Christiane
Paul
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Neural
Networks vs. Computer-Networked Environments:
Cognition and Communication in Digital Art
Christiane Paul
Similarities between the functional principles of neural networks and
computer networks have recently been discussed in fields as varied as
new media theory, computer science, neurobiology, cognitive science, or
mathematics. On a more metaphorical level, these parallels are obvious:
the neural networks of our brains allow for the transmission and analysis
of multiple kinds of information – on a sensoric and cognitive level;
computer networks, such as the Internet and World Wide Web, equally establish
environments of linked nodes that allow for the processing of information.
The dream of hypermedia applications (even if it hasn’t been quite
fulfilled yet) is to establish webs of associative references that mimic
the processes of the brain. Apart from these obvious parallels between
neural and computer networks as linked ‘information processing environments’
in the broadest sense, what exactly are the functional principles that
connect the brain and computer networks?
This essay focuses on two broad areas: the analogies between computer
and neural networks; and the relevance of neural networks and cognitive
science (in particular natural language processing) in the context of
Artificial Intelligence and what is known as ‘evolutionary computation.’
I will discuss both of these areas in connection to digital art works
that range from projects addressing network structure to projects focusing
on artificial life and intelligence.
Small World Networks: Parallels between Computer Networks and the Brain
1. Social Networks
In 1998, Duncan Watts and Steve Strogatz (from Cornell University) published
a paper titled “Collective Dynamics of Small-World Networks”
in the magazine Nature. Their research focused on so-called small-world
graphs, diagrams outlining networks – for example, between people
– that seem to be neither ordered nor random. [Fig 1] Watts and
Strogatz reexamined several by now very well known experiments in the
realm of social networks. In 1960, psychologist Stanley Milgram had discovered
the paradigm of ‘6 degrees of separation’ – the fact
that all the people on this planet seem to be linked by an average of
six connections. In his original experiment, Milgram had sent letters
to a random selection of people in Nebraska and Kansas and asked each
of them to forward the letter to one of Milgram’s friends, a stockbroker
in Boston whose address he didn’t give them. The people in Nebraska
and Kansas were asked to only send the letter to someone who they thought
might be socially closer to the stockbroker. The experiment has now been
recreated various times (always with the same results) – for example
by the German newspaper Die Zeit, which tried to connect a Kebab-shop
owner in Frankfurt to his favorite actor, Marlon Brando. What interested
Watts and Strogatz in these earlier experiments was the interplay of randomness
and control in networks.
Another researcher whose work had been crucial to the idea of randomness
in network theory was the Hungarian mathematician Paul Erdös who
authored numerous papers on random graphs during the 1950s and 60s. Erdös
discovered that no matter how many points there might be, a small percentage
of randomly placed links between them is always enough to combine them
into a more or less completely connected network. The percentage of links
required dwindles as the network gets bigger. For a network of 300 points,
there are nearly 50,000 possible links that could run between them. If
no more than about 2 percent of these links are in place, the network
will be completely connected. For a network of 1,000 points, the crucial
fraction is less than 1 percent.
Fascinated by Milgram’s 6 degrees of separation, Mark Granovetter,
a professor at Johns Hopkins University, further examined the nature of
the small-world connections and discovered that the ‘bridges’
and crucial links in a social network are in fact the ‘weak’
links between people, meaning a direct link between people (who may only
be acquainted) that connects separate social networks. It is a weak link
that really holds together a social network, which would crumble if that
link is removed. Strong links between close friends, on the other hand,
are less important since they usually take a triangular structure. It
is most likely that your two closest friends also know each other and
if you would remove a link to one of them, the connection would still
exist; it would only be one step further removed. Granovetter published
his findings in the 1973 paper “The Strength of Weak Ties.”
