Katherine Hayles: Narratives
of Artificial Life
In contrast to the circular processes of Maturana's
autopoiesis, the figure most apt to describe the third
wave is a spiral. Whereas the second wave is
characterized by an attempt to include the observer
in an account of the system's functioning, in the third
wave the emphasis falls on getting the system to
evolve in new directions. Self-organization is no
longer enough. The third wave wants to impart an
upward tension to the recursive loops of selforganizing
processes so that, like a spring
compressed and suddenly released, they break out of
the pattern of circular self-organization and leap
outward into the new.
Just as von Foerster served as a transition figure
between the first and second waves, so Francisco
Varela bridges the transition between the second
and third waves. We saw in Chapter 6 that Maturana
and Varela extended the definition of the living to
include artificial systems. After co-authoring The
Embodied Mind,1 Varela began to work in a new field
known as Artificial Life and co-edited the papers
from the first European conference in that field. In
the introduction to the conference volume, Towards
a Practice of Autonomous Systems, he and his coauthor
Paul Bourgine lay out their view of what
Artificial Life should be.[2] They locate its origins in
cybernetics, referencing Grey Walter's electronic
tortoise and Ross Ashby's homeostat. Although some
characteristics of autopoiesis are reinscribed on the
successor field of Artificial Life, especially the idea
that systems are operationally closed, other features
are new. The change is signaled in Varela's subtle
reconception of autonomy. He and his co-author
write, "Autonomy in this context refers to [the
living's] basic and fundamental capacity to be, to
assert their existence and bring forth a world that is
significant and pertinent without being pre-digested
in advance. Thus the autonomy of the living is
understood here both in regards to its actions and to
the way it shapes a world into significance. This
conceptual exploration goes hand in hand with the
design and construction of autonomous agents and
suggest an enormous range of applications at all
scales, from cells to societies" (p. xi). For Maturana,
"shap[ing] a world into significance" meant that
perception was linked primarily to internal
processes rather than external stimuli.[3] We have
seen the difficulties he had with evolution, because
he sought to put the emphasis instead on the
organism's holistic nature and autopoietic
circularity. When Varela and his co-author speak of
"shap[ing] a world into significance," the important
point for them is that the system's organization, far
from remaining unchanged, can transform itself
through emergent behavior. The change is not so
much an absolute break, however, as a shift in
emphasis and a corresponding transformation in the
kind of questions the research programs pose, as
well as new strategies for answering them. Thus the
relation of the third wave to the second is again one
of seriation, an overlapping pattern of replication
and innovation.
The shift in questions and methodologies is not, of
course, neutral. For researchers who come to the
field from backgrounds in cognitive science and
computer science, rather than from autopoiesis as
Varela does, the underlying assumptions all too
easily lend themselves to reinscribing a disembodied
view of information. But as Varela's presence in the
field indicates, not everyone who works in the field
agrees that disembodied "organisms" are the best
way to construct Artificial Life. Just as there were
competing camps in the Macy conferences, one
arguing for a disembodied view of information and
one for a contextualized embodied view, so in
Artificial Life some researchers concentrate on
simulations, insisting that embodiment is not
necessary, whereas others argue that only embodied
forms can fully capture the richness of an organism's
interactions with the environment. Our old friend
the observer, who was at the center of the
epistemological revolution Maturana sparked, in the
third wave retreats to the periphery, with a
consequent loss of the sophistication that Maturana
brought to epistemological questions. The observer
has, however, not altogether vanished from the
scene, remaining in the picture as narrator and
narratee of stories about Artificial Life. To see how
the observer's presence helps to construct the field,
let us turn now the consider the strange flora and
fauna of the world of Artificial Life.
The Nature and Artifice of Artificial Life
At the Fourth Conference on Artificial Life in the
summer of 1994, evolutionary biologist Thomas S.
Ray put forth two proposals.[4] The first was a plan
to preserve biodiversity in Costa Rican rain forests;
the second was a suggestion that Tierra, his software
program creating Artificial Life forms inside a
computer, be released on the Internet so that it could
"breed" diverse species on computers all over the
world. Ray saw the two proposals as
complementary. The first aimed to extend biological
diversity for protein-based life forms; the second
sought the same for silicon-based life forms. Their
juxtaposition dramatically illustrates the
reconstruction of nature going on in the field of
Artificial Life, affectionately known by its
practitioners as AL. "The object of an AL
instantiation," Ray wrote recently, "is to introduce
the natural form and process of life into an artificial
medium" (emphasis added).[5] The lines startle. In
Ray's rhetoric, the computer codes comprising these
"creatures" become natural forms of life; only the
medium is artificial.
How is it possible in the late twentieth century to
believe, or at least claim to believe, that computer
codes are alive? And not only alive, but natural? The
question is difficult to answer directly, for it involves
assumptions that are not explicitly articulated.
Moreover, these presuppositions do not stand by
themselves but move in dynamic interplay with
other formulations and ideas circulating through the
culture. In view of this complexity, the subject is
perhaps best approached through indirection, by
looking not only at the scientific content of the
programs but also at the stories told about and
through them. These stories, I will argue, constitute a
multilayered system of metaphoric and material
relays through which "life," "nature," and the
"human" are being redefined.
The first level of narrative with which I will be
concerned is the Tierra program and various
representations of it by Ray and others. In these
representations, authorial intention,
anthropomorphic interpretation, and the program's
operations are so interwoven that it is impossible to
separate them. As a result, the program operates as
much within the imagination as it does within the
computer. The second level of narrative focuses on
the arguments and rhetorical strategies that AL
practitioners use as they seek to position Artificial
Life as a valid area of research within theoretical
biology. This involves telling a story about the state
of the field and the contributions that AL can make to
it. As we shall see, the second-level story quickly
moves beyond purely professional considerations,
evoking a larger narrative about the kinds of life that
have emerged, and are emerging, on earth. The
narrative about the present and future of terrestrial
evolution comprises the third level. It is constituted
through speculations on the relation of human
beings to their silicon cousins, the "creatures" who
live inside the computer. Here, at the third level, the
implication of the observer in the construction of all
three narratives becomes explicit. To interrogate
how this complex narrative field is initiated,
developed, and interpolated with other cultural
narratives, let us begin at the first level, with an
explanation of the Tierra program.
Conventionally, Artificial Life is divided into three
research fronts. Wetware is the attempt to create
artificial biological life through such techniques as
building components of unicellular organisms in test
tubes. Hardware is the construction of robots and
other embodied life forms. Software is the creation of
computer programs instantiating emergent or
evolutionary processes. Although each of these areas
has its distinctive emphases and research agendas,
they share the sense of building life from the
"bottom up." In the software branch, with which I am
concerned here, the idea is to begin with a few
simple local rules, then through structures that are
highly recursive, allow complexity to emerge
spontaneously. Emergence implies that properties or
programs appear on their own, often developing in
ways not anticipated by the person who created the
simulation. Structures that lead to emergence
typically involve complex feedback loops in which
the outputs of a system are repeatedly fed back in as
input. As the recursive looping continues, small
deviations can quickly become magnified, leading to
the complex interactions and unpredictable
evolutions associated with emergence.[6]
Even granting emergence, it is still a long jump from
programs that replicate inside a computer to living
organisms. This gap is bridged largely through
narratives about the programs that map them into
evolutionary scenarios traditionally associated with
the behavior of living creatures. The narratives
translate the operations of computer codes into
biological analogues that make sense of the
program's logic. In the process, the narratives
transform the binary operations that, on a physical
level, amount to changing magnetic polarities into
the high drama of a Darwinian struggle for survival
and reproduction. To see this transformation in
action, consider the following account of the Tierra
program. This account is compiled from Thomas
Ray's published articles and unpublished working
papers, conversations I had with him about his
program, and public lectures he has given on the
topic.[7]
When I visited him at the Santa Fe Institute, he
talked about the genesis of Tierra. Frustrated with
the slow pace of natural evolution, he wondered if it
would be possible to speed things up by creating
evolvable artificial organisms within the computer.
One of the first challenges he faced was designing
programs robust enough to withstand mutation
without crashing. To induce robustness, he
conceived of building inside the regular computer a
"virtual computer" out of software. Whereas the
regular computer uses memory addresses to find
data and execute instructions, the virtual computer
uses a technique Ray calls "address by template."
