Machine Vision in 5 Dimensions

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import os from flask import Flask, render_template, request # import our OCR function from ocr_core import ocr_core # define a folder to store and later serve the images UPLOAD_FOLDER = ‘/static/uploads/’ # allow files of a specific type ALLOWED_EXTENSIONS = set([‘png’, ‘jpg’, ‘jpeg’]) app = Flask(__name__) # function to check the file extension def allowed_file(filename): return ‘.’ in filename and \ filename.rsplit(‘.’, 1)[1].lower() in ALLOWED_EXTENSIONS # route and function to handle the home page @app.route(‘/’) def home_page(): return render_template(‘index.html’) # route and function to handle the upload page @app.route(‘/upload’, methods=[‘GET’, ‘POST’]) def upload_page(): if request.method == ‘POST’: # check if there is a file in the request if ‘file’ not in request.files: return render_template(‘upload.html’, msg=’No file selected’) file = request.files[‘file’] # if no file is selected if file.filename == ‘’: return render_template(‘upload.html’, msg=’No file selected’) if file and allowed_file(file.filename): # call the OCR function on it extracted_text = ocr_core(file) # extract the text and display it return render_template(‘upload.html’, msg=’Successfully processed’, extracted_text=extracted_text, img_src=UPLOAD_FOLDER + file.filename) elif request.method == ‘GET’: return render_template(‘upload.html’) if __name__ == ‘__main__’: app.run()

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start

input input input input > >

{

domain object recognition 1> 60 116 114 97 102 102 105 99 95 108 105 103 104 116 62 2> 60 112 105 110 119 104 101 101 108 62 3> 60 99 111 109 112 117 116 101 114 95 107 101 121 98 111 97 114 100 62 4> 60 115 111 99 99 101 114 95 98 97 108 108 62

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 226 128 156 79 117 114 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 115 32 97 114 101 32 102 105 108 108 101 100 32 119 105 116 104 32 101 120 97 109 112 108 101 115 32 111 102 32 109 97 99 104 105 110 101 45 116 111 45 109 97 99 104 105 110 101 32 115 101 101 105 110 103 32 97 112 112 97 114 97 116 117 115 101 115 46 46 46 84 104 101 115 101 32 115 121 115 116 101 109 115 32 97 114 101 32 111 110 108 121 32 112 111 115 115 105 98 108 101 32 98 101 99 97 117 115 101 32 100 105 103 105 116 97 108 32 105 109 97 103 101 115 32 97 114 101 32 109 97 99 104 105 110 101 45 114 101 97 100 97 98 108 101 32 97 110 100 32 100 111 32 110 111 116 32 114 101 113 117 105 114 101 32 97 32 104 117 109 97 110 32 105 110 32 116 104 101 32 97 110 97 108 121 116 105 99 32 108 111 111 112 226 128 157 32 10 40 80 97 110 103 108 101 110 44 32 50 48 49 57 41 46’ } ] } ] } ]

“Our built environments are filled with examples of machine-to-machine seeing apparatuses...These systems are only possible because digital images are machine-readable and do not require a human in the analytic loop” (Panglen, 2019).

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e76” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 99 107 110 111 119 45 10 108 101 100 103 109 101 110 116 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘83 105 110 99 101 114 101 115 116 32 97 112 112 114 101 99 105 97 116 105 111 110 32 116 111 10 77 105 99 104 97 101 108 32 65 108 105 110 103 32 102 111 114 32 116 104 101 32 118 97 108 117 97 98 108 101 32 116 104 111 117 103 104 116 115 32 97 110 100 32 100 105 115 99 117 115 115 105 111 110 115 32 10 84 111 32 109 121 32 102 97 109 105 108 121 32 97 110 100 32 102 114 105 101 110 100 115 32 102 111 114 32 116 104 101 32 117 110 99 111 110 100 105 116 105 111 110 97 108 32 115 117 112 112 111 114 116 32 ---------70 111 114 32 116 104 101 58 10 70 117 116 117 114 101 10 72 117 109 97 110 105 116 121 10 67 111 45 104 97 98 105 116 97 110 116’ } ] } ] } ] }

Acknowledgment

Sincerest appreciation to Michael Aling for the invaluable thoughts and discussions My family and friends for the unconditional support

for

Future Humanity

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e78” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 97 98 108 101 10 111 102 10 67 111 110 116 101 110 116 115 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘65 99 107 110 111 119 108 101 100 103 109 101 110 116 32 9 9 9 48 48 55 10 65 98 115 116 114 97 99 116 9 9 9 9 48 49 49 10 71 108 111 115 115 97 114 121 9 9 9 9 48 49 51 10 10 73 110 116 114 111 100 117 99 116 105 111 110 9 9 9 48 49 55 10 66 105 111 108 111 103 105 99 97 108 32 97 110 100 32 97 114 116 105 102 105 99 105 97 108 32 118 105 115 105 111 110 9 48 49 57 10 65 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 9 9 48 50 49 10 77 97 99 104 105 110 101 32 118 105 115 105 111 110 9 9 9 48 50 51 10 83 101 101 105 110 103 9 9 9 9 48 50 53 10 65 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 9 9 9 48 50 55 10 82 101 115 101 97 114 99 104 32 77 101 116 104 111 100 32 83 116 97 116 101 109 101 110 116 9 9 48 50 57 10 10 10 49 68 10 66 97 114 99 111 100 101 32 10 67 111 108 111 117 114 32 97 110 100 32 105 108 108 117 115 105 111 110 10 84 104 101 32 115 112 97 116 105 97 108 32 108 97 121 111 117 116 32 99 111 110 115 101 113 117 101 110 99 101 115 32 111 102 32 116 104 101 32 98 97 114 99 111 100 101 10 73 109 112 97 99 116 32 111 102 32 98 97 114 99 111 100 101 32 111 110 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 100 101 115 105 103 110 32 10 10 50 68 32 10 70 108 97 116 32 105 109 97 103 101 115 32 10 68 97 116 97 32 99 111 108 108 101 99 116 105 111 110 10 68 97 116 97 32 100 101 109 111 99 114 97 99 121 32 97 110 100 32 98 105 97 115 10 67 111 110 115 101 113 117 101 110 99 101 115 32 111 102 32 100 97 116 97 32 109 105 110 105 110 103 10 65 32 104 117 109 97 110 45 109 97 99 104 105 110 101 32 101 110 118 105 114 111 110 109 101 110 116 10 10 51 68 10 86 111 108 117 109 101 10 79 98 106 101 99 116 32 114 101 99 111 110 115 116 114 117 99 116 105 111 110 32 97 110 100 32 105 110 115 112 101 99 116 105 111 110 32 10 87 104 101 110 32 100 97 116 97 32 103 111 101 115 32 119 114 111 110 103 10 83 117 114 118 101 105 108 108 97 110 99 101 58 32 116 104 101 32 100 105 102 102 101 114 101 110 116 32 112 101 114 99 101 112 116 105 111 110 32 10 65 110 116 105 45 115 117 114 118 101 105 108 108 97 110 99 101 10 66 117 105 108 100 105 110 103 32 114 101 115 116 111 114 97 116 105 111 110 10 66 117 105 108 100 105 110 103 32 105 110 115 112 101 99 116 105 111 110 10 10 52 68 10 65 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 10 84 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 116 101 115 116 98 101 100 10 84 114 111 108 108 101 121 32 112 114 111 98 108 101 109 32 10 82 101 97 108 45 116 105 109 101 32 116 114 97 99 107 105 110 103 10 83 69 78 83 69 97 98 108 101 32 99 105 116 121 32 10 82 101 100 101 115 105 103 110 32 116 104 101 32 117 114 98 97 110 32 102 97 98 114 105 99 10 77 111 98 105 108 105 116 121 32 112 108 97 110 10 226 128 156 67 105 116 121 32 108 111 103 103 105 110 103 226 128 157 10 10 53 68 10 84 104 101 32 105 110 118 105 115 105 98 108 101 32 101 121 101 10 84 104 105 110 107 105 110 103 32 109 97 99 104 105 110 101 10 65 108 103 111 114 105 116 104 109 105 99 32 119 111 114 108 100 32 97 110 100 32 112 114 111 99 101 100 117 114 97 108 105 115 109 10 84 111 111 32 115 109 97 114 116 32 99

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Table of Contents

Acknowledgment Abstract Glossary Introduction Biological and artificial vision Artificial intelligence Machine vision Seeing Autonomous agent Research Method Statement

1D 2D 3D 4D 5D

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051

Barcode The spatial layout Aesthetic and function

Flat images Data collection Data democracy and bias Consequences of data mining A human-machine environment

075

Depth Object reconstruction and inspection Images by machine Surveillance: the different perception Anti-surveillance Building restoration Building inspection

097

Autonomous agent The autonomous vehicle testbed Trolley problem Real-time tracking Senseable city Redesign the urban fabric Mobility plan “City logging”

123

The invisible eye Thinking machine Algorithmic world Too smart city Society of control

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Epilogue Mediography Appendices