One of the main questions Watts and Strogatz were pondering in their small-world
research was, what if a network needed to describe a social world is neither
ordered nor random but somewhere in between? Using computer programs,
Watts and Strogatz did experiments for hundreds of graphs. Each of them
started out as an ordered network, which they then subjected to some random
rewiring. [Fig 2] They monitored the number of degrees of separation and
computed how clustered the network was. In the original network, each
point had 10 neighbors and there was a potential total of 45 links that
could run between them. In reality, only 2 or 3 of these points were actually
linked and so the degree of clustering was 2/3 or 0.67. Starting from
a network of 5000 wires, they had the computer add 50 more at random,
which means that one percent of links was randomly established. The result
was a clustering of 0.65 percent as opposed to 0.67 of the original, not
a major decrease. The degrees of separation, however, had fallen from
an initial number of 50 to about 7.
The mapping of social networks and communication has also become a broad
area of inquiry in digital art. Many of these maps have been created within
MIT’s Sociable Media Group and among the most well-known of these
works are Warren Sack’s Conversation Map and Judith Donath’s
Chat Circles.
Warren Sack’s Conversation Map (since 2000) is one example of a
possible mapping of communication: the Conversation Map is a browser that
analyses the content of large-scale online e-mail exchanges (such as newsgroups)
and uses the results of the analysis to create a graphical interface that
allows users to see social and semantic relationships. Participants in
the conversation are represented as little nodes with names and their
exchanges are displayed as lines connecting them (proximity of the nodes
indicates the amount of messages that have been exchanged). A menu of
discussion themes lists the most commonly discussed topics in hierarchical
order, and an overview panel presents the history of all messages exchanged
over a given period of time. Terms in the conversation that are synonyms
or have similar meaning are connected in a semantic network.
An earlier, well-known graphical representation of large-scale communication
is Chat Circles by Judith Donath and Fernanda B. Viégas. Each person
connected to the chat environment is represented as a colored circle with
the person’s name attached to it. If users post a message it appears
within their respective circle, makes the circle grow and then gradually
fades as time passes. Activity within the communication environment is
indicated through changes in the size, color, and location of the graphics.
While users see all the other participants in the entire system, they
need to be physically close to other participants to be able to 'read'
their conversation. Users outside the person's 'hearing' range are rendered
differently-their circles appear as outlines. Donath also created maps
of the social patterns of an electronic community with the project Visual
Who.
While none of these projects was specifically based on the idea of small-world
networks or set out to prove their principles, they nevertheless illustrate
basic functionalities of small worlds.
The study of networks is part of the general area of science known as
complexity theory. The phenomenon of small-world networks seems to suggest
that there is hidden principle at work that organizes our world, a combination
of randomness and order that hasn’t been fully explained. The concept
of the small world network theory turns out to be applicable to anything
from social networks and power networks to cell structure -- that is,
the communication between specialized cells -- as well as the WWW.
2. The Internet and WWW as Small-World Networks
In 1964, the RAND corporation, the foremost Cold War think-tank, developed
a proposal for ARPA (Advanced Research Projects Agency) within the Department
of Defense that conceptualized the Internet as a communication network
without central authority that would be safe from a nuclear attack. Paul
Baran, working for the RAND Corporation, wrote several papers that examined
different types of distributed networks, one resembling a fishing net,
another a hierarchical decentralized system. [Fig 3] Baran concluded that
the fishnet structure would be more survivable. The clustering of computers
on the current Internet turns out to be over a hundred times greater than
one would expect for a random network. It isn’t an ordered network
either as Baran envisioned. It is another small-world network.
By now, there are numerous visualization studies of the Internet -- among
them the well-known visualizations of NSFNET by Donna Cox and Robert Patterson
from the NCSA that were created in 1992. Bill Cheswick (from Bell Labs)
and Hal Birch (from Carnegie Mellon) created maps of the Internet via
tracerouting – a process that traces the routes taken by the packets
of information traveling the network -- which unveiled the structure of
the Internet as hierarchical decentralized network. [Cheswick and Birch
took traceroute-style path probes, one to each registered Internet entity.
From this, they built a tree showing the paths to most of the nets on
the Internet.] In 1999, computer scientists Michalis, Petros and Christos
Faloutsos used 1997 and 1998 data for the network of the Internet to study
the number of links a packet ordinarily has to traverse in going between
one point and another. Despite the size of the Internet, the number is
4.
