Fenland, England
21st century: a machine age. Inspired by Trevor Paglen’s invisible images, the project focuses on the current extraordinary time-frame where the relationship between the sentience and the autonomous become ambiguous. It starts in a machine era when society is marching towards the fourth industrial revolution. “The City of Tomorrow” written by Carlo Ratti and Matthew Claudel points out the core question of this project - cities had been evolved for thousand years, but in a linear form. Nonetheless, the presence of digital layers will inevitably bring radical changes to our cities. Ratti and Claudel call it futurecraft, a new approach to envisioning cities. Everyone on planet Earth, professionals or the public, are contributing and in control of shaping our future; with machine’s assistance. Yet, in what form of human-machine collaboration do we need to achieve our dream? With that, the project questions the future of the built environment with the omnipresence of autonomous agents such as self-driving cars and home robots. It is a 50-year operation origin from the Architecture Experiment Lab, which carries out deep data analysis and experiments on the current built environment. New forms of the built environment will be generated under the collaboration of human and machine. However, it also questions the level of consciousness involved in machine techniques; and how or when shall human designers be involved in the process. Setting at the Fenland, it will be the ideal site for the experimental events due to its abundance of natural energy resources, vastness and nakedness in technology. At the hinterland, the operation provides a real-life safe site that allows the autonomous agents to learn and analyse the surrounding. Lastly, the site will become a safe place for the inhabitation of both sentience and autonomous agents over a three phases blueprint. However, the operation promises that humans will continue to lead planet Earth with the aid of artificial intelligence and machines.
“ We [humans] no longer look at images-images look at us. They [machine] no longer simply represent things, but actively intervene in everyday life... We need to unlearn how to see like humans. ” Trevor Paglen
“ Cities are, by definition, plural, public, and productive. They are created by society itself, and they function as culture’s petri dish for progress... Designers produce mutations, some of which will grow, evolve, and develop into tangible artifacts that cause global change. ”
Ratti and Claudel, 2016
HUMAN-MACHINE COLLABORATION | GAUGANS INPUT
The images are machine readable segmentation map. Data is assigned to each colour to convert the colour into pleasant image for humans. Large amount of training data is need to created a desired output.
HUMAN-MACHINE COLLABORATION | GAUGANS OUTPUT
The output images then post-processed by human to create a form of visual representation that is readable by human. Through the process, human shall understand more about the new visual language created by machines.
MASS SURVEILLANCE | PRIVACY
Today, the society become a modern Panopticon with the omnipresent machine vision. Machine vision is widely adopted across different fields which invisibly bring fears among humans. Machine eye becomes the enemy of humans due to the lack of understanding of this piece of new technology.
A NEW BUILT ENVIRONMENT
The presence of machine vision is non-negligible as if it is a future “smart dust”. A new built environment is needed to provide a safe place for humans and machines inhabitation.
Factors and considerations for a future of humans and machines
13
20 hours of practice 45 hours
serious car crashes.
2x
car crashes per million miles driven on public roads compare to human driving, but tend to be less severe.
driving lessons + past experience on a car
275 miles
(25 years) of training + as many useful footage
Efficiency of regulation
Industry investment
EV charging station
Population living on site
Government funds
Innovation
4G coverage
Digital skills
Data-sharing environment
Cybersecurity
Quality of roads
Acceptance
Future orientation
Clouds, IoT and AI
Readiness
Society technology use
DEFINE THE PROBLEMS AUTONOMOUS | ACCIDENT
There needs to be a public site for the autonomous agents to be tested on; a site that encompassed real lives. The autonomous agents are designed to improve humans’ daily life. But it does not seem so with the reports on accidents caused by autonomous agents in recent years. While the technology is advancing and preparing machines to serve mankind, is the built environment ready to welcome the new co-habitant?
DEFINE THE PROBLEMS UNKNOWN | FEARS | BALANCE
Humans shall not be afraid. Correct channel is needed to spread the knowledge on machines and artificial intelligence to the general public. Following the increase of machine vision among society, the general public lost confidence in the technology due to the lack of understanding. Meanwhile, most of the technologies are used as a surveillance tool. Humans do not find any benefits and starting to hide away from it. Humans and machines need to develop a balanced relationship to march towards a better future.