Taking its cue from the topological matching of DNA
bases, in which one base finds its appropriate
partner by diffusing through the medium until it
locates another base with a surface it can fit into like
a key into a lock, address by template matches one
code segment to another by looking for its binary
inverse. For example, if an instruction is written in
binary code 1001, the virtual computer searches
nearby memory to find a matching segment with the
code 0110. The strategy has the advantage of
creating a container for the organisms that renders
them incapable of replicating outside the virtual
computer, for the address by template operation can
occur only within a virtual computer. Presented with
a string such as 0110, the regular computer would
read it as data rather than instructions to replicate.
Species diversify and evolve through mutation. To
introduce mutation, Ray creates the equivalent of
cosmic rays by having the program flip a bit's
polarity once in every 10,000 executed instructions.
In addition, replication errors occur about once in
every 1,000 to 2,500 instructions copied, introducing
another source of mutation. Other differences spring
from an effect Ray calls "sloppy reproduction,"
analogous to the genetic mixing that occurs when a
bacterium absorbs fragments of a dead organism
nearby. To control the number of organisms, Ray
introduced a program that he calls the "reaper." The
"reaper" monitors the population and eliminates the
oldest creatures and those who are "defective," that
is, those who most frequently have made errors in
executing their programs. If a creature finds a way to
replicate more efficiently, it is rewarded by moving
down in the reaper's queue and so becomes
"younger."
The virtual computer starts the evolutionary
process by allocating a block of memory that Ray
calls the "soup," by analogy with the primeval soup
at the beginning of life on earth. Inside the soup are
unleashed self-replicating programs, normally
starting with a single 80-byte creature called the
"ancestor." The ancestor is comprised of three
segments. The first segment counts its instructions
to see how long it is (this procedure ensures that the
length can change without throwing off the
reproductive process); the second segment reserves
that much space in nearby memory, putting a
protective membrane around it (by analogy with the
membranes enclosing living organisms); and the
third segment copies its code into the reserved
space, thus completing the reproduction and
creating a "daughter cell" from the "mother cell." To
see how mutation leads to new species, consider that
a bit flip occurs in the last line of the first segment,
changing 1100 to 1110. Normally the program
would find the second segment by searching for its
first line, encoded 0011. Now, however, the program
searches until it finds a segment starting with 0001.
Thus it goes not to its own second segment but to
another string of code in nearby memory. Many
mutations are not viable and do not lead to
reproduction. Occasionally, however, the program
finds a segment starting with 0001 which will allow
it to reproduce. Then a new species is created, as this
organism begins producing offspring.
When Ray set his program running overnight, he
thought he would be lucky to get a one- or two- byte
variation from the 80-byte ancestor. Checking it out
the next morning, he found that an entire ecology
had evolved, including one 22-byte organism. Among
the mutants were parasites that had lost their own
copying instructions but had developed the ability to
invade a host and hijack its copying procedure. One
45-byte parasite had evolved into a benign
relationship with the ancestor; others were
destructive, crowding out the ancestor with their
own offspring. Later runs of the program saw the
development of hyperparasites, which had evolved
ways to compete for time as well as memory.
Computer time is doled out equally to each organism
by a "slicer" that determines when it can execute its
program. Hyperparasites wait for parasites to invade
them. Then, when the parasite attempts to
reproduce using the hyperparasite's own copy
procedure, the hyperparasite directs the program to
its own third segment instead of returning it to the
parasite's ending segment. Thus the hyperparasite's
code is copied on the parasite's time. In this way the
hyperparasite greatly multiplies the time it has for
reproduction, for in effect it appropriates the
parasite's time for its own.
This, then, is the first-level narrative about the
program. It appears with minor variations in Ray's
articles and lectures. It is also told in the Santa Fe
Institute videotape "Simple Rules . . . Complex
Behavior," in which Ray collaborated with a graphic
artist to create a visual representation of Tierra,
accompanied by his voiceover.[8] If we ask how this
narrative is constituted, we can see that statements
about the program's operation and interpretations of
its meaning are in continuous interplay with each
other. Consider the analogies implicit in such terms
as "mother cell," "daughter cell," "ancestor,"
"parasite" and "hyperparasite." The terms do more
than set up parallels with living systems; they also
reveal Ray's intention in creating an appropriate
environment in which the dynamic emergence of
evolutionary processes could take off. In this respect
Ray's rhetoric is quite different from that of Richard
Dawkins in The Selfish Gene, a work also deeply
informed by anthropomorphic constructions.[9]
Dawkins's rhetoric attributes to genes human agency
and intention, creating a narrative of human-like
struggle for lineage. In this construction, Dawkins
overlays onto the genes strategies, emotions, and
outcomes that properly belong to the human
domain. Ray, by contrast, is working with artificial
systems designed by humans precisely so they would
be able to manifest these qualities. This is the primary
reason explanation and interpretation are
inextricably entwined in the first-level narrative.
Ray's biomorphic namings and interpretations
function not so much as an overlay as an explication
of an intention that was there at the beginning.
Analogy is not incidental or belated but central to the
program's artifactual design.
Important as analogy is, it is not the whole story.
The narrative's compelling effect comes not only
from analogical naming but also from images. In
rhetorical analysis, of course, "image" can mean
either an actual picture or a verbal formulation
capable of evoking a mental picture. Whether an
image is a visualization or visually evocative
language, it is a powerful mode of communication
because it draws upon the high density of
information that images convey. Visualization and
visually evocative language collaborate in the
videotape the Santa Fe Institute made to publicize its
work, entitled "Simple Rules . . . Complex Behavior.
As the narrative about Tierra begins, the camera flies
over a scene representing the inside of a computer.
This stylized landscape is dominated by a block-like
structure representing the CPU (Central Processing
Unit) and dotted with smaller upright rectangles
representing other integrated circuits. Then the
camera zooms into the CPU, where we see a grid
upon which the "creatures" appear and being to
reproduce. They are imaged as solid polygons strung
together to form three sections, representing the
three segments of code. Let us linger at this scene
and consider how it has been constructed. The
pastoral landscape upon which the creatures are
visualized instantiates a transformation
characteristic of the new information technologies
and the narratives that surround them. A material
object (the computer) has been translated into the
functions it performs (the programs it executes)
which in turn have been represented in visual codes
familiar to the viewer (the bodies of the "creatures").
The path can be represented schematically as
material base--> functionality--> representational
code. This kind of transformation is extremely
widespread, appearing in popular venues as well as
scientific applications. It is used by William Gibson in
Neuromancer, for example, when he represents the
data arrays of a global informational network as
solid polygons in three-dimensional space that his
protagonist, transformed into a point of view or pov,
can navigate as though he were flying through the
atmosphere.[10] The schematic operates in
remarkably similar fashion in the video, where we
become a disembodied pov flying over the lifeworld
of the "creatures," comfortingly familiar in its threedimensional
spaces and rules of operation. Whereas
the CPU landscape corresponds to the computer's
interior architecture, however, the lifeworld of the
creatures does not. The seamless transition between
the two elides the difference between the material
space inside the computer and the imagined space
that, in actuality, consists of computer addresses and
magnetic polarities on the computer disk.
To explore how these images work to encode
assumptions, consider the bodies of the "creatures,"
which resemble stylized ants. In the program, the
"creatures" have bodies only in a metaphoric sense,
as Ray recognizes when he talks about their bodies
of information (itself an analogy).[11] These bodies
of information are not, as the expression might be
taken to imply, phenotypic expressions of
informational codes. Rather, the "creatures" are their
codes. For them, genotype and phenotype amount to
the same thing; the organism is the code, and the
code is the organism. By representing them as
phenotypes, visually by giving them threedimensional
bodies and verbally by calling them
ancestors, parasites, and such, Ray elides the
difference between behavior, properly restricted to
an organism, and executing a code, applicable to the
informational domain. In the process, assumptions
we have about behavior, in particular thinking of it
as independent action undertaken by purposive
agents, are transported into the narrative.
Further encoding takes place in the plot. Narrative
tells a story, and intrinsic to story is chronology,
intention, and causality. In Tierra, the narrative is
constituted through the story that emerges of the
creatures' struggle for survival and reproduction.