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{translated_ 8-bit sign integer: ‘77 97 99 104 105 110 101 32 118 105 115 105 111 110 32 105 110 32 102 105 118 101 32 100 105 109 101 110 115 105 111 110 115 32 105 115 32 97 32 115 116 117 100 121 32 111 102 32 116 104 101 32 116 114 97 110 115 102 111 114 109 97 116 105 111 110 32 105 110 32 116 104 101 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 97 110 100 32 105 116 115 32 114 101 108 97 116 105 111 110 32 116 111 32 116 104 101 32 115 101 110 116 105 101 110 116 32 97 103 101 110 116 32 102 111 108 108 111 119 105 110 103 32 116 104 101 32 100 101 118 101 108 111 112 109 101 110 116 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 97 110 100 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 44 32 100 105 115 99 117 115 115 105 110 103 32 97 99 114 111 115 115 32 100 105 102 102 101 114 101 110 116 32 118 105 115 117 97 108 32 100 105 109 101 110 115 105 111 110 115 46 32 84 104 105 115 32 114 101 115 101 97 114 99 104 32 98 101 103 105 110 115 32 119 105 116 104 32 99 114 105 116 105 113 117 105 110 103 32 116 104 101 32 112 114 111 98 108 101 109 115 32 97 110 100 32 97 112 112 108 105 99 97 116 105 111 110 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 116 101 99 104 110 111 108 111 103 121 32 105 110 32 116 104 101 32 112 114 101 115 101 110 116 32 100 97 121 44 32 117 115 105 110 103 32 105 116 32 116 111 32 114 101 102 108 101 99 116 32 117 112 111 110 32 97 114 99 104 105 116 101 99 116 117 114 101 32 100 101 115 105 103 110 32 97 110 100 32 102 111 114 109 32 97 32 115 101 114 105 101 115 32 111 102 32 115 112 101 99 117 108 97 116 105 118 101 32 116 104 111 117 103 104 116 115 32 111 110 32 116 104 101 32 102 117 116 117 114 101 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 116 104 97 116 32 99 97 110 32 97 99 99 111 109 109 111 100 97 116 101 32 98 111 116 104 32 115 101 110 116 105 101 110 116 32 97 110 100 32 97 114 116 105 102 105 99 105 97 108 32 97 103 101 110 116 115 46 32 10 10 77 97 99 104 105 110 101 32 118 105 115 105 111 110 32 105 115 32 97 32 102 111 114 109 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 118 105 115 105 111 110 32 115 121 115 116 101 109 32 116 104 97 116 32 105 110 116 101 110 100 115 32 116 111 32 114 101 112 108 105 99 97 116 101 32 104 117 109 97 110 32 118 105 115 105 111 110 32 115 121 115 116 101 109 44 32 97 108 108 111 119 105 110 103 32 104 117 109 97 110 115 32 116 111 32 115 101 101 32 98 101 121 111 110 100 32 116 104 101 105 114 32 118 105 115 117 97 108 32 108 105 109 105 116 97 116 105 111 110 115 46 32 84 104 101 32 116 104 101 115 105 115 32 119 97 108 107 115 32 116 104 114 111 117 103 104 32 97 32 106 111 117 114 110 101 121 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 97 99 114 111 115 115 32 100 105 109 101 110 115 105 111 110 115 58 32 32 32 10 32 32 10 49 68 58 32 65 32 119 111 114 108 100 32 116 104 97 116 32 98 117 105 108 100 115 32 117 112 32 111 102 32 108 105 110 101 115 44 32 101 97 99 104 32 111 98 106 101 99 116 32 112 111 115 115 101 115 115 101 115 32 97 32 117 110 105 113 117 101 32 99 111 100

Abstract

Machine vision in 5 dimensions

is a study of the multiple ways that machines, more recently artificial intelligence, visually compute the world, and how machine vision system is setting a new guideline for future architectural designs. The discussion is building around different spatial dimensions. This thesis will first discuss the problems and applications of the machine vision system in the past and present. Recent breakthroughs in artificial intelligence and machine learning enhance the machine vision system. Following that is the rising numbers of data centre and training data that brings up a series of problems in the age of datafication. Through dissecting the machine vision system, a set of parameters is defined and reflected on architecture design, forming a series of speculative thoughts on the future built environment that can accommodate both human and artificial agents.

In our current surveillance age, the machine vision system able to detect humans from all perspectives. While the machine vision is shifting towards an invisible form, it creates a new visual culture that lingers between physical and virtual space. In recent years, designers and architects are using machine vision technology in the design process, such as site analysis and construction inspection. This thesis argues that autonomous agents such as fully autonomous vehicle will arrive soon, suggesting that the machine will coexist with humans. Therefore, it requires architects to consider machine vision requirements when designing a space. This thesis will address the current status of autonomous vehicle technology, using it as arguments to redefine the built environment Machine vision is part of the artificial vision system that intends to replicate the human vision system, allowing humans to see beyond their visual limitations. The thesis walks through a journey of machine vision across dimensions: 1D A space that builds up of lines in which each object possesses a unique code as its identity.

2D A space that appears flat, conveyed via patterns or electronic screens.

3D A space with depth and distance.

4D A space with the presence of time and speed. It is a collaboration between human and machine vision.

5D A space with a new visual culture. The machine starts to think and perceive.

Dimensions Figure 0.1 (Leong, 2021) Source: Author

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{translated_ 8-bit sign integer: ‘65 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 58 10 65 32 118 101 104 105 99 108 101 32 99 97 112 97 98 108 101 115 32 111 102 32 116 114 97 99 107 105 110 103 32 97 110 100 32 114 101 97 99 116 105 110 103 32 116 111 32 105 116 115 32 115 117 114 114 111 117 110 100 105 110 103 115 32 98 121 32 117 115 105 110 103 32 65 73 32 116 101 99 104 110 111 108 111 103 121 46 32 73 116 32 99 97 110 32 116 114 97 110 115 112 111 114 116 32 102 114 111 109 32 111 110 101 32 112 111 105 110 116 32 116 111 32 97 110 111 116 104 101 114 32 119 105 116 104 111 117 116 32 116 104 101 32 105 110 116 101 114 118 101 110 116 105 111 110 32 111 102 32 104 117 109 97 110 115 46 32 84 104 101 114 101 32 97 114 101 32 115 105 120 32 108 101 118 101 108 32 111 102 32 100 114 105 118 105 110 103 32 97 117 116 111 109 97 116 105 111 110 44 32 114 97 110 103 105 110 103 32 102 114 111 109 32 108 101 118 101 108 32 48 32 116 111 32 108 101 118 101 108 32 53 32 97 115 32 100 101 102 105 110 101 100 32 98 121 32 116 104 101 32 83 111 99 105 101 116 121 32 111 102 32 65 117 116 111 109 111 116 105 118 101 32 69 110 103 105 110 101 101 114 115 32 40 83 65 69 41 46 10 10 68 97 116 97 58 10 84 104 101 32 102 117 110 100 97 109 101 110 116 97 108 32 101 108 101 109 101 110 116 32 105 110 32 65 73 32 97 110 100 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 46 32 73 116 32 99 97 110 32 98 101 32 100 105 118 105 100 101 100 32 105 110 116 111 32 117 110 115 116 114 117 99 116 117 114 101 100 32 100 97 116 97 44 32 115 116 114 117 99 116 117 114 101 100 32 100 97 116 97 44 32 97 110 100 32 97 117 103 109 101 110 116 101 100 32 100 97 116 97 46 10 10 71 101 110 101 114 97 108 32 65 73 58 10 65 32 102 111 114 109 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 116 104 97 116 32 99 97 110 32 98 101 32 117 115 101 100 32 116 111 32 99 111 109 112 108 101 116 101 32 97 32 98 114 111 97 100 32 114 97 110 103 101 32 111 102 32 116 97 115 107 115 32 105 110 32 97 32 119 105 100 101 32 114 97 110 103 101 32 111 102 32 101 110 118 105 114 111 110 109 101 110 116 115 32 116 104 97 116 32 97 32 104 117 109 97 110 32 99 97 110 32 112 101 114 102 111 114 109 46 32 40 65 112 112 108 105 101 100 32 73 110 116 101 108 108 105 103 101 110 99 101 32 71 108 111 115 115 97 114 121 32 124 32 65 99 99 101 110 116 117 114 101 44 32 110 46 100 46 41 32 10 10 71 101 110 101 114 97 116 105 118 101 32 97 100 118 101 114 115 97 114 105 97 108 32 110 101 116 119 111 114 107 32 40 71 65 78 115 41 58 10 80 97 105 114 115 32 111 102 32 97 108 116 101 114 110 97 116 101 108 121 32 116 114 97 105 110 101 100 32 109 111 100 101 108 115 32 117 115 105 110 103 32 99 111 109 112 101 116 105 110 103 32 100 101 101 112 32 108 101 97 114 110 105 110 103 32 97 108 103 111 114 105 116 104 109 115 46 32 72 101 114 101 44 32 116 104 101 32 102 105 114 115 116 32 109 111 100 101 108 32 105 115 32 116 114 97 105 110 101 100 32 117 115 105 110 103 32 97 32 115 101 99 111 110 100 32 109 111 100 101 108 44 32 116 111 32 100 105 115 99 114 105 109 105 110 97 116 101 32 98 101 116 119 101 101 110 32 97 99 116

Glossary

Algorithm: An algorithm is a process that follows a set of rules, a problem solver mainly used by computers (Iriondo, 2020).

Artificial Neural Network (ANN): A network system that learns from training data, translating a data input to a desired output through layers of structured algorithm network. It is inspired by biology neural network.

Autonomous vehicle: A vehicle that capables of tracking and reacting to its surroundings by using AI technology. It can transport from one point to another without the intervention of humans. There are six level of driving automation, ranging from level 0 to level 5 as defined by the Society of Automotive Engineers (SAE).

Data: The fundamental element in AI and machine learning. It can be divided into unstructured data, structured data, and augmented data.

General AI: A form of artificial intelligence that can be used to complete a broad range of tasks in a wide range of environments that a human can perform (Applied Intelligence Glossary | Accenture, n.d.).

Generative adversarial network (GANs): GANs is an unsupervised machine learning model. It works on pairs of alternately trained models using competing deep learning algorithms. Here, the first model is trained using a second model, to discriminate between actual data and synthetic data. This ability to capture and copy variations within a dataset is widely apply in image-to-image translation and generate realistic data. (Applied Intelligence Glossary | Accenture, 2021).

Human-in-the-loop: A system that involved both artificial intelligence and human intelligence in the processes.

Intelligent agent: A program that can make decisions or perform a service based on its environment, user input and experiences, autonomously when prompted by the user in real time (TechTarget Contributor, 2019).