SELF DRIVING CAR | PROPOSED LOCATIONS FOR CURRENT OR FUTURE TESTING IN PUBLIC, CONTROLLED, AND VIRTUAL ENVIRONMENTS
K-CITY, KOREA 79-ACRE
MCITY, MICHIGAN 32-ACRE
SELF DRIVING CAR | CONTROLLED ENVIRONMENT 1 Handling & stability track 2 Off-road test track 3 Testing facilities 4 Narrow road 5 Rural road 6 Motorway 7 Urban area 8 Pedestrian-centric area 9 Autonomous parking facilities 10 Tunnel 11 Roundabout 12 Asphalt/ concrete road 13 Unpaved road 14 Road safety features test ground Steering pad Universal road Low friction track 15 Tree-line street 16 Road construction noise 17 Open test area
18 Metal bridge deck 19 Building 2D facade 20 Ramp metering 21 Software controlled traffic 22 Bike lanes 23 Open test area 24 Roundabout 25 Blind curve 26 Intersection 27 Brick paver road 28 Tunnel
SELF DRIVING CAR | AFFECTING FACTORS
project note
project note
FRI 03/01/2021
TUES 14/01/2021
Research: Machine vision across different dimensions.
Research: Image recogni�on - how machine perceive and interpret images?
Scenario Scenario 1: Alteration 1: Alteration of colourof colour project note
1D: A space that builds up of lines in which each object possesses a unique code as its iden�ty.
FRI 03/01/2021
project project note note TUES 14/01/2021 TUES 14/01/2021 Research: Research: Image recogni�on - how Image recogni�on how machine perceive -and machine perceive interpret images? and interpret images?
project note
Research: Machine vision across different dimensions.
TUES 14/01/2021 Research: Image recogni�on - how machine perceive and interpret images?
2D: A space that appears flat, conveyed via pa�erns or electronic screens.
1D: A space that builds up of lines in which each object possesses a unique code as its iden�ty.
3D: A space with depth and distance.
1D
2D
process4D:through AI [Convolution neural network (CNN)] Straw processA space through AI Straw [Convolution neural network (CNN)] with the presence of
Wig
Wig
Zebra
�me and speed. It is a collabora�on between human and machine vision.
Zebra
Scenarios when machine vision may not obtain accurate result: Scenario 1: Alteration of colour
1D
2D 5D A space with a new visual culture. The machine starts to think and perceive.
4D: A space with the presence of �me and speed. It is a collabora�on between human and machine vision.
Image processing process through AI [Convolution neural network (CNN)]
IMAGE PROCESSING PROCESS THROUGH AI [CONVOLUTION NEURAL NETWORK (CNN)]
3D: A space with depth and distance.
2D: Image processing A spaceImage that appears flat, processing conveyed via pa�erns or electronic screens.
Image processing process through AI [Convolution neural network (CNN)]
Scenarios when machine vision may not obtain accurate result: Straw
Wig
Zebra
Road
Agriculture land
Carpet
Scenario 1: Alteration of colour
5D A space with a new visual culture. The machine starts to think and perceive. Straw
3D
Road
4D
Road
Wig
AgricultureAgriculture land land Carpet Road
Zebra
Carpet
Scenario 2: Implementing noise
Agriculture land
Carpet
Scenarios when machine vision may not obtain accurate result: 3D 4D SCENARIOS ACCURATE RESULT: Scenarios when machine vision may not obtain accurate result: WHEN MACHINE VISION MAY NOT OBTAIN Scenario 2: Implementing noise
Scenario Scenario 2: Implementing 2: Implementing noise noise
Scenario 1: Alteration of colour Scenario 1: Alteration of colour Scenario 1: Alteration of colour
Scenario 2: Implementing noise
Scenario 3: Object displacement
Research Outer-space Test Record 53% Confidence
Wig Wig Wig
Zebra Zebra Zebra
by: Research Test Record
Road Road Road
by:
Agriculture land Agriculture land Agriculture land 5D
Michelle & Eve
Scenario 2: Implementing noise Scenario 2: Implementing noise
Carpet Carpet Carpet
by: Research Test Record
Outer-space by: 53% Confidence
Michelle & Eve Outer-spaceOuter-space 7 53% Confidence 53% 7 Confidence
Michelle & Eve 7
Scenario Scenario 3: Object3:displacement Object displacement
Research Test Record
Research Research Research TestTest Test Record Record Record
Glass 98% Confidence Glass 98% Confidence
Scenario 3: Object displacement Scenario 3: Object displacement
by: by: Michelle & Eve 7 7
7 7
Michelle & Eve
8 8
8 8
8
7
8
Glass 98% Confidence
Glass Glass 98% Confidence 98% Confidence
AI | MACHINE LEARNING | IMAGE RECOGNITION
Outer-space 53% Confidence Outer-space 53% Confidence
Glass 98% Confidence
Scenario 3: Object displacement 5D
Michelle & Eve
Glass 98% Confidence
Scenario 3: Object displacement
Research Test Record
Straw Straw Straw
Outer-space 53% Confidence
8
8
project note
project note
FRI 17/01/2021 Test: CNN through Google Deep Dream Generator Google deep dream generator uses convolu�on neural network to process an image and enhance features. Google deep dream trains on ImageNet, which the training model is dominant by images of cat and dog. Thus, giving the dream-like hallucinogenic appearance result.
