The scientific and
philosophical scope of artificial life
Mark Bedau
Department of Philosophy,
Reed College, Portland OR 97202, USA mab@reed.edu, http://www.reed.edu/~mab
Abstract
The new interdisciplinary science
of artificial life has had a connection with the arts from its inception. This
paper provides an overview of artificial life, reviews its key scientific
challenges, and discusses its philosophical implications. It ends with a few
words about the implications of artificial life for the arts.
Artificial life is a young
interdisciplinary collection of research activities aimed at understanding the
fundamental behavior of life-like systems by synthesizing that behavior in
artificial systems. As befits a journal for artists who use science and
developing technologies, papers discussing artificial life regularly appear in
the pages of Leonardo. There is also traffic in the other direction; for
example, the biennial International Conference on Artificial Life is the
primary vehicle for publishing all the latest scientific developments in
artificial life, but more than five percent of the articles published in the
proceedings of the last conference [1] concerned the application of artificial
life to art and music [2-5]. People in both communities believe that the arts
and artificial life have much to offer each other. Given this, it would useful
for the two communities to know each other better.
The opportunity to counteract
the hype and misleading publicity surrounding artificial life is also welcome.
The truth is often more interesting and surprizing than fiction, and it is
always more valuable.
This paper aims
primarily to provide an overview of artificial life, explaining its approach to
science and technology and outlining its main open problems, and sketching its
broader philosophical implications. It ends with a few words about the
implications of artificial life for the arts.
Overview of
artificial life
Life is an interconnected web of
adaptive systems produced spontaneously by the process of evolution. Living
systems exhibit impressively robust and flexible functionality at many levels
of analysis. Examples range from the genomic and proteomic regulatory systems
that control how biological organisms develop and function, to the evolving
ecological networks through which members of different species interact.
Man-made adaptive systems like the myriad communication networks that span the
globe are beginning to approach the complexity of adaptive systems found in
nature. Learning how to engineer flexible and robust adaptive complexity is one
of the biggest challenges facing society in the twenty-first century.
Traditionally,
adaptive systems of different kinds were modeled independently in different
disciplines. Artificial life is now bringing together biologists, physicists,
chemists, psychologists, economists, and anthropologists with computer
scientists and philosophers to create a unified understanding of adaptive
systems of all types. Artificial life (also known as “ALife”) studies life and life-like processes by
synthesizing them in artificial media, most often using computer technology.
The goals of this activity include modeling and even creating life and
life-like systems; the goals also include developing practical applications
involving new technologies that exploit intuitions and methods taken from
living systems. The phrase “artificial life” was coined by Christopher Langton.
He envisioned a study of life as it could be in any possible setting, and he
organized the first conference that explicitly recognized this field [6]. There
has since been a regular series of conferences on artificial life and a number
of academic journals have been launched to publish work in this new field.
Artificial
life borrows from other, older disciplines, especially computer science,
cybernetics, biology, and the study of complex systems in physics. Its closest
intellectual cousin is artificial intelligence (AI). But there is a crucial
difference between the modeling strategies AI and ALife typically employ. Most
traditional AI models are top-down-specified serial systems involving a
complicated, centralized controller that makes decisions based on access to all
aspects of global state. The controller’s decisions have the potential to
affect directly any aspect of the whole system. On the other hand, many natural
living systems exhibiting complex autonomous behavior are parallel, distributed
networks of relatively simple low-level “agents” that simultaneously interact
with each other. Each agent’s decisions are based on information about only its
own local situation, and its decisions directly affect only its own local
situation. ALife’s models follow nature’s example. The models themselves are
bottom-up- specified parallel systems of simple agents interacting locally. The
local interactions are repeatedly iterated and the resulting global behavior is
observed. The whole system’s behavior is represented only indirectly. It arises
out of the interactions of a collection of directly represented parts.
The synthetic
methodology of artificial life has several virtues. The discipline of
expressing a theory synthetically, especially in computer code, forces
precision and clarity. It also insures that hypothesized mechanisms are
feasible. Computer models also facilitate the level of abstraction required for
maximally general models of phenomena. The bottom-up architecture of artificial
life models creates an additional virtue. Allowing micro-level entities
continually to affect the context of their own behavior introduces a realistic
complexity that is missing from analytically studied mathematical models. Analytically solvable
mathematical models can reveal little about the global effects that emerge from
a web of simultaneous nonlinear interactions. The obvious way to study the
effects of these interactions is to build bottom-up models and then empirically
investigate their emergent global behavior through computer simulations.
