Knowledge Ecologies - Stephan Sigl, Prof.C.Pasquero, M.Kuptsova

KNOWLEDGE ECOLOGIES Stephan Sigl

KNOWLEDGE ECOLOGIES Stephan Sigl

Master Thesis submitted in fulfilment of the requirements for the degree Diplom-Ingenieur to the Leopold-Franzens-University of Innsbruck Faculty of Architecture Supervision Univ.-Prof. Dr. Claudia PASQUERO Co-Supervision Maria KUPTSOVA, MA Institute of Urban Design - Landscape Architecture ioud / synthetic landscape lab Innsbruck, August 2020

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PREFACE

The conditions how knowledge is being accumulated, understood, interpreted and applied is subject to a perpetual change. Artificial Intelligence currently pushes means and methods for the development of autonomous, intelligent machines. It seems that every aspect of our life and environment faces rapid modifications. So how could a proposal for a future knowledge environment get along without the ability to adapt radically? Digitisation affects all areas of life. The quantity and quality of accessible digital data is growing exponentially. Conventional data Storing consumes huge amounts of energy and space. And it seems likely, that capacities will run out. At the same time, ground-breaking technologies such as data storage in DNA strands are appearing on the horizon. (see: Ceze et al. 2019) In the foreseeable future, this may not even require laboratory space, since data can also be stored in living organisms. What could be more obvious than to conceive of Knowledge Ecology as a landscape, in the open air?

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Pr o p o s a l

This Proposals shows in a first step an essayistic specu-

of reframing traditional approaches of future knowl-

lation of a distributed autonomous information storing

edge environments. Speculative thinking operates as

and processing machine that is inherent in an enig-

a tool for undogmatic visual prospects. The complexity

matic terrain, partially created by its own intelligence.

of society, science, economy and technology in this

In a further step the sampled results are tested and

context could seem to us to be an almost impenetra-

applied on a specific site that is strongly transformed.

ble matter. This is precisely why the thoughts of Dennis

The redesigned landscape stands for an aesthetic

Gabor, the inventor of holography, is particularly

reflection of a virtual landscape of the mind, which

appropriate here:

should make knowledge spontaneously and openly accessible, perhaps with the help of augmented reality devices.

“The future cannot be predicted, but futures can be invented. It was man’s ability to invent which has made human society what it is. The mental processes

The connection between man and machine is con-

of inventions are still mysterious. They are rational but

ceived here by referencing from the address coordi-

not logical, that is to say, not deductive.�

nates of a city to the virtual level. Addresses, in turn,

(Gabor 1964 207)

are each a reference to a location where humans can be found. As coordinates, they outlast any structural change, so to speak.

Thus, on the basis of aesthetic manifestations, an understanding of future needs and possibilities in con-

This conception of knowledge ecologies may be a

nection with the acquisition and transfer of knowledge

proposal for an antrophocene - framed environment

could be considered. The goal must be to make

typology, analogous to the basic idea of archives and

knowledge and its acquisition accessible to all people,

libraries. It is an aesthetic design conception for neural

both informally and spatially. Editing and control of

networks and their interface and storage structures on

knowledge needs many participants as well as a

a landscape scale. In a way the conceived design

culture and spaces of discourse. Some of these rooms

may appear thoroughly speculative for the purpose

might be virtual in nature.

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Preface Contents Proposal [A] THIRST FOR KNOWLEDGE KNOWLEDGE MILESTONES CASE STUDIES

[C] AN ESSAYISTIC SPECULATION EMERGENCE DISTIBUTED NEURAL NETWORKS EVALUATION THE MACHINE AND THE UNCONSCIOUS

[B] DIGITAL TURN CONTEMPORARY KNOWLEDGE STRUCTURES

[D] APPLICATION ON SITE MORPHOLOGY

SIGNIFICANT FACTORS

EVALUATION

DNA DATA STORAGE

DISTRIBUTION GRANULARITY INWEAVING HUMAN AND NON-HUMAN [E] SOURCE REFERENCES [F] APPENDIX

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[ A] T HI R S T FOR KNOWLEDGE

