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Do Robots Dream of Digital Sheep?

1 Refik Anadol, StyleGAN generated building study using a data base of designs by ZHA

Neil LeachFIU/Tongji/EGS

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ABSTRACTAI is playing an increasingly important role in everyday life. But can AI actually design?

This paper takes its point of departure from Philip K Dick’s novel, Do Androids Dream of

Electric Sheep? and refers to Google’s DeepDream software, and other AI techniques such

as GANs, Progressive GANs, CANs and StyleGAN, that can generate increasingly convincing

images, a process often described as ‘dreaming’. It notes that although generative AI does

not possess consciousness, and therefore cannot literally dream, it can still be a powerful

design tool that becomes a prosthetic extension to the human imagination. Although the

use of GANs and other deep learning AI tools is still in its infancy, we are at the dawn of an

exciting – but also potentially terrifying – new era for architectural design. Most impor-

tantly, the paper concludes, the development of AI is also helping us to understand human

intelligence and 'creativity'.

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The movie Blade Runner (1982), directed by Ridley Scott,

is based on the novel by Philip K Dick, Do Androids Dream

of Electric Sheep? (1) It depicts a dystopian future world

involving ‘replicants’ – bio-engineered robots – manu-

factured by the Tyrrell Corporation to have super human

abilities so that they can survive in the hostile conditions

of off-world colonies. Replicants are therefore potentially

dangerous, and as a safety measure are given a limited

life span of four years. In the movie a group of six repli-

cants return to earth in a bid to extend their lives. Rick

Deckard, played by Harrison Ford, is a ‘blade runner’, a

kind of policeman/bounty hunter, charged with hunting

down and ‘retiring’ – killing – these replicants. The problem,

however, is that replicants – especially the advanced Nexus

7 model – look almost identical to human beings, and can

only be distinguished by using the elaborate ‘Voight-Kampff’

test designed to check whether their emotional responses

and eye reflexes meet the standard of human beings (2).

The movie is thus primarily about the difference between

human beings and replicants, such that replicants become

a mirror in which to understand what it is to be human. As

the late Rutger Hauer, who plays the leader of the repli-

cants, Roy Batty, comments, ‘In many ways, Blade Runner

wasn’t about the replicants. It was about what does it mean

to be human.’ (Roxborough)

Fast forward to 2019 – the year in which Blade Runner is

set – and it is worth reflecting on how prescient the movie

has proved to be. We don’t have replicants infiltrating

society, but we do have AI personal assistants, Siri, Alexa

and Google Assistant, colonizing our everyday lives, and

we do have AI filtering our spam, and performing other

tasks on our cell phones. We don’t have flying cars, but

we do have Maglev trains, drones and self-driving cars.

We don’t have the Tyrrell Corporation, but corporate life

is dominated nonetheless by hi-tech companies, such as

Google, Amazon, Apple and Microsoft. And, as predicted in

Blade Runner, we do talk to our computers, and do have

LED advertising all over our buildings, especially in cities

like Shanghai. Clearly Blade Runner has proved to be highly

prescient.

Although replicants are not necessarily controlled by AI,

they are clearly an artificial life form endowed with some

kind of ‘intelligence’. They therefore make a productive

vehicle by which to introduce the topic of AI, especially in

the context of debates about human intelligence. The title

of the novel, Do Androids Dream of Electric Sheep?, can

also be extended to raise the interesting question as to

whether AI can not only dream, but also design. Of course,

‘dreaming’ is not the same as ‘designing’, in that dreaming

– at least according to some commentators – refers to a

2 Rudger Hauer as Roy Batty in Blade Runner, directed by Ridley Scott, 1982

bottom-up process of unleashing repressed ideas, whereas

designing must also entail some measure of top down

control. (Freud) Nonetheless, it provides a useful starting

point for a discussion about AI and creativity.

CAN AI DREAM?It is generally assumed that computers cannot be creative.

This is the conclusion, at any rate, reached by Japanese

computational architect, Makoto Sei Watanabe, writing

about the potential applications of artificial intelligence (AI)

in the field of design: ‘Machines are better than people at

solving complex problems with many intertwined conditions.