MON 20/01/2021 Test
Input
Output Neural network layer
01: Basic
5
10
15
20
Increasing in neural network layer causing the increase in detail features.
02: Noise alteration
Noise level 5%
50%
90%
Research: Image genera�on through machine learning [Genera�ve adversarial networks (GANs)]
GANs architecture
GANS is a system that pits two AI systems (generator and discriminator) against each other to improve the quality of their results.GANs create new contents that resemble the training data. It is a type of machine unsupervise learning. Reference works Mario Klingemann - Memories of Passerby
03: Pixel Value alteration
Pixel value Original
Bright spot
Black and white
04: Two inputs
Trevor Paglen - Adversarially Evolved Halluc
AI | MACHINE LEARNING | GOOGLE DEEP DREAM Google deep dream generator uses convolution neural network to process an image and enhance features. Google deep dream trains on ImageNet, which the training model is dominant by images of cat and dog. Thus, giving the dream-like hallucinogenic appearance result.
Research Test Record
Research Test Record
GANS ARCHITECTURE
WORKS DONE THROUGH GANS Mario Klingemann - Memories of Passerby
Trevor Paglen - Adversarially Evolved Hallucinations
AI | MACHINE LEARNING | GENERATIVE ADVERSARIAL NETWORKS (GANS) GANS is a system that pits two AI systems (generator and discriminator) against each other to improve the quality of their results. GANs create new contents that resemble the training data. It is a type of machine unsupervised learning.
Test image: The Aftermath of the First Smart War Adversarial Evolved Hallucination, 2017, Trevor Paglen
Material gathering: The GANS model used to produce the image composes of desertification, birth defects,burning oil fields, depleted uranium and other effects of first Gulf war.
Output:
GANS IMAGE VS HUMAN REGENERATION This test is a reverse interpretation of the image generated by machine. It compares the differences between the machine image and the image that is familiar to human eyes.
CONCEPTULISE GANS MACHINE LEARNING PROCESS
CONCEPT IMAGE
OUTPUT IMAGE
IMAGE ANALYSIS
2D input
Sky_53.9% Canal_2.6% Road_7.2% Built_0.3%
Vision projection
3D REALISATION
COLLABORATION WITH MACHINE | EXPERIMENTAL SCENE GENERATION
CONCEPT IMAGE
OUTPUT IMAGE
IMAGE ANALYSIS
2D input
Sky_53.9% Tree_20.2% Farmland_10.2% Canal_2.6% Built_0.3%
Vision projection
3D REALISATION
COLLABORATION WITH MACHINE | EXPERIMENTAL SCENE GENERATION
15 megawatt
data centre used up to
360,000 gallons
of water a day to cool down the server floor consume
contribute to
global electricity
carbon emissions
3%
2%
ENERGY FORECAST
DATA | ENERGY
6 layers of physical protections 01- property boundary 02- security perimeter 03- building access 04- security operation room 05- data center floor 06- disk destroy room
MACHINE LEARNING IN BUILDING MANAGEMENT SYSTEM
1940
1990
2000
2010
Exploration of AI
AI as a service tool
AI as an interacting tool
AI as an interacting and interpretation tool
1956, the term artificial intelligence was coined at Dartmouth conference. Throughout the period of exploration, AI winter happened at 1973 and 1988 which allow scientist reset the direction and aim of AI research. First ever autonomous robot-Grey Walter’s Machine Speculatrix was created. Also, first autonomous vehicle by NovLab was created at 1986. Meanwhile, Alan Turing proposed Turing test to test intelligence behaviour within machines.
