The Makers Museum (Part II)-M.Arch-Thesis-Chia Sheng Wei by CHIA_SHENG_WEI

FA L L 2 0 2 0

M.ARCH

THESIS

THE MAKERS ? MUSEUM PA RT I I

CHIA SHENG WEI

MENTOR: IMMANUEL KOH

THE MAKERS ? MUSEUM

overview

Site PL

-o

p ro

e nc

unloading bays

LANNING N

c an tr

pt ul sc

The site is planned according to the grid, with the 32 x 32 grid situated at the centre of the plot, surrounded by a series of supporting zones.

Site PL

-o

p ro

e nc

unloading bays

LANNING

c an tr

pt ul sc

Holding zones are utilized for the temporary storage of unused modules and parts of the museum. Featuring 740 lots for module storage, the number of modules can be further stacked to accomodate more should the need arise. These zones are within the working radius of the trackmounted tower cranes.

Site PL

The Museum features being a street-level while the second le 12m below street le the event plaza, a remains cool and sh MakerFaire, and gene

-o

p ro

e nc

unloading bays

LANNING N

a two-tiered drop-off with the first l drop-off for visitors to the park eads to the main entrance situated evel. Connecting the two floors is series of large terraced steps that haded in the day for events such as eral use by the public.

c an tr

pt ul sc

drop

The two-tiered drop-off allows for direct access to the museum for visi

p-off

itors as well as the option to access the park spaces via street-level. 61

Site PL

-o

p ro

e nc

The Sculpture Park connects with the rest of the programme experience within a semi-arti is exhibited in this zone and destroyed over time. Visitors larger than life sculptures untouchable.

The park also connects via an main volume of the Makers Muse

unloading bays

LANNING N

c an tr

pt ul sc

the Changi business park es and creates an immersive ificial nature. Process art allowed to be weathered and s can go up close to these and touch the previously

n underpass entrance to the eum.

sculptur

Proxy art in the Sculpture Park allows visitors to observe the transient nature o

re park

of the artworks up close and critiques the exclusive nature of contemporary art. 63

wayfindin destination query input

route overview

Within an everchanging architecture, wayfinding via a precise Wifi local posit helps users to locate the best routes to their intended destination. By mean based on oneâ&#x20AC;&#x2122;s purpose, the system allocates possible destinations for the u and alerts the user of the distance, duration and relevant information.

tioning system ns of a query user to select

sect

The section depicts the incorporation of the vertical sys Because of their optimal locations, the assembl

tion

stems of the building and highlights the assembly spaces. ly spaces appear as museum exhibits themselves. 65

el 12

packing p The usage of space within the Makerâ&#x20AC;&#x2122;s Museum can be approached as a series of reconfigurable spaces made up of smaller units. While the machinery itself is irreducible, the environment can be altered via load-bearing structural panels. Functional Furniture Classes

-Formal

-Semi-Formal

-Informal

-Roughwork Surfaces

-Partition Walls -Exhibition Walls

Machinery Classes -3D Printer -Wood Lathe -Robotic Arm -CNC Milling

-Soldering Station -Metal Lathe -Panel Saw -Table Saw

-Drill Press -Wood Lasercutter -Mitre Saw -Regular Lasercutters

-Sander -Aspirator -Planar -Metal Press

Module Variation

The usage of space within the Makerâ&#x20AC;&#x2122;s Museum is approached as a series of reconfigurable An infinite amount of variation is possible to suit functional needs with limited pieces. spaces made up of smaller units. While the machinery itself is irreducible, the environment can be altered via load-bearing structural panels.

programme 5

Option 01: Automatic Configuration Blurring Boundaries

Level 12

Identifying Boundary Conditions

Points on either side of the boundary check their

zone as well as their neighbouring zones. Points on either side of the boundary check distance the as point to the neighbouring boundary detetheirThezone as from well their rmines the probability of it being a programme of zones. distance from point region. to the its The own region or that of a the neighbouring boundary determines the probability of it The closer it is to the boundary, the higher the beingprobability a programme of its own region or that that it is a programmes of the neighzone. of a bouring neighbouring region. The closer it is to the boundary, theof higher probability This allows a form programmethe interpolation. are blurred. that Boundaries it is a programmes of the neighbouring zone. This allows a form of programme interpolation.

Determining Safety Determining SafetyDistances Distances Equipment within Makerspaces have safety Equipment within Makerspaces have safety distances. distances. By utilizing a physics solver to By utilizing a physics solver to pack the prograprogrammes can programmes be more evenly can spaced pack mmes, the equipment, beout. more evenly spacedand out. Equipment furniture that require more space are represented as larger circles.

Equipment and furniture that require more space are represented as larger circles.

Locking Specific Programmes to the to GridGrid Locking Specific Programmes

As certain infrastructure are connected to As certain infrastructure are connected to the flthe floor and ceiling panels, particularly oor and ceiling panels, particularly heavy equipmthese can bethese anchored in the while heavyent, equipment, can be algorithm anchored in still being solved for collision prevention. the algorithm while still being solved for Here, larger circles are locked to the grid. collision prevention. Here, larger circles are locked to the grid.

Reconfiguration for Spontaneous Exhibits

Reconfiguration for New Exhibits As the Gallery Space is deemed as a As the Gallery Space is deemed as a continuous and continuous the temporalandtemporal affair, the AI affair, needs to be ableAI to needs reconthe spaces with the reallocation of furnitto be figure able to reconfigure the spaces with the ures and machines so as to create routes from the reallocation furnitures and machines exit pointsof of the cores to the exhibited piece.so as toThe create routes from the exit points of shortest route is calculated taking into account obstacles and shortest distances. the cores to the exhibited piece. The shortest route is calculated taking into account obstacles and shortest distances. Route Creation Between Exhibits

Route Creation between Exhibits In the event of multiple display locations, the

ground plane is further divided to allow for pas-

sage between the spontaneous exhibition spaces. In the event of multiple display locations, Floor Plan Render the ground plane is further divided to allow for passage between the spontaneous exhibition spaces.

internal r

Option 02: Branch, Com

Branching allows users to explore new configurations from the original.

Editin config a comm branch keep t flows.

reconfig.

mmits & Pull Requests, an open-source approach

ng and creating gurations is mit to the h and helps track of work .

Pull Requests initiate discussion among other floor users by notifying them to review the userâ&#x20AC;&#x2122;s commits.

This is a process that allows for the sharing of ideas and potential conflict resolution among floor users.

Once a general consensus is achieved, the configuration is submitted to the A.I for implementation and final testing.

Within a duration of 7-days, if no issues arise, the new configuration is merged and implemented as the main configuration.

internal r

Illustrating the concept of an internal reorganisation, this image depi

reconfig.

icts the bare bones of the museum where no programme has been assigned. 68

internal r

Through the collaboration of the AI with human users, these

reconfig.

e versatile spaces are reconfigured to suit various needs. 69

internal r

Perhaps taking on the skin of a makerspace and supported by the necessary equip amount of enclosure eg:woodworking or painting, as well as connecting to

reconfig.

pment and infrastructure, such as the partition of subspaces that require some the existing services via the service cavities in the module structures. 70

internal r

In the event of an exhibition, the space can be adjusted to accommodate works of ar tiques the formal white cube with a blurring of the gallery space; as well as t

reconfig.

rt and people, perhaps becoming a space for the exhibition of maker art, which crito create opportunities for visitors and artists to mingle around the artwork. 71

THE MAKERS ? MUSEUM

overview

external 1

projection

In the reconfiguration process of the museum, the result of the popular vote is projected as the endgoal in a two year process such that change is gradual, allowing the users of the museum to readjust at a manageable pace. The voted result and present museum design as points in the Artificial Intelligenceâ&#x20AC;&#x2122;s latent space are traversed such that key relationships are potentially preserved.

reconfig.

Comparing the degree of change, 80% of the museumâ&#x20AC;&#x2122;s mass remains consistently the same between each 2-month interval of transition, as compared to an instantatenous change from A to B.

external 2

reallocation 2.1

reconfig.

external 2

reallocation 2.1

The internal reorganisation of the interior equipment and furniture follo

reconfig.

ows the changes in massing and redistributes into the additional spaces.

external 2

reallocation

reconfig.

external 2

reallocation

reconfig.

external 2

reallocation

reconfig.

external 2

reallocation

reconfig.

proxi

The small signature of the robot allows for change to happen as unobtrusively as possible, even in close And these robots are as much a fabric of the museum as the museum and occupants itself working tirelessly to change to allow new relationships between Human, A.I and Architec

imity

e proximity to artist in residence quarters. o change the museum even as preferences and needs cture. 80

Whil e th e Ma ke r s M u s e u m m a y seem l ik e a sp e c u l a t i v e real it y to da y, t h e v a r i o u s comp ut at io na l, a r c h i t e c t u r a l and en gi ne er in g t e c h n o l o g i e s for th e mo st p a r t , a l r e a d y exis t. The th es is t hu s e x p l o r e d the im pl em en ta t i o n s o f s u c h tech no lo gy i n t h e c o n t e x t o f a museum that is more than just a repository but also a space for the act of creation; a n d t h e nece ss ar y sy st e m s t o s u p p o r t reco nf ig ur at io n . With g re at er a d v a n c e m e n t s i n tech no lo gy , pe r h a p s s u c h a Muse um , a pr od u c t o f H u m a n and Ar ti fi ci al i m a g i n a t i o n , may ju st b e on t h e e d g e o f tomo rr ow .

I pra is e yo u, f o r I a m f e a r f u l l y a n d wonderfully made. Wonde rf ul a re y o u r w o r k s ; m y s o u l k nows it very well. Psalm 139:14

thank you

appendix

the symbol

â&#x20AC;&#x2DC;As t he Unicorn becomes popular to th e m a s s e s b e c a u s e o f its singularity, t he demand for it be c o m e s t o o g r e a t . T h e uniqueness of the unicorn dies as imitat i o n i n c r e a s e s . â&#x20AC;&#x2122; Citizens of no pl ace by Jimenez Lai, 201 2

the symbol

Such a n architect u ral s ymbol s peaks l itt l e o f t h e c o l l e c t i v e , but of authority in the hands of the inf l u e n t i a l .

assembly

Assembly Space locations for each floor is optimized to accessible and visible. While it is understood that multip using an evolutionary solver reduces the complexity in de

Assembly Space locations for each distribution about the massing whi

Objective 01

Obje

Minimize distance from point to building envelope

Project Managers Fittest Objective 01

Fitness Obj 01

e to provide expert bureucratic and coy the A.I and propoactivity towards the

Jul

Aug

Sep

Gen 474 Indv 00

Fitness Obj 03

Fitness Obj 02

ibutions in the Year Less

tion

Fittest Objective 02

Fitness Obj 01

Fitness Obj 03

ated by the AI, the utions edited within Maker community then useumâ&#x20AC;&#x2122;s website cast um.

ty votes for the redetermined mecale of 0.0-1.0. the average for loor using a selfl data.

Fitness Obj 02

Fitness Obj 01 Fittest Objective 03

I tion)

Gen 228 Indv 02

Gen 462 Indv 02

Fitness Obj 03

Fitness Obj 02

*Fitness Objectives are Minimized

Maximize point to oth

y spaces

allow an even distribution about the massing while remaining ple optimal solutions exist, the multi-goal optimization process ecision-making for the human collective.

floor is optimized to allow an even ile remaining accessible and visible.

ective 02

Fl Ea

Objective 03

distance from her closest points

Maximize the connectivity of the selected point in network

Pr Th se

Best Average Fitness

Visualized Chosen Solution

Fitness Obj 01

Le Gen 494 Indv 42

Fitness Obj 03

Smallest Relative Difference

Fitness Obj 02

-Co -Ad -Fa -Le -Le

Fitness Obj 01

Le Gen 443 Indv 17

Fitness Obj 03

Fitness Obj 02

-Ad -Fa -Le -St

-Ad -Fa -Le -St 86

thesis prep

FA L L 2 0 2 0

M.ARCH

CHIA SHENG WEI

C A

I T

A E

â&#x20AC;&#x2122;

L D

I F

N O

THESIS

T R

MENTOR: IMMANUEL KOH

on the edge of tomorrow Th e t h es i s s e e k s to s p e c u late m u s eu m d e s i g n o f th e f u t u re ex p e r i men t i n g w i t h t h e to o l o f A r t i f i ci a l I n te lli ge n ce to generate fo r m f ro m 3D d ata

Contents i

thesis overview

Abstract Overview of Issues Museums Machines Research Question

museums in the information age

A history of Museums The Starchitect Issue Artificial Intelligence in Museums Icons in Architecture Democracy in Architecture Democracy in the Digital The Makerâ&#x20AC;&#x2122;s Museum

iii digital tool Understanding the Machine State of the Art-ificial in Architecture Digital Workflow Digital Explorations

iv the makerâ&#x20AC;&#x2122;s museum Against a Symbol Transient Dynamic Publicizing Probability Unfinished Bibliography Image References

PART I

abstract Machines & Museums

Mu se u ms i n to d ay â&#x20AC;&#x2122;s age o f d i g i tal co n s umpti o n h as i n m a n y i n sta n ce s b e co m e th e o b j e ct o f e x h i b i t , tak i n g th e p lace o f the a r t i fa ct . V i si to r s t rave l large d i stan ce s to mar v e l at th e arch i te cture of m u se u m s, ta ke a pict u re wi th i t an d p o st i t o n s o ci al me d i a, tag g i n g i ts l o cat i o n i n t h e pro ce s s . I n co n trast , th e multi p li ci ty o f arti facts wi thi n do n ot e n j o y a s m u ch atte n ti o n . Mus e ums wo rld wi d e atte st to th i s s upe r f i c i a l qu a l i t y a n d re pre se n tati o n o f th e i r i d e n ti ty, co mmi s s i o n i n g â&#x20AC;&#x2DC;star a rc h i te ctsâ&#x20AC;&#x2122; to de si g n ico nic, p ro v o cati v e an d f las h y b ui ld i n gs to d raw i n t h e c ro w ds. I s t h i s whe re th e f uture o f mus e um arch i te cture i s h ead e d to wa rds, w i t h ea c h b u ildin g co mp eti n g wi th th e n e xt to b e a mo re p o p ula r sym b o l ?

U t iliz in g th e d i gi tal to o ls avai lab le to d ay, th e th e s i s qu e st i o n s t h e ro l e o f t he arch i te ct i n d e s i gn i n g th e n e xt s y mb o li c mus e u m b y pro po si n g a n alte rnati v e : A Mus e um ge n e rate d b y an A rti f i ci al I n te l l i ge n ce f ro m co l le ct ive re p o s i to ri e s o f 3 - d i me n s i o n al d ata as an an ti -t h e si s to t h e a rc h i te ct u ral s y mb o l.

A R CHI T ECTU RA L D ES I GN

MUS EU M S

th esubj e ct

sp e culation

A RT I F I C I A L I N T E LLI G E N C E

dig ita lto o l

A mi sm atch o f te n e x is t s b et w e e n t h e applicat io n o f cu t t i n g -ed g e tec h n o logy to a lo calize d d e s ig n S u b je ct . T im e is ne e d e d fo r t h e tec h n o l o g y to mature , k no w le d g e o f it s u s ag e to pro life rate and fo r d em o n s t ra t i o n s o f i t s app licat io n to b e te s te d .

Wi t hout k no w ing w h at a To o l is g o o d fo r, can it b e u s ed to d es i g n a n ew t ype of b u ild ing ? It is d if f icu lt to te ll. A n e x plo rato r y s tep h a s to b e ta ken .

The tools t h at w e h av e at h and are rapid ly ch ang ing a n d a rc h i tec tu re h a s to keep u p. H o w w e u s e t h e m to d e s ig n is a qu e s t io n t h a t ever y n ew to o l develope d b ring s u po n arch ite ct s .

overview of pertinent Issues

i nfo r m at i o n

To d ay â&#x20AC;&#x2122;s i n fo rmati o n age e n j o y â&#x20AC;&#x2122;s an i n f lu x o f te c h n o l o g i e s t h at h a v e a lte re d th e way th e wo rld i s e x p e ri e n ce d . I n fo r m at i o n i s m a de m o re a cce s s i b le to i n d i v i d uals as lo n g as th e y h av e a de v i ce t h at ca n b e co n n e cte d to th e i n te rn et an d h as e n ri ch e d th e h um a n e x pe r i e n ce t h ro u g h t h e sh e e r p leth o ra o f i n fo rmati o n . Wi e ld i n g th e s e te c h n o l o g i e s a s a to o l h a s e n ab le d h uman i ty to s h are d ata at un p re cede n te d rate s.

Wh at co n sti tute s d ata? D ata can b e a li n e o f w o rds i n a m e ssa ge , a n i m age , a v i d e o, a 3 - d i me n s i o n al o b j e ct , i n fo r m at i o n e n co de d i n a l a n g u a ge th at we d o n ot i n sti n cti v e ly un d e rstan d , a n d re -t ra n sl ate d i n to t h e fa m i li ar. Th e co n stan t stream o f s uch i n fo rmat i o n co n st i t u te s a co n sc i o u sn e ss th at re f le cts th e glo bal co mmun i ty th at ex i sts b ot h i n ph ysi ca l spa ce a n d v i rtual s pace , th e taste s an d p re fe re n ce s , fe e l i n g s a n d o pi n i o n s, w h i c h i s i n turn s h ap e d b y A rti f i ci al I n te lli ge n ce a n d pre se n te d i n m o re ta rgete d co n te n t to us acco rd i n g to o ur i n d i v i d ua l pre fe re n ce s.

mu s e um s

Mus e ums o n th e oth e r h an d , s e r v e as re po si to r i e s o f i n fo r m at i o n a s w e l l . Th e y co n tai n arti facts an d oth e r o b j e cts o f a r t i st i c , c u l t u ra l , h i sto r i ca l , o r s ci e n ti f i c i mp o rtan ce . De s p i te th e adva n ce m e n ts i n te c h n o l o g y, su c h as v i rtual an d augme n te d reali ty, mus e u m s re m a i n i r re pl a ca b l e . I n stea d o f b e i n g s up p lan te d b y th e s e te ch n o lo gi e s, to da y, m u se u m s h a v e b eg u n i n co rp o rati n g te ch n o lo gi cally ad van ce d e x h i b i ts t h at fa b r i cate a n ot h e r lay e r o f i n te racti o n b etwe e n th e us e r an d t h e co n te n t .

Wh i le Mus e ums e x i st i n man y fo rms , f ro m sm a l l pr i vate co l l e ct i o n s to p ub li cly acce s s i b le mus e ums th at h av e e n o u r m o u s a n d e xte n si v e ga l l e ri e s s uch as th e Lo uv re , th e th e s i s wi ll b e fo c u si n g o n a ddre ssi n g t h e spate o f i co n i c co n te mp o rar y mus e ums th at are a ppea r i n g o n e a f te r t h e ot h e r s i n ce th e 1 9 7 0 s , each o n e mo re fan tastica l t h a n t h e pre v i o u s a s a po i n t o f co mpari s o n an d d i s ag re e me n t .

“Man’s stock of tools marks out the stages of civilization, the stone age, the bronze age, the iron age. Tools are the result of successive improvement; the effort of all generations is embodied in them. The tool is the direct and immediate expression of progress; it gives man essential assistance and essential freedom also.” Le Corbusier, Towards a new Architecture, p13

to o l s

Th e ma r k o f a n in te l l ige n t c iv il izat io n ca n be s e e n f ro m t h e s o ph i st i cat i o n of its to o l s . W hil e t he s m a r te st a n im a l s us e p r im it iv e to o l s t h at a re wi del y availab l e in n at ure , s uch a s a Chim pa n ze e’s us e o f a t wi g to fi s h ter m i tes from th e ir n e sts , hum a n it y ha s p ro g re s s e d fa r be y o n d t h e pr i m i t i ve to o l , with to o l s fo r e v e r y im a g in a bl e p ur p o s e a va il a bl e . To da y ’s to o l s ex i st i n an invi s ibl e rea l m , t he d ig ita l . Un s e e n y et im m e n s e l y po wer fu l , t h e di g ital is a s its ro ots , a m et ho d o f ca l cul at in g a n d p re s e n t i n g i n fo r m at i o n at speeds fa ste r t ha n t he hum a n m in d . In a s e n s e , o ur to o l s h a ve s u r pa s s ed our ori g in a l co n d it io n a n d a ug m e n t o ur a bil it ie s to create m o re s o ph i st icated a n d bea ut if ul t hin g s .

(00) Pounding with hammers to pounding the keyboard in the creative process

a rch ite c t ure

The to o l s w e ha v e d ire ct l y in f l ue n ce t he wa y we b u i l d. I n t h e h a n ds o f t h e a rc hite ct , t he n e w to o l s o f t he e ra o ug ht s h a pe t h e n e w a rc h i tect u re. Su c h wa s t he d il e m m a t hat Le Co r bus ie r fa ce d wh en h e m a r vel l ed at h o w t h e in d ust r ia l a ge ha d e n d o w e d e n g in e e r s w i t h t h e a b i l i t y to b u i l d h u ge o cea n l in e r s , w hil e a rchite cts l a g ge d be hin d in o u r t ra di t i o n a l wa y s , s pu r r i n g him to ret hin k a rc hite ct ure a s a m a c hin e fo r l i vi n g .

The a rchite ct o f to d a y d e s ig n s w it h t he to o l i n h a n d, per h a ps fi r st wi t h a p e n cil to s ketch be fo re m o v in g o n to C o m pu ter A i ded D es i g n ( C A D ) i n t he l atte r ha l f o f t he 20t h ce n t ur y. Arc hitect u re a s we k n o w i t , h a s a l wa y s be e n d e s ig n e d in its in ce pt io n . The co n s c i o u s t h o u g h t pu t b eh i n d t h e co l l e ct io n o f p ie ce s t hat m a ke up t he w ho l e a re a ppa ren t i n i ts o vera l l co he re n ce . A st r uct ura l p il l a r, a wa l k wa y, a wa l l , a do o r, a n o r n a m en t , t h es e a re t he p ie ce s t hat f it to get he r p re cis e l y a cco rdi n g to a n a n t h ro po cen t r i c in te n t io n a n d hin t to wa rd s t he hum a n a gen c y o f t h e des i g n er.

But to d a y, o ur to o l s p o s s ibl y s ur pa s s us a n d per h a ps , o u r h u m a n co n dit io n l im its us to t hin k in g v e r y hum a n t h o u g h ts a n d h a vi n g ver y h u m a n in te r p retat io n s o f in fo r m at io n .

artificial

Ar t if ic ia l In te l l ige n ce (AI) is t he a bil it y fo r m a c h i n es to ta ke o n ta s k s t h at

i ntel l ig e n ce

re q uire hum a n in te l l ige n ce . The a bil it y to c reate i s defi n ed a s ‘ t h e a b i l i t y to co m e up w it h id ea s o r a r te fa cts t hat a re n e w, s u r pr i s i n g , a n d va l u a b l e.’ (Bo ud e n , 2004) W hil e e xist in g a s p o p ul a r s c i en ce fi ct i o n i dea s i n t h e 9 0 s , s uch a s Dick’s n o v e l ‘Do An d ro id ’s Drea m o f E l ect r i c Sh eep’ s u c h n ot i o n s o f a m a chin e in te l l ige n ce ha v e be co m e rea l i t y to da y.

If a r t if ic ia l in te l l ige n ce is t he to o l o f t he 2 1 st cen t u r y, h o w i t a ffects t h e wa y w e d e s ig n a rchite ct ure o ug ht to be qu est i o n ed. A l rea dy, data s c i en t ists a re a hea d o f a rc hite cts ut il iz in g AI to a n a l y s e data a n d c reate vi r t u a l a s s ista n ts , e v e n s e l f- d r iv in g ca r s . S im il a r to Le C o r b u s i er ’s m u s i n g s , wh at t he n s ho ul d t he ‘n e w a rchite ct ure’ be ?

An ot he r p e r t in e n t q ue st io n t hat a r is e s is t h e exten t o f wh i c h des i g n de c is io n s s ho ul d be l e f t to t he a r t if ic ia l i n tel l i gen ce. Aftera l l , des i g n er s m a y n ot f ul l y un d e r sta n d t he rea s o n a n A I m a kes cer ta i n dec i s i o n s e ven t ho ug h t he re is a l o g ica l rea s o n fo r it d u e to t h e co m pl ex i t y o f h o w i n fo rm at io n is p ro ce s s e d by t he m a chin e . At w h i c h sta ges o f t h e des i g n s h o u l d t he a rchite ct in te r v e n e ? Is t he re a n e e d to fu l l y u n der sta n d t h e m a c h i n e? The t he s is o f fe r s p o s s ibl e in s ig ht in to t he s e a rea s .

(02) Dom-Ino House by Le Corbusier, 1914-15

(01) Primitive Hut engraving by Charles Eisen, 1755

(03) Elements of Architecture by Rem Koolhaus, 2018

(04) Do Androids Dream of Electric Sheep by Phillip K. Dick, 1968

museums Introduction (05) Old Ashmolean Museum, erected 1683

(06) Ashmolean Museum of Art and Archeology (Current), erected 1845

M u se u m s today are built upon a l o n g histo r y o f t he hum a n

co n s cio us n e s s ’. ( Le wi s , 1 9 9 9 ) A ‘c u l t u ra l co n s c i o u s n es s ’ ca n

d e sire to ‘acquire and inquire’ wit h m us e um s a s a s pa ce fo r

be d e f in e d a s a n a wa ren es s o f o n e’s o wn c u l t u re wi t h i n t h e

‘ p re se r vat ion and interpretation’. H o w e v e r, t he te r m ‘m us e -

s e l f o r t he c ul t u res o f ot h er s a n d ex pa n d to e ven b roa der

u m’ w ith its classical Greek origin s m o u s ei o n m ea n t ‘s eat o f

co n te xts , s uc h a s t h e c u l t u re o f t h e h u m a n ra ce. I t i m pl i es

the m u se s’ and was designated a s a n in st it ut io n fo r p hil o -

a co l l e ct iv e , wh ere t h e o b j ects wi t h i n a m u s eu m a re a fa cet

sop hi cal discussion. Th e term on l y be ca m e w id e l y us e d to

o f a l a rge r p hen o m en o n a n d t h e i n di vi du a l o b j ects b u i l d u p

d e scri b e wh at we understand as a m us e um to d a y in t he 17t h

a n in te r p rete d n a r rat i ve fo r a c u l t u re. T h e per i o d o f t h e E n -

ce ntu r y wh ere it was utilized in E uro p e to d e s cr ibe a co l l e c-

l ig hte n m e n t s p u r red t h e reco g n i t i o n t h at k n o wl edge o u g h t

ti on of curiosities. (Le wis, 1999)

to be m a d e p u b l i c to h a ve l a st i n g s i g n i fi ca n ce. I n a ddi t i o n , to p ro m ote co n t i n u i t y, s c h o l a r s h i p a n d go ver n m en ta l fa-

Pu rp ose s of each museum may va r y, ea ch p e r ha p s s p e c ia l-

v o r (Le w is , 199 9 ) m a n y pr i vate co l l ect i o n s were do n ated to

i z ing i n a certain f ield of study, to p ro m ote pat r iot is m a n d

p ubl ic bo d ie s . T h i s res u l ted i n t h e o pen i n g o f m u s eu m s to

i d e ntity o r e ven some a form of re creat io n . H o w e v e r, t he y

t he p ubl ic w he re pre vi o u s l y t h e y b el o n ged to t h e rea l m o f

are b ou nded by a common aim: ‘t he p re s e r vat io n a n d in-

w ea l t hy in d iv idu a l s , i n st i t u t i o n s o f l ea r n i n g a n d ecc l es i a st i -

te rp retation of some materi al a s p e ct o f s o c iet y ’s c ul t ura l

ca l e sta bl is hm en ts .

“The origins of the twin concepts of preservation and interpretation, which form the basis of the museum, lie in the human propensity to acquire and inquire.” Lewis, 1999, Encyclopaedia Britanica Th e arch itectu re o f m us e um s ha s cha n ge d since th e f irst buil d in g p ur p o s e f ul l y buil t to h ouse a private co l l e ct io n t hat wa s m a d e p ub licly available, t he A s hm o l ea n M us e um . ‘A s a building th at ho us e s ite m s o f e xce l l e n ce , it is expected to matc h t he q ua l it y a n d d ist in ct io n th at th e exh ib its br in g it ’ (Le w is , 1999) A s a consequence o f t his l o g ica l in fe re n ce , m us e ums of old h a v e be e n d e s ig n e d a s g ra d io s e arch itecture, wit h t he in te n t io n to in s p ire

(07) A Temple of the Muses-The Yorkshire Museum, 1830. From Thomas Allen, A new and complete history of the county of York.

awe and wonde r a n d a s a s y m bo l o f k n o w ledge. E arly mus e um s s uc h a s t he A s hm o l ea n Museum and the Yo r k s hire M us e um to o k re f-

ge n he im Bil ba o b y Fra n k G eh r y. Su c h co n tem po -

erence f rom the e n t y m o l o g y o f t he m us e um ,

ra r y m us e um s h a ve depa r ted fro m m o re t ra di t i o n a l

with cues f rom cl a s s ica l a rchite ct ure s ha p in g

id ea s o f t he a rc h i tect u re b ei n g a n em b o di m en t o f

its form. Musue m s ha v e a l s o be e n in s p ire d by

t he m us e um’s i dea s o r i ts co n ten ts a n d h a ve b e -

cath edrals in t he 19t h ce n t ur y, a s a s a n ct um

co m e ico n s fo r t h e c i t y, t h e m a r k s o f a fa m o u s a r-

for knowledge a n d l ea r n in g a s re f l e cte d in

c hite ct , to d ra w i n t h e c ro wds .