In that realm, people are no match for machines. But people

are the only ones who can create an image that does not yet

exist. Machines do not have dreams.’ (3) (Watanabe, 2017)

The term ‘dream’, however, has been used – albeit meta-

phorically – in connection with a technique, DeepDreaming,

discovered by Alex Mordvintsev of Google Artists and

Machine Intelligence [AMI], while analyzing the operations

at work in the process of recognition using artificial neural

networks. Mordvintsev found that he was able to generate

images by reversing the flow of information in a neural

network.

Artificial neural networks are often used to recognize

images. They are used, for example, to recognize faces on

FaceBook and to classify images on Instagram, and are

often based on a class of deep neural networks, known as

convolutional neural networks [CNNs]. Generally speaking,

a network consists of 10-30 stacked layers of artificial

neurons. As Alex Mordvintsev et al. comment: ‘Each image

is fed into the input layer, which then talks to the next layer,

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3 50 iterations of DeepDream trained to perceive dogs, Martin Thoma, 2015

until eventually the “output” layer is reached. The network’s

“answer” comes from this final output layer.’ (Mordvintsev,

Olah, Tyka)

With DeepDream, however, the neural network operates in

the opposite direction. Instead of recognizing an image and

assigning it a category, DeepDream starts with a category

and proceeds to generate an image. For example, whereas

a standard neural network can recognize an image of a

bird, and categorize it as a ‘bird’, DeepDream is able to start

with the category ‘bird’, and generate an image of a bird.

Thus instead of operating ‘from image to media’, DeepDream

operates ‘from media to image’.

But how exactly can a neural network typically used for

recognizing images also be used to generate – or ‘synthe-

size’ – images? Importantly, although computational neural

networks are trained to discriminate between images, it

is essential that they have some understanding of those

images in order to distinguish them. This allows them to

work in reverse, and generate images, instead of merely

categorizing them (4).

However, the process of inverting the operation of a neural

net in order to generate images is not so straightforward

(5). Also, it often produces a somewhat ‘trippy’ picture that

appears vaguely surrealistic with a multiplicity of objects

generated in a variety of poses (6).

These generated images are referred to as ‘hallucinations’.

Technically, however, they cannot strictly be hallucina-

tions as such, in that true hallucinations are phantasms or

figments of the imagination, composed of images that do

not actually exist. These ‘hallucinations’, by contrast, are a

form of overly strong perceptual prediction of what is there

already, and are generated by ‘maximizing the activation of

the semantic neurons at the end of a recognition network.’

(8) (Agüera y Arcas)

GENERATIVE ADVERSARIAL NETWORKS (GANS)The next step in the development of generative AI came

with the introduction of Generative Adversarial Networks

(GANs), a technique first proposed by Ian Goodfellow in

2014, which has undergone a process of rapid develop-

ment. (Goodfellow, 2014). GANs represent a significant

step forward in the quest to ‘synthesize’ images, in that the

issue of invariance to ‘pose’ is no longer a problem, and the

images generated have significantly better resolution.

GANs are based on a competition between two neural

networks. There is always a bottom-up generator – or

‘artist’ – (typically a deconvolutional neural network) that

generates images, and a top-down discriminator – or ‘critic’

– (typically a convolutional neural network) that evaluates

those images. In the competition, the generator attempts to

fool the discriminator by producing images so realistic that

the discriminator is unable to distinguish them from a real

data set (9).

Effectively GANs are a way of training a computer to

perform complex tasks through a generative process. ‘[A]

Generator (an artist) generates an image. The Generator

does not know anything about the real images and learns

by interacting with the Discriminator. Discriminator (an art

critic) determines whether an object is “real” and “fake” .

. . The Generator keeps creating new images and refining

its process until the Discriminator can no longer tell the

difference between the generated images and the real

training images.’ (Nayak 2018) The two work in tandem, so

that the ‘artist’ trains the ‘critic’, and the ‘critic’ trains the

‘artist’. Once the ‘artist’ has been trained, the ‘critic’ can be

removed (10). Although GANs have been used primarily

to generate faces, they have also been used for clothing,

shoes, industrial design objects, and even interiors and

exteriors of buildings.

A number of variations of GANs have now been devel-

oped. A recent variation of GANs – Progressive Growing of

GANs – has improved the speed and stability of the training

process, by starting with a very low resolution image and

increasingly the resolution progressively with every layer.