More than an analogy or an image, this is a drama
that, if presented in a different medium, one would
not hesitate to identify as an epic. Like an epic, it
portrays life on a grand scale, depicting the rise and
fall of races, some doomed and some triumphant,
recording the strategies they invent as they play for
the high stakes of establishing a lineage. The epic
nature of the narrative is even more explicit in Ray's
plans to develop a global ecology for Tierra. In his
proposal to create a digital "biodiversity reserve,"
the idea is to release the Tierra program on the
Internet so that it can run in background on
computers across the globe. Each site will develop its
own microecology. Because background programs
run when demands on the computer are at a
minimum, the programs will normally be executed
late at night, when most users are in bed. Humans
are active while the "creatures" are dormant; they
evolve while we sleep. Ray points out that someone
monitoring activity in Tierra programs would
therefore see it as a moving wave that follows
darkfall around the world. Linking the creatures'
evolution to the human world in a complementary
diurnal rhythm, the proposal edges toward a larger
narrative level that interpolates their story into ours,
our into theirs.
A similar interpolation occurs in the video. The
narrative appears to be following the script of
Genesis, from the lightning that flickers over the
landscape, representing the life force, to the
"creatures" who, like their human counterparts,
follow the Biblical imperative to be fruitful and
multiply. When a death's head appears on the scene,
representing the reaper program, we understand
that this pastoral existence will not last for long. The
idyll is punctured by competition between species,
strategies of subversion and co-optation, and
exploitation of one group by another--in short, all the
trappings of rampant capitalism. To measure how
much this narrative accomplishes, it is helpful to
remember that what one actually sees as the output
of the Tierra program is a spectrum of bar graphs
tracking the numbers of programs of given byte
lengths as a function of time. The strategies emerge
when human interpreters scrutinize the binary
codes that constitute the "creatures" to find out how
they have changed and determine how they work.
No one knows this better, of course, than Ray and
other researchers in the field. The video, as they
would no doubt want to remind us, is merely an
artist's visualization that has no scientific standing. It
is, moreover, intended for a wide audience, not all of
whom are presumed to be scientists. This fact in
itself is interesting, for the tape as a whole is an
unabashed promotion of the Santa Fe Institute. It
speaks to the efforts that practitioners in the field
are making to establish Artificial Life as a valid,
significant, and exciting area of scientific research.
These efforts are not unrelated to the visual and
verbal transformations discussed above. To the
extent that the "creatures" are biomorphized, their
representation reinforces the strong claim that the
"creatures" are actually alive and extends its
implications. Nor do the transformations appear only
in the video, although they are particularly striking
there. As the discussion above demonstrates, they
are also inscribed in published articles and
commentary. In fact, they are essential to the strong
claim that the computer codes do not merely
simulate life but are themselves alive. At least some
researchers at the Santa Fe Institute recognize the
relation between the strong claim and the stories
researchers tell about these "organisms." Asked
about the strong claim, one respondent insisted, "It's
in the eye of the beholder. It's not the system, it's the
observer."[12]
In the second wave of cybernetics, accounting for the
observer was of course a central concern. What
happens when the observer is taken into account in
Artificial Life research? To explore further the web of
connections between the program's operations,
descriptions of its operation by observers, and the
contexts in which these descriptions are embedded,
we will follow the thread to the next narrative level,
where arguments circulate about the contributions
Artificial Life can make to scientific knowledge.
Positioning the Field: The Politics of Artificial
Life
Christopher Langton, one of the most visible of AL
researchers, explains the reasoning behind the
strong claim. "The principle [sic] assumption made
in Artificial Life is that the 'logical form' of an
organism can be separated from its material basis of
construction, and that 'aliveness' will be found to be
a property of the former, not of the latter."[13] It
would be easy to dismiss the claim on the basis that
the reasoning behind it is tautological: Langton
defines life in such a way as to make sure the
programs qualify, and then, because they qualify, he
claims they are alive. But more is at work here than
tautology. Resonating through Langton's definition
are assumptions that have marked Western
philosophical and scientific inquiry at least since
Plato. Form can logically be separated from matter;
form is privileged over matter; form defines life,
while the material basis merely instantiates it. The
definition is a site of reinscription as well as
tautology. This convergence suggests that the
context for our inquiry should be broadened beyond
the definition's logical form to the field of inquiry in
which such arguments persuade precisely because
they reinscribe.
This context includes attitudes, deeply by many
researchers in scientific communities, about the
relation between the complexity of observable
phenomena and the relatively simple rules they are
seen to embody. Traditionally the natural sciences,
especially physics, have attempted to reduce
apparent complexity to underlying simplicity. The
attempt to find the "fundamental building blocks" of
the universe in quarks is one example of this
endeavor; the mapping of the human genome is
another.[14] The sciences of complexity, with their
origins in nonlinear dynamics, complicated this
picture by demonstrating that for certain nonlinear
dynamical systems, the evolution of the system could
not be predicted, even in theory, from the initial
conditions (as Ray did not know what creatures
would evolve from the ancestor). Thus the sciences
of complexity articulated a limit on what
reductionism could accomplish. In a significant
sense, however, AL researchers have not
relinquished reductionism. In place of predictability,
traditionally the test of whether a theory works, they
emphasize emergence. Instead of starting with a
complex phenomenal world and reasoning back
through chains of inference to what the fundamental
elements must be, they start with the elements and
complicate them through appropriately nonlinear
processes so that the complex phenomenal world
appears on its own.[15]
Why is one justified in calling the simulation and the
phenomena that emerge from it a "world"? Precisely
because they are generated from simple underlying
rules and forms. AL reinscribes, then, the
mainstream assumption that simple rules and forms
give rise to phenomenal complexity. The difference
is that AL starts at the simple end where synthesis
can move forward spontaneously, rather than at the
complex end where analysis must work backward.
Christopher Langton, in his explanation of what AL
can contribute to theoretical biology, makes this
difference explicit. "Artificial Life," he writes, "is the
study of man-made systems that exhibit behaviors
characteristic of natural living systems. It
complements the traditional biological sciences
concerned with the analysis of living organisms by
attempting to synthesize life-like behaviors within
computers and other artificial media. By extending
the empirical foundation upon which biology is
based beyond the carbon-chain life that has evolved
on Earth, Artificial Life can contribute to theoretical
biology by locating life-as-we-know-it within the
larger picture of life-as-it-could-be ."[16]
The presuppositions informing such statements
have been studied by Stefan Helmreich, an
anthropologist who spent several months at the
Santa Fe Institute.[17] Helmreich interviewed
several of the major players in the American AL
community, including Christopher Langton and
Thomas Ray, about whom we have already heard, as
well as John Holland and others. He summarizes the
views of his informants about the "worlds" they
create. "For many of the people I interviewed, a
'world' or 'universe' is a self-consistent, complete,
and closed system that is governed by low level laws
that in turn support higher level phenomena which,
while dependent on these elementary laws, cannot
be simply derived from them."[18] Helmreich uses
comments from the interviews to paint a fascinating
picture about the various ways in which simple laws
are believed to underlie complex phenomena.
Several informants thought that the world was
mathematical in essence. Others held the view, also
extensively articulated by Edward Fredkin (about
whom we will hear more shortly), that the world is
fundamentally comprised of information.[19] From
these points of view, phenomenological experience is
itself a kind of illusion, covering over an underlying
reality of simple forms. For them, a computer
program that generates phenomenological
complexity out of simple forms is no more or less
illusory than the "real" world.
The form/matter dichotomy is intimately related to
this vision, for reality at the fundamental level is
seen as form rather than matter, specifically as
informational code whose essence lies in a binary
choice rather than a material substrate. Fredkin, for
example, says that reality is a software program run
by a cosmic computer, whose nature must forever
remain unknown to us because it lies outside the
programs that run on it.[20] For Fredkin, AL
programs are alive in precisely the same sense as
biological life--because they are complex phenomena
generated by underlying binary code. The
assumption that form occupies a foundational
position relative to matter is especially easy to make
with information technologies, since information is
defined in theoretic terms (as we have seen) as a
probability function and thus as a pattern or form
rather than a materially instantiated entity.
Information technologies seem to realize a dream
impossible in the natural world--the opportunity to
look directly into the inner workings of reality at its
most elemental level. The directness of the gaze does
not derive from the absence of mediation. On the
contrary, our ability to look into programs like
Tierra is highly mediated by everything from
computer graphics to the processing program that
translates machine code into a high-level computer
language such as C++. Rather, the gaze is privileged
because the observer can peer directly into the
elements that the world is before it cloaks itself with
the appearance of complexity. Moreover, the
observer is presumed to be cut from the same cloth
as the world he inspects, inasmuch as he is also
constituted through binary processes similar to
those he sees inside the computer. The essence of
Tierra as an artificial world is no different from the
essence of the observer or the world he occupies: all
are constituted through forms understood as
informational patterns. When form is triumphant,
Tierra's "creatures" are, in a disconcertingly literal
sense, just as much life forms as any other organisms.
