Breeding
the Evolutionary:
Interactive Emergence in Art and Education
By Dan Collins
This
paper first given at the 4th Annual Digital Arts Symposium: Neural Net{work}, University of Arizona,
Tucson, AZ April 11 – 12, 2002
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Learning is not a
process of accumulation of representations of the environment; it is a
continuous process of transformation of behavior…
--Humberto Maturana (1980)[1]
Art is not the
most precious manifestation of life.
Art has not the celestial and universal value that people like to
attribute to it. Life is far more
interesting.
--Tristan Tzara,
“Lecture on Dada” (1922)[2]
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INTRODUCTION
In August 2000, researchers at
Brandeis University made headlines when they announced the development of a
computerized system that would automatically generate a set of tiny robots—very
nearly without human intervention.
“Robots Beget More Robots?,” asked the New York Times somewhat
skeptically on its front page. Dubbed the Golem project (Genetically Organized Lifelike
Electro Mechanics) by its creators, this was the first time that robots had
been designed by a computer and robotically fabricated. While machines making machines is
interesting in and of itself, the project went one step further: the robot offspring were “bred” for
particular tasks. Computer scientists Jordan Pollack
and colleague Hod Lipson had developed a set
of artificial life algorithms—evolutionary instruction sets—that allowed them
to “evolve” a collection of “physical locomoting machines” capable of goal
oriented behavior.[3]
The Golem project is just one
example of a whole new category of computer-based, creative research that seeks
to mimic--somewhat ironically given its dependence on machines--the
evolutionary processes normally associated with the natural world. Shunning fixed conditions and idealized
final states, the research is characterized by an interest in constant
evolutionary change, emergent behaviors, and a more fluid and active
involvement on the part of the user.
The research suggests a new and
evolving role for artists and designers working in computer aided design
process and interactive media, as well as an expanded definition of the
user/audience. Instead of exerting
total control over the process and product where “choices” are made with
respect to every aspect of the creative process, the task of the
artist/researcher becomes one of interacting effectively with machine-based
systems in ways that supercharge the investigative process. Collaboration is
encouraged: projects are often structured enabling others—fellow artists,
researchers, audience members—to interact with the work and further the
dialogue. While the processes often
depend on relatively simple sets of rules, the “product” of this work is
complex, open-ended, and subject to change depending on the user(s), the data,
and the context.
A healthy mix of interdisciplinary
research investigating principles of interaction, computational evolution, and
so-called emergent behaviors inform and deepen the work. Artists are finding new partners in fields
as diverse as educational technology, computer science, and biology. There are significant implications for the
way we talk about the artistic process and the ways we teach art.
As an introduction to this new
territory, I will trace some of the conceptual and historical highlights of
“evolutionary computing” and “emergent behavior.” I will then review some of
the artists, designers, and research scientists who are currently working in
the field of evolutionary art and design. Finally, I will consider some of the
pedagogical implications of emergent art for our teaching practices in the
arts.
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Most natural and living systems are both productive and
adaptive. They produce new material
(e.g, blood cells, tissue, bone mass) even while adapting to a constantly
changing environment. While natural and
living systems are "productive" in the sense of creating new
"information," human-made machines that can respond with anything
more than predictable binary "yes/no" responses are a relatively
recent phenomenon. To paraphrase media artist Jim Campbell, most machines are
simply "reactive," not interactive.[4] To move beyond reactive mechanisms,
the system needs to be smart enough to produce output that is patently new—that
is, not already part of the system
"Intelligent" machines,
being developed with the aid of "neural networks" and "artificial
intelligence," can actual learn new behaviors and evolve their responses
based upon user input and environmental cues. Over time, certain properties
begin to "emerge" such as self-replication or patterns of
self-organization and control. These so-called "emergent properties"
represent the antithesis of the idea that the world is simply a collection of
facts waiting for adequate representation. The ideal system is a generative
engine that is simultaneously a producer and a product.