‘The key idea is to grow both the generator and discrimi-

nator progressively: starting from a low resolution, we add

new layers that model increasingly fine details as training

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4 Edmond de Belamy (2018): A Creative Adversarial Network portrait painting generated by the Paris-based arts collective Obvious

5-6 StyleGAN Faces, generated using www.thispersondoes- notexist.com

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progresses.’ (Karras, Aila, Laine, Lehtinen) This allows the

whole process to produce some highly convincing faces of

non-existent people with far greater realism than previ-

ously achieved. The results, while still not perfect, show

that true photo realism is not far away (11).

Likewise, a version of GANs has now been developed to

produce Creative Adversarial Networks [CANs]. CANs

have been used in particular to generate art; ‘The system

generates art by looking at art and learning about style;

and becomes creative by increasing the arousal potential

of the generated art by deviating from the learned styles.’

(Elgammal, Liu, Elhoseiny, Mazzone) The intention here is to

open up the range of creative possibilities ‘by maximizing

deviation from established styles and minimizing deviation

from art distribution.’ (Elgammal, Liu, Elhoseiny, Mazzone)

Interestingly, it was found that human beings were unable

to distinguish ‘art’ created by CANs from art created by

artists. As such, it would appear that CANs are capable of

passing the Turing Test.

A further version, StyleGAN, is the latest in this line of

development and the most sophisticated GAN system to

date. StyleGAN is an open-source style based generator

architecture that borrows from style transfer literature to

produce images, and offers remarkable improvements in

terms of resolution and quality. In particular, it overcomes

the problem of ‘entanglement’ whereby any slight tweak or

amendment to one feature would have a knock on effect

on other features, by reducing the correlation between

different features (12). (Horev) As a result, it generates

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faces so convincing that it is often almost impossible to tell

the difference between the generated artificial faces and

those of real human beings.

Perhaps the most significant improvement, however, is that

images can simply be fed in, and do not need to be tagged

or classified as with previous versions of GAN. This saves

considerable time. As the authors note: ‘The new archi-

tecture leads to an automatically learned, unsupervised

separation of high-level attributes (e.g., pose and identity

when trained on human faces) and stochastic variation in

the generated images (e.g., freckles, hair), and it enables

intuitive, scale-specific control of the synthesis.’ (Karras,

Laine, Aila)

ARCHITECTURAL APPLICATIONS OF GANSOne of the first applications of GANs to an architectural

project was a projection on to the Walt Disney Concert Hall

[WDCH], by media artist, Refik Anadol, in collaboration with

Google AMI and computational sound consultant, Parag

Mital, and others, commissioned to mark the 100th anni-

versary of the LA Philharmonic in October 2018.

‘When we dream, our minds process memories to form

new combinations of images and ideas.’ (Google Arts and

Culture) This, at any rate, is the premise behind the projec-

tion. The intention was to give ‘consciousness’ to the WDCH

in three distinct stages. The first stage of the projection

– ‘Memory’ – involved the ‘training’ of a neural network by

classifying thousands of images and sound recordings of

events from the archives of LA Philharmonic. These were

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then processed by the network, and the results projected

onto the surface of the WDCH (13). The second stage –

‘Consciousness’ – involved the use of a neural network to

categorize and rearrange the material, by finding similar-

ities within the material (14). The third stage – ‘Dreaming’

– was probably the most spectacular. This was when the full

‘hallucination’ takes place (15).

More recently Anadol has explored the use of StyleGAN as

a means of ‘hallucinating’ an animated movie from which

still images of fictional buildings can be extracted, drawing

upon a data base of thousands of photographs of more

progressive buildings, designed by architects such as

Gehry and Partners, Zaha Hadid Architects, Toyo Ito, Tadao

Ando and so on.

This work raises the interesting question as to whether

StyleGAN might be able to generate designs that are even

more experimental. Indeed, it might equally be possible to

use alternative data, such as natural forms like flora and

fauna – orchids, perhaps – or even rocks and landscapes,

to ‘hallucinate’ novel hybrid or cross-pollinated forms.

Xkool, an AI startup in Shenzhen, China, whose CEO is

Wanyu He, has probably engaged with the most sophisti-

cated application of AI to architectural design to date. The

intention is to streamline the design process and make

it more efficient and creative by using deep learning to

not only search through a vast range of possibilities, but

also automatically generate designs based on the trained

models, and then, evaluate and return the outcomes from

various evaluation models.

