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The Info-computational Nature of Morphological Computing

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Abstract

Morphological computing emerged recently as an approach in robotics aimed at saving robots computational and other resources by utilizing physical properties of the robotic body to automatically produce and control behavior. The idea is that the morphology of an agent (a living organism or a machine) constrains its possible interactions with the environment as well as its development, including its growth and reconfiguration. The nature of morphological computing becomes especially apparent in the in-fo-computational framework, which combines informational structural realism (the idea that the world for an agent is an informational structure) with natural computationalism (the view that all of nature forms a network of computational processes). Info-computationalism describes morphological computation as a process of continuous self-structuring of information and shaping of both interactions and informational structures. This article argues that natural computation/morphological computation is a computational model of physical reality, and not just a metaphor or analogy, as it provides a basis for computational framing, parameter studies, optimizations and simulations – all of which go far beyond metaphor or analogy.
TheInfocomputationalNatureof
MorphologicalComputing
Gordana Dodig-Crnkovic
Mälardalen University, Computer Science and Networks Department,
School of Innovation, Design and Engineering, Västerås, Sweden;
E-mail: gordana.dodig-crnkovic@mdh.se
Abstract
Morphological computing emerged recently as an approach in robotics
aimed at saving robots computational and other resources by utilizing
physical properties of the robotic body to automatically produce and con-
trol behavior. The idea is that the morphology of an agent (a living organ-
ism or a machine) constrains its possible interactions with the environment
as well as its development, including its growth and reconfiguration. The
nature of morphological computing becomes especially apparent in the in-
fo-computational framework, which combines informational structural
realism (the idea that the world for an agent is an informational structure)
with natural computationalism (the view that all of nature forms a network
of computational processes). Info-computationalism describes morpholog-
ical computation as a process of continuous self-structuring of information
and shaping of both interactions and informational structures. This article
argues that natural computation/morphological computation is a computa-
tional model of physical reality, and not just a metaphor or analogy, as it
provides a basis for computational framing, parameter studies, optimiza-
tions and simulations – all of which go far beyond metaphor or analogy.
Introduction
In recent years, morphological computing emerged as a new idea in robot-
ics, (Pfeifer 2011), (Pfeifer and Iida 2005), (Pfeifer and Gomez 2009)
(Paul 2004). This presents a fundamental change compared with traditional
Author's manuscript published by SPRINGER
Chapter in a book
Philosophy and Theory of Artificial Intelligence
Volume 5 of the series Studies in Applied Philosophy,
Epistemology and Rational Ethics pp 59-68
2
robotics which, based on the Cartesian tradition, treated the body/machine
and its control (computer) as completely independent elements of a robot.
However, it has become increasingly evident that embodiment itself is es-
sential for cognition, intelligence and generation of behavior. In a most
profound sense, embodiment is vital because cognition (and consequently
intelligent behavior) results from the interaction of the brain, body, and
environment. (Pfeifer 2011) Instead of specifically controlling each
movement of a robot, one can instead use morphological features of a body
to automatically create motion. Here we can learn from specific structures
of biological life forms and materials found in nature which have evolved
through optimization of their function in the environment.
During the process of its development, based on its DNA code, the body of
a living organism is created through morphogenesis, which governs the
formation of life over a short timescale, from a single cell to a multi-
cellular organism, through cell division and organization of cells into tis-
sues, tissues into organs, organs into organ systems, and organ systems in-
to the whole organism. Morphogenesis (from the Greek “generation of the
shape"), is the biological process that causes an organism to develop its
shape.