London’s Museum o f N at ura l H isto r y. Tho ug h less common, suc h id ea s st il l l in ge r t il l to d a y,

W hil st t he n u m b er s o f s u c h i co n i c m u s eu m s co n-

as seen f rom M a r io Botta’s S a n Fra n c is co M u -

t in ue to g ro w wo r l dwi de, t h e t h es i s qu est i o n s t h e

seum of Modern Ar t . H o w e v e r, in to d a y ’s a ge

in d iv id ua l ro l e o f t h e a rc h i tect i n t h e fo r m -m a k i n g

of information a n d v is ua l o v e r l oa d , t he st r ug-

p ro ce s s o f a b u i l di n g t h at i s u l t i m atel y m ea n t fo r a

gle for museums to a p p ea l to t he m a s s e s ha s

co l l e ct iv e re po s i to r y a n d a u di en ce.

led to an e ven g reate r e v o l ut io n in m us e um arch itecture, s pa r ke d o f f in pa r t by t he Gug-

(08) Denver Art Museum by Daniel Libeskind, 2006

machines Architectural Potential (09) Fun Palace by Cedric Price, 1964

(10) The Centre Pompidou by Renzo Piano, Richard Rogers and Gianfranco Franchini, 1977

T he p ote n tial for arch itecture to be a l te re d by t he p ro ce s s e s

atta c he d , d etac h ed, rotated a n d c h a n ged fo r t h e o rc h est ra -

and te chn ologies it incorporates in its c reat io n ca n be w it-

t io n o f d if fe re n t fo r m s o f a ct i vi t y.

ne sse d i n C edric P rice and J oan L itt l e w o o d ’s Fun Pa l a ce o f 1 9 6 4 . W hile originally intended to be a n e w fo r m o f l e is ure fo r

P r ice , in v o l v e d i n t h e em erg i n g fi el ds o f ‘c y b er n et i c s , ga m e

the p e op l e of London, ‘Th e Fun Pa l a ce wa s n ot a buil d in g in

t he o r y, a n d co m pu ter tec h n o l o g i es ’ ( M att h e ws , 2 0 0 5 ) rea l -

any conv entional sense, but was in stea d a s o c ia l l y in te ra ct iv e

is e d t he n e e d to u s e t h em to to reg u l ate s u c h a n i n deter m i-

machi ne , h igh ly adaptable to th e s hif t in g cul t ura l a n d s o cia l

n ate s y ste m a n d i n vo l ved t h e h el p o f c y b er n et i c i a n G o rdo n

cond i ti on s of its time and place.’ (M att he w s , 2005)

Pa s k in d e s ig n i n g t h es e s y stem s . W h i l e t h e Fu n Pa l a ce wa s ul t im ate l y un b u i l t , i t repres en ted ‘a n u n preceden ted a rc h i-

W ith this vision, a dy namic spa ce t hat wa s co n sta n t l y in

te ct ura l s y n t hes i s o f tec h n o l o g y, c y b er n et i c s , a n d ga m e t h eo -

flu x re sp o nding to th e wh ims an d fa n cie s o f t he v is ito r s in

r y ’ (M att he w s , 2 0 0 5 ) a s a n ‘ i n st r u m en t to s o c i a l i m pro vem en t ’

the ir i nd ividual pursuits was con ce iv e d by P r ice . In a n un -

a n d in s p ire d m a n y a rc h i tects o f t h e t i m e t i l l to da y, s u c h a s

conv e nti o nal sense, th e space wa s n ot p e rce iv e d a s a s o l id

t he Archig ra m g ro u p a n d b u i l t wo r k s u c h a s t h e Po m pi do u

archite ctural building, but a stru ct ura l s ca f fo l d t hat w o ul d

Ce n t re . The d y n a m i c a rc h i tect u re i s s een a s a gen erato r fo r

allow mach ines, arch itectural pie ce s a n d co n t ra pt io n s to be

n e w hum a n a ct i vi t y.

(12) A Walking City by Ron Heron, 1964

(11) Plug In City by Peter Cook, 1964 Th e conceptua l id ea t hat a rchite ct ure n e e d n ot be

f l ue n ce wa s l a rgel y fo r m a l a n d a est h et i c ’ ( M att h e ws ,

static was expl o re d by Arc hig ra m’s s e r ie s o f d ra w-

2005 ) , t h e co n cept o f a s pa ce t h at a l l o ws fo r m u l t i pl e

ings such as ‘P l ug - In Cit y ’ w hich im a g in e d t he cit y

c ha n g i n g po s s i b i l i t i es a cco rdi n g to t h e n eeds o f t h e

as massive mo d ul a r un its p l ug ge d in to m a chin e - l ike

p ro g ra m m e do es b ea r s o m e s i m i l a r i t y to t h e pa ra -

megastructure s by g ia n t c ra n e s . ‘ Wa l k in g Cit y ’ o n

d ig m s h i ft i n a rc h i tect u re i n i t i ated b y P r i ce. T h i s wa s

th e oth er h an d im a g in e s t he c it y a s a r t if icia l l y in-

e xe c u ted b y di s pl a y i n g t h e u s u a l l y h i dden b u i l di n g

telligent machin e s t hat t ra v e l to w he re re s o urce s fo r

s e r v i ces o u t o n to t h e fa ca de, freei n g u p t h e i n ter i o r

its inh abitants a re n e e d e d , e v e n co m bin in g to fo r m

ga l l e r y s pa ces , a n i dea t h at wa s i n t u r n i n fl u en ced

large mobile m et ro p o l is e s . W hil e s p e cul at iv e , t he s e

by A rc h i g ra m’s ex pl o rat i o n s i n i n ver t i n g t ra di t i o n a l

envisionings o f a p o s s ibl e f ut ure q ue st io n t he ro l e o f

buil di n g h i era rc h i es .

arch itecture as un in te l l ige n t , un cha n g in g buil t fo r m . The se pro j ects po s i t a rc h i tect u re a s m a c h i n e, dy n a mTh e Fun Palace a l s o s e r v e d a s a m o d e l fo r in s p ir in g

ic a n d a da pta b l e. B u t wi t h i n c rea s i n g l y i n tel l i gen t

Th e Pompidou Ce n t re d e s ig n e d by Re n zo P ia n o, Ric h -

m a c h i n es , h o w a rc h i tect u re ca n b e c reated di fferen t l y

ard Rogers and Gia n f ra n co Fra n chin i. Tho ug h t he ‘in -

is a qu est i o n y et to b e fu l l y ex pl o red.

(13) Plug In City by Peter Cook, 1964

RESEARCH question

makers

of the Thesis T he th esis seeks to propose a n e w k in d o f m us e um t hat re b e ls against th e notion of th e m us e um a s a n Ico n , d e s ig n e d

i nfo r m at i o n

b y ‘star arch itects’. Instead, it p ro p o s e s a n o n - s y m bo l a s a p ossible counterpoint , a Ma ke r ’s M us e um , w he re t he a ge n c y of the arch itect is almost no n - e xiste n t .

thor, Artificial Intelligence, a s a to o l to ge n e rate a co l l e ct iv e re p resentation for th e Make r s M us e um f ro m co l l e ct iv e d ata .

K e y questions to ask are, ‘Ho w d o e s a M a c hin e s e e d if fe re n tly?’, ‘H ow does a Mach ine t hin k d if fe re n t l y ?’ a n d ‘H o w d o e s a M ach ine create differentl y ?’ a n d w il l be a d d re s s e d in t he

d ig ita l w o rk f l o w

To do so, th e th esis explores t he ro l e o f a m o re im pa r t ia l a u-

artificial i ntel l ig e n ce

the sis.

By understanding th e Machin e , t he t he s is a im s to p ro v id e a d igital workf low for conv e r t in g d ata in to a l a n g ua ge und e rstandable by th e mach in e , a n d t ra n s l at in g it ba ck to t he p hysical. Explorations in th e d ig ita l co n d ucte d g iv e in s ig ht a s

i nfo r m at i o n

to th e tech nical understan d in g o f t he M a c hin e , its be n e f its and sh ortfalls.

T he th esis would speculate ho w t he s e ge n e r ic fo r m s ge n e rate d from collective data b y t he M a chin e ca n be pa r t o f a n architectural narrative for th e M a ke r s M us e um , a n d p o st ul ate a sy stem for its implementat io n .

m a k e r ’s mu s e u m

PART II

m u s e u m s i n t h e i n f o r m at i o n

n age

PART II

museums a brief history Mankind’s tendency to collect

Muse u m s have e xiste d sin ce an ti q ui ty, h o we v e r th e i r p ur po se and nat u re has ch an ge d d rasti cally i n th e past cent u rie s. Whe re pre vio u sly th e y we re co lle cti o n s o f th e r ic h as a display o f wealt h an d k n o wle d ge to re i n fo rce p rest ige , to day, t he y are m o stly acce s s i b le to th e p ub li c to s e r ve an e du cat io nal o r co n s e r vati o n al p urp o s e . Tracing a histo r y o f m u se u m s she d s li gh t o n th e ch an g i n g o b -

Princess Ennigaldi’s small educational museum of antiquities. Tablets describing 21st-century-bce artifacts was discovered. Possibility of a educational museum.

Greek pinakotheke a house for paintings honouring the gods. Art was abundant in public, but there was no ‘museum’ as we know it.

ject ive s o f m u se u m s and its p ote n ti al f uture as p i rati o n s .

O ne o f t he o lde st m u se u m s k n o wn to h uman i ty was th e Ennigaldi- Nanna m u se u m b ui lt b y a P ri n ce s s En n i gald i at

6 th ce nt u r y B C E

5 th ce nt u r y B C E

Ur, A n c i e nt B ab yl o n

G re e ce

th e e nd o f t he Ne o - Bab y lo n i an Emp i re , d ati n g f ro m c 5 3 0 BC E, which co ntaine d art ifa cts f ro m earli e r ci v i li zati o n s . (Wilke ns, 2011) Early m u se ums we re p ri vate co lle cti o n s of t he wealt hy and co ntain e d a large vari ety o f stran ge a r tifacts. Acce ss to t he se mus e ums we re re stri cte d to th e res pe cte d in so ciet y at t he d i s creti o n o f th e o wn e r. Th e muse u m s we re u su ally e nc y clo pae d i c i n n ature (Fi n d le n , 1994 ) whe re o wne rs st ro ve to co lle ct an d d i s p lay as much info rm at io n as po ssib le . Ho we v e r, i n th e 1 8 th ce n tur y, th e age o f Enlighte nm e nt ch an ge d th e way mus e um co llect io ns we re cu rate d and p lace d a g reate r e mp h as i s o n ‘organizat io n and taxano m y ’ (Fi n d le n , 1 9 9 4 ).

Collection by Cosimo de Medici was one of the most extensive collections and was expanded by his descendents, and donated to the state in 1743. Some collections were made available to the public and were included in guides for tourists to the city.

In other parts of Europe, patronage by royalty resulted in the amassment of collections of art. Displays of arms and armour were intended for public benefit.

1 5 th ce nt u r y - 1 7 4 3

1 5 th ce nt u r y

F l o re n ce , Ita ly

Eu ro p e

“Do not lay up for yourselves treasures on earth, where moth and rust destroy and where thieves break in and steal” Matthew 6:19, ESV I n 1 6 8 3 , t h e f i r st m o de r n pu b l i c m u se u m wa s o pe n e d co ns i sti n g t h e pe r so n a l co l l e ct i o n o f E l i a s A sh m o l e ( S wa n n ,

BUT MAYBE IN MUSEUMS

2 0 0 1 ) i n t h e Un i v e r si t y o f Ox fo rd. H o w e v e r, t h e se ‘ pu b l i c ’ mus e um s w e re st i l l o n l y a cce ssi b l e to t h e m i ddl e a n d u p p e r classe s o f so c i et y a n d w e re di f f i c u l t to ga i n a cce ss to. Yet , w i t h t h e su g ge st i o n t h at m u se u m s o u g h t to b e e d ucator s o f t h e pu b l i c o f pro pe r co n du ct a n d c i v i l i ze d b e h av i o u r dra w i n g o n t h e i dea s o f l i b e ra l go v e r n m e n t b y Fo ucault , t h e u n a cce ssi b i l i t y o f m u se u m s wa s g ra du a lly e rad i cate d ( B e n n ette , 1 9 9 5 ) a n d b e ca m e t r u l y pu b l i c d uri n g t h e 1 9 t h ce n t u r y, su c h a s t h e M et ro po l i ta n M u se um o f A r t i n 1 8 7 0 a m d t h e V i cto r i a a n d A l b e r t M u se u m in 1852.

The Todai Temple built in the 8th century housed treasures gifted by the emporer Shomu in a Shoso Repository built for such a purpose.

The tomb of Qinshihuang attest to the collection of rare and valuable goods. Collecting is noted to commence at least as early as mid-16th to mid-11th BCE in the Shang dynasty.

The idea of ‘waqf’ formalized by the prophet Muhammad led to collections of objects given up for the public good.

Relics and treasures held economic importance and were usually in possession of the church or royalty.

3 rd ce nt ur y B C E

8 th ce nt u r y

9 th ce nt u r y

C h i na

Jap a n

Eu ro p e

8 th ce nt u r y The first known instance of a public institution receiving a private collection was recorded to be the gift by the Grimani family to the Venetian republic in 1583. Specialized personal collections grew with an increased interest in natural history. A spirit of system and rational inquiry was emerging as reflected by a work by Samuel von Quicheberg on the nature of collections in 1565.

Isl a m i c Co m mun it i e s

The first instance of a building built to house collections for the public’s viewing was the Ashmolean Museum. The collection was gifted by Elias Ashmole to the University of Oxford.

Opening of Museums to the Public The Age of Enlightenment ushered in a spirit of intellectual discourse and philosophy and an increased taste for the exotic. Collections were made available to the public as a means to educate and uplift the public consciousness. The British Museum in London and the Louvre Museum in Paris were opened in 1759 and 1793 repectively.

Similarly, new collections were being made available to the public in other parts of Europe, from donations by royalty and initiatives of public authorities. By the 19th century, the European model had spread to the rest of the world, such as the United States.

1 6 th ce nt u r y

1683

1759, 1793

1 8 th ce nt ur y

Euro p e

E ngl a n d

E ngl a n d , Fra n ce

Eu ro p e

p a s t, p r e s e n t, f u t u r e A s mu se u m s b e cam e m o re p ub li cly acce s s i b le an d d e sti nati o ns fo r le isu re , so did th e arch i te cture . P ro gramme s th at we re m o re pu b lic o rie n te d s uch as e v e n t s pace s , g i f t sh ops and cafe s cate re d to th e n e e d s an d d e s i re s o f th e ma sse s while t he way s in wh i ch th e b ui lt arch i te cture interacts wit h t he su rro u nd i n g urban fab ri c are curate d to create a m o re invit ing an d o p e n e n tran ce s e q ue n ce . Afte r all, m u se u m s have b e co me mas s i v e re v e n ue ge n e rators att ract ing approxim ate ly 8 5 0 mi lli o n v i s i to rs each yea r to Am e rican m u se u m s alo n e . (A me ri can A lli an ce o f Under the influence of colonialism, museums in contries under colonial rule began to appear, such as the Batavia Society of Arts and Science in Jakarta, Indonesia in 1778.

Muse u m s)

W ith pre se nt te chno lo g y, th e co n te n t o f mus e ums h as ch a n ge d to do cu m e nt hu m an i ty ’s p ro g re s s i n th e areas

Increased interest in antiquities led to greater development of archeological research which had affected the development of museums. Museums were seen as a means for entertainment, promoting national pride, and celebrating scientific achievements. The Great Exhibition of 1851 at the Crystal Palace was one hallmark of the century that contributed to subsequent museum collections.

of scie nce as we ll as inco rpo rate th e late st te ch n o lo gi e s . Muse u m s su ch as t he Art Sci e n ce Mus e um i n S i n gap o re

1 8 th ce nt u r y

1 9 th ce nt u r y

incorpo rate high-te ch visu a l p ro j e cti o n s an d i n te racti v e

R e st o f th e w o rl d

Eu ro p e

a ugm e nte d realit y e xhib iti o n s i n an i mme rs i v e f uture d r ive n narrat ive o f spe cu lati o n wh i le mus e ums s uch as

Diversification of Museums to Science and

th e Ho u se o f Ele ct ro nic Arts i n B as e l q ue sti o n th e ro le o f tec hno lo g y in disru pt ing ar t . Th e wo rld ’s f i rst d i g i tal art muse u m , Bo rde rle ss b y tea mL ab s e e k s to create a ‘s h i f t

s uch as ke e pi n g v i si to r s i n a qu e u e . I n te re st i n g atte m pts

in the re lat io nship b et we e n th e i n d i v i d ual v i e we r an d th e

to e n gage d i g i ta l l y w i t h a u di e n ce s t h ro u g h A r t i f i c i a l l y I n-

crowd’ wit h inte ract ive installati o n s th at are co n n e cte d

te lli ge n ce h a v e a l so b e e n m a de b y m u se u m s, su c h a s Re c-

to the inte rnet . I ncreasingly, th e s e te ch n o lo gi e s create a

o g n i ti o n , a pro g ra m m e t h at co m pa re s rea l t i m e ph oto -

ne w dim e nsio n to ho w t he arti fact i s v i e we d .

j o urn ali s m w i t h B r i t i sh a r t f ro m t h e Tate co l l e ct i o n . ( Tate , 2 0 1 6 ) U s i n g m a c h i n e l ea r n i n g a l go r i t h m s, i t sea rc h e s t h e

T he ro le o f Art ificial I nte lli ge n ce h as als o b e e n s e e p i n g

d atabas e o f a r t co l l e ct i o n s a n d atte m pts to m atc h o n l i n e

into t he m u se u m sce ne . I n 2 0 1 6 , a ro b ot n ame d B e re n s o n

n e ws i mage s w i t h t h e m at i c o r v i su a l l y si m i l a r a r t pi e ce s.

wa s de signe d to wande r t h e h alls o f Mus e e d u q uai o f Pa r is re spo nding to art wo r k bas e d o n i ts learn i n g f ro m

A part f ro m t h e te c h n o l o g i ca l i n c l i n at i o n s o f m u se u m s

rea ct io ns o f real hu m an sub j e cts to artwo rk . Th ro ugh

to d ay, mus eu m s a re st i l l i n t h e m i dst o f c h a n ge i n a n at-

th is u nde rstanding o f what i s ‘ p o s i ti v e’ an d ‘ n egati v e’

te mpt to addre ss i ts re l e va n ce to to da y’s a u di e n ce s. T h e

a r t , t he art ificial inte llige n ce d e v e lo p s i ts o wn cri ti ci s m

re o rgan i zati o n o f i n te r i o r s, sh o w ca se o f te m po ra l e x h i b i-

towards ne w pie ce s o f art it s e e s fo r th e f i rst ti me . (Pan g-

ti o n s , s p e ci a l pro g ra m m e s a n d e v e n ts a re a da ptat i o n s to

bur n, 2016) Art ificial I nte lli ge n ce h as als o p lay e d a part

e n gage aud i e n ce s i n n e w wa ys. Yet u n su r pr i si n g l y de spi te

in enhancing t he m u se u m e x p e ri e n ce . Th e S mi th s o n i an

th e n e e d for a f l e x i b l e c h a ra cte r, t h e e xte r n a l re m a i n s

Inst it u te e m plo y s Pe ppe r, a ro b ot d e v e lo p e d b y S o f t-

un ch an geabl e , a f i xe d sym b o l t h at g i v e s t h e m u se u m a n

ba n k Ro b ot ics to inte ract wi th v i s i to rs (Walch , 2 0 2 0 ) an d

i d e n ti ty, b e sto w e d u po n i t pe r h a ps b y a ce r ta i n ‘sta r a r-

to ho ld co nve rsat io ns wit h th e m, an s we ri n g co mmo n ly

ch i te ct’.

a ske d qu e st io ns. The ro b ot i s ab le to teach v i s i to rs h o w to inte ract wit h t he e xhib i ts as we ll as carr y o ut tas k s

‘A museum is a public, collective process by which people are enabled, through understanding their relationship to the tangible and intangible heritage of humanity and its environment, to contribute to the long-term well-being of communities and sustainability of environments, globally and locally.’ Peter Stott

The latter half of the 19th century saw an explosion of museums with more than 100 opened in Britain in the 15 years before 1887) South America and Asia saw an increase in museums as well.

Museums continued to be developed despite constraints in public funding. In 1977, the Pompidou Centre was built which held galleries for modern art collections and additional exhibition and cultural activity spaces, a new type of museum for the city.

A reassesment of museums and their contribution to society due to society’s changing needs could be in part attributed to the two world wars. New types of museums appeared in this period.

1 9 th ce nt u r y

Post wa r

2 0 th & 2 1 st ce nt ur y

Inte r nat i o na l

Fra n ce

Inte r n at i o na l

d Technology

Mu s e um s To d ay

Rethinking Museums: Programme, Adaptations, Form

Robotic art critic, ‘Berenson’ was employed in the Musee du quai of Paris and reacted to artwork negatively or positively having learned from humans.

2016

Museums have begun to inhabit buildings of previous significance such as the Musee d’Orsay in 1986 which was a former railway station. The Tate Modern opened in 2000 was housed in a former power station on the South Bank in London.

‘Recognition’ was an artificial intelligence programme that found the best match for real time photojournalism from an online database. Employed image recognition techniques.

2016 Artificial Intelligence in Museums

Pepper a small humanoid robot deployed at 6 Smithsonian spaces designed to interact with visitors and answer questions, successfully enhancing visitor experience.

2018

t h e s ta r c h i t e c t an interpretation of architecture for a cultural collective

‘SHAPES AND TEXTURES OF A FISH’

(14) The Guggenheim Bilbao by Frank Gehry, 1997

T he or igin s o f su ch a phe no m e no n , th e mus e um as artwo rk

cultural e n e rg y, to t h e e xte n t t h at i t wa s te r m e d t h e ‘ B i l ba o

a nd attra cto r, can b e t race d to t h e G ug ge n h e i m Mus e um, B i l -

Ef fe ct’.

ba o. Built in 1997 and de signe d by arch i te ct Fran k G e h r y, th e sc ulptura l fo rm s and u ndu lat ing s ai ls o f th e B i lbao gli tte r

Wh i le th e o ri g i n a l i n te n t i o n wa s fo r t h e m u se u m to b e a n a r t

a nd outlin e t he cit y sky line . The ci ty o f B i lbao was d e li b e r-

p i e ce i n i ts e lf, a s i n t h e ca se o f t h e G u g ge n h e i m , c r i t i c s c l a i m

ately looking fo r a way to re invigo rate i ts e lf i n th e wake o f

th at th e arch i te ct u re u psta ge s t h e a r t a n d t h at t h e fo c u s o n

its p ost-indu st rial de cline . To do s o, i t to o k th e ri s k o f h av i n g

i ts e x p re s s i v e fo r m co m e s at t h e det r i m e n t o f ga l l e r i e s t h at

th e mus eum as a fo cal po int fo r its urban re n e wal. Th e re s ult

d warf th e artw o r k o r a re o ddl y si ze d. I n t h i s se n se , t h e a r t

is, a ccording to t he o riginal visi o n o f th e mus e um’s d i re c-

wi th i n b e co mes su b se r v i e n t to t h e sym b o l o f t h e m u se u m

tor, Jua n I gnacio Vidarte , ‘a t ran s fo rmati o n al p ro j e ct’ th at

an d o f te n th e sym b o l i s fet i sh i ze d. I n ‘ Li tt l e Fra n k a n d H i s

beca me ‘a drive r o f e co no m ic ren e wal’ an d e n j o y s a stead y

Carp’ th e p e rfo r m a n ce a r t i st A n drea Fra se r pa ro di e s t h i s fe -

strea m of m o re t han a m illio n visi to rs each y ear. Th e s ucce s s

ti s h i zati o n b y re spo n di n g i n a n e x pre ssi v e a n d se n su a l m a n-

of th e mu se u m as cu lt u ral de st in ati o n an d lan d mark s ub s e -

n e r to th e cue s o f t h e n a r rato r to ce l e b rate t h e w o n de r o f t h e

q uently resu lte d in t he su cce ssful re v i tali zati o n o f th e ci ty,

mus e um, th e e n t i re o f w h i c h ta ke s u p t h e f i r st 6 - m i n u te s o f

w ith a p o u ring in o f inve st m e nts , to uri sts , n e w j o b s an d a

th e n arrati o n se qu e n ce . ( S h i n e r, 2 0 0 7 )

Narrat them.

i n f l u -

‘TO PENETRATE THE HISTORIC ARSENAL’

(15) Dresden War Museum by Daniel Libeskind, 2011

‘THE DESERT ROSE’ (16) National Museum of Qatar by Jean Nouvel, 2019

On th e oth e r h an d , th e mus e u m h a s b e e n pra i se d fo r b e i n g a ‘cataly st fo r arti sti c b ri lli an ce’ ( S pe cto r, 2 0 1 7 ) a s w e l l a s h av i n g a ‘ tran s fo rmi n g e n e rg y’ ( H o l l , 2 0 1 7 ) o n t h e si te . A cco rd i n g to Fran k G e h r y, mo st a r t i sts wa n t to se e t h e i r w o r k i n b ui ld i n g s th at are to o a wo rk of a r t a n d t h at t h e m u se u m wa s d e v e lo p e d i n co n s ultati o n w i t h m u se u m re pre se n tat i v e s. Man y ci ti e s aro un d th e wo rld h a v i n g se e n i ts e f fe ct so u g h t to re p li cate i t wi th var y i n g d eg re e s o f su cce ss, co m m i ssi o n i n g ‘star arch i te cts’ to b ui ld rad i ca l n e w fo r m s i n h o pe s o f g i v i n g th e e co n o my a b o o st . S uch atte m pts i n c l u de t h e D e n v e r A r t Mus e um, D re s d e n War Mus e um b y D a n i e l Li b e ski n d, t h e N ati o n al Mus e um o f Qatar b y J ea n N o u v e l , a n d t h e M i l wa u ke e A rt Mus e um b y Calatrava, to n a m e a fe w.

tor: “You’ll see people going up to the walls and stroking You might feel the desire to do so yourself”

(17) ‘Little Frank and His Carp’ by Andrea Fraser, 2001

Wh i le i t can n ot b e d e n i e d th at ‘sta r a rc h i te cts’ po ssi b l y gi v e a mus e um i ts d raw, th e pa ra di g m pote n t i a l l y ra i se s t h e q ue sti o n o f wh eth e r th e e x p ressi o n o f t h e m u se u m do e s i ts co n te n ts j usti ce .

t h e cambrian explos S TA R C H I T E C T

The Guggenheim Bilbao marks the start to an era of ‘star architecture’. Designed by Frank Gehry, the expressive form of the building bearing no relation to its interior function drew crowds creating the ‘Bilbao Effect’ that many other cities wanted to emulate.

1997

Museum of Pop Culture Frank Gehr y 2000 Seattle, Was hington, US

B i l b a o , Sp a i n

Museums as Icons

City of Arts and Sciences Santiago Calatrava 1998-2005 Valencia, Spain

Jewish Museum in Berlin Daniel Libes k in d 2001 Berlin, German y

Quadracci Pavilion Santiago Ca l atra va 2001 M ilwaukee , Wi s co ns i n, US

Contemporary Arts Center Za h a H a d i d 2003 C i nc i nnati , O h i o, US

Royal Ontario Museum D a ni e l Li be s ki nd 2007 To ro nto, C a na d a

Denver Art Museum D a ni e l Li be s ki nd 2006 D e nve r, C o l o ra d o, US

Pompidou Metz S h i ge r u Ba n 2010 Metz, Fra nce

to name a fe w

a non-exhaustive li

sion

Maxxi National Musuem Za h a Had id 2 010 Ro m e, Ital y

Military History Museum Dan iel Lieb es kind 2011 Dresd en , Germa ny

Art Science Museum Mosh Sa fdie 2011 Singa pore

Ordos Museum MAD Arc h itects 2011 Ordos. C h ina

Heydar Aliyev Center Zaha Hadid 2012 Baku, Azerbaijan

Museum of Tomorrow Santiago C a l atra va 2015 Rio de Jane i ro, Bra zi l

Louis Vuitton Foundation Frank Gehr y 2014 Paris , France

Museum of Rock M VR DV 2016 Roski l d e , D e nm a r k

The Louvre Abu Dhabi J ea n No u ve l 2017 S a a d i yat Is l a nd , Abu D h a bi

Qatar National Museum J ea n No u ve l 2019 D o h a , Q ata r

s t o f s ta r c h i t e c t m u s e u m s

icons in architecture icons and symbols

i co n

A p icto r ia l re p re s e n tat io n o r s ig n w ho s e fo r m s u g gests m ea n i n g

s y mb o l

S o m et hin g t hat sta n d s fo r o r s ug ge sts s o m et h i n g el s e b y rea s o n o f rel a t io n s hip, a s s o c iat io n , co n v e n t io n , o r a cc i den ta l res em b l a n ce.

s y mb o l

ideal

a rch ite c t u re

ideal

Archite ct u re and sy m b o ls are o f te n i n te rtwi n e d w h ere o ne atte m pts to co n v e y th e oth e r o r b e co me s a ne w sy m b o l fo r so m et hin g e ls e . Th e parli ame n tar y building can b e a sy m b o l fo r d e mo crac y, th e j ud icia l co u rts a sym b o l o f ju sti ce , th e li b rar y a s y mb o l for kno wle dge . The se arc h i te cture s h av e b e co me symb o ls o f t he pro gram m e s o r i d eals th e y e mb o d y w h ile pe rhaps b e co m ing so b y d rawi n g o n s y mb o ls of earlie r ideals. Fo r e xam p le th e us e o f clas s i cal o rd er s to re fe re nce b eau t y, an ti q ui ty an d k n o wle d ge in the case o f t he A shm o l ean Mus e um o r th e i d eal of de m o crac y in go ve rnm e n t i n sti tuti o n s s uch as th e V irginia State Capito l de sig n e d b y Th o mas J e f fe rs o n ba se d o n Ro m an classicism , as i n te rp rete d b y Palla d i o fo r t he m o de rn wo rld, as i t h ad ach i e v e d th e â&#x20AC;&#x153;a p pro bat io n o f t he age sâ&#x20AC;? (Loth , 2 0 1 2 ) s e e k i n g to b e com e a sy m b o l o f go o dne ss an d b eauty.