Intelligence machine move towards the direction of human servicing, leading to the raising of Online services and entertainment. 1997, IBM-built machine won world chess champion Garry Kasparov. Robot also replaced human in amazon fulfillment centre. The first robot for home-Roomba invented.
Launching of Facebook at 2004 starts the rise of Online connection with the rest of the world. Follow by the starting of social media such as WhatsApp, SnapChat and WeChat. Robot starts to interact with people through speech recognition, e-payment system and popstar live concert. Facebook face recognition system and ImageNet drive AI to a new era.
AI deep learning involved in Generative adversarial network (GANs) and other machine learning framework which suggests that AI have the capability to carry out selflearning. Google Deep Mind Alpha Go beat human in the Go game. Move 37 particularly portrait the creativity within AI.
KEY QUESTION
HOW WILL AUTONOMOUS AGENTS AND AI BRING CHANGES TO THE FUTURE BUILT ENVIRONMENT AND HUMANITY?
2025-2075
Future
Click here to view film [00:08:50]
TYPE OF VISIONS
This proposal draws from two different perspectives to dive into the world of machine vision via humans eyes. Participants will be able to sense the change of view by detecting the edge of the display board. Below are the two views:
SENTIENT AGENT
Contents label with
Google Play store:
AUTONOMOUS AGENT
can be viewed thorough Artivive app which can be downloaded from:
Apple App store:
DAY 00001
CHAPTER 1 2025 | EARLY EXPLORATION: MACHINE TAKE-OVER Day 00001 Curious Dawn of the machine age, humans are now co-exist on planet Earth with the machine – the agent that views the surrounding differently. What makes sense to them do not make sense at all to humans. Actions should be taken to evolve a language that can link between both.
DAY 00029
VIDEO RECORD: First documentation and thoughts toward Fenland District.
data collection big data (inputs) image processing recreation output
Click here to view full film
DAY 00046
WHAT IS FENLAND DISTRICT?
data collection big data (inputs) image processing recreation output
DAY 00050
INITIAL SITE SURVEY AND SELECTION
data collection big data (inputs) image processing recreation output
DAY 00134
GNF001: WHAT IS F.E.N.S.?
data collection big data (inputs) image processing recreation output
DAY 00143
GNF001: ANALYSING F.E.N.S.
data collection big data (inputs) image processing recreation output
DAY 00216
GNF001: ANALYSING INPUT IMAGES.
data collection big data (inputs) image processing recreation output
DAY 00241
GNF001: RECREATION OF F.E.N.S. THROUGH GAUGANS.
data collection big data (inputs) image processing recreation output
DAY 00249
GNF001: RECREATION OF F.E.N.S. THROUGH GAUGANS.
data collection big data (inputs) image processing recreation output
DAY 00260
GNF001: GENERATING RESULT.
data collection big data (inputs) image processing recreation output
DAY 00260
GNF001: OUTPUT.
data collection big data (inputs) image processing recreation output
DAY 00260
GNF001: OUTPUT.
data collection big data (inputs) image processing recreation output
FL1: ENCODING THE SITE.
52o30’31.6”N 0O08’42.5”E Scale 1-10000 on A1
DAY 00299
FL2: DATA COLLECTION FOR DRAWING FL2.
Legend 01 City grid 02 Extruded volume (Black) 03 Subtracted volume (grey)
DAY 00311
FL2: DATA ANALYSING FOR DRAWING FL2.
DAY 00320
FL2 OUTPUT: β MASTERPLAN
Legend Building Empty volume Main building Data centre Energy zone Existing building Main road Road Scale 1-10000 on A1
DAY 00332
50-YEAR PLAN: DEFINING β.
What is β?
46.0% Sky
Nature
Farmland
Road
2.1% Road F.E.N.S. Built
Data centre (SPINE) 0.4%
Canal Tree
β
2.6% Power resources (SPINE)
19.2% Artificial Tree 29.7% Edifice
Testbed 1 (SPADES) Testbed 2 (HEART) Testbed 3 (DIAMONDS)
DAY 00333
50-YEAR PLAN: MASTERPLAN STRATEGY.
