THE POSTHUMAN ARCHITECT ARTIFICIAL INTELLIGENCE, AESTHETICS AND NOVELTY
BY JEFFREY BRAY
Cover Image: Neural Zoo Image fig.1(Crespo, 2018)
ABSTRACT Jeffrey Bray Unit 14 JB5821M 001041244-1 jb5821m@gre.ac.uk
We are in the information age and real AI systems are being used to augment our lives. Popular media has turned most of us against the idea of AI however by understanding real AI we can apply it as a tool without fear. The recent development of neural networks that process visual media has led to a movement of artists collaborating with AI in developing novel
solutions
and
results.
This
essay
discusses how AI algorithms work and suggests how they might augment the neural processing of the human in producing meaningful art and architecture. A design process is suggested that uses AI to mix forms, and a human designer to consciously guide the evolution of a novel aesthetic. This creative collaboration blurs the line between nature and technology, and leads us further into a posthuman era where architects and computers continue to augment one
another
in
a
symbiotic
relationship.
CONTENTS 1\ Introduction
1
2\ Demystifying AI
3
3\ Blurring the Line
5
4\ Neural Networks
8
5\ Design Process
12
7\ Posthuman Architect
16
7\ Conclusion
20
\\ Figures
22
\\ References
24
1\ INTRODUCTION Design
processes
understanding
are
and
limited
our
ability
to to
human
recall
knowledge,
and
organise
information. This limits the possible range of aesthetic design
solutions
to
the
cognitive
capabilities
of
the
human mind. As such human cognitive limitations are holding back the possibilities of creative design; in this essay it
is
Data
suggested is
being
that
AI
augmentation
collected
and
offers
information
a
solution.
shared
at
an
unprecedented rate and humans are not able to process vast amounts of information into a useful form, as such we are turning to machines to help us, the most recent development in machine cognition is neural networks. These are being used in professions such as journalism to help write articles and
visualise
neural
big
networks,
data
(fig.1).
specifically
Recent
the
advancements
ability
of
in
Generative
Adversarial Networks (GAN) to process imagery, brings AI into the field of aesthetic design practice and many artists are beginning to explore this new field of visual processing. The biologist Richard Dawkins has commented on the similarity of biological and computational systems (River Out of Eden, 1995), this analogy will help to describe AI Neural Networks and how they are similar to and can literally augment the neural pathways of the brain. The biological processes of genetics and evolution can be used to describe a design process with AI Neural Networks as imitation to biological processes and Visualising Big Data fig.2 (Davis, 2013)
mix
(using
online
visuals)
and
AI the
app
Artbreeder.com
designer
as
genetic
to
create
selector
or “agent of mutation” as described by Ratti and Claudel
1
(The City of Tomorrow, 2016). The final “evolved” images are used as design generators and reference material for humans
to
develop
new
architectural
design
aesthetics.
Humans have collaborated with machines in physical labor since the first industrial revolution we are now at a level of computational power where we are able to collaborate with machines at a creative level. There have been 3 industrial revolutions each have had a profound effect on architectural design practice, the most recent digital revolution utilised electronics and information technology to develop parametric design and the B-spline curve resulting in architectural discourse such as Folding, Blobs and Parametricism (Oxman and Oxman, 2014). Mario Carpo describes a second digital revolution in his book ‘The Second Digital Turn’ (2016) one that begins to describe human collaboration with computers by recognising and utilising machine thinking as unique to that of human thought, not superior, but with each possessing unique strengths. This
essay
will
propose
how
the
unique
capabilities
of
humans and AI can be combined in a digital design system that equals more than the sum of its parts and indicates the progression of the designer into the posthuman era in collaboration and of the
details machine
with a
new
learning
uniquely
human
AI
in
Architectural
workflow as
a
that
utilises
cybernetic
strengths
design
that
tool
the that
designers
practice strengths augments possess.