This phenomenon is perfectly illustrated in ART+COM’s installation
Ride the Byte (1998), which used tracerouting to visually translated the
routes of information through the global communication network. Users
of the system could choose a website from a predefined selection on a
display. On a large projection, they could then see the route taken by
the actual data packet travelling to the requested site in order to retrieve
the information. As an artwork, Ride the Byte reinstates the paradigms
of a physical map and its emphasis on geographical location for the process
of data travel, a process that usually is not visible but vanishes behind
the concept of a global network that transcends time and place.
There are few nodes have a huge number of links and act as hubs. The Faloutsos
team studied a subset of 4,389 nodes in the network, linked by 8,256 connections
and created a graph of it. The curve they found follows what mathematicians
call a “power law”: each time the number of links doubles,
the number of nodes with that many links becomes less by about 5 times.
It is unlikely that this is a mere coincidence, there seems to be a ‘hidden
order.’
The same results were found by physicist Albert-László Barabási
and colleagues at Notre Dame University who built a crawler -- software
that traces and ‘crawls along’ all the links leading from
one webpage to others -- to investigate the structure of websites. Starting
with the Notre Dame University site of 325,729 documents connected by
1,469,680 links, they found a pattern nearly identical to that of the
Internet. Each time the number of links doubled, the number of websites
having that many links decreased by about 5 times
Barabási’s experiments also showed that a natural engine
of small-world architecture is what is known as ‘the rich-getting-richer
mechanism’: networks grow by preferential attachment in the simplest
way possible.
Crawlers have also been used in various art projects to create structural
maps of the Internet or websites that offers us a view of the network
that usually remains unseen in the conventional ways of filtering and
searching the Internet through portals such as search engines. Among the
projects is Lisa Jevbratt’s 1:1, which used a crawler to assemble
a database of the Internet’s IP addresses, which were then color-coded
to create different views of the network. These maps are particularly
interesting in comparison to the ones done by Cheswick.
Jevbratt continued her investigation with the project Mapping the Web
Infome Imager, an application that allows users to customize and set parameters
for their own travels trough the structure of the Internet. The focus
of the project is the exploration of variables, both structural and aesthetic,
that create a context for the structure of websites. Jevbratt’s
software uses ‘crawlers’ to access web sites by following
links between them and collect data. Users can decide where they would
like to begin ‘crawling’ (on a specific page, a randomly picked
one, or one returned by a search engine); how they would like to navigate
(by sequentially following all the links on one page or jumping around
on it); and how this search should be visualized. Jevbratt’s software
creates visual maps of the structures of sites as they are encountered
on the itineraries chosen by the user. Depending on the parameters chosen,
these visualizations reveal information about a page itself, the users’
interests as they manifest themselves in the routes selected, and about
the way in which different visual models for the display of information
affect the way we understand it. By allowing users to choose between pixels
or ‘degrees’ (length of lines) as a visual model for the creation
of the crawler’s map, Jevbratt introduces two basic aesthetic paradigms
for processing information.
Temporal and spatial characteristics of the crawled pages -- for example,
date on the client’s computer or the date the page was modified;
color and attributes of the page design -- become part of the mapping
process itself, which blurs the boundaries between the map and the territory.
Jevbratt’s maps effectively merge the inherent structure of sites
with the journey of the user whose choices in turn affect the balance
between these two realms. The project documents transitions from the micro-
to the macro-level --the transition from one page to the next, from randomness
to user control, as well as the transitions between meanings conveyed
by different models of representation.
The Internet and World Wide Web are networks that have evolved without
any centralized control -- potentially, everyone can connect a server
to the network or create their website. The small-world architecture of
these self-organizing networks suggests that this structure seems to be
a form of evolutionary principle, a particularly efficient form of communication
(in the broadest sense) that allows quick transmission of signals and
stability of the network even if links are removed.
3. The Brain as Small-World Network
The neural network of the brain exhibits the same fundamental structure
as that of social or computer networks. The brain can be understood as
an assembly of distinct modules, each of them responsible for different
tasks, such as speech, language, vision. In neuroscience labs, magnetic
resonance imaging techniques -- which use radio waves to probe the pattern
of blood flow in the brain, revealing how much oxygen its various parts
are using at any moment -- are used to see these modules in action. This
process reflects the level of neural activity.