‘The duck is the special building that is a symbol,’ while ‘the decorated shed is the conventional shelter that applies symbols.’ Venturi et al., 1977

I n th e g ra ph i c n o v e l b y J i m e n e z L a i , ‘ C i t i ze n s o f n o p lace’ sym b o l i sm a n d re pre se n tat i o n s o f fo r m wa s d i s cus se d i n t h e c h a pte r ‘ O n t ype s o f se du ct i v e ro b ustn e ss’. T h e c h a ra cte r i s a cc u se d b y dete ct i v e s fo r creati n g a n a rc h i te ct u ra l fo r m t h at h a d n o pre ce de n t an d thu s wa s n ot qu a l i f i e d. D e spi te so, t h e c h a ra cte r ma n a ge d to co n v i n ce t h e dete ct i v e s t h at h e wa s n ot gu i l t y o n t h e pre m i se t h at ‘ h e wa s i n l o v e w i t h i t an d ca n n ot h e l p h i m se l f ’. T h e dete ct i v e s ca pi t u l ate d th at su c h l o v e i s ‘ i n n o ce n t a n d i n de e d pre c i o u s’ an d a ssi g n e d t h e fo r m a u n i qu e re fe re n ce n u m b e r so th at ot h e r s co u l d ‘fo r n i cate’ w i t h i t . T h e sto r y m o c ks s e v e ra l a spe cts o f a rc h i te ct u ra l sym b o l s to da y. T h e fact that w h at i s pre po ste ro u s i s de e m e d j u st i f i a b l e b e cau se o f t h e a r t i st i c te n de n c i e s o f t h e a rc h i te ct . Th e arc h i te ct i s de e m e d su b j e ct to h i s o w n po w e r f u l i n sti n cts. T h e se co n d, i s t h at a rc h i te ct u ra l sym b o l s h av i n g b e e n de e m e d a s va l u a b l e , e co n o m i ca l l y pe r(18) Learning from Las Vegas by Venturi et al. 1972

h ap s , i n to da y’s co n te xt a re t h e n re pro du ce d a n d re p li cate d su c h t h at t h e i co n l o se s i ts u n i qu e n e ss a n d re co g n i za b i l i t y a s a n i co n .

(19) On types of seductive robustness, Citizens of no place by Jimenez Lai, 2012

d u c k s & d e c o r at e d s h e d s learning from las vegas

In t he co nt ro ve rsial b o o k b y Ve n turi ,

S cott B ro wn to geth e r an d

Izen o u r, ‘Learning fro m Las Vegas’, arch i te cture i s re d uce d to two typ es o f b u ildings, t he ‘du c k’ an d th e ‘d e co rate d s h e d ’.

T he ‘du ck’ e m plo ys u nive rsal s y mb o li s m as fo rm to s i g n i f y i ts f un ction s, while t he ‘de co rate d s h e d ’ co mmun i cate s th ro ugh ap p li e d symb o ls, su ch as a signb oard o r o rn ame n tati o n . Co n te mp o rar y arch ite cts have re co gnize d t he p o we r o f th e s y mb o l i n arch i te cture in creat ing an im m e diate con n e cti o n wi th p e o p le an d man y re ce n t w or ks have leane d to wards th e ‘d uck’ e n d o f th e s p e ctrum. B ui ld ings su ch as t he Lego Ho u se an d D an i s h N ati o n al Mari ti me Mus e um by BI G e m plo y su ch im age r y to g reat s ucce s s .

T he wo rk was a crit iqu e o n th e v i lli f i cati o n o f th e us e o f o rn ame ntation b y Mo de rnism and hi gh li gh te d th e i mp o rtan ce o f s y mb o li s m in archite ct u re , and can b e co n s i d e re d as an early wo rk o n Po stmode rnism .

T he t ype o f ab st ract sym b oli s m th at arch i te cts s uch as G e h r y e xp resse s in t he Gu gge nhe im re q ui re s a catego r y o f i ts o wn .

W h i le t his du alism m ay ho ld true fo r mo st b ui ld i n g s , th e ri s e o f ‘star a rchite cts’ create s a ne w ble n d o f b oth th ro ug h th e creati o n o f a signat u re e xpre ssive st y le . T h e b ui ld i n g i n th i s cas e i s th e n th e s y mbol fo r itse lf o r t he archite ct wh i le n ot s p eak i n g o f i ts i n te rn al f un ction s. Applie d sy m b o ls are th e re fo re sti ll n e cce s s ar y i n i n te rp reti n g its f u nct io n. Fo r e xam ple , th e G ug ge n h e i m Mus e um B i lbao ap p ears to be an ab st ract re pre se n tati o n o f a s h i p o r a f i s h , “a fan tasti c d ream ship o f u ndu lat ing form i n a cloak o f ti tan i um,” i ts b ri lli an tly refle ct ive pane ls also re m in i s ce n t o f f i s h s cale s . (To mp k i n s , 1 9 9 7 ) It is ho we ve r, not a re pre se n tati o n o f i ts f un cti o n as a mus e um o r its art ist ic co nte nts b u t rat h e r a s y mb o l i n i ts e lf o f th e ci ty ’s as p iratio ns and t he inge nu it y of th e d e s i gn e r. A co mb i n ati o n o f b oth , a ‘d eco rate d du ck’ is t hu s t he e n d re s ult .

DUC

(20) Architectural Ducks, base image of Denise Scott Brown by Venturi et al. 1977

DEC

ORA

TED

DUC

(21) Architectural Ducks, base image of Denise Scott Brown by Venturi et al. 1977

WHOSE SYMBOL

whose memory? whose collective consciousness? Libe skind ’s statement underscores t he im p ortance o f th e sy mbol in arch ite ct ure a s a method of communication of a n a r rat iv e . Yet it i s ironically dif f icult to under sta n d t he symbolism with out reading about w hat he thi nk s the design is about . Th is hig hl ig hts the d ange r of sy mbolism in arc hite ct ure , w he re a single person, or a small g ro up o f p e op le takes upon th emselves th e ta s k o f e x p re ssi ng a sy mbol in an obscure a n d un in tu itiv e manner. Extending Venturi’s a n a l o g y, w hile the building claims innate rep re s e n tation, it is actually an innate repre s e n tat io n of the arch itect’s abstraction. A co n f l at io n of u niv e rsal sy mbol with personal in te r p re tation occurs and it becomes a ‘De co rate d She d M asq uerading as a Duck’, a ‘Fa ke Duc k’.

A single person’s conceptual narrative in the design of the Imperial War Museum is compared with the Memorial to the Murdered Jews in Europe, a field of stelae that evokes multiple subjective narratives and memories.

Pete r Eise nman on th e oth er end of t he s p e ct r um re co g n ize s t hat m e m o r y is s ubje ct i ve a n d per s o n a l wh i l e b u i l di n g s a re i n a n i m ate obje cts, re f using to give a narrative to his M e m o r ia l to t he M urd e re d Je w s o f Euro p e i n B er l i n wh en i t fi r st o pen ed a n d a vo i di n g a n y form of e x plicit sy mbolism, instead c reat in g a p o s s ibl e a rc hite ct ura l co n d it io n w here s y b o l i s m i s r i c h o n l y b eca u s e i t i s o pen to a m u ltip li ci ty of interpretations, succe s s f ul y e v o k in g m e m o r y a n d e m ot io n w hil e re d u c i n g ‘ t h e po s s i b i l i t y o f a s i n g l e i n ter pretat i o n’ (Oli n, 2008).

= (23)

Memorial to the Murdered Jews in Europe by Peter Eisenmann

“Unless the building or memorial communicates something then people won’t identify with it.” Libeskind, 2013

= (22)

Imperial War Museum North by Daniel Libeskind

Ce n t ra l to his id ea s o f w hat t h e M em o r i a l o u g h t to b e, wa s t he creat io n o f a ‘f ie l d o f ot h er n es s ’ a s m en t i o n ed i n a n in te r v ie w w it h t he Lo uis a n a Ch a n n el . T h e i n ten t i o n wa s to c reate a s e n s at io n o f be in g a n ‘ot h er ’ a s a J e w i n G er m a n y. W hil e t his co ul d co n st it ute a s a n a r rat i ve fo r t h e pro j ect , t he fa ct t hat it is n ot d ire ct l y t ra n s l ated i n to ph y s i ca l s y mbo l s a l l o w s fo r a p ro je ct io n o f t h e s y m b o l wi t h i n t h e m i n d o f t he v is ito r to a n a rg ua bl y e ven st ro n ger effect . W h i l e t he s y m bo l o f ‘M e m o r ia l ’ ha s b een a s s i g n ed to i t , i ts i n te r io r f un ct io n s re m a in a bst ra ct a n d ex i sts a s a ‘A m b i g u o u s De co rate d S he d ’.

W it hin t he s a m e in te r v ie w, Eis en m a n em ph a s i zes t h e n eed fo r ‘a rchite ct ure to m a r k co l l e ct i ve m em o r y ’. A cco rdi n g to t he st ud io’s p ro f il e , t his is d o n e t h ro u g h a pro ces s o f co n s id e r in g ‘t he l a y e r s o f p hy s ica l a n d c u l t u ra l a rc h a eo l o g i es at ea ch s ite , n ot just t he o bvi o u s co n texts a n d pro g ra m s o f a buil d in g .’ (Eis e n m a n Arch i tects ) T h e i dea o f t h e co ll e ct iv e , d ra w s ta n ge n ts w it h to da y ’s vi r t u a l co l l ect i ve, a n d t he n e e d fo r a rchite ct ure to re s po n d to i t . I f t h e co l l ect i ve is d ig ita l , p e r ha p s t he p ro ce s s o f u n ea r t h i n g t h e l a y er s o ug ht to be a s w e l l .

democracy in architecture can architecture be democratic? Instead of an auth oritarian a p p roa ch t hat ‘star ach itects’ or arch itects in ge n e ra l ta ke towards design, h ow h as archite ct ure a s a p rofe ss ion attempted to be mo re d e m o c ratic? Pi o neering modernist archite cts s uch a s Lou i s S ullivan and Frank Lloyd W r ig ht ha v e claim e d to be designing demo c rat ic a rchite ctu re and h as attempted to p ro m ote d e m ocratic Arch itecture in th e form o f a uto p ia n sche me, Broadacre C ity, gath e r in g t he s up p ort of many prominent perso n s o f t he e ra . (Ock man, 2011) To date, Arch ite cts ha v e a n sw e re d th is question in various wa y s to va r ying d eg rees of success.

public space? O t he r a rc hite cts s uch a s R i c h a rd Ro ger s , en vi s i o n ed a dem o c rat i c a rchite ct ure a s ret ur n i n g s pa ce to t h e pu b l i c , ‘ wh en pu b l i c s pa ce is e ro d e d , o ur d e m o c ra c y s u ffer s .’ ( Ro ger s , 2 0 1 7 ) T h i s i s e vi n ced i n Fo ste r a n d Pa r t n e r ’s ren o vat i o n ato p t h e G er m a n Pa r l i a m en t B u i l din g , a l l o w in g bot h t h e P u b l i c a n d Po l i t i c i a n s to en j o y t h e a rc h i tect ure a n d in t he w o rd s o f J a n -W er n er M u el l er o f P r i n ceto n U n i ver s i t y t he p e o p l e ‘a s t he u l t i m ate s o verei g n , a re s y m b o l i ca l l y el e vated

style?

a bo v e t he ir re p re s e ntat i ves .’

Thomas J ef ferson saw Roma n Cl a s s icis m as su i table to represent th e d e m o c ra c y o f Am e ri ca as a sty le th at embodi e d ‘go o d n e s s ’ and d re w links to th e classica l id ea l s o f d e m ocrac y wh ile distancing from t he id ea o f au thoritarian colonial sty le archite ct ure . (d e B le e ckere, 2007)

(25)

(26) Virginia State Capitol by Thomas Jefferson, 1788

“Of the people, by the people, for the people” Abraham Lincoln, 1863

Reichstag, New German Parl

m at e r i a l s

“Representing democracy and facilitating democracy are two different things” “architecture should be less authoritarian” Mueller, 2015 Libertecture, 2014

community engagement? A rchite ctu re h as also been considere d d e m o c rat ic by e n ga g in g d e m o c rat ic p roce sse s su c h as including th e commun it y t hro ug h m et ho d s o f pa r t icipato r y d esign. In the rebuilding at ‘Ground Ze ro’ fo l l o w in g t he 9/ 11 atta ck s , a s e r ie s of participati o n exercises were h eld w it h t he p ubl ic to hea r t he ir o p in io n s a n d h op e s for the rebuilding. Th e stronge st co n ce pts w e re t he n d ete r m in e d a n d g uid e d the se l ection of th e proposals fo r re buil d in g .

symbols

(24)

Ground Zero Masterplan by Daniel Libeskind, 2003

S y m bo l is m s up p o s e d l y im bue d w it h d e m o c rat i c i dea l s a re a l s o a wa y a rc h i tect u re atte m pts to e m bo d y s uc h va l ue s . ‘Fre e d o m To wer ’ at ‘ G ro u n d Zero’ wa s m ea n t to b e a s y m bo l ic he ig ht o f 1776 fe et a n d d re w re feren ces to a n ot h er s y m b o l , t h e o u tst retc h ed a r m o f t he Stat ue o f L ibe r t y to m a r k Am e r i ca n i n depen den ce a s wel l a s va l u es o f dem o cra c y a n d l ibe r t y. H o w e v e r a s M ue l l e r p uts s u cc i n t l y, ‘ T h e pro b l em i s t h at s u c h repres entat io n s o f d e m o crat ic va l ue s a re n ot e q u a l l y co m preh en s i b l e o r, i f y o u l i ke, a cces s i b l e. To t he in n o ce n t o bs e r v e r, it n e e d s to be ex pl a i n ed t h at t h e to wer i s 1 7 7 6 feet h i g h a n d p e r ha p s a l s o fo r w hat t hat n um be r is s uppo s ed to sta n d’ ( M u el l er, 2 0 1 5 )

who we build for? Arc hite cts s uch a s L ibe s k in d , bo l d l y refu s e to wo r k fo r a u t h o r i ta r i a n reg i m es wh i l e Gün te r Be hn is c h wa r n s a ga in st buil d in g ‘ b i g , u n i qu e a r tefa cts ’ t h at o n l y di ctato r s wi t h a bs o l ute p o w e r a re a bl e to a f fo rd a ga in st a l l i m pra ct i ca l i t y. ( B eh n i s c h , 2 0 0 2 ) . By m a k i n g a state m e n t o f n ot a cce pt in g s uc h un - d e m o c rat i c c l i en ts , a rc h i tects m a ke k n o wn t h ei r p o l it ica l sta n ce a n d a s a co n s e q ue n ce t h at o f t h ei r a rc h i tect u re.

liament by Foster and Partners, 1999 T h e u se of ce rtain materials such as gl a s s in a rc hite ct ure is o f te n he ra l de d to symbolise democrac y. Of th e use o f g l a s s in t he Lo n d o n Cit y H a l l , the archi te cts note th at th e project ‘e xp re s s e s t he t ra n s pa re n c y o f t he d em ocrati c p rocess’ by allowing th e p ubl ic to ha v e a v is ua l co n n e ct io n to the A sse m bly process. Glass is give n a s y m bo l ic va l ue in bot h t he Re i c hstag b u i ld ing and C ity H all, h owe ve r, s uch ca s e s a re re p re s e n tat io n s of d e m ocrac y but do not truly facilitate d e m o cra c y in a rchite ct ure a s t he p ublic i s u nable to affect th ese proces s e s it is p r iv y to. (27)

London City Hall by Foster and Partners, 2002

d e m o c r a c y i n t h e d i g i ta l can the digital create democratic architecture?

Archi tecture th at struggles to be m o re d e mocratic is juxtaposed with th e s ucce s s e s in starchitecture th at generates a n a rchite ct ure that u pstages its contents for t he p ur p o s e o f a sp e ctacle as ech oed by H al Fo ste r in ‘De s ig n and Crime’. Cultural centers s uch a s m us e u m s originally meant as forums o f c iv ic e n gage ment ser ve a wh ole differe n t p ur p o s e o f sp e ctatorsh ip. H ow many more s p e cta c l e s o f ind i v iduality can we h ave befo re a rc hite ct ure lose s its meaning or th ese s p e cta c l e s l o s e the i r i mpressiveness? J imine z L a i l ike n s s uch archi tecture as a unicorn in h is g ra p hic n o v e l , initially a rare and ch erish ed o cc ura n ce , y et once co mmodified, loses its m ea n in g . But be -

(25) On Uniqueness, Citizens of no place by Jimenez Lai, 2012

cau se of th is, h istor y is ch ange d . W hat is n e xt the n i s th e question th e th esis a s k s a n d s e e k s

the internet the world 60% 2020 90% 2030 4.5billion 7.5billion 3.8billion 6hrs43min 44zetabytes 2.5QUINTILLION

to answer with th e digital.

of the world’s population has internet access

The internet is th e reason fo r t he In fo r m at io n Age . W it h

the lower cost of an Internet co n n e ct io n to d a y a s w e l l a s cheap er available consumer te c hn o l o g y t hat e n a bl e s o n e

of the world’s population has internet projected access

social media users

to connect to th e internet , it h a s be co m e a cce s s ibl e to t he

per day

m ajori t y of th e world’s populat io n . The a m o un t o f in fo r m a tion, o r data, th at is constantly ge n e rate d by its us e r s is a n

u nim aginable amount , accelerate d by t he us e o f s o c ia l m e d i a, w h ich facilitates th e uploa d o f d ata create d by e v e r y-

worldwide average time spent online

d ay u sers wh eth er intentiona l l y o r un in te n t io n a l l y. W hil e

1 ZETABYTE =1021 BYTES

the Internet as a tech nological to o l is p o l it ica l l y n e ut ra l , its acce ssibility to users is a form o f d e m o c ra c y. An y o n e w it h

an internet connection h as a cce s s to (m o st ) in fo r m at io n

of information comprises the internet

and is able to contribute to t his d ig ita l p ubl ic- s ca p e in a m u ltip l icity of forms. Most comm o n l y in t he fo r m o f ha v in g a ‘ v oice’, an avenue to give an o p in io n a bo ut s o m et hin g .

bytes of information uploaded per day

“Gehry evokes an individuality that seems more exclusive than democratic. Rather than “forums of civic engagement,” his cultural centers appear as sites of spectacular spectatorship, of touristic awe.” Foster, 2015 W i th ad vancements in 3D tech nolo g y, s uc h a s 3D m o d e l l in g s o f t wa re s , 3D scanni ng de vices and meth ods, an d 3D p r in t in g ca pa bil it ie s at in c rea s in g ly afford able prices, space and for m its e l f is be co m in g pa r t o f t he l a rge st netw ork in th e world, existing in th e fo r m o f d ata .

M u se u ms today h ave begun digital izat io n t he ir a r t ifa cts in a bid to c reate d i gi tal re positories of information a s ‘ba c kup s ’ s uc h t hat a r t ifa cts ca n e xist b e yond th eir lifespan virtually a n d to m it igate t he im pa ct o f un p re d icatble e v ents th at could damage t he s e a r t ifa cts . (Al d e r to n , 2016) W hil e thi s i s the primar y goal, th e demo crat izat io n o f s uch in fo r m at io n is t he ne xt ste p, in allowing th e public, re s ea rc he r s a n d e d ucato r s to ut il ize t his informati o n for personal, teach ing o r re s ea rc h p ur p o s e s fo r f re e . (28) 3Dscan of artifact by The British Museum

d’s largest public space? Data according to Digital 2020 Global Digital Overview and Cybersecurity Ventures

“If you think about the limited number of people in the long run who are able to actually visit the museum in person, it pales in comparison with the number of people who have Internet access in the United States and worldwide.” Adam Metallo, Digitization Program Office at the Smithsonian Institution in Washington, D.C. (Alderton, 2016) Thi s m ark s a trend of th e digital a l l o w in g fo r g reate r a cce s s ibil it y to informati on wh ere wh at was pre vio us l y o n l y v is ibl e in t he m us e um is n o w

physical object

museum

p ossi b ly v ie wed in 3D online. Th e s ha r in g o f 3D d ata o cc ur s n ot just by institu tions of knowledge with the ir a ud ie n ce s but a l s o w it hin o n l in e comm u nities. Ech oing Abrah am Li n co l n’s Gett y s burg a d d re s s , d e m o c ra c y as ‘of the people, by th e people, for t h e pe o ple’ s uc h e xc ha n ge s o f d ata fo r the com m unity and by th e commun it y e xa m p l if ie s t he id ea o f t he in te r n et be ing a p ublic space th at h as eleme n ts o f a d e m o c rat ic id ea l .

Can the cross -pollination of such id ea l s e xist in g in t he v ir t ua l a f fe ct t he p hysi cal as well? C an th e digital s e r v e to create a m o re d e m o c rat ic a r-

[0,1,1] [0,0,1]

[1,1,1] [1,0,1]

chi te ctu re using th e availability o f d ata ? A s a n a sto un d in g a m o un t o f informati o n is uploaded each da y, a co l l e ct iv e co n s cio us n e s s o f id ea s , creations, information is being a m a s s e d a n d a n a l y s e d by t he a r t if icia l inte llige nces of corporations to dete ct p ro f ita bl e t re n d s . H o w t he n ca n archi te cture enter a ne w more dem o crat ic e ra in t he In fo r m at io n Age ?

[0,1,0] [0,0,0]

[1,1,0] [1,0,0]

digital representation

community

t h e d i g i ta l d u c k a critique of starchitecture, leveraging on the digital to create a non-symbol

“A museum is the memory of mankind.” Philippe de Montebello, former Metropolitan Museum of Art director

T he statement th at th e mus e um is t he m e m o r y of mankind implies th at it o ug ht to e m bo d y a ce rtain collective memor y. T his m a ke s o n e q ue s ti on th e sy mbol generating c ha ra cte r o f m us e um architecture today. A s illustrate d ea r l ie r, a n d e xpan ding on Venturi’s idea o f ‘d uck s ’ a n d ‘d e corated sh eds’, th e arch itect ura l e xa m p l e s c ite d i n t h e th esis can be furth er d ist in g uis he d in to finer categories as depicted o n t he fa cin g pa ge . T hi s segment explores th e p o s s ibil it y o f a ‘d ig i tal duck’ in th e information a ge .

W hile th e experience of a v ir t ua l m us e um w il l ne v er replace th at of th e p hy s ica l , t he re v e r s e of digitalizing th e museum i s co n s id e re d in l ig ht of creating a ne w ty pe of m us e um t hat a cts a s a counterpoint to starch ite ct ure , by l e v e ra g in g on t h e digital collective to create a n o n - s y m bo l , the ‘digital duck’.

form generated from a collective consciousness?

A non-sy mbol refers to arc hite ct ure t hat d o e s museum

non-symbol

not h ave an explicit sy mb o l is m o f a n y t hin g .

N e ith er is it an explicit sy mbo l o f t he a rc hite ct ’s ab st ract expression, or does it st r iv e to be o n e .

T he striving towards th e op p o s ite , o f creat in g a non -sy mbol, th e likes of Eis e n m a n’s M e m o r ia l , i s proposed as a digital pro ce s s w hic h a l l o w s for th e agenc y of th e arch ite ct to be re p l a ce d

artificial intelligence

w ith an artificial intelligence t hat create s l ea r ni ng f rom a collective. Th e in te n t is to c reate a rchitecture th at is more true to its co n te n ts a n d more democratic.

[0,1,1] [0,0,1] [0,1,1] [0,1,1] [0,1,1]

[1,1,1] [1,1,1] [1,1,1]

[0,0,1] [1,1,0] [0,1,0]

[1,1,0] [1,1,0]

[0,0,0] [1,0,0] [0,0,0] [0,0,0][1,0,0] [1,0,0]

digital representations

collective

fake duck

decorated duck

Catalogue of Ducks

Function:

Claims innate representation of function

Assigned representation of function

Symbol:

Innate representation of architect’s abstraction

Innate representation of architect’s ab-

Conflation of universal symbol with personal in-

straction

terpretation

Example:

Guggenheim Bilbao

duck

decorated shed

Imperial War Museum North

Function:

Innate representation of function through

Assigned representation of function

building form

through applied symbols

Symbol:

Building is symbol

Symbol applied to building

Example:

Longaberger Company Basket Building

Most buildings

implicit symbol

ambiguous decorated shed

digital duck

explicit symbol

or non-symbol

Function:

Assigned representation of function

Symbol:

Function in terms of actual usage

Abstract symbol as a result of collective

remains ambiguous

abstract representation

Symbol: Abstract symbol as a result of architect’s process Example: Eisenman’s Memorial for the Murdered Jews in Berlin

the maker’s museum the Thingiverse digital collective T he Maker movement first b ega n in 2005 w it h t he p ubl icat io n fo un d e d b y Dale Dough erty, th e ‘Ma ke’ m a ga z in e w hich wa s a p ubl icat io n t hat he lped people start h obbie s a n d l ea r n n e w s k il l s (Do ug he r t y, 2012) by ‘d oing it th emselves’. Wh ile d ista n c in g t he m s e l v e s f ro m t he te r m ‘in v e n tor ’, makers prefer th e more in fo r m a l te r m to d e s c r ibe t he m s e l v e s a s a group of people wh o enjoy e n r ic hin g t he ir l iv e s a n d l ea r n in g t hro ug h creating ph y sical objects wit h te c hn o l o g y. M a ke r s s e e t he m s e l v e s a s part of a larger maker comm un it y w it h o p e n a n d f re e e xcha n ge o f k n o w l e d ge and ideas. E arly consum e r 3d - p r in te r bus in e s s e s s uc h a s M a ke r bot

1.5million

3d printers bought in 20

projected sales according to 3d printing indust

52%

year-on-year growth 3d printer volume sal

analysis according to research firm Context

Ind ustries tapped into th e ma ke r s ubcul t ure a n d o p e n s o urce d t he ir in iti al self-assembled 3d prin te r s , m a r kete d a s t he co n s um e r p ro d uct o f the future th at would ‘demo c rat ize m a n ufa ct ur in g a n d m a ke 3D p r in t in g more accessible to e ver y one’ (Pett is , 2011). In a wa y, w it h t he in c rea s in g affordability of desktop 3D p r in te r s , s uc h a state m e n t ha s co m e t r ue tod a y. 3D printer sales h av e in crea s e d ea c h y ea r w it h 1.5 m il l io n 3D p ri n ters projected to be sold in 2020. H o w e v e r, t his co ul d n ot ha v e be e n p ossible with out correspond in g o n l in e p l at fo r m s t hat he l p ge n e rate d e mand for objects th at a des kto p 3D p r in te r ca n c reate by in crea s in g t he ease of 3D data exch ange w it hin t he m a ke r co m m un it y. The p hy s ica l d e mocratization of manufact ur in g , is e n a bl e d by t he d ig ita l d e m o crat ization of information.

1,876,840

3d models uploaded at least

M akerbot’s Th ingiverse is an o n l in e p l at fo r m t hat s e r v e s a s a re p o s ito r y for open source designs ma d e by t he Thin g iv e r s e co m m un it y. It wa s f ir st created in 2008 by Zach S mi t h, o n e o f t he co fo un d e r s o f M a ke r bot In d us trie s as a companion site to t he ir p r in te r s .

Today, it is th e largest onl in e 3D p r in t in g co m m un it y a n d f il e re p o s itor y and h as at least 1,876, 840 3D m o d e l s up l oa d e d by its us e r s . A s 3D p ri n ting tech nolog y and expe r t is e co n t in ue s to p ro l ife rate , t hat n um be r i s e xpected to grow. C atego r ie s o f 3D m o d e l s a re a l s o w e l l - o rga n is e d acco rding to a dash board that re d ire cts us e r s to re l e va n t d ata .

Ce ntral to th e community is t he id ea t hat a n y o n e ca n s ha re t he ir w o r k , d ownload th e designs of ot he r s , o r im p ro v e up o n it v ia re m ixe s . In t he sp ir it of openess and acces s ibil it y, a n y o n e ca n p o st a co m m e n t , q ue r y or re vie w about th e 3D f ile t he y d o w n l oa d e d . A rat in g s y ste m f ro m t he nu mber of ‘likes’ and ‘down l oa d s ’ g iv e in d icat io n o f w hat is m o re o r l e s s p opular among th e commun it y.

(29)

Tools

Household

Toys & Games

Math Physics & Astronomy Animals Buildings & Structures Creatures Food & Drink Model Furniture Model Robots People Props Vehicles Hand Tools Machine Tools Tool Holders & Boxes Chess Construction Toys Dice Games Mechanical Toys Playsets Puzzles Toy & Game Accessories

Models

Hobby

Art

h les

Gadgets

try

3DPrinting

s 020

3D Printer Accessories 3D Printer Extruders 3D Printer Parts 3D Printers 3D Printing Tests 2D Art Art Tools Coins & Badges Interactive Art Math Art Scans & Replicas Sculptures Signs & Logos Accessories

Fashion

3D Printing

Bracelets Costume Earrings Glasses Jewelry Keychains Rings Audio Camera Computer Mobile Phone Tablet Video Games Automotive DIY Electronics Music R/C Vehicles Robotics Sports & Outdoors Bathroom Containers Decor Household Supplies Kitchen & Dining Office Organization Outdoor & Garden Pets Biology Engineering

Learning

All Things

Categories

“The contents of this massive repository of 3D objects are interpreted as a digital collective, a manifestation of the maker culture, with implicit rules governing what is desirable, or undesirable, popular or not.”

The catego r i es t h at T h i n g i ver s e o rga n i zes i ts repo s i to r ie s in to a re a refl ect i o n o f t h e i n terests a n d n eeds o f t he m a ker s wh o u t i l i ze t h e pl at fo r m . W h i l e a dm i ttedl y, t he d i g i ta l a r t i fa cts wi t h i n t h e co l l ect i ve a re u n a b l e to ca pt ure t h e fu l l est es s en ce o f a c u l t u re, i m pl i c i t r u l es t hat go ver n fo r m o r fu n ct i o n ca n po s s i b l y b y u n ea r t h ed by l ea r n i n g fro m t h es e di g i ta l a r t i fa cts .