2\ DEMYSTIFYING AI “AI can give you the most likely answer to any question that can be answered with a number. It involves quantitive prediction... AI is statistics on steriods.” (Broussard, 2018, p.32) Upon reading an article about the advancements in artificial intelligence and of machines making sense of what they see in the world I was inspired and started researching, upon doing so three things became apparent: AI is not what most expect, machines are not anthropomorphically intelligent and humans are still very much required for intelligent results. Science Fiction has always set the cultural expectation of technological development; and AI is no exception. The popular perception of AI is that of Sci-fi films such as 2001: A Space Odyssey (1968) in which HAL9000, a malevolent computer with self awareness, turns against its creators. The belief is that AI is evil and will enslave humans as depicted in The Matrix (1999). At a time when AI is being developed and used in reality it is important to make a clear distinction between the AI of hollywood and that of reality and to separate the cultural associations of fictional AI from that of the realistic capabilities of AI. The two are so spectacularly different it will be useful to differentiate between them using the terms defined by Broussard in her book Artificial Unintelligence (2018, p.10); General AI is the AI of sci-fi and Narrow AI is the real AI in use at the moment. In debunking the perceived magic of computing it may be useful to revisit the definition of the word ‘computer’ itself. Dictionary.com (2020) uses this definition “a programmable electronic device
2
3
designed to accept data, perform prescribed mathematical and logical operations at high speed”. AI is made up of algorithms which
are
sets
of
rules
to
be
followed
when
performing
calculations, essentially, AI is a sophisticated calculator. This is the version of AI to be discussed in this essay. There are currently two main branches of research in Narrow AI; that of producing machine consciousness and that of producing useful AI systems to augment human cognition (Chomsky and Krauss, 2015, 5:50). Machine consciousness is an interesting topic of which scientists and researchers know very little; we
don’t
even
know
the
nature
of
consciousness,
what
consciousness is or where it originates. We need answers to some of these questions to know if conscious machines are even possible, for the moment at least it is a topic of discussion among philosophers and scientific theorists. This essay will focus on the latter research of productive AI, specifically on neural networks in the advancement of machine learning and cognition to augment the creative practice of architectural design. It is however difficult to detach creativity from that which makes us human. It is, however, difficult to see how our brains could be augmented by digital systems in the posthuman sense, to help frame this we must first talk about the similarities between genetics and digital systems; by
discussing
AI
pragmatically
we
started
to
3\ BLURRING THE LINE “Up until 1953, it was still possible to believe that there was something fundamentally and irreducibly mysterious in living protoplasm. No longer.” (Dawkins, 1995, p.17) Dawkins is referring to the unravelling of the gene in 1953 by Watson and Crick, and goes on to describe, in his book River out of Eden (1995), that genes are coded as purely digital information stored in DNA and represented in a way that is remarkably similar to computers. Digital information is binary (0 & 1) and is stored physically as a switch called a transistor being in one of two states on (1) and off (0) (fig.3). Genetic information is quaternary (G, C, A & T) and stored physically in the form of 1 of 4 chemicals guanine (G), cytosine (C), adenine (A) and thymine (T) (fig.4). In both systems each bit is discrete, it either is or isn’t, a digital signal for example is represented by a square wave whereas an analogue system is a continuous curve represented
demystify
“digital life” we will follow by demystifying natural life.
Binary Code fig.3 (Binary Code, n.d.)
4
5
by a sine wave where information is degraded when copied
Samuel Butler proposed a darwinian analogy for the evolution
or transmitted as the wave loses energy. The information
of technology in 1853 in his text Darwin among the Machines
contained within the genetic code is the digital blueprint
(Butler, 1863), in it he suggests the “task of classifying
of life and the code is copied almost perfectly from one
machines into the genera and subgenera, species, varieties
generation
can
and sub-varieties, and so forth, of tracing the connecting
represent the blueprint of a programme and can be copied almost
to
the
next,
just
as
digital
information
links between machines of widely different characters”. From
perfectly from one computer to the next. (Dawkins, 1995)
this Ratti describes a Darwinian approach to object design evolution where the designer becomes an agent of mutation in
the
design
Similarities information understand
process
in and
how
the
(Ratti
and
Claudel,
2016,
workings
of
digital
and
advances AI
could
in
AI
augment
neural the
networks
human
brain
p.6).