Many
artificial life models are designed not to represent known biological systems
but to generate wholly new and extremely simple instances of life-like
phenomena. The simplest example of such a system is the famous cellular
automaton called the “Game of Life”, devised by the mathematician John Conway
in the 1960s [7]. Computer simulation is crucial for the study of complex
adaptive systems. It plays the role that observation and experiment play in
more conventional science. The complex self-organizing behavior of the Game of
Life would never have been discovered without simulating thousands of
generations for millions of sites. The same holds for virtually all other
systems studied by artificial life.
Rather than
merely producing computer simulations, some artificial life research aims to
implement system in the real world. The products of this activity are physical
devices such as robots that exhibit characteristic life-like behavior. Some of
these implementations are motivated by the concern to engineer practical
devices that have some of the useful features of living systems, such as
robustness, flexibility, and autonomy. But some of this activity is primarily
theoretical, motivated by the belief that the best way to confront the hard questions
about how life occurs in the physical world is to study real physical systems.
Examples range from evolvable hardware, which attempts to use
biologically-inspired adaptive processes to shape the configuration of micro-
electronic circuitry, to biologically-inspired robotics, such as using
evolutionary algorithms to automate the design of robotic controllers and
swarms of robots communicating locally to achieve some collective goal.
Grand challenges in
artificial life
A good way to understand a scientific
community is to grasp its central aims. The fact that a second generation of
scientists is commencing work in artificial life prompted the organizers of the
last International Conference on Artificial Life to publish list of grand
challenges [8]. Since there is still so much unknown about the emergence and
evolution of living systems, the list emphasizes scientific understanding
rather than applications, and the challenges are unabashedly long term. This
section reviews those challenges.
The
challenges fall into three broad categories: the origin of life, life’s
evolutionary potential, and life’s connection to mind and culture.
A. How does life arise from the
non-living?
1. Generate a molecular
proto-organism in vitro.
2. Achieve the transition to
life in an artificial chemistry in silico.
3. Determine whether
fundamentally novel living organizations can arise from inanimate matter.
4. Simulate a unicellular
organism over its entire lifecycle.
5. Explain how rules and
symbols are generated from physical dynamics in living systems.
B. What are the potentials and
limits of living systems?
6. Determine what is inevitable
in the open-ended evolution of life.
7. Determine minimal conditions
for evolutionary transitions from specific to generic response systems.
8. Create a formal framework
for synthesizing dynamical hierarchies at all scales.
9. Determine the predictability
of evolutionary manipulations of organisms and ecosystems.
10. Develop a theory of
information processing, information flow, and information generation for
evolving systems.
C. How is life related to mind,
machines, and culture?
11.
Demonstrate the emergence of intelligence and mind in an artificial
living system.
12.
Evaluate the influence of machines on the next major evolutionary
transition of life.
13.
Provide a quantitative model of the interplay between cultural and
biological evolution.
14.
Establish ethical principles for artificial life.
Challenges in the third category
are more speculative, and some are interwoven with non-scientific issues. Some
areas in which artificial life plays a significant role, such as robotics and
art, do not appear on the list. In part this is simply a practical expedient to
shorten the list as much as possible. In the rest of this section I will
briefly explain a representative selection of these challenges. More
information about them all can be found in the original source.
The first
challenge involves no less than constructing a novel life form in the
laboratory from scratch. The first targets should be the simplest possible
forms of life—self-reproducing molecular structures that construct and maintain
themselves in a simple environment and evolve. The environment would involve
only simple forms of energy and material, and the goal would be to create an
encapsulated biochemical system that can derive energy from simple chemicals or
light and use information carried in primitive genes. The attempt to create a
proto-organism that self-replicates and evolves, using energy and nutrients
from its environment, illustrates artificial life’s concern with understanding
life by synthesizing it. It also shows that artificial life’s interests are not
just fanciful abstractions. A fundamental understanding of real life in the
real world is a key part of what artificial life hopes to provide.
Few
questions concerning living systems are as fundamental as the spontaneous
generation of life, and the second challenge explores this issue in artificial
chemistries. Artificial chemistries are computer-based model systems comprised
of objects (abstractions of molecules) which are assembled by collisions among
simpler objects according to predefined interaction rules. The chemistry must
be constructive rather than merely descriptive, with rules that determine
arbitrarily complex products from arbitrarily complex collisions.