We are now in a position to understand the deep
reasons why some practitioners think of programs
like Tierra not as models or simulations but life
itself. As Langton and many others point out, in the
analytic approach reality is modeled by treating a
complex phenomenon as if it were comprised of
smaller constituent parts. These parts are broken
down into still smaller parts, until one arrives at
parts sufficiently simplified so that they can be
treated mathematically. Most scientists would be
quick to agree that the model is not the reality,
because they recognize that many complexities had
to be tossed out by the wayside in order to lighten
the wagon sufficiently to get it over the rough places
in the trail. Their hope is that the model nevertheless
captures enough of the relevant aspects of a system
to tell them something significant about how reality
works. In the synthetic approach, by contrast, the
complexities emerge spontaneously as a result of the
system's operation. The system itself adds back in
the baggage that had to be tossed out in the analytic
approach. (Whether it is the same baggage remains,
of course, to be seen.) In this sense Artificial Life
poses an interesting challenge to the view of
nineteenth-century vitalists, who saw in the analytic
approach a reductionist methodology that could
never adequately capture the complexities of life. If it
is true that the analytical approach murders by
dissection, by the same reasoning the synthetic
approach of AL may be able to procreate by
emergence.
In addition to these philosophical considerations,
there are also more obviously political reasons to
make a strong claim for the "aliveness" of AL. As a
new kid on the block, Artificial Life must jockey for
position with larger, more well-established research
agendas. A common reaction from other scientists is,
"Well, this is all very interesting, but what good is
it?" Even AL researchers joke that AL is a solution in
search of a problem. When applications are
suggested, they are often open to cogent objections.
As long as AL programs are considered to be
simulations, any results produced from them may be
artifacts of the simulation rather than properties of
natural systems. So what if a certain result can be
produced within the simulation? It is artifactual and
therefore non-signifying with respect to the natural
world unless the same mechanisms can be shown to
be at work in natural systems.[21] These difficulties
disappear, however, if AL programs are themselves
alive. Then the point is not that they model natural
systems but rather that they are, in themselves, also
alive and therefore as worthy of study as
evolutionary processes in naturally-occurring media.
This is the tack that Christopher Langton takes when
he compares AL simulations to synthetic
chemicals.[22] In the early days, he observes, the
study of chemistry was confined to naturally
occurring elements and compounds. Although some
knowledge could be gained from these, the results
were limited by what lay ready at hand. Once
researchers learned to synthesize chemicals, their
knowledge took a quantum leap forward, for then
chemicals could be tailored to specific research
problems. Similarly, theoretical biology has been
limited to the case that lay ready to hand, namely the
evolutionary pathways taken by carbon-based life. It
is notoriously difficult to generalize from a single
instance, but theoretical biology had no choice;
carbon-based life was it. Now a powerful new
instance has been added to the repertoire, for AL
simulations represent an alternative evolutionary
pathway followed by silicon-based life forms.
What theoretical biology looks for, in this view, are
similarities that cut across the particularities of the
media. In "Beyond Digital Naturalism," Walter
Fontana and his coauthors lay out a research agenda
"ultimately motivated by a premise: that there exists
a logical deep structure of which carbon chemistrybased
life is a manifestation. The problem is to
discover what it is and what the appropriate
mathematical devices are to express it".[23] Such a
research agenda presupposes that the essence of life,
understood as logical form, is independent of the
medium. More is at stake in this agenda than
expanding the frontiers of theoretical biology. By
positing AL as a second instance of life, researchers
affect the definition of biological life as well, for now
it is the juxtaposition that determines what counts as
fundamental, not carbon-based forms by themselves.
This change hints at how far-reaching the
implications can be of the narrative of Artificial Life
as an alternate evolutionary pathway for life on
earth. To explore these implications, let us turn to
the third level of narrative, where we will consider
stories about the relation of humans to our silicon
cousins, the Artificial Life forms who represent the
road not taken--until now.
Reconfiguring the Body of Information
As research on Artificial Life forms continues and
expands, the construction of human life is affected as
well. Two different narratives of how the human will
be reconfigured in the face of artificial bodies of
information are told by Rodney Brooks of the
Artificial Intelligence Laboratory at MIT, and Hans
Moravec, of whom we have already heard. Whereas
Moravec privileges consciousness as the essence of
human being and wants to preserve it intact, Brooks
speculates that the more essential property is the
ability to move around and interact robustly with the
environment. Instead of starting with the most
advanced qualities of human thought, Brooks starts
with locomotion and simple interactions and works
from the bottom up. Despite these different
orientations, both Brooks and Moravec see the
future of human being inextricably bound up with
Artificial Life. Indeed, in the future world they
envision, it will be difficult or impossible to
distinguish between natural and artificial life, human
and machine intelligence.
In Mind Children: The Future of Robot and Human
Intelligence, Moravec argues that the age of carbonbased
life is drawing to a close.[24] Humans are
about to be replaced as the dominant life form on the
planet by intelligent machines. Drawing on the work
of Cairns-Smith, Moravec suggests that such a
revolution is not unprecedented. Before protein
replication developed, a primitive form of life existed
in certain silicon crystals that had the ability to
replicate. But protein replication was so far superior
that it soon left the replicating crystals in the dust.
Now silicon has caught up with us again, in the form
of computers and computerized robots. Although the
Cairns-Smith hypothesis has been largely
discredited, in Moravec's text it serves the useful
purpose of increasing the plausibility of his vision by
presenting the carbon-silicon struggle as a rematch
of a earlier contest rather than an entirely new
event.
A different approach is advocated by other members
of the Artificial Life community, among them Rodney
Brooks, Pattie Maes, and Mark Tilden.[25] They
point to the importance of having agents who can
learn from interactions with a physical environment.
Simulations, they believe, are limited by the
artificiality of their context. Compared to the rich
variety and creative surprises of the natural world,
simulations are stick worlds populated by stick
figures. No one argues this case more persuasively
than Brooks. When I talked with him at his MIT
laboratory, he mentioned that he and Hans Moravec
were roommates in college (a coincidence almost
allegorical in its neatness). Moravec, for his senior
project, had built a robot that used a central
representation of the world to navigate. The robot
would go a few feet, feed in data from its sensors to
the central representation, map its new position, and
move a few more feet. Using this process, it would
take several hours to cross a room. If anyone came in
during the meantime, it would be thrown hopelessly
off. Brooks, a loyal roommate, stayed up late one
night to watch the robot as it carried out its
agonizingly slow perambulation. It occurred to him
that a cockroach could accomplish the same task in a
fraction of the time, and yet the cockroach could not
possibly have as much computing power aboard as
the robot. He decided that there had to be a better
way and began building robots according to a
different philosophy.
In his robots, Brooks uses what he calls
"subsumption architecture." The idea is to have
sensors and actuators connected directly to simple
finite state machine modules, with a minimum of
communication between them. Each system "sees"
the world in an entirely different way from the
others. There is no central representation, only a
control system that kicks in to adjudicate when there
is a conflict between the distributed modules. Brooks
points out that the robot does not need to have a
coherent concept of the world; instead it can learn
what it needs directly through interaction with its
environment. The philosophy is summed up in his
aphorism: "the world is its own best model."[26]
Subsumption architecture is designed to facilitate
and capitalize on emergent behavior. The idea can be
illustrated with Genghis, a six-legged robot
somewhat resembling a oversize cockroach, that
Brooks hopes to sell to NASA as a planetary
explorer.[27] Genghis's gait is not programmed in
advance. Rather, each of the six legs is programmed
to stabilize itself in an environment that includes the
other five. Each time Genghis starts up, it has to learn
to walk anew. For the first few seconds it will
stumble around; then, as the legs begin to take
account of what the others are doing, a smooth gait
emerges. The robot is relatively cheap to build, more
robust than the large planetary explorers NASA
currently uses, and under its own local control
rather than dependent on a central controller who
may not be on site to see what is happening. "Fast,
cheap, and out of control" is another aphorism that
Brooks uses to sum up the philosophy behind the
robots he builds.