The processing centers of the brain reside in the cerebral cortex, which
contains most of the brain’s neurons. The modules of the brain have
to communicate in order to coordinate overall brain activity. A region
of the human brain no larger than a marble contains as many neurons as
there are people in the US – which are 287,400,000 (mid 2002). Each
neuron is a single cell with a central body from which numerous fibers
project. The shortest fibers (dendrites) are the neuron’s receiving
channels, the longer fibers (axons) are the transmission lines.
Axons from any neuron eventually link up with dendrites of other neurons,
and some axons link up with neurons in neighboring brain areas. The brain
also has a small number of ‘long-distance’ axons.
Focusing on the brains of cats and monkeys, Jack Scannell (from the University
of Newcastle, England) spent more than a decade mapping out connections
between different regions of the cerebral cortex. The cat has 55 regions
of the cerebral cortex associated with different functions, with 400 to
500 significant links connecting them. In order to determine how these
links are arranged, Vito Latora of the University of Paris and Massimo
Marchiori of MIT used Scannel’s maps and analyzed the brain networks
in the terms set out by Watts and Strogatz. They found a strikingly efficient
network architecture with a number of degrees of separation in the cat
brain between two and three.
Strogatz and Watts had also studied the possible communication links between
fireflies in order to solve the synchronization puzzle of their simultaneous
‘blinking’ over large distances. In 1999, Luis Lago-Fernandez
and colleagues from the Autonomous University of Madrid studied networks
of neurons in a similar way Watts and Strogatz had studied fireflies by
creating a virtual model of the locust’s olfactory antennal lobe
(a group of about 800 neurons that takes info from the olfactory smell
receptors and relays it to higher regions of the brain). They build several
simulations, with detailed models for the behavior of each of 800 neurons,
in which they applied a stimulus to a small fraction of neurons in the
network and then monitored the way it spread through it. While an ordered
network resulted in an inadequate response, a small-world network yielded
surprising results.Neural Networks, Evolutionary Computation, and Artificial
Life and Intelligence Projects Models of brain and behavioral processes
are commonly applied to computer technologies and networks in fields including
computer science, neurobiology, and cognitive science.
The effort of building naturally intelligent systems has become its own
area of research. Computational neural networks or neurocomputers are
designed to mimic the architecture of the brain. They are infomation processing
systems inspired by the structure of biological neural systems and mimic
the functions of the central nervous system and the sensory organs attached
to it. Humans are estimated to have 10 billion neurons, a fly about a
million, and the largest neurocomputers currently have about a few million
-- which means that they have only little more than fly power.
Computational neural networks are distinguished by the following characteristics:
-
they are not programmed in computer languages as conventional computers
are, but trained in the way we want them to.
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they communicate through neurodes, interconnections with variable weights
and strengths
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the information in neural networks is processed by constantly changing
patterns of activity
As
opposed to having a separate memory and controller like a digital computer,
a neural network is controlled by 3 properties: the transfer function
of the neurodes, the structure of the connection among the neurodes and
the learning law the system follows
Neural networks have 3 basic building blocks: neurodes (artificial models
of biological neurons); interconnects (links between neurodes); and synapses
(junction where interconnect meets neurode).
Neural networks deal with sensory tasks (such as the processing of visual
stimuli), motor tasks (controlling arm movements), or the decision-making
by which sensory tasks drive motor tasks. Neural networks imitate behaviors
and are better suited for processing at the cognitive level -- for example,
motor control, association, and speech recognition.
1. Small-world Architecture in the Structure of Human Language
Language and speech, as well as association are obviously an important
area of an intelligent human system. The architecture of a small world
also seems to form the basic structure of human language. Physicists Ricard
Solé and Ramon Ferrer i Cancho used the database of the British
National Corpus -- a 100-million-word collection of samples of written
and spoken language from a wide range of sources -- to study the grammatical
relationships between 460,902 words in the English language. They considered
two words to be linked if they appeared next to one another in English
sentences. Again, the system proved to be a small-world network, in which
words such as ‘a,’ ‘the,’ or ‘at’
turned out to be well-connected hubs. The typical distance between words
in the language was less than three, and the clustering of the network
was 5000 times higher than for a random network.