Xkool has also used StyleGAN and other AI techniques

to generate images of buildings that do not exist (16). On

the whole these buildings are more traditional that those

generated by Anadol, and are largely modernist in their

aesthetic, reflecting the content of the database of images

used. These images also reveal how much depends on the

quantity of data, in that they draw upon a larger data-

base, and are therefore somewhat clearer and more

detailed than those generated by Anadol. However, they

do not reach the same standard as the faces generated by

StyleGAN, and at present there are still too many tell-tale

glitches giving away the fact that they are not images of

actual buildings.

Although StyleGAN can already generate some star-

tlingly innovative images, there a number of constraints

holding back its development. Firstly, the use of StyleGAN

is constrained by the power of Graphic Processing Units

(GPUs) available. As a result, Anadol, for example, would

not have been unable to generate images so quickly without

the support of extremely powerful GPUs provided by

NVIDIA. Secondly, it should be borne in mind that at present

StyleGAN has so far been restricted largely to the gener-

ation of 2D images. In order to generate truly convincing

‘designs’, it would be necessary to move beyond the domain

of two dimensions and operate in three dimensions (17).

Thirdly, it would be necessary to deepen the level of infor-

mation to incorporate some more highly refined details, and

to engage with performative constraints.

Since GANs are already in use for the purposes of optimi-

zation, some aspects of the introduction of performative

constraints should be fairly straightforward. Moreover, the

introduction of KoolPlan, an AI assist for floor plans and

façade design, which builds upon the logic of StyleGAN, is

Do Robots Dream of Digital Sleep? Leach

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7-8 Refik Anadol, StyleGAN generated building studies using a database of designs by ZHA

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already helping to increase the refinement of the detailing.

Meanwhile Autodesk is continuing to explore the poten-

tial use of AI into structural optimization through Project

Dreamcatcher, automation in design through Fusion 360,

and even AI based software for risk assessment in the

construction industry. (Rajagopal, 2018)

Indeed, given the logic of Moore’s Law, it is surely safe to

assume that that the process of development might even

accelerate, and that a robust AI based technique for gener-

ating 3D architectural drawings will be available within the

next few years (18).

CAN AI BE CREATIVE?Can AI be creative? In some senses, the answer would

appear to be clearly ‘yes’. Take the example of the highly

novel Move 37 made by AlphaGo in the second game of

its match against Lee Sedol. As Fan Hui comments, ‘When

AlphaGo chose that move, I assumed that it had made a

mistake. I immediately looked to see Lee Sedol’s reaction. At

first, he seemed to smile – as though he too thought it had

made a mistake – but as the minutes rolled by it was clear

that he was starting to realize its brilliance. In fact, after the

match, he said that when he saw this move he finally real-

ised that AlphaGo was creative.’ (Hassabis, Hui, 89)

If, as Richard and Daniel Susskind note, AlphaGo could

come up with such an original move, and it can be viewed

a creative does this not suggest that creativity amounts

to little more than imagining a range of already available

options that have not been thought about before? (Susskind,

Susskind, x) In other words, could we even say that being

‘creative’ is little more than searching for the unexpected,

or merely proposing that which has not been proposed

before? As the Susskinds note: ‘Contrary to widespread

belief, machines are now capable of generating novel

outcomes, entirely beyond the contemplation of their orig-

inal human designers.’ (Susskind, Susskind, xi)

In order to answer the question more comprehensively,

however, we would need to understand the nature of

creativity in more detail. According to Margaret Boden,

‘Creativity can be defined as the ability to generate novel,

and valuable, ideas.’ (Boden, 2009) For Boden ‘valuable’ has

many meanings: ‘interesting, useful, beautiful, simple, richly

complex and so on’. Meanwhile ‘ideas’ could take many

forms from ideas as such, ‘concepts, theories, interpreta-

tions, stories,’ but also artefacts, such as ‘graphic images,

sculptures, houses and jet engines.’ (Boden, 2009)

Boden notes that there is nothing magical about creativity

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9-11 Xkool, StyleGAN generated building images

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even shocking – results, as something might have been

generated that could never have been previously imagined.

Importantly, for Boden, exploratory creativity can lead to

transformational creativity. (Boden, 2009)

For Boden collage would be an example of combinational

creativity. Painting, architecture and music, and artistic

production in general, would be an example of explor-

atory creativity. Boden offers the example of a shape

grammar study of the works of Frank Lloyd Wright, which

suggested many other possible designs that could have

been produced. (Boden, 1999) Finally, science offers some

of the best examples of transformational creativity, where

paradigmatic shifts in knowledge can occur (20).