Brooks's program has been carried further by Mark
Tilden, a Canadian roboticist who worked under
Brooks and now is at the University of Waterloo. In
my conversation with him, Tilden mentioned that he
grew up on a farm in Canada and was struck by how
chickens ran around after they had their heads cut
off, performing, as he likes to put it, complicated
navigational tasks in three-dimensional space
without any cortex at all. He decided considerable
computation had to be going on in the peripheral
nervous system. He used the insight to design insectlike
robots that operate on nervous nets
(considerably simpler than the more complex neural
nets) comprised of no more than 12 transistor
circuits. These robots use analogue rather than
digital computing to carry out their tasks. Like
Genghis, their gait is emergent. They are remarkably
robust, able to right themselves when turned over,
and can even learn a compensatory gait when one of
their legs is bent or broken off. [28]
Narratives about the relation of these robots to
humans emerge when Brooks and others speculate
about the relevance of their work to human
evolution. Brooks acknowledges that the robots he
builds have the equivalent of insect intelligence. But
insect intelligence is, he says, nothing to sneer at.
Chronologically speaking, by the time insects
appeared on earth, evolution was already 95% of the
way to creating human intelligence.[29] The hard
part, he believes, is evolving creatures who are
mobile and who can interact robustly with their
environment. Once these qualities are in place, the
rest comes relatively quickly, including the
sophisticated cognitive abilities that humans
possess. How did humans evolve? In his view,
through the same kind of mechanisms that he uses in
his robots, namely distributed systems that interact
robustly with the environment and that
consequently "see" the world in very different ways.
Consciousness is a relatively late development,
analogous to the control system that kicks in to
adjudicate conflicts between the different
distributed systems. Consciousness is, as Brooks
likes to say, a "cheap trick," that is, an emergent
property that increases the functionality of the
system but is not part of system's essential
architecture. Consciousness does not need to be, and
in fact is not, representational. Like the robot's
control system, consciousness does not require an
accurate picture of the world; it only needs a reliable
interface. As evidence that human consciousness
works this way, Brooks adduces the fact that most
adults are unaware that they go through life with a
large blank spot in the middle of their visual field.
This reasoning leads to yet another aphorism that
circulates through the AL community: consciousness
is an epiphenomenon. The implication is that
consciousness, although it thinks it is the main show,
is in fact a latecomer, a phenomenon dependent on
and arising from deeper and more essential layers of
perception and being. The view is reminiscent of the
comedian Emo Phillips's comment. "I used to think
that the brain was the most wonderful organ in the
body," he says. "But then I thought, who's telling me
this?"
It would be difficult to imagine a more contrarian
position to the one Hans Moravec espouses when he
equates consciousness with human subjectivity. In
this respect Moravec aligns with artificial
intelligence (AI), whereas Brooks and his colleagues
align with Artificial Life (AL).[30] Michael Dyer, in
his comparison of the two fields, points out that
whereas AI envisions cognition as the operation of
logic, AL sees it as the operation of nervous systems;
AI starts with human-level cognition, AL with insector
animal-level; in AI, cognition is constructed as if it
were independent of perception, whereas in AL it is
integrated with sensory/motor experiences.[31]
Brooks and his colleagues argue forceful that AI has
played itself out and that the successor paradigm is
AL. Brooks and Ray both believe that it will
eventually be possible, using AL techniques, to
evolve the equivalent of human intelligence inside a
computer. For Brooks, that project is already
underway with "Cog," a head-and-torso robot with
sophisticated visual and manipulative capability. But
AL researchers go about creating high-level
intelligence in dramatically different ways that AI.
Consider the implications of this shift for the
construction of the human. The goal of artificial
intelligence was to build an intelligence inside a
machine comparable to that of a human. The human
was the measure, the machine the attempt at
instantiation in a different medium. This assumption
deeply informs the Turing test, dating from the early
days of the AI era, which defined success as building
a machine intelligence that cannot be distinguished
from a human intelligence. By contrast, the goal of
Artificial Life is to evolve intelligence within the
machine through pathways found by the "creatures"
themselves. Rather than serving as the measure to
judge success, human intelligence is itself is
reconfigured in the image of this evolutionary
process. Whereas AI dreamed of creating
consciousness inside a machine, AL sees human
consciousness, understood as an epiphenomenon,
perching on top of the machine-like functions that
distributed systems carry out.[32] In the AL
paradigm, the machine becomes the model for
understanding the human. Thus the human is
transfigured into the posthuman.
To indicate how widespread is this refashioning of
the human into the posthuman, in the following
section I want to sketch with broad strokes some of
the research contributing to this project. The sketch
will necessarily be incomplete. Yet even this
imperfect picture will be useful in indicating the
scope of the posthuman. So pervasive is this
refashioning that it amounts to a new world view--
still in process, highly contested and often
speculative, yet with enough links between different
sites to be edging toward a vision of what we might
call the computational universe. In the
computational universe, the essential function for
both intelligent machines and humans is processing
information. Indeed, the essential function of the
universe as a whole is processing information. In a
different way than Norbert Wiener imagined, the
computational universe realizes the cybernetic
dream of creating a world in which humans and
intelligent machines can both feel at home. That
equality derives from the view that our world--and
the great cosmos itself--is a vast computer, and we
are programs it runs.
The Computational Universe
Let us start our tour of the computational universe at
the most basic level, the level that underlies all life
forms, indeed all matter and energy. The units that
comprise this level are cellular automata. From their
simple on-off functioning, everything else is built up.
Cellular automata were first proposed by John von
Neumann in his search to describe self-reproducing
automata. Influenced by Warren McCulloch and
Walter Pitts's work on the on-off functioning of the
neural system, von Neumann used the McCullochPitts
neuron as a model for computers, inventing
switching devices that could perform the same kind
of logical functions McCulloch had outlined for
neurons. He also proposed that the neural system
could be treated as a Turing machine. Biology thus
provided him with clues to build computers, and
computers provided clues for theoretical biology. To
extend the analogy between biological organism and
machine, he imagined a giant automaton that could
perform the essential biological function of selfreproduction.[33]
(Maturana referred to it, as we
saw in Chapter 6, when he made the point that what
von Neumann modeled were biologists' descriptions
of living processes rather than the processes
themselves.)
Stanislaw Ulam, a Polish mathematician who worked
with von Neumann at Los Alamos during World War
II, suggested to von Neumann that he could achieve
the same result by abstracting the automaton into a
grid of cells. Thus von Neumann reduced the massive
and resistant materiality of the self-reproducing
automaton as he had originally envisioned it to
undifferentiated cells with bodies so transparent
they were constituted as squares marked off on
graph paper and later as pixels on computer
screens.[34]
Each cellular automaton (or CA) functions as a
simple finite state machine, with its state determined
solely by its initial condition (on or off), rules telling
it how to operate, and the state of its neighbors at
each moment. For example, the rule for one group of
CAs might state "On if two neighbors are on,
otherwise off." Each cell checks on the state of its
neighbors and updates its state in accordance with
its rules, at the same time that the neighboring cells
also update their states. In this way the grid of cells
goes through one generation after another, in a
succession of states that (on a computer) can easily
stretch to hundreds of thousands of generations.
Extremely complex patterns can build up, emerging
spontaneously from interactions between the CAs.
Programmed into a computer and displayed on the
screen, CAs give the uncanny impression of being
alive. Some patterns spread until they look like the
designs of intricate Oriental carpets, others float
across the screen like gliders, and still others flourish
only to die out within a few hundred generations.
Looking at the emergence of complex dynamical
patterns from these simple components, more than
one researcher has had the intuition that such a
system can explain the growth and decay of patterns
in the natural world. Edward Fredkin took this
insight further, seeing in cellular automata the
foundational structure from which everything in the
universe is built up.
How does this building up occur? In the
computational universe, the question can be rephrased
by asking how higher-level computations
can emerge spontaneously from the underlying
structure of cellular automata. Christopher Langton
has done pioneering work analyzing the conditions
under which cellular automata can support the
fundamental operations of computation, which he
analyzes as requiring the transfer, storage, and
modification of information.[35] His research
indicates that computation is most likely to arise at
the boundary between ordered structures and
chaotic areas. In an ordered area, the cells are tightly
tied together through rules that make them
extremely interdependent; it is precisely this
interdependence that leads to order. But the tightly
ordered structure also means that the cells as an
aggregate will be unable to perform some of the
essential tasks of higher-level computation,
particularly the transfer and modification of
information. In a chaotic area, by contrast, the cells
are relatively independent of one another; this
independence is what makes them appear
disordered. While this state lends itself to
information transfer and modification, here the
storage of information is a problem, because no
pattern persists for long. Only in boundary areas
between chaos and order is there the necessary
tension between innovation and replication that
allows patterns to build up, be modified, and travel
over long distances without dying out.