natural help
to
and
an
opportunity could arise for a new creative design process. The AI neural network serves as the processor (calculator) of information and coder of ‘genes’ and the human designer is the ‘agent of mutation’ and the designers conscious choice serves to replace the process of ‘natural selection’. In this process we should be able to trace the lineage of a design in units of generations. The genetics of the design can be altered at any stage in the process to create a new species or subspecies of the design and seemingly discontinuous images could be linked through a digital genealogy. (see p.17-19) Gene Sequencing fig.4 (University of California, 2016)
“We know that genes themselves, within their minute internal structure, are long strings of pure digital information.” (Dawkins, 1995, p.17) We can see here, even considering this clearly mechanistic view of the world, these systems work in very similar ways and by comparing fig.3 and fig.4 the lines between the natural and the digital begin to blur. The posthuman world view is to believe that the human is inextricably linked to the rest of the universe, including computers and the digital world.
6
7
4\ NEURAL NETWORKS “My CPU is a neural net processor; a learning computer.” Arnold Schwarzenegger (Terminator 2, 1991) Biological Neuron fig.5 (Raschka, 2015)
After the augmentation of physical labor during the industrial age and automation in the digital age we are now at a third technological revolution as described by Carpo (2018) and moving into the second digital age where advancements in the use of AI Neural Networks to process big data and augment intellectual labor have already begun. At this point it may be useful to revise the definition of intelligence as defined in the Oxford Dictionary “The ability to acquire and apply
Artificial AI Neuron fig.6 (Raschka, 2015)
knowledge and skills” (Lexico, 2020). Although Narrow AI may not be anywhere near the complexity expected of General AI we may surmise that AI neural networks are, in fact, gaining a level of intelligence but as Chomsky says “asking whether a machine thinks is like asking whether a submarine swims…
these
are
terminological
questions”
and
the
same
could be said for machine intelligence and consciousness.
GAN Generator Schematic fig.7 (Radford and Metz, 2016)
AI neural networks are inspired by the physical workings of the brain where data travels via weighted connections (nerves) to neurons which process the data from input to output. These connections can be extremely complicated with many layers of processing to learn deeper levels of detail and features from the input. Neural Networks are arranged into many different types of Machine Learning algorithms, this essay will focus on the Generative Adversarial Networks (GAN) where 2 neural networks use competition and feedback to teach each other
GAN Framework fig.8 (GAN Framework, n.d.)
8
9
based on a given dataset as input. GANs are particularly
“you scored a 9.5 probability, and are almost definitely
interesting to the posthuman architect as they are able to
human. However you have some grammatical errors, replace
learn patterns and schema from entire images pixel by pixel.
there with they’re, their with there and they’re with their.”
To explain how GANs work I will use a story to describe
GANs teach each other via an initial dataset that could
the
got
comprise anything from 1 image to 100 million images and
us this far I will use General AI to frame this analogy.
then via trial, error and feedback. The interesting thing
elements
described
above,
seeing
as
Sci-fi
has
about GANs is their ability to create something novel from Imagine there is a robot trying to write sentences like a
a
human and is called an impersonator (generator network) who is
algorithm from a simple data processing tool to a creative
finite
dataset.
trying to appear human to another robot called an interrogator
augmentation,
to
(discriminator network) who, adversely, is trying to discern
that
this
real humans from fake ones. The impersonator has never met
brings artists and AI together in one creative exercise.
inspires
It a
is
human essay
this
designer. and
a human before so has no idea what one may say, and the interrogator has only met a few humans (input dataset) to compare against. To begin the process the impersonator says a random string of characters, the interrogator communicates “you scored a probability score of 0.2. You are a fake human, any human I have met would not say that. However if you try changing character x for character y that might be more convincing” at the same time the interrogator makes a note of the attempted string of characters and learns a bit more, making it harder for the impersonator to trick it next time. The robots continue taking turns and improving each others intelligence by competition and feedback until the impersonator randomly gets a string of characters correct and the interrogator communicates “you scored a 0.7, and may be a human however some of the things you said are still not quite right. Try changing string x for string y” and then updates its own notes on what strings it deemed to be fake. This continues until both AI systems are intelligent and the impersonator produces the sentence “There going to love going there, I heard they’re food is the best!” at this point the interrogator is intelligent enough to be able to discern grammatical features in sentences and comments
10
ability
11
a
new
It
that is
design
elevates this
the
ability
process
that
images in
5\ DESIGN PROCESS “Routines in an artificial neural network become responsible for authorship and the human artist (with non-artificial neurons) acts as the muse.” Sofia Crespo
enabling
the
the
aesthetics
of
price the
of
bitcoin
tulips.