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{translated_ 8-bit sign integer: ‘ 105 99 32 100 97 116 97 46 32 84 104 105 115 32 97 98 105 108 105 116 121 32 116 111 32 99 97 112 116 117 114 101 32 97 110 100 32 99 111 112 121 32 118 97 114 105 97 116 105 111 110 115 32 119 105 116 104 105 110 32 97 32 100 97 116 97 115 101 116 32 99 97 110 32 98 101 32 97 112 112 108 105 101 100 32 102 111 114 32 117 115 101 115 32 115 117 99 104 32 97 115 32 117 110 100 101 114 115 116 97 110 100 105 110 103 32 114 105 115 107 32 97 110 100 32 114 101 99 111 118 101 114 121 32 105 110 32 104 101 97 108 116 104 99 97 114 101 32 97 110 100 32 112 104 97 114 109 97 99 111 108 111 103 121 46 32 40 65 112 112 108 105 101 100 32 73 110 116 101 108 108 105 103 101 110 99 101 32 71 108 111 115 115 97 114 121 32 124 32 65 99 99 101 110 116 117 114 101 44 32 50 48 50 49 41 10 10 72 117 109 97 110 45 105 110 45 116 104 101 45 108 111 111 112 58 32 10 65 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 32 109 111 100 101 108 32 116 104 97 116 32 105 110 118 111 108 118 101 100 32 98 111 116 104 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 97 110 100 32 104 117 109 97 110 32 105 110 116 101 108 108 105 103 101 110 99 101 32 105 110 32 116 104 101 32 112 114 111 99 101 115 115 101 115 46 32 10 10 73 110 116 101 108 108 105 103 101 110 116 32 97 103 101 110 116 58 10 65 32 112 114 111 103 114 97 109 32 116 104 97 116 32 99 97 110 32 109 97 107 101 32 100 101 99 105 115 105 111 110 115 32 111 114 32 112 101 114 102 111 114 109 32 97 32 115 101 114 118 105 99 101 32 98 97 115 101 100 32 111 110 32 105 116 115 32 101 110 118 105 114 111 110 109 101 110 116 44 32 117 115 101 114 32 105 110 112 117 116 32 97 110 100 32 101 120 112 101 114 105 101 110 99 101 115 44 32 97 117 116 111 110 111 109 111 117 115 108 121 32 119 104 101 110 32 112 114 111 109 112 116 101 100 32 98 121 32 116 104 101 32 117 115 101 114 32 105 110 32 114 101 97 108 32 116 105 109 101 46 32 40 84 101 99 104 84 97 114 103 101 116 32 67 111 110 116 114 105 98 117 116 111 114 44 32 50 48 49 57 41 10 10 73 110 116 101 114 110 101 116 32 111 102 32 116 104 105 110 103 115 32 40 73 111 84 41 58 10 67 111 108 108 101 99 116 105 111 110 115 32 111 102 32 100 101 118 105 99 101 115 32 116 104 97 116 32 99 97 110 32 99 111 109 101 32 116 111 103 101 116 104 101 114 32 119 105 116 104 32 97 117 116 111 109 97 116 101 100 32 115 121 115 116 101 109 32 116 104 97 116 32 97 108 108 111 119 115 32 116 111 32 103 97 116 104 101 114 44 32 97 110 97 108 121 115 101 44 32 97 110 100 32 112 114 111 99 101 115 115 32 105 110 102 111 114 109 97 116 105 111 110 46 10 10 77 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 58 32 10 84 104 101 32 115 117 98 102 105 101 108 100 32 111 102 32 65 114 116 105 102 105 99 105 97 108 32 73 110 116 101 108 108 105 103 101 110 99 101 32 116 104 97 116 32 111 102 116 101 110 32 117 115 101 115 32 115 116 97 116 105 115 116 105 99 97 108 32 116 101 99 104 110 105 113 117 101 115 32 116 111 32 103 105 118 101 32 99 111 109 112 117 116 101 114 115 32 116 104 101 32 97 98 105 108 105 116 121 32 116 111 32 -30 -128 -100 108 101 97 114 110 -30 -128 -99 46 32 40 65 73 32 71

Glossary

Internet of things (IoT): Collections of devices that can come together with automated system that allows to gather, analyse, and process information.

Machine learning: The subfield of Artificial Intelligence that often uses statistical techniques to give computers the ability to “learn” (AI Glossary | Curated by Data Scientists and ML Experts, 2021).

Narrow AI: An application of artificial intelligence where the model has been optimized to perform a limited number of tasks (Applied Intelligence Glossary | Accenture, 2021).

Posthuman: A creature born into this binary condition, into a world of converged digital and material, where each individual’s mental and social existence is enabled, sustained, and improved by technologies (Ratti and Claudel, 2016).

Sentient agent: An agent that possesses consciousness and responsive to senses, which include humans and some animals.

Strong AI: A computer program thought to be capable of performing similarly to a human mind in terms of perception, beliefs and other cognitive capabilities associated with humans (Applied Intelligence Glossary | Accenture, 2021).

Training data: A subset of available data that a data scientist selects for the training phase of the development of a model (AI Glossary | Curated by Data Scientists and ML Experts, 2021).

Unconscious bias: Human bias that happens out of conscious awareness. This bias is typically a learned stereotype that is automatic and mostly unintentional (Man, n.d.).

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0 Z E RO

In tro d u ction

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Introduction In this information age, ubiquitous machines are penetrating every bit of human’s life. It is setting out a new machine landscape where human’s privacy is coming under threat. With artificial intelligence, the machine develops a visual culture that is largely illegible by a human.

Therefore, this thesis critically analyses the machine vision system to understand how the machine sees the world. This thesis will break down the machine vision system into five different spatial dimensions, exploring its application and problems, respectively.

Each chapter will end with architectural responses. It becomes the parameters for the speculations design. The first part of the thesis investigates the application of the machine vision system in the built environment and the urgency to change the status quo, such as machine bias, human control, and surveillance. The second part engages with the autonomous agent, specifically on the autonomous vehicle, to understand the built environment from a different perspective and suggests how machine vision may transform one’s daily life. The final chapter draws a series of speculative thoughts to complete the argument.

Dimensions II Figure 0.2 (Leong, 2021) Source: Author

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Biological and artificial vision

The visual system occupies a significant amount (one third) of the human’s brain processing system compared to other senses (Yasinski, 2020). The eye is the organ that connects humans to the world most of the time, but it takes more than seeing to perceive the world. Visuality is a form of accumulative experience; the brain encoding strategy work to store images in the human subconscious system. The human visual system often associates with past experiences while continually engaging with new perceptions.

The visual experience is inseparable from other senses. For instance, when a person perceives a sour plum, their salivation nervous system will automatically generate saliva. It is a form of consciousness that is absent in intelligent agents.

Since the 20th century, computer scientists have attempted to understand biological systems to impose human characteristic in artificial life forms. Instead of biological neurons in humans’ brain, Perceptron is an artificial neuron algorithm that processes signals. The artificial neural network (ANN) imitates a biological neural network to perform layers of connection and machine interpretation. Rather than the life experiences humans gained during their lifetime, intelligent agents rely on training data to gain experience. A person can figure out a banana in all shapes and perspectives effortlessly, whereas layers of ANN are required for an intelligent agent to recognise a yellowish-green curved object as a banana in two-dimensional space. Besides the similarity in the structure of human and machine vision system, the machine interprets things in a completely different way. For humans to understand the machine vision system, more research into the algorithmic world is needed. Only by that, the human can have the confidence to coexist with the machine in the future.

John Berger: Ways of seeing Figure 0.3 (Leong, 2021) Source: Author

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Artificial intelligence (AI) AI pioneer Marvin Minksy define AI as “the science of making machines do things that would require intelligence if done by men (Marsden, 2017)”, which is still relevant nowadays. AI no longer merely duplicates human behaviours but performs deep learning and creative reflection, which show a sign of intelligence. AI can be classified into three groups; narrow, general, and strong AI (Jajal, 2019). Within this thesis, AI will be referring to narrow AI that focuses on performing designated tasks. Narrow AI is an extension of the human. It triggers social discussion, such as the replacement of human jobs. However, AI robots that appear in the movies are known as general AI that possesses a range of skills. It is an idealised version of the current narrow AI that is not coming anything soon (Broussard, 2019). The current AI process is a seemingly unthreatening one; it is a series of procedures, a system that works on human instruction, and no one shall be thinking for itself as a malevolent threat (Broussard, 2019: 39). AI can be designed to do anything, however. It is not a one-size-fits-all approach but finding the right fit for every task. Besides, a significant amount of energy and training data that take up physical space on Earth is needed to power AI. Following AI development, architects need to react to the unignorable impacts that AI brings to both built and natural environments.

Types of Artificial intelligence Diagram 0.1 (Leong, 2021) Source: Author

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Machine vision Machine vision is not made to replace humans but creates a new way of seeing through a machinic eye. The machine does not read an object as humans do, but both are ineffectual without the presence of light. Machine vision carries no meaning without training data due to the lack of common sense in the machine. As an inventor of the technology system, humans need to ensure that training data does not capture the existing bias and prejudice. As training data is the core of the system, there is no need for a replica reproduction of human vision.

A machine vision system requires an imager, processor, and output device. Machines pick up targeted pattern, analyse with the designated programmer and create a human-understandable output.

With an ANN machine learning model, the machine can define each pixel on the image, simplifies it in its binary language to analyse the input. The result of machine vision needs to be highly accurate in events that involve life and death, such as the autonomous vehicle system. Since the industrial era, the invention of machine vision replaced human position in the monotonous and repetitive tasks in mass production, working non-stop around the clock. Ratti and Claudel (2016) discuss machine vision as ‘smart dust’ in the future urban environment. Before implementing machine vision in the future smart city, critical social dilemmas caused by this potential omnipresent technology need careful solutions from everyone.

Machine eyes Figure 0.4 (Leong, 2021) Source: Author

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Seeing In Edwin Abbott Abbott’s Flatland, he introduces the terms Pointland (0D), Lineland (1D), Spaceland (3D) and the fourth dimension that illustrates the meaning of seeing (Abbott, 1994). Humans can perceive up to the third dimension, what beyond is visual speculation. However, this thesis will explore the forth and fifth dimension, which involved machine vision in the process of seeing, suggesting a world beyond human’s perception. In this machinic world, human needs to perceive instead of seeing, thinking instead of looking. Seeing has simplified with the pervasive internet of things.

As suggested by Bridle (2018: 43), “we refashion our understanding of the world to better accommodate the constant alerts and cognitive shortcuts provided by the automated system. Computation replaces conscious thought. We think more and more like the machine, or we do not think at all.”

Who is looking at who?

Figure 0.5 (Leong, 2021) Source: Author

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Original Bernie Sanders Figure 0.6 (Smialowski, 2021)

Bernie Sanders’ photo went viral Figure 0.7 (Bendett, 2021)

Nowadays, ubiquitous image manipulation technology can easily alter the original meaning of an image. As Berger (1972: 19) mentioned, “when the camera reproduces a painting, it destroys the uniqueness of its image… its meaning multiplies and fragments into many meanings”. It is hard to differentiate the authentic from the synthetic. “Something has changed in the nature of images today; they seem to take on a life of their own instead of staying on the page or the screen…affecting not just what we see, but how we see” (Bridle, 2019a). In the recent United State 46th presidential inauguration, a cut out of Bernie Sanders inserted in different artwork backdrops went viral on social media. It had successfully gained public attention due to the inundation of the image. Through the act of image editing, the original intention of the photograph loses its meaning. However, “the more images we see, the less we know. The result is fear, confusion and often anger” (Bridle, 2019a).