These results are strikingly similar to those
discovered by Stuart Kauffman in his work on the
origins of life. Kauffman was Warren McCulloch's last
protégé; McCulloch said in several interviews that he
regarded Kauffman as his most important
collaborator since Walter Pitts.[36] Kauffman argues
that natural selection alone is not enough to explain
the relatively short time scale on which life
arose.[37] Some other ordering principle is
necessary, which he locates in the ability of complex
systems spontaneously to self-organize. Calculating
the conditions necessary for large molecules to
organize spontaneously into the building blocks of
life, he found that life is most likely to arise at the
edge of chaos. This means that there is a striking
correspondence between the conditions under
which life is likely to emerge and those under which
computation is likely to emerge--a convergence
regarded by many researchers as an unmistakable
sign that computation and life are linked at a deep
level. In this view, humans are programs that run on
the cosmic computer. When humans build intelligent
computers to run AL programs, they replicate in
another medium the same processes that brought
themselves into being.
An important reason why such connections can be
made so easily between one level and another is that
in the computational universe, everything is
reducible at some level to information.
Yet among proponents of the computational
universe, not everyone favors disembodiment, just
as they did not in the Macy conferences when the
idea of information was being formulated. Consider,
for example, the different approaches taken by
Edward Fredkin and the new field of evolutionary
psychology. When Fredkin asserts that we can never
know the nature of the cosmic computer on which
we run as programs, he puts the ultimate material
embodiment out of our reach. All we will ever see, as
human beings, are the informational forms of pure
binary code that he calls cellular automata. By
contrast, the field of evolutionary psychology seeks
to locate modular computer programs in embodied
human beings whose physical make-up is the result
of hundreds of thousands of years of evolutionary
processes.
The agenda for this new field is set out by Jerome H.
Barkow, Leda Cosmides and John Tooby in The
Adapted Mind: Evolutionary Psychology and the
Generation of Culture. Like Minksy, they argue that
the model (or metaphor) of computation provides
the basis for a wholesale revision of what counts as
human nature.[38] They aim to overcome objections
by cultural anthropologists and others to the idea of
"human nature" by offering a more flexible version
of how that nature is constituted. They argue that
behavior can be modeled as modular computer
programs running in the brain. The underlying
structure of these programs is the result of
thousands of years of evolutionary tinkering.
Adaptations which conferred superior reproductive
fitness survived; those that did not died out. The
programs are structured to enable certain
functionalities to exist in humans, and these
functionalities are universally present in all humans.
These functionalities, however, represent potentials
rather than actualities. Just as the actual behavior of
a computer program is determined by a constant
underlying structure and varying inputs, so actual
human behavior results from an interplay between
the potential represented by the functionalities and
inputs provided by the environments. All normal
human infants, for example, have the potential to
learn language. If they are not exposed to language
by a certain critical age, however, this potential
disappears and they can never become linguistically
competent. Although human behavior varies across a
wide spectrum of actualization, it nevertheless has
an underlying universal structure determined by
evolutionary adaptations. Thus a science of
evolutionary psychology is possible, for the existence
of a universal underlying structure guarantees the
regularities that any science needs to formulate
knowledge that will be coherent and consistent.
This cybernetic/computer vision of human behavior
leads to a very different account of "human nature."
Although the evolutionary programs the
brain/computer runs do not lead to universal
behavior, they are nevertheless rich with content.
The potentials lie not just in the structure of the
general machine but much more specifically in
environmentally adaptive programs that proactively
shape human responses. Thus children are not
merely capable of learning language; they actively
want to learn language and will invent it among
themselves if no one teaches them.[39] Like
Wiener's cybernetic machine, the cybernetic brain is
responsive to the flow of events around it and
adaptive over an astonishingly diverse set of
circumstances. It is a measure of how much our
vision of machines has changed since the Industrial
Revolution that only the intelligent machine is seen
to be light enough on its feet to do justice to human
variability.
It will now perhaps be clear why the most prized
functionality is the ability to process information, for
in the computational universe, information is king.
Luc Steels, an Artificial Life researcher, reinscribes
this value when he distinguishes between first-order
and second-order emergence (surely it is no accident
that the terminology here echoes the distinction
between first- and second-order cybernetics, the
grandparent and parent of Artificial Life). First-order
emergence denotes any property that is generated
by interactions between components, that is,
properties that emerge as a result of those
interactions, in contrast to properties inherent in the
components themselves. Among all such emergent
properties, second-order emergence grants special
privilege to those that bestow additional
functionality on the system, particularly the ability to
process information.[40] To create successful
Artificial Life programs, it is not enough to create
just any emergence. Rather, the programmer
searches for a design that will lead to second-order
emergence. Once second-order emergence is
achieved, the organism has in effect evolved the
capacity to evolve. Then evolution can really take off.
Humans evolved through a combination of chance
and self-organizing processes until they reached the
point where they could take conscious advantage of
the principles of self-organization to create
evolutionary mechanisms. They used this ability to
build machines capable of self-evolution. Unlike
humans, however, the machine programs are not
hampered by the time restrictions imposed by
biological evolution and physical maturation. They
can run through hundreds of generations in a day,
millions in a year. Until very recently, humans have
been without peer in their ability to store, transmit
and manipulate information. Now they share that
ability with intelligent machines. To foresee the
future of this evolutionary path, we have only to ask
which of these organisms, competing in many ways
for the same evolutionary niche, has the informationprocessing
capability to evolve more quickly.
This conclusion makes clear, I think, why the
computational universe should not be accepted
uncritically. If the name of the game is processing
information, it is only a matter of time until
intelligent machines replace us as our evolutionary
heirs. Whether we decide to fight them or join them
by becoming computers ourselves, the days of the
human race are numbered. The problem here does
not lie in the choice between these options; rather, it
lies in the framework constructed so as to make
these options the only two available. The
computational universe becomes dangerous when it
goes from being a useful heuristic to an ideology that
privileges information over everything else. As we
have seen, information is a socially constructed
concept that could have been, and was, envisioned
differently than its currently accepted definition. Just
because information has lost its body does not mean
that humans or the world have lost theirs.
Fortunately, not all theorists agree that it makes
sense to think about information as an entity apart
from the medium that embodies it. Let us re-visit
some of the sites in the computational universe, this
time to locate those places where the resistance of
materiality does useful work within the theories.
From this perspective, fracture lines appear that
demystify the program(s) and make it possible to
envision other futures than the one sketched above,
futures in which human beings feel at home in the
universe because they are embodied creatures living
in an embodied world.
Murmurs from the Body
One of the striking differences between researchers
who work with flesh and those who work with
computers is how nuanced the sense of the body's
complexity is for those who are directly engaged
with it. The difference can be seen in the contrast
between Marvin Minsky's "Society of Mind"
approach and that of the evolutionary psychologists.
Although Minsky frequently uses evolutionary
arguments to clarify a program's structure, his main
interest clearly lies in building computer models that
can accomplish human behaviors.[41] He
characteristically thinks in terms of computer
architecture, about which he knows a great deal,
rather than human physiology. In his lectures (less
so in his writing), he rivals Moravec in his consistent
downplaying of the importance of embodiment. At
the public lecture he delivered on the eve of the Fifth
Conference on Artificial Life in Nara, Japan, he
argued that only with the advent of computer
languages has a symbolic mode of description arisen
adequate to account for human beings, whom he
defines as complicated machines.[42] "A person is
not a head and arms and legs," he remarked. "That's
trivial. A person is a very large multiprocessor with a
million times a million small parts, and these are
arranged as a thousand computers." It is not
surprising, then, that he shares with Moravec the
dream of banishing death by downloading
consciousness into a computer. "The most important
thing about each person is the data, and the
programs in the data that are in the brain. And some
day you will be able to take all that data, and put it
on a little disk, and store it for a thousand years, and
then turn it on again and you will be alive in the
fourth millennium or the fifth millennium."