In Steven Johnson’s recent book, Emergence,
he offers the following explanation of emergent systems:
In
the simplest terms (emergent systems) solve problems by drawing on masses of
relatively stupid elements, rather than a single, intelligent “executive
branch.” They are bottom-up systems,
not top-down. They get their smarts
from below. In a more technical
language, they are complex adaptive systems that display emergent behavior. In these systems, agents residing on one
scale start producing behavior that lies one scale above them: ants create colonies; urbanites create
neighborhoods; simple pattern-recognition software learns how to recommend new
books. The movement from low-level
rules to higher-level sophistication is what we call emergence.[5]
In terms of the hard science underlying the concept of
emergence, it should be said that many approaches to evolutionary design,
artificial life, adaptive systems, evolutionary computation, share similar
characteristics but have distinct operating principles that are beyond the
scope of this essay to review. For the
purposes of this essay, the word “emergent” will refer to both the behaviors
of emergent systems and the metamorphosed families of objects created through
the use of evolutionary, emergent, or “artificial-life” principles.
Emergent behavior is the name given to the observed function of an
entity, and is, generally speaking, unrelated to the underlying form or
structure. These behaviors exist because of the nature of the interaction
between the parts and their environment, and cannot be determined through an
analytical reductionist approach.[6]
Novel forms generated using genetic algorithms exhibit “emergent
properties” as well. Instead of laying
the stress on the function of the object, an “artificial-life” (“a-life”)
designer will often focus on the form or changing morphology of discrete,
synthetically derived entities. To a
certain extent, early “evolutionary art” emphasized the fantastic power of
these techniques for unpredictable form generation. Later interactive and emergent artworks
have focused more on function—i.e., emergent behavior.
For evolution to occur, the system must be capable of
reproduction, inheritance, variation, and selection. Further, unlike natural
evolution, artificial evolution using evolutionary algorithms (EAs) also
require three other important features:
intitalisation, evaluation, and termination. EAs are “given a head start” by initialising (or seeding)
them with solutions that have fixed structures, behaviors, or meanings, but random
values (often generated by pseudo random number generators). Evaluation in EAs is responsible for
guiding evolution towards better solutions.
Finally, explicit termination criteria are built into the program
to halt evolution, typically when a “best fit” or acceptable solution is
achieved, or when a predetermined number of interations have been tested.[7]
Irrespective of its
particular manifestation, the key concept—the thing that makes a phenomenon
“emergent” —is that forms, self-sustaining behaviors, and meanings can evolve
over time through a simple set of rules to produce completely non-predictable
results.
History of Emergence as a Concept
The historical foundations of the
concept of “emergence” can be found in the work of John Stewart Mill in the 19th
century who, in A System of Logic (1843), argued that a combined effect
of several causes cannot be reduced to its component causes. Early 20th century proponents of
“emergent evolution” developed a notion of emergence that echoes Mill’s idea: elements interact to form a complex whole
which cannot be understood in terms of the elements; the whole has emergent
properties that are irreducible to the properties of the elements.[8]
The first real statement of the possibility of linking machines to
evolutionary principles was developed as a thought experiment by the
mathematician John Von Neumann in the late 1940s who conceived of
non-biological, kinematic self-reproducing machines.[9]
At first Von Neumann investigated mechanical devices floating in a
pond of parts that would use the parts to assemble copies of themselves. Later
he turned to a purely theoretical model. In discussions with Polish
mathematician Stanislaw Ulam the idea of cellular automata
was born. Just before his death in 1957, von Neumann designed a
two-dimensional, 27-state cellular automaton—a self replicating machine--which
carried code for constructing a copy of itself. His unpublished notes were
edited by Arthur Burks who published them in 1966 as a book, The Theory of
Self-Reproducing Automata.[10]
Other influential thinkers in the
history of emergent systems and evolutionary art include Richard Dawkins who
inadvertently founded the field of evolutionary art in 1985 when he wrote his
now famous Blind Watchmaker algorithm[11]
to illustrate the design power of Darwinian evolution. The algorithm demonstrates very effectively
how random mutation followed by non-random selection can lead to complex forms. These forms, called “biomorphs,” are visual
representations of a set of “genes.”
Each biomorph in the Blind Watchmaker applet has the following 15
genes:
- genes 1-8 control the
overall shape of the biomorph,
- gene 9 the depth of
recursion,
- genes 10-12 the color of
the biomorph,
- gene 13 the number of
segmentations,
- gene 14 the size of the
separation of the segments,
- gene 15 the shape used
to draw the biomorph (line, oval, rectangle, etc).
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The science of emergent behavior is closely related to the work
done using machines to model evolutionary principles in the arts. Among the
first “evolutionary artists” to understand the significance of Richard Dawkins’
work is the British sculptor, William Latham.