From this perspective, the use of StyleGANs in architec-

ture would seem to fall into the category of exploratory

H-creativity, in that the material generated is novel from

a global perspective, and depends upon an initial set of

constraints. Although not limited to fixed forms, as in the

case of shape grammars, the operations of StyleGAN

would seem to amount to a blending of pre-existing forms.

Although novel forms can be generated, the search space

of possibilities is constrained by the range of forms in the

data base.

CONCLUSIONAnd so do robots dream of digital sheep? Or – to be more

precise – can AI dream of digital buildings? Is Watanabe

correct to claim that humans are the only ones that can

create an image that does not exist, and that machines do

not have dreams? (21) (Watanabe, 2017)

Let us start by stating the obvious. All faces or buildings

generated by StyleGAN do not actually exist (22). In terms

of dreams, however, what becomes clear is that the word

‘dream’ appears in many of the AI applications and projects

cited above, whether it be ‘DeepDream’, ‘Dreamcatcher’ or

‘WDCH Dreams’. Clearly, in one sense, computers are able

to ‘dream’ not just of sheep but also of buildings through the

use of various forms of GANs. However, the term, ‘dream’ –

along with the terms, ‘memory’ and ‘hallucination’ – should

not be taken literally. They need to appear in inverted

commas. Until computers have consciousness, they cannot

literally dream (23). In this sense, Watanabe is correct (24).

But can AI really be as creative as humans beings? For

Boden, this is a philosophical question unanswerable for

now, as many highly contentious philosophical issues would

need to be resolved. (Boden, 2009) For Boden, however, the

key issue is not whether AI can match human creativity, but

how AI has helped us to understand human intelligence,

(19). Although we have come to think of creativity as a

mysterious process, that often depends on the ‘black box’

of intuition, this is simply because most people do not

understand how it operates. In this sense, it is similar to

magic. For magicians do not perform magic. They simply

conceal the processes that actual happen, so that the

audience comes to attribute them to magic. (Leach, 1999;

Boden, 2009) Nor does creativity need to operate at a high

level, and Boden offers examples of some relatively banal

jokes generated by AI that nonetheless display a low level

of creativity, such as, ‘What do you call a strange market? A

bizarre bazaar.’ (Boden, 2009)

Boden distinguishes between what she calls psycho-

logical creativity (P-creativity) and historical creativity

(H-creativity). P-creativity could be defined as something

novel in terms of the person who created it, whereas

P-creativity refers to something that has been created for

the first time in history. She further divides creativity into

three distinct categories, combinational, exploratory and

transformational.

Combinational creativity ‘produces unfamiliar combina-

tions of familiar ideas, and works by making associations

between ideas that were only indirectly linked.’ (Boden,

2009) Exploratory creativity, meanwhile, is based on a

culturally accepted style of thinking, or ‘conceptual space’,

defined by a set of generative rules. Finally, in transfor-

mational creativity, ‘the space or style is transformed by

altering (or dropping) one or more of its defining dimen-

sions’. (Boden, 2009) This can lead to surprising – or

12-13 Xkool, AI assist-generated drawings

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14 Xkool, StyleGAN generated building images

‘Thanks in part to AI, we have already begun to understand

what sort of phenomenon creativity is.’ (Boden, 2009)

Certainly, there are a number of specific questions about

human creativity that we might pose in the light of what

we know about AI. Could the process of classifying objects

to form a data base in machine learning be similar to how

children themselves are taught to identify objects, ‘This is

a cow’; ‘this is a horse’, and so on? Could we even surmise

that the teaching of architectural design is based on a

similar principle, since architectural design is not taught

as an overarching theory, but rather by way of examples:

‘this is a good design by Le Corbusier’; ‘this is a good design

by Rem Koolhaas’, and so on? Could we even claim that the

architectural design process consists of a directed search

of latent space based on a database of such examples, not

dissimilar to how StyleGANs work? Does the reversal in

direction of operation of a neural network, pioneered with

the development of DeepDream, suggest that the process

of interpretation is – in some senses – the opposite of the

process of creativity? If so, might this help to explain why

architectural theorists/critics tend not to be so creative,

while architectural designers tend to be less theoretical?