Humans are no longer the only agent who perceive the world.

The machine is developing a process of image-making based on images given to it without human involvement. “We no longer look at images – images look at us. Images no longer solely represent things, but actively intervene in everyday life” (Young, 2019: 27).

It is the fifth dimension, an evolution of the visual language that is unintelligible to humans. It will be a newly awoken visual culture, a rather strange one to humans that require one to relearn the process of seeing.

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e730” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 65 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 32 105 110 99 108 117 100 101 115 32 114 111 98 111 116 105 99 32 117 110 105 116 44 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 97 110 100 32 117 110 109 97 110 110 101 100 32 97 101 114 105 97 108 32 118 101 104 105 99 108 101 32 119 105 108 108 32 98 101 32 105 110 116 114 111 100 117 99 101 100 32 105 110 32 116 104 101 32 116 104 101 115 105 115 46 32 32 65 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 115 32 101 113 117 105 112 32 119 105 116 104 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 115 121 115 116 101 109 115 32 115 117 99 104 32 97 115 32 99 97 109 101 114 97 44 32 114 97 100 97 114 32 97 110 100 32 108 105 100 97 114 32 116 111 32 100 101 116 101 99 116 32 97 110 100 32 97 110 97 108 121 115 101 32 116 104 101 32 51 54 48 45 100 101 103 114 101 101 32 115 117 114 114 111 117 110 100 105 110 103 32 101 110 118 105 114 111 110 109 101 110 116 44 32 109 111 118 101 109 101 110 116 115 44 32 97 110 100 32 99 104 97 110 103 101 115 32 117 115 105 110 103 32 118 97 114 105 111 117 115 32 65 73 32 109 111 100 101 108 46 32 68 105 102 102 101 114 101 110 116 32 102 114 111 109 32 116 104 101 32 99 111 110 118 101 110 116 105 111 110 97 108 32 100 114 105 118 101 114 32 115 121 115 116 101 109 44 32 103 114 111 117 110 100 32 112 108 97 110 32 112 114 111 99 101 115 115 101 115 32 97 115 32 110 101 119 32 97 108 103 111 114 105 116 104 109 105 99 32 109 97 116 114 105 120 32 103 117 105 100 97 110 99 101 32 102 111 114 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 46 32 65 109 97 122 111 110 8217 115 32 114 111 98 111 116 105 99 32 100 114 105 118 101 114 32 117 110 105 116 32 112 111 114 116 114 97 105 116 32 97 110 32 101 120 97 109 112 108 101 32 111 102 32 104 111 119 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 115 32 99 97 110 32 100 101 112 108 111 121 32 105 110 32 97 32 99 111 110 116 114 111 108 108 101 100 32 101 110 118 105 114 111 110 109 101 110 116 44 32 102 111 114 109 105 110 103 32 97 32 104 117 109 97 110 45 109 97 99 104 105 110 101 32 101 99 111 115 121 115 116 101 109 46 32 73 110 32 116 104 101 32 111 112 101 110 32 101 110 118 105 114 111 110 109 101 110 116 44 32 104 111 119 101 118 101 114 44 32 116 104 101 114 101 32 97 114 101 32 109 111 114 101 32 117 110 112 114 101 100 105 99 116 97 98 108 101 32 102 97 99 116 111 114 115 32 119 104 105 99 104 32 99 97 110 32 100 101 99 114 101 97 115 101 32 116 104 101 32 114 101 108 105 97 98 105 108 105 116 121 32 111 102 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 46 10 84 97 107 105 110 103 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 97 115 32 97 110 32 101 120 97 109 112 108 101 44 32 105 116 32 114 101 109 97 105 110 115 32 97 110 32 117 110 115 97 102 101 32 111 112 101 114 97 116 105 111 110 32 97 115 32 116 104 101 32 99 114 97 115 104 101 115 32 114 97 116 101 32 112 101 114 32 109 105 108 108 105 111 110 32 109 105 108 101 115 32 100 114 105 118 101 110 32 111 110 32 112 117 98 108 105 99 32 114 111 97 100

Autonomous agent This thesis will include robotic driver units, autonomous vehicles and unmanned aerial vehicles as autonomous agents. Autonomous agents equip with machine vision systems such as camera, radar and lidar to detect and analyse the 360-degree surrounding events through the artificial intelligence model. As opposed to the conventional driver system, the ground plan transforms into algorithmic matrix guidance for the autonomous agent.

According to the Society of Automotive Engineers (SAE), there are six different autonomation level within autonomous vehicles (diagram 0.2). The current autonomous vehicle is achieving level 3 autonomy.

Level of Driving Automation Diagram 0.2 (Leong, 2021) Source: The 6 Levels of Vehicle Autonomy Explained (Synopsys, n.d.)

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e732” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </82 101 115 101 97 114 99 104 32 77 101 116 104 111 100 32 83 116 97 116 101 109 101 110 116 class WeightReader:

def __init__(self, weight_file): with open(weight_file, ‘rb’) as w_f: major, = struct.unpack(‘i’, w_f.read(4)) minor, = struct.unpack(‘i’, w_f.read(4)) revision, = struct.unpack(‘i’, w_f.read(4)) if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:

w_f.read(8)

else:

w_f.read(4) transpose = (major > 1000) or (minor > 1000) binary = w_f.read() self.offset = 0 self.all_weights = np.frombuffer(binary, dtype=’float32’)

def read_bytes(self, size): self.offset = self.offset + size return self.all_weights[self.offset-size:self.offset]

def load_weights(self, model):

for i in range(106):

try: conv_layer = model.get_layer(‘conv_’ + str(i)) t import requests response = requests.post(ocr_url, headers=headers, params=params, json=data) response.raise_for_status() analysis = response.json() print analysis { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 105 115 32 116 104 101 115 105 115 32 105 115 32 97 100 100 114 101 115 115 105 110 103 32 97 32 99 114 105 116 105 99 97 108 32 97 110 97 108 121 115 105 115 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 44 32 117 115 105 110 103 32 105 116 32 116 111 32 100 101 118 101 108 111 112 32 97 32 115 121 109 98 105 111 116 105 99 32 114 101 108 97 116 105 111 110 115 104 105 112 32 98 101 116 119 101 101 110 32 115 101 110 116 105 101 110 116 32 97 103 101 110 116 32 40 104 117 109 97 110 41 32 97 110 100 32 116 104 101 32 105 110 116 101 108 108 105 103 101 110 116 32 97 103 101 110 116 32 40 65 73 32 109 97 99 104 105 110 101 41 32 116 104 114 111 117 103 104 32 97 32 110 101 119 32 102 111 114 109 32 111 102 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 46 32 73 116 32 115 101 101 107 115 32 116 111 32 105 100 101 110 116 105 102 121 32 99 117 116 116 105 110 103 45 101

<< This side of the page is intended for machine reading

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Research Method Statement

>> This side of the page is intended for human reading

Critical thinking This thesis conducts a critical analysis of machine vision, using it to develop a symbiotic relationship between the sentient agent (human) and the intelligent agent (AI machine) through a new form of the built environment. It seeks to identify cuttingedge technology of machine vision and the problem of artificial intelligence, reflecting on Meredith Broussard’s theory of ‘artificial unintelligence’ (Broussard, 2019). It gives a hypothesis which the built environment will reshape through the lens of machine vision. This research establishes around past and present discourses on the machine vision system; aims to create discussion and transformative impact across all relevant sectors on the future of the built environment. This thesis focuses equally on the ubiquitous artificial intelligence technology and nature environmental issues that cause noticeable changes to human lives. It provokes critical thinking to reshape the urban fabric and placing the natural order back in place.

building

car person person person

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e734” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

</ 77 101 116 104 111 100 111 108 111 103 121 10 </80 114 105 109 97 114 121 32 114 101 115 101 97 114 99 104 32 109

{

101 116 104 111 100 </83 101 99 111 110 100 97 114 121 32 114 101 115 101 97 114 99 104 32 109 101 116 104 111 100

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 105 115 32 116 104 101 115 105 115 32 105 115 32 97 32 99 97 115 101 32 115 116 117 100 105 101 115 32 114 101 115 101 97 114 99 104 44 32 114 101 97 108 105 115 101 100 32 116 104 114 111 117 103 104 32 109 105 120 101 100 32 109 101 116 104 111 100 115 46 32 84 104 101 32 114 101 115 101 97 114 99 104 32 99 111 110 115 105 115 116 115 32 109 111 115 116 108 121 32 111 102 32 113 117 97 108 105 116 97 116 105 118 101 32 100 97 116 97 44 32 113 117 97 110 116 105 116 97 116 105 118 101 32 100 97 116 97 32 105 115 32 114 101 102 108 101 99 116 101 100 32 105 110 32 115 116 97 116 105 115 116 105 99 97 108 32 100 97 116 97 32 119 104 101 114 101 32 110 101 101 100 101 100 46 32 83 101 99 111 110 100 97 114 121 32 114 101 115 101 97 114 99 104 32 104 97 115 32 98 101 101 110 32 112 114 101 100 111 109 105 110 97 110 116 108 121 32 117 115 101 100 46 32 73 116 32 105 110 99 108 117 100 101 100 32 116 104 101 111 114 105 101 115 32 97 110 100 32 115 116 97 116 101 109 101 110 116 32 102 114 111 109 32 114 101 115 101 97 114 99 104 101 114 115 44 32 99 111 109 112 117 116 101 114 32 115 99 105 101 110 116 105 115 116 115 44 32 97 114 116 105 115 116 115 44 32 104 105 115 116 111 114 105 97 110 115 44 32 97 110 100 32 97 114 99 104 105 116 101 99 116 115 32 116 104 97 116 32 113 117 111 116 101 100 32 105 110 32 97 114 116 105 99 108 101 115 44 32 106 111 117 114 110 97 108 115 44 32 119 101 98 32 97 114 116 105 99 108 101 115 44 32 98 111 111 107 115 44 32 100 111 99 117 109 101 110 116 97 114 105 101 115 44 32 97 110 100 32 101 120 104 105 98 105 116 105 111 110 115 46 32 67 97 115 101 32 115 116 117 100 105 101 115 32 102 114 111 109 32 97 32 119 105 100 101 32 114 97 110 103 101 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 105 110 110 111 118 97 116 105 111 110 32 114 101 115 101 97 114 99 104 32 112 97 112 101 114 115 32 102 114 111 109 32 100 105 102 102 101 114 101 110 116 32 111 114 103 97 110 105 115 97 116 105 111 110 115 32 105 115 32 99 111 110 100 117 99 116 101 100 32 116 111 32 98 117 105 108 100 32 117 112 32 102 117 110 100 97 109 101 110 116 97 108 32 117 110 100 101 114 115 116 97 110 100 105 110 103 32 111 102 32 116 104 101 32 97 112 112 108 105 99 97 116 105 111 110 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 105 110 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 46 32 84 104 105 115 32 105 115 32 105 109 112 111 114 116 97 110 116 32 116 111 32 112 114 111 118 105 100 101 32 97 32 112 114 111 118 105 110 103 32 103 114 111 117 110 100 32 102 111 114 32 116 104 101 32 112 114 97 99 116 105 99 97 108 105 116 121 32 111 102 32 116 104 101 32 115 112 101 99 117 108 97 116 105 118 101 32 100 101 115 105 103 110 32 99 111 110 99 108 117 115 105 111 110 46 10