Ge n e rat i n g a M a ker ’s M u s eu m t h at i s t h e em b o di m en t o f t he data t h at i s co n t r i b u ted b y t h e co m m u n i t y, s u c h a p ro ces s ex pl o res t h e po s s i b i l i t y fo r a m o re dem o c rat i c a p p roa c h to c reat i n g a rc h i tect u ra l fo r m .

Makerbot’s Thingiverse Webpage

(30) Item download and options

the maker’s museum an experiment for machine learned form generated from Thingiverse data The id ea o f t he ‘Dig ita l Duc k’ is o n e w he re d es i g n i s di sta n ced fro m h u m a n a ut ho r s hip s uc h t hat t he d ig ita l co l l e ct iv e in t h e fo r m o f data ca n b e pro ces s ed in a n un bia s e d m a n n e r. Ad m itte d l y, t he a ge n c y o f t h e a rc h i tect ca n n ot b e fu l l y o m itte d at t he c ur re n t sta ge o f Ar t if ic ia l In te l l i gen ce’s ( A I ) de vel o pm en t a n d t h e H um a n In te l l ige n ce a n d Ar t if icia l In te l l ige n ce h a ve to co l l a b o rate i n t h e des i g n p ro ce s s . W hil e t he a rc hite ct d e cid e s t he ge n e ra l r u l es to fra m e t h e pro b l em , s u c h a s t he catego r y o f fo r m s to co m p r is e t he d ata s et fo r t h e A I to t ra i n o n , i t i s t h e Ar t if icia l In te l l ige n ce t hat ge n e rate s re p re s e n tat i o n s ba s ed o n t h e i m pl i c i t r u l es it ha s l ea r n t . W it hin s uch a p ro ce s s its e l f, a cer ta i n i m pa r t i a l i t y o f t h e m a c h i n e create s t he ega l ita r ia n fo r m .

A s th e Artif ic ia l In te l l ige n ce ge n e rate s n e w d ata f ro m l ea r n ed data , i t i s critical th at t he in it ia l d ata s et is ca re f ul l y s e l e cte d by t he des i g n er. Po s sible catego r ie s fo r s e l e ct io n co ul d in c l ud e ‘1000 m o st- l iked’ o r ‘ 1 0 0 0 most-popul a r fo r t he y ea r ’. Catego r ica l s e l e ct io n s s uc h a s ‘ B u i l di n g s & Structures’ o r a cco rd in g to s im il a r o bje ct s ca l e co ul d co m p r i s e t h e data set such th at t he m a c hin e l ea r n s a s im il a r s ca l e o f o bje cts .

O n ce t he d ata s et ha s be e n p re pa red, a m a c h i n e l ea r n i n g m o del ca n b e t ra in e d o n t he 3D d ata . In t his t hes i s , a gen erat i ve m o del i s u s ed. A l a rge d ata s et w o ul d a l l o w fo r bette r l ea r n i n g , t h o u g h i t wo u l d s i g n i fi ca n t l y in crea s e t ra in in g t im e s a n d co m pu t i n g res o u rces . A go o d n u m b er wo u l d be bet w e e n 1000- 3000 ba s e d o n e x pl o rat i ve res ea rc h do n e i n t h e t h es i s .

A s th e mod e l t ra in s , it c reate s a w e l l - p o p ul ate d l ate n t s pa ce t h at i s t h e abstract rep re s e n ta io n o f its un d e r sta n d in g o f t he im p l ic i t r u l es t h at govern form . The l ate n t s pa ce ca n be s a m p l e d a n d m a n ipu l ated fo r a variety of fo r m s .

S e l e ct io n o f fo r m s f ro m t he ge n erated data h a s to b e s el ected b y t h e d e s ig n e r a n d m a d e a rc hite ct ura l . D es pi te t h e des i g n er ’s ro l e i n t h i s pa r t o f t he d e s ig n , fo r m s ha v e ul t im ate l y b een c reated b y t h e m a c h i n e fro m a d ig ita l co l l e ct iv e co n s cio us n e s s . T h e des i g n er s i m pl y pl a y s t h e ro l e o f a curato r a n d in te r p rete r o f a r t if ic i a l l y gen erated fo r m .

Method in General

Selection of Thingiverse Data

Training on Data

artificial intelligence

human intelligence

Generate Latent Space

Selection of Form

Makerâ&#x20AC;&#x2122;s Museum

PART III

d i g i ta l t o o l s

A.I

I np ut s

Outputs

Th e I n fo r mat i on Age t oday o ffers a vast l y dif ferent toolset . Ar tif ic ia lly intelligent tools s uch as Neural Net works th at lea r n f rom data of fer a potential to change t he way we d esign. Th e m eth ods for designing with thes e ne w tools wo ul d al ter t h e wa y we th ink abou t Arc h itecture and could pos s ibly capt ure facets th at were pre viou sly inv isible to us .

In t he d esig n for a Ma ker â&#x20AC;&#x2122;s Mu seu m , th e resea rch of the Tool explores t he capab il it ies a nd potentia l for 3 -D im ensional forms to be learnt an d recreated by G enerative Adv er sar ia l Netw ork s (GAN s ).

timeline of theories & too a brief history of architecture & technology in the past century Tech nolog y h as ch ange d a n d co n t in ue s c ha n g-

m o v e m e n t p io n eered t h e u s a ge o f C AT I A i n a rc h i-

ing th e way arch itects t hin k a bo ut d e s ig n . W he re

te ct ure , a p ro g ra m m e m ea n t to des i g n a i rc ra ft ,

pre viously our forerun n e r s ut il is e d t im e -te ste d

meth ods to build archite ct ure , t he pa st ce n t ur y

a d va n ce d a n d u s er-fr i en dl y s o ft wa res s u c h a s

h as seen th e rise of a ne w to o l : t he co m p ute r. W it h

S ketchUp a n d Re vi t were de vel o ped a l o n g s i de t h e

th e computer, th e first s o f t wa re s t hat a id e d t he

Third In d ust r ia l Re vo l u t i o n e ven a s co m pu t i n g ca-

mech anical drawing pro ce s s e m e rge d w it h P ro n to

pa bil it ie s o f m a c h i n es b eca m e m o re po wer fu l a n d

th e first C omputer Aid e d M a n ufa ct ur in g s o f t wa re

a f fo rd a bl e . The s e s o ft wa res a l l o wed fo r 3 D repre -

by H anratty in 1957. This wa s s o o n fo l l o w e d by

s e n tat io n s o f in fo r m at i o n . G ra s s h o pper c reated b y

d e s cr ibe

u n co n ven t i o n a l

MODERNISM

geo m et r i es .

M o re

POSTMODERNISM

Ro be r t Ve ntu r i & D e ni s e S cott Bro w n 1960s

1920s

ARTIFICIAL INTELLIGENCE

J o h n Mc C a r th y at D a r tm o u th C o l l ege co nfe re nce 1956

NEURAL NETWORKS Theories

Warren M cCulloch & Walter P itts 1943

TURING TEST Alan Turing 1950

PERCEPTRON Fra nk Ro s e nbl att 1958

MODULAR ARCHITECTURE

Digital Tools

Lu i gi Mo retti 1971

COMPUTATIONAL DESIGN

B u c km inster Fu ller, D ymaxion 1920s

1920

PARAMETRIC D

1960s

1950

1960

1970

Pronto (First CAM Software) Patr i c k J. H a nratty Fi r st Re l ea s e 1957 ENIAC (First Commercial Computer) John M auchly & J.P res p e r Ec ke r t First Releas e 1951

Sketchpad I va n S u th e r l a nd Fi r st Re l ea s e 1960

SECOND INDUSTRIAL REVOLUTION

THIRD INDUSTRIAL REVOLU

Division of labour, mass production, electricity

High level automation through

S ketch pad, in 1960 by Iva n S ut he r l a n d w ho te r m e d

Da v id Rutte n in 2 0 0 7 , a pa ra m et r i c m o del l i n g to o l

it a ‘Robotic Draftsman’. The id ea o f t he co m p ute r

fo r Rhin o ce ro s b y Ro b er t M c N eel a n d A s s o c i ates ,

as a tool for arch itects to d ra w wa s f ur t he r re f in e d

ga v e a rchite cts a cces s to des i g n i n g wi t h pa ra m e -

and expanded in th e fo l l o w in g d e ca d e s w it h co m -

te r s , ke y p ie ce s o f i n fo r m at i o n t h at co u l d b e co n-

puter aided drawing (C AD) to o l s s uc h a s Auto CAD.

t ro l l e d by t he us er to a l ter a rc h i tect u ra l fo r m .

In 1988, Frank Geh r y, pa r t o f t he d e co n st r uct iv ist

t he pa ra m ete r s o f t h ei r des i g n s vi a a n e vo l u t i o n a r y

sof tware were ideas o f d r iv in g d e s ig n in a m o re

a l go r it hm . Un der l y i n g t h e i dea s o f gen erat i ce de -

computational way. C o m p utat io n a l d e s ig n in a r-

s ig n is t he co n cept o f a m a c h i n e c reat i n g wi t h o u t

ch itecture beginning in t he 1960s wa s p io n e e re d

s ig n if ica n ce in ter feren ce fro m t h e h u m a n a n d fa l l s

with th e work of Nic ho l a s N ego p ro n te at M IT’s

un d e r t he d e f i n i t i o n o f A r t i fi c i a l I n tel l i gen ce.

Arch itecture Mach ine Gro up t hat st ud ie d n e w a p proach es to h uman-co m p ute r in te ra ct io n s . S ubs e -

A s d e f in e d in t h e o ver vi e w, a n A r t i fi c i a l I n tel l i-

quent ideas of compu tat io n d e s ig n co n t ro l l e d by

ge n ce ha s ‘t h e a b i l i t y to co m e u p wi t h i dea s o r

a set of parameters b y M o rett i f ur t he r d e v e l o p e d

a r te fa cts t hat a re n e w, s u r pr i s i n g , a n d va l u a b l e.’

arch itectural th ough t a s a s y ste m o f r ul e s t hat in-

(Bo ud e n , 2004 ) T h e n ot i o n o f a m a c h i n e i n tel l i -

teract and give rise to fo r m , a st ud y o f “t he re l a-

ge n ce wa s f ir st di s c u s s ed b y A l a n Tu r i n g i n 1 9 5 0

tionsh ips between th e d im e n s io n s ” (M o rett i, 1971).

a n d e sta bl is hed a s a fi el d o f st u dy b y J o h n M c-

Generative sy stems, b uil d in g up o n co m p utat io n a l

Ca r t hy in 1956 . Fa st fo r wa rdi n g to to da y, gen era-

capabilities, were uniq ue l y id e n t if ie d in t he a rchi-

t iv e a l go r it hms a re ca pa b l e o f c reat i o n s t h at a n -

tectural discours in 1991 by Fid che r a n d H e r r w ho

s w e r to s uc h a defi n i t i o n o f i n tel l i gen ce. U t i l i z i n g n e ura l n et w o rk s fo r m a c h i n e l ea r n i n g , a co n cept t hat bega n in t h e 1 9 4 0 s a n d de vel o ped i n 1 9 5 8 a s

DECONSTRUCTIVISM 1980s

DESIGN

GENERATIVE DESIGN Fisc h er & Herr 1991

PARAMETRICISM Patr i c k S c h u m a c h e r 2008

1980

1990

AutoCAD J o hn Walker F irst Rel ea se 1 9 8 2

CATIA V3 D assa u lt System es Used Arc h itectu ra lly 1988

2000

3D Studio Max Autodes k First Releas e 1996

2010

Revit Charles River Software First Releas e 2000

Grasshopper Davi d R u tte n Fir st Re l ea s e 2007

2020

Generative Adversarial Networks Ia n G o o d fe l l o w et a l . P u bl i s h e d 2014

Rhino Robert M cN eel & A s s ociates First Releas e 1998 SketchUp @ Last Software First Releas e 1999

TION

FOURTH INDUSTRIAL REVOLUTION

electronic and computation systems

Data and digital systems

defined th e approach a s “d ur in g t he d e s ig n p ro -

Ro s e n bl att ’s ‘ Percept ro n’, G en erat i ve A dver s a r i a l

cess th e designer does n ot in te ra ct w it h m ate r ia l s

N et w o r k s to d a y a re a b l e to c reate ph oto rea l i st i c

and products in a dire ct wa y, but v ia a ge n e rat iv e

im a ge s o f p e o pl e a n d o b j ects t h at a re i n di st i n-

sy stem of some sort .” (H e r r, 2002) Re f l e cte d in

g uis ha bl e f ro m t h e rea l . W h i l e a n equ i va l en t to o l

th e tools available toda y, Ga l a pa go s Ev o l ut io n a r y

in a rchite ct ure h a s y et to ex i st , t h e c h a l l en ge fo r

S olver is a componen t in Gra s s ho p p e r by Da v id

a rchite ct ure to u t i l i ze s u c h a r t i fi c i a l l y i n tel l i gen t

Rutten th at gives desig n e r s a cce s s to o pt im iz in g

m e c ha n is m s to des i g n i s a n exc i t i n g po s s i b i l i t y.

D ef init ions referenced from C om pu tat i o na l des i g n i n a rchi tect u re: D efi ni ng pa ra m et r i c , gen erat i ve, a n d a l go r i t h m ic d e s ig n (C a eta n o, S a n to s , & Le itão, 2 0 20)

ols

Beh ind th ese breakthro ug hs in co m p ut in g a n d

u n d e r s ta n d i n g t h e m a c h i n e introduction to machine learning

M ach ine learning, a branc h o f a r t if icia l in te l l ige n ce , is t he stu dy of h ow mach ines can be ta ug ht to re co g n ize d ata , cate gorize data, predict data an d e v e n ge n e rate d ata ‘w it ho ut be i ng explicitly programmed’ (S im o n , 2015). Fo r s im p l e p ro bl e m s w ith a def inite answer, a pro g ra m m e fo r a co m p ute r ca n be w ri tten and executed to obta in t he s o l ut io n . H o w e v e r fo r p ro b -

ARTIFICIAL INTEL A ny te chniq u e t hat al l ow s comp u te r s

le ms with multiple variables a n d m o re a d va n ce d ta s k s , it ca n b e dif f icult for a programm e r to d ete r m in e s p e cif ic p ro g ra m s for each possible input situat io n .

M ach ine learning models ut il ize l a rge a m o un ts o f d ata to a d just

MACHINE LEAR

a math ematical model th at is re p re s e n tat iv e o f t he d ata . This

Su p e r v ise d , U nsu p

allo ws it to make prediction s o n n e w a n d va r ie d d ata us in g t he

Re inforce me nt Le

optimized model or determin e p ote n t ia l s o l ut io n s .

T he effects of such tech no l o g y p e r ha p s un s e e n ca n be c l ea rly felt . Facebook’s algorit hm s t hat s ug ge st a d v e r t is e m e n ts acco rding to y our vie wing p re fe re n ce s , Go o g l e’s s ea rc h a l go rithms th at find th e best data a cco rd in g to t he ke y w o rd s t y p e d as well as suggesting Youtu be v id e o s ba s e d o n y o ur s ea rc he s are examples of h ow machin e l ea r n in g ha s a l rea d y in f il t rate d

DEEP LEARN

ou r daily activities. More a ct iv e e n ga ge m e n ts w it h a r t if ic ia l i ntelligence includes utilizin g v o ice a s s ista n ts s uch a s Ap p l e’s Siri and self-driving cars which l ea r n f ro m l a rge d ata s ets o f lang uage and feedback res p e ct iv e l y to im p ro v e w il l co n t in ue to permeate our lives as the s e s y ste m s be co m e s m a r te r a n d more af fordable.

As the thesis is interested Unsupervised Deep Learn (31) Daily utilities that use machine learning

e The re a re t hre e broa d catego r ie s i n wh i c h m a c h i n e l ea r n i n g ca n b e a p p roa che d .

LL IGENCE to m i m i c i n te l l igen ce

RN ING

per v i s ed ,

ear n i n g

NI NG

Supervised Fir st l y, Su per v is ed Lea rn in g.

The co m p ute r is g iv e n a s et o f i n pu ts a n d des i red o u t pu ts , fo r exa m p l e , a n im a ge a n d t he co r re s po n di n g c l a s s i fi cat i o n o f t h e o b j ect in t he im a ge . By t ra in in g o n t he s e i n pu ts a n d o u t pu ts , t h e co m pu ter l ea r n s to m a p t he in p uts to t he o u t pu ts a n d reco g n i zes t h e feat u res t hat d ete r m in e it to cl a s s if y.

unSupervised S e co n d l y, Un s u per v is ed Lea rn in g.

In t his in sta n ce , n o l a be l s (o r d es i red o u t pu ts ) a re g i ven to t h e co m p ute r. The co m p ute r is l e f t to l ea r n a n d c l a s s i fy c h a ra cter i st i c s o f t h e in p ut o n its o w n . This ca n be us efu l wh ere t h ere a re n o l a b el s a va i la bl e fo r t he d ata s et .

reinforcement Third l y, Rein fo rcemen t Lea rn in g.

The p ro g ra m is d e s ig n e d s uch t h at i t i n tera cts wi t h a dy n a m i c en vi ro n m e n t . Thro ug h t he in te ra ct io n s wi t h t h e en vi ro n m en t wh ere po s it iv e be ha v io r is re wa rd e d w hil e n egat i ve b eh a vi o r i s pen a l i s ed, t h e co m p ute r l ea r n s a ce r ta in be ha v i o r s u c h t h at re wa rds a re o pt i m i zed.

deep learning D eep lea rn in g is a s ubs et o f m a c h i n e l ea r n i n g wh ere deep n eu ra l n etw o r k s a re ut il ize d in t he l ea r n in g m o del . A deep n eu ra l n et wo r k i s a n a r t if icia l n e ura l n et w o r k w it h m u l t i pl e h i dden l a y er s b et ween t h e in p ut a n d o ut p ut l a y e r s . This a l l o ws fo r pro g res s i vel y h i g h er o rder s o f feat ure s to be e xt ra cte d f ro m t he ra w i n pu t data .

d in the implicit formal rules that govern the 3-dimensional, ning would be utilized.

u n d e r s ta n d i n g t h e m a c h i n e neural networks, how a machine thinks

T he Neural Network , a po w e r f ul te chn iq ue u se d in Mach ine Learning, d ra w s in s p irat io n from th e H uman Brain to c reate a st r uct ure of a rtificial neurons organize d in l a y e r s w it h each neuron relay ing infor m at io n f ro m o n e layer to connected neurons in t he n e xt . Ea ch

‘Black Box?’

ne uron is a math ematical fun ct io n t hat ca rrie s a certain weigh t and bia s ; t he n e uro n su ms th e total number of inputs m ul t ip l ie d by i ts corresponding weigh ts an d a d d s a bia s . An activation f unction is th en a p p l ie d to t he s um and gives an output value. A s t ra in in g in p ut

Inputs

Outputs

i s fed into th e Neural Netwo r k , t he p re d icte d ou tput is compared with th e e xp e cte d o ut p ut and th e weigh ts optimized to re d uce t he d is cre panc y th rough a process o f ‘ba ck- p ro pa gati on’. Utilizing deep learnin g m et ho d s , w he re the re are multiple h idden l a y e r s ,

Ar t if ic ia l

Intelligence can recognize patte r n s a n d fea tu res with in data th at are n ot im m e d iate l y ob v ious to us nor encoded fo r w it hin t he m achine. Th e relationsh ip and co n n e ct io n s be tw e en data remains obscure to us , a s e e m in g ly ‘ black box’, unth inkable by hum a n m in d s .

Artificial Neuron Structu r e Bia s

Inputs

W e ig hts

∑

S um m in g Fun ct io n

T h res h o l d

A ct i vat i o n Fu n ct i o n

N eu ro n Ou t pu t

Simple Fully Connected Neural N e t w o r k

Fe e d Fo r wa rd H id d e n L a y e r

Input La y e r

Ou t pu t L a y er

N et work Inputs N et wo r k Ou t pu t

Ba ck- P ro pa gat io n

Deep Neural Network (DNN)

Inp u t Lay er

Hi d d en L a yer

Hidden L a yer

H i d d e n L a ye r

Ou t pu t L a y er

u n d e r s ta n d i n g t h e m a c h i n e convolution neural networks, how a machine sees

i np ut i mage

convolution + relu

im age values

0 0

11 1 1

kernel/filter

0 1

con v o l u t i o n + relu

0 11

1 0

flatten

feature map

2 2

A C onvolutional Neural Net w o r k (CN N ) is a ke y a p -

A s t he ke r n e l o r ca m era i n t h i s a n a l o g y m o ves o ver

paratus to th e th esis and t hus w il l be e xp l a in e d in

t he im a ge , t he o u t pu t o f ea c h co n vo l u t i o n i s co m-

sligh tly greater detail.

bin e d in to a n o u t pu t m at r i x wh i c h i s ca l l ed t h e A ct ivat io n M a p a n d i s repres en tat i ve o f t h e feat u res i n

A C NN is a ty pe of neural n et w o r k in w hic h co n v o -

t he im a ge . An a ddi t i o n a l n o n -l i n ea r o perat i o n i s a p -

lu tion operations are per fo r m e d o n in p ut d ata ,

p l ie d to a cco un t fo r n o n -l i n ea r i t y a fter ea c h co n vo -

w hich in most cases, are RGB im a ge s . Co n v o l ut io n

l ut io n . In a CN N, t h i s pro ces s o cc u r s m u l t i pl e t i m es

operators in th e context o f n e ura l n et w o r k s a re a

a n d t he o ut p ut i s fl atten ed a n d co n n ected to fu l l y

re placement to th e neuron s in a co n v e n t io n a l f ul l y

co n n e cte d l a y e rs . Fo r exa m pl e, i n a fa ce reco g n i za -

con nected neural network l a y e r, a n d a ct a s feat ure

t io n p ro bl e m , t h e fi r st fe w co n vo l u t i o n s l ea r n t h e ke y

e xtractors. C onvolution p re s e r v e s t he s pat ia l re l a-

p o in ts w it hin t h e i n pu t i m a ge data , wh i l e t h e s u b -

tionsh ip between pixels b y ta k in g â&#x20AC;&#x2DC;s n a p s hots â&#x20AC;&#x2122; o f a

s e q ue n t co n v o u t i o n l a y er s l ea r n t h e edges , s h a pes

group of pixels instead o f a n in d iv id ua l p ixe l a n d

a n d p e r ha p s co l o r to n es wh i l e t h e fi n a l co n vo l u t i o n

ou tputs a value th at is repre s e n tat iv e o f t he va l ue s

l a y e r s co m p re he n d wh at m a kes a fa ce, i n i n c rea s i n g

w i t h in th e snap sh ot .

hie ra rchie s o f co n preh en s i o n .

“The continuous analogue human eye is to be supplanted by a ‘discrete digital vision’.” Koh, 2019

first lay er r ep re se nt at ion

second layer representatio n

third layer representation

(32) CNN facial feature maps by Killian Levacher

3d Convolutions

3- di m en s i o n a l co n vo l u t i o n s a re po s s i b l e a s wel l . T h e ke rn el m o ves i n t h e x ,y a n d z di rect i o n s a n d o u t pu ts Advant ag es o f Co nv ol utional Neural Networks - Capture features and relatio n s hip s w e l l - Re duces th e number of para m ete r s - Re duces computation requi re m e n ts - High er classif ication accura c y - N d imensional C onvolutions a re p o s s ibl e

a 3 D vo l u m e s pa ce. T h i s i s a n a rea o f res ea rc h wh i c h ha s l es s preceden t du e to t h e s i g n i fi ca n t l y l a c k o f co mpreh en s i ve 3 D data s ets a va i l a b l e a s wel l a s hig h er co m pu tat i o n a l dem a n ds t h at a n a ddi t i o n a l d im en s i o n n eces s i tates . Si m i l a r to 2 D C o n vo l u t i o n s , 3D feat u res wh i c h a re c l u ster s o f po i n ts o r voxel s ca n be l ea r n t b y t h e m a c h i n e. T h i s i s t h e m et h o d t h at t h e t hes i s ex pl o res u s i n g 3 D data fro m T h i n g i ver s e a n d ot h er s o u rces a s t ra i n i n g i n pu t .

G E N E R AT I V E A D V E R S A R I A L N E T generative machine learning

G e ne rative Models are mach in e l ea r n in g m o d e l s t hat a re ab le to generate ne w forms of d ata ha v in g l ea r n t f ro m in p ut d ata. S ummarizing f rom th e inp ut d ist r ibut io n o f d ata , t he s e mod e ls are able to generate data t hat f it w it hin t he d ist r ib u ti on and are perh aps indist in g uis ha bl e f ro m t he o r ig in a l i np u t dataset . G en erat i ve Ad ve rsari al N et wo rk s (GAN s ) a re o n e s uc h m o d e l. (G o odfellow et al, 2014) A s t he n a m e s ug ge sts , a n a dv e rsarial strateg y is utilized in t his m o d e l . Tw o s ub - m o d e l s , w hi ch comprise deep neural n et w o r k s a re s et a ga in st o n e another. O ne acts as a Generato r, t he ot he r a cts a s t he Dis -

(33) People who do not exist - generated from StyleGAN (Karras, Laine, & Aila, 2019)

crim inator.

generator

s a m pl es t h at t h e D i s c r i m i n ato r i s u n a b l e to di fferen t i ate.

NEURAL NETWORK

H o w e ver, i t i s n ot n eces s a r y to rea c h t h i s sta ge to h a ve a us ea b l e m o del .

T he Ge ne rato r: samples a ran d o m v e cto r f ro m a m ul t i- d i-

At t he en d o f t h e t ra i n i n g , t h e G en erato r ca n b e s a ved a n d

me nsional space and generates o ut p ut d ata w hich is pa s s e d

us e d to gen erate n e w s a m pl es i n depen den t o f t h e D i s c r i m-

on to th e Discriminator. Th is s a m p l e s pa ce fo r m s t he l ate n t

in ato r. I t h a s l ea r n t h o w to gen erate s a m pl es t h at a re â&#x20AC;&#x2DC; re -

space in wh ich points with in the s pa ce co r re s p o n d to p o in ts

a l ist ic â&#x20AC;&#x2122;.

i n the problem domain.

discriminator

Ge n e rat i ve A dver s a r i a l N et wo r k s , i n pa r t i c u l a r, D eep C o n vo -

NEURAL NETWORK

l ut io n a l G en erat i ve A dver s a r i a l N et wo r k s ( D CG A N ) h a ve b een p ro v en to b e a b l e to l ea r n ext rem em l y wel l fro m data s ets o f im a ge s . M a c h i n e Lea r n i n g res ea rc h St y l eG A N ( K a r ra s , L a i n e,

T he Disc ri mi nato r: trains on bot h rea l a n d fa ke d ata f ro m

& Ail a , 2 0 1 9 ) i s o n e s u c h preceden t wh ere t h e gen erated i m-

the G e nerator and classifies the m a s e it he r rea l o r fa ke .

a ge s o f peo pl e wh o do n ot ex i st ca n b e fi n el y a dj u sted b y va r y in g t h e st y l e vecto r s a n d n o i s e. F i g 2 9 depi cts o n e s u c h

T he se models are set against ea c h ot he r, a s a d v e r s a r ie s , in

e xa m pl e wh ere t h e st y l e o f fa ces gen erated i s va r i ed fro m

w hi ch both tr y to out do th e ot he r. The Ge n e rato r t r ie s to

l e f t to r i g h t , ea c h pro du c i n g a rel at i vel y u n i qu e fa ce wh o s e

ge ne rate more outputs similar to rea l d ata to fo o l t he Dis -

o w n er do es n ot ex i st i n rea l i t y.

crim inator, wh ile th e Discrimin ato r atte m pts to d if fe re n t iate b etw e en real and fake data wit h e v e n hig he r a cc ura c y. At

Ca n th e s a m e b e do n e i n a rc h i tect u re? W h ere t h e m a c h i n e

each training epoch , both th e Dis cr im in ato r a n d Ge n e rato r

a uto n o m o u s l y gen erates 3 D fo r m fro m wh at i t h a s l ea r n t?

are u pdated based on th eir s ucce s s e s in ea c h re s p e ct iv e

The fo l l o wi n g preceden ts co ver t h e state -o f-t h e -a r t i n m a-

class, specif ically, th eir respect iv e cro s s e n t ro p ie s . This p ro -

chin e l ea r n i n g a ppl i cat i o n s i n a rc h i tect u re.

ce ss i s repeated until convergen ce . In an idealized case, th e Generato r ha s l ea r n t to ge n e rate

TWORKS

ANALOGY FORGER

D E T E CT I V E

REAL DATA IS EVALUATED AT TH E S AME TIME

RESULTS OF REAL/FAKE CL ASSIFICATION ARE USED TO UP DATE BOTH

GEN ERATOR

D I S C R I M I N ATO R

GAN CODE STRUCTURE

RA ND O M I NP UT NO I SE VECTO R

G EN ERATO R M O DEL

G ENERATED DATA

R E AL DATA

UPDAT E MOD EL

DI S C R I MI N ATO R MO DEL

U P DAT E MODEL BI NA RY C L A SSI FI CATI O N REA L /FA K E

s tat e o f t h e a r t- i f i c i a l machine learning precedents in Architecture

Ma c hi ne learni ng h as bee n a p p l ie d in t he f ie l d o f architecture to accomplish va r io us ta s k s . Apa r t f ro m the area of arch itectural ro bot ic fa br icat io n , m a c hin e learning models h ave been a p p l ie d to tea ch a m a chin e to recognize arch itectural feat ure s f ro m im a ge s .