Over a long timescale, morphological computing governs the evolution of
species. From an evolutionary perspective it is crucial that the environment
provides the physical source of the biological body of an organism as well
as a source of energy and matter to enable its metabolism. The nervous
system and brain of an organism evolve gradually through the interaction
of a living agent with its environment. This process of mutual shaping is a
result of information self-structuring. Here, both the physical environment
and the physical body of an agent can at all times be described by their in-
formational structurei. Physical laws govern fundamental computational
processes which express changes of informational structures. (Dodig
Crnkovic 2008)
The environment provides a variety of inputs in the form of both informa-
tion and matter-energy, where the difference between information and
matter-energy is not in the kind, but in the type of use the organism makes
of it. As there is no information without representation, all information is
3
carried by some physical carrier (light, sound, radio-waves, chemical mo-
lecules able to trigger smell receptors, etc.). The same object can be used
by an organism as a source of information and as a source of nourish-
ment/matter/energy. A single type of signal, such as light, may be used by
an organism both as information necessary for orientation in the environ-
ment, and for the photosynthetic production of energy. Thus, the question
of what will be used 'only' as information and what will be used as a
source of food/ energy depends on the nature of the organism. In general,
the simpler the organism, the simpler the information structures of its
body, the simpler the information carriers it relies on, and the simpler its
interactions with the environment.
The environment is a resource, but at the same time it also imposes con-
straints which limit an agent’s possibilities. In an agent that can be de-
scribed as a complex informational structure, constraints imposed by the
environment drive the time development (computation) of its structures,
and thus even its shape and behavior, to specific trajectories.
This relationship between an agent and its environment is called structural
coupling by (Maturana & Varela 1980) and is described by (Quick and
Dautenhahn 1999) as “non-destructive perturbations between a system and
its environment, each having an effect on the dynamical trajectory of the
other, and this in turn affecting the generation of and responses to subse-
quent perturbations.”
This mutual coupling between living systems and the environment can be
followed on the geological time scale, through the development of the first
life on earth. It is believed that the first, most primitive photosynthetic or-
ganisms contributed to the change of the environment and produced oxy-
gen and other compounds enabling life on earth. For example, Catling et
al. (2001) explain how photosynthesis splits water into O2 and H, and me-
thanogenesis transfers the H into CH4. The release of hydrogen after CH4
photolysis therefore causes a net gain of oxygen. This process may help
explain how the earth's surface environment became successively and irre-
versibly oxidized, facilitating life on earth.
When talking about living beings in general, there are continuous, mutual-
ly shaping interactions between organisms and their environment, where
4
the body of some organisms evolved a nervous system and a brain as con-
trol mechanisms. Clark (1997) p. 163 talks about "the presence of conti-
nuous, mutually modulatory influences linking brain, body and world."
Morphological Computing
In morphological computing, the modelling of an agent’s behavior (such as
locomotion and sensory-motor coordination) proceeds by abstracting the
principles via information self-structuring and sensory-motor coordination,
(Matsushita et al. 2005), (Lungarella et al. 2005) (Lungarella and Sporns
2005) (Pfeifer, Lungarella and Iida 2007). Brain control is decentralized
based on sensory-motor coordination through interaction with the envi-
ronment. Through embodied interaction with the environment, in particu-
lar through sensory-motor coordination, information structure is induced
in the sensory data, thus facilitating perception, learning and categoriza-
tion. The same principles of morphological computing (physical compu-
ting) and data self-organization apply to biology and robotics.
Morphology is the central idea in the understanding of the connection be-
tween computation and information. It should be noted that material also
represents morphology, but on a more basic level of organization – the ar-
rangements of molecular and atomic structures. What appears as a form on
a more fundamental level of organization (e.g. an arrangement of atoms),
represents 'matter' as a higher-order phenomenon (e.g. a molecule). Iso-
mers show how morphological forms are critical in interaction processes
such as pharmacology, where the matching of a 'drug' to a 'receptor' is only
possible if the forms are correct. The same is true for processes involving
molecules in a living cell.
Info-computational naturalism (Dodig Crnkovic 2009) describes nature as
informational structure – a succession of levels of organization of informa-
tion. Morphological computing on that informational structure leads to
new informational structures via processes of self-organization of informa-
tion. Evolution itself is a process of morphological computation on struc-
tures of organisms over a long time scale. It will be instructive within the
info-computational framework to study in detail processes of self organiza-
tion of information in an agent (as well as in a population of agents) able to
re-structure themselves through interactions with the environment as a re-
5
sult of morphological (morphogenetic) computation. Kauffman (1993) cor-
rectly identifies the central role of self-organization in the process of evo-
lution and development. The order within a living organism grows by self-
organization, which is lead by basic laws of physics.