(25)

Blade Runner, as has been noted, has proved to be extraor-

dinarily prescient in predicting the future. But are there

not also uncanny parallels between Blade Runner and

the operations of AI? Does the victory at chess of repli-

cant, Roy Batty, over Dr Eldon Tyrrell, not foreshadow the

eventual moment when DeepBlue, IBM’s super computer,

beat the then leading chess player Gary Kasparov at chess

in 1997? Does not the way in which replicants are almost

indistinguishable from human beings, echoe how StyleGAN

generated faces are almost indistinguishable from human

faces? And, does not the Voight-Kampff test, used to distin-

guish replicants from human beings, echo the Turing test,

used to establish whether the operations of a computer

might be indistinguishable from those of human beings?

There is, however, a further parallel that deserves to be

mentioned. Just as for Hauer, Blade Runner is primarily

about the difference between human beings and repli-

cants, such that replicants become a mirror in which to

understand what it is to be human, so too AI – and robotic

intelligence in general - can become a mirror in which to

understand what it means to be human. As Hiroshi Ishiguro

notes, ‘The robot is a kind of mirror that reflects humanity

and by creating intelligent robots we can open up new

opportunities to contemplate what it means to be human.’

(Ishiguro, 179)

37 years ago Ridley Scott released a movie, Blade Runner,

set in October-November 2019, that predicted a world

inhabited by artificial life forms almost identical to human

beings. Indeed the fact that two replicants, Rachael and

Deckard, could fall in love, and that Roy Batty could deliver

a soliloquy as eloquent as ‘Tears in the Rain’ implies that

these artificial life forms had developed at least some form

of consciousness. But whether AI will ever achieve full

consciousness and match human creativity remains an

open question.

NOTES1. With this very title, the author raises an important question

about the potential for androids/robots to ‘dream’. Indeed the

theme of dreaming runs throughout both Blade Runner movies.

In the original movie, Blade Runner, Deckard has a dream of a

unicorn, while in the sequel, Blade Runner 2049, Anna Stelline,

whom we find out to be the daughter of Deckard and his lover

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5. The problem, as Blaise Agüera y Arcas explains, is that the

process cannot literally be inverted, as it is a non-linear

operation, and therefore some ingenuity is required: ‘With a

convolutional net you can’t do this directly exactly, as it is not a

linear operator, and therefore not invertible. But you can ‘cheat’,

by basically saying that I am going to optimize the pixels, so I’ll

start maybe with noise – with pure noise – and run the same

gradient ascent algorithms that you use to learn a neural net

on the pixels until the output corresponds to the embedding

vector that I want.’ [Agüera y Arcas]

6. ‘The problem,’ as Agüera y Arcas notes, ‘is that the convolu-

tional nets are designed to be invariant to pose, which means

that when they are run backwards they do not know what pose

to render things in. This leads to an aggregation of several

poses at once.’ [Agüera y Arcas]

7. As Oliver Sacks notes, ‘Precise definitions of the word “hallu-

cination” still vary considerably, chiefly because it is not always

easy to discern where the boundary lies between hallucina-

tion, misperception and illusion. But generally, hallucinations

are defined as percepts arising in the absence of any external

reality – seeing things or hearing things that are not there.’

[Sacks, ix]

8. It is perhaps not insignificant that according to neuroscientist,

Anil Seth, perception is a form of perceptual prediction, and is

actively generated – ‘hallucinated’ – on the part of the viewer.

Thus perception becomes a form of ‘controlled hallucination’:

Rachel, is a designer of dreams for replicants. Since replicants

are not born and brought up like humans, they need to be given

dreams, just as they need to be given memories.

2. Rachael, who ultimately has an affair with Deckard, is herself a

Nexus 7 model. While previous models were required to answer

20 to 30 questions before they could be identified as a replicant,

it took 100 questions before Rachael could be positively identi-

fied as a replicant.

3. While Watanabe does not use the term ‘design’ here, he does

refer to a process of ‘dreaming’ whose role would seem to be to

imagine that which does not yet exist. For Watanabe, the term

‘designing’ would therefore seem to imply ‘imagining that which

does not yet exist.’