Initially, Latham focused on the form generation capabilities of
evolutionary systems.
Trained as a Fine Artist,
Latham’s early drawings explored the growth and mutation of images of organic-looking
shapes. In the mid 80s, he became an
IBM Fellow and began using computers to develop mutating images. This idea was
applied to other areas like architecture and financial forecasting, where
interesting mutations of scenarios could be selected and bred, with the user
acting like a plant breeder. Between
the years 1987 – 1994 at IBM, Latham established his characteristic artistic
style and began working with IBM mathematician Steven Todd. IBM’s sponsorship
of this ground breaking work led to Todd developing the “Form Grow Geometry
System” (aka FormGrow) which was designed to create the bizarre organic forms
for which the artist is known.[12]
In 1992, Latham published a book, Evolutionary Art And
Computers (with Stephen Todd)[13],
documenting the development of his extraordinary form of art. Latham set up programs on the basis
of aesthetic choices in such a way that the parameters of style and content in
the images are established but the final form is not predetermined. Much of the
time, a fixed 'final form' may never materialize. Random mutation allows the artist to
navigate through the space of the infinitely varied forms that are inherent in
his program. His early FormGrow program provided rules through which the
'life-forms' are subject to the processes of 'natural selection.' The results
of such "Darwinian evolution driven by human aesthetics" are
fantastic organisms whose morphologies metamorphose in a sequence of animated
images.[14]
In the last few years, Latham has moved beyond the creation of images
into the world of interactive gaming. One of his newest applications is a
computer game called Evolva. Released in early 2000,
Evolva enacts the process of evolution -- but it is the game warriors
themselves who evolve. Picture this scenario:
sometime in the future, the human race has mastered the art of genetic
engineering and created the ultimate Darwinian warrior -- the Genohunter. A
Genohunter kills an enemy, analyses its DNA, and then mutates, incorporating
any useful attributes—strength, speed, bionic weapons—possessed by the victim.[15]
In the area of robotics, the artist David Rokeby saw the potential
of emergent properties to mitigate the “closed determinism” of some interactive
robotic artwork several years ago. In
1996 he wrote an essay which referenced the work of robot artist, NormanWhite.
One of White's robots, Facing Out, Laying Low, interacts with its
audience and environment, but, if bored or over-stimulated, it will become
deliberately anti-social and stop interacting.
Rokeby writes:
This kind of behaviour may seem counter-productive, and
frustrating for the audience. But for White, the creation of these robots is a
quest for self-understanding. He balances self-analysis with creation,
attempting to produce autonomous creatures that mirror the kinds of behaviours
that he sees in himself. These behaviours are not necessarily willfully
programmed; they often emerge as the synergistic result of experiments with the
interactions between simple algorithmic behaviours. Just as billions of simple
water molecules work together to produce the complex behaviours of water (from
snow-flakes to fluid dynamics), combinations of simple programmed operations
can produce complex characteristics, which are called emergent properties, or
self-organizing phenomena.[16]
Interactive
Emergence
Educational technologist
Ellen Wagner defines interaction as "… reciprocal events that require at
least two objects and two actions. Interactions occur when these objects and
events mutually influence one another."[17]
High levels of “interactivity” are achieved in
human/human and human/machine couplings that enable reciprocal and mutually
transforming activity.
Interactivity—particularly the type that harnesses emergent forms of
behavior—requires that both parties—human users or machines—be engaged in
open-ended cycles of productive feedback and exchange. Beyond simply providing an on/off switch or
a menu of options leading to “canned” content, users should be able to interact
intuitively with a system in ways that produce new information. Interacting with a system that produces
emergent phenomena is what I am calling “interactive emergence.”
Concrete examples from art and technology research illustrate how
different individuals, groups, and communities are engaging in interactive
emergence—from the locally controlled parameters characteristic of the video
game and the LAN bash, to large scale interactions involving distributed collaborative
networks over the Internet. Artists and
Scientists such as Eric Zimmerman (game designer, theorist, and artist); John
Klima (artist and webgame designer); Hod Lipson and Jordan B.