As an example of morphological computing, in botany phyllotaxis is the
arrangement of leaves on a plant stem (from ancient Greek phýllon "leaf"
and táxis "arrangement").
“A specific crystalline order, involving the Fibonacci series, had until
now only been observed in plants (phyllotaxis). Here, these patterns are
obtained both in a physics laboratory experiment and in a numerical simu-
lation. They arise from self-organization in an iterative process. They are
selected depending on only one parameter describing the successive ap-
pearance of new elements, and on initial conditions. The ordering is ex-
plained as due to the system’s trend to avoid rational (periodic) organiza-
tion, thus leading to a convergence towards the golden mean.” Douady and
Couder (1992)
Morphological computing is information (re)structuring through computa-
tional processes that follow/implement physical laws. It is physical compu-
ting or natural computing in which physical objects perform computation.
Symbol manipulation, in this case, is physical object manipulation.
Information as a Fabric of Reality
“Information is the difference that makes a difference. “ (Bateson, 1972)
More specifically, Bateson’s difference is the difference in the world that
makes the difference for an agent. Here the world also includes agents
themselves. As an example, take the visual field of a micro-
scope/telescope: A difference that makes a difference for an agent who can
see (visible) light appears when she/he/it detects an object in the visual
field. What is observed presents a difference that makes the difference for
that agent. For another agent who may see only ultra-violet radiation, the
visible part of the spectrum might not bring any difference at all. So the
difference that makes a difference for an agent depends on what the agent
is able to detect or perceive. Nowadays, with the help of scientific instru-
6
ments, we see much more than ever before, which is yet further enhanced
by visualization techniques that can graphically represent any kind of data.
A system of differences that make a difference (information structures that
build information architecture), observed and memorized, represents the
fabric of reality for an agent. Informational Structural Realism (Floridi,
2008) (Sayre, 1976) argues exactly that: information is the fabric of reali-
ty. Reality consists of informational structures organized on different le-
vels of abstraction/resolution. A similar view is defended by (Ladyman et
al. 2007). Dodig Crnkovic (2009) identifies this fabric of reality (Kantian
Ding an sich) as potential information and makes the distinction between it
and actual information for an agent. Potential information for an agent is
all that exists as not yet actualized for an agent, and it becomes informa-
tion through interactions with an agent for whom it makes a difference.
Informational structures of the world constantly change on all levels of or-
ganization, so the knowledge of structures is only half the story. The other
half is the knowledge of processes – information dynamics.
Computation. The Computing Universe: Pancomputationalism
Konrad Zuse was the first to suggest (in 1967) that the physical behavior
of the entire universe is being computed on the basic level, possibly on cel-
lular automata, by the universe itself, which he referred to as "Rechnender
Raum" or Computing Space/Cosmos.
The subsequently developed Naturalist computationalism/ pancomputatio-
nalism (Zuse, 1969) (Fredkin, 1992) (Wolfram, 2002), (Chaitin, 2007), (Lloyd,
2006) takes the universe to be a system that constantly computes its own
next state. Computation is generally defined as information processing, see
(Burgin, 2005)
Info-computationalism
Information and computation are two interrelated and mutually defining
phenomena – there is no computation without information (computation
understood as information processing), and vice versa, there is no informa-
tion without computation (information as a result of computational
processes). (Dodig Crnkovic 2006) Being interconnected, information is
7
studied as a structure, while computation presents a process on an informa-
tional structure. In order to learn about foundations of information, we
must also study computation. In (Dodig-Crnkovic, 2011) the dynamics of
information is defined in general as natural computation.