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Research Method Statement

>> This side of the page is intended for human reading

Methodology This thesis is a case study research on computerlogy, realised through mixed methods. It consists essentially of qualitative data, where quantitative data is viewing in statistical information where needed. Secondary research has been predominantly used. It included theories and statement from researchers, computer scientists, artists, historians, and architects; found in articles, journals, web articles, books, documentaries, and exhibitions. This thesis also builds upon a wide range of artificial intelligence research papers that allow a theoretical understanding of the application of artificial intelligence in machine vision. The research is crucial to provide a proving ground for the practicality of the speculative design conclusion. Furthermore, existing online artificial intelligence systems are applied to generate machine-readable text and images.

Primary research method An interview is conducted with the leading 3D scanning practice, ScanLAB projects, to gain insight into the cutting-edge technology. The studio has a rich working experience with machine vision which can provide accurate and honest information about the technology.

Secondary research method The research gains latest information of machine vision technology and artificial intelligence from: Open AI > text and image processing using AI WIRED > current news of technology BLDGBLOG > discussion and speculation on machine vision MIT Senseable city lab > data collection and analysis using AI Documentaries & Podcast BBC Radio 4 (2019). James Bridle // New Ways of Seeing tw19751 (2012). John Berger / Ways of Seeing , Episode 1 -4 (1972) YouTube originals (2020). The Age of A.I. Netflix (2020). The Social Dilemma Key books and theories Bridle, J., 2018.

New Dark Age. Brooklyn, NY: Verso.

Broussard, M., 2019.

Artificial Unintelligence.

Massachusetts: MIT press.

Greenfield, A., 2017.

Radical Technologies.

Brooklyn, NY: Verso.

Ratti, C. and Claudel, M., 2016.

The City of Tomorrow.

New Haven and London: Yale University Press Shepard, M., 2011.

Sentient city.

Massachusetts: MIT Press.

Young, L., 2019.

Machine Landscapes: Architectures of the Post-Anthropocene.

Architectural Design, (257).

> Chasm and complexity of machine and artificial intelligence > Chasm and complexity of machine and artificial intelligence > Automation, machine learning and artificial intelligence > Futurecraft

> Sentient city/ too smart city

> Application of machine vision and artificial intelligence

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e736” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

</82 101 97 100 101 114 115 104 105 112 </82 101 108 101 118 97 110 99 101 32 97 110 100 32 99 111 110 115

101 113 117 101 110 99 101 115 </83 101 108 102 45 99 114 105 116 105 99 105 115 109 115

Object recognition files import from keras.layers import Conv2D from keras.layers import Input from keras.layers import BatchNormalization from keras.layers import LeakyReLU from keras.layers import ZeroPadding2D from keras.layers import UpSampling2D from keras.layers.merge import add, concatenate from keras.models import Model

def _conv_block(inp, convs, skip=True):

x = inp

count = 0

for conv in convs:

if count == (len(convs) - 2) and skip:

skip_connection = x

count += 1

if conv[‘stride’] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top

x = Conv2D(conv[‘filter’],

conv[‘kernel’],

strides=conv[‘stride’] import requests response = requests.post(ocr_url, headers=headers, params=params, json=data) response.raise_for_status() analysis = response.json() print analysis { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 105 115 32 116 104 101 115 105 115 32 116 97 114 103 101 116 101 100 32 112 114 101 100 111 109 105 110 97 110 116 108 121 32 97 116 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 112 114 111 102 101 115 115 105 111 110 115 32 119 105 116 104 32 97 32 98 97 115 105 99 32 117 110 100 101 114 115 116 97 110 100 105 110 103 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 97 110 100 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 46 32 71 108 111 115 115 97 114 121 32 119 97 115 32 112 114 111 118 105 100 101 100 32 116 111 32 104 101 108 112 32 116 104 101 32 114 101 97 100 101 114 32 117 110 100 101 114 115 116 97 110 100 115 32 116 104 101 32 116 101 114 109 105 110 111 108 111 103 105 101 115 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 46 32 66 101 115 105 100 101 115 44 32 105 116 32 98 114 111 117 103 104 116 32 97 116 116 101 110 116 105 111

<< This side of the page is intended for machine reading

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Research Method Statement

>> This side of the page is intended for human reading

Readership This thesis predominantly targets architectural professions with a basic understanding of artificial intelligence and machine learning. A glossary is provided to assist readers in understanding the terminologies of artificial intelligence. Besides, it brings attention to the urban planner, social theorists, tech giants and other relevant professionals. person

building

person

car person

Relevance and consequences This thesis highlights the urgent need for architects and other professionals to (re)define a symbiosis relationship between sentience agents and intelligent agents that brings an underlying value to the built environment. As much as artificial intelligence has impacted human’s daily life, this thesis set out the effects and problems of intelligent machine. Available research and discussions have essentially focused on applications of machine vision in various sectors. However, there is still a lack of discourse on how machine vision may impact architectural design. By viewing through different machine vision dimensions, this thesis provides a new research opportunity and draws out initial propositions for the future city that is ready for intelligent agents. This thesis hopes to create a dialogue between the professionals (architect and others) and the users (public), promoting close collaboration between the different perspectives to invoke a different outcome.

Self-criticisms An initial obstacle lies in this thesis to operate in a higher dimension (fourth and the fifth dimension) that is beyond human’s visual perception. The difficulty increases with the limitation to project all information on a two-dimensional surface. Machine vision and human vision is a parallel system that humans only understand through our language. There might be undiscovered issues due to the existing barrier of both agents. Science fiction illustrations often become an unrealistic visualisation reference, which raises the conflict between the realist and the virtualist. Therefore, the thesis will perform critical studies through existing literature and concepts to support the arguments and increase the credibility of the speculation.

Besides, the research shifts away from core books, referring to online resources such as online news, web article and video due to the nature of the research topic that highly relies on current events. Machine vision also appears as a new topic among architects and designers, which leads to a lack of architectural case studies. Thus, this thesis will investigate examples from other sectors such as product design and computer science, using them to reflect upon the architectural design and forms an initial architectural discourse on machine vision and the built environment.

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e738” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </70 111 114 109 32 </83 116 114 117 99 116 117 114 101 32 </83 112 101 99 117 108 97 116 105 111 110 32 97 110 100 32 99 111 110 99 108 117 115 105 111 110 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 105 115 32 116 104 101 115 105 115 32 100 114 97 119 115 32 111 117 116 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 97 110 100 32 104 117 109 97 110 32 118 105 115 105 111 110 32 105 110 32 102 105 118 101 32 100 105 102 102 101 114 101 110 116 32 100 105 109 101 110 115 105 111 110 115 46 32 80 108 97 99 105 110 103 32 98 111 116 104 32 118 105 115 105 111 110 115 32 110 101 120 116 32 116 111 32 101 97 99 104 32 111 116 104 101 114 32 112 114 111 118 105 100 101 32 97 110 32 105 110 115 116 97 110 116 32 100 105 115 116 105 110 99 116 105 111 110 32 98 101 116 119 101 101 110 32 116 104 101 32 112 97 114 97 108 108 101 108 32 118 105 115 117 97 108 32 115 121 115 116 101 109 46 32 84 104 101 32 118 105 115 117 97 108 32 99 111 110 116 101 110 116 32 111 102 32 116 104 105 115 32 116 104 101 115 105 115 32 97 105 109 115 32 116 111 32 114 101 97 99 104 32 111 117 116 32 116 111 32 116 104 101 32 112 117 98 108 105 99 32 114 101 97 100 101 114 115 44 32 116 114 97 110 115 108 97 116 105 110 103 32 99 111 109 112 108 101 120 32 97 114 103 117 109 101 110 116 115 32 105 110 116 111 32 97 32 109 111 114 101 32 101 110 103 97 103 105 110 103 32 102 111 114 109 97 116 46 32 84 104 105 115 32 105 115 32 116 111 32 105 110 99 114 101 97 115 101 32 99 111 108 108 101 99 116 105 118 101 32 97 119 97 114 101 110 101 115 115 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 32 97 110 100 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 97 110 100 32 116 111 32 105 110 99 108 117 100 101 32 116 104 101 32 118 111 105 99 101 32 111 102 32 112 117 98 108 105 99 32 100 105 114 101 99 116 108 121 32 105 110 116 111 32 116 104 101 32 100 101 115 105 103 110 32 111 102 32 116 104 101 32 102 117 116 117 114 101 32 99 105 116 121 46 32 32 10 ---------84 104 101 32 116 104 101 115 105 115 32 98 114 101 97 107 115 32 100 111 119 110 32 105 110 116 111 32 102 105 118 101 32 100 105 102 102 101 114 101 110 116 32 100 105 109 101 110 115 105 111 110 115 44 32 119 104 105 99 104 32 105 115 32 116 104 101 32 102 117 110 100 97 109 101 110 116 97 108 32 101 108 101 109 101 110 116 32 111 102 32 118 105 115 117 97 108 32 115 121 115 116 101 109 115 46 32 69 97 99 104 32 99 104 97 112 116 101 114 32 97 110 97 108 121 115 101 115 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 105 110 32 114 101 115 112 101 99 116 105 118 101 32 100 105 109 101 110 115 105 111 110 115 32 97 110 100 32 99 111 110 99 108 117 100 101 100 32 119 105 116 104 32 116 104 101 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 114 101 115 112 111 110 115 101 46 32 73 116 32 105 110 99 108 117 100 101 115 32 116 104 101 32 115 101 99 114 101 116 32 100 105 109 101 110 115 105 111 110 115 32 40 102 111 117 114 116 104 32 97 110

<< This side of the page is intended for machine reading

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>> This side of the page is intended for human reading

Research Method Statement Form This thesis draws out machine vision and human vision in five different dimensions, aiming to unveil the invisible machine vision. It places both visual representations next to each other to provide an instant distinction between the parallel systems. The left side of the spread is a machine-readable document, in which letters appear in numeric forms and images are translating to patterns or codes. The visual content aims to reach out to public readers, translating complex arguments into a more engaging format. By this, architects will gather the public’s opinion that is crucial for the future built environment design.