Pollack (The Golem Project), Pablo Funes (computer scientist and
EvoCAD inventor); Christa Sommerer, and Laurent Mignonneau (Interactive
systems); Ken Rinaldo (Artificial Life); Yves Amu Klein (Living Sculpture); are
doing pioneering work in an area that could be called “evolutionary art and
design”. Other artists approaching
using evolutionary and emergent principles in their work include Jeffrey Ventrella (Gene Pool), The Emergent Art Lab, David Rokeby / very nervous systems, Thomas Ray, Jon McCormack, Bill
Vorn and Louis-Phillipe Demers, Simon Penny,
Erwin
Driessens and Maria Verstappen, Steven
Rooke, Nik
Gaffney, Troy
Innocent, and Ulrike Gabriel.
What differentiates the work of these artists
from more traditional practices? What
educational background, perceptual skills, and conceptual orientations are
required of the artist—and of the viewer/participant? What systems, groups, or individuals are acknowledged and
empowered by these new works?
Creating
an experience for a participant in an interactive emergent artwork must take
into account that interactions are, by definition, not "one-way"
propositions. Interaction depends on feedback loops[18]that
include not just the messages that preceded them, but also the manner in which
previous messages were reactive. When a fully interactive level is reached,
communication roles are interchangeable, and information flows across and
through intersecting fields of experience that are mutually supportive and
reciprocal. The level of success at which a given interactive system attains
optimal levels of reciprocity could offer a standard by which to critique interactive
artwork in general. Interactive
emergence could be gauged by the degree to which the parties achieved optimal
reciprocity (sex is an appropriate analogy here) along with the degree to which
the system was “productive” of new content, not predicted by the contents of
its memory.
Many
artists have developed unique attempts at interactive emergence exploring
different forms of display, user control processes, navigation actions, and
system responses. Different works have varying levels of audience participation,
different ratios of local to remote interaction, and different levels of
emergent phenomemona. Moreover, the range of artistic attempts at interactive
emergence provide us with a variety of approaches that could be used for
diverse kinds of learners in a variety of educational settings. Understanding
experiments with interactive emergence in an art context may help us to better
understand its use in pedagogical settings.
Many projects in recent years
exhibit various levels of interaction.
But the capability of exhibiting or tracking "emergent
properties" is seen by the author as a future hallmark of high level
interactive systems. With projects that enable these heightened levels of
interactivity, we may begin to see the transformation of the discrete and
instrumental character of “information” into a broad—and unpredictable--
“knowledge base” that honors the contexts and connections essential to global
understanding and exchange.
With these challenges in mind, I
would like to discuss briefly the work of an artist team, an independent
artist, an animator, and a computer scientist who are exploring “emergent
systems” in their research: Christa
Sommerer and Laurent Mignonneau, Ken Rinaldo, Karl Sims, Pablo Funes.
Christa Sommerer and Laurent Mignonneau: Interactive Plant Growing
(1993)
Austrian-born Christa Sommerer and French-born Laurent Mignonneau
teamed up in 1992, and now work at the ATR Media Integration and Communications
Research Laboratories in Kyoto, Japan.
In nearly a decade of collaborative work, Sommerer and Mignonneau have
built a number of unique virtual ecosystems, many with custom viewer/machine
interfaces. Many of their early projects allow audiences to create new plants
or creatures and influence their behavior by drawing on touch screens, sending
e-mail, moving through an installation space, or by touching real plants wired
to a computer.
Artist's
rendering of the installation showing the five pedestals with plants and the
video screen.
Interactive Plant Growing is an example of one such project. The
installation connects actual living plants, which can be touched or approached
by human viewers, to virtual plants that are grown in real-time in the
computer. In a darkened installation space, five different living plants are
placed on 5 wooden columns in front of a high-resolution video projection
screen. The plants themselves are the interface. They are in turn connected to
a computer that sends video signals from its processor to a high-resolution
video data projection system. Because the plants are essentially antennae
hard-wired into the system, they are capable of responding to differences in
the electrical potential of a viewer's body. Touching the plants or moving your
hands around them alters the signals sent through the system. Viewers can
influence and control the virtual growth of more than two dozen computer-based
plants.
Screen shot of the video projection during one interactive
session.
Viewer participation is crucial to the life of the piece. Through their
individual and collective involvement with the plants, visitors decide how the
interactions unfold and how their interactions are translated to the screen.