Information self-structuring (self-organization)
The embodiment of an agent is both the cause and the result of its interac-
tions with the environment. The ability to process and to structure informa-
tion depends fundamentally on the agent’s morphology. This is the case
for all biological agents, from the simplest to the most complex. According
to (Lungarella et al. 2005), “embodied agents that are dynamically coupled
to the environment, actively shape their sensory experience by structuring
sensory data (…).” Because of the morphology which enables dynamic
coupling with the environment, the agent selects environmental informa-
tion which undergoes the process of self-structuring (by organizing the sta-
tistics of sensory input) in the persistent loops connecting sensory and mo-
tor activity. Through repeated processing of typically occurring signals,
agents get adapted to the statistical structure of the environment. In (Lun-
garella & Sporns, 2004) it is argued that:
” in order to simplify neural computations, natural systems are optimized,
at evolutionary, developmental and behavioral time scales, to structure
their sensory input through self-produced coordinated motor activity. Such
regularities in the multimodal sensory data relayed to the brain are criti-
cal for enabling appropriate developmental processes, perceptual catego-
rization, adaptation, and learning.” (Lungarella 2004)
In short, information self-structuring means that agents actively shape their
sensory inputs by interactions with the environment. Lungarella and
Sporns use entropy as a general information-theoretic functional that
measures the average uncertainty (or information) of a variable in order to
quantify the informational structure in sensorimotor data sets. Entropy is
defined as:
  log 
where p(x) is the first order probability density function.
8
Another useful information-theoretical measure is mutual information
(Lungarella & Sporns, 2004). In terms of probability density functions, the
mutual information of two discrete variables, X and Y, is be expressed as:
, , log  / ,
thus measuring the deviation from the statistical dependence of two va-
riables.
In sum, statistical methods are used in order to analyze data self-
structuring, which appears as a result of the dynamical coupling between
the (embodied) agent and the environment. (Lungarella & Sporns, 2004)
Cognition as Restructuring of an Agent in the Interaction with
the Environment
As a result of evolution, increasingly complex living organisms arise that
are able to survive and adapt to their environment. This means that they
are able to register input (data) from the environment, to structure it into
information, and, in more complex organisms, to structure information into
knowledge. The evolutionary advantage of using structured, component-
based approaches such as data – information – knowledge is the improved
response-time and the efficiency of cognitive processes of an organism.
All cognition is embodied cognition in all living beings – microorganisms
as well as humans. In more complex cognitive agents, knowledge is built
not only as a direct reaction to external input information, but also on in-
ternal intentional information processes governed by choices, dependent
on value systems stored and organized in the agent’s memory as
'implemented' in the agent’s body.
Information and its processing are essential structural and dynamic ele-
ments which characterize the structuring of input data (data information
knowledge) by an interactive computational process going on in the
agent during the adaptive interplay with the environment.
There is a continuum of morphological development from the automaton-
like behaviors of the simplest living structures to the elaborate interplay
9
between body, nervous system and brain, and the environment of most
complex life forms. Cognition thus proceeds through the restructuring of
an agent in its interaction with the environment and this restructuring can
be identified as morphological computing.
Morphogenesis as Computation (Information Processing).
Turing's Reaction-Diffusion Model of Morphogenesis
Morphology (Greek morphê - shape) is a theory of the formative principles
of a structure.
Morphogenesis is a study of the creation of shape during the development
of an organism. It is one of the following four fundamental, interconnected
classes of events in the development: Patterning - the setting up of the po-
sitions of future events across space at different scales; Regulation of tim-
ing - the 'clock' mechanisms and Cell differentiation: changes in a set of
expressed genes (molecular phenotype) of a cell.
Interesting to note is that in 1952 Alan Turing wrote a paper proposing a
chemical model as the basis of the development of biological patterns such
as the spots and stripes on animal skin, (Turing 1952).