M ost prominently, image c l a s s if icat io n m o d e l s ha v e b e en applied to arch itectura l im a ge s to c l a s s if y a rchi te ct ural sty les. S uch examp l e s in c l ud e ‘Cl a s s if icat io n of Mexican h eritage buildin g s ’ a rc hite ct ura l st y l e s ( Ob eso et al., 2017) wh ere a d ata s et o f 16,000 l a be l l e d i mages in 4 categories were us e d to t ra in t he m o d e l to re cognize th e ‘pre -h ispanic ’, ‘co l o n ia l ’, ‘m o d e r n’ a n d ‘oth er ’ sty le categories. A d e e p co n v o l ut io n a l n e ura l network (DC NN) th at was st r uct ure d to in c l ud e co nv olutional and pooling lay e r s wa s hy p ot he s ize d to re d uce th e number of lay ers w it ho ut d e crea s in g a cc urac y. O th er approach es suc h a s t he ‘Cl a s s if icat io n o f architectural h eritage elem e n ts ’ (L l a m a s et a l ., 2017) also used an AlexNet DC NN m o d e l to l ea r n to c l a s s if y i mages into 10 ty pes of archite ct ura l e l e m e n ts .

deep learning architect

(34) Specific features for prehispanic, colonial, and modern buildings (Obeso et al., 2017)

(35) Activation maps of Deep Learning Architect (Yoshimura, Cai, Wang, Ratti, 2019)

The applications of Machine Learning in the world are enormous. In the interest of this thesis, architectural applications will be the primary focus.

(36) Clustering by Principal Component Analysis (Yoshimura, Cai, Wang, Ratti, 2019) â&#x20AC;&#x2DC; D e e p Learning Arch itect : C las s if icat io n fo r Archite ctu ral Design th rough th e Ey e o f Ar t if ica l In te l l ige n ceâ&#x20AC;&#x2122; (Yoshi mura, C ai, Wang, Ratti, 2019) is o n e s uch e xam p le wh ere a deep convolut io n a l n e ura l n et w o r k (D CNN) was trained to be capa bl e o f cl a s s if y in g t he w orks o f 34 dif ferent arch itects w it h a 73% a cc ura c y. By e xamining th e weigh ts in t he t ra in e d m o d e l , t he team was able to quantitativ e l y m ea s ure t he v is ua l sim ilar ities between arch itects t hat w e re im p l icit l y d e te rm ined by th e neural network . Us in g l in ea r P r in c ip l e Comp o nent Analy sis (P C A) to re d uce t he d im e n s io nality of th e output data, th e tea m wa s a bl e to cl uste r archi tects and compare th eir f in d in g s w it h co n v e n t ia l v i e w s of arch itectural h istor y.

relevance Thi s research precedent (Yosh im ura , Ca i, Wa n g , Ratt i, 2019) i s usef ul in classif y ing a s pat ia l st y l e a s o p p o s e d to an a rch itectural feature, an un d e r sta n d in g o f s pa ce that a mach ine can compreh en d p e r ha p s bette r t ha n a p e rson. Arch itectural feature s s ho ul d l ike w is e be learnable f rom 3D data. S uch p re ce d e n ts a l s o hig htlight the ef fectiveness of using Co n v o l ut io n a l N e ura l Netw ork (C NN) as a tool for a m a c hin e to l ea r n a rc hi te ctu ra l features effectively.

(37) Literature study on classification of visual elements in art, architecture, and urban studies (Yoshimura, Cai, Wang, Ratti, 2019)

s tat e o f t h e a r t- i f i c i a l machine learning precedents in Architecture

M ach ine learning h as also be e n a p p l ie d in architectural research in h o w s pa ce s ca n b e analy sed and understoo d by a m a c hin e and utilised to generate n e w a rc hite ct ura l configurations th at retain t he s a m e im p l ic it characteristics of th e origin a l .

In ‘ Discrete S ampling’ (Koh , 2019) e xist in g architectural plans are dig ita l l y d e co mp osed into discrete figure g ro un d ce l l s w here th e notions of walls , f l o o r s a n d co l u mns are ‘f lattened’ into gen e r ic va l ue s a n d sorted ‘only according to t he ir f re q ue n c y d istribution’. Th e tradition a l a rc hite ct ura l notions of ‘f igure’ and ‘groun d ’ a re d is c ra d e d in favor of ‘Discrete F ield s ’. A s s e e n in t he D iscrete -Mies project , th e or ig in a l Ba rce l o n a Pav ilion is decomposed into v o l um et r ic ce l l s and a self-super vised learnin g m o d e l l ea r n s the statistical structure at d if fe re n t s pat ia l

(38) Mies Van der Rohe’s Barc Pavilion is discretely sampled original 1 x 1 metre grid (Koh

scales.

At t h e s ca l e o f a c i t y, t h e ‘ Reco m b in a n t ’ s er i es ex pl o res fo r m s y n t h es i s t h ro u g h t h e stat i st i ca l l ea r n i n g o f s atel l i te i m a ges o f c i t i es fo r fu r t h er pro ba b i l i st i c reco m b i n at i o n a n d ext ra po l at i o n , c reat i n g fi ct i o n a l i m a ges o f c i t i es t h at reta i n s o m e a m o u n t o f t h e o r i g i n a l ’s s pat i a l rel at i o n s h i ps .

T h e res ea rc h attem pts to b r i dge t h e ga p b et ween t h e a n a l o g a n d t h e di g i ta l a n d m a kes a t h eo ret i ca l c l a i m to a ba n do n t h e a n a l o g fi g u re -g ro u n d o r o b j ect-fi el d co n cept u a l di c h oto m y. (Koh, 2019)

(39) ‘Recombinant’ series, trained on satellite raster-map tiles of Barcelona (Koh,2017)

(40) Miesâ&#x20AC;&#x2122; Barcelona Pavilion is placed amongst newly inferred ones (Koh,2018)

celona d in the h,2018)

(41)

The original Bauhaus amongst inferred instances (Koh,2018)

In th e Discrete -Bauh aus p ro je ct (Ko h, 2018), Wa l te r

ate n e w co n fi g u rat i o n s . I t i s a l s o rel e va n t to t h i s t h es i s

G ropiusâ&#x20AC;&#x2122; 1926 Bauh aus buil d in g in De s s a u is d e -

in p ro p o s in g a n e w wa y o f des i g i n g wi t h t h e a i d o f a

co mposed and statisticall y s a m p l e d us in g d if fe re n t

m a chin e l ea r n i n g m o del to c reate fi ct i o n a l repres en-

m eth ods and parameters to ge n e rate n e w co n f ig ura -

tat io n s t hat reta i n m em o ra b l e c h a ra cter i st i c s , wi t h o u t

tions of th e Bauh aus. Th e m a chin e ge n e rate d im a ge s

e xp l icit r ul e -ba s e n ot i o n s . Le vera g i n g o n t h i s a dva n-

retain ch aracteristics of t he o r ig in a l but ca n n ot be

ta ge is a ce n t ra l t h em e o f t h i s t h es i s , to a l l o w t h e m a -

said to h ave been designed by a hum a n d e s ig n e r.

chin e to d is co ver t h es e i m pl i c i t r u l es fro m data a n d to ge n e rate n e w co n fi g u rat i o n s t h at a re s i m i l a r y et n ot

relevance Th is research is notable in e xp l o r in g t he p o s s ibil it ie s of h ow a mach ine can be ta ug ht s pat ia l co n ce pts f ro m p recedents and th rough t he id ea o f s a m p l in g , ge n e r-

t he s a m e , a n a b st ra ct repres en tat i o n o f rea l i t y wi t h o u t a n in n ate bi a s n es s .

s tat e o f t h e a r t- i f i c i a l GAN precedents in Architecture

More rece n t l y, Ge n e rat iv e Ad v e r s a r ia l N et w o r k m o d e l s fo r A rc h i tect u ra l research ha s p ro g re s s e d w it h p ro je cts t hat l e v e ra ge o n i ts a b i l i t y to l ea r n and generate n e w d ata .

In â&#x20AC;&#x153;AI + A rchite ct ure , To wa rd s a N e w Ap p roa c hâ&#x20AC;? (Cha il l o u , 2 0 1 9 ) a m a c h i n e understa n d in g o f f l o o r p l a n s wa s buil t t ra in in g o n a d ata s et o f a rc h i tectural flo o r p l a n s to catego r ize t he w id e ra n ge o f d e s ig n s ba s ed o n t h e Footprin t , P ro g ra m a n d Fur n is hin g . A m ul t i- ste p p ip e l in e i n t h i s s equ en ce allows th e a rc hite ct to in te r v e n e at t he o ut p ut o f ea ch m o del b efo re pro ceeding to t he n e xt .

(42) Training results of floorplan program organization (Chaillou, 2019) By creat in g a n in te r fa ce fo r t he us e r to s p e c if y t he e n t ra n ce a n d fen est ra tions on t he fo ot p r in t , t he m o d e l co ul d be us e d to s ca l e to en t i re b u i l d ings, con ta in in g m ul t ip l e a pa r t m e n t un its .

(43) Generated plans on larger massings (Chaillou, 2019) Th e f lexi bil it y o f t he GAN in s o l v in g hig hl y co n st ra in e d pro b l em s a l l o wed for th e ge n e rate d f l o o r p l a n s to re s p o n d f l e xibiy to va r io u s t y pes o f b u i l ding footpr in t .

(44) Style transfer pipeline and style metrics (Chaillou, 2019) In addit io n to ge n e rat in g n e w f l o o r p l a n s , st y l e t ra n s fe r u s i n g G A N s wa s explored in t his p re ce d e n t . N e w m o d e l s w e re t ra in e d o n fo u r s pec i fi c st y l es : Baroqu e , Ro w H o us e , Victo r ia n S ubur ba n H o us e & M a n h atta n U n i t , i n a n attempt to un ea r t h t he im p l ic it r ul e s t hat go v e r n s uc h st y l es . A m a s s i n g generate d ba s e d o n s ite co n st ra in ts in M a n hatta n wa s t h en po pu l ated to compri s e a patchw o r k o f a pa r t m e n t un its o f va r io us st y l es .

(45) Patchwork of apartment styles in massing (Chaillou, 2019) F inally, t he va r io us un it st y l e s w e re a s s e s s e d a cco rd in g to s i x c r i tera t h at would a l l o w t he us e r to q ua l if y t he f l o o r p l a n d e s ig n : Fo ot pr i n t , P ro g ra m , Orientat io n , Thick n e s s & Te xt ure , Co n n e ct iv it y a n d Circ u l at i o n .

s tat e o f t h e a r t- i f i c i a l GAN precedents in Architecture G e nerative Adversarial Netw o r k s ha v e a l s o b e en utilised for 3D Arch ite ct ura l d e s ig n e x p lorations in recent pap e r s . The s e pap e rs of fer a glimpse of h ow 3D s pa ce ca n b e visualized from trainin g re s ul ts f ro m G ANs.

In t h e research paper, ‘3D M o d e l Ge n e ration on Arch itectural P la n a n d S e ct io n Training th rough Mach ine Lea r n in g ” (Zha n g

(46) Training results of plan drawings (Zhang & Blasseti, 2019)

& Blasseti, 2019), th e auth o r s e xp e r ie m e n t w ith h ow to better train st y l e GAN s o n a rchitectural drawings and e v e n t ua l l y us in g a meth od similar to th e ge n e rat io n o f 3D models from CT scans, gen e rate 3D a rchite ct ural models f rom 3D gen e rate d fo r m s . Various pitfalls and suggestio n s to im p ro v e G AN training was raised in t his pa p e r. In the example below, plan dra w in g s o f va r iou s sty les were collected an d t ra in e d o n to li mited degree of success. T he re s ea rc he r s p ostulate th at a lack in var iat io n a f fe cte d

(47) Training results of plan and section drawings (Zhang & Blasseti, 2019)

the training process and that d ata s ets re q u ire clear design purpose.

To test th eir th eor y, a collect iv e t ra in in g o n Plan and S ection drawings wa s co n d ucte d su ch th at th e larger varian ce bet w e e n a n architectural plan and se ct io n d ra w in g w ould allow th e ‘opposite’ in p ut re s o urce s to be noticed better and th us co n t ro l t he convergence more easily. A s p re d icte d , the sty le -mixing results sh o w t he in te ra cti on of multiple sty les und e r t w o - ge n e ra l

(48) 3D models generated from truncation trick images of previous trainin

d irections. To generate a 3D m o d e l t hat captures th e transition state , p ixe l s o f a sp e cific color gamut was o bta in e d f ro m p re vious training results to f un ct io n a s the single -lay er informatio n . This in fo rmat ion was th en sequential l y sta cke d . The re sulting 3D model was rich in s pat ia l in formation, h owe ver its spat ia l m ea n in g is q u e stionable and not direct l y t ra n s l ata bl e to a rch itectural form.

(49) Two different approaches to capturing 3D information in a 2D format 2019)

T he au thors o f th is paper h ave also re searche d oth er tech niques for cap t u ri ng 3D information for GAN train i n g in a se p erate paper, â&#x20AC;&#x2DC;3D Arch i te ctu ral Form Sty le Transfer th rough Machi ne Learningâ&#x20AC;&#x2122;. Th e first is a seria l stack method th at takes slice im a ge s of a 3d model, wh ile th e second us e s a mu ltip l e vie w point approach to e xe cu te style transfer in 2D before

(50) Results of style transfer using stack method. : (a) Original input Model. (b) Target input Model. (c) Image Results with Preprocess. (d) Image Results without Preprocess. (e) 3D Result with Preprocess. (f) Section Model with Preprocess. (g) 3D Result without Preprocess. (h) Section Model without Preprocess. (Zhang & Blasseti, 2019)

re constru cti ng th e 3D model, reduci n g com p u tati o n resources.

Whi le the fi rst meth od captures th e i n te ri or i nformation of th e 3D mod el i n the slices, th e training results a rgu ably lack 3-dimensional clarity, whi le the se cond meth od of multip l e v i e w s ap pears more successf ul es p e cially w ith color tagging of th e d i ffe re nt arch itectural features, th e i n te ri or i nformation is lost .

(51) Results from CycleGAN (unpaired) and Pix2Pix (paired) : (a) Original input Model. (b) Target input Model. (c) Image Results from CycleGAN. (d) Image Results from Pix2Pix. (e) 3D Result from CycleGAN. (f) 3D Result from CycleGAN. (g) 3D Result from Pix2Pix. (h) 3D Result from Pix2Pix. (Zhang & Blasseti, 2019)

ng results (Zhang & Blasseti, 2019)

(Zhang & Blasseti,

(52) Results through CycleGAN with color tagged, double direction and additional input of style transfer: (a) Original input Model. (b) Style B Model. (c) 3D Result from Style A to B. (d) 3D Result from Style B to A. (e) Additional Input Model. (f) 3D re sult of Additional Model from Style A to B. (Zhang & Blasseti, 2019)

s tat e o f t h e a r t- i f i c i a l GAN precedents in Architecture

G AN’s can also be trained on data s ets that captu re th e relationsh ips betwe e n b u i ld ing spaces. ‘Artificial intelligen ce i n archite cture: Generating concept ua l d e sign v ia deep learning’ (A s, Pa l , & Basu , 2 0 1 8) is a research paper t hat u se s grap h mapping tech niques in conju cti on with deep neural netw o r k s ( D N N) to identif y essential buildin g b lock s of design represented as s ub -

(53) Graph representation of house, nodes have attributes such as type, area (indicated by circle size and volume (As, Pal, & Basu, 2018)

grap hs. T h is identification proces s is d e p e nd e nt on a performance crite r ia w hi ch i s as def ined by users, such a s sle e pab i li ty or liveability.

U si ng this meth od, a graph co r re sp ond i ng to a user ’s requireme n ts can be ge n erated by first discove r in g b u i ld ing blocks th at respond to t he p e rform ance

criteria

score

th ro ug h

training the DNN independently fo r each criteria and identify ing w hic h b u i ld ing blocks h ave h igh regress io n and activation scores. Th ese build in g b lock s can th en be merged with gra p h me rging algorith ms.

T hi s re search aff irms th e notion o f form follows function by provindin g a n A rti ficial Intelligent approach to d e -

(54) Composition of subgraphs into larger assemblies. Additional edges are added to nodes according to the edge’s probability of occurance (As, Pal, & Basu, 2018)

si gn base d on a user ’s needs. H owe v e r, i t giv e s little additional informat io n apart from th e proximity for each p ro gram m e to th e oth er and does not t r ul y hav e e ffe cts on th e form of space. N e v e rthe le ss, it is an innovative meth o d o f u nd e rstanding programmatic relat io nship s giv e n a sizeable dataset .

(55) DNN-based representation learning of types of rooms in a latent vector space while obeying proximities of types of rooms in design samples. (As, Pal, & Basu, 2018)

relevance T he se p re cedents of fer much insig ht to wa rd s t he d iffe re nt meth ods Artificial Intelligen ce ca n be us e d i n archi te ctural design as well as hig hl ig ht t he p o -

Summary

workflow for designer intervention

te ntial d ifficulties of training such m o d e l s .

Chai llou ’s “AI + Arch itecture, Towa rd s a N e w Ap p roach” d e monstrates a compreh ensiv e a n d re m a r k-

possible categorization

ab le w ork flow for AI to learn, create a n d s e l f- e va lu ate to p rovide options for end-user s in d e s ig n in g apartm e nts th at h ave a unique sty le a n d o pt im ize d accord ing to th eir preferences in s ix d if fe re n t cr ite ri a. T he p rominence of a controlle d p ip e l in e t hat allow s for a designer to inter vene a s w e l l a s ha v in g

comprehensive datasets

a form of categorization of generate d un its m a ke s his ap p roach practical and powerful .

Z hang and Blasseti take a step toward s 3D GAN st y l e transfe r by touch ing on various met ho d s to d o s o,

2.5D approaches to 3D ML

v ia the se ction and plans, th e stacke d a p p roa c h a s w e ll as the multi-vie w meth od with va r y in g re s ul ts . K e y takeaway points are th e seemi n g l y g reate r e ffe ctiv e ne ss of c y cleGAN as compared to p ix2p ix, a s w e ll as the dif f iculties th e y encounte re d w hil e t ra in i ng w i th a limited dataset , or datas ets t hat d o n ot

utilizing three-channel CNN

hav e e nou gh variation. An additiona l l ea r n in g p o in t w ou ld be the ef fectiveness of labell in g t he d ata a ccord i ng to th e arch itectural feature us in g co l o r to max im ise th e th ree -ch annel learning a bil it y o f CN N s to re d u ce training time.

little true 3D ML precedent

In A rti ficial intelligence in arch ite ct ure : Ge n e rati ng conce pt ual design via deep lea r n in g ’ (A s , Pa l , & B asu , 2 0 18), a dif ferent approach is s ug ge ste d that i nte re stingly captures 3D data in a co m p re s s e d grap h re p re s entation, wh ere nodes ha v e ce r ta in attribu te s that can be translated into a r ra y s a n d fe d i nto a ne u ral net . Th e advantage o f t his a p p roa c h i s the se e m ingly less demanding co m p utat io n a l re sou rce ne e ded. H owe ver, th e prereq uis ite fo r t his ap p roach is th e need for well-labelle d d ata t hat ha s vari ou s attributes of spaces encode d a l rea d y. This ap p roach may h ave much potentia l in t he f ut ure w he n B IM becomes more widely use d , a n d d ata s ets more fre e ly available.

An o v e ra rc hin g t he m e i s t h e i m po r ta n ce o f t h e data s et a va il a bl e . The m o re co m preh en s i ve, t h e b etter t he t ra in in g . In t he e ven t o f a s m a l l data s et , data a ug m e n tat io n te chn iqu es wo u l d h a ve to b e employed.

In a d d it io n to n ote , t h ere a re fe w preceden ts t h at a d o pt a t r ue 3- d im e n s i o n a l a pproa c h o f u t i l i z i n g GAN s in d e s ig n , p o s s i b l y du e to t h e di ffi c u l t y i n t ra in in g , co m p utat io n a l dem a n ds a s wel l a s t h e l a c k o f a va il a bil it y o f 3- d im en s i o n a l data s ets .

d i g i ta l w o r k f l o w Implementing the GAN

T hi s pre vious section h ighl ig hte d t he d if fe re n t a p p roa ch es and learning poin ts gat he re d f ro m re s ea rc h d one in th is relatively ne w â&#x20AC;&#x2DC;in - bet w e e nâ&#x20AC;&#x2122; f ie l d , w he re A rch itecture and Artif icial In te l l ige n ce m e et . The p re ce d e nts are not many, perh a p s be ca us e t he k n o w l e d ge i s e xtremely specialised and st il l in t he p ro ce s s o f be i ng de veloped and applied. Excit in g n e w p o s s ibil it ie s re main, and of ten th rough a p ro ce s s o f t r ia l a n d e r ro r ne w obser vations are made o f t he s e m o d e l s t hat buil d u p t h e body of knowledge.

W hile h aving emph asized t he a d va n ta ge s o f ut il iz in g G ANs and th e potential d if f ic ul t ie s in us in g s uc h a ge nerative model, each pro bl e m s pa ce w o ul d p re s e n t i ts o wn unique issues and w il l ha v e to be t ho ro ug hl y i nv estigated in th e th esis.

One of th e contributions of t his re s ea rch is to d e v e l o p a d igital workf low for 3D ma chin e l ea r n in g t hat w il l be su b s equently incorporated in to a n o v e ra l l d e s ig n w o r kflow for designing th e Make r â&#x20AC;&#x2122;s M us e um . This w o r k f l o w w ill document th e processe s ut il ize d in d eta il in ho p e s of being not too tech nical, s uc h t hat it ca n be a p p ro p ri ated and expanded for f ut ure re s ea rch by a rc hite cts and not just data scientists.

It would mainly concern its e l f w it h t he Ar t if icia l In te l li ge nce processes such as pre pa r in g d ata s ets , t ra in in g the mach ine, monitoring the re s ul ts , a n d e xp l o r in g t he latent space generated by t he GAN . The s p e c if ic to o l s u se d for each step will be d o cum e n te d w it hin t he o v e rall digital workflow diagram .

incorporating digital workflow

Method in General

Selection of Thingiverse Data

Training on Data

artificial intelligence 3D DCGAN

human intelligence

Generate Latent Space

Selection of Form

Makerâ&#x20AC;&#x2122;s Museum

d i g i ta l w o r k f l o w Designer Intervention

Artificial Intelligence today still requires human intervention to interprete the results at each stage of a workflow. Especially across systems that are unable to directly communicate, the human acts as the mediator between interfaces. In a way, the agency of the designer is not nullified but augmented by the machine and the architect creates the architecture of the AI augmented workflow.

Monitoring Training

Workflow

Visualize 1

Data Collection

Conversion

Input

Data Set

Designer Intervention

Failure Detected 3

Model Training

Visualize

Analysis of Training Results

If Unsatisfactory

Adjust Model Curate Dataset

Available Datasets have to be vetted to remove possible defects that would hinder training. Tools used: Rhino Grasshopper Python Key libraries: open3d pyntcloud

Digital Tools

Formats: Pointclouds Meshes Nurbs

3D data has to be converted into a format that is understood by TensorFlow. In this case, data is voxelized and stored as a binary array. Tools used: Python Key libraries: numpy

Using Jupyter Notebook, GAN code comprising TensorFlow and Keras libraries are trained for a fixed number of Epochs.

Results of training are analysed by random sampling of the latent space. Visualization is within Python using Matplotlib library.

Tools used: Python

Key libraries: TensorFlow Keras

Key libraries: Matplotlib numpy

Format: .npz

Rhinoceros 3D is a CAD modelling software developed by Robert McNeel & Associates.

Tarsier is a point cloud and 3D scanning library for use in Grasshopper by camnewham.

Python is an interpreted, hi el, general-purpose program language created by Guido Rossum.

Grasshopper is a visual programming language & environment that runs within Rhinoceros 3D created by David Rutten.

Volvox is a point cloud editing plugin for use in Grasshopper by Henrik Leader Evers & Mateusz Zwierzycki.

Yellow is a mesh and voxel manipulation plugin for use Grasshopper by Amir Habib

Designer Intervention

If Satisfactory

Latent Space Exploration

Sampling

Designer Intervention

Catalogue of Desired Coordinates

3D Software

Spatial Quality Analysis

Evaluate

Proceed to Next Phase

y If Unsatisfactory

Evaluate Data Scope

Latent space interpolation serves as a second check for training results. Done in Python. Tools used: Python Key libraries: Matplotlib numpy

Coordinates of key variations are saved and mapped to form a catalogue of form variations and to understand the implicit rules learnt. Tools used: Python Key libraries: Matplotlib numpy

Generated spatial characterisics are further evaluated in 3D modelling software to determine what is learnt. Tools used: Rhino Grasshopper Key plugins: Volvox Tarsier Yellow

igh-levmming o van

Tensorflow is an open source platform for machine learning created by Google Brain Team.

Numpy is a library for scientific computing in Python that support s large, multi-dimensional arrays and matrices, created as a community project.

Matplotlib is a plotting library for creating static, animated & interactive visualizations in Python by John D. Hunter.

e in bi.

Keras is an open source neural-network library written in Python created by Francoise Chollet.

Pyntcloud is a Python library for working with 3D point clouds craeted as a community project.

Open3D is an open source library that supports development of software that deals with 3D data created by the Open3D team.

d i g i ta l w o r k f l o w 1. data collection

W el l catego rise d archite ct ural 3 D d ata i s n ot co mmo n . S h ape net ( Chang et al., 201 5 ) i s o n e o f th e large st we ll a nn otate d 3D m o de l datasets co mp ri s i n g 5 5 o b j e ct catego rie s and 51,300 dist inct mo d e ls . H o we v e r, mo st d o not have an archite ct u ral catego r y. A s s uch th e re i s a nee d to o btain a dataset fo r th e mo d e l to trai n o n i f it is de sire d fo r archite ct u ral fo rm to b e ge n e rate d b y th e GAN.

S ome alte rnat ive so u rce s o f arch i te ctural 3 D mo d e ls i nclude Go o gle M aps, and cit i e s th at h av e 3 D mo d e ls s uch

data augmentation: rotation

a s Ne w Yo rk Cit y. Co llat ing an d clean i n g d ata can b e a la bo rio u s pro ce ss as pro ve n b y th e i n i ti al d ata s et th at th e au t ho r co lle cte d to te st th e G A N mo d e l o n .

T he data set of 52 arch itect ura l m o d e l s a s v is ua l ize d o n the following page includes m o d e l s o bta in e d f ro m Go o gle Maps and S ketch up Ware ho us e . The buil d in g s w e re chosen on th e criteria of bein g in t he â&#x20AC;&#x2DC;hig hr is eâ&#x20AC;&#x2122; catego r y ( above 75m) and with intere st in g m a s s in g st rateg ie s .

A s t h e 3D models obtained f ro m Go o g l e M a p s w e re bro ke n and disjointed, much t im e wa s s p e n t c l ea n in g t he e x p orted mesh to include o n l y t he re l e va n t m a s s in g . D ata augmentation tech niq ue s m a y a l s o be n e ce s s a r y to expand th e dataset .

data augmentation: mirror

data augmentation: add noise

(56) Small dataset of architectural buildings collected from Google Maps & Sketchup

d i g i ta l w o r k f l o w 2. data conversion

A s t he Ge ne rato r in t he GAN wo uld b e d e s i gn e d to o utp ut data t hat is dire ct ly t ran s latab le to 3 D fo rm, i n p uts to the Discrim inato r wo u ld li ke wi s e h av e to b e 3 D d ata.

RAW MESH DATA

Most 3D data availab le is i n th e fo rm o f me s h e s an d w ould have to b e co nve rte d i n to a fo rm th at can b e

.stl .ply formats

inp ut into t he ne u ral net wo rk . Me s h e s are e x p o rte d to .p ly fo rm at if not already so an d are s amp le d v i a p o i n t cloud sam pling u sing Ope n 3 d an d P y n tclo ud li b rari e s in P yt ho n to re du ce t he data co mp le x i ty an d eas e th e com pu tat io n load in t he v oxe li zati o n p ro ce s s . Po i n tcloud data can b e dire ct ly co n v e rte d to v oxe ls i n th i s

POPULATING MESH WITH POINTS

p roce ss. The voxe lize d data i s th e fo rm o f a b o o lean num py array, 1==Tru e , 0== Fals e an d th e p o s i ti o n o f ea ch valu e in t he array is i n d i cati v e o f i ts co o rd i n ate s

pyntcloud library

in 3D.

POINT CLOUD

numpy array

VOXELIZED POINT CLOUD

(57) Mesh to Voxelized Point Cloud

d i g i ta l w o r k f l o w 3. model training

generator neural network

G ene rat i ve Ad ve rsari al N et wo rk s (GAN s ) ha v e be e n p ro v e n to produce images of bette r q ua l it y a s co m pa re d to ot he r ge nerative tools such as Va r iat io n a l Auto - En co d e r s (VAEs ).

16384

128

A lth ough th e y are dif f icult to t ra in , t he t he s is w il l atte m pt to de velop an intuition of h o w m o d e s o f fa il ure s ca n be p re v e nted in th e process of expl o r in g its ca pa bil it ie s .

A s th e th esis is concerned w it h t he e xp l o rat io n o f 3- Dim e nsi on al understanding by a M a c hin e , 3 D Co n vo lu tio n Neu ra l Net wo rks (3DC NN) will be ut il ize d w it hin t he GAN d e s p ite the h igh computational dema n d s . Ten so rFlow is th e software l ibra r y o f c ho ice fo r t he m a chine learning aspect of th is t he s is . It is t he m o st co m m o n ly used librar y for mach in e l ea r n in g a n d wa s d e v e l o p e d b y research ers from Google Bra in , m a d e o p e n - s o urce a n d p rovides Application P rogra m m in g In te r fa ce s (AP Is ) in m o st major languages and enviro n m e n ts n e e d e d fo r d e e p l ea r n-

discriminator neural network

i ng projects, such as P y th o n . It is a bl e to r un o n bot h CP U and GP U smooth ly and was d e s ig n e d w it h p ro ce s s in g p o w e r li mitations in mind. In addit io n , ba s ic t uto r ia l s o n m a chin e learning are made available o n te n s o r f l o w.o rg / t uto r ia l s o f w hi c h th is th esis builds upo n . Keras is a h igh -le vel librar y t hat is buil t o n to p o f Te n s o rFlow. It provides a scikit-lea r n t y p e AP I w r itte n in P y t ho n that allows for neural netw o r k s to be buil t q uic k l y w it ho ut hav ing to learn th e backend in d eta il .

Both TensorF low and Keras w e re us e d to buil d t he GAN in Python.