Structure The thesis breaks down into five chapters, which form around the basics of the spatial dimension system. Each chapter analyses machine vision in respective spatial dimensions and concluded with architectural responses. It includes the unknown dimensions (fourth and fifth), which beyond human vision capability to speculate around the future of spatial experiences. The fourth dimension suggests a physical space with time, and the fifth dimension is projecting towards virtual space. It stops at the fifth dimension to leave suspense and an open ending discussion among the readers.

Speculation and conclusion The speculations focus on architectural designs and urban environments changes through critical analysis of the machine vision system. They assume that the successful transformation to the future city will create an ideal environment for sentience agents and intelligent agents. The thesis predicts that autonomous agents will be the cartographer of the future urban environment. Humans and machines will achieve a symbiosis relationship. Thus, present actions are required as “any strategy for living in the new [machine] age depends upon attention to the here and now” (Bridle, 2018: 252). Ratti’s senseable city, Shepard’s sentient city, and Archigram’s instant city are adopted to envision the future city in this thesis.

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e740” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </60 47 82 101 97 100 105 110 103 95 109 101 116 104 111 100 111 108 111 103 121 # line drawing import box # Initializing the box b = box.Box() l = 50 b.line(l) # Palindrome Python One-Liner phrase.find(phrase[::-1]) # Swap Two Variables Python One-Liner a, b = b, a

<< This side of the page is intended for machine reading

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# Sum Over Every Other Value Python One-Liner sum(stock_prices[::2]) # Read File Python One-Liner [line.strip() for line in open(filename)] # Factorial Python One-Liner reduce(lambda x, y: x * y, range(1, n+1)) # Performance Profiling Python One-Liner python -m cProfile foo.py # Superset Python One-Liner lambda l: reduce(lambda z, x: z + [y + [x] for y in z], l, [[]]) # Fibonacci Python One-Liner lambda x: x if x<=1 else fib(x-1) + fib(x-2) # Quicksort Python One-liner lambda L: [] if L==[] else qsort([x for x in L[1:] if x< L[0]]) + L[0:1] + qsort([x for x in L[1:] if x>=L[0]]) # Sieve of Eratosthenes Python One-liner reduce( (lambda r,x: r-set(range(x**2,n,x)) if (x in r) else r), range(2,int(n**0.5)), set(range(2,n))) > text input _ 8-bits signed integer 104 111 119 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 99 104 97 110 103 101 32 101 110 118 105 114 111 110 109 101 110 116 32 10 119 104 97

Research Method Statement

>> This side of the page is intended for human reading

Reading methodology

How machine vision change the built environment?

4D Time Digital dust and collective data Senseable city

Architectural responses

What is the future of the built environment?

5D Invisibility Code and algorithm Speculation

1

Speculative design Conclusion

3D Light and depth Application and impact Boston dynamics & ScanLAB 2D Patterns and movement Data impact Amazon fulfillment centre & amazon go 1D Lines and codes Barcode building

Operation of machine vision system in the built environment

Abstract Introduction Glossary RMS

0

Reading methodology Diagram 0.3 (Leong, 2021) Source: Author

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e742” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </ 49 68 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 65 32 115 112 97 99 101 32 116 104 97 116 32 98 117 105 108 100 115 32 117 112 32 111 102 32 108 105 110 101 115 32 105 110 32 119 104 105 99 104 32 101 97 99 104 32 111 98 106 101 99 116 32 112 111 115 115 101 115 115 101 115 32 97 32 117 110 105 113 117 101 32 99 111 100 101 32 97 115 32 105 116 115 32 105 100 101 110 116 105 116 121 46 32 32 10’ } ] } ] } ] }

1D

C H A PTE R ON E

A sp a c e t h a t b u i l d s u p o f lin es in wh ich e a ch o b je c t pos s e s s e s a unique c ode as its ide ntity.

First dimension Figure 1.1 (Leong, 2021) Source: Author

440

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e744” headers = {‘Ocp-Apim-Subscription-Key’: sub} { {

</102 141 162 143 157 144 145

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 102 141 162 143 157 144 145 40 143 157 156 163 151 163 164 163 40 157 146 40 141 40 163 145 162 151 145 163 40 157 146 40 154 151 156 145 163 40 141 156 144 40 143 157 156 164 162 141 163 164 151 156 147 40 143 157 154 157 165 162 54 40 167 151 164 150 40 144 151 146 146 145 162 145 156 164 40 167 151 144 164 150 40 141 156 144 40 147 141 160 163 56 40 111 164 40 151 163 40 141 40 163 171 155 142 157 154 157 147 171 40 164 150 141 164 40 141 144 157 160 164 145 144 40 147 154 157 142 141 154 154 171 40 163 151 156 143 145 40 61 71 67 64 56 40 111 164 40 144 157 145 163 40 156 157 164 40 157 156 154 171 40 141 160 160 145 141 162 40 157 156 40 164 150 145 40 147 162 157 143 145 162 171 40 160 162 157 144 165 143 164 163 40 156 157 167 141 144 141 171 163 54 40 142 165 164 40 157 156 40 164 150 145 40 141 151 162 40 164 151 143 153 145 164 40 157 162 40 141 156 171 40 157 164 150 145 162 40 151 156 166 145 156 164 157 162 151 145 163 40 164 150 141 164 40 156 145 145 144 40 164 157 40 142 145 40 164 162 141 143 153 145 144 40 141 156 144 40 162 145 141 144 40 151 156 163 164 141 156 164 154 171 56 40 124 150 145 40 154 151 156 145 163 40 151 156 143 162 145 141 163 145 40 160 162 157 160 157 162 164 151 157 156 141 154 154 171 40 151 156 40 167 151 144 164 150 40 164 157 40 162 145 160 162 145 163 145 156 164 40 144 151 146 146 145 162 145 156 164 40 156 165 155 142 145 162 163 40 164 150 141 164 40 145 156 143 157 144 145 40 151 156 146 157 162 155 141 164 151 157 156 56 40 124 150 145 40 142 141 162 143 157 144 145 40 151 163 40 162 145 141 144 141 142 154 145 40 142 171 40 164 150 145 40 157 160 164 151 143 141 154 40 163 143 141 156 156 145 162 40 145 166 145 156 40 151 156 40 165 160 163 151 144 145 40 144 157 167 156 40 160 157 163 151 164 151 157 156 54 40 142 145 143 141 165 163 145 40 157 146 40 164 150 145 40 143 150 145 143 153 40 144 151 147 151 164 40 164 150 141 164 40 143 157 155 145 40 151 156 40 164 150 145 40 145 156 144 40 157 146 40 164 150 145 40 156 165 155 142 145 162 40 143 150 141 151 156 40 141 156 144 40 164 150 145 40 144 151 146 146 145 162 145 156 164 40 147 141 160 163 40 142 145 164 167 145 145 156 40 164 150 145 40 154 151 156 145 163 40 151 156 40 144 151 146 146 145 162 145 156 164 40 163 145 143 164 151 157 156 163 40 157 146 40 164 150 145 40 142 141 162 143 157 144 145 56 40 124 150 145 40 142 141 162 143 157 144 145 40 151 163 40 141 40 163 151 155 160 154 151 146 151 145 144 40 163 171 155 142 157 154 40 146 157 162 40 164 150 145 40 160 162 157 144 165 143 164 163 54 40 167 151 164 150 40 151 164 163 40 151 156 146 157 162 155 141 164 151 157 156 40 163 165 143 150 40 141 163 40 160 162 151 143 145 40 141 156 144 40 163 164 157 143 153 40 156 165 155 142 145 162 40 163 164 157 162 145 40 151 156 40 164 150 145 40 143 157 156 156 145 143 164 145 144 40 143 157 155 160 165 164 145 162 40 144 141 164 141 142 141 163 145 56 40 125 156 154 151 153 145 40 164 150 145 40 156 157 162 155 141 154 40 154 151 147 150 164 54 40 154 141 163 145 162 40 151 163 40 165 163 145 144 40 164 157 40 162 145 141 144 40 142 141 162 143 157 144 145 40 142 145 143 141 165 163 145 40 157 146 40 151 164

Barcode Barcode consists of a series of lines and contrasting colour, with different width and gaps. It remains the simplest form of the machine vision system that still widely adopted in retail and manufacturing, appearing on any inventories that need to be tracked and read instantly. The lines increase proportionally in width to represent a series of numbers that link to inventories information. The check digit (diagram 1.1) comes at the end of the number chain, and the specific gap systems across sections of the barcode allow the machine to detect the correct barcode orientation and information in a split second. The barcode is a simplified symbol for the products, with information such as price and stock number store in the connected computer database.

The barcode is readable by the optical scanner. Unlike ordinary light, the laser beam uses to read barcode because of its coherence, as it emits narrow light beams that remain at a similar wavelength over a great distance. A laser can also focus on a tiny spot, which is the ideal tool for accurate detection.

Left hand guard bars pattern (BWB)

Number system digit

Today, the barcode remains a standardised global identifier due to its simplicity and the affordability of the machine vision system.

Module check digit bars

Centre bars (WBWBW)

Number system Manufacturer bars ID bars

Manufacturer ID number

Item bars

Item number

Right hand guard bars pattern (BWB)

Module check digit

Barcode Digram 1.1 (Leong, 2021) Source: You Can Learn to Read Barcode! (Electronic Imaging Materials, Inc., n.d.)