“Patterns resulting from the sole interplay between reaction and diffusion
are probably involved in certain stages of morphogenesis in biological
systems, as initially proposed by Alan Turing. Self-organization phenome-
na of this type can only develop in nonlinear systems (i.e. involving posi-
tive and negative feedback loops) maintained far from equilibrium.” (Du-
los et al. 1996)
Turing did not originally claim that the physical system producing patterns
actually performs computation through morphogenesis. Nevertheless, from
the perspective of info-computationalism (Dodig Crnkovic 2009) we can
argue that morphogenesis is a process of morphological computing. Physi-
cal process, even though not 'computational' in the traditional sense,
presents natural (unconventional), physical, morphological computation.
An essential element in this process is the interplay between the informa-
tional structure and the computational process – information self-
structuring (including information integration), both synchronic and diach-
10
ronic, proceeding through different scales of time and space. The process
of computation implements (represents) physical laws which act on infor-
mational structures. Through the process of computation, structures
change their forms.
All of computation on some level of abstraction is morphological compu-
tation – a form-changing/form-generating process.
Info-Computationalism and Morphological Computing are
Models of Computation and not just Metaphors
“Perhaps every science must start with metaphor and end with algebra –
and perhaps without the metaphor there would never have been an alge-
bra.” (Black, 1962) p.242
According to the dictionary definition, metaphor is a figure of speech in
which a term or phrase is applied to represent something else. It uses an
image, story or tangible thing to represent a quality or an idea.
In the case of morphological computing, some might claim that morpho-
logical computing is just a metaphor, or a figure of speech, which would
mean that morphogenesis can metaphorically be described as computing,
for example, while in fact it is something else.
On the other hand, analogy (from Greek 'αναλογία' – 'proportion') is a
cognitive process of transferring information or meaning from one particu-
lar subject to another particular subject, and a linguistic expression corres-
ponding to such a process. An analogy does not make identification, which
is the property of a metaphor. It just establishes similarity of relationships.
If morphological computing were just an analogy, it would establish only a
similarity of some relationships, which is definitely not all it does.
Unlike metaphors and analogies, models are not primarily linguistic con-
structs. They have substantial non-linguistic, interactive spatio-temporal
and visual qualities. Models are cognitive tools often used not only for de-
scription but also for prediction and control and interactive studies of
modeled phenomena. Black (1962) noticed the line of development from
metaphor to computational model:
11
“Models, however, require a greater degree of structural identity, or iso-
morphism, so that assertions made about the secondary domain can yield
insight into the original field of interest, and usually the properties of the
second field are better known than those of their intended field of applica-
tion. Mathematical models are paradigmatic examples for science, and in
physics and engineering, at least, their primary function is conventionally
taken to be the enabling of predictions and the guiding of experimental re-
search. Kant went so far as to identify science with mathematization...”
(Black, 1962) p.242
The process of modeling, designing and creating robots that are more life-
like in their morphological properties, can both advance our understanding
of biological life and improve embodied and embedded cognition and in-
telligence in artificial agents. Morphological computing is a model of
computing, i.e. data/information processing. It is a type of natural (physi-
cal) computing, and as a model it has both important practical and theoret-
ical implications.
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... Computational naturalism (pancomputationalism, naturalist computationalism, computing nature) [46][47][48], see even [3,4], is the view that the entirety of nature is a huge network of computational processes, which, according to physical laws, computes (dynamically develops) its own next state from the current one. Among prominent representatives of this approach are Zuse, Fredkin, Wolfram, Chaitin, and Lloyd, who proposed different varieties of computational naturalism. ...
... Embodiment is central because cognition arises from the interaction of brain, body, and environment [90]. Agents' behavior develops through embodied interaction with the environment, in particular through sensory-motor coordination, when information structure is induced in the sensory data, thus leading to perception, learning, and categorization [48]. Morphological computing has also been applied in soft robotics, self-assembly systems, and molecular robotics, embodied robotics, and more. ...
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The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.
... On the contrary, adopting a different intellectual perspective, which directly attributes cognitive capacities to living bodies and pays preeminent attention to this aspect, physical and biological entities present different kinds of ignorance. For example, from the rich and eclectic info-computational point of view proposed in Dodig-Crnkovic (2013, there is an important difference between nonliving and living bodies. Biological bodies possess cognition on different levels, from the lowest to the highest organization level. ...