DAY 00334
50-YEAR PLAN: RELATIONSHIP DIAGRAM.
SECTION
RELATIONSHIP
DAY 00335
50-YEAR PLAN: TIMELINE AND COLOUR.
DAY 00336
50-YEAR PLAN: SPATIAL ORGANIZATION CHART AND CODE SYSTEM.
Infrastructure Home Infrastructure
DAY 00340
50-YEAR PLAN: DETAILING MASTERPLAN - TESTBED 1
OUTPUT 2 Overlay output 1 as noise to the initial masterplan to generate detail components.
INPUT CORPUS [Text to image generation]
image 1 image image image image image image image image
1: 2: 3: 4: 5: 6: 7: 8:
image 2
image 3
image 4
image 5
floors road plan view error on the building plan building section wall error in building plan floor corridor
OUTPUT 1 [Machine generated testbed 1]
image 6
image 7
image 8
DAY 00355
50-YEAR PLAN: DETAILING MASTERPLAN - TESTBED 2
OUTPUT 2 Overlay output 1 as noise to the initial masterplan to generate detail components.
INPUT CORPUS [Text to image generation]
image 1 image image image image image image image
1: 2: 3: 4: 5: 6: 7:
image 2 glitches glitches error on building glitches window windows
image 3
image 4
image 5
within the building plan on a landscape within the building on the Manhattan grid the triangular building on the Manhattan grid within the building plan
OUTPUT 1 [Machine generated testbed 2]
image 6
image 7
DAY 00363
50-YEAR PLAN: DETAILING MASTERPLAN - TESTBED 3
OUTPUT 2 Overlay output 1 as noise to the initial masterplan to generate detail components.
INPUT CORPUS [Text to image generation]
image 1 image image image image image image
1: 2: 3: 4: 5: 6:
image 2
image 3
image 4
image 5
nature error on the liner building plan building wall anomaly walls stair
OUTPUT 1 [Machine generated testbed 3]
image 6
DAY 00365
DATA COLLECTION FROM THE SURROUNDING TOWN: MANEA
DAY 00375
DETAIL INTERPRETATION: Wall [D01] OUTPUT:
5mm, 300mm, 100mm, 200mm, 5mm; layers upon layers, some give a strong collision, some a gentle touch, and some hiccups in between. 10 seconds after, here comes an increase in degree Celsius, I know I am at the other end.
DAY 00390
DETAIL INTERPRETATION: Stair [D02] OUTPUT:
The way we meet is rather distressing, manoeuvre at the edge of something, almost falling, and takes a few trials to figure out the right way; the transition of depth, colour, height, width, as if a romantic encounter of things; I am lost in it.
DAY 00405
DETAIL INTERPRETATION: Floor [D03] OUTPUT:
Like a deja-vu, a similar feeling when I penetrate through the first object, but different; the further I go, I can almost feel the long lost feeling of interacting with the ground, the particles cannot wait to cuddle me, I join in the party of nature, dancing in it.
DAY 00425
DETAIL INTERPRETATION: Window [D04] OUTPUT: Reaching maximum lux level, my light sensors get distracted by the anomaly, it went out of function; the white thing almost get me blind, what I can see was the frame that holds it up, please tell me where am I.
DAY 00525
β OPERATION OUTPUT SET 1 | ARCHITECTURE EXPERIMENT LAB [1DE056]
The architecture experiment lab is the ground-zero of the β operation. It plays a fundamental role in experimenting and generating suitable edifices under different machine vision requirements and conditions. The experiment is requisite because machine vision differs significantly from humans’ perception. The lab is classifying into three sections that carry out respective tests on facade materials and architectural structure. Data collected from the autonomous device that manoeuvre around the city will be sent to the data office to carry out further data analysis and test in real-life condition. Through this, the machine will generate a set of architectural outputs that suit the machine environment. The output will also turn into physical and virtual 3D model, acting as a study model for later research. Architectures across all three testbeds will be the outcomes of this lab.