4. It is also possible to produce an image by starting with an

arbitrary image instead of ‘noise’ or a specific embedding, and

allowing the network to analyze and optimize it. It is important,

however, to specify a layer, and ask the network to enhance

what it sees. Whereas lower-level layers simply produce

‘strokes’ and ‘simple ornament-like patterns’, higher–level

layers ‘identify more sophisticated features in images, complex

features and even whole objects’. Higher-level layers are there-

fore more productive, and can potentially work by feedback

loops to enhance the image that they are fed, to unexpectedly

generate a further image. As Mordvintsev notes, ‘We ask the

network: “Whatever you see there, I want more of it!” This

creates a feedback loop: if a cloud looks a little bit like a bird,

the network will make it look more like a bird. This in turn will

make the network recognize the bird even more strongly on

the next pass and so forth, until a highly detailed bird appears,

seemingly out of nowhere.’ [Mordvintsev]

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15 Refik Anadol with Google AMI, WDCH projection, October 2019, ‘Dream’ section

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‘If hallucination is a kind of uncontrolled perception, then

perception. . . is also a kind of hallucination, but a controlled

hallucination in which the brain's predictions are being reined

in by sensory information from the world.’ (Seth, 2017). As Anil

Seth notes, instead of passively recognizing an object, the brain

actively partakes in trying to make sense of what it is sensing

through a form of perceptual prediction: ‘Instead of perception

depending largely on signals coming into the brain from the

outside world, it depends as much, if not more, on perceptual

predictions flowing in the opposite direction. We don't just

passively perceive the world, we actively generate it. The world

we experience comes as much, if not more, from the inside

out as from the outside in.’ (Seth) Seth goes on to illustrate this

with an algorithm based on Google’s DeepDream to simulate

the effects of an overly strong perceptual prediction: ‘When

perceptual predictions are too strong, as they are here, the

result looks very much like the kinds of hallucinations people

might report in altered states, or perhaps even in psychosis.’

(Seth) Seth’s comments about altered states is highly relevant,

in that research has shown that altered states can also result

from a combination of top down and bottom up processes, on

which computational hallucinations are based. In fact Seth

uses the ‘hallucinations’ of DeepDream to illustrate how the

brain itself works. Others, such as Agüera y Arcas, go further,

and claim that the brain itself is literally computational. (Agüera

y Arcas, 2016) Whether or not the brain is actually computa-

tional, there are clear parallels between the operations of AI

and the operations of the brain.

9. As Horev notes, ‘The generator input is a random vector

(noise) and therefore its initial output is also noise. Over time, as

it receives feedback from the discriminator, it learns to synthe-

size more “realistic” images. The discriminator also improves

over time by comparing generated samples with real samples,

making it harder for the generator to deceive it.’ (Horev)

10. This echoes the way in which architect and critic work

together in architectural culture. It also echoes the dialectical

relationship between architectural design and architectural

criticism, in that the critic needs to think creatively, while the

designer needs to generate work critically.

11. ‘While the quality of our results is generally high compared

to earlier work on GANs, and the training is stable in large

resolutions, there is a long way to true photorealism. Semantic

sensibility and understanding dataset-dependent constraints,

such as certain objects being straight rather than curved,

leaves a lot to be desired. There is also room for improvement

in the micro-structure of the images. That said, we feel that

convincing realism may now be within reach, especially in

CELEBA-HQ.’ [Karras, Aila, Laine, Lehtinen]

12. As Rani Horev notes, ‘ProGAN generates high-quality images

but, as in most models, its ability to control specific features

of the generated image is very limited. In other words, the

features are entangled and therefore attempting to tweak the

input, even a bit, usually affects multiple features at the same

time. A good analogy for that would be genes, in which changing

a single gene might affect multiple traits.’ (Horev)

13. As Anadol notes: “We take every folder, every image, every

sound recording in the archive, one by one, and let the archive

load itself into a RAM or buffer so the building goes through

its nostalgic folders, finds the first music directors, finds the

first iconic moments in time, and the building plots them for us.”

(Huewe)

14. ‘The second part,’ notes Anadol, ‘is completely organic. The

building starts to look at its own entire history. Suddenly, we

see a bunch of Big Bang moments. Data universes appear in

front of us, as emotional as possible.’ (Huewe)

15. As Anadol comments, ‘It is completely a machine hallucination.

An architectural cultural beacon reconstructs its own skin, and

even reconstructs a memory.’ (Huewe)

16. Wanyu He used to work for OMA, hence the name ‘Xkool’ (ie

ex-Koolhaas). According to the Urban Dictionary, Xkool also

means ‘super cool’. [Urban Dictionary]

17. Matias del Campo, however, is exploring the use of a 2d to

3D style transfer GAN in research work that has yet to be

published.