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collaborative aesthetics ethics natural artificial exterior interior human-centered

non-human human

knowledge accumulation and transfer cave painting, oral, books

urge for knowledge

basic human characteristic

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arts

interpretation and expression of Knowledge

subject

exploring discovering

1450 Gutenberg letterpress printing

development of sciences rational thinking

mundaneum

Jorge L. Borges infinite library

technological development technocratic thinking

libraries universities

computation machinic processing

server room: similar typologies

digitization digital storage

Knowlege Milestones

collaborative aesthetics ethics natural artificial exterior

interior human-centered non-human human

voyager project golden record

seed vault infinite monkey theorem computing in place of storing

science & bio-tech progress dna-storage, big data, AI computation capacities digital turn parametric design

reframing anthopocenic strategies

the unconcius of the machine

farming

bio-computing

distributed computing organism

knowledge ecologies

energy extraction

transfigurated campus

integrating interior and exterior

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fig01: Le RĂŠpertoire Bibliographique Universel vers 1900 Author: Patrick Tombelle

fig03: Svalbard Global Seed Vault, Spitsbergen Author: Svalbard Global Seed Vault, Matthias Heyde

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fig02: Voyager Golden Record Author: NASA

C a s e St u d i e s Knowledge Machine The Mundaneum, called as “Google de papier” by Le Monde (see: Mundaneum website 2020), was established by the Belgian librarian Paul Otlet in 1889.

This is the early and fascinating attempt of a universal knowledge transfer system, which shows some similarities with today’s tools of the web. “In 1895, Otlet and La Fontaine (...) began the creation of a collection of index cards that would reach more than 15 million entries. Later Otlet set up a fee-based service to answer questions by mail, by sending the requesters copies of the relevant index cards for each query.” (Wikipedia contributors 2020, May 15) Voyager Project The Voyager Project is an example for transferring.

It´s a story about an exploring spacecraft travelling through space, but also carrying data intended to transmit to extraterrestrial life. The Voyager Golden Records are two phonograph records that were included aboard both Voyager spacecrafts launched in 1977.

The records contain sounds and images selected to portray the diversity of life and culture on earth, and are intended for any intelligent extraterrestrial life form who may find them. (see: Wikipedia contributors 2020 August 01) Seed Vault It´s a story about storing gene information of plants for future generations.

The Svalbard See Vault is an underground facility with ideal conditions for the long-term storage of rare seeds. The aim is to preserve the biodiversity of plants.

Rare duplicates from seed banks all over the world are stored here as a backup. On the remote island of Spitsbergen in Norway, it is hoped that these specimens can also be preserved in view of the threat of natural hazards worldwide. (see: Wikipedia contributors 2020, July 03)

We humans always have been driven by a thirst for knowledge. There are some milestones in history, at least seen from today’s rational view of the world.

This diagram on the previous pages shows some interconnections of milestones regarding certain attributes and subjects. From the oral transmission of information to the printing of books a lot of time passed, from here the growth of information increased rapidly until today. The Big Data age is leading to a similar information explosion with completely different dimensions.

The case studies cited show how deep storing and transferring knowledge is an everlasting necessity in human life.

Man has the desire to explore the universe and at the same time to present himself, to hand knowledge to descendants or even extra-terrestrial life, bringing knowledge to immortality. Knowledge includes communication, storing and transferring, among other issues.

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[ B ] D I GI TAL TUR N

“With different nuances, ... the second digital turn in architecture is largely about finding new ways to design, ...�

(Mario Carpo, 2017)

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Ethics Aesthetics Exploring artificial

Server Farms Library

KNOWLEDGE Generated Landscape

Typology Evaluation

natural Archive

Transfer

Transformation

Distribution

i interior exterior

Essayistic Speculation Void Reframing Parallel computing

Access

Editing

DNA Storage

subdivide Augmented Reality

KNOWLEDGE ECOLOGIES

Undogmatic prospects Partizipants Displacement

non-human

h Workbench

Elevation Big Data

Workflow

Space

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Erosion

Application: Site

Growth E Energy

locations: campus/lib b

shortest path

Resolution

DIGITISATION

Granularity

Artificial Intelligence

Theory vs. Formula human

ECOLOGIES

Digitization of books changes workflow One aspect of digitisation is the full-text digitisation of books and since all these texts are available digitally, not only humans - also machines can read and understand them. Moreover, these data are accessible independently of time and place, which calls into ques-

“In order to understand and

tion the need for space in libraries. But the demand

chronicle the emerging con-

for physical library space seems unbroken, as Graham

dition that the data centre

Matthews, British Professor of Information Management

embodies, we will push open

states “ … at the same time, there has been a great investment in university library space both before and

the pressurised doors and cross the lines of the human

since the millennium, with major projects in England

exclusion zones to trespass

…” and “...elsewhere in the world...” (Matthews 2013).

through the machine land-

The digitalization of information has radically changed

scapes that run the world.”