T he model was built accord in g to re co m m e n d e d be st p ra cti ce s for Deep C onvolution a l Ge n e rat iv e Ad v e r s a r ia l N etw or ks (DCGAN), such as a Lea k y Re Lu a ct ivat io n f un ct io n w ith a slope of 0.2, a convo l ut io n ke r n e l st r id e o f 2 a n d u si n g th e Adam Optimizer to up d ate t he w e ig hts o f t he n etw or k . A s th e output is an a r ra y o f v oxe l s , t he Re Lu a ct i vation f unction was used fo r t he Ge n e rato r â&#x20AC;&#x2122;s f in a l o ut p ut layer. A Glorot initializer (Gl o rot & Be n g io, 2010) wa s a l s o u se d to initialize th e weigh ts in t he m o d e l .

128

tf.generator.summary() Model: “sequential” Layer

Output Shape

Param #

Dense

(None, 4096)

524,288

BatchNormalization

(None, 4096)

16,384

LeakyReLU

(None, 4096)

Reshape

(None, 4,4,4,64)

Conv3DTranspose

(None, 8,8,8,32)

131,072

BatchNormalization

(None, 8,8,8,32)

128

LeakyReLU

(None, 8,8,8,32)

Conv3DTranspose

(None, 16,16,16,16)

32,768

BatchNormalization

(None, 16,16,16,16)

LeakyReLU

(None, 16,16,16,16)

Conv3DTranspose

(None, 32,32,32,1)

1024

Total Params: 705,728 Trainable Params: 697,440 Non-trainable Params: 8,288

tf.discriminator.summary() Model: “sequential_1” Layer

Output Shape

Param #

Conv3D

(None, 16,16,16)

2,016

LeakyReLU

(None, 16,16,16)

Dropout

(None, 16,16,16)

Conv3D

(None, 8,8,8,32)

64,032

LeakyReLU

(None, 8,8,8,32)

Dropout

(None, 8,8,8,32)

Conv3D

(None, 4,4,4,64)

256,064

LeakyReLU

(None, 4,4,4,64)

Dropout

(None, 4,4,4,64)

Flatten

(None, 4096)

Dense

(None, 1)

4097

Total Params: 326,209 Trainable Params: 326,209 Non-trainable Params: 0

d i g i ta l w o r k f l o w 4. analysis of results

5. latent space explotation

W hile th e model is training , re s ul ts ca n be v is ua l ize d

Tw o m et ho ds o f l aten t s pa ce ex pl o rat i o n , l i n ea r i n-

and obser ved for modes of fa il ure . W hil e t he l o s s e s o f

te r p o l at io n a n d s ph er i ca l l i n ea r i n ter po l at i o n ( Sl er p)

the generator and discrimin ato r in GAN s be ha v e d if-

ba s e d o f f Bro wn l ee’s exa m pl es ( B ro wn l ee, 2 0 1 9 ) were

fe rently from classification n e ura l n et w o r k s a s t he y

te ste d in p re l i m i n a r y t ra i n i n g a n d co m pa red. L i n ea r

te nd to flunctuate in th e ze ro - s um ga m e , t re n d s fo r

in te r p o l at io n a s s u m es t h at t h e l aten t s pa ce i s di st r i b -

non -convergence can still be o bs e r v e d . Id ea l l y, t he

ute d un ifo r ml y i n a n n -di m en s i o n a l equ i va l en t o f a

accurac y for th e discriminato r s ho ul d sta y a bo v e 80%

s q ua re o r a c u b e wh i l e Sl er p ( Sh o em a ke, 1 9 8 5 ) ta kes

acco rding to preliminar y an a l y s is o f t he n et w o r k . The

in to a cco un t t h e ‘c u r vi n g o f t h e s pa ce’ ( Lee, 2 0 1 9 )

learning rates of both th e ge n e rato r a n d d is c r im in a-

a n d s ho ul d pro du ce res u l ts t h at i s a m o re a cc u rate

tor can be adjusted such that m o re e q ua l t ra in in g ca n

o r s m o ot h in ter po l at i o n b et ween po i n ts . Sl er p a l s o

occur. Evaluating th e GAN mo d e l is a t r ic k y o p e rat io n

‘p re v e n ts d iv erg i n g fro m a m o del ’s pr i o r di st r i b u t i o n

as t h ere is no objective los s f un ct io n fo r t he ge n e ra -

a n d p ro d uce s s h a r per s a m pl es ’ ( W h i te, 2 0 1 6 ) . Vecto r

tor model since its loss fun ct io n is d e p e n d e n t o n t he

a r it hm et ic (L a r s en et a l . , 2 0 1 6 ) o f b u i l di n g fo r m b y gen-

d iscriminator ’s ability to mi s jud ge ge n e rate d s a m p l e s .

e rat in g a n d s a vi n g t h e l aten t vecto r s o f di st i n g u i s h i n g feat ure s s ho u l d a l s o a l l o w t h e res u l t o f ‘a ddi n g ’ a n d

Since th ere is no objective l o s s f un ct io n , it is d if f icul t

‘s ubt ra ct in g ’ b u i l di n g s a n d po s s i b l y i ts co m pl ex i t i es ,

to know wh en to stop trainin g . A s s uc h, t he re is a n e e d

a n ot io n t hat s eem s l es s i n t u i t i ve to a rc h i tects , to b e

to periodically save model s a n d o ut p uts at va r io us

p o s s ibl e .

stages of training for post e va l uat io n by hum a n s ub j e cts.

T here are se veral quantitat iv e m et ho d s fo r e va l uati ng GANs. H owe ver some met ho d s s uc h a s t he o r ig i nal “Average Log-likelih ood” (Go o d fe l l o w et a l , 2014) met h od wh ich e valuates th e a bil it y o f t he GAN to ca p tu re th e probability distribu t io n o f t he ge n e rato r ha v e b e en found inef fective. (Bor ji, 2019) M o re w id e l y us e d te ch niques include th e Ince pt io n S co re (S a l im a n s et al. , 2016) and Frech et Incept io n Dista n ce (H e us e l et al. , 2017). H owe ver, both th e s e m et ho d s re l y o n a p re trained network for image cl a s s if icat io n a n d t hus is not transferable to th e th es is . A s s uc h t he t he s is w il l re ly mainly on qualitative an a l y s is to e va l uate t he GAN .

“Many GAN practitioners fall back to the evaluation of GAN generators via the manual assessment of images synthesized by a generator model.” Brownlee, 2019

(58) Spherical Linear Interpolation of Latent Space of a model trained on Shapenet ‘Airplane’ Dataset

d i g i ta l w o r k f l o w 6. catalogue of coordinates

7. spatial quality analysis

Creating a catalogue of gen e rate d fo r m feat ure s co ul d

Fo r m s ge n e rated b y t h e G A N o u g h t to b e e va l u ated to

p ossibly ser ve as a design to o l fo r a rc hite cts to un -

d ete r m in e t h e po s s i b i l i t y o f t ra n s l at i o n to a rc h i tect u r-

d e rstand th e implicit relat io n s hip s d ist il l e d by t he

a l fo r m .

mach ine, between different a rchite ct ura l fo r m s . M et h od s of clustering th is generate d d ata s uch a s K- m ea n s

Q ua l itat iv e c r i ter i a co u l d en co m pa s s t h e fo l l o wi n g :

clu stering will h ave to be exp l o re d f ur t he r in t he cre -

- The ge n e rated fo r m o u g h t n ot b e a voxel i zed repl i ca

ation of th is catalogue since a GAN d o e s n ot a uto m at-

o f t he o r ig in a l .

i cally cluster data. A Variab l e Auto - En co d e r (VAE- GAN )

- Yet s ho ul d reta i n s o m e 3 D s pat i a l qu a l i t i es o f t h e

( Larsen et al., 2016) may h a v e to be us e d to get m o re

o r ig in a l .

accurate cataloguing of data . Us in g VAE- GAN ho w e v e r,

-S e l e ct io n s h o u l d b e m a de a cco rdi n g to t h e em ergen t

w ould require greater computat io n a l re s o urce s .

q ua l it ie s o bs er ved fo r exa m pl e: a t ra n s i t i o n state fo r a rc hite ct ura l fo r m .

The in t uit iv e u n der sta n di n g o f t h e voxel s pa ce gene rate d is a co m po s i t i o n o f reg u l a r l y s pa ced g r i ds o f w hic h t he a g g l o m erat i o n c reates i ts o vera l l fo r m . T h i s co ul d be a n ex pl o rat i o n i n m o du l a r o r di s c rete a rc h ite ct ura l s y stem s a s a m o re di rect rea di n g .

O r p e r ha p s a s a n o rga n i zat i o n a l fra m e wo r k i f t h e pro g ra m m at ic f u n ct i o n s ca n b e ca pt u red i n t h e t ra i n i n g o f t he GAN , w here t h e voxel s pa ce dem a rcates t h e pro g ra m m at ic rel at i o n s h i ps wh i l e a l l o wi n g t h e po s s i b l e fo r m to be mo re free.

In t he e xa m pl e o n t h e fa c i n g pa ge, a m a c h i n e l ea r n ed re p re s e n tat io n o f a 3 D C h a i r i s vi s u a l i zed a s a s er i es o f co l o ur s t h at co r res po n d to di fferen t pro ba b i l i t i es . The â&#x20AC;&#x2DC;wa r m e r â&#x20AC;&#x2122; t h e co l o u r, t h e h i g h er t h e pro ba b i l i t y o f it be in g l ea r n t a s pa r t o f a c h a i r. A n a g g regat i o n o f s o l id e l e m e n ts i s m a de to o cc u r wh ere t h e pro ba b i l i t y i s l o w e r, p e r ha p s s er vi n g a s a st r u ct u ra l el em en t .

(59) Image by author produced during Discrete Sampling Workshop, Digital Futures 2020

d i g i ta l E X P L O R AT I O N S in GAN training

A s t raining a h y perparamete r Ge n e rat iv e Ad v e r s a r i al ‘can be a ch allenging ta s k’ be ca us e t he t ra in in g p rocess is ‘inh erently unstabl e’ (Bro w n l e e , 2019) d ue to t h e adversarial nature of t he m o d e l , it is re l e va n t for th e th esis, th ough it may be a l a rge l y s p e c ul at iv e one, to understand th e inne r w o r k in g s o f t he m a chin e and its limitations. S e veral e xp l o rat io n s w it h d if fe re nt datasets were trained us in g t he 3D DCGAN m o d e l

GAN training is “inherently unstable” Brownlee, 2019

d ocumented in th e pre vious s e ct io n . This s eg m e n t i s divided into th e follow in g s ub - s eg m e n ts . Ea ch e x p loration was useful in im p ro v in g t he a ut ho r ’s und e rstanding of GANs in vario us wa y s a s s um m a r ize d . W hile th e dataset to be used in t he d e s ig n o f t he M a ke r ’s Museum would be comp il e d f ro m Thin g iv e r s e , the se datasets were usef ul fo r e xp l o r in g t he l im its o f the GAN mach ine learning wo r k f l o w.

52 Skyscrapers Dataset 3766 HDBs Dataset RGB Self-Generated Dataset RGB Chair-Piano Dataset

preliminary explorations in: collection of datasets data augmentation testing hyperparameters modes of failure explorations in: large datasets understanding how scale affects training visualizations in probability latent space catalogue indexing explorations in: rgb data generation in grasshopper rgb 3-channel GAN learning explorations in: multi category rgb 3-channel GAN learning rgb latent space exploration

Recap

T he t h es is s e e k s to s p e c u late m u s eu m d e s i g n o f t h e f u tu re ex p e r i men t i n g wi t h t h e to o l o f A r t i fi c i a l I n te l l i ge n ce to generate fo r m f ro m 3 D d ata

d i g i ta l E X P L O R AT I O N S i D ATA S ET : 5 2 S K Y S C R A P E R S To test th e GAN model illust rate d in t he p re v i ou s section, a dataset of 5 2 a rchite ct ura l m o de ls obtained f rom Google M a p s a n d S ketc hup Wareh ouse were ch osen on t he c r ite r ia o f be in g i n t h e â&#x20AC;&#x2DC;h igh riseâ&#x20AC;&#x2122; categor y ( a bo v e 75m ) a n d w it h d iv erse massing strategies . The d ata wa s a ugme nted by 90 degree rotati o n s , v oxe l is e d w it hin a 3 2x32x32 grid and 64x64x64 g r id a n d co m p il e d i nto a dataset of 208 model s .

W h ile understandably sma l l a s a d ata s et , t he training of th is initial data s et hig hl ig hte d s e ve ral ke y takeaway s th at co n t r ibute s to t he re search of GAN training for a rc hite ct ura l a p p l i cat ions.

52 Skyscrapers Dataset 3766 HDBs Dataset RGB Self-Generated Dataset RGB Chair-Piano Dataset

D ATA S ET : 5 2 S K Y S C R A P E R S T R A I N I N G & R E SU L T S

64 AR R AY

Th e GAN wa s t ra in e d fo r 2000 e p o chs a n d t he t ra in in g stat i st i c s vi s u a l i s ed. Fro m t h e graph gene rate d , it ca n be s e e n t hat t he m o d e l co n v e rge d a fter 1 0 0 0 epo c h s . V i s u a lizing th e pl ots w e s e e t hat a s t he d is cr im in ato r be co m e s mo re a cc u rate at di s cer n i n g â&#x20AC;&#x2DC;fakesâ&#x20AC;&#x2122; its lo s s f un ct io n d e crea s e s w hil e t hat o f t he ge n e rato r i n c rea s es . I m pro vem en ts of th e discrim in ato r co m e s at t he e xp e n s e o f t he ge n e rato r, y et t h i s do es n ot m ea n t h at

TRAINING LENGTH:

th e generate d s a m p l e s be co m e w o r s e .

2200 EPOCHS CONVERGENCE: >1 0 0 0 EPOCHS

By study ing t he q uir k s o f t he ge n e rato r l o s s g ra p h, s o m e i n terest i n g po i n ts ca n b e obser ved. In pa r t icul a r, w he re t he re a re d ra st ic cha n ge s in t h e l o s s va l u e o f t h e gen erator wh ic h a re in d icat iv e o f p e r ha p s a s hif t in t he atte n t i o n o f t h e gen erato r t h at resh apes th e l ate n t s pa ce s uc h t hat a m o re s ucce s s f ul st rateg y ca n b e atta i n ed to fo o l th e discrimin ato r. This is e v id e n t at Ep o ch 500 w he re a s h i ft i n t h e fo r m s gen erated is obser ved.

Interesting l y, t he d is c r im in ato r d o e s n ot a p p ea r to ha v e a s i m i l a r c h a n ge i n l o s s wh ere th e generato r d o e s at t hat s p e c if ic p o in t w hic h co ul d in d icate t h at t h e a ct i vat i o n m a p region of th e d is c r im in ato r is d if fe re n t .

Discriminator Accuracy

Discriminator Loss

0.96

0.6

0.94 0.92

0.5

0.9 0.88

0.4

0.86 0.3

0.84 0.82

0.2

0.8 0.78

0.1

0.76 0.74

0.72 0

200

400

600

800

1.2k

1.4k

1.6k

1.8k

2.2k

200

400

600

800

1.2k

1.4k

1.6k

1.8k

2.2k

Generator Loss 5 4.5

~Epoch 500

4 3.5 3 2.5 2 1.5 1 0.5 0 0

200

400

600

800

1.2k

1.4k

1.6k

1.8k

2.2k

Epoch 200

Epoch 300

Epoch 400

Epoch 490

Epoch 500

Epoch 510

Epoch 600

Epoch 700

Epoch 800

Epoch 900

Epoch 1000

Epoch 1100

Latent Space Interpolation Generator at 500 Epochs

Latent Space Interpolation Generator at 600 Epochs

Latent Space Interpolation Generator at 2000 Epochs

Usi ng a relatively s ma ll data s et , th e laten t s pa ce in te r p o l at i o n i s d o ne by t he s a mplin g o f 1 4 u n iqu e po in ts with in th e laten t spa ce a nd â&#x20AC;&#x2DC; wa l ki ngâ&#x20AC;&#x2122; between th es e two po in ts . By co u n tin g th e n u mb e r o f uni q ue fo r m s fo r eac h 1 4 data po in ts s a mpled, we s ee a ref lectio n o f t he d i ve rsi t y o f t he laten t s pa ce.

Ge ne rato r at Ep o ch 5 0 0 : 14 unique fo r m s

Ge ne rato r at Ep o ch 6 0 0 : 4 close l y id e n t ica l fo r m s a n d m a n y s im il a r ot he r fo r m s

Ge ne rato r at Ep o ch 2 0 0 0 : 3 main fo r m s a n d ot he r fo r m s w it h v e r y s l ig ht va r iat io n s

Apartment Tower

Pencil Tower

Pearl Tower

T hi s gi ves th e in dicatio n th at th e laten t s pa ce beco m e s m o re uni fo r m as t rai n in g pro g res s es a n d m od e colla p se h a s o ccu re d l i ke l y d ue to t he si ze o f th e data s et a va ila ble. Th e gen erato r lea rn s t hat p e r ha p s t he se t hre e fo rms wo u ld g ive th e g reatest s u cces s in redu c i ng i ts l o ss f unct i o n w h ich creates a mo re s pa rs ely po pu lated laten t spa ce .

Additio n a l s a m p l in g o f t he L ate n t S pa ce wa s co n d ucted wi t h ot h er ra ndom see d s . The s e s a m p l e s f ur t he r a f f ir m t he hy p ot hes i s t h at m o de co llapse h a s o cc ure d .

d i g i ta l E X P L O R AT I O N S i i D ATA S ET : 3 7 6 6 H D B s Pre liminar y tests in th e pre v io us s e ct io n m a ke a p pa re n t t he n e e d fo r a l a rger data s et . U t i l i z i n g data ( B i l jecki, Ledoux, & Stoter, 2017) o n p ubl ic ho us in g (H o us in g De v e l o p m e n t B oa rd) i n Si n ga po re, a d ataset of an initial 10,966 H DB m o d e l s wa s c urate d a n d w hitt l e d d o w n to 3 , 7 6 6 m o del s .

T hese models were voxelized a n d co n v e r te d to n um p y a r ra y s fo r t ra in in g , d epi cted i n pl a n vi e w o n t h e follo wing page. Th ough voxe l ize d , t he p ro f il e o f t he H DBs a re a p pa re n t .

52 Skyscrapers Dataset 3766 HDBs Dataset RGB Self-Generated Dataset RGB Chair-Piano Dataset

D ATA S ET : 3 7 6 6 H D B s T R A I N I N G & R E SU L T S 0 1

32 AR R AY

Learning f ro m t ra in in g w it h a s m a l l d ata s et , s im il a r p r in c i pl es were a ppl i ed i n t h e training of a m uc h l a rge r d ata s et . The s e p r in c ip l e s in cl ud e a ppl y i n g a s i g m o i d a ct i vation funct io n to t he l a st l a y e r o f t he Ge n e rato r N et w o r k i n stea d o f a rel u fu n ct i o n as well as i n crea s in g t he ra n ge o f va l ue s in t he l o s s f un ct i o n s fo r wh i c h b i n a r y c ro s s entropy com p ute s t he l o s s bet w e e n t he g ro un d t r ut h to t h e o u t pu ts o f t h e res pect i ve

TRAINING LENGTH:

networks. T his ha d t he e f fe ct o f s ig n if ica n t l y l o w e r in g t he l o s s fu n ct i o n o f t h e G en era-

2000 EPOCHS CONVERGENCE: >1 0 0 0 EPOCHS

tor wh ich il l ust rate s m o re ba l a n ce d t ra in in g . Initially, th e d ata s et o f H DBs wa s p ro ce s s e d s uch t hat t he b o u n di n g b ox fo r ea c h m o del was base d o n t he ta l l e st H DB. H o w e v e r, t his re s ul te d in a data s et o f 1 0 , 9 6 6 m o del s th at was ske w e d to wa rd s ha v in g re l at iv e l y s m a l l e r m a s s in g b ei n g t h e m a j o r i t y o f t h e dataset . Wh il e l ea r n in g wa s q uic k , re s o l ut io n o f t he re s u l ta n t m o del s wa s l o w. T h i s emph asized t he n e e d fo r a d ata s et t hat is m o re ba l a n ce d i n ter m s o f di st r i b u t i o n o f variations. Th e dataset wa s t he n re create d by e xcl ud in g H DBs t hat were o n ei t h er en d o f t h e h eigh t spect r um . This re s ul te d in a d ata s et o f 3,766 m o d e l s t h at h a d a m o re ba l a n ced variation. A s s e e n f ro m t he g ra p hs , t his wa s he l p f ul in sta b i l i z i n g t h e G A N t ra n i n i n g . Time for ea c h Ep o c h: ~8s

Discriminator Accuracy

Discriminator Loss 0.72

0.984

0.715 0.71

0.982

0.705

0.98

0.7 0.695

0.978

0.69

0.976

0.685 0.68

0.974

0.675

0.972

0.67 0.665

0.97

0.66

200

400

600

800

1.2k

1.4k

1.6k

1.8k

200

400

600

800

1.2k

1.4k

1.6k

1.8k

Generator Loss 1.83 1.82 1.81 1.8 1.79 1.78 1.77 1.76 1.75 1.74 1.73 1.72 0

200

400

600

800

1.2k

1.4k

1.6k

1.8k

ORIGINAL DATASET TRIMMED DATASET

Epoch 10

Epoch 25

Epoch 50

Epoch 75

Epoch 100

Epoch 115

Epoch 10

Epoch 50

Epoch 100

Epoch 200

Epoch 400

Epoch 1000

D ATA S ET : 3 7 6 6 H D B s T R A I N I N G & R E SU L T S 0 2

32 AR R AY

A s o bse r ved f ro m th e pre v io u s laten t s pa ce cata lo g u e, ge ne rate d m o d e l s a p p ea r mo re amo rph o u s a n d les s o rth o go n a l in co mpa ris o n to th e o r i g i na l d ata set . To te st i f f urt he r t ra in in g wo u ld improve th e mo del, th e GA N wa s t ra i ni ne d to a n a d d i t i o na l 6000 Ep o c h s . Whi le re search s u g gests th at u s in g a la rge batch s ize fo r t he d ata set p ro m ote s b et-

TRAINING LENGTH:

te r learni ng beca u s e th e g ra dien ts a re a mo re sta ble est i m ate o f t he f ul l d ata set

6000 EPOCHS CONVERGENCE: >1 0 0 0 EPOCHS

not muc h l iteratu re is men tio n ed a bo u t h ow th e batch si ze o f t he no i se , Z a f fe cts t rai ni ng as well. Sin ce both th es e fa cto rs h a ve co n s equ e nce s o n co m p utat i o na l re so urce s ne eded, th e batch s ize o f Z wa s in crea s ed f ro m 4 to 3 2 to d ete r m i ne i ts e f fe ct o n t ra in in g a s well.

It is interest in g to n ote t hat co m pa r in g w it h t he p re v io us t ra i n i n g , a l a rger batc h s i ze results in fa ste r co n v e rge n ce t ho ug h t he t im e ta ke n fo r ea c h epo c h i s l o n ger at a p proximately 18 s e co n d s p e r e p o c h. In crea s in g t he batc h s i ze b y 8 t i m es di d n ot res u l t in a corresp o n d in g in c rea s e in e p o c h t im e , in d icat in g t hat t h e t ra de -o ff i s wo r t h i t . Ti me fo r each Epo ch : ~ 1 8 s

Discriminator Accuracy

Discriminator Loss

0.995

0.705

0.99

0.7

0.985

0.695

0.98

0.69

0.975

0.685

0.97

0.68

0.965

0.675

Generator Loss 1.83 1.82 1.81 1.8 1.79 1.78 1.77 1.76 1.75 1.74

Epoch 10

Epoch 50

Epoch 100

Epoch 200

Epoch 400

Epoch 1000

Epoch 2000

Epoch 3000

Epoch 4000

Epoch 5000

Epoch 6000

Epoch 6600

Catalogue o f 1 2 0 p o i n t s in Latent Space Generator a t 2 0 0 0 E p o c h s

Catalogue o f 1 2 0 p o i n t s in Latent Space Generator a t 6 0 0 0 E p o c h s

index 12

index 13

index 63

index 07

C o m pa r i n g t h e gen erated o u t pu t fro m t h e m o del t ra i n ed to 2 0 0 0 E po c h s a n d t h e m o del t ra i n ed to 6 0 0 0 E po c h s , we s ee t h at t h e n u m b er o f n o i s y m o d index 95

el s t ra i n ed i n t h i s catal o g u e, c i rc l ed i n o ra n ge, h a s g reat l y redu ced. T h i s s h o ws t h at wi t h a l a rge a n d di ver s e en o u g h t ra i n -

index 105

i n g s et , m o de co l l a ps e i s u n l i kel y to o cc u r e ven wh en t ra i n i n g fo r l o n g du rat i o n s .

Selectiv e L a t e n t S p a c e Interpolation

index 07

index 63

index 105

index 95

index 12

index 13

5 i n dexes were ch osen from t he cata l o g ue o f 120 p o in ts in t he p re v io us page. T h e l aten t vecto r s were called from th e catalogue a n d a l ate n t wa l k wa s d o n e bet w e e n t he t w o p o i n ts i n l aten t s pa ce u s i n g sp h erical linear interpolatio n . The s m o ot hn e s s o f t he in te r p o l at io n s a re in di cat i o n s o f a wel l t ra i n ed model th at h as a suf f icien t l y w e l l - p o p ul ate d l ate n t s pa ce re p re s e n tat io n . T h es e gen erated m o del s are unique and not repetitio n s o f t he in p ut d ata s et .

d i g i ta l E X P L O R AT I O N S I I i self-generated datasets in rgb Input Data Array Shape

To dete rmi ne how muc h i nfo rmatio n a 3 D C o n v

[ 3 2 , 3 2 , 3 2 , 3 ]

GA N can capt ure f ro m a voxelis ed mo del, a n experime nt was co nd ucte d . Si mila r to th e 2 D Co n volu tio nal laye r i n Ke ras, t h e 3 D C o n vo lu tio n a l la yer has an RGB c hanne l, m ea n in g th at RG B in-

[ R , G , B ]

form at i o n can p o ssi bly be e m bedded in th e in pu t data .

[ 2 5 5 , 2 5 5 , 2 5 5 ]

Ontop of capturing th e spat ia l fo r m o f 3D d ata ,

Normalized

p e rha ps th rough a representat io n o f co l o ur, t he

[ 1 . 0 , 1 . 0 , 1 . 0 ]

sp e ci fic programmatic functio n o r s pat ia l q ua l it y of these spaces can be capture d a s w e l l .

Possible Color Combinations R G B values are input as intege r s o f e it he r 0 o r 255. Thi s value would h ave to be n o r m a l ize d to a va l ue betw een 0 and 1 to streamli n e t he l ea r n in g p ro ce ss. Th eoretically, a well tra in e d m o d e l s ho ul d be able to pick up on any com bin at io n o f RGB va lu e s, howe ver, as th ese value s a re re p re s e n tat iv e of p ro bability as well, it is per ha p s un w is e to ha v e combinations of colors th at a re n ot m a d e up o f a m ax imum or minimum of th e p r im a r y co l o ur s , co nsid e ring th e limitations of co m p utat io n a l re s o urce s and time th at th e th esis h a s a cce s s to.

[ 1 . 0 , 0 . 0 , 0 . 0 ] [ 1 . 0 , 1 . 0 , 0 . 0 ] [ 1 . 0 , 0 . 0 , 1 . 0 ] [ 0 . 0 , 1 . 0 , 0 . 0 ] [ 0 . 0 , 1 . 0 , 1 . 0 ] [ 0 . 0 , 0 . 0 , 1 . 0 ] [ 1 . 0 , 1 . 0 , 1 . 0 ]

52 Skyscrapers Dataset 3766 HDBs Dataset RGB Self-Generated Dataset RGB Chair-Piano Dataset

RGB 3D AGGREGATIONS

g_vertical

r_vertical

g_horizontal

r_horizontal

Connection Rule 01:

Connnection Rule 02:

g_vertical>g_vertical

g_horizontal>b_horizontal g_horizontal>r_horizontal r_horizontal>b_horizontal b_horizontal>r_horizontal

aggregation 01

variation 01

b_vertical

b_horizontal

aggregation 02

x 1000

creat i n g a â&#x20AC;&#x2DC; f a k e â&#x20AC;&#x2122; r g b d a t a s e t

To c reate a data s et to test R G B C o n vo l u t i o n , a s equ en t ia l a g g regat i o n m et h o d wa s u s ed wh ere voxel s o f a

variation 02

ce r ta in co l o u r a re a g g regated a cco rdi n g to co n n ect i n g r ul e s . Fi r st l y. g reen voxel s a re a g g regated ver t i ca l l y,

x 1000

re p re s e n t i n g b u i l di n g co res .

Re d a n d b l u e voxel s were t h a n a g g regated a ro u n d t h es e co re s a n d co n n ected to ea c h ot h er vi a a c h ec ker b oa rd patte r n . T h e ra n do m a g g regat i o n wi t h i n r u l e co n st ra in ts wa s t h en repeated a t h o u s a n d t i m es fo r ea c h

variation 03

va r iat io n to fo r m a n R G B data s et o f 3 0 0 0 .

x 1000

The d ata wa s t h en ex po r ted a s a po i n tc l o u d i n . x yz fo r m at to b e co n ver ted i n to [ 3 2 , 3 2 , 3 2 , 3 ] n u m py a r ra y s .