Viewers can control the size of the virtual plants, rotate them, modify their
appearance, change their colors, and control new positions for the same type of
plant. Interactions between a viewer's body and the living plants determine how
the virtual three-dimensional plants will develop. Five or more people can
interact at the same time with the five real plants in the installation space.
All events depend exclusively on the interactions between viewers and plants.
The artificial growing of computer-based plants is, according to the artists,
an expression of their desire to better understand the transformations and
morphogenesis of certain organisms (Sommerer et al, 1998).
Sommerer and Mignonneau: Verbarium (1999)
In a more recent project the artists have created an interactive
"text-to-form" editor available on the Internet. At their Verbarium
web site, on-line users are invited to type text messages into a small pop up
window. Each of these messages functions as a genetic code for creating a
visual three-dimensional form. An algorithm translates the genetic encoding of
text characters (i.e., letters) into design functions. The system provides a
steady flow of new images that are not pre-defined but develop in real-time
through the interaction of the user with the system. Each different message
creates a different organic form. Depending on the composition of the text, the
forms can either be simple or complex. Taken together, all text images are used
to build a collective and complex three-dimensional image. This image is a
virtual herbarium, comprised of plant forms based on the text messages of the
participants. On-line users help to not only create and develop this virtual
herbarium, but also have the option of clicking on any part of the collective
image to de-code earlier messages sent by other users.
Screen
shot of the Verbarium web page showing the collaborative image created by
visitors to the site.
The
text to form algorithm translated the author’s phrase "purple people
eater" into the image at the upper left. This image
was subsequently collaged into the collective "virtual herbarium."
In the following passage, the artists discuss future goals for the
work:
We anticipate that as more users participate,
increasingly complex image structures will emerge over time…While our prototype
system succeeds in modeling some of the features of complex system, future
updates of the systems should include the modeling of genetic exchange of
information (text characters) between forms, creating offspring forms through standard
genetic crossover and mutation operations as we have used them in the past. The
potential benefit of such an extended system will be the expansion of
diversity, the reaction to neighbors and to external control, exploration of
their options, and replication, basically the remaining features that are
commonly associated with complex adaptive systems, as described by Crutchfield.
Another further update of the system should also include the capacity to
simultaneously display all messages in the browser's window; this should make
it possible for users to retrieve all messages ever sent and to follow the
whole evolution of interaction history.[19]
Ken Rinaldo: Autopoiesis (2000)
Overview
of all fifteen robotic arms of the Autopoiesis installation.
Photo credit: Yehia Eweis.
A work by American artist Ken Rinaldo was recently exhibited in
Finland as part of "Outoaly, the Alien Intelligence Exhibition 2000,"
curated by media theorist Erkki Huhtamo.[20] Rinaldo, who has a background in both
computer science and art, is pursuing projects influenced by current theories
on living systems and artificial life. He is seeking what he calls an
"integration of organic and electro-mechanical elements" that point
to a "co-evolution between living and evolving technological
material."
Rinaldo's contribution to the Finnish exhibition was an installation entitled Autopoiesis[21],
which translates literally as "self making." The work is a
computer-based installation consisting of fifteen robotic sound sculptures that
interact with the public and modify their behaviors over time. These behaviors
change based on feedback from infrared sensors that determine the presence of
the participant/viewers in the exhibition, and the communication between each
separate sculpture.
The series of robotic sculptures--mechanical arms that are suspended from an
overhead grid--"talk" with each other (exchange audio messages)
through a computer network and audible telephone tones. The interactivity
engages the participants who in turn affect the system's evolution and
emergence. This interaction, according to the artist, creates a system
evolution as well as an overall group sculptural aesthetic. The project
presents an interactive environment that is immersive, detailed, and able to
evolve in real time by utilizing feedback and interaction from audience
members.
Karl Sims
One of the most
innovative creators of “artificial life” is Karl Sims. Sims’s cross
disciplinary background at MIT in Biology and computers gave him insights into
how computers could be used to model genetic systems that evolved
"creatures" -- objects with bodies and the capacity to develop
evolving intelligent behaviors over time. In earlier experiments with growing
"plants," he specified instructions for evolving the development of
the organisms. With his creatures, he
added movements and intelligent behaviors that could also evolve. Removing
himself from the selection of the "fittest" from one generation to
the next, Sims established goals, then let the computer determine how well
offspring in a generation met the fitness criteria, and produce the next
generation.