Furthermore, the chemical
interaction rules should be simple compared with the ultimate products that
they create. This challenge illustrates artificial life’s emphasis on
understanding the amazing spontaneous emergence of structure and hierarchy that
characterizes life. It also shows how artificial life uses abstraction to
capture the essence of such a process.
Life as we
know it encodes information about hierarchically organized spatially localized
individuals in genetic structures. The third challenge involves determining
whether this or any other particular form of organization is necessary for
life. The question is relevant to the search for extra-terrestrial life in the
universe. Examples of fundamentally different organizations include those
without a genetic code, without spatially localized individuals, without
hierarchical organization, without a genotype-phenotype distinction or, indeed,
without any symbolic representation scheme. The debate about what organizations
are “fundamentally different” will clarify our understanding of the nature of
life, and pursuing this challenge will expand our horizons and challenge our
preconceptions about life.
The sixth
challenge concerns life’s contingency. Artificial life is trying to discern the
features common to all evolutionary processes, or to broad classes of
evolutionary processes. It aims to determine whether different kinds of evolutionary processes have
different potential creativity. Artificial life expects that many of the most
fundamental features of the evolution of life on Earth are independent of the
physical media that happen to embody the process. Digital information
processing in computers is a very different medium from molecular biology, yet
artificial life has been building digital organisms based on genetic and
cellular principles from its inception. Digital media provide considerable scope
to vary the “physics” underlying the evolutionary process, so it is
straightforward to investigate evolutionary contingency in that context. But we
do not know whether digital systems and physical systems have the same
potential for evolutionary innovation. Artificial life’s commitment to a
synthetic methodology shows itself here. Not content with mere verbal
speculation about kinds of evolutionary creativity, artificial life insists on
making systems that actually demonstrate those capacities.
The forms of
life that we know all have a complex organization that enables them to act
autonomously and in their own interests. Organisms can be transgenically
manipulated to express different genes, but the evolutionary consequences and
limits of such manipulations are unknown. This raises artificial life’s ninth
challenge: determining how well we can predict the evolutionary consequences of
making new forms of life. To what extent can one redesign organisms to fulfill
novel functions without disrupting their viability? Is there a tradeoff between
size of modification and viability? Better understanding of the genetics of
development will enable us to create novel multicellular organisms, but they
might not flourish or they might unleash unanticipated and uncontrollable
ecological consequences. Perhaps major changes to organisms can be perfected
only by lengthy coevolutionary optimization. Along with genetic engineering,
artificial life must confront questions like these since it unleashes novel
autonomous beings with lives of their own. Furthermore, artificial life is
ideally poised to address such questions, since it can synthesize all kinds of
genetic manipulation in isolated digital contexts.
Once life
originated, biological evolution underwent a number of major evolutionary
transitions, such as the origin of eukaryotes, the origin of multicellular
life, and the origin of culture. Presumably there will be more major
transitions in the future. Once culture originates, it has the capacity to
evolve on its own. The past century has seen the explosion of technological
culture including the creation of computing machines and complex distributed
networks connecting them. Many agree it is only a matter of time before
artificial life creates machines that are alive, intelligent, reproduce their
own kind, have their own purposes, set their own goals, and evolve
autonomously. These machines will be part of our world and their evolution will
affect our future. Think of how machines currently influence the nature and
rate of human communication and interconnection. All this suggests that
machines might play an unprecedented role in the next major evolutionary
transition. Artificial life’s twelfth challenge is to predict and explain this
role. Machines will certainly play at least a supporting role in the next major
evolutionary transition since they provide an infrastructure that influences
the rate and direction of change. They might even be central players, if
autonomously evolving machines have proliferated. This will again push the
boundaries of what it means to be alive and embody new forms of the unbounded
creativity of evolution.
Culture is one of
the products of human existence, and culture itself evolves. Artificial life’s
thirteenth challenge is to understand the connection between biological and
cultural evolution. Examples of cultural evolution include the development of
economic markets, the changes in technological infrastructure (see the previous
challenge), and growth and revolution in scientific opinion. Some treatments of
cultural evolution (e.g., sociobiology and evolutionary psychology) consider
how cultural traits evolve due to their impact on biological fitness. But one
can also consider how cultural traits evolve in their own right, as Dawkins did
when he coined the word “meme” [9]. This sort of “pure” cultural evolution is
driven by mechanisms similar to those behind biological evolution, but there
are important differences. In each case traits exhibit variation, heritability,
and differential fitness, but cultural traits are transmitted not genetically
but psychologically, and their fitness concerns not biological survival and
reproduction but retention in and proliferation across minds. One question
concerns the similarities and differences in the behavior of biological and
cultural evolution. Do both exhibit the same kind of creative explosions, and
for similar reasons? Another question concerns how they are interconnected.