(Young 2019)

the way we work with data. Instead of a linear work with physical papers a complex and multi directional workflow has developed.

If the Tools change, the workbench

Big Data and AI

has to change as well.

Big Data not only stands for an awful lot of data, it

Conventional storage of large amounts of data

also stands for very clever methods of combining and

consumes large amounts of energy and space. Digital

evaluating information. And it’s more about calculat-

data is apparently dematerialized, its resource require-

ed correlations and probabilities.

ments prove the opposite. It seems likely, that capaci-

(see: Mayer-Schönberger 2013)

ties will run out.

Powerful computing technics, such as parallel computing or quantum computing are very efficient and

So the consideration to connect the archives with

powerful in comparison to today´s classic (serial)com-

environment is charming. By now, architectural typol-

puting. Although quantum computers are not yet able

ogies are similar (grid, raster-based), baroque library,

to perform all the calculations that classical computers

modern archive or server room: their design principles

can do, it is assumed that quantum computers can

didn´t change in the same range as the digital shift

solve many problems faster. (see: Wikipedia contribu-

would demand. In addition, server rooms are basically

tors 2020 August 02).

pure machines, as Liam Young notes:

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This leads directly to an improvement in artificial intelligence: “Today, the learning curve for machines appears to be sharply steeper than it is for human beings, which is fundamentally changing the relationship between humans and machines.” (Ramge 2019) Through growing capacities of computed calculations storing of data could become obsolete. Information could be calculated in real time, based on formulas, algorithms.

Data storing remains a subject of development.

In 2018 Word´s total data adds up to 33 Zetabyte. And will rise to 175 zetabyte in 2025. (see: Reisel et al. 2018) “If you could download the entire 2025 Global Datasphere at an average of 25 Mb/s, ... , then it would take one person 1.8 billion years to do it, ...” (Reisel et al. 2018) It is likely that capacity will eventually run out given the current production of data. Growing amounts of data require improved methods of storage.

How does Knowledge differ from Data?

In his short story “The Library of Babel”, first published in 1941, the Argentinean writer and librarian Jorge Luis Borges tells an atmospheric story about the inhabitants of a library of infinite dimensions. The library consists of an infinite number of hexagonal rooms of always the same size, which in turn are always equipped with the same number of books. The content of the books consists of all possible combinations of 25 letters or punctuation marks. This means that in the infinitely large number of books, among all the seemingly meaningless combinations, there are also all conceivable literarily meaningful combinations in the volumes.

“All - the detailed history of the future, the autobiographies of the archangels, the faithful catalog of the Library, thousands and thousands of false catalogs, the proof of the falsity of those false catalogs, ...” fig 04: Infinite Monkey Theorem Author: New York Zoological Society

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(Borges 1998)

Si g n i f i c a n t f a c t o r s However, it is very difficult - and the inhabitants of this library have been trying for generations - to find the intelligible ones in the infinite number of books.

The inhabitants are aware that they are sitting on a great treasure, which is fatally not easily accessible to them. Even the catalogues, in which the locations of the meaningful works would be listed, are tragically not to be found. (see: Borges 1998) What could be more Today the use of artificial intelligence could probably

obvious than to conceive

help us out of this mess if we were the inhabitants of

of Knowledge Ecology

such a library. In fact, we no longer need to imagine

as a landscape, in the

such a library today, consisting of printed books. Basi-

open air?

cally, all we need is an incredibly powerful computer that simply tries out all the letter combinations and, with the help of programmed intelligence, stores the meaningful combinations.