DAY 00525
525 days
DAY 00574
ARCHITECTURE EXPERIMENT LAB
DAY 00678
DAY 00891
EDIFICE TESTING AND GENERATION PROCESSES
1_ DATA INPUT
2_FACADE AND MATERIAL EXPERIMENT
3_STRUCTURAL EXPERIMENT
4_3D MODEL PRINTING
5_1:1 MODEL ASSEMBLY, RESEARCH AND REAL-LIFE TESTING
DAY 01235
GENERATING A SPACE
1DE056
Data DATA input INPUT set SET 11
MACHINE VISION OUTPUT:
[Machine vision] 01_Point cloud :5.0% 02_Lines :10.0% 03_Anomaly :75.0% 04_Normal :10.0% [Architecture elements] 01_wall :8.3% 02_wall II :39.5% 03_structure :32.3% 04_detail structure :5.3% 05_floor :8.5% 06_opening :6.1% 07_circulation :0.0% DATA INPUT SET 2 [Segmentation map] Wall Wall II Structure Detail structure Floor Opening
DATA INPUT SET 3 [plan and location] =2;010203040506;3
DATA INPUT SET 4 [Scene set up]
HUMAN VISION
ARCHITECTURE EXPERIMENT LAB Date: 28/09/2028 [DE0456] 2;010203040506;3 Shot distance: approx. 1m
DAY 01286
β OPERATION OUTPUT SET 2 RESEARCH HUB [1D0456] | HUMAN ACTIVITY ZONE [1D06] | MACHINE WORKSHOP [1DE06]
RESEARCH HUB
HUMAN ACTIVITY ZONE
MACHINE WORKSHOP
The research hub is the gathering point for global research in artificial intelligence and machine learning. It houses researchers (computer scientist, architect, urban planner, sociologist, ecologist and other professions) from different backgrounds to study the existing problem between machine and human. Upon entrance, the researchers will direct to the respective lift to travel to their research base. Open space is provided for group discussion and research. It is a place for collective brainstorming on the future built environment.
The human activity centre is created to introduce the machine back to the humans. It is a place to spread the knowledge of machines and reduce the fear of human towards the unknown agent. This is a crucial step to prepare humans for a future machine age.
The machine workshop is a space for handson experiments with the products created from Architecture Research Lab and Research Hub. It also provides space to accommodate self-driving car repair and upgrade facilities.
DAY 01342
1342 days
DAY 01825
RESEARCH HUB
DAY 01843
DAY 01867
GENERATING A SPACE DATA INPUT SET 1 [Machine vision] 01_Point cloud :5.0% 02_Lines :10.0% 03_Anomaly :75.0% 04_Normal :10.0% [Architecture elements] 01_wall :30.3% 02_wall II :18.7% 03_structure :8.9% 04_detail structure :0.8% 05_floor :6.2% 06_opening :21.1% 07_circulation :14.0% DATA INPUT SET 2 [Segmentation map] Wall Wall II Structure Detail structure Floor Opening Circulation
DATA INPUT SET 3 [plan and location] =2;0506;3
DATA INPUT SET 4 [Scene set up]
1D0456 MACHINE VISION OUTPUT:
HUMAN VISION
RESEARCH HUB | ENTRANCE Date: 31/01/2030 [DE0456] 2;0506;3 Shot distance: approx. 3m
DAY 01923
1923 days
DAY 02640
HUMAN ACTIVITY ZONE
DAY 02653
DAY 02674
GENERATING A SPACE DATA INPUT SET 1 [Machine vision] 01_Point cloud :5.0% 02_Lines :10.0% 03_Anomaly :75.0% 04_Normal :10.0% [Architecture elements] 01_wall :14.7% 02_wall II :4.9% 03_structure :17.7% 04_detail structure :33.7% 05_floor :9.6% 06_opening :19.4% 07_circulation :0.0% DATA INPUT SET 2 [Segmentation map] Wall Wall II Structure Detail structure Floor Opening
DATA INPUT SET 3 [plan and location] =0;040506;2
DATA INPUT SET 4 [Scene set up]
1D06 MACHINE VISION OUTPUT:
HUMAN VISION
HUMAN ACTIVITY ZONE | INTERACTION ZONE Date: 06/12/2032 [DE0456] 0;040506;2 Shot distance: approx. 3m
DAY 02920
2920 days
DAY 03015