640

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e746” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

</84 104 101 32 115 112 97 116 105 97 108 32 108 97 121 111 117 116

{

32 99 111 110 115 101 113 117 101 110 99 101 115 32 111 102 32 116 104 101 32 98 97 114 99 111 100 101

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 68 117 114 105 110 103 32 116 104 101 32 97 103 101 32 111 102 32 105 110 100 117 115 116 114 105 97 108 105 115 97 116 105 111 110 44 32 98 97 114 99 111 100 101 32 98 101 99 111 109 101 115 32 116 104 101 32 99 117 116 116 105 110 103 45 101 100 103 101 32 116 101 99 104 110 111 108 111 103 121 32 116 104 97 116 32 115 104 105 102 116 101 100 32 103 108 111 98 97 108 32 101 99 111 110 111 109 121 32 112 101 114 115 112 101 99 116 105 118 101 46 32 66 97 114 99 111 100 101 32 97 115 32 116 104 101 32 105 110 102 111 114 109 97 116 105 111 110 32 97 110 100 32 99 111 109 109 117 110 105 99 97 116 105 111 110 115 32 116 101 99 104 110 111 108 111 103 121 32 40 73 67 84 41 44 32 98 105 103 32 114 101 116 97 105 108 115 32 97 98 108 101 32 116 111 32 116 114 97 110 115 102 111 114 109 32 116 104 101 105 114 32 109 111 100 101 32 111 102 32 114 101 116 97 105 108 105 110 103 32 97 110 100 32 109 97 110 117 102 97 99 116 117 114 105 110 103 46 32 66 117 116 32 105 116 32 114 101 113 117 105 114 101 115 32 105 110 118 111 108 118 101 109 101 110 116 115 32 102 114 111 109 32 97 108 108 32 105 110 100 117 115 116 114 105 97 108 32 115 101 99 116 111 114 115 32 116 111 32 100 101 114 105 118 101 32 98 101 110 101 102 105 116 115 32 102 114 111 109 32 105 116 46 32 73 116 32 119 97 115 32 97 32 115 105 103 110 32 111 102 32 103 108 111 98 97 108 32 99 97 112 105 116 97 108 105 115 109 32 97 116 32 116 104 97 116 32 109 111 109 101 110 116 44 32 119 104 105 99 104 32 116 104 101 32 98 105 103 32 114 101 116 97 105 108 115 32 104 97 100 32 116 104 101 32 97 98 105 108 105 116 121 32 116 111 32 105 110 118 101 115 116 32 105 110 32 115 99 97 110 110 101 114 115 32 119 104 105 108 101 32 116 104 101 32 115 109 97 108 108 32 111 110 101 32 115 116 114 117 103 103 108 101 32 116 111 32 112 97 121 32 116 104 101 32 117 110 110 101 99 101 115 115 97 114 121 32 99 111 115 116 32 97 110 100 32 112 108 97 99 101 32 116 104 101 109 115 101 108 118 101 115 32 111 117 116 32 102 114 111 109 32 116 104 101 32 105 110 100 117 115 116 114 105 97 108 32 108 111 111 112 46 32 84 104 105 115 32 101 102 102 101 99 116 32 116 104 101 32 115 99 97 108 101 32 111 102 32 116 104 101 32 114 101 116 97 105 108 115 44 32 116 104 101 32 99 111 110 118 101 110 105 101 110 99 101 32 105 110 32 109 97 110 97 103 105 110 103 32 105 110 118 101 110 116 111 114 105 101 115 32 97 108 108 111 119 32 114 101 116 97 105 108 101 114 115 32 116 111 32 112 117 114 99 104 97 115 101 32 109 111 114 101 32 112 114 111 100 117 99 116 115 46 32 65 108 115 111 32 108 101 97 100 32 116 111 32 116 104 101 32 114 101 99 111 110 115 105 100 101 114 97 116 105 111 110 32 111 102 32 116 104 101 32 116 105 108 108 32 97 114 114 97 110 103 101 109 101 110 116 32 97 110 100 32 113 117 101 117 101 32 115 112 97 99 101 32 111 114 103 97 110 105 115 97 116 105 111 110 32 100 117 101 32 116 111 32 116 104 101 32 115 104 111 114 116 101 114 32 113 117 101 117 101 32 114 101 116 97 105 108 101 114 32 105 115 32 100 101 97 108 105

The spatial layout In 1974, the first working barcode was found on the Wrigley Juicy Fruit gum in an Ohio grocery store. Since then, the barcode system becomes the cutting-edge information and communications technology that shifted the global economic perspective. The big retailer can upgrade their retailing and manufacturing mode through barcode system, which may directly affect the retail scale.

Furthermore, the convenience in managing inventories allow retailers to purchase more products. The barcode system also boosts checkout processes, and leads to the reconsideration of the till arrangement and queue space organisation. Overall retailing and shopping experience increase with the redesign of inventory packaging to include barcode and remodel of the rack system to incorporate the general price tag on the rack instead of each item. Andrea Gursky’s 99 Cent (figure 1.2) captures a retail space with the different inventories arrangement and inventories variety increment. The barcode system is transforming the spatial layout even during the age of early industrialisation and mass production. The built environment can incorporate a similar labelling system to clusterise and label places in this fourth industrialisation revolution1.

1

Fourth industrialisation revolution (Industry 4.0) is automatising the traditional manufacturing. It is using smart technology such as artificial intelligence to blur the lines between physical, digital and biological worlds. It will create new opportunities as well as challenges that cannot be ignored (Department for Business, Energy & Industrial Strategy, 2019).

99 Cent Figure 1.2 (Gursky, 1999)

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e748” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

</73 109 112 97 99 116 32 111 102 32 98 97 114 99 111 100 101 32 111

{

110 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 100 101 115 105 103 110 32

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 79 110 32 116 104 101 32 111 116 104 101 114 32 115 105 100 101 44 32 116 104 101 32 98 97 114 99 111 100 101 32 100 111 101 115 32 108 105 116 116 108 101 32 105 110 102 108 117 101 110 99 101 32 111 110 32 99 117 114 114 101 110 116 32 97 114 99 104 105 116 101 99 116 117 114 101 32 100 101 115 105 103 110 44 32 105 116 32 97 99 116 115 32 109 111 115 116 108 121 32 97 115 32 97 110 32 97 101 115 116 104 101 116 105 99 32 105 110 115 112 105 114 97 116 105 111 110 46 32 80 117 98 108 105 99 32 103 101 116 32 97 110 32 105 109 112 114 101 115 115 105 111 110 32 111 102 32 98 97 114 99 111 100 101 32 115 101 101 105 110 103 32 116 104 101 32 109 111 110 111 99 104 114 111 109 101 44 32 118 101 114 116 105 99 97 108 32 115 116 114 105 112 32 102 97 99 97 100 101 46 32 84 104 101 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 100 101 115 105 103 110 101 114 115 32 103 101 116 32 100 105 114 101 99 116 32 105 110 115 112 105 114 97 116 105 111 110 32 102 114 111 109 32 116 104 101 32 105 99 111 110 105 99 32 98 97 114 99 111 100 101 32 116 111 32 112 114 101 115 101 110 116 32 116 104 101 32 109 111 115 116 32 115 116 114 97 105 103 104 116 32 102 111 114 119 97 114 100 32 114 101 115 112 111 110 115 101 32 111 110 32 116 104 101 32 101 120 116 101 114 110 97 108 32 102 97 -61 -89 97 100 101 44 32 97 115 32 105 102 32 98 117 105 108 100 105 110 103 32 105 116 115 101 108 102 32 98 101 99 111 109 105 110 103 32 97 32 103 105 97 110 116 32 115 105 103 110 98 111 97 114 100 46 32 82 101 99 97 108 108 32 73 110 102 111 114 109 97 116 105 111 110 32 67 101 110 116 114 101 32 108 111 99 97 116 101 100 32 97 116 32 78 101 119 32 83 111 117 116 104 32 87 97 108 101 115 44 32 65 117 115 116 114 97 108 105 97 44 32 99 108 97 100 100 101 100 32 105 116 115 32 119 97 114 101 104 111 117 115 101 32 119 105 116 104 32 98 108 97 99 107 32 97 110 100 32 119 104 105 116 101 32 115 116 101 101 108 32 102 97 -61 -89 97 100 101 32 116 111 32 112 111 114 116 114 97 105 116 32 97 32 98 97 114 99 111 100 101 32 116 104 97 116 32 97 115 115 101 109 98 108 101 32 116 104 101 105 114 32 99 111 109 112 97 110 121 32 98 117 115 105 110 101 115 115 32 110 117 109 98 101 114 46 32 -30 -128 -100 66 97 114 99 111 100 101 115 32 97 114 101 32 97 116 32 116 104 101 32 99 101 110 116 114 101 32 111 102 32 101 118 101 114 121 116 104 105 110 103 32 119 104 105 99 104 32 82 101 99 97 108 108 32 100 111 101 115 44 -30 -128 -99 32 115 97 105 100 32 116 104 101 32 97 114 99 104 105 116 101 99 116 32 119 104 111 32 112 114 111 112 111 115 101 100 32 116 104 101 32 98 97 114 99 111 100 101 32 102 97 -61 -89 97 100 101 32 105 100 101 97 32 40 76 89 83 65 71 72 84 44 32 110 46 100 46 41 46 32 84 104 111 117 103 104 44 32 116 104 101 32 98 97 114 99 111 100 101 32 105 115 32 98 121 32 110 111 32 109 101 97 110 115 32 116 111 32 98 101 32 114 101 97 100 97 98 108 101 32 98 121 32 104 117 109 97 110 44 32 117 110 108 101 115 115 32 116 104 111 115 101 32 105 110 100 105 118 105 100 117 97

Aesthetic and function

Hitherto the barcode system does little influence besides acting predominantly as aesthetic inspiration in architectural design. The designer gets direct inspiration from the iconic barcode to present the most straight forward response on the external façade as if the building itself becomes a giant signboard. Recall Information Centre (figure 1.4) finished its warehouse with black and white steel façade that resembles a barcode language of its company business number. “Barcodes are at the centre of everything which Recall does,” said the architect who proposed the barcode façade scheme (LYSAGHT, n.d.). The customised and individualised barcode façade affects the building status, making it stands out from the sea of warehouses (Specifier, n.d.). Beyond the pure aesthetic purposes, the barcode façade transforms into a purposeful architectural element. Shtrikh Kod Building (figure 1.3) applies vertical slits window in between the solid cladding, creating a contrasting barcode-like effect while bringing light into the internal space. The project consists of two red boxes located side by side, retaining the same architectural language but different barcodes. The dissimilarity is doubtful if it carries no significant purpose.