18. ‘Moore’s Law’ refers to a comment made in 1965 by Gordon

Moore, then CEO of Fairchild Semiconductor, and later to

become the co-founder of Intel, that the number of transistors

on an integrated circuit board would double every year, while

unit costs would fall correspondingly. This leads to exponential

growth. It has since been applied by Ray Kurzweil and others to

all forms of technology. (Kurzweil) The recent development of

ArchiGAN by Stanslav Chaillou - too recent to be included in this

article - is surely a sign of things to come.

19. Do we not therefore need to question our understanding of

human creativity? Is not human creativity, as has been claimed,

something of a ‘myth’? (King, Churchill, Tan, xi)

20. By way of example, Boden offers the redefinition in our

understanding of benzene, which opened up a whole new field

of enquiry for chemists: ‘If all organic molecules are basically

strings of molecules, then benzene can’t be a ring structure.

In suggesting that this is indeed what benzene is, the chemist

Friedrich von Kekule had to transform the constraint string

(open curve) into that of a ring (closed curve).’ (Boden, 2009)

21. The very notion that there might be a form that ‘does not

yet exist’ is itself contentious. According to Kostas Terzidis,

for example, what AI establishes above all else is that every

possible form is already out there, and it is simply a matter

of searching for it. (Terzidis, 2006, 11; 2014, 85) For example,

in the game of Go against Sedol, the possibility of Move 37

had always already existed. It is just that no human beings

had ‘encountered’ it before. According to this logic, the notion

that architects are genius creators constantly ‘dreaming up’

new forms is clearly absurd, in that it would be simply impos-

sible to invent forms that do not yet exist. Arguably, then, we

308

could claim that when we design something novel, we are not

inventing anything, but merely finding out about something that

was there in the first place.

22. Indeed there is a website that automatically generates faces,

‘This person does not exist’: www.thispersondoesnotexist.com

23. One of the main criticisms of AI is that machines do not have

consciousness. As such, although DeepBlue and AlphaGo both

appeared to be good at ‘playing’ chess and Go, neither was

conscious of actually playing the game. Nor indeed does AI have

emotions, thus establishing a further parallel with replicants.

Here we must differentiate between ‘narrow AI’ or ‘weak AI’ –

AI, that is, designed to work on one general task – and ‘general

AI’ or ‘strong AI’ – AI, that is, that can perform any task that a

human can, in some cases involving full consciousness. While

many have speculated about the possibility of general AI, it is

clearly a long way off. Until such time as AI can be endowed

with consciousness, it will never be able to hold a meaningful

conversation, still less dream. As such, attempts to produce

humanoid robots, such as Hansen Electronics’ robot, Sophia,

that not only look human – much like replicants – but also

supposedly ‘think’ like humans, are clearly misguided. For

Rodney Brooks, Sophia is ‘completely bogus and a total sham’

while for Benedict Evans Sophia is merely ‘a tape recorder with

a rubber head on it’. [Sinapayen]

24. But is consciousness so important? What if we were to judge

AI not in terms of human intelligence, but on its own terms, as

a form of alternative, alien intelligence? (Leach, 2019) As such,

the question of consciousness becomes largely irrelevant. In

any case, ultimately the issue is not competition between AI and

human intelligence, but rather a potential synergy between the

two. Instead of pure AI, we should therefore be taking about

‘Intelligence Augmentation’ [IA], whereby AI operates in tandem

with human intelligence, and becomes a prosthetic extension to

the human imagination. (Leach, 2018) As such, with IA there will

always be consciousness on the part of the human operator.

25. This is not to overlook the fact that theorists need a degree of

creativity, and designers need a degree of criticality. In other

words, the processes of interpreting and creating are to some

extent reciprocal.

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IMAGE CREDITSFigure 1: © Refik Andadol, 2019

Figure 2: © Bernard Goldbach, Wiki Commons, 2008

Figure 3: © Martin Thoma, 2015

Figure 4: © Obvious, 2018

Figures 5-6: © Neil Leach, images generated using StyleGAN

website, www.thispersondoesnotexist.com

Figures 7-8: © Refik Andadol, 2019

Figures 9-14: © Wanyu He, 2019

Figure 15: © Refik Andadol, 2018