MACHINE WORKSHOP
DAY 03276
DAY 03297
GENERATING A SPACE DATA INPUT SET 1 [Machine vision] 01_Point cloud :5.0% 02_Lines :10.0% 03_Anomaly :75.0% 04_Normal :10.0% [Architecture elements] 01_wall :61.3% 02_wall II :10.0% 03_structure :11.4% 04_detail structure :11.2% 05_floor :5.9% 06_opening :0.0% 07_circulation :0.0% DATA INPUT SET 2 [Segmentation map]
Wall Wall II Structure Detail structure Floor
DATA INPUT SET 3 [plan and location] =2;030405;4
DATA INPUT SET 4 [Scene set up]
1DE06 MACHINE VISION OUTPUT:
HUMAN VISION
MACHINE WORKSHOP | SELF-DRIVING CAR REPAIR ZONE Date: 31/10/2035 [DE0456] 2;030405;4 Shot distance: approx. 1m
DAY 03315
SUPPORTING FACILITIES: VERTICAL ACCESS
DAY 03387
GENERATING A SPACE DATA INPUT SET 1 [Machine vision] 01_Point cloud :5.0% 02_Lines :10.0% 03_Anomaly :75.0% 04_Normal :10.0% [Architecture elements] 01_wall :1.0% 02_wall II :0.5% 03_structure :20.2% 04_detail structure :0.8% 05_floor :0.5% 06_opening :51.9% 07_circulation :25.1% DATA INPUT SET 2 [Segmentation map] Wall Wall II Structure Detail structure Floor Opening Circulation
DATA INPUT SET 3 [plan and location] =2;040506;4
DATA INPUT SET 4 [Scene set up]
003 MACHINE VISION OUTPUT:
HUMAN VISION
TESTBED 1 | VERTICAL ACCESS Date: 12/02/2034 [DE0456] 2;040506;4 Shot distance: approx. 10m
DAY 03413
DAY 03612
TESTBED 1 MASTERPLAN STRATEGY
DAY 03650
CHAPTER 2 2035 | MID EXPLORATION: HUMAN & MACHINE INTERACTION Day 03650 A little lost but hopeful A new architecture system has built by the machine with the inputs of humans knowledge. Humans are fellow machine inventors but also the greatest obstacles. New chemistry is needed between the two to ensure the safety of this novel ecosystem. The site is no longer solely a working place; it is becoming a new paradise for the sentience and autonomous. It is a huge experiment ahead, but it will be a brand new way of living.
DAY 03660
β OPERATION OUTPUT SET 3 HOME [2EF067] | GARDEN [004]
HOME
GARDEN
“We will shift from living in a home to living with a home (Ratti and Claudel, 2016).”
The first step towards a self-sustainable city. This vertical garden set among the Eden, becoming part of the functional artificial landscape within nature. It provides the main source of foods, driven by human and machine system. The different atmospheric conditions ensure nation across all counties enjoy their local foods in this new city.
This Home questions the form of living space with the presence of machines and artificial intelligence. What if the bed can perceive users’ routine? What if windows can detect light level and transform by themselves to the most suitable configuration? It is designed in the simplest cubic form that is transformable into different spatial quality, customised to its occupants.
DAY 05700
5700 days
DAY 05761
HOME
DAY 05780
HOME CONCEPT
DAY 07665
7665 days
DAY 07703
GARDEN
DAY 07749
β OPERATION OUTPUT SET 4 POWER ZONE [002] | PICK-UP POINT [005]
POWER ZONE
PICK-UP POINT
Electricity is fundamental in this new ecosystem. The power zones located along the street will be the charging point for self-driving cars and other machines. It also acts as an emergency office for humans to report any machine errors.
The pick-up point is the modern bus station. It comes with wavelength releasers to interact with other machines. This intervention is to ensure human safety when they are engaging with selfdriving cars on the street level.