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In the first two chapters of this book we have stressed that eco-cognitive computationalism sees computation in context, following some of the main tenets advanced by the recent cognitive science views on embodied, situated, and distributed cognition. We have also described the new attention in computer science devoted to the relevance in computation of the morphological features. It is by further deepening and analyzing the perspective opened by these novel fascinating approach that we see ignorant bodies as domesticated to become useful “mimetic bodies” from a computational point of view.
... Computational naturalism (pancomputationalism, naturalist computationalism, computing nature) [29][30] [31][3] [4] is the view that the entire nature is a huge network of computational processes, which, according to physical laws, computes (dynamically develops) its own next state from the current one. Among prominent. ...
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At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.
... On the contrary, adopting a different intellectual perspective, which directly attributes cognitive capacities to living bodies and pays preeminent attention to this aspect, physical and biological entities present different kinds of ignorance. For example, from the rich and eclectic info-computational point of view proposed in Dodig-Crnkovic (2013, there is an important difference between nonliving and living bodies. Biological bodies possess cognition on different levels, from the lowest to the highest organization level. ...
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Eco-cognitive computationalism considers computation in context, following some of the main tenets advanced by the recent cognitive science views on embodied, situated, and distributed cognition. It is in the framework of this eco-cognitive perspective that we can usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities by the morphological features: ignorant bodies can be domesticated to become useful “mimetic bodies”, that is able to render an intertwined computation simpler, resorting to that “simplexity” of animal embodied cognition, which represents one of the main quality of organic agents. Through eco-cognitive computationalism we can clearly acknowledge that the concept of computation changes, depending on historical and contextual causes, and we can build an epistemological view that illustrates the “emergence” of new kinds of computations, such as the one regarding morphological computation. This new perspective shows how the computational domestication of ignorant entities can originate new unconventional cognitive embodiments. In the last part of the article I will introduce the concept of overcomputationalism, showing that my proposed framework helps us see the related concepts of pancognitivism, paniformationalism, and pancomputationalism in a more naturalized and prudent perspective, avoiding the excess of old-fashioned ontological or metaphysical overstatements.
... Consequently, biological metaphors have served as inspirations for models of amorphous computation, for which cellular computing is a promising implementation technology 162 , especially at scales inaccessible to silicon. Robotics has also drawn inspiration from biological computation, particularly in relation to morphological computing, which takes advantage of the physical properties of computing agents in order to achieve more efficient computations 163 . By using intrinsic physical properties of the computational substrate to "outsource" parts of the computation, increasingly complex computations can be carried out whilst maintaining relatively simplistic control structures 164 . ...
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Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found. Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.
Chapter
Taking advantage of the logical and cognitive studies illustrated in the previous chapters, which emphasize the crucial role played in abductive cognition by the so-called “optimization of eco-cognitive openness and situadedness”, “knowledge in motion”, and the concept of “epistemic irresponsibility”, the present chapter will introduce the concept of overcomputationalism, to help interpret the related concepts of pancognitivism, paninformationalism, and pancomputationalism and their impact on discoverability. In the second part of the chapter I will submit to the attention of the reader a question that in my opinion synthesizes many of the problems described in this book: will the future of eco-cognitive settings computationally-tailored or humanly-tailored? The challenges against human abduction and epistemic rigor on the part of what I call computational invasive “subcultures” and unwelcome effects of selective ignorance are illustrated.
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This paper presents the properties of ontological information. We claim that ontological information is characterized by epistemic neutrality (EN), physical embodiment (PE), and formative nature (FN). We also have formulated two corollaries for ontological information: (C1) information is quantifiable, and (C2) changes in the organization of physical objects are denoted as computation or information processing.