In an interview with
the late visionary theorist, Steven Holtzman,
Sims stated: "I've always been interested in getting computers to
do the work. While I worked in Hollywood, I was doing animations with details
that no one would ever want to do by hand. For example, I used a computer to
create an animated waterfall, when I could never have designed each drop of
water for each frame in the animation. Instead, I created it with a completely
procedural method." -- that is, he created a general description of the
waterfall and then let the computer generate the waterfall itself.
Sims soon began
playing with relationships between his creatures. Holtzman described one such
project in detail:
… competing creatures were positioned at opposite ends of an open
space and a single cube was placed in the middle with the goal of creating
competition between the two creatures. Whichever creature got to and maintained
control over the cube first won. What was most interesting was how the species
developed strategies to counter an opponent's behavior. Some creatures learned
to push their opponent away from the cube, while others moved the cube away
from their opponents. One of the more humorous approaches was a large creature
that would simply fall on top of the cube and cover it up, so its opponent
couldn't get to it. Some counterstrategies took advantage of a specific
weakness in the original strategy but could be foiled easily in a few
generations by adaptations in the original strategy. Others permanently
defeated the original strategy.[22]
The power of Sims's
artificial evolution is its ability to come up with solutions we couldn't
otherwise imagine. Sims states:
"When you witness the process, you get to see how things slowly evolve,
then quickly evolve, get stuck, and then get going again. Mutation, selection,
mutation, selection, mutation, selection -- the process represents the ability
to surpass the complexity that we can handle with traditional design
techniques. Using the computer, you can go past the complexity we could
otherwise handle; you can go beyond equations we can even understand."
Sims’s work explores both the concept of evolving emergent “forms” as well as
emergent behaviors.[23]
In more recent work, Sims has introduced the possibility for
audience members to interact with the organisms of his world. In Galapagos, Computer simulated organisms
in abstract forms display themselves on twelve monitors. Participants select an
organism and consciously choose to let it continue to exist, copulate, mutate
and reproduce itself by pressing sensor- equipped foot pedals located in front
of the monitors. This is a work in which virtual "organisms" undergo
an interactive Darwinian evolution.
Galapagos (1997)
The process in this
exhibit is a collaboration between human and machine. The visitors provide the
aesthetic information by selecting which animated forms are most interesting,
and the computers provide the ability to simulate the genetics, growth, and
behavior of the virtual organisms. But the results can potentially surpass what
either human or machine could produce alone. Although the aesthetics of the
participants determine the results, they are not designing in the traditional
sense. They are rather using selective breeding to explore the
"hyperspace" of possible organisms in this simulated genetic system.
Since the genetic codes and complexity of the results are managed by the
computer, the results are not constrained by the limits of human design ability
or understanding.[24]
Pablo Funes: EvoCAD
According to
computer scientist Pablo Funes, the new field of Evolutionary Design may open up an unexpected
creative role for the computer in CAD (computer aided design). In a CAD system designed using evolutionary
design principles, Funes maintains that
“not only can designs can be drawn (as in CAD), or drawn and simulated
(as in CAD+simulation), but (they can also be) designed by the computer following
guidelines given by the operator.” His
EvoCAD program successfully combines the theory of evolutionary design with the
practical outcomes associated with CAD.
In its initial
iteration, the EvoCAD system takes the form of a mini-CAD system to design 2D
Lego structures. Some success has also been demonstrated with fully 3D Lego
structures (see “Table” structure below, fig. B). His application allows the user to manipulate Lego structures,
and test their gravitational resistance using a simplified structural
simulator. It also interfaces to an evolutionary algorithm that combines
user-defined goals with simulation to evolve possible solutions for user-defined
design problems. The results of the evolutionary process set in motion are sent
back to the CAD front-end to allow for further re-design until the desired
solution is obtained.