Infinite monkey theorem Not quite dissimilar is the idea based on the infinite monkey theorem: If a monkey had the opportunity and an infinite amount of time to randomly type letters

We are talking about huge amounts of data.

on a typewriter, after an infinite amount of time he

Conventional storing (magnetic, oprical) consumes

would have produced all meaningful and meaning-

huge amounts of energy and space.

less texts, even “translated� in all languages. The task

Digital information can be encoded either as a se-

of separating the useful from the meaningless would

quence of 0 and 1 or as a sequence of letters.

also be challenging here. For this, one would probably

In laboratories, digital data can now be stored directly

need an infinitely long period of time again. This the-

in the molecules of the DNA after it has been encoded

orem is based on the Borel-Cantelli-Lemma, which is

using the 4 letters of the nucleobases of DNA.

part of the mathematics of probability. (see: Wikipedia

This also works in the molecules of living organs.

contributors 2020, July 10)

An existing DNA strand is taken from an organsim, copied several times, recoded and inserted into a DNA

But these considerations seem to be particularly

strand again. Data can continiously be read wirh DNA

fascinating today, in a time when we are learning to

Sequencers. The data-structure never changes.

handle very large amounts of data. Will it perhaps one

If DNA is damaged, error correcting codes are im-

day be possible, for example, to anticipate important

plemented to restore data. This happens in a DNA

research results by simply artificially producing scien-

sequencer, mainly automatically. (see: Ceze 2019)

tific articles in which they are to be published without

The sequencer is virtually a black box.

having done any research at all?

In this proposal, it is a pure machine.

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D NA data stora g e

Exterior DNA storage in transformed landscape as a distributed system of Knowledge Ecologies

A (Adenine) C (Cytosine) T (Thymine) G (Guanine)

01 11 10 00

A C T G Sequencer //encoding

interior

(source information see: Ceze et al. 2019)

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storage density low

storage robustness

high

energy input

in vitro storage

in vivo storage (living cells)

extremely high

exterior

very robust very low

01 11 10 00 Sequencer \\decoding

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[C] E S S A YIS TIC KN O W L ED G E EC O L O G Y

„At the moment when the electronics revolution seems about to melt all that is solid - to eliminate all necessity for concentration and physical embodiment it seems absurd to imagine the ultimate library.“ (R.Koolhaas, Strategy of the Void)

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This leads to the essayistic speculation of a generic Knowledge Ecology, based on elements of these changes. A catalogue of generated landscapes which are transformed by massing tools, displacement, distortion is formed. Iteratively and due to the needs of the system.

Starting from an appropriate geological field, 1000 x 1000 units, in convenient atmospheric conditions, the system creates its own terrain, which is subsequently exposed to natural processes like aging and erosion.

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Morphogenesis

thermal and hydro erosion embankment vs. excavation geolocical transformations dry / humid hot / cool calm / windy wind erosion biogenic sediment accumulation

eastern wet side high wind forces heavy erosion

atmospheric and geolocical forces according to prevalent conditions

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fn:20191105_cat_0E geo 0E erode 1: hydro thermal freeze@20 erode 2: hydro + thermal water flow + TpVw

fn20191104_cat_0Aa geo 0Aa erode 1: hydro+ freeze@40 erode 2: hydro + thermal TpVw

fn 20191104_cat_0A geo 0A erode 1: hydro freeze@20 erode 2: hydro ++ thermalHF distortion: swirl TpVw

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Catalogue

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speculation about condition below the ground level through further erosion simulation

surface investigation through granularity

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swamp-land network: foamy sponge rather than grid

disintegration: component parts of equal size

Eva l u a t i o n s

The landscape is being evaluated with tools like further scaling or reframing by interpretation.

Details below the surface sediment can be interpreted as substrate containing repositories, as environments in which organisms live. Branching structures provide nutritive substances.

Cell bodies of neurons are insinuated.

base terrain

redrawn segments 01

r e dr a wn s e g me n t s 0 2

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Iterative steps of massing and transforming of parts feed new specualtions about surface and conditions

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Sp e c u l a t i ve m i c r o s c a l e d r a w i n g s

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Distribution of elements by

Ridge distribution

Plains distribution

steep ridge:

higher plains

certain characteristics

distribution by slope

Processing units are spread in

distinctive and

minerals or metals, networks

protective feature

and height

neurologic systems, energy

remote area

[slope: 0.00°-30.00°]

is generated and extracted

distribution by heigth

from plants, cooling comes

and characteristics

from atmospheric forces

[height 12.00-120.00m a.s.l.]

(wind, ocean, water) and

[slope 30.00°-90.00°]

an infinite amount of data is

assembling, gathering

stored right in the protein of

computing elements

[height: 0.00-4.00m a.s.l.]

Boxes as an archetypical form

coast direction

and direction

for shelter, storing, accumu-

distribution by heigth

lating

and direction:

e.g. CPUs , capacitors (low

[height: 20.00-104.00m a.s.l.]

temp.)