D ATA S ET : R G B S E L F - G E N E R AT E D T R A I N I N G & R E SU L T S

The mo d e l was t rai ne d fo r 5 0 epo ch s a f ter wh ich

Th e ima ges he re show 4 ra nd o m l y sa m p l e d ge ne rate d

mo d e co llap se was ap pa ren t . Th es e prelimin a r y re -

ma s s in g s fro m t he l ate nt spa ce , w i t h R GB c ha nne l s

sults show t hat whi le mo de co lla ps e h a s o ccu red

s plit a n d sub se q ue nt l y co m b i ne d i n Gra ssho p p e r. I n

due to t he li tt le vari at i o n in fo rm in th e data s et , th e

reg io n s whe re t he re i s a n e q ua l p ro ba b i l i t y o f ‘ b l ue’

mac hi ne has succe ssf ully lea rn t th e featu res o f th e

a n d ‘ red’ o cc ur i ng , we se e p ur p l e . T hi s co ul d b e i n-

data, suc h as t he gre e n co res a n d ch eckerboa rd pat-

dicative o f a p o ssi b l e hy b r i d spa ce w he re i t i s a spa -

te rn o f re d and blue . The lower th e pro ba bility, th e

tia l qu a lity t hat ex i sts b et we e n t he t wo. F i ne r d i st i nc-

mo re t ransluce nt t he colo u r. Th e s in g u la r tower h a s

tio n s ca n gi ve r i se to i nf i ni te l y va r i e d yet co he si ve

the mo st ‘so li d i t y’ p e rhaps beca u s e it is th e co n sta n t

s patia l co n f i g urat i o ns.

feat ure i n all t he d atasets .

d i g i ta l E X P L O R AT I O N S i v rgb chair-piano dataset

Chair Dataset tagged Re d

Piano Dataset tagged Blu e

D ATA S ET : R G B S E L F - G E N E R AT E D T R A I N I N G & R E SU L T S 0 2

To dete rmi ne how d i f fe re nt catego ries o f fo rms ca n be t rai ne d at t he sam e time yet rema in ‘recogni zable’, t wo catego r ies o f o bj ects f ro m S h a pe net (Chang et al., 2015 ), ‘ch a irs’ a n d ‘ pi a n os’ we re co lo ure d ‘re d ’ and ‘ blu e’ res pective ly. The t wo cate rgo ri e s we re th en co mbin ed a n d s h u ffle d i nto a d ataset o f 300 0 o bj ects a n d th e 3 D DCGAN was t rai ne d fo r 2000 Epo ch s .

A s se en f rom th e catalogue of 60 p o in ts , t he re w e re m any ‘blank’ and incomprehe n s ibl e o bje cts ge ne rate d amidst better def ined o n e s . This co ul d p o te nti a lly be due to th e fact that t he d is cr im in ato r has learnt th at it is more likel y fo r d ata in p ut to be ne ither red nor blue th an to be bot h re d a n d bl ue .

Taking 5 indexes from th e cata l o g ue o f l ate n t p oi nts, th e interpolation betwe e n o bje cts o f d if fe re nt categories was explored. De s p ite t he p ote n t ia l m od e of failure of ‘blank’ po in ts w it hin t he l ate n t space, th e interpolation bet w e e n catego r ie s wa s su rp risingly legible.

52 Skyscrapers Dataset 3766 HDBs Dataset RGB Self-Generated Dataset RGB Chair-Piano Dataset

S e lective Latent Space Inter p o l a t i o n

C a t alogue of 60 points in Lat e n t S p a c e

index 13

index 19

index 30

index 48

index 22

index 23

d i g i ta l E X P L O R AT I O N S conclusions Findings from th e digital e xp l o rat io n o f t he To o l : 3DCO N V GAN s ca n be d ifferentiated into th e follo w in g catego r ie s . Te chn ica l , The o ret ica l , P ra cti cal. Th ese f indings are h el p f ul in un d e r sta n d in g t he to o l s uch t hat it ca n b e used according to its capa bil it ie s d e s p ite p o s s ibl e l im itat io n s .

technical W hile not claiming to be a n e xp e r t , us in g ce r ta in re co m me nded h y perparameters a s w e l l a s t hro ug h a p ro ce s s o f

HYPERPARAME-

trial and error, th e following w e re ke y e f fe ct iv e te c hn iq ue s in

Ce r ta in hyper pa ra m eter s s u c h a s t h e g l o rot u n i fo r m wei g h t

the training of th e GAN Mod e l .

in it ia l ize r wa s u s ed a cco rdi n g to reco m m en dat i o n s a s wel l a s

LARGE & DIVERSE DATAIt is not suff icient th at data s ets a re l a rge , t he y n e e d to be d iv erse as well in terms of fo r m va r iat io n in t he ca s e o f a 3D G AN to pre vent mode collaps e .

BATCHBoth th e batch size of z and t ra in in g batch s ize o ug ht to be su ff iciently large. Initially, a s m a l l e r batc h s ize wa s us e d in hopes th at it would reduce t he co m p utat io n RAM n e e d e d . Howe ver, it was realized th at d o in g s o n egat iv e l y a f fe cts t he training because it results in t he e st im ate d g ra d ie n ts to be le ss representative of th e d ata s et . Us in g a batc hs ize o f 32 was found to be a good numbe r fo r sta bl e t ra in in g .

ACTIVATION FUNCTIONS U si n g a sigmoid function wa s m o re he l p f ul fo r t he l ea r n in g a s compared to a tanh f unctio n fo r t he f in a l l a y e r a ct ivat io n o f the generator model. Th e ta n h f un ct io n ha s ste e p e r g ra d ie n ts

ha v in g a h i g h er gen erato r l ea r n i n g rate o f 0 . 0 0 1 a s o ppo s ed to t he d is c r i m i n ato r gen erato r o f 0 . 0 0 0 1 were u s ed wi t h n o d et r im e n ta l effect o n t h e t ra i n i n g .

theoretical The re a re s e vera l t h eo ret i ca l i m pl i cat i o n s fro m t h es e st u di es t hat ca n b e co n s i dered i n ter m s o f h o w a M a c h i n e’s percep t io n o f s pa ce s h o u l d a ffect t h e wa y we des i g n .

spatial relationships The m a ch i n e i s a b l e to ca pt u re t h e s pat i a l rel at i o n s h i ps o f fo r m . Co n vo l u t i o n s a l l o w a s pat i a l u n i t to b e co n s i dered a ga in st a l a rger co n text . T h i s po s s i b l y a l l o ws t h e m a c h i n e to un ea r t h impl i c i t r u l es o r o rga n i zat i o n a l patter n s t h at a re n ot im m e d iate l y o b vi o u s to u s . T h e m a c h i n e’s i n t u i t i o n i s t h en a to o l t hat i n fo r m s a rc h i tect u re.

w hi c h reduce th e variability in t he o ut p ut .

learning

IMPAIRING THE DISCRIMINATOR

At t he s a me t i m e, t h e m ea n i n g o f t h es e s pa ces i s co n t ro l l ed

By allowing th e ‘true’ value s to fa l l w it hin a ra n ge o f 0.7- 1.2 and th e ‘false’ values to fa l l w it hin a ra n ge o f 0- 0.3 in t he calculation of th e loss f unct io n , t he ge n e rato r wa s a bl e to ‘compete’ better with th e dis c r im in ato r w hic h re s ul ts in bette r t raining and pre vents mo d e co l l a p s e . This is a l s o t he reason wh y a ‘ReLu’ function wa s n ot us e d fo r t he ge n e rato r f in a l layer activation as it disall o w s a hig he r m a rg in o f e r ro r to b e possible, wh ich makes th e d is c r im in ato r to o co m p ete n t .

o r l im ite d b y wh at t h e a rc h i tect c h o o s es a s t h e data s et to ‘tea ch’ t he m a c h i n e. Per h a ps wh at i s l ea r n t i s fo r m , o r pro g ra m m e , o r s pat i a l qu a l i t i es a n d a r ra n gem en ts , o r s o m et h i n g e l s e e n t ire l y. Ha vi n g l ea r n t , t h e m a c h i n e t h en gen erates wh at is s im il a r i n n at u re to t h e i n pu t . W i t h a s u ffi c i en t l y l a rge a n d d iv e r s e in pu t , t h e m a c h i n e i s a b l e to c reate o r i g i n a l o b j ects in stea d o f repl i cate, b eco m i n g a to o l t h at wi den s t h e des i g n s pa ce in f i n i tel y.

probability W hat a mach ine regurgitates is th e p ro ba bil it y o f e xiste n ce o f a space from wh at it h as learned. This is a d is c rete va l ue in the se nse th at it is absolute, but co n t in uo us in its d e r ivat io n , in thi s case, th e sigmoid activatio n f un ct io n , w hich is co n t inu ou s. A rch itects rarely consider w hat it m ea n s fo r s pa ce to be a p robab ility. Th e possibility of e xiste n ce s ug ge sts a t y p e o f d ynam ic and potential for one spa ce to t ra n s fo r m to a n ot he r, or be come a void wh ere it pre vious l y is o cc up ie d . Pe r ha p s a m achi ne understands th ese conce pts bette r t ha n w e d o, a n d can p ossibly birth a ne w ty pe of a rc hite ct ure t hat f un d a m e n tally sp ea ks of its probable nature .

By v i su alizing such probabilities , fo r m is n o l o n ge r s im p l y form, b u t th e possibility of form. In t he s e d e p ict io n s , v oxe l s that hav e a relatively h igh er proba bil it y a p p ea r d a r ke r w hil e v oxe ls that h ave lower probabilit y a re l ig hte r. S uch a re p re se ntati on allows us to consider t he t he o ret ica l im p l icat io n s of a mach ine -learned space.

Probable HDB forms Epoch 100

PART IV

the makerâ&#x20AC;&#x2122;s museum

against the symbol digital to architectural speculation T he Maker â&#x20AC;&#x2122;s Museum as a n o n - s y m bo l is t he t ra n s l at io n

O n t he ot her h a n d, a s y m b o l , es pec i a l l y t h at o f a rc h i tec-

of a digital collective to a n a rc hite ct ura l o bje ct . W hat

t ure , e xud es a cer ta i n per m a n en ce, a s b ei n g ro oted to t h e

categorizes such a building a n d ho w ca n it be e f fe cte d ?

g ro un d a n d at a s ca l e t h at m a kes i t s eem a n u n c h a n gea b l e

A comparison between an exp l ic it a n d im p l icit s y m bo l is

pa r t o f t he u r ba n l a n ds ca pe. A st r u ct u re t h at c h a n ges a n d

made. Wh ile th e Art S cience M us e um m a y in t he m in d o f

d o e s n ot a l wa y s rem a i n t h e s a m e i s t h en o n e t h at reb -

the visitor be reduced to a n ico n ic s il ho ette , t he M e m o -

e l s a ga in st t h e n ot i o n o f per m a n en ce, a n d di s a l l o ws a n y

rial is irreducible. Reducing it to its ba s ic co m p o n e n t o f

a s s o c iat io n to b e m a de wi t h a s i n g u l a r o u t l i n e o r fo r m .

a single stela negates its m ea n in g a n d re n d e r s t he m e -

At t he s a me t i m e, i t i s a n a b st ra ct repres en tat i o n o f i ts

morial unrecognizable. Th e m ul t ip l icit y o f t he M e m o r ia l

co n te n ts a n d l i ke a c l o u d i n t h e s k y s o l i c i ts m u l t i pl e re -

created from a single ty pe o f o bje ct a r ra n ge d reg ul a r l y

s p o n s e s a s to wh at i t co u l d b e, g i vi n g h i n t to i ts i n ter i o r

i n a f ield gives it a certain va g ue - n e s s , n ot be in g o f o n e

e xhibits a n d i n vi t i n g per u s a l .

thing but open to interpretat io n a cco rd in g to e xp e r ie n ce .

symbol reducible

non-symbol

the human vision

irreducible

human consciousness

collective non-symbol

the artificial vision

collective consciousness

Symbol

Non-Symbol

Singular

Multiplicity

Distinct

Vague

Permanence

Transient

transient

dy n a m i c

architectural form that changes with time A ke y ch aracteristic of a no n - s y m bo l ic a rc hite ct ure is its i mpermanence. Learning fro m Ce d r ic P r ice’s Fun Pa l a ce , ‘ Pri ce th ough t of th e Fun Pa l a ce in te r m s o f p ro ce s s , a s e v e nts in time rath er th an o bje cts in s pa ce , a n d e m bra ce d i nd eterminac y as a core d e s ig n p r in c ip l e .’ (M att he w s , 2 0 0 5) th e dy namism of spa ce a s a co re p r in cip l e fo r a n e x p erience of creativity. To d o s o, a s y ste m o f c ra n e s span ning th e scaf fold were us e d to a s s e m bl e p re fa br icate d modules and wall compo n e n ts a cco rd in g to t he n e e d s and wh ims of th e participan ts , a l l o w in g fo r a t y p e o f d e mocrac y in design, wh ere a l l ha d t he p o w e r to a f fe ct t he

“It was a scaffold of constant activity which would never reach completion, because the ultimate plan, programme and goal were never finite and always changing.” Mathhews, 2005 (Cedric Price’s experiment in architecture and technology)

b u i l t environment . Th e plan a s a n o p e n g r id w it h a ce n t re

(60) Pivoting escalators and flexible partitions allow for changing configurations in the Fun Palace, 1964. Cedric Price Archives, Canadian Centre for Architecture, Montreal. le ft unobstructed encourage d t he d y n a m ic be ha v io r o f participants.

Si milarly but on a different s ca l e a n d t hro ug h a d if fe re n t d im ension (big data), th e Ma ke r ’s M us e um e m p l o y s a co l le ctive digital democrac y, to e xp re s s a d y n a m icis m bot h e xternal and internal as a b o l d state m e n t in t im e , t hat its architecture does not stand a ga in st t im e but cha n ge s w it h i t according to th e collective co n s cio us n e s s .

(61)No main entry to the Fun Palace, 1964. Cedric Price Archives, Canadian Centre for Architecture, Montreal.

“Let us imagine a true museum, one that contained everything, one that could present a complete picture after the passage of time, after the destruction by time.” Le Corbusier

Le Corbu sie r speculated a ty pe of m us e um , t he M us e um o f U nli mited Growth , th at would be a t r ue r re p re s e n ta-

(62) Le Corbusier’s sketches of the Museum of Unlimited Growth, taking inspiration from a mollusc.

t ion of i tse lf and would potentially e xpa n d in t he f ut ure with a grow ing collection. C entral to his id ea s fo r a m us e u m was th e removal of h ierarch y, a s a cha l l e n ge to t he prom ine nt museum model of th e ti m e by Jea n N icho l a s Lou i s D u rand (C h in, 2016) wh ich h ad a s e r ie s o f co ur ts a n d monu me ntal fixtures. Taking inspirat io n f ro m t he s he l l o f a m ollu sc, he reinterpretes th e spira l in g g ro w t h a s a s e r ie s o f m od u le s th at wh ile initially bein g pa r t o f t he e xte r n a l facad e , be co mes an internal partitio n wa l l a s t he m us e um e v olv e s. ‘ Le C orbusier imagined th at a m o d e r n m us e um , a tru e m u se um, would include th e p re s e n t d a y, t he e v e r y day, the q u otidian.’ (C h in, 2016) Rea l ist ica l l y, t he m us e um cou ld not g row infitely due to ph y s ica l co n st ra in ts , but tod ay, the d igital repositor y loosens t he s ha c k l e s o f s uc h

(63) Le Corbusier’s renderings of the Museum of Unlimited Growth.

constraints with a seemingly endless a n d g ro w in g sto ra ge capaci ty.

Withi n the frame work of a Maker ’s Mus e um , a s l o n g a s t he Arti ficial Intelligence continues to l ea r n f ro m t he g ro wing d ata ocean of artifacts and creat io n s , its re p re s e n t io n will always be a culmination of th e k n o w l e d ge o f t he t im e , from histor y to th e present day.

C e ntral to both th e Fun Palace and t he M us e um o f Unl im ite d growth is th e potential o f d is crete e l e m e n ts , s caffold i ng and th e module respect iv e l y, a s a m ea n s fo r facilitating a ch anging arch itecture, d ue to its a bil it y to conne ct and form an arch itecture t hat is m o re t ha n t he s u m of i ts parts. Th is will be potent ia l l y e xp l o re d f ur t he r in the the si s. (64) Possible Module explorations.

p u b l i c i z i n g s p at i a l p r o b a b i minority representation? A s a mach ine generates in p ro ba bil it ie s , ho w ca n the se probabilities be rep re s e n te d a rc hite ct ura l l y and wh at do th e y mean? Th e p ro ba bil it y o f e xiste n ce as learned by th e Mach ine is he re re p re s e n te d t hro ug h color as a signif ier. C ould th e m us e um t he n p o s s e s s a lang uage of representing its co n te n ts t hro ug h t he s e p robabilities? Wh ere ‘low pro ba bil it y ’ s pa ce s co n ta in valuable y et not as ‘popular ’ a r t ifa cts ?

W here one learned categor y o f o bje cts o v e r l a p s w it h anoth er categor y, perh aps a d if fe re n t k in d o f s pa ce re sults as represented by pur p l e a n d c ya n a m o r p ho us

CAT 02 0.95<Probability<0.999

v olumes.

CAT 01 0.999<Probability CAT 01 0.95<Probability<0.999

CAT 01 0.9<Probability<0.95

ilities

Probability<0.9

CAT 02 0.9<Probability<0.95

CAT 02 0.999<Probability

unfinished from one transitory state to another Th e Make r â&#x20AC;&#x2122;s Muse um i s t hus ima g in ed a s a n a rch i-

H o w its p ro g ra m m es , c i rc u l at i o n a n d m ec h a n i s m s

tecture always und e r c han ge, f ro m o n e ex h ibitio n

w o r k w il l be ex pl o red i n t h e m a i n t h es i s res po n di n g

to t he ot he r as t he make r co mmu n ity g rows a n d its

to a r t if ic ia l l y gen erated fo r m s t ra i n ed o n a data s et

l ike s and d i sli ke s e vo lve w ith time, a n o n - s y bo lic

o bta in e d f ro m T h i n g i ver s e. C atego r i es fo r t h e data-

zeitge i st .

s et s e l e ct io n wi l l a l s o b e dec i ded i n t h e fo l l o wi n g s eg m e n t .

Temporary Exhibition 01

Transiti

time

precedent

test cas

fun palace

makerâ&#x20AC;&#x2122;s

ion

s museum

W hile today, 3D mach ine learn in g is a bst ra cte d at a

A s s uch t he t he s i s po st u l ates t h at t h e ro l e o f t h e a r-

low re solution voxel space of 32x32x32x3, t he e xp o -

c hite ct co ul d p o ss i b l y c h a n ge i n t h e fu t u re i f b i g data

ne ntial growth in computing po w e r a n d us e r- ge n e rat-

is t he d r iv e r to wa rds a n e w dem o c rat i c a rc h i tect u re.

e d d ata in th e information age he ra l d s t he p o s s ibil it y

The M a ke r â&#x20AC;&#x2122;s M us eu m i s b u t a t ra n s i to r y state to o, a

for su ch an approach to be ap p l ie d m ul t ifa r io us l y in

te st ca s e a n d d e mo n st rat i o n fo r a n A r t i fi c i a l l y I n tel-

archi tecture at a much lower co st a n d at a m uc h hig h-

l ige n t Archite ct ure.

e r accessibility.

Temporary Exhibition 02

future

a more democratic architecture?

BIBLIOGRAPHY

Alderto n , M . (2016). Dig it izat io n a n d t he Fut ure o f M u s eu m s . Reds h i ft b y Autodes k . htt p s :/ / w w w.a uto d e s k .co m / re d s hif t /d ig it izat i o n -fu t u re -o f-m useums/

A s, I., Pa l , S ., & Ba s u, P. (2018). Ar t if icia l in te l l ige n ce in a rc h i tect u re: Generat in g co n ce pt ua l d e s ig n v ia d e e p l ea r n in g . In te r n at i o n a l J o u r n a l o f Arch ite ct ura l Co m p ut in g , 16(4), 306– 327. d o i: 10.1177/ 1 4 7 8 0 7 7 1 1 8 8 0 0 9 8 2

Bastéa El e n i. (2004). M e m o r y a n d a rc hite ct ure . Al buq u erqu e: U n i v. o f N e w Mexico P re s s .

Bil jeck i, F., Le d o ux, H ., &a m p ; Stote r, J. (2017). Ge n e rat i n g 3 D c i t y m o del s with out e l e vat io n d ata . Co m p ute r s , En v iro n m e n t a n d U r ba n Sy stem s , 6 4 , 1-18. d o i:10.1016/ j.co m p e n v ur bs y s .2017.01.001

Borji, A. (2019). P ro s a n d co n s o f GAN e va l uat io n m ea s u res . C o m pu ter Vision a n d Im a ge Un d e r sta n d in g , 179, 41– 65. htt p s :/ /do i .o rg /1 0 . 1 0 1 6 / j . c viu.2018.10.009

Brownl e e , J. (2019, Jul y 12). H o w to Eva l uate Ge n e rati ve A dver s a r i a l N etworks. M a c hin e Lea r n in g M a ste r y. htt p s :/ / m a chin e l ea r n i n g m a ster y.co m / h ow-to - e va l uate - ge n e rat iv e - a d v e r s a r ia l - n et w o r k s / .

Brownl e e , J. (2019, N o v e m be r 21). H o w to Exp l o re t he G A N L aten t Spa ce Wh en Ge n e rat in g Fa ce s . M a c hin e Lea r n in g M a ste r y. h tt ps : //m a c h i n e l e a r n i n g m a s t e r y . c o m / h o w -t o - i n t e r p o l a t e - a n d - p e r f o r m - v e c t o r - a r i t h m e tic-wit h- fa ce s - us in g - a - ge n e rat iv e - a d v e r s a r ia l - n et w o r k /.

Budds, D. (2016, M a y 21). Re m Ko o l ha a s : “Archite ct u re Ha s A Ser i o u s P roblem To d a y ”. Ret r ie v e d Ap r il 9, 2020, f ro m htt p s ://www. fa stco m pa n y. com/3060135/ re m - ko o l ha a s - a rc hite ct ure - ha s - a - s e r io u s -pro b l em -to da y

C aetan o, I., S a n to s , L ., &a m p ; Le itã o, A. (2020). Co m pu tat i o n a l des i g n in archite ct ure : De f in in g pa ra m et r ic, ge n e rat iv e , a n d a l go r i t h m i c de sign. Fro n t ie r s o f Arc hite ct ura l Re s ea rch, 9(2), 287- 3 0 0 . do i : 1 0 . 1 0 1 6 / j . foar.2019.12.008

C h aillo u, S . (2019). AI + Archite ct ure | To wa rd s a N e w A pproa c h . Ha r va rd Univers it y, 188.

C h ang, A. X., Fun k ho us e r, T., Guiba s , , L ., H a n ra ha n , P., Hu a n g , Q. , L i , Z . , … Yu, F. (2015). S ha p e N et : An In fo r m at io n - Rich 3D M o d e l Repo s i to r y. H ammin g , R. W. (1980). The Un rea s o n a bl e Ef fe ct iv e n es s o f M at h em at i c s . Th e Ame r ica n M at he m at ica l M o n t hl y, 87(2), 81. d o i: 10 . 2 3 0 7 /2 3 2 1 9 8 2 de Blee c ke re , S . (2007). St y l e a n d Archite ct ure in a D em o c rat i c Per s pective. 4( 1), 13– 20.

Dough e r t y, D. (2012). The M a ke r M o v e m e n t . 7(3). htt p s : //do i .o rg /1 0 . 1 1 0 9 / F IE .200 3.1264741 Forgan , S . (2005). Buil d in g t he M us e um : Kn o w l e d ge, C o n fl i ct , a n d t h e

BIBLIOGRAPHY

Power o f P l a ce . Is is , 96(4), 572- 585. d o i:10.1086/ 49859 4

Glorot , X., & Be n g io, Y. (2010). Un d e r sta n d in g t he d i ffi c u l t y o f t ra i n i n g deep fe e d fo r wa rd n e ura l n et w o r k s . htt p s :/ /d o i.o rg /d o i = 1 0 . 1 . 1 . 2 0 7 . 2 0 5 9 H ay den . (1995). The p o w e r o f p l a ce : ur ba n l a n d s ca p e s a s pu b l i c h i sto r y. C ambrid ge , M A: The M IT P re s s .

H eusel , M ., Ra m s a ue r, H ., Un te r t hin e r, T., N e s s l e r, B . , & Ho c h rei ter, S. (2017). GAN s Tra in e d by a Tw o Tim e -S ca l e Up d ate Rul e C o n verge to a Lo cal Nas h Eq uil ibr ium . htt p s :/ /d o i.o rg /a r Xiv :1706.08500 v6

H istor y o f M us e um s - Fro m O l d e st to M o d e r n M us e ums . ( n .d. ) . Ret r i e ved J uly 26, 2020, f ro m htt p :/ / w w w.histo r y o f m us e um s .co m/

IK P rize 2016: Re co g n it io n – Exhibit io n at Tate Br ita i n | Tate. ( n .d. ) . Re trie ved Jul y 26, 2020, f ro m htt p s :/ / w w w.tate .o rg .uk /wh ats -o n / tate -b r i tain/exhibit io n / ik- p r ize - 2016- re co g n it io n

Inside t he w o r l d ’s f ir st d ig ita l a r t m us e um | Br it is h C o u n c i l . ( n .d. ) . Re trie ved Jul y 26, 2020, f ro m htt p s :/ / w w w.br it is hco un c i l .o rg /a n y o n e -a n y wh ere/e xp l o re /d ig ita l - creat iv it y/ f ir st- d ig ita l - a r t- m us e u m

C h in, I. (2016). Le Co r bus ie r ’s M us é e à cro is s a n ce il l i m i té e: A L i m i t l es s Diagra m fo r M us e o l o g y. 1– 15. htt p s :/ /d o i.o rg / 10.4995/ l c 2 0 1 5 . 2 0 1 5 . 5 8 4

Karras, T., L a in e , S ., & Ail a , T. (2019). A St y l e - Ba s e d Gen erato r A rc h i tecture fo r Ge n e rat iv e Ad v e r s a r ia l N et w o r k s . 2019 IEEE/CV F C o n feren ce on C omp ute r Vis io n a n d Patte r n Re co g n it io n (CVP R). d o i : 1 0 . 1 1 0 9 / c vpr.20 19.00453

Koh , I. (2019). Dis crete S a m p l in g : The re is N o O bje ct o r F i el d … J u st Statistical Dig ita l Patte r n s . Archite ct ura l De s ig n , 89(2), 102 – 1 0 9 . do i : 1 0 . 1 0 0 2 / ad.2418

Lai, J. (2012). Cit ize n s o f n o p l a ce : An a rc hite ct ura l g ra ph i c n o vel . N e w York : Pr in ceto n Archite ct ura l P re s s .

Larsen , An d e r s Bo e s e n L in d bo, S ø n d e r by, S ø re n Ka a e, L a ro c h el l e, Hu go, Winth e r, O l e . Auto e n co d in g be y o n d p ixe l s us in g a l ea rn ed s i m i l a r i t y m etric. h tt p s :/ /a r xiv.o rg /a bs / 1512.09300 2016

Le wis, G. D. (1999). m us e um | De f in it io n , H isto r y, Types , & Operat i o n | Britann ica . En c y cl o pa e d ia Br ita n n ica . htt p s :/ / w w w.br i ta n n i ca .co m / to pi c / museum - cul t ura l - in st it ut io n

Mansso ur, Y. M ., & M o r s i, N . K. (n .d .). The histo r ica l e v o l u t i o n o f m u s eu m s arch ite ct ure .

Math e ws H o ba r t , S ., & S m it h Co l l ege s , W. (2005). The Fu n Pa l a ce: C edr i c P rice’s e xp e r im e n t in a rc hite ct ure a n d te chn o l o g y. Te c h n o et i c A r ts : A J o u r-

BIBLIOGRAPHY

nal of S p e c ul at iv e Re s ea rc h, 3(2). htt p s :/ /d o i.o rg / 10 . 1 3 8 6 / tea r. 3 . 2 . 7 3 /1 Muelle r, J.-W. (n .d .). Ca n Archite ct ure be De m o crat ic ? - P u b l i c Sem i n a r. Retrie v e d Aug ust 1, 2020, f ro m htt p s :/ / p ubl ic s e m in a r.o rg /2 0 1 5 /0 6 /ca n -a rch itect ure - be - d e m o c rat ic /

O C KMA N , J. (2011). W hat Is De m o crat ic Archite ct ure ? : T h e P u b l i c L i fe o f Buildin g s . Dis s e n t (00123846), 58(4), 65– 72. htt p :/ / 10.0 . 5 . 7 3 /ds s . 2 0 1 1 . 0 1 0 0 O lin, M. (2008). The Sto n e s o f M e m o r y : Pete r Eis e n m a n i n C o n ver s at i o n . Images, 2(1), 129– 135. d o i: 10.1163/ 187180008x408636 Palliste r, J. (2013, Fe br ua r y 21). Da n ie l L ibe s k in d : ‘I’m n ot i n terested i n buildin g g l ea m in g st re ets fo r d e s p ots ’. Ret r ie v e d Apr i l 1 0 , 2 0 2 0 , fro m h ttps:// w w w.a rchite cts jo ur n a l .co.uk / ho m e /d a n ie l - l ibe s k i n d-i m -n ot-i n ter ested-i n - buil d in g - g l ea m in g - st re ets - fo r- d e s p ots / 864313 4 . a r t i c l e

Pangbu r n , D. (2016). This Da p p e r Ro bot Is a n Ar t Cr it ic . h tt ps : //www.vi ce. com/en _ us /a r t ic l e /a e n q 45/ ro bot- a r t- cr it ic- be re n s o n

Ponzin i, D. (2011). L a rge s ca l e d e v e l o p m e n t p ro je cts an d sta r a rc h i tect u re in th e a bs e n ce o f d e m o c rat ic p o l it ic s : The ca s e o f Abu D h a b i , UA E . C i t i es , 28(3), 251– 259. htt p s :/ /d o i.o rg / 10.1016/ j.cit ie s .2011.02 . 0 0 2

S alima n s , T., Go o d fe l l o w, I., Za re m ba , W., Che un g , V., R a dfo rd, A . , & a m p; C h en, X. (2016). Im p ro v e d Te chn iq ue s fo r Tra in in g GA N s . h tt ps : //do i .o rg / arX iv:1 606.03498v 1

S h iner, L . (n .d .). Arc hite ct ure v s . Ar t : The Ae st het ic s o f A r t M u s eu m D e sign1. Ret r ie v e d Jul y 26, 2020, f ro m htt p s :/ / w w w.co n tem pa est h et i c s .o rg / ne wvo l um e / pa ge s /a r t ic l e .p hp ? a r t icl e ID=487

S h oema ke , K. (1985). An im at in g rotat io n w it h q uate r n i o n c u r ves P ro ceed ings of t he 12t h An n ua l Co n fe re n ce o n Co m p ute r Gra ph i c s a n d I n tera ct i ve Tech niq ue s - S IGGRAP H ‘85. htt p s :/ /d o i.o rg / 10.1145/ 3 2 5 3 3 4 . 3 2 5 2 4 2

S imon, P. (2015). To o big to ig n o re : t he bus in e s s ca s e fo r b i g data . Ho b o ken, NJ : W il e y.