Yet anyone who actually works with embodied
forms, from the relatively simple architecture of
robots to the vastly more complicated workings of
the human neural system, knows that it is by no
means trivial to deal with the resistant materialities
of embodiment. To Minsky, these problems of
embodiment are nuisances that do not even have the
virtue of being conceptually interesting. In his
plenary lecture at Artificial Life V, he asserted that a
student who constructed a simulation of robot
motion learned more in six months than the
roboticists did in six years from building actual
robots.[43] Certainly simulations are useful for a
wide range of problems, for they abstract a few
features out of a complex interactive whole and then
manipulate those features to get a better
understanding of what is going on. Compared to the
real world, they are more efficient precisely because
they are more simplified. The problem comes when
this mode of operation is taken to be fully
representative of a much more complex reality, and
everything that is not in the simulation is declared to
be trivial, unimportant, or uninteresting.
Like Varela in his criticisms and modifications of
Minsky's model (discussed in Chapter 6), Barkow,
Tooby and Cosmides are careful not to make this
mistake. They acknowledge that the mind-body
duality is a social construction which obscures the
holistic nature of human experience. Another
researcher who speaks powerfully to the importance
of embodiment is Antonio Damasio in Descartes'
Error: Emotion, Reason, and the Human Brain.44
Discussing the complex mechanisms by which mind
and body communicate, he emphasizes that the body
is more than a life support system for the brain. The
body "contributes a content that is part and parcel of
the workings of the normal mind" (p. 226). Drawing
upon his detailed knowledge of neurophysiology and
his years of experience working with patients who
have suffered neural damage, he argues that feelings
constitute a window through which the mind looks
into the body. Feelings are how the body
communicates to the mind information about its
structure and continuously varying states. If feelings
and emotions are the body murmuring to the mind,
then feelings are "just as cognitive as other
precepts," part of thought and indeed part of what
makes us rational creatures (p. xv). He finds it
significant that cognitive science, with its
computational approach to mind, has largely ignored
the fact that feelings even exist (with some notable
exceptions such as The Embodied Mind, discussed in
Chapter 6). One can guess what his response to the
scenario of downloading human consciousness
would be from the following passage. "In brief,
neural circuits represent the organism continuously,
as it is perturbed by stimuli from the physical and
sociocultural environments, and as it acts on those
environments. If the basic topic of those
representations were not an organism anchored in
the body, we might have some form of mind, but I
doubt that it would be the mind we do have" (226).
Human mind without human body is not human
mind. More to the point, it doesn't exist.
What are we to make, then, of the posthuman? As
the liberal humanist subject is dismantled, many
parties are contesting to determine what will count
as (post)human in its wake. For most of the
researchers discussed in this chapter, becoming a
posthuman means much more than having
prosthetic devices grafted onto one's body. It means
envisioning humans as information-processing
machines with fundamental similarities to other
kinds of information-processing machines, especially
intelligent computers. Because of how information
has been defined, many people holding this view
tend to put materiality on one side of a divide and
information on the other side, making it possible to
think of information as a kind of immaterial fluid
that circulates effortlessly around the globe while
still retaining the solidity of a reified concept. Yet
this is not the only view, and in my judgment, it is not
the most compelling one. Other voices insist that the
body cannot be left behind, that the specificities of
embodiment matter, that mind and body are finally
the "unity" Maturana insisted upon rather than two
separate entities. Increasingly the question is not
whether we will become posthuman, for
posthumanity is already here. Rather, it is what kind
of posthumans we will be. What the narratives of
Artificial Life reveal is that if we acknowledge that
the observer must be part of the picture, bodies can
never be made of information alone, no matter which
side of the computer screen they are on.
Endnotes
1 Francisco Varela, Evan Thompson and Eleanor Rosch, The
Embodied Mind: Cognitive Science and Human Experience
(Cambridge: MIT Press, 1991).
[2]Francisco Varela and Paul Bourgine, editors, Toward a Practice
of Autonomous Systems: Proceedings of the First European
Conference on Artificial Life (Cambridge: MIT Press, 1992).
[3] Humberto R. Maturana and Francisco Varela, Autopoiesis and
Cognition: The Realization of the Living (Dordrecht: D. Reidel,
1980).
[4]Thomas S. Ray, "A Proposal to Create Two Biodiversity
Reserves: One Digital and One Organic," presentation at Artificial
Life IV, Cambridge MA, July 1994.
[5]Thomas S. Ray, "An Evolutionary Approach to Synthetic
Biology: Zen and the Art of Creating Life," Artificial Life I, no. 1/2
(Fall 1993/Winter 1994): 179-209, especially 180.
[6]Luc Steels offers useful definitions of emergence in "The
Artificial Life Roots of Artificial Intelligence," Artificial Life I, no.
1/2 (Fall 1993/Winter 1994): 75-110. He distinguishes between
first-order emergence, defined as a property not explicitly
programmed in, and second-order emergence, an emergent
behavior that adds additional functionality to the system. In
general AL researchers try to create second-order emergence, for
then the system can use its own emergent properties to create an
upward spiral of continuing evolution and emergent behaviors.
James P. Crutchfield makes a similar point in "Is Anything Ever
New? Considering Emergence," in Integrative Themes, edited by G.
Cowan, D. Pines, and D. Melzner, Santa Fe Institute Studies in the
Sciences of Complexity XIX (Redwood City CA: Addison-Wesley,
1994): 1-15. For a criticism of emergence, see Peter Cariani,
"Adaptivity and Emergence in Organisms and Devices," World
Futures 32 (1991): 111-32.
[7]The Tierra program is described in Thomas S. Ray, "An
Approach to the Synthesis of Life," Artificial Life II, edited by
Christopher G. Langton, Charles Taylor, J. Doyne Farmer, and
Steen Rasmussen, Proceedings Volume X, Santa Fe Institute
Studies in the Sciences of Complexity (Redwood City CA: AddisonWesley,
1992), pp. 371-408. "An Evolutionary Approach to
Synthetic Biology" explains and expands on the philosophy
underlying Tierra, working paper, ATR Human Information
Processing Research Laboratories, Kyoto Japan. Further
information about Tierra can be found in "Population Dynamics of
Digital Organisms," Artificial Life II Video Proceedings, edited by
Christopher G. Langton (Redwood City CA: Addison-Wesley,
1991). A popular account can be found in "Electronic Ecosystem,"
John Travis, Science News 140, no. 6 (August 10, 1991): 88-90.
[8]"Simple Rules . . . Complex Behavior," produced and directed by
Linda Feferman for the Santa Fe Institute, 1992.
[9]Richard Dawkins, The Selfish Gene (Oxford: Oxford University
Press, first edition 1976).
[10]William Gibson, Neuromancer (New York: Ace, 1984).
[11]In "An Evolutionary Approach," Ray writes that "The 'body' of
a digital organism is the information pattern in memory that
constitutes its machine language program" (184).
[12]Quoted in Stefan Helmreich, "Anthropology Inside and Outside
the Looking-Glass Worlds of Artificial Life," unpublished
manuscript (1994), p. 11. An earlier version of this work was
published as a working paper at the Santa Fe Institute under the
title "Travels through 'Tierra," Excursions in 'Echo':
Anthropological Refractions on the Looking-Glass Worlds of
Artificial Life," 94-04-024. Helmreich included in this version
some remarks that the administrators of SFI evidently found
offensive, including a comparison between the scientists' belief in
the "aliveness" of Artificial Life to the seemingly bizarre (to
Westerners) beliefs held by such marginal cultural groups as the
Trobriand Islanders. Objecting that Helmreich's work was not
scientific and misrepresented the science done at SFI, the
administrators had the working paper removed from the shelves
and deleted from the list of available publications.
[13]Christopher Langton, "Artificial Life," in Artificial Life, edited by
Christopher Langton (Redwood City CA: Addison-Wesley, 1989),
pp. 1-47, especially p. 1.
[14]Richard Doyle has written on the simplification of body to
information in the human genome project in On Beyond Living:
Rhetorics of Vitality and Post-Vitality in Molecular Biology
(Stanford: Stanford University Press, 1997).
[15]Actually, both inference and deduction are at work in most AL
research, as they usually are in scientific projects. AL researchers
study the complex--> simple route for clues on how to construct
programs that will be able to move from simple--> complex.
[16]Christopher Langton, "Artificial Life," p. 1.