In his essay “Rules of Language,” Steven Pinker outlined that
language and cognition have been explained according to two basic and
different principles:
- as
the products of a homogenous associative memory structure. Associationism
describes the brain as a homogenous network of interconnected units,
which are modified by a learning mechanism. This mechanism records correlations
among frequently co-occurring input patterns.
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as a set of genetically determined computational modules in which rules
manipulate symbolic representations. Rule-and-representation theories
describe the brain as a computational device in which rules and principles
operate on symbolic data structures. (Some rule theories further propose
that the brain is divided into modular computational systems that have
an organization that is largely specified genetically.)
The study of one phenomenon of English grammar and how it is processed
and acquired suggests that both theories are partly right. Regular verbs
(such as learn, learned) are computed by a suffixation rule in a neural
system for grammatical processing, while irregular verbs (such as run,
ran, run) are retrieved from associative memory.
The above-mentioned two principles connect to the different models employed
by neural networks (the computational kind) and Artificial Intelligence.
Neural networks basically act as an associative memory while AI attempts
to generate heuristics or rules to find solutions for problems of control,
recognition, and object manipulation. The underlying assumption is that
problems can be solved by applying formal rules for symbol manipulation
– a task digital computers handle well.
Neural networks attempt to solve these problems at the level of the structure
of the machine itself. In neural networks, symbolic processing is a result
of the low-level structure of the physical system. While neural networks
imitate behaviors, AI describes behaviors with rules and symbols.
2. Artificial Intelligence
As early as 1936, Mathematician Alan Turing (1912-1954) -- one of the
early influential theoreticians of AI who gave the famous Turing Test
its name -- outlined the Turing machine, a theoretical apparatus which
established a connection between the process of the mind, logical instructions,
and a machine. Turing's paper “Computing Machinery and Intelligence”
(1950) was a major contribution to the philosophy and practice of Artificial
Intelligence, a term that was officially coined in the 1960s by computer
scientist John McCarthy. AI had one of its groundbreaking victories in
May 1997, when IBM’s Deep Blue Supercomputer beat the reigning world
chess champion, Garry Kasparov. Deep Blue’s ‘intelligence’
is a strategic and analytical one and an example of a so-called ‘expert
system’ that has expertise in a specific area and is able to draw
conclusions by applying rules based on that knowledge.
AI is more successful on the level of expert systems than at the level
of speech recognition, which is part of the area of AI research that focuses
on man-machine communication. The best-known artificial intelligence ‘characters’
are Eliza and ALICE – software programs you can talk to, also known
as chatbots (chat robots). Eliza was developed by Joseph Weizenbaum who
joined the MIT Artificial Intelligence Lab in the early 1960s. Admittedly,
Eliza does not have much ‘intelligence’ but works with what
could more or less be considered tricks, for example, string substitution
and pre-programmed responses based on keywords. Eliza’s much more
advanced colleague ALICE (Artificial Linguistic Computer Entity) was designed
by Dr. Richard S. Wallace and operates on the basis of AIML, or Artificial
Intelligence Markup Language, a markup language that allows to customize
ALICE and program how she could respond to various input statements. Both
Eliza and ALICE are accessible online and people can chat with them through
their respective websites.
The AIML Pattern Language Committee is working on what is known as AIML
pattern language, a currently very restricted subset of regular expression
syntax, plus some rules that allow the inclusion of certain AIML tags.
At the Alice Foundation’s site, Richard Wallace keeps a gallery
of ALICE Brain Pictures. The pictures are based on the AIML's pattern
matching of language, which is done by means of a so-called ‘Graphmaster.’
The Graphmaster consists of a collection of nodes called Nodemappers,
which map the branches from each node. The root of the Graphmaster is
a Nodemapper with about 2000 branches, one for each of the first words
of all the patterns (40,000 in the case of the ALICE brain).
The eye-shaped log spiral plots all 24,000 categories in the ALICE Brain.
The spiral itself represents the root. The trees emerging from the root
are the patterns recognized by ALICE. The branching factor for the root
is about 2000. As Richard Wallace points out there is a similarity between
the graph of ALICE’s brain and the Graphmaster plot of cortical
algorithms for visual processing, which, in his opinion, is no coincidence.