Tomkin s , C. (1997, Jun e 30). The M a v e r ick | The N e w Yo r ker. h tt ps : //www. ne wy or ke r.co m / m a ga z in e / 1997/ 07/ 07/ t he - m a v e r ic k

Treib, M . (2009). S pat ia l re ca l l : m e m o r y in a rchite ct u re a n d l a n ds ca pe. Ne w Yo r k , N e w Yo r k : Ro ut l e d ge . d o i: htt p s :/ /d o i- o rg . l i b ra r y. s u td.edu . sg:2443/ 10.4324/ 9781315881157

Venturi, R. (1977). Co m p l e xit y a n d co n t ra d ict io n in a rch i tect u re. N e w Yo r k : Museum o f M o d e r n Ar t ; d ist r ibute d by N e w Yo r k Gra p h i c .

Venturi, R., Bro w n , D. S ., & Ize n o ur, S . (1972). Lea r n i n g fro m L a s Vega s . C ambrid ge , M A: M IT P re s s .

BIBLIOGRAPHY

Walch , K. (2020). AI Re v o l ut io n iz in g The M us e um Exp e ri en ce At T h e Sm i t h sonian . htt p s :/ / w w w.fo r be s .co m / s ite s /co g n it iv e w o r l d/2 0 2 0 /0 3 /2 6 /a i -re volution iz in g -t he - m us e um - e xp e r ie n ce - at-t he - s m it hs o n i a n /# 3 0 f4 5 a 0 0 5 6 fd

Wh ite, T. (2016). S a m p l in g Ge n e rat iv e N et w o r k s . htt p s ://do i o rg /1 6 0 9 . 0 4 4 6 8

Yosh imura , Y., Ca i, B., Wa n g , Z., & Ratt i, C. (2019). De e p Lea r n i n g A rc h itect : C l a s s if icat io n fo r Archite ct ura l De s ig n t hro ug h t h e e y e o f A r t i fi c i a l Intellige n ce . Co m p utat io n a l Ur ba n P l a n n in g a n d M a n a gem en t fo r Sm a r t C ities, 249â&#x20AC;&#x201C; 265. d o i: 10.1007/ 978- 3- 030- 19424- 6_ 14

Zh ang, H . (2019). 3D M o d e l Ge n e rat io n o n Arc hite ct ura l P l a n a n d Sect i o n Trainin g t hro ug h M a c hin e Lea r n in g . Te c hn o l o g ie s , 7(4) , 8 2 . do i : 1 0 . 3 3 9 0 / tech no l o g ie s 7040082

Zh ang, H ., & Bl a s s et i, E. (2019). 3D Archite ct ura l Fo r m St y l e Tra n s fer th roug h M a chin e Lea r n in g . d o i: 10.13140/ RG.2.2.16791 . 5 2 6 4 5

IMAGE REFERENCES

00. Too l s o f t he Age s Stone a ge ha m m e r im a ge o bta in e d f ro m htt p :/ /o l d e uro pea n c u l t u re. b l o g spot .co m / 2015/ 12/ ba ba - ha m m e r- a n d - a n v il .ht m l Bronze a ge ha m m e r im a ge o bta in e d f ro m htt p s :/ /a n ci en tto u c h .co m /n eo lith ic-bro n ze - a ge %20sto n e .ht m Iron a ge ha m m e r im a ge o bta in e d f ro m htt p s :/ / www. pi n terest .co m / pin/285415695121961645/ Mech an ica l ha m m e r im a ge o bta in e d f ro m htt p s :/ / p i cc l i c k .co. u k /B l a c ker-B-Po w e r- H a m m e r- 202395074060.ht m l Wrought iro n m a chin e im a ge o bta in e d f ro m htt p s :/ /e l l s en o r n a m en ta l i ro nmach in e s .co m / ha m m e r- fo rg in g / F irst co m m e rc ia l p c im a ge o bta in e d f ro m htt p s :/ /o bs er ver.co m /2 0 1 5 /0 8 / ibms -f ir st- p e r s o n a l - co m p ute r- wa s - re l ea s e d - 34- y ea r s - a go -to da y/

01. P rimit iv e H ut e n g ra v in g by Cha r l e s Eis e n f ro m Fro n t i s pi ece o f M a rc-A ntoine L a ug ie r : Es s a i s ur l ’Arc hite ct ure 2n d e d . 1755 b y C h a r l es E i s en (1720-1778). Al l ego r ica l e n g ra v in g o f t he Vit r uv ia n p r im i t i ve h u t . O btain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/ The _ P r im i t i ve_ Hu t # /m edi a / F ile:E ss a i_ s ur _ l ’Archite ct ure _ - _ Fro n t is p ie ce . jp g

02. Per s p e ct iv e v ie w o f t he Do m - in o s y ste m , 1914. Im age fro m Le C o r b u s i er & P ie r re Jea n n e ret , O Euv re Co m p l ète Vo l um e 1, 1910 – 1 9 2 9 , Les Edi t i o n s d’Arch ite ct ure Ar te m is , Zür ic h, 1964 O btain e d

f ro m

htt p s :/ / w w w.d e ze e n .co m / 2014/ 03/ 20 /o pi n o n -j u st i n -m c-

guirk-le - co r bus ie r- s y m bo l - fo r- e ra - o bs e s s e d - w it h- custo m i s at i o n /

03. E le m e n ts o f Archite ct ure by Re m Ko o l ha us , 2018. O btain e d f ro m htt p s :/ /o m a .e u/ p ubl icat io n s /e l e m e n ts - o f-a rc h i tect u re

04. Do An d ro id s Drea m o f El e ct r ic S he e p by P hil l ip K. D i c k , 1 9 6 8 Ha r perC ollins P ubl is he r s O btain e d

f ro m

htt p s :/ / w w w.e ba y.co m / i t m /D O-A N D R OI D S -

DREAM-OF-ELECTRIC-SHEEP-Filmed-as -Blade -Runne -by-Philip -KDick-/302511545383

05. H isto r y o f S cie n ce M us e um , Oxfo rd , e re cte d 1683 O btain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/ H isto r y_ o f_ Sc i en ce_ M u s e um,_Oxfo rd #/ m e d ia / Fil e :O l d _ A s hm o l ea n _ 2006.JP G

06. A shm o l ea n M us e um o f Ar t a n d Arc he o l o g y, e re cte d 1 8 4 5 O btain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/A s hm o l ea n _ M u s eu m # /m e dia/F il e :A s hm o l ea n _ M us e um _ in _ Jul y _ 2014. jp g

07. A Te m p l e o f t he M us e s -The Yo r k s hire M us e um , 183 0 . Fro m T h o m a s A l len, A n e w a n d co m p l ete histo r y o f t he co un t y o f Yo r k . O btain e d f ro m Fo rga n , S . (2005). Buil d in g t he M us e um : K n o wl edge, C o n f lict , a n d t he Po w e r o f P l a ce . Is is , 96(4), 572- 585. d o i:1 0 . 1 0 8 6 /4 9 8 5 9 4

08. Den v e r Ar t M us e um by Da n ie l L ibe s k in d , 2006. O btain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/ De n v e r_ A r t _ M u s eu m # /m e -

IMAGE REFERENCES

dia/F il e :De n v e r _ a r t _ m us e um _ n ig ht _ a rchip re n e ur _ a d a m _ c ra i n . j pg

09. Fun Pa l a ce by Ce d r ic P r ice , 1964. Obtain e d f ro m htt p s :/ / w w w.m o m a .o rg /co l l e ct io n / w o r k s /8 4 2

10. Th e Ce n t re Po m p id o u by Re n zo P ia n o, Richa rd Ro ger s a n d G i a n fra n co Francin i, 1977 Obtain e d

f ro m

htt p s :/ / w w w.k l o o k .co m /a ct iv it y/ 39 9 4 -po m pi do u -cen-

ter-mu s e um -t icket- pa r is /

11. P lug In Cit y by Pete r Co o k , 1964. Obtain e d f ro m htt p s :/ / w w w.m o m a .o rg /co l l e ct io n / w o r k s /7 9 5

12. A Wa l k in g Cit y by Ro n H e ro n , 1964. Obtain e d

f ro m

htt p :/ / t het r ic y cl e s 101.bl o g s p ot .co m/2 0 1 2 /0 8 /ext ra -re -

search - wa l k in g - c it y.ht m l

13. P lug In Cit y by Pete r Co o k , 1964. Obtain e d

f ro m

htt p s :/ / w w w.re s ea rc hgate .n et / f ig ure/A rc h i g ra m s -P l u g -

I n - C i t y - 1 9 6 2 - 1 9 6 4 - I n te re st i n g l y - P i ete r- B r u ege l - pa i n te d - i n -t h e - s i xte e n t h _ fig2_30874784

C ollage 01. Co l l a ge co m p r is in g : Th e Met im a ge o bta in e d f ro m htt p s :/ / w w w.n y t im e s .co m /2 0 1 9 /0 3 /2 1 /a r ts / d e s i g n / t h e - m et- w i l l - u s e - i ts - fa ca d e - a n d - g reat- h a l l -to - s h o w ca s e - co n te mporar y - a r t .ht m l Dresede n M il ita r y H isto r y M us e um im a ge o bta in e d fro m h tt ps : //www. a rc h d a i l y.co m / 1 7 2 4 0 7 /d re s d e n % 2 5 e 2 % 2 5 8 0 % 2 5 9 9 s - m i l i ta r y - h i sto r y - m u seum-d a n ie l - l ibe s k in d Ne w

Ar t

M us e um

im a ge

o bta in e d

f ro m

htt p s :/ /di vi s a re.co m /pro j -

e c t s / 2 8 9 2 8 3 - s a n a a - k a z u y o - s e j i m a - r y u e - n i s h i za w a - d e a n - k a u f m a n - n e w art-mus e um Th e Lo uv re im a ge o bta in e d f ro m htt p s :/ /o n e l ifeto u r s .ca /6 6 6 -pa n es -o fglass -a n d -t he - a ct ua l - co n t ro v e r s y - o f-t he - l o uv re - p y ra mi d/ Maxxi M us e um im a ge o bta in e d f ro m htt p s :/ / w w w.a rc h i tect u ra l reco rd. com/ar t ic l e s / 7850- m a xxin at io n a l - m us e um - o f-xxi- ce n t u r y -a r ts

Timelin e 01 co m p r is in g : Ruins in t he To w n o f Ur, S o ut he r n Ira q im a ge o bta i n ed fro m h tt ps : // en.wikipedia.org /wiki/Ennigaldi-Nanna%27s_museum#/media/ F ile:Ur- N a s s ir iya h. jp g

Greek P in a kot he ke r uin s im a ge o bta in e d f ro m htt p s :/ /co m m o n s .wi k i m e dia.org / w ik i/ Fil e :P in a kot he ke - Ac ro p o l is - At he n s - 1980. j pg

Tomb o f Q in s hihua n g im a ge o bta in e d f ro m htt p s :/ /www.week i n c h i n a . com/cha pte r/a n - a - z- o f- c hin e s e - histo r y/q in - s hihua n g /

Todai-ji S ho s ho - in re p o s ito r y im a ge o bta in e d f ro m htt ps : //en .wi k i pedi a . org /wik i/ S h%C5%8Ds %C5%8Din #/ m e d ia / Fil e :S ho s o - in . j pg

IMAGE REFERENCES

Relics t ra f f ic ke d in m e d ie va l c hr ist ia n it y im a ge o bta i n ed fro m h tt ps : // www.met m us e um .o rg / toa h/ hd / re l c/ hd _ re l c .ht m

C osimo d e M e d ic i im a ge o bta in e d f ro m htt p s :/ / w w w.br i ta n n i ca .co m /b i o g raph y/Co s im o - d e - M e d ici

S amue l v o n Q uic he r be rg In s cr ipt io n e s v e l t it ul i t heat ri a m pl i s s i m i i m a ge obtaine d f ro m htt p s :/ / w w w.histo r y o f in fo r m at io n .co m /deta i l . ph p ? i d= 3 2 7 9

H istor y o f S cie n ce M us e um , Oxfo rd , e re cte d 1683 ima ge o bta i n ed fro m h tt p s : / /e n .w i k i p e d i a .o rg / w i k i / H i sto r y _ o f _ S c i e n ce _ M u s e u m , _ Ox fo rd # / m e dia/F il e :O l d _ A s hm o l ea n _ 2006.JP G

British M us e um im a ge o bta in e d f ro m htt p s :/ / w w w.mu s eu m s u m m i t . go v. h k/en/

15th ce n t ur y a r m o ur im a ge o bta in e d f ro m htt p s :/ / w ww. a n c i en t .eu / i m age/8933/ k n ig hts - in - a r m o ur- 15t h- ce n t ur y - ce /

Timelin e 02 co m p r is in g : Royal B ata v ia n S o c iet y o f Ar ts a n d S c ie n ce im a ge o bta i n ed fro m h tt ps : // e n .w i k i p e d i a .o r g / w i k i / R o ya l _ B a ta v i a n _ S o c i e t y _ o f _ A r t s _ a n d _ S c i e n ce s # / m e d i a / F i l e : CO L L E CT I E _ T R O P E N M U S E U M _ H e t _ g e b o u w _ a n n e x _ m u s e u m _ van_h et _ Bata v ia a s ch_ Ge n o ots c ha p._ TM n r _ 60005154. j pg

Arch eo l o g y im a ge o bta in e d f ro m htt p s :/ / w w w.st ud y in g -i n -u k .o rg / to p -u n i versitie s -to - st ud y - a rc ha e o l o g y - in - uk /

Great Exhibit io n o f 1851 at t he Cr y sta l Pa l a ce im a ge o bta i n ed fro m h tt p s : / / w w w.t h eg u a rd i a n .co m / s c i e n ce / b l o g / 2 0 1 5 /a u g / 2 8 / h o w -t h e - g reatexh ibit io n - o f- 1851- st il l - in f l ue n ce s - s c ie n ce -to d a y

Pompid o u

Ce n te r

im a ge

o bta in e d

f ro m

h tt ps : //www.de zeen .

com/2019/ 11/ 05/ce n t re - p o m p id o u- p ia n o - ro ge r s - hig h-tec h -a rc h i tect u re/ Musee d â&#x20AC;&#x2122;O r s a y im a ge o bta in e d f ro m htt p s :/ / w w w.a fa r.co m /pl a ces /m u see -do r s a y - pa r is

Tate Mo d e r n im a ge o bta in e d f ro m htt p s :/ / w w w.br i ta n n i ca .co m / to pi c / Tate -ga l l e r ie s

Robotic a r t cr it ic Be re n s o n im a ge o bta in e d f ro m htt p s : //n e ws . a r t n et .co m / art-wor l d / ro bot- a r t- c r it ic- be re n s o n - 436739

Recogn it io n by Tate M o d e r n , im a ge o bta in e d f ro m htt p : //reco g n i t i o n .tate. org.uk/ #ho w it w o r k s

Pepper ro bot im a ge o bta in e d f ro m htt p s :/ / w w w.d ig ita l t ren ds .co m /co o l tech /pe p p e r- ro bot- s m t hs o n ia n /

IMAGE REFERENCES

14. Th e Gug ge n he im Bil ba o by Fra n k Ge hr y, 1997. Obtain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/Gug ge n h ei m _ M u s eu m _ B i lbao#/m e d ia / Fil e :Bil ba o _ - _ Gug ge n he im _ a uro re . jp g

15. Dre s d e n Wa r M us e um by Da n ie l L ibe s k in d , 2011. Obtain e d

f ro m

htt p s :/ / w w w.a rc hd a il y.co m /1 7 2 4 0 7 /dres -

den% 25e 2%2580%2599s - m il ita r y - histo r y - m us e um - d a n i el -l i b es k i n d

16. Nat io n a l M us e um o f Q ata r by Jea n N o uv e l , 2019. Obtain e d f ro m htt p s :/ / w w w.a rc hito n ic .co m /e n / p ro ject / j ea n -n o u vel -n ational-m us e um - o f- q ata r/ 20053122

17. ‘Litt l e Fra n k a n d H is Ca r p’ by An d rea Fra s e r, 2001. Obtain e d f ro m htt p s :/ / w w w.y o ut ube .co m / watc h? v =E3u 5 YV 4 R HA 4

Timelin e 03 co m p r is in g : Th e Gug ge n he im Bil ba o by Fra n k Ge hr y, 1997 im a ge o bta i n ed fro m h ttps:// w w w.g ug ge n he im - bil ba o.e us /e n / t he - buil d in g

C ity of Ar ts a n d S cie n ce s im a ge o bta in e d f ro m htt p s : //www. go o g l e.co m / s e a r c h ? q = c i t y + o f + a r t s + a n d + s c i e n c e s & r l z = 1 C 1 E J FA _ e n S G 7 9 1 S G 7 9 1 & s x s r f = A L e K k 0 0 p M 2 Q v s v s f m L 9 r X zY E g 0 U - G R z 6 t g : 1 5 9 6 7 9 0 4 4 3 0 1 7 & s o u r c e = l nms&tbm=isch&sa=X&ved=2ahUKEwiN0Iv524jrAhUS7XMBHWVTB_wQ_ AUoA X o ECB4QAw &biw =1278&bih=878#im g rc =STr L 0U6b y W M E E M

Museum o f Po p Cul t ure im a ge o bta in e d f ro m htt p s :/ / www.c n t ra vel er.co m / activitie s / s eatt l e / m us e um - o f- p o p - cul t ure

J e wish M us e um in Be r l in im a ge o bta in e d f ro m htt ps : //www. a rc h da i l y. com/91 273/a d - c l a s s ic s - je w is h- m us e um - be r l in - d a n ie l -l i b es k i n d

Quadra cc i

Pa v il io n

im a ge

o bta in e d

f ro m

htt ps : //www. a rc h da i l y.

com/53 1290/ s p ot l ig ht- s a n t ia go - ca l at ra va

C ontemp o ra r y Ar ts Ce n te r im a ge o bta in e d f ro m h tt ps : //a rc h i di a l o g . com/20 12/ 02/ 12/ za ha - ha d id - a n d -t he - m e c ha n ics - o f- in s pi rat i o n /

Denver Ar t M us e um im a ge o bta in e d f ro m htt p s :/ / w w w. fl i c k r.co m /ph oto s / lulek/47217546422

Royal On ta r io M us e um im a ge o bta in e d f ro m htt p s :/ / w w w. ro m .o n .ca /en

Pompido u M et z im a ge o bta in e d f ro m htt p s :/ / m a ga z i n e. b el l es dem eu res . co m /e n / l u x u r y/ l i fe st y l e / p o m p i d o u - m et z- ce n t re - 1 0 - y ea r s - e x h i b i tions -a n d - st ro n g - l o ca l - a n cho r in g - a r t icl e - 35539.ht m l

Maxxi N at io n a l M us e um im a ge o bta in e d f ro m htt p s ://s i tes . go o g l e.co m / site/apa r t histo r y he n r y cl a y s cho o l /a r t- histo r y - 250- 1/ 24 9

Art S cie n ce M us e um im a ge o bta in e d f ro m htt p s :/ / w ww. m u s eu m . red-dot .

IMAGE REFERENCES

sg /marin a - ba y - a r t- a n d - d e s ig n - g uid e - a r ts cie n ce - m us eu m

O rdos M us e um im a ge o bta in e d f ro m htt p :/ / w w w.a rchta l en t .co m /pro j ects / ordos -m us e um

Militar y H isto r y M us e um im a ge o bta in e d f ro m htt p s :/ / w ww.t r i ph o b o.co m / places /d re s d e n - ge r m a n y/ m il ita r y - histo r y - m us e um

H e y dar Al iy e v Ce n te r im a ge o bta in e d f ro m htt p s :/ / www. reddi t .co m /r/ A rc h i te ct u re Po r n /co m m e n ts / f y v x s w/co o l e st _ b u i l d i n g _ o u t _ t h e re _ h a n d s _ down_ he y d a r/

Louis Vuitto n Fo un d at io n im a ge o bta in e d f ro m htt p s :/ / www.wo r l da r t fo u ndation s .co m / fo un d at io n / fo n d at io n - l o uis - v uitto n /

Museum o f To m o r ro w im a ge o bta in e d f ro m htt p s :/ / www. a rc h i tect m a gazine.co m / p ro je ct- ga l l e r y/ t he - m us e um - o f-to m o r ro w _ o

Museum o f Ro ck im a ge o bta in e d f ro m htt p s :/ / w w w.a rc h da i l y.co m /7 8 6 4 8 9 / ragnaro c k- m v rd v - p l us - co be

Th e Lo uv re Abu Dha bi im a ge o bta in e d f ro m htt p :/ / w w w. i per i o n c h .eu /co ns e r va t i o n - s c i e n t i st- p o s i t i o n - a t-t h e - l o u v r e - a b u - d h a b i - u n i t e d - a ra b - e m i r ates -dea d l in e - ja n ua r y - 20- 2019/

Q atar N at io n a l M us e um im a ge o bta in e d f ro m htt p :/ /a r ta s i a pa c i fi c .co m / Blog /N at io n a l M us e um O f Q ata r O p e n s In Do ha

18. Lea r n in g f ro m L a s Vega s by Ro be r t Ve n t ur i et a l . 19 7 7 . O btain e d f ro m Ve n t ur i, R., Bro w n , D. S ., & Ize n o ur, S . (1 9 7 2 ) . Lea r n i n g fro m Las Vega s . Ca m br id ge , M A: M IT P re s s .

19.On t y p e s o f s e d uct iv e ro bust n e s s , Cit ize n s o f n o p l a ce b y J i m en a z L a i , 2012. O btain e d f ro m L a i, J. (2012). Cit ize n s o f n o p l a ce : An arc h i tect u ra l g ra ph i c novel. N e w Yo r k : P r in ceto n Arc hite ct ura l P re s s .

20. C ol l a ge 02. C ollage co m p r is in g : Image o f De n is e S cott Bro w n im a ge o bta in e d f ro m htt ps : //a rc h i n ect .co m / f ea t u r e s /a r t i c l e / 1 4 9 9 7 0 9 2 4 / l ea r n i n g - f r o m - l ea r n i n g - f r o m - l a s - v e ga s - w i t h denise - s cott- bro w n - pa r t- i-t he - fo un d at io n

S h oe ho us e im a ge o bta in e d f ro m htt p s :/ / w w w.d e s ig n i n g b u i l di n g s .co. u k / wiki/Ha in e s _ S ho e _ H o us e

Longab e rge r Co m pa n y im a ge o bta in e d f ro m htt p s :/ / w ww.des i g n i n g b u i l d ings.co.uk / w ik i/ The _ Big _ Ba s ket

S emino l e H a rd Ro c k H o l l y w o o d im a ge o bta in e d f ro m h tt ps : //www. b u s i -

IMAGE REFERENCES

n e s s i n s i d e r. c o m / s e m i n o l e - h a r d - r o c k - g u i t a r - s h a p e d - h o t e l - n o w - a c c e p t ing-res e r vat io n s - 2019- 7

Long Is l a n d Big Duc k im a ge o bta in e d f ro m htt ps : //www. a rc h da i l y. com/87 5022/ 9- w e ird - a n d - w o n d e r f ul - a rc hite ct ura l - d uc k s

Nation a l Fis he r ie s De v e l o p m e n t Boa rd im a ge o bta i n ed fro m h tt ps : // laugh in g s q uid .co m / f is h- s ha p e d - o f f ice - buil d in g - in - in d i a /

21. C ol l a ge 03. Co l l a ge co m p r is in g : Image o f Ro be r t Ve n t ur i o bta in e d f ro m htt p s :/ / w w w.f ra m e web.co m /n e ws / united- state s - d e n is e - s cott- bro w n - ro be r to - v e n t ur i

C h aoya n g P l a za im a ge o bta in e d f ro m htt p s :/ / w w w.m u l t i fa m i l y exec u t i ve. co m /d e s i g n - d e v e l o p m e n t /d e s i g n / m a d - a r c h i t e c t s - co m p l e t e s - c h a o y ang-pa r k- p l a za - in - be ijin g _ o

Art S ci e n ce M us e um im a ge o bta in e d f ro m htt p s :/ / w w w. m a r i n a ba y s a n ds . com/m us e um /e v e n ts / l et- s -ta l k- a bo ut .ht m l

Milwau ke e Ar t M us e um Exte n s io n im a ge o bta in e d f ro m h tt ps : //u wm .edu / w e l co m e /e v e n t /u n i o n - a r t- ga l l e r y - s p o n s o r s - a -t r i p -to -t h e - m i l wa u ke e - a r tmuseum - 2/

Th e H i v e N TU im a ge o bta in e d f ro m htt p s :/ /e n .w ik ipedi a .o rg /wi k i /T h e_ H ive,_Sin ga p o re #/ m e d ia / Fil e :The _ H iv e ,_ N TU_ (I). jp g

Gate to t he Ea st im a ge o bta in e d f ro m htt p s :/ /e n .w ik ip e di a .o rg /wi k i /G ate_ to_th e_ Ea st #/ m e d ia / Fil e :Gate _ to _ t he _ Ea st _ S uz ho u_ No vem b er _ 2 0 1 7 . j pg

Oly mpic Fis h Pa v il io n , Ba rce l o n a , S pa in im a ge o bta i n ed fro m h tt ps : // www.a rchite ct ura l d ige st .co m /ga l l e r y/ be st- o f- f ra n k- geh r y -s l i des h o w

Lego Ho us e im a ge o bta in e d f ro m htt p s :/ / t he ur ba n d e vel o per.co m /a r t i cles/ste l l a r- s ho r t l ist- a n n o un ce d - fo r- a d e l a id e - a r t- ga l l e r y -a rc h i tect

22. Imp e r ia l Wa r M us e um by Da n ie l L ibe s k in d , 2002. Obtain e d f ro m htt p s :/ / w w w.iw m .o rg .uk / histo r y/ 8-t hin g s -y o u -di dn t-k n o wabout-t he - iw m - n o r t h- buil d in g

23. Memo r ia l to t he M urd e re d Je w s in Euro p e by Pete r E i s en m a n , 2 0 0 5 . Obtain e d

f ro m

htt p :/ /a rchjo ur n e y.o rg / p ro je cts / m e m o r i a l -to -t h e -m u r-

dered-je w s - o f- e uro p e /

24. Gro un d Ze ro M a ste r p l a n by Da n ie l L ibe s k in d , 2003 . Obtain e d f ro m htt p s :/ / l ibe s k in d .co m / w o r k /g ro un d - ze ro -m a ster-pl a n /

25. Reichsta g , N e w Ge r m a n Pa r l ia m e n t by Fo ste r a n d Pa r t n er s , 1 9 9 9 . Obtain e d

f ro m

htt p s :/ / w w w.fo ste ra n d pa r t n e r s .co m /pro j ects /rei c h-

stag-ne w - ge r m a n - pa r l ia m e n t /

IMAGE REFERENCES

26. Virg in ia State Ca p ito l by Tho m a s Je f fe r s o n , 1788. O btain e d f ro m htt p s :/ /e n .w ik ip e d ia .o rg / w ik i/ Virg in ia _ State_ C a pi to l # /m e dia/F il e :Virg in ia _ Ca p ito l _ 1865. jp g

27. Lon d o n Cit y H a l l by Fo ste r a n d Pa r t n e r s , 2002. O btain e d f ro m htt p :/ /a rc hjo ur n e y.o rg / p ro je cts / l o n d o n -c i t y -h a l l /

28. Gra n ite hea d o f Am e n e m hat III by t he Br it is h M us e u m F ile

o bta in e d

f ro m

htt p s :/ / s ketc hfa b.co m /3 d-m o del s /g ra n -

ite -h ea d - o f- a m e n e m hat- iii- 64d 0b7662b59417986e 9d 69 3 6 2 4 de9 7 a

29. Ma ke r bot ’s Thin g iv e r s e W e bpa ge . O btain e d f ro m htt p s :/ / w w w.t hin g iv e r s e .co m /

30. Th in g iv e r s e ite m d o w n l oa d o pt io n s . O btain e d f ro m htt p s :/ / w w w.t hin g iv e r s e .co m / t hin g :403 8 1 8 1

31. Logo s o f ut il it ie s t hat us e m a chin e l ea r n in g . O btain e d f ro m htt p s :/ / s e e k l o go.co m /

32. C N N fa cia l feat ure m a p s by Kil l ia n Le va c he r. O btain e d f ro m htt p s :/ / m e d ium .co m / n o d e f l ux/ t he - e vo l u t i o n -o f-co m pu ter-visio n -te chn iq ue s - o n - fa ce - d ete ct io n - pa r t- 2- 4a f 3b22 df7 c 2

60,61. Fun Pa l a ce s e ct io n , c. 1964 O btain e d f ro m Ce d r ic P r ice Arc hiv e s , Ca n a d ia n Ce n t re fo r A rc h i tect u re, Montrea l . M at he w s , S . (2005). The Fun Pa l a ce : Ce d r ic P r i ce’s ex per i m en t in archite ct ure a n d te c hn o l o g y. Te chn o et ic Ar ts , 3(2), 7 3 – 9 2 . h tt ps : //do i . org /10 .1386/ tea r.3.2.73/ 1

62,32. Le Co r bus ie r ’s M us e um o f Un l im ite d Gro w t h, 19 3 1 . O btain e d

f ro m

htt p s :/ /e v o l ut io n a r y ur ba n is m .co m/2 0 1 7 /0 2 /2 8 /m u s e -

um-of- un l im ite d - g ro w t h/

thank you