Confronting these questions invites us to reconceptualize life, culture, and technology.
Artificial life gives us an increasingly constructive role in our future. Even
if we do not try to shape our future to fit our current preconceptions of what
is possible, artificial life can help us to understand and appreciate the
open-ended creative process in which we are all embedded.
Artificial life is not just a
scientific and engineering enterprise. It offers a new perspective on the
essential nature of many fundamental aspects of reality like life, adaptation,
and creation. Thus is has rich implications for a number of broad philosophical
issues. In fact, philosophy and artificial life are natural intellectual
partners, for a variety of reasons. Both seek to understand phenomena at a
level of generality that is sufficiently deep to ignore contingencies and
reveal essential natures. In addition, by creating wholly new kinds of
life-like phenomena, artificial life continually forces us to reexamine what it
is to be alive, intelligent, creative, etc. Furthermore, artificial life’s
computational methodology is a direct and natural extension of philosophy’s
traditional methodology of a priori thought
experiment. In the attempt to capture the simple essence of vital processes,
artificial life models abstract away as many details of living systems as
possible. These models are thought experiments that are explored with the help
of a computer. Like the traditional armchair thought experiments, artificial
life simulations attempt to answer “What if X?” questions, but the premises
they pose are complicated enough that their implications can be explored only
by computer simulation; armchair analysis is simply inconclusive. Synthesizing
thought experiments on a computer brings a new kind of clarity and constructive
evidence to philosophy. In this section I illustrate artificial life’s broad
implications for a handful of philosophical topics: emergence, evolution, life,
and mind.
Emergence.
One of life’s amazing
features is how the whole is more than the sum of the parts. This is called
emergence. In general, emergent phenomena share two broad hallmarks: they both
depend on and are autonomous from underlying phenomena. Although apparent
emergent phenomena are all around us, the two hallmarks of emergence
seem inconsistent or philosophically illegitimate. How can something be
autonomous from underlying phenomena if it depends on them? This is the
traditional philosophical problem of emergence. A solution to this problem
would both dissolve the appearance of illegitimate metaphysics and show how
emergence plays a constructive in scientific explanations of phenomena
involving life and mind.
The aggregate
global behavior of complex systems studied in artificial life offers a new way
to view of emergence. On this view, a system’s macrostate is emergent just in
case it can be derived from the system’s boundary conditions and its
micro-level dynamical process but only through the process of iterating and
aggregating all the micro-level effects [10]. This new view explains the two
hallmarks of emergence. Micro-level phenomena clearly depend on macro-level
phenomena; think of how a bottom-up artificial life model works. At the same
time, macro-level phenomena are autonomous because the micro-level interactions
in the bottom-up models produce such complex macro-level effects that the only
way to recognize or predict them is by observing macro-level behavior. This
form of emergence is common in complex systems found in nature, and artificial
life’s models also exhibit it. This view attributes the unpredictability and
unexplainability of emergent phenomena to the complex consequences of myriad,
non-linear and context-dependent local micro-level interactions. Emergent
phenomena can have causal powers on this view, but only by means of aggregating
micro-level causal powers. There is nothing inconsistent or metaphysically
illegitimate about underlying processes constituting and generating phenomena
by iteration and aggregation. Furthermore, this form of
emergence is prominent in scientific accounts of exactly the natural phenomena
like life and mind that apparently involve emergence.
This shows
how artificial life will play an active role in future philosophical debates
about emergence and related notions like explanation, reduction, complexity,
and hierarchy. Living systems are one of the primary sources of emergent
phenomena, and artificial life’s bottom-up models generate impressive
macro-level phenomena wholly out of micro-level interactions.
Artificial life expands our
sense of what is possible, and it provides a constructive way to explore it.
Evolution.
The evolution of life has shown
a remarkable growth in complexity. Simple prokaryotic one-celled life lead to
more complex eukaryotic single-celled life, which then lead to multicellular
life, then to large-bodied vertebrate creatures with sophisticated sensory
processing capacities, and ultimately to highly intelligent creatures that use
language and develop sophisticated technology. This illustration of evolution’s
creative potential leads to a deep question about evolution’s creative
potential: Does evolution have an inherent tendency to create greater and
greater adaptive complexity, or is the complexity of life just a contingent and
accidental by-product of evolution?