[17]Extensive interviews with AL researchers have also been
conducted by Steven Levy, as recounted in his useful
popularization, Artificial Life: The Quest for a New Creation (New
York: Pantheon Books, 1992). A more technical account covering
much the same material as Levy can be found in Claus Emmeche,
The Garden in the Machine: The Emerging Science of Artificial Life
(Princeton: Princeton University Press, 1994). Although Emmeche
says in the opening pages that his book is intended for the general
reader, he soon leaves the simplistic style that characterizes the
first sections and moves into more interesting and demanding
material. Especially noteworthy is his discussion of the deep
problems raised about the nature of computation.
[18]Helmreich, p. 5.
[19]Edward Fredkin is something of a cult figure for researchers
interested in computational philosophies. After achieving financial
independence through the company he founded, he bought and
occasionally lives on his own island in the Caribbean. Although he
has himself published very little, several articles and part of a
book have been written about him. He is a faculty member at MIT
and has a research group there working out a universal theory of
cellular automata intending to show how cellular automata can
account for all the laws of physics. See for an account of his work,
see Robert Wright, Three Scientists and Their Gods: Looking for
Meaning in an Age of Information (New York: Times Books, 1988).
One of Fredkin's rare publications is "Digital Mechanics: An
Information Process Based on Reversible Universal Cellular
Automata," Physica D 45 (1990): 254-70. See also Julius Brown, "Is
the Universe a Computer?", New Scientist 14 (July 1990): 37-39.
Levy and Lemmeche both mention Fredkin.
[20]G. Kampis and V. Csanyi in "Life, Self-Reproduction and
Information: Beyond the Machine Metaphor," Journal of
Theoretical Biology 148 (1991): 17-32 have an important analysis
of the idea of self-reproduction in a machine context. They point
out that one's account of what happens in self-reproduction
changes depending on how the framing context is constructed. For
all machine (re)production, there is always a context in which
outside agency is needed to complete reproduction, in contrast to
the reproduction of [asexual] living organisms. By placing the last
computer out of sight, as it were, Fredkin has erased this context
from view, although he still has to posit it to explain how things
come into existence.
[21]For a research program that takes this objection into account,
see David Jefferson et al., "Evolution as a Theme in Artificial Life:
The Genesys/Tracker System," Computer Science Department
Technical Report CSD-900047 (December 1990), University of
California-Los Angeles. In the Tracker simulation, designed to
generate social behavior and food gathering strategies
characteristic of ants, Jefferson and his colleagues used two very
different kinds of algorithms to demonstrate that the behavior
generated by the simulations were not artifacts. They reasoned
that because the underlying structures of the simulations were
different, similarities of behavior could not be attributed to the
algorithms, only to the dynamics conceptualized through those
algorithms.
[22]Christopher Langton, "Editor's Introduction," Artificial Life I,
no. 1/2 (Fall 1993/Winter 1994): v-viii, especially v-vi.
[23]Walter Fontana, Gunter Wagner, and Leo W. Buss, "Beyond
Digital Naturalism," Artificial Life I, no. 1/2 (Fall 1993/Winter
1994): 211-227, especially 224.
[24]Hans Moravec, Mind Children: The Future of Robot and Human
Intelligence (Cambridge: Harvard University Press, 1988).
[25]See Pattie Maes, "Modeling Adaptive Autonomous Agents,"
Artificial Life I, no. 1/2 (Fall 1993/Winter 1994): 135-162; Rodney
Brooks, "New Approaches to Robotics," Science 253 (13
September 1991): 1227-1232; Mark Tilden, "Living Machines--
Unsupervised Work in Unstructured Environments," Los Alamos
National Laboratory, CB/MT-v1941114.
[26]Rodney A. Brooks, "Intelligence Without Representation,"
Artificial Intelligence 47 (1991): 139-159. See also The Artificial
Life Route to Artificial Intelligence: Building Embodied, Situated
Agents, edited by Luc Steels and Rodney Brooks (Hillsdale, NJ: L.
Erlbaum Associates, 1995).
[27]Genghis is described, among other places, in Rodney A. Brooks
and Anita M. Flynn, "Fast, Cheap, and Out of Control: a Robot
Invasion of the Solar System," Journal of the British Interplanetary
Society, 42 (1989): 478-485.
[28]Mark Tilden lectured and demonstrated his mobile robots at
the Center for the Study and Evolution of Life, University of
California, January 1995, where I had an opportunity to talk with
him.
[29]Brooks, "Intelligence without Representation."
[30]In my conversation with Moravec at the University of Illinois
"Cyberfest" in March 1997, he defended the top-down approach
by comparing the success of a robot-piloted car he designed with
Rodney Brooks' robots. While Moravec's robot car successfully
drove several hundred miles, Brooks' robots have scarcely been
out of the laboratory. The point is well taken, and future research
may well use a combination of both approaches. Moravec declared
himself a pragmatist, willing to use whatever works.
[31]Michael G. Dyer, "Toward Synthesizing Artificial Neural
Networks that Exhibit Cooperative Intelligent Behavior: Some
Open Issues in Artificial Life," Artificial Life I, no. 1/2 (Fall
1993/Winter 1994): 111-135, especially p. 112.
[32] Edwin Hutchins demystifies this proposition in Cognition in
the Wild (Cambridge: Mit Press, 1996), when he elegantly
demonstrates that humans normally act in environments where
cognition is distributed among a variety of human and non-human
actors, from graph paper and pencil to the sophisticated naval
guidance systems he discusses. His book, by grounding its
arguments in existing naval navigation techniques of the past and
present, shows that distributed cognition has been around about
as long as humans have.
[33]Steven Levy gives an account of von Neumann's selfreproducing
machine in Artificial Life. His account is based on the
rather sketchy information given by Arthur W. Burks (who edited
and compiled von Neumann's incomplete manuscript after his
death) of what Burks calls the kinematic model of selfreproduction.
Burks' version can be found in John von Neumann,
Theory of Self-Reproducing Automata (Urbana: University of
Illinois Press, 1966), pp. 74-90.
[34]Cellular automata are described in detail in von Neumann, pp.
91-156. See also Stephan Wolfram, one of the foremost
researchers on cellular automata, in "Universality and Complexity
in Cellular Automata," Physica D 10 (1984): 1-35, and "Computer
Software in Science and Mathematics," Scientific American 251
(August 1984): 188-203. In these articles, Wolfram concentrates
on one-dimensional CAs, where each generation appears as a line
and patterns appear as the lines proliferate down the screen (or
graph paper).
[35]Chris G. Langton, "Computation at the Edge of Chaos: Phase
Transition and Emergent Computation," Physica D 42 (1990); 12-
37.
[36]In Warren McCulloch's papers there is a letter of reference that
McCulloch wrote for Kauffmann, Warren McCulloch Papers,
American Philosophical Library, B/M139, Box 2. In several
lectures and interviews that McCulloch gave in the few years
before his death, he mentioned Kauffman as an important
collaborator.
[37]Stuart A. Kauffman, The Origins of Order: Self-Organization and
Selection in Evolution (New York: Oxford University Press, 1993).
See also his popularized version, At Home in the Universe: The
Search for the Laws of Self-Organization and Complexity (New
York: Oxford University Press, 1995).
[38]Jerome H. Barkow, Leda Cosmides and John Tooby, The
Adapted Mind: Evolutionary Psychology and the Generation of
Culture (New York: Oxford University Press, 1992); see especially
Tooby and Cosmides, "The Psychological Foundations of Culture,"
pp. 19-136. Tooby and Cosmides have also been instrumental in
forming the "Human Behavior and Evolution Society," which holds
annual conferences centered on the ideas of evolutionary
psychology. In some ways the HBES is a successor to sociobiology,
although with a more flexible framework of interpretation.
[39]Steven Pinker makes this point in The Language Instinct (New
York: W. Morrow, 1994). This model provides an interesting
corrective to Maturana's largely passive model of "languaging"
between "observers."
[40]Luc Steels; see note 6.
[41] Marvin Minsky, The Society of Mind (New York: Simon and
Schuster, 1985), especially pp. 17-24.
[42]Marvin Minsky, Public Lecture, "Why Computer Science is the
Most Important Thing That Has Happened to the Humanities in
5,000 Years," Nara, Japan, May 15, 1996. I am grateful to Nicholas
Gessler for providing me with his transcript of the lecture.
[43]Marvin Minsky, Plenary Lecture, "How Computer
Science Will Change Our Lives," Fifth Conference on
Artificial Life, Nara, Japan, May 17, 1996.
44Antonio R. Damasio, Descartes' Error: Emotion, Reason,
and the Human Brain (New York: G. P. Putnam, 1994).