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We have already delineated some basic aspects of the so-called eco-cognitive computationalism, for example the fact that computation is always seen in context, exploiting the ideas developed in those projects that have originated the recent views on embodied, situated, and distributed cognition. As illustrated in the previous chapter Turing’s original intellectual perspective has already clearly depicted the evolutionary emergence in humans of information, meaning, and of the first rudimentary forms of cognition, as the result of a complex interplay and simultaneous coevolution, in time, of the states of brain/mind, body, and external environment.
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A transdisciplinary theory of cognition and communication based on the process self-organizing and autopoietic system theory of Niklas Luhmann integrated with a triadic semiotic paradigm of experience and interpretation with phenomenological and hermeneutical aspects of C.S. Peirce, goes beyond info-computationalism in its integrating of phenomenological and hermeneutical aspects of Peircean semiotic logic with a cybernetic and autopoietic systemic emergentist process view. This makes the emergence of mind and transdisciplinary view of sciences possible.
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The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.
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Within the framework of info-computationalism, morphological computation is described as fundamental principle for all natural computation (information processing).
Book
Stuart Kauffman here presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt remain poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biological science itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences.
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This book argues that the only kind of metaphysics that can contribute to objective knowledge is one based specifically on contemporary science as it really is, and not on philosophers' a priori intuitions, common sense, or simplifications of science. In addition to showing how recent metaphysics has drifted away from connection with all other serious scholarly inquiry as a result of not heeding this restriction, this book demonstrates how to build a metaphysics compatible with current fundamental physics ("ontic structural realism"), which, when combined with metaphysics of the special sciences ("rainforest realism"), can be used to unify physics with the other sciences without reducing these sciences to physics itself. Taking science metaphysically seriously, this book argues, means that metaphysicians must abandon the picture of the world as composed of self-subsistent individual objects, and the paradigm of causation as the collision of such objects. The text assesses the role of information theory and complex systems theory in attempts to explain the relationship between the special sciences and physics, treading a middle road between the grand synthesis of thermodynamics and information, and eliminativism about information. The consequences of the books' metaphysical theory for central issues in the philosophy of science are explored, including the implications for the realism versus empiricism debate, the role of causation in scientific explanations, the nature of causation and laws, the status of abstract and virtual objects, and the objective reality of natural kinds. © James Ladyman, Don Ross, David Spurrett, and John Collier 2007. All rights reserved.
Article
It is suggested that a system of chemical substances, called morphogens, reacting together and diffusing through a tissue, is adequate to account for the main phenomena of morphogenesis. Such a system, although it may originally be quite homogeneous, may later develop a pattern or structure due to an instability of the homogeneous equilibrium, which is triggered off by random disturbances. Such reaction-diffusion systems are considered in some detail in the case of an isolated ring of cells, a mathematically convenient, though biologically unusual system. The investigation is chiefly concerned with the onset of instability. It is found that there are six essentially different forms which this may take. In the most interesting form stationary waves appear on the ring. It is suggested that this might account, for instance, for the tentacle patterns on Hydra and for whorled leaves. A system of reactions and diffusion on a sphere is also considered. Such a system appears to account for gastrulation. Another reaction system in two dimensions gives rise to patterns reminiscent of dappling. It is also suggested that stationary waves in two dimensions could account for the phenomena of phyllotaxis. The purpose of this paper is to discuss a possible mechanism by which the genes of a zygote may determine the anatomical structure of the resulting organism. The theory does not make any new hypotheses; it merely suggests that certain well-known physical laws are sufficient to account for many of the facts. The full understanding of the paper requires a good knowledge of mathematics, some biology, and some elementary chemistry. Since readers cannot be expected to be experts in all of these subjects, a number of elementary facts are explained, which can be found in text-books, but whose omission would make the paper difficult reading.
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All approaches to high performance computing is naturally divided into three main directions: development of computational elements and their networks, advancement of computational methods and procedures, and evolution of the computed structures. In the paper the second direction is developed in the context of the theory of super-recursive algorithms. It is demonstrated that such super-recursive algorithms as inductive Turing machines are more adequate for simulating many processes, have much more computing power, and are more efficient than recursive algorithms.