The
to
be
second
communicated interesting
point is that the tulips are novel, highlighting the GAN’s unique capability to create something new. As explained by Ridley on her website “What is nice about getting an AI to ‘imagine’ or ‘dream’ tulips is that it echoes 17th century Dutch
still
realism,
are
life
flower
‘botanical
paintings
which,
impossibilities’”
despite (Ridler,
their 2019).
This is a GAN trained with a single form, i.e. the tulip as a single idea, the true power of the GAN can be seen most
There are many varieties of GANs each designed to complete a specific task. Most of which you have to be proficient in coding to understand or even to get working on your own PC, many of the artists currently working with AI are part of an open source community in which progress and code is shared to be freely altered to produce results for different creative goals. In this chapter I will outline the work of 2 of these artists and how they have utilised GANs unique capabilities. In her work, Myriad (tulip), Anna Ridler builds a dataset of
Ridlers Hand Labelled Dataset fig.9 (Ridler, 2018)
10,000 images of tulips throughout the blooming season and hand labels each image to train a GAN, this highlights the human input to train the AI exactly what is important within the images to link the forms with labels (Ridler, 2018). The tulip GAN was then used in her work, Mosaic Virus, in which she linked the generation of tulips to the price of Bitcoin. The more expensive the coin the more stripes in the petals of the tulips, this reflects the symptoms of the mosaic virus and the tulip mania of the 17th century in which the price of tulips was dependant on the presence of the stripes caused by the virus (Ridler, 2019). What must be drawn from this artwork in the context of the essay is that, firstly, the price of bitcoin and the aesthetic of a tulip are vastly discontinuous ideas that are connected by the ability of the GAN to find patterns between words and detail within
AI Generated Mosaic Virus Tulips fig.10 (Ridler, 2018)
12
13
clearly when forms are mixed to create new visual forms. Sofia Crespo uses GANs in her work Neural Zoo to mix forms of life and generate novel life forms from a specific class of animals such as fish or arthropods. This work is the antithesis of the computer science term ‘garbage in: garbage out’ and highlights the importance of the human to curate a concise dataset as input to ensure meaningful output in the process of training any AI. Sofia Crespo describes her work’s ability to augment the brain’s neural pathways in accepting discontinuous mixed forms as relating to a feeling like déjà vu. The novel creature looks familiar even though you know you haven’t seen it before - “These images resemble nature, but an imagined nature that has been rearranged. Our visual cortex recognizes the textures, but the brain is simultaneously aware that those elements don’t belong to any arrangement of
reality
that
it
has
access
to.”
Sofia
Crespo
(2019)
What is clear from these examples is that the technology is restricted to the quality of the data it learns from, it cannot truly create something entirely new from nothing. However nothing,
it we
is can
arguable only
that
build
humans
from
our
cannot own
create
from
knowledge
base
and only when combining 2 or more ideas is something novel created in the linking of discontinuous ideas and creating perceived order where there once was chaos (Pepperell, 2003).
Image from Sofia Crespo’s Neural Zoo fig.11 (Crespo, 2018)
14
15
All images in this chapter were created by the author using the AI web app artbreeder.com. The images show the AI genes individually and multisociated into the first generation and the evolutionary process to a final image.
In The Posthuman Condition Pepperell (2003) explains
how
the
brain
understands
BEAVER GENE
and
connects ideas via neural pathways that are well linked in a continuous idea or concept
7\ POSTHUMAN ARCHITECT
and therefore requires little energy for the signal to travel from one neuron to the next and when we learn we strengthen connections
BUTCHER GENE
and create new continuous pathways between
“The mother of art is architecture. Without an Architecture of our own we have no soul of our own civilization” Frank Lloyd Wright
neurons. However the brain’s understanding is limited to its network of neurons which is the product of its exposure to stimulus.