[southeast winds]

collocation: swarming, gather-

energy transformation

ing, assembling

and accumulation

are assembled by biological

molecules.

and cooling elements

[height: 4.00-12.00m a.s.l.] plant, grow: dna-storing elements lower plains distribution by slope [slope: 0.00°-8.00°] western: farming plots neurologic network energy generation growing veins 0-12m a.s.l. slope 00°-08° bio-neurologic networks cover all over the plains farming plots lower plains substrate atmosphere (protection by ridge) humidity storage plots

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Eva l u a t i o n a n d Di s t r i b u t i o n

mountains

island tower

lower plains

thermal and hydro erosion embankment vs. excavation

western dry side

steep ridge

fall off

geolocical transformations dry / humid hot / cool calm / windy wind erosion biogenic sediment accumulation

eastern wet side high wind forces heavy erosion

lower plains

higher plains

open waters

atmospheric and geolocical forces according to prevalent conditions

plateau

open fields

mountains

mountains

island tower

furrow

western dry side

steep ridge distinctive feature

western dry side

open waters

open waters plateau

plateau

open fields

open fields

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water flow Ffelds (growing veins) serve as supply network for farming plots, shortest path connections

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Se c o n d a r y n e t w o r k

shortest path connections form a communicative neural system integrating machine and environment

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The Unconcious of the machine Concealed in the initial source code of the system there is a gate implemented - possibly by humans that represents the unconscious of the bio-computer.

The system doesn´t know about it.

The unconscious forces the system to generate elements beyond its own agenda. It is a void core (poetic, irrational, dream-driven), a MacGuffin*- like singular object, that provides shadow from the systems intelligence. *Plot Device, an object, that pushes or disturbs the action. (see: Wikipedia contributors 2020, July 11 )

Maybe it is a reminiscence to a predigital era.

This is an enigmatic space in-between.

The human trace within the system´s autonomous ecology. Maybe the void core is the system´s initial intention, the reason why the system was developed.

The system reinterprets its results, selects parts and implements changes again .

No matter how much data or layers of data are being stored or processed, there is likely the need to pick parts of it in certain moments and arrange them in sceneries, again and again, as the parameters change. This conception sees “world”, digital or analogue, as always to be constructed. Moment stands as well for the state of charge in a computational system. (e.g. infinite number of states in quantum computing) Specific states are to be measured.

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autonomous growing (rather than assembling) surface

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Gr a n u l a r i t y

radically augmenting the number of polygon faces

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stages of growth 1

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Gr o w t h

stages of growth 2-3

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[ D ] A ppl i c a t ion on S it e

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Woodland area and Hydrology

larger area - Tirol region, source map: Land Tirol, tiris

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fig05: Innsbruck, southbound Author: Stephan Sigl information source: Stadtmagistrat Innsbruck

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Si t e a r e a

The city of Innbruck is both an economic centre and

Today, the Alpine region is often portrayed as a largely

an important traffic junction due to its geographical

unspoilt, natural recreational area. In fact, “the Alps

position. Situated in a wide alpine valley crossed by

are neither a natural nor a near-natural landscape,

the river Inn, it is surrounded by very high mountains

but have been profoundly ecologically changed and

and mountain ranges. The city is strongly character-

reshaped by man in the course of its history.”(Baetzing

ized by a dynamic altitude development.

2018 99). Since then, “natural and cultural landscapes have interlocked on a small scale” (Bätzing 2018 10).

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fig06: Innsbruck, cultural and natural landscapes interlocked Author: Stephan Sigl information source: Tirol Atlas, Dep. for Geography, University Innsbruck online: http://tirolatlas.uibk.ac.at [retrieved: 2020, September 04] 51

x

y

Campus Innrain 79682,88 236647,26 Campus CCB 79355,39 236222,78 Campus Sport 77452,88 235843,68 Campus Technik 76399,3 236653,31 Campus Universitätsstraße

