies emerge from the nonlinear interaction of assemblies. Repeated several times, one gets the model of the brain as an emergent dynamic hierarchy.

In brain research, it is assumed that all mental states are correlated to cell assemblies. The corresponding cell assemblies must empirically be identified by observational and measuring instruments. In brain reading, for example, active cell assemblies correlated with words and corresponding objects can be identified. A single neuron is not decisive and may differ among different persons. There are typical distribution patterns with fuzzy shapes which are represented in computer simulations. Brain research is still far from observing the activities of each neuron in a brain. Nevertheless, the formal hierarchical scheme of dynamics, at least, allows an explaining model of complex mental states like, for instance, consciousness. In this model, conscious states mean that persons are aware of their activities. Self-awareness is realized by additional brain areas monitoring the neural correlates of these human activities (e.g., perceptions, feeling, or thinking). This is a question of empirical tests, not of armchaired reflection (Chalmers 2010). For example, in medicine, physicians need clear criteria to determine different degrees of consciousness as mental states of patients, depending on states of their brain.

Thus, we aim at clear working hypotheses for certain applications and not at a “complete” understanding what “consciousness” means per se. Besides medicine, the assumption of different degrees of self-awareness opens new perspectives of technical applications. Robots with a certain degree of self-awareness can be realized by self-monitoring and self-control which are useful for self-protection and cooperation in robot teams. In technical terms, these robots have internal representations of their own body and states. They can also ...

Table of contents

• Cover
• Title Page
• Copyright
• Table of Contents
• Introduction
• Part 1: Augmented Reality and Historical Issues
• Part 2: Ontological Problems in Augmented Reality
• Part 3: The Epistemology of Augmented Reality
• Part 4: Negative Knowledge Through Augmented Reality
• Part 5: Educational Applications and Implications of Augmented Reality
• Notes on Contributors
• Author index
• Subject index