To help understand how AI processes this information we will employ Plato’s theory of forms. Specifically the theory that each object has an ultimate ‘form’ that humans use to compare all objects to decide if the object in question is or is not a chair for example. We cannot see this ultimate form, it only exists in our minds (Macintosh, 2012). The AI learns the form of an image of a chair from a dataset of images of real chairs, it then uses this form which is a dataset of pixel probabilities, to generate a new image of a chair that humans can recognise. It
has
been
discussed
that
AI
systems
are
very
basic
cognitive machines that have no sociocultural understanding of the material they are processing. The neural net has no
understanding
of
what
a
chair
is
it
merely
reduces
the information within the images into data within which it finds patterns and uses statistics to derive a set of “DNA” blueprints that define the invisible platonic form of an image of a chair. The meaning of ‘chair’ is placed by the human viewing the image and recognising the patterns within
as
representative
of
a
chair
and
all
that
is
connected to the sensorial experience of a chair. A symbol which has gained personal meaning through exposure to the object
in
question
either
physically
16
or
intellectually.
The neural network is able to connect many
CASTLE GENE
different forms into one novel object, in the way a human may be able to connect two different ideas into one through the process
of
thinking
alone.
However
it
takes a human a lot of time and energy and may potentially require study of and
COMIC GENE
around the two ideas to forge a new neural pathway. Arthur Koestler coined the term ‘bisociation’
in
his
book
The
Act
of
Creation (1964) and describes it as the creative process of connecting two ideas at the axis of two different planes of
CRANE GENE
understanding, for example the creativity and humor of puns lies in the understanding of two distinct contexts for a single word. This is how we can explain the creative capacity
of
the
GAN
in
its
ability
to
learn two or more forms and create a new discontinuous image that is a combination of these forms via bisociation, in fact the
AI
can
‘multisociate’
by
connecting
many different forms into one new image.
17
FLY GENE
GEN1
The definition of meaningful art according
and speed of novel production; once trained the network
to Pepperell is as follows: Good art must be
can produce infinite varying images for any given genetic
aesthetically stimulating by simultaneously
makeup. In doing so the AI can offer the architect a neural
presenting continuity and discontinuity in
augmentation
the same event or object (Pepperell, 2003).
discontinuous ideas, ideas that may be too discontinuous for
that
may
bridge
the
pathways
between
two
the human mind to apply meaning without being pre-cognated GEN2
With this definition of art, and in the
by the AI. In bridging the ideas it may well expedite the
aesthetic frame of this essay it is not useful
learning or cognition of our human mind that is capable of
to consider architecture as a commodity but
applying meaning to the images as an individual designer
instead
who
to
consider
architecture
in
its
understands
the
sociocultural
most creative form as an aesthetic object removing
it
from
its
economic
context.
GEN3 Architectural
design,
just
as
GEN7 CURRENT GENERATION
art,
therefore requires humans that understand the matrices of the symbols processed by the AI, not only as the creator but as GEN4
an
observer.
concept
of
Without
this
continuity
or
there
is
no
discontinuity
and therefore no meaning. To refer back to
the
analogy,
understand
the
humans input
are
required
objects
to
to
form
a
multisociated pun that is the output of GEN5
the GAN, and then again humans are required to
recognise
therefore
the
intuit
social the
continuity
discontinuity
and of
the output to give it artistic meaning. The AI itself does not need to provide GEN6
meaning in this process. It is a symbiotic relationship
between
human
and
machine
where the machine does some of the work and
the
humans
provide
What
the
network
offers
the
form
of
18
energy
the is
meaning.
utility
(processing
in
power)
19
context
of
the
input.
vu and a middle ground between two discontinuous concepts. With research and development continuing in Neural Networks
7\ CONCLUSION
we will better understand how the AI processes information and will be able to improve the symbiosis with the human mind and there will be tools to curate the data input to
“this digital revolution at the very core of life has dealt the final, killing blow to vitalism - the belief that living material is deeply distinct from nonliving material” (Dawkins, 1995, p.17)
inform better output. However in doing so we do not want to constrain the AI so that we end up with predictable results. Perhaps the fact we do not understand it is what makes machine thinking so alluring, perhaps this tool is
So what is the use of AI neural networks and how does it define the posthuman architect? AI Neural Networks are a pre-biocognitive
neural
augmentation
to
the
posthuman
architect which functions to supplement neural pathways and to broaden the posthuman capacity for the ‘multisociation’ of
discontinuous
in
making
at
greater
novel
ideas
uniquely speed,
variations
or
concepts,
meaningful with
of
a
the given
therefore
architectural ability genetic
to
at
its
most
of
its
unpredictability
useful
for
creative and
our
inspiration lack
a whole new way of thinking, in collaboration with machines.