Boiled
down to its basics, the process combines a particular genetic representation
with various fitness functions in order to create physical simulations that
“solve” hypothetical problems. These elements are in turn regulated by a “plain
steady-state” genetic algorithm. Funes describes the process of meeting certain
design objectives as follows:
To
begin an evolutionary run, a starting structure is first received, consisting
of one or more bricks, and "reverse-compiled" into a genetic
representation that will seed the population. Mutation and crossover operators
are applied iteratively to grow and evolve a population of structures. The
simulator is run on each new structure to test for stability and load support,
needed to calculate a fitness value. The simulation stops when all objectives
are satisfied or when a timeout occurs.[25]
This
set of techniques allows Funes to create various evolving structures in
simulated form. By altering the fitness functions, Fune’s team has successfully
evolved and built many different structures, such as bridges, cantilevers,
cranes, trees and tables. While these
are not intended as “art” per se, they are highly expressive and
non-predictable structures that bring a new twist to the old adage, “form
follows function.” They provide an
important benchmark for artists and designers interested in using evolutionary
design principles in realizing a new class of graphic and sculptural objects.
Pablo Funes, Evolved Lego structures:
cantilevered bridge (a); table, an example of 3D evolution (b); crane, early
and final stages (c, d); tree, internal representation and built structure (e,
f)
IMPLICATIONS FOR ART AND EDUCATION
The pendulum has
swung to a conservative extreme of late with a reemphasis of high stakes
testing and the development of curricular “standards” for educational policy
makers. This is not the place to
develop an argument against standards in schools; suffice it to say that
demonstrations of competencies resulting from “schooling” are a far cry from
real learning. (footnote: for persuasive
essay on what’s wrong with standards in schools see Elliot Eisner. (1998).[26] Learning is an evolving process that, at its
essence, is proven by the ability of the learner to apply received knowledge in
multiple and unrelated settings.
Demonstrating this level of competency involves “knowledge transfer”—the
concept of transfer being an effective measure of real knowledge—and
significant interaction with content, teachers, peers, and discipline
specialists.
Among the quotations at the lead of this essay is one by Humberto
Maturana, Biologist,
Cybernetician, Scientist – and the creator of the theory of autopoiesis--the concept of continual self renewal (see
also Rinaldo above). For Maturana, autopoiesis
is the process by which an organism continously reorganizes its own
structure.. This has implications for
not only how we interact with our environment, but how we learn.
Maturana wrote in
1980 that:
"Learning is not a process of accumulation of representations
of the environment; it is a continuous process of transformation of behavior
through continuous change in the capacity of the nervous system to synthesize
it. Recall does not depend on the indefinite retention of a structural
invariant that represents an entity (an idea, image or symbol), but on the
functional ability of the system to create, when certain recurrent demands are
given, a behavior that satisfies the recurrent demands or that the observer
would class as a reenacting of a previous one."[27]
Put another way,
learning is not about fixed reactions to a static environment, but about the
ability of the organism to evolve behaviors that meet the challenges of a
constantly changing environment.
Is it possible for
artists and educators to provide students with rich, evolving
content—translated into “living curricula” that not only convey essential
skills for success, but evolve to meet the ever changing needs of students
attempting to develop higher order cognitive skills that can transfer into a
variety of settings?
Imagine the ability
to interact with a system that not only provided a real time mirror of
performance levels (the cognitive equivalent to a computerized rowing machine),
but also provided timely feedback. Now
tie this vision of a “smart system” into a distributed network of “learning
nodes” that harnessed the power of multiple CPUs and provided opportunities for
interactions among affiliates of “learning communities”. John Laird, at the University of Michigan,
an expert in the intersection of artificial life and gaming, has proposed new
avenues for gaming that may provide part of the answer. Laird writes:
Although
we have found computer games to be a rich environment for research on
human-level behavior representation, we do not believe that the future of AI in
games lies in creating more and more realistic arenas for violence. Better AI
in games has the potential for creating new game types in which social
interactions,not violence, dominate. The Sims9 provides an excellent example of
how social interactions can be the basis for an engaging game. Thus, we are
pursuing further research within the context of creating computer games that
emphasizes the drama that arises from social interactions between
humans
and computer characters.[28]
And another example, again referencing artists Christa
Sommerer and Laurent Mignonneau who have developed a number of projects
expressly for the internet. They
write:
The Internet nowadays contains more than a
billion documents, and the amount of text, image and sound data increases by
the minute. One could even argue that the Internet itself is one of the best
examples of a complex system. It provides an ideal platform for knowledge
discovery, data mining and data retrieval and systems that make use of a
dynamic and constantly evolving data base.[29]
In both the localized computer
installations and web-based projects realized by Sommerer and Mignonneau, the
interaction between multiple participants operating through a common interface
represents a reversal of the topology of information dissemination. The pieces
are enabled and realized through the collaboration of many participants
remotely connected by a computer network.