80651,64

237331,76

Campus Theologie 80555,86 237198,41 Archiv für Baukunst

80708,01

236346,84

Claudiana 80163,67 237184,85 Fakultät für Bildungswissenschaften

80221,97

236536,81

Forschungsinstitut 80699,81 237956,57 Forschungsschwerpunkt

82604,8 237005,27

Institut für Botanik

79124,52

237100,72

Institut für Erziehungswissenschaften

80620,29

236111,77

Institut für LehrerInnenbildung

77288,38

235957,59

Haus der Musik

80450,24

237229,79

Institut für Psychosoziale Intervention

80699,81

237956,57

Michael-Popp-Forschungsinstitut

80213,24

236117,18

Zentrum für Alte Kulturen

82604,8

237005,27

ULB Aussenmagazin 78948,91 236708,74

coordinates of educational sites and libraries source: Stadtmagistrat Innsbruck

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Academic campus and libraries

closer area Innsbruck, source map: Land Tirol, tiris. educational sites and libraries source: Stadtmagistrat Innsbruck. Projection: MGI Austria GK West (M28)

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D ef ining dis tricts thr oug h shor test p a th a l g o r i t h m

shortest path algorithm starting from libraries and iniversity locations. Stopped by forest borders. shortest path networks not inweaving [any to each] [00] base terrain DEM/DSM model from ogov 5m HF Resample [resolution scale 2, filter scale 1.5] convert HF [triangles, density 1] [01_02_shpth] shortest path analysis convert HF (mesh) [density 0.65; alternating triangles] attribute wrangle-remove points [treshold 0.834; seed 0.478] start group [200618_06_all_edu_locations.dxf] end group: mesh findshortestpath [any to each]

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university (campus) and libraries as knowledge substrate

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Sh o r t e s t p a t h f r o m L i b - Ed u s i t e s

as starting points library and educational site coordinates have been determined as nuclei for a growing algorithm

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Applying eleva ti on ma ni p ul a ti on

manipulating elevation of areas generated by shortest

maximum distance 625, Value 1;

path; borders - hills and valleys emerge

blur method: blur; blur radius: 15]

200623_T7_ctA_01_shpth_areas

HF noise

HF mask by object (shortest path)

HF resample

[input wire from 02_shpth(smooth); wire radius 0.9]

[resolution scale: 1.5, filter: gaussian, filter scale 1.5]

hills and valleys emerge those are transformed by massing tools

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a new topology is generated by manipulating the elevation of areas generated by shortest path end branches

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D isplacement

200623_T7_ctA_02_shpth_areas_distortion HF displacement [control blur radius: 0; substeps: 1 rotate displacement: -90 displace by: vector control layer HF resample [resolution scale: 1.7 filter: gaussian, filter scale 1.5] convert HF [triangles, density 1]

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in a next step these districts are transformed by displacement passes

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E r os ion simulati on

erosion E_01_T7_ctA 200623_T7_ctA_04_E01_f20_d12 freeze 20/80 main [global erosion rate: 1.63] [hydro| erodibility: 1; erosion rate: 0.4; bank angle: 70; spread iterations 48] [thermal | erodibility: 0; erosion rate: 0; bank angle: 35]

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erosion simulations generate more details and a higher resolution

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catalogue of ter r a i n tr a nsfor ma ti ons

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cat A 5/5 dist. v1

cat B 7/7dist. v1

evaluation catalogue

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selection f or f u r ther i nv esti g a ti on

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c a t B 02 d i s t . v3

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In a next step of detailing / zooming in, the source geometry is subdivided in several passes which leads to fragmentation, to a surface growth like - network.

Fragmentation is executed by varying scaling of the deviations per division, adjusting amplitude of deviation and selecting the direction of fractalization (either direction of source normal or divergent).

Tests with duplication, triplication and quadruplication of faces were made.

Depending on source mesh (from 2.1 to 8.4 million faces) new polygon-meshes with 17 to 35 million faces - as the largest possible number of faces that could have been processed - were generated.

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Gr a n u l a r i t y , F r a g m e n t a t i o n , Su r f a c e Gr o w t h

fragmentation process

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subdivision of the source surface assisted by particles and wind force upmost particles moved by wind force form a basis of vertices for a new porous structured surface

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Windforce - growth from particles

which is treated with the granularity algorithm

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structural grid of fragmented surface

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Implementation of particles

testing of Implementing particles as elements

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cat B frct 7000m

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Gr a n u l a r i t y t e s t i n g 7k / 2. 5k

cat B frct 2500m

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cat B02 frct05 5000m

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Gr a n u l a r i t y t e s t i n g 5k / 3k

cat B02 frct05 3000m

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Design conception for neural networks and their interface and storage structures on a landscape scale. (1000m)

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Di s t r i b u t i o n o f e l e m e n t s

Proposal for the purpose of reframing traditional approaches of future knowledge environments.