Stephen Jay
Gould [11] devised a clever way to address this issue: the thought experiment
of replaying the tape of life. Imagine that the process of evolution were
recorded on a tape. The thought experiment is to rewind the evolutionary
process backward in time, erasing the tape, and then playing it forward again
but allowing it to be shaped by wholly different contingencies. It is not clear
what the outcome of the thought experiment is. Gould himself suggests that “any replay of the
tape would lead evolution down a pathway radically different from the road
actually taken.” He concludes that the contingency of evolution destroys any
possibility of a necessary growth in adaptive complexity. Daniel Dennett [12]
draws exactly the opposite conclusion. He argues that complex features like
sophisticated sensory processing provide such a distinct adaptive advantage
that natural selection will almost inevitably discover it in one form or
another. Dennett concludes that replaying life’s tape will almost inevitably
produce highly intelligent creatures that use language and develop
sophisticated technology.
Artificial life can make a number of contributions to this debate. Experience in artificial life has
shown time and again that armchair expectations about the outcome of thought
experiments like replaying life’s tape are highly fallible. The only sure way
to know what to expect is to create the relevant system and observe the results
of repeated simulation. In fact, artificial life is exactly where the activity
of creating and studying such systems occurs.However, we cannot yet conduct
the experiment of replaying life’s tape because no one has been able to create
a system that exhibits continual open-ended evolution. Achieving this goal is a
key open problem in artificial life, related to its sixth grand challenge. All
conjectures about evolution’s inherent creativity will remain unsettled until
we actually study what happens when the tape of life is replayed.
Life.
Philosophers from Aristotle to
Kant have addressed the nature of life. But philosophers today ignore the
issue, perhaps because it seems too scientific.
At the same time, most biologists also ignore the issue, perhaps because
it seems too philosophical. The advent of artificial life has revitalized the
question. This is partly because one can simulate or synthesize living systems
only if one has some idea what life is. Artificial life’s self-conscious aim to
discern the essence of life encourages liberal experimentation with novel
life-like organizations and processes. Thus, artificial life both fosters a
broad perspective on life. In the final analysis, the question of the nature of
life will be settled by whatever perspective provides the best explanation of
the rich range of natural phenomena that living systems exhibit. Better
understanding of how to explain these phenomena will also help resolve a
cluster of puzzles about life, such as whether life admits of degrees, how the
notion of life applies at different levels in the biological hierarchy, and the
relationship between the material embodiment of life and the dynamical
processes in which those materials participate.
Artificial
life highlights the question whether artificial constructions, especially
purely digital systems existing in computers, could ever literally be alive.
This question will be easier to answer once there is agreement about the nature
of life; but that agreement should not be expected until we have experienced a
much broader range of possibilities. So the debate over whether real but
artificial life is possible continues. Some people complain that it is a simple
category mistake to confuse a computer simulation of life with a real instance
of it [13]. A flight simulation for an airplane, no matter how detailed and
realistic, does not really fly. A simulation of a hurricane does not create
real rain driven by real gale-force winds. Similarly, a computer simulation of
a living system produces merely a symbolic representation of the living system.
The intrinsic ontological status of this symbolic representation is nothing
more than certain electronic states inside the computer (e.g., patterns of high
and low voltages). This
constellation of electronic states is no more alive than is a series of English
sentences describing an organism. It seems alive only when it is given an
appropriate interpretation. But this charge of category mistake can be blunted.
Artificial life systems are
typically not simulations or models of any familiar living system but new
digital worlds. Conway’s Game of Life, for example, is not a simulation or
model of any real biochemical system but a digital universe that exhibits
spontaneous macroscopic self-organization. So, when the Game of Life is actually
running in a computer, the world contains a new physical instance of
self-organization. Processes like self-organization and evolution are multiply
realizable and can be embodied in a wide variety of different media, including
the physical media of suitably programmed computers. So, to the extent that the
essential properties of living systems involve processes like self-organization
and evolution, suitably programmed computers will actually be novel
realizations of life.
Mind.
Life forms are sensitive to the
environment in various ways, and this environmental sensitivity affects their
behavior in various ways. So forms of life have broadly mental capacities.