DAY 07793
SUPPORTING FACILITIES: POWER ZONE
DAY 07830
SUPPORTING FACILITIES: PICK-UP POINT
DAY 07899
GENERATING A SPACE DATA INPUT SET 1 [Machine vision] 01_Point cloud :2.0% 02_Lines :20.0% 03_Anomaly :38.0% 04_Normal :40.0% [β elements] 01_edifice I :0.4% 02_artificial tree :59.3% 03_edifice II :0.8% 04_circulation :14.6% 05_nature :24.9%
DATA INPUT SET 2 [Segmentation map]
Edifice I Artificial tree Edifice II Circulation
Nature
DATA INPUT SET 3 [plan and location] =0;020304;2
DATA INPUT SET 4 [Scene set up]
Ef067 MACHINE VISION OUTPUT:
HUMAN VISION
TESTBED 2 | JUNCTION OF MACHINES AND HUMANS Date: 16/06/2048 [BC03456] 0;020304;2 Shot distance: AERIAL, approx. 30m
DAY 08210
TESTBED 2 MASTERPLAN STRATEGY
DAY 08395
β OPERATION OUTPUT SET 5 DATA CENTRE [EZCDE05671] | WATER TOWER [EZCDE05672] | WIND CATCHER [EZCDE0671] | SOLAR PANEL [EZCDE0672]
DATA CENTRE
Data is the new food of the century. Data centre becomes the new architecture landscape. Considering the Fenland condition, water that is once threatening to the hinterland may become an advantage to data farm. Instead of forming a banal landscape on the ground, the data farm submerges into pools of underground water.
WATER TOWER
Processed water will be used to maintain the temperature of the server floors while providing clean water to the city.
WIND CATCHER Energy captors
SOLAR PANEL Energy captors
DAY 08591
ENERGY ZONE MACHINE VISION OUTPUT: PLAN
UNDERWATER ELEVATION
DAY 08642
DAY 08951
DAY 10935
CHAPTER 3 2055 | LATE EXPLORATION: A NEW HOME FOR HUMAN Day 10953 Excited, confident Dusk, thirty-year since the β operation. The operation is reaching the end of the tunnel. The city is now a self-sustain paradise with the collaboration from both agents. More spaces are created to turn the site into a human-familiar place. It is not a normal city; it is a kindergarten for humans to relearn the way of seeing. Once testbed 3 is ready, the site will be open for public inhabitation.
DAY 10950
β OPERATION OUTPUT SET 6 BUNKER [3C03] | CITY HALL [3C041] | AVS CENTRAL STATION [3C08]
AVS CENTRAL STATION
CATHEDRAL
CITY HALL
A human-machine encounter.
A new belief and norm.
A place of carnival and celebration.
DAY 10950
β OPERATION OUTPUT SET 6 EDUCATION HUB [3BC05] | CATHEDRAL [3C042] | MACHINE ARCHIVE AND MUSEUM [3C043]
EDUCATION HUB
BUNKER
MACHINE ARCHIVE AND MUSEUM
Not the normal school.
An escape from the machine world.
A machine graveyard.
DAY 11351
[AVS CENTRAL]
DAY 12045
[CATHEDRAL]
DAY 12410
[CITY HALL]
DAY 12697
[EDUCATION HUB]
DAY 13870
[BUNKER]
DAY 14965
[ARCHIVE AND MUSEUM]
DAY 16316
TESTBED 3 MASTERPLAN STRATEGY
DAY 16425
β CITY WELCOMING MANUAL
Testbed 1 09/08/2048 11;54 pm Shot distance: approx. 35km
Testbed 2 21/10/2053 12:13 pm Shot distance: approx. 35km
Testbed 3 23/11/2064 02:09 pm Shot distance: approx. 40km
VIEW FROM B1098 Date: 28/06/2056 [C045] Shot distance: STREET VIEW +1.8m, approx. 0.5km
TESTBED 1 | AERIAL VIEW Date: 06/08/2045 [DE0456] Shot distance: AERIAL, approx. 10m
TESTBED 1 | ON THE ARCHITECTURE EXPERIMENT STREET Date: 06/01/2038 [D05] Shot distance: STREET VIEW +1.5m, approx. 0.1km
TESTBED 2 | MEET AT THE JUNCTION OF HUMANS AND MACHINES Date: 01/04/2050 [F07] Shot distance: STREET VIEW +3m, approx. 0.1km
TESTBED 2 | EDEN Date: 31/05/2060 [E07] Shot distance:+10m, approx. 3m
ENERGY ZONE | UNDERWATER SERVER FARM Date: 26/04/2060 [D07] Shot distance: -30m, approx. 2m
ENERGY ZONE | THE POWER BANK Date: 14/02/2065 [D06] Shot distance: AERIAL, approx. 20m
TESTBED 3 | A NEW PARADISE Date: 04/09/2075 [BC03456] Shot distance: STREET VIEW +1.8m, approx. 0.1km
THE β CITY | DUSK Date: 31/12/2075
THANK Y O U E
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