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Archetypical white box devices (operating as black-boxes, pure machines) execute steam-sterilisation-like, well controlled saving and erasing commands.

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Di s t r i b u t i o n a n d Sc a l e

From machinic point of view back to a human scale: larger elements sized 30m-40m and smaller sized 8m-12m

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The redesigned landscape stands for an aesthetic reflection of a virtual landscape of the mind, which should make knowledge spontaneously and openly accessible, perhaps with the help of augmented reality devices.

The connection between man and machine is conceived here by referencing from the address coordinates of a city to the virtual level.

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I n vw e a vi n g h u m a n - n o n - h u m a n

Addresses, in turn, are each a reference to a space where humans can be found. As coordinates, they outlast any structural change, so to speak. A flow of information packets is simulated by a particle flow addressing the network devices and the real-world address coordinates of the city.

coordinates of addresses source data: Land Tirol, tiris. Projection: MGI Austria GK West (M28)

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90

91

92

93

subdividing

transforming fr 030.50

fr 060.20

elevation

res 2.5 filter 1.5

manipulation

any each

interaction

start end

edu/lib locations

fr 010.00

site characteristic t c city ibk lat.47°16'11" long 11°23'39"

94

fr 060.40

distribution o

h s

connectivity

fr 020.10

shortest path alg.

x x

subdividing

fr 020.30

shortest path

fragmentation

fr 050.70

hydroerosion simulation

fr 040.80

displacement a distort b by layer

e. hy

referencing fr 080.40

x2 x3

address referencing virtual/real

fr 070.30

height slope

distribution

sc 30-40 sc 08-10

scale experiments

fr 060.90

.r. 1.63 ydr 0.4

granularity

wind force

fragmentation

virtual real

fr 060.70 res 1.3v filter -10

implementation

wind force

particles

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[ E ] S OUR CES

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Bibliography

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Table of figures fig01 Le Répertoire Bibliographique Universel vers 1900 Author: Collections de la Fédération Wallonie-Bruxelles, en dépôt au Mundaneum (Mons, Belgique), Patrick Tombelle https://commons.wikimedia.org/wiki/File:Le_R%C3%A9pertoire_Bibliographique_Universel_vers_1900.jpg [retrieved 2020-08-07] fig02 Voyager Golden Record Author: NASA https://commons.wikimedia.org/wiki/File:The_Sounds_of_Earth_Record_Cover_-_GPN-2000-001978.jpg [retrieved 2020-08-07] fig03 Svalbard Global Seed Vault, Spitsbergen Author: Svalbard Global Seed Vault, Matthias Heyde https://www.flickr.com/photos/landbruks-_og_matdepartementet/15412648967/in/album-72157623004641656 [retrieved 2020-0807] fig04 Chimpanzee seated at typewriter Author: New York Zoological Society https://en.wikipedia.org/wiki/File:Chimpanzee_seated_at_typewriter.jpg [retrieved 2020-08-07] fig05 Innsbruck, southbound Author: Stephan Sigl fig06 Innsbruck, cultural and natural landscapes interlocked Author: Stephan Sigl

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[ F ] APPENDI X

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appendix 01: 3d print prototypes

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appendix 02: T7_B02a_is_20200807_T7_A0a_dgm_dem_5_1000_nodes

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appendix 03: refining resolution

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appendix 04: approaching cartographic tools

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Eidesstattliche Erklärung Ich erkläre hiermit an Eides statt durch meine eigenhändige Unterschrift, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Alle Stellen, die wörtlich oder inhaltlich den angegebenen Quellen entnommen wurden, sind als solche kenntlich gemacht.

Die vorliegende Arbeit wurde bisher in gleicher oder ähnlicher Form noch nicht als Magister/Master-/Diplomarbeit/Dissertation eingereicht.

Datum

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Unterschrift

dedicated to Mother and Father †thank you for your reliable support many thanks to my friends and colleagues and all who have supported me particularly Julia and Mike Wolfgang Teresa, Michela special thanks to Claudia Pasquero, Marco Poletto Maria Kuptsova and Filippo Nassetti for your inspiring, constructive and passionate supervision thanks to you this thesis became an exciting beginning

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What could be more obvious than to conceive of Knowledge Ecology as a landscape, in the open air?

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