The same cortical architecture that enables real-time, attention-based
visual processing can in fact be applied to linguistic processing as well.
Artists have frequently been incorporating artificial intelligence and
speech programs (mostly based on AIML) into their art. Although their
works are based on the current research, they cannot be simply labelled
as ‘AI projects’ since they are broader in their scope and
metaphoric implications. Among the well-known AI-related artworks are
Ken Feingold’s If / Then and David Rokeby’s Giver of Names.
In If/Then, two eerily humanoid heads are sitting in a box surrounded
by what resembles styrofoam nuggets normally used as packaging materials.
As Feingold explains, he wanted them ‘to look like replacement parts
being shipped from the factory that had suddenly gotten up and begun a
kind of existential dialogue right there on the assembly line’.
The heads are involved in an ever-changing dialogue probing the philosophical
issues of their existence as well as their separateness and likeness.
Their conversation, based on a complex set of rules and exceptions, points
to larger issues of human communication: picking up on syntax structure
and strings of words in their respective statements, the heads’
communication at times may seem conditioned, limited and random (as human
conversations sometimes do) but highlights the meta-levels of meaning
created by failed communication, misunderstandings, and silences. The
heads’ dialogue unveils crucial elements of the basics of syntax
structure and the way we construct meaning, with often extremely poetic
results.
While distinctly different from Feingold’s heads, Giver of Names
(1990–present) by Canadian artist David Rokeby (b. 1960) addresses
similar issues of ‘machine intelligence’ in an equally poetic
way that transcends the merely technological fascination with AI and becomes
a reflection on semantics and the structure of language. The Giver of
Names is a computer system that quite literally gives objects names by
trying to describe them. The installation consists of an empty pedestal,
a video camera, a computer system, and a small video projection. Visitors
can choose an object or set of objects from those in the space or from
the ones they might carry with them, and place them on the pedestal, which
is observed by a camera. When an object is placed on the pedestal, the
computer grabs an image and then performs many levels of image processing
(outline analysis, division into separate objects or parts, colour analysis,
texture analysis, etc.). These processes are visible on the life-size
video projection above the pedestal. The computer’s attempts to
arrive at conclusions about objects chosen by visitors lead to increasing
levels of abstraction that open up new forms of context and meaning. Giver
of Names is an exploration of the various levels of perception that allow
us to arrive at interpretations and creates an anatomy of meaning as defined
by associative processes. The project ultimately is a reflection on how
machines think (and how we make them think).
3. Evolutionary Computation, Behavioral Algorithms, and Artificial Life
The sets of genetically determined computational modules (in which rules
manipulate symbolic representations) that were mentioned earlier also
connect to the concepts of computation in decentralized systems and genetic
computation.
The first theories about computation with decentralized systems date back
to 1948, when Hungarian-born mathematician John von Neumann (1903-1957)
gave a lecture on the “General and Logical Theory of Automata.”
His concepts would be later expanded by the Polish-born mathematician
and physicist Stanislaw Ulam (1909-1984) who suggested that systems could
be modeled on a grid of ‘cells, ’ which could behave on the
basis of a set of rules. Further elaborating on von Neumann’s ideas,
the American philosopher and computer scientist Arthur Burks -- who, with
his wife Alice, helped build and program ENIAC, the first computer --
introduced the term ‘cellular automaton’ in the 1950s.
According to the International Society of Genetic and Evolutionary Computation
, genetic and evolutionary computing (GEA) are computer methods based
on natural selection and genetics to solve problems across the spectrum
of human endeavor. Evolutionary computation and artificial life are two
relatively new but fast-growing areas of science. Some people believe
that artificial life and evolutionary computation are very distinct areas
which only overlap in the occasional use of evolutionary computation techniques
such as genetic algorithms by artificial life researchers; others argue
that artificial life and evolutionary computation are very closely related
and evolutionary computation is an abstracted form of artificial life,
since both strive to represent "solutions" to an environment,
deciding which "solutions" get to reproduce and how things reproduce.