In an educational setting, this
heightened sense of interaction needs to be understood as crucial. Students and
instructors alike are capable of both sending and receiving messages across a
myriad of pathways. Many
educators continue to be stuck in a method of teaching that echoes the
structure of the one-way "broadcast"--a concept that harks back to
broadcast media such as radio. In the typical lecture the teacher as "source"
transmits information to passive "receivers." This notion of a
"one-to-many" model that reinforces a traditional hierarchical
top-down approach to teaching is at odds with truly democratic exchange. Everyone is a transmitter and a receiver, a publisher and a
consumer. In the new information ecology, traditional roles may become
reversed--or abandoned. Audience members become active agents in the creation
of the new networked/artwork learning community. Teachers spend more time
facilitating and "receiving" information than lecturing. Students
exchange information with their peers and become adept at disseminating
knowledge. Participant/learners
interacting with such systems are challenged to understand that cognition is
less a matter of absorbing ready made "truths" and more a matter of
finding meaning through iterative cycles of inquiry and interaction.
Ironically, this may be what good teaching has always done.
So would we be justified in building a
"machine for learning" that does essentially the same thing that good
teachers do? One argument is that by designing such systems we are forced to
look critically at the current manner in which information is generated,
shared, and evaluated. Further, important questions are surfaced such as
"who can participate"; "who has access to the information;"
and "what kinds of interactions are enabled?" The traditional
"machine for learning" (the classroom) with its single privileged
source of authority (the teacher) is hardly a perfect model. Most of the time,
it is not a system that rewards boundary breaking, the broad sharing of
information, or the generation of new ideas. It IS a system that, in general,
reinforces the status quo. Intelligent machines such as Rinaldo's Autopoiesis,
or Sommerer’s and Mignonneau’s Verbarium can help us to draw connections
between multiple forms of inquiry, enable new kinds of interactions between
disparate users, and increase a sense of personal agency and self-worth. While
intelligent machines will surely be no smarter than their programmers,
pedagogical models can be more easily shared and replicated. Curricula
(programs for interactions) can be modified or expanded to meet the special
demands of particular disciplines or contexts. Most importantly, users are free
to interact through the system in ways that are suited to particular learning
styles, personal choices, or physical needs.
Conclusion
The power of the
networked computing coupled with our understanding of emergent systems and
evolutionary computing holds real promise.
The
unpredictable nature of the outcomes provides an ideational basis for both art
making and art teaching that is less deterministic, less bound in inherited
style and method, less totalizing in its aesthetic vision and, perhaps, less
about the ego of the individual artist/teacher. In addition to the mastery of materials and harnessing the powers
of the imagination that we expect of the professional artist, our new breed of
artist—call her an evolutionary—is equally adept at developing new
algorithms, envisioning useful and beautiful interfaces, and
managing/collaborating with machines and/or humans exhibiting non-deterministic
and emergent behaviors. Like a
horticulturalist who optimizes growing conditions for particular species but is
alert to the potential beauty of mutations in evolutionary strains, the evolutionary
works to prepare and optimize the conditions for conceptually engaging and
aesthetic outcomes. In order to do
this, this new breed of artist must have a fuller understanding of
interactivity, an healthy appreciation of evolutionary theory, and a gift for
setting into motion emergent behavior.
Because what we are
doing is modeling processes and behaviors that more closely approximate the
complexity of “real life”—seen as such, we put ourselves in a position of
appreciation rather than agents of domination and control. Interacting in collaboration with our
environment and seeking out unexpected outcomes through systems of emergence
provide new models for living on a tightly packed but richly diverse planet.
There is no question that the uses
of technology outlined here need to be held against the darker realities of
life in a hi-tech society. The insidious nature of surveillance and control,
the assault on personal space and privacy, the commodification of aesthetic
experience, and the ever-widening "digital divide" between the
technological “haves and have nots” are constant reminders that technology is a
double edged sword.
But there is at least an equal chance that a clearer understanding of the
concepts of interaction, evolution, and emergence—enabled by technology—will
yield a broader palette of choices from which human beings can come together to
create meaning. In watching these processes unfold, educators will surely find
new models for learning.
References and Notes