many at
speed, the posthuman architect is able to choose from a set of images in a similar fashion to generative design. The question of synthetic consciousness is very interesting but in the context of this essay is not a useful question to ask as we are so far away from that reality if it is even possible. A more relevant topic is the power of AI to augment our neural pathways and broaden our imagination and creative capacity to multisociate in creative design, a new line of artistic exploration is uncovered in combining many different platonian forms to create something new that has meaning because of the human participant making choices that affect the evolution of the visual media used for inspiration which in turn invigorates the imagination of the designer to
produce
something
entirely
new
together
with
the
because
understand how it works or perhaps we are at the beginning of
aesthetics
composition
now
understanding.
Perhaps like card magic, AI will lose its intrigue when we
assisting
produce
of
AI
network. In turn the viewer in engaged by a sense of déjà
fig.12 (Mahdavian, 2018)
20
21
fig.6
Raschka, S. (2015). Schematic of Rosenblatt’s Perceptron. [image] Available at: https:// sebastianraschka.com/Articles/2015_ singlelayer_neurons.html [Accessed 7 Jan. 2020].
\\ FIGURES fig.1
Crespo, S. (2018). Neural Zoo Image [image] Available at: https://www.flickr. com/photos/160825680@N02/31282443157/in/ dateposted-public/ [Accessed 7 Jan. 2020].
fig.7
Radford, A. and Metz, L. (2016). DCGAN Generator. [image] Available at: https:// arxiv.org/pdf/1511.06434.pdf [Accessed 7 Jan. 2020].
fig.2
Davis, S. (2013). Visualising Big Data. [image] Available at: http://steffondavis. com/visualizing-taste-in-art-with-big-datacuration-analysis-curalytics-and-curiator/ [Accessed 7 Jan. 2020].
fig.8
Generative Adversarial Network Framework. (n.d.). [image] Available at: https:// www.freecodecamp.org/news/an-intuitiveintroduction-to-generative-adversarialnetworks-gans-7a2264a81394/ [Accessed 7 Jan. 2020].
fig.3
Binary Code. (n.d.). [image] Available at: https://phoneky.com/ wallpapers/?id=w42w1665439 [Accessed 7 Jan. 2020].
fig.9
Ridler, A. (2018). Myriad Tulip Data. [image] Available at: http://annaridler.com/myriadtulips [Accessed 7 Jan. 2020].
fig.4
University of California (2016). Gene Sequencing UC San Francisco. [image] Available at: https://www.universityofcalifornia.edu/ news/gene-signature-could-lead-new-waydiagnosing-lyme [Accessed 7 Jan. 2020].
fig.10 Ridler, A. (2018). Mosaic Virus AI Tulips. [image] Available at: https://howtospendit. ft.com/art-philanthropy/205746-artificialintelligence-the-art-world-s-weird-andwonderful-new-medium [Accessed 7 Jan. 2020].
fig.5
Raschka, S. (2015). Schematic of Biological Neuron. [image] Available at: https:// sebastianraschka.com/Articles/2015_ singlelayer_neurons.html [Accessed 7 Jan. 2020].
fig.11 Crespo, S. (2018). Neural Zoo Image. [image] Available at: http://www.aiartonline.com/ art/sofia-crespo/ [Accessed 7 Jan. 2020].
22
fig.12 Mahdavian, N. (2018). They Just Want to Dance. [image] Available at: https://www.newyorker. com/cartoons/issue-cartoons/cartoons-fromthe-march-5-2018-issue?verso=true [Accessed 7 Jan. 2020]. 23
Koestler, A. (1964). The Act of Creation. London: Hutchinson, pp.35-50.
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the
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