Technological Nature: Adaptation and the Future of Human Life (MIT Press)

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Kahn describes his investigations of children's and adults' experiences of cutting-edge technological nature.

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He and his team installed "technological nature windows" inch plasma screens showing high-definition broadcasts of real-time local nature views in inside offices on his university campus and assessed the physiological and psychological effects on viewers. He studied children's and adults' relationships with the robotic dog AIBO including possible benefits for children with autism. And he studied online "telegardening" a pastoral alternative to "telehunting". Kahn's studies show that in terms of human well-being technological nature is better than no nature, but not as good as actual nature.

We should develop and use technological nature as a bonus on life, not as its substitute, and re-envision what is beautiful and fulfilling and often wild in essence in our relationship with the natural world. Technological Nature is a deeply compelling book. Our species spent , years as hunter-gatherers of the African savannah, and Kahn clearly demonstrates that ancestral memories of this are with us still, leaving us with an emotional need for nature and the desire to find substitutions for it. His thesis is unique, his work is breathtakingly original, and his presentation has created a real page-turner.

In this engaging and provocative book, Peter Kahn explores how technology can simulate the natural world. Kahn has written something unusual and important—a fascinating review of ongoing scientific research, a considered exploration of human development, and a passionate defense of the value of nature.

Many today believe human life has become the product of mainly invention and technology. To be modern, they believe, is to separate from the animal world, becoming something different from the rest of living creation. In this world, they wonder, who needs real nature? Yet, as a species, are we necessarily richer for all these gains in terms of health, happiness, and biological fitness?

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Despite our remarkable capacity to reach far beyond our biology, does our inventiveness continue to rely on having evolved in a natural, not human-created world? This book helps confront this question of the role of modern technology in our lives and where we fit in nature. Peter Kahn, a pioneering researcher on the human relationship with nature, offers a beautifully written, sometimes disturbing, and always provocative tour of the disappearing borderland between machinery and humanity.

Kahn thinks at the cutting edge. Understanding our interactions with 'technological nature' is one of the most pressing concerns of this century.

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Peter Kahn's outstanding and insightful book delivers the first comprehensive treatment of this critical topic. Peter H. Kahn, Jr. Maria Kronfeldner. Computer science has exploited artificial evolution extensively, initially with genetic algorithms Holland, ; Mitchell et al. The main purpose of using evolutionary algorithms is to search suitable solutions in problem spaces that are difficult to explore with more traditional heuristic methods.

In hard ALife, evolution has been used also for further removing the influence of the designer with the development of evolutionary robotics Cliff et al. This approach continues to be a popular tool for the ALife community Nolfi and Floreano, ; Harvey et al. One response has been to apply insights from organisms to better design the internal organization of artificial agents such that they can spontaneously re-organize, for example, by incorporating some capacity for homeostatic adaptation and habit formation Di Paolo, Initial attempts followed Ashby proposal of ultrastability, but the problem of heteronomous design quickly resurfaced.


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Artificial development is, on one hand, inspired by the developmental processes and cellular growth seen in nature biological development , and on the other hand, it is interested in studying developmental processes related to cognition cognitive development. These systems have been traditionally divided into two groups: 1 those that are based on self-organizing chemical processes in and between cells, and 2 those that follow a grammatical approach.

Turing seminal paper on the chemical basis of morphogenesis is probably the earliest work belonging to the first group. In that paper, Turing used a set of differential equations to propose a reaction-diffusion model, which led him to suggest that an initially homogeneous medium might develop a structured pattern such as certain radial and dappling patterns observed in the skin of many animals due to an instability of the homogeneous equilibrium, triggered by small random disturbances.

They proposed that pattern formation was the result of local self-activation coupled with lateral inhibition. The most famous result of their theory is the simulation of seashell patterns Meinhardt, Regarding those systems that follow a grammatical approach, Lindenmayer proposed the so-called L-Systems, which are a formal grammar with a set of symbols and a set of rewriting rules. They were introduced as a mathematical formalism for modeling development of simple multicellular organisms.

These systems were applied to modeling the development of plants and trees Prusinkiewicz et al. His results include biomorphs that resemble tree-like structures, insects, crustaceans, and mammals. This model allowed him to demonstrate the existence of intrinsic properties found in natural development, such as bilateral symmetry and repeating patterns with and without variation.

There has also been interest in creating software platforms as tools for experimenting with simulated developmental processes.


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  • For example, Stewart et al. The artificial life community has also been interested in creating computational models of cognitive development. Mareschal and Thomas defined them as formal systems that track the changes in information processing taking place as a behavior is acquired. Several approaches have been taken to tackle this problem, such as neural networks [e.

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    For recent reviews of this topic, see Elman and Schlesinger and McMurray Learning is a fundamental aspect of adaptive behavior for living organisms. In the context of artificial life, several approaches have been taken to model learning, some of which have influenced the field of machine learning Bishop, Artificial neural networks Rojas, ; Neocleous and Schizas, are a well known approach to learning, which are inspired by the structure and functional aspects of biological neural networks. Another common form of machine learning, inspired by behaviorist psychology, is reinforcement learning Kaelbling et al.