At the basis of digital art projects in the realm of artificial life are
inherent characteristics of digital technologies: the possibility of infinite
‘reproduction’ in varying combinations according to specified
variables; as well as the feasibility of programming certain behaviors
(such as ‘ fleeing,’ ‘seeking,’ ‘attacking’)
for so-called ‘autonomous’ information units or characters.
Numerous artificial life projects, such as Karl Sims’ Genetics Images
and A-Volve by Christa Sommerer and Laurent Mignonneau establish an explicit
link between aesthetics and evolution.
Issues of the transformation of information and the survival of the (aesthetically)
fittest form the basis of A-Volve (1994/5), which establishes a direct
connection between the physical and virtual world. The interactive environment
allows visitors to create virtual creatures and interact with them in
the space of a water-filled glass pool. By drawing a shape with their
finger on a touch screen, visitors produce virtual three-dimensional creatures
that automatically become ‘alive’ and start swimming in the
real water of the pool as simulated appearances. The movements and behaviors
of the virtual creatures are dependent on their forms, which ultimately
determine their fitness for survival and ability to mate and reproduce
in the pool -- aesthetics becomes the crucial factor in the survival of
the fittest. The creatures also react to the visitors’ hand movements
in the water: people can ‘push’ them forward or backward or
stop them (by holding their hands right over them), which may protect
them from their predators. A-Volve literally translates evolutionary rules
into the virtual realm and at the same time blends the virtual with the
real world. Human creation and decision play a decisive role in this virtual
ecosystem: A-Volve is a reminder of the complexity of any life-form (organic
or inorganic) and of our role in shaping artificial life. Allowing visitors
to interact with the creatures in the pool, A-Volve reinstates human manipulation
of evolution.
An example of the computational determination of the behavior of characters,
as it is often found in gaming, is John Klima’s project Jack &
Jill, which defines behavioral patterns for the characters in the familiar
children’s story. The project was created for an exhibition called
CODeDOC, which I organized for the Whitney Museum's artport website. The
exhibition per se has nothing to do with the subject of this paper but
was meant to look at the relationship between the language of code and
the (visual) artwork produced by this code. Both the code and its results
were published side by side on the website, For Jack & Jill, Klima
created a small expert system, and visitors to the site can actually take
a look at the code of a small, condensed expert system. (This is by no
means a complex one since the artists had to work with severe restrictions
and their main code could not be longer than 8K.) John Klima basically
wrote a story in Visual Basic that retells the nursery rhyme "Jack
& Jill": the characters have a set of behaviors, such as indecisive
or reluctant emotional states. What the characters are doing at any given
point is ultimately determined by the system: parameters such as 'willing,'
'unwilling,' and 'indecisive' allow for an interplay between and evolution
of behavioral characteristics. If you are familiar with the gaming pantheon,
you may recognize that the characters in this story are Super Mario and
the princess from the original Donkey Kong game.
The issues outlined here are embedded in the much larger cultural context
of human perception, for example, the relationship between the observer
and the observed and between real and virtual interfaces. The central
questions we are facing are: in how far do the systems we create in the
physical world actually replicate the structure of bodies and brains (in
terms of the networks of cells, synapses etc.)? And in how far do these
systems and 'interfaces' we create in turn change our brain, our perception,
and our body? Visual representations of culture (in the broadest sense)
and the technologies that are used to produce them may in turn feed back
into culture and ultimately change our perception. The relationship and
feedback loop between the observer, cognitive processes, and cultures
is becoming one of the pressing issues of our time, and will profoundly
shape our understanding of art and the visual image.
The theories
and research summarized in this article are outlined in detail in:
Albert-László Barabási, Linked: The New Science of
Networks (Perseus Publishing: Cambridge, MA, 2002)
Mark Buchanan, Nexus: Small Worlds and the Groundbreaking Science of Networks
(W. W. Norton & Co.: New York / London, 2002)
The theories and research summarized in this article are outlined in detail
in:
Albert-László Barabási, Linked: The New Science of
Networks (Perseus Publishing: Cambridge, MA, 2002)
Mark Buchanan, Nexus: Small Worlds and the Groundbreaking Science of Networks
(W. W. Norton & Co.: New York / London, 2002)
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