Furthermore, the sophistication of these mental capacities seems to correspond
to the complexity of those forms of life. So it is natural to ask whether life
and mind have some deep connection. Since all forms of life must cope in one
way or another with a complex, dynamic, and unpredictable world, perhaps this
adaptive flexibility inseparably connects life and mind.
It is well
known in the philosophy of mind and artificial intelligence that the emergent
dynamical patterns among human mental states are especially difficult to
describe and explain. Descriptions of these patterns must be qualified by
“ceteris paribus” clauses, as the following example illustrates: If someone
wants a goal and believes that performing a certain action is a means to that
goal, then ceteris paribus they will
perform that action. For example, if
someone wants a beer and believes that there is one in the kitchen, then he
will go get one—unless, as the “ceteris paribus” clause signals, he does not
want to miss any of the conversation, or he does not want to offend his guest
by leaving in midsentence, or he does not want to drink beer in front of his
mother-in-law, or he thinks he had better flee the house since it is on fire,
etc. This pattern exhibits a special property which I will call “suppleness”.
Suppleness is involved in a distinctive kind of exceptions to the patterns in
our mental lives—specifically, those exceptions that reflect our ability to act appropriately in the face
of an open- ended range of contextual contingencies. These exceptions to the
norm occur when we make appropriate adjustment
to contingencies. The ability to adjust our behavior appropriately in context
is a central component of the capacity for intelligent behavior.
A promising
strategy for explaining mental suppleness is to follow the lead set by
artificial life [14]. For there is a similar suppleness in vital processes such
as metabolism, adaptation, and even flocking. For example, a flock maintains
its cohesion not always but only for the most part, only ceteris paribus, for the cohesion can be broken when the flock
flies into an obstacle (like a tree). In such a context, the best way to
“preserve” the flock might be for the flock to divide into subflocks.
Artificial life models of flocking exhibit just this sort of supple flocking
behavior. Or consider another example concerning the process of adaptation
itself. Successful adaptation depends on
the ability to explore anappropriate number of viable
evolutionary alternatives; too many or too few can make adaptation difficult or
even impossible. In other words, success requires striking a balance between
the competing demands for “creativity” (trying new alternatives) and “memory”
(retaining what has proved successful). Furthermore, as the context for
evolution changes, the appropriate balance between creativity and memory can
shift in a way that resists precise and exceptionless formulation.
Nevertheless, artificial life models can show a supple flexibility in how they
balance creativity and novelty.
Implications for the arts
Artificial life’s central aim is
to develop a coherent theory of life in all its manifestations. It embraces the
possibility of discovering life in unfamiliar settings and creating unfamiliar
forms of life. In the long run artificial life will contribute to the
development of practical adaptive systems in many fields of application, such as
software development and management, design and manufacture of robots including
distributed swarms of autonomous agents, automated trading in financial
markets, pharmaceutical design, ecological sustainability, and extraterrestrial
exploration. The economic potential of harnessing natural adaptive systems can
be compared with cracking the genetic code. Natural adaptive systems vastly
exceed the complexity of anything humans have yet created. Understanding and
harnessing life’s adaptive creativity will spawn a wealth of new technologies
and entrepreneurial opportunities.
Artificial
life also has aesthetic applications. There are at least three ways in which
artists might find artificial life useful. First, artificial life technology
can be used for a variety of aesthetic purposes. They range from commercial
applications in computer animations of life forms to new kinds of active art,
evolving art, and interactive art (e.g., [3, 4, 15, 16]). Second, artificial
life is radically changing human culture and technology, and art often responds
to and comments on such changes (e.g., [17, 18]). Third, art has a long
tradition of representing and responding to our understanding of nature, so new
insights about life revealed by artificial life can spark new aesthetic objects
(e.g., [19, 20, 21]).
Just as
artificial life can be beneficial for artists, artists can provide
complementary benefits to artificial life. For one thing, artists that use
artificial life techniques and insights can be counted among the consumers of the
product that artificial life produces, and one spur to producing better
products is consumer demand. Scientists can also gain a broader perspective on
their own scientific activity when artists explore the implications of the
science and subject it to commentary and social criticism. Finally, human
aesthetic activity is itself one distinctive manifestation of the creative
potential contained within life. It would behoove those who want to understand
nature’s creative potential to keep an eye on the latest aesthetic
developments.
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Copied from http://people.reed.edu/~mab/publications/papers/leonardo.pdf
on 03.29.17
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