Dynamic
texts offer new possibilities for reading and new
challenges in how we approach the reading object,
forcing the final object away from the idea of a
fixed form on a fixed surface. In order to read
such an object, one must look deeper, into the code
itself, and one must consider the various
ramifications inherent in a code-based work.
Ultimately, one must explore the edge where
language apparatuses engage.

Artificial
Intelligence (AI) has long been seen, by both its supporters
and its critics, as an attempt to develop formal
representations which are equivalent to human abilities. The
complexity of human behaviour and existence in the world, by
this way of thinking, is reduced to or replaced by formal
algorithms. When all physical objects and agents are encoded
as logical propositions and all action is encoded as changes
to those propositions, then behaving can be reduced to
thinking, which in turn can be understood as search through
sets of logical propositions. The messy properties of the
real world can be left behind, as we enter the rarefied
realm of the "closed world" of logic, based on the Closed
World Assumption: All knowledge can be logically derived
from what is already formalized; i.e. the world and
everything in it is fully encodable.

Supporters
of classical AI rejoice in the formal elegance of this
reduction, while detractors lament the limitations of this
"closed world" of AI. In particular, discussions on social
and cultural issues inherent in AI are difficult. AI
researchers tend to have difficulty reflecting on AI's
social embeddedness because technology is seen as existing
in a separate sphere.

For
this reason, many artists and others critical of AI have
supported an oppositional strand in AI termed situation
action. These critics within AI, notably Rodney Brooks,
Philip Agre, and David Chapman, argued that instead of
modeling the world, one should act on it, following the
slogan "the world is its own best representation." Situated
action states explicitly that the world is not adequately
encodable, and that intelligence comes into being in
interaction with this complex world. Its commitments to
embodiment and the incompleteness of formal approaches to
understanding the world seem, on the surface, more
compatible with artists' and humanists' ways of
conceptualizing reality and friendly to discussions about
the limits of AI as a formalism itself.

Yet,
for many situated action researchers, while the world is not
encodable, the mind is. Intelligent behavior is often said
to be reducible to simple rules. The apparent complexity of
behavior is due largely to the complexity of the world with
which an agent acts. Consciousness itself, it is said, is an
epiphenomenon - an illusion overlaying the simple encodings
which science has revealed as our actual essence. Subjective
human experience as discussed in the arts and humanities is
irrelevant, replaced by objective scientific fact. Rather
than a "closed world," we are here dealing with something we
might call "closed minds."

Yet a
radically different epistemological stance is becoming
apparent in contemporary offshoots of AI, including
statistical natural language processing and machine learning
as well as the work of hybrid artist-technologists like
Warren Sack and Michael Mateas. By this way of thinking, the
power of formal representations is not based on their
existence in a separate, clean closed world, but on their
embeddedness in a complex, incompletely formalizable outside
world. Web search systems like Google, for example, work not
by creating an abstract, formal representation of human
life, but by computing directly on language and links
created by humans for one another. The goal with such
systems is not to replace the human world with a formalized
ideal, but to opportunistically discover patterns in partial
formalizations of a much more complex reality.

In
these cases, instead of a closed world, we can speak of an
"embedded world" in which formalisms can tell us something
about social and cultural issues *not* because they
represent them but because they *arise* from them. We can
create technical systems which explore such issues without
needing to create limited formalisms for them. Warren Sack's
Conversation Map, for example, uses simple algorithms to
identify links between and topics discussed by participants
in netnews groups - links and topics whose meaning can then
be interpreted by the human observer, rather than the system
itself [1]. Similarly, Richard Rogers and Noortje
Marres's project mapping the web around the debate around
greenhouse warming uses formal link structure to illustrate
and talk about the power relations and politicking inherent
in web structure - for example, the number of links out of
the site of a non-governmental organization(NGO) tends to be
inversely proportional to its power; small NGOs link
promiscuously, while Greenpeace sees no need to link at all
[2]. In these cases, we can speak naturally in one
breath about formalisms, algorithms, politics, and the
social embeddedness of technologies, with an awareness of
the relationships between the representations in the program
and nonrepresented realities outside.