    There have been several other ALife approaches to learning in conjunction with other themes, e. At a high level of abstraction, ecological studies in ALife can be described as interactions between individuals from different species and with their environment. Coevolution involves species interaction across generations, having strong relations with ecology. These creatures compete for a resource and evolve interesting morphologies and behaviors. Also, related with evolution, ecological studies of ALife can offer insights into relationships such as symbiosis, parasitism Watson et al.

    At a global level, the living properties of biospheres have been studied. ALife models can study how regulation can occur as a consequence of multiple ecological interactions McDonald-Gibson et al. ALife ecological models, including cellular automata and agent-based Grimm et al.

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    Societies are defined by the interactions of individuals of the same species. The computational modeling of social systems has become very popular because it enables the systematic exploration of possibilities of social interaction, which are very difficult to achieve with complex societies Gilbert and Conte, ; Epstein Axtell, ; Gershenson, ; Epstein, For example, the evolution of cooperation has been a popular research topic Burtsev and Turchin, This approach has also been used to study multilevel selection Traulsen and Nowak, ; Powers et al. Central to human societies, the evolution of language and communication has been widely studied, beginning within the ALife community Cangelosi and Parisi, ; Kirby, ; Steels, The evolution of language can be seen as a special case of semiotics, i.

    Language is also a part of culture, which is beginning to be modeled within computational anthropology Axtell et al. The modeling of societies has led to the development of popular ALife games, such as Creatures Grand, and The Sims Wikipedia, In several cases, artificial societies include models of individual behavior [e. Some of the differences between artificial intelligence and artificial life can be seen in their contrasting views of and approaches to synthesizing behavior.

    Put in somewhat simplified terms, AI reduces behavior to something that is specified to take place inside an agent independently and on its own terms. This internal processing is often implemented in terms of a sense-model-plan-act architecture, which means that the agents behavior has more to do with logical inferences based on internal representations rather than with interacting with the world in real time.

    This traditional view was widely criticized from scientific, engineering, and philosophical perspectives. These have agreed that the structure of behavior is primarily to be conceived, designed, and analyzed in terms of the dynamics of a closed sensorimotor loop Braitenberg, ; Brooks, ; Cliff, ; Dreyfus, ; Clark, ; Pfeifer and Scheier, ; Pfeifer et al. This has led to the study of adaptive behavior, mainly based on ethology Maes, ; Meyer, This widespread paradigm shift made it evident that the contributions of the body and of the environment cannot be ignored, which is why this research is often referred to as embodied and situated or embedded cognition Varela et al.

    Since the s this paradigm has continued to grow in popularity Wheeler, ; Chemero, ; Robbins and Aydede, ; Beer, b , so much that the next step is to disentangle the many versions that have been proposed Kiverstein and Clark, ALife has benefited from this paradigm shift because it has always preferred to study the conditions of emergence to pre-specified behavior, and because it has closely linked the notion of life with biological embodiment and its environment. Therefore, we can expect that ALife will take the place of AI as the most important synthetic discipline of cognitive science.

    It is ALife, not traditional AI, which has the tools in order to investigate the general principles of the biologically embodied mind Di Paolo, ; Pfeifer et al. At the same time, given the increasing interest in the science of consciousness, it is likely that these efforts will be complemented by a growing emphasis on synthesizing and using new kinds of immersive and life-like human-computer interfaces to explore life- and mind-as-it-could-be from the first-person perspective Froese et al. Theoretical biology Waddington, b preceded ALife in the abstract study of living systems.

    In return, ALife has contributed to theoretical biology with the development of computational models and tools. Computers have enabled the study of complex systems i. Systems biology Kitano, has also required computers to study the complexity of biological systems at different scales, overlapping with ALife in several aspects. The transmission, storage, and manipulation of information at different scales are essential features of living systems, and several ALife models focus on one or more of these. Cellular automata were already mentioned Wolfram, ; Wuensche, Similar models have been used to study other aspects of biology.

    Studying ensembles of such networks, the functional effects of topologies, modularity, degeneracy, and other structural properties can be measured Gershenson, , providing insights into the nature of adaptability and robustness. These models of genetic regulatory networks have been useful for theoretical biology, as they have demonstrated the role of criticality in evolution Balleza et al.

    The study of biological neural networks led to the proposal of several models of distributed computation Rojas, In a similar way, the computational study of immune systems Bersini, ; Forrest et al.

    Artificial chemistries are used to study questions related to the origin of life from chemical components, as well as prebiotic and biochemical evolution Dittrich et al. This is because chemical components are considered non-living, while they form living organisms. Perhaps the first computer simulation of the formation of a simple protocell consisting of a metabolic network and a boundary was that which introduced the concept of autopoiesis Varela et al.