It raises the question what kind of algorithmic information should be encoded into architecture if there is a giant machine that moves around the city, constantly seeing and interpreting the built environment?

Shtrikh Kod Building, Russia Figure 1.3 (Vutuvius&Sons architectural studio, n.d.)

Recall Information Centre, New South Wales, Australia Figure 1.4 (Recall Information Centre, New South Wales, Australia, n.d.)

050

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e750” headers = {‘Ocp-Apim-Subscription-Key’: sub} { {

</50 68

‘language’:’en’, ‘orientation’:’Up’ {translated_ 8-bit sign integer: ‘

65 32 115 112 97 99 101 32 116 104 97 116 32 97 112 112 101 97 114 115 32 102 108 97 116 44 32 99 111 110 118 101 121 101 100 32 118 105 97 32 112 97 116 116 101 114 110 115 32 111 114 32 101 108 101 99 116 114 111 110 105 99 32 115 99 114 101 101 110 115 46’ }

}

]

}

]

}

]

< Image generated by GauGAN

2D

C H APTE R T WO

A sp a c e th a t a p p e a r s fla t, co n v e y e d v ia patte r ns or e le c tronic s c re e ns .

Second dimension Figure 2.01 (Leong, 2021) Source: Author

250

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e752” headers = {‘Ocp-Apim-Subscription-Key’: sub} { {

</84 104 101 95 102 108 97 116 95 105 109 97 103 101 115

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 101 32 105 110 118 101 110 116 105 111 110 32 111 102 32 50 68 32 98 97 114 99 111 100 101 32 97 108 108 111 119 115 32 109 111 114 101 32 105 110 102 111 114 109 97 116 105 111 110 32 116 111 32 98 101 32 101 110 99 111 100 101 100 32 105 110 32 116 104 101 32 112 97 116 116 101 114 110 46 32 84 104 101 32 50 68 32 98 97 114 99 111 100 101 32 105 115 32 114 101 97 100 97 98 108 101 32 98 121 32 108 97 115 101 114 32 115 99 97 110 110 101 114 32 97 110 100 32 105 109 97 103 101 114 115 46 32 81 117 105 99 107 32 114 101 115 112 111 110 115 101 32 40 81 82 41 32 99 111 100 101 32 116 104 97 116 32 101 110 99 111 100 101 115 32 119 105 116 104 32 119 101 98 115 105 116 101 32 111 114 32 97 112 112 108 105 99 97 116 105 111 110 32 97 99 99 101 115 115 32 108 105 110 107 32 105 115 32 119 105 100 101 108 121 32 97 100 111 112 116 101 100 32 116 111 32 115 104 111 119 32 105 110 102 111 114 109 97 116 105 111 110 32 105 110 32 97 32 109 111 114 101 32 97 99 99 101 115 115 105 98 108 101 32 119 97 121 46 32 84 97 107 105 110 103 32 97 109 97 122 111 110 32 102 117 108 102 105 108 109 101 110 116 32 99 101 110 116 114 101 32 97 115 32 97 110 32 101 120 97 109 112 108 101 44 32 81 82 32 99 111 100 101 115 32 97 114 101 32 108 97 121 105 110 103 32 97 114 111 117 110 100 32 116 104 101 32 119 97 114 101 104 111 117 115 101 32 111 110 32 116 104 101 32 103 114 105 100 32 112 97 116 116 101 114 110 44 32 108 101 116 116 105 110 103 32 116 104 101 32 114 111 98 111 116 105 99 32 100 114 105 118 101 114 32 117 110 105 116 32 40 82 68 85 41 32 116 111 32 105 100 101 110 116 105 102 121 32 105 116 115 32 119 97 121 32 116 111 32 116 104 101 32 112 105 99 107 101 114 46 32 84 104 101 32 81 82 32 99 111 100 101 32 105 115 32 97 32 114 111 97 100 32 115 105 103 110 32 102 111 114 32 116 104 101 32 114 111 98 111 116 44 32 116 104 97 116 32 105 115 32 99 111 110 110 101 99 116 105 110 103 32 116 111 32 97 32 99 101 110 116 114 97 108 32 99 111 110 116 114 111 108 32 115 121 115 116 101 109 32 115 117 112 101 114 118 105 115 101 115 32 98 121 32 104 117 109 97 110 46 32 84 104 101 32 82 68 85 115 32 100 111 32 98 101 121 111 110 100 32 115 101 110 100 105 110 103 32 105 110 118 101 110 116 111 114 105 101 115 32 116 111 32 116 104 101 32 112 105 99 107 101 114 44 32 105 116 32 99 97 114 114 105 101 115 32 111 117 116 32 111 98 106 101 99 116 32 115 111 114 116 97 116 105 111 110 46 32 66 97 99 107 32 116 104 101 110 44 32 104 117 109 97 110 32 108 97 98 111 117 114 115 32 97 114 101 32 110 101 101 100 101 100 32 116 111 32 115 111 114 116 32 111 117 116 32 116 104 101 32 112 114 111 100 117 99 116 115 32 97 99 99 111 114 100 105 110 103 32 116 111 32 116 104 101 32 116 114 97 100 105 116 105 111 110 97 108 32 105 110 101 102 102 105 99 105 101 110 116 32 97 108 112 104 97 98 101 116 105 99 97 108 32 115 111 114 116 97 116 105 111 110 32 109 101 116 104 111 100 46 32 84 104 101 32 109 97 99 104 105 110 101 32 105 110 116 101 108 108 105 103 101 110 99 101 32 97 110 97 108 121 115 101 115 32 116 104 101 32 97 108 103 111 114 105 116 104 109 32 111 102 32 98 117 121 101 114 32 112 114 101 102 101 114 101 110 99 101 32 97 110 100 32 115

The warehouse seems like complete chaos to human.

RDUs at Amazon Fulfillment Centre Figure 2.02 (Young, n.d.)

Flat images The invention of the matrix barcode allows the 2D patterns to contain more information.

Different from the 1D barcode, the matrix barcode is readable by laser scanners, and imagers. The matrix barcode such as Quick response (QR) code is encoded with a website or application access link to show information in a more accessible way. Taking Amazon fulfilment centre as an example, the robotic driver units (RDUs) can figure out their way to the picker via the QR codes. The QR codes lay around the warehouse on a grid pattern, connecting to the central human control system. Before this, manual labours sort out the products according to the traditional inefficient alphabetical sortation method. Now, machine intelligence carries out object sortation by analysing the algorithm of buyer preference and seasonal changes. The RDUs reflect on the algorithms and place the relevant inventories in the most accessible area to increase their efficiency. The human workers can no longer find a product without going through a hand-held machine device because of the new organisation system. As Bridle (2018: 116) states, “arranging the world from the perspective of the machine renders it computationally efficient but make it completely incomprehensible to humans.

Moreover, it accelerates their oppression.” The warehouse seems like complete chaos to human.

450

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e754” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 95 102 108 97 116 95 105 109 97 103 101 115 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 83 101 103 109 101 110 116 97 116 105 111 110 32 109 97 112 32 111 102 116 101 110 32 117 115 101 32 105 110 32 105 109 97 103 101 32 99 108 97 115 115 105 102 105 99 97 116 105 111 110 46 32 73 116 32 105 115 32 100 111 110 101 32 116 104 114 111 117 103 104 32 116 104 101 32 115 105 109 112 108 105 102 105 99 97 116 105 111 110 32 111 102 32 97 110 32 105 109 97 103 101 44 32 116 117 114 110 105 110 103 32 116 104 101 32 105 110 112 117 116 32 105 110 102 111 114 109 97 116 105 111 110 32 105 110 116 111 32 99 111 108 111 117 114 32 112 97 116 99 104 32 116 104 97 116 32 99 97 110 32 98 101 32 97 110 97 108 121 115 101 32 98 121 32 116 104 101 32 99 111 109 112 117 116 101 114 32 115 121 115 116 101 109 46 32 77 97 99 104 105 110 101 32 118 105 115 105 111 110 32 99 97 110 32 100 101 116 101 99 116 32 116 104 101 32 115 117 114 114 111 117 110 100 105 110 103 32 111 98 106 101 99 116 32 98 121 32 106 117 115 116 105 102 121 105 110 103 32 116 104 101 32 101 100 103 101 32 97 110 100 32 115 104 97 112 101 32 111 102 32 111 98 106 101 99 116 115 32 105 110 32 116 104 101 32 50 68 32 105 110 112 117 116 32 105 109 97 103 101 46 32 87 111 114 107 105 110 103 32 105 110 32 116 104 101 32 111 112 112 111 115 105 116 101 32 119 97 121 44 32 109 97 99 104 105 110 101 32 99 97 110 32 97 110 97 108 121 115 101 32 97 32 115 101 103 109 101 110 116 97 116 105 111 110 32 109 97 112 32 97 110 100 32 112 114 111 100 117 99 101 32 97 32 114 101 97 108 105 115 116 105 99 32 50 68 32 105 109 97 103 101 32 116 104 97 116 32 99 111 109 112 108 121 32 116 111 32 104 117 109 97 110 32 112 101 114 99 101 112 116 105 111 110 46 32 78 86 73 68 73 65 32 71 97 117 71 97 110 115 32 105 115 32 97 32 100 101 101 112 32 108 101 97 114 110 105 110 103 32 109 111 100 101 108 32 98 97 115 101 32 111 110 32 103 101 110 101 114 97 116 105 118 101 32 97 100 118 101 114 115 97 114 105 97 108 32 110 101 116 119 111 114 107 32 40 71 65 78 115 41 32 119 104 105 99 104 32 97 108 108 111 119 115 32 109 97 99 104 105 110 101 32 116 111 32 112 114 111 100 117 99 101 32 112 104 111 116 111 114 101 97 108 105 115 116 105 99 32 112 97 105 110 116 105 110 103 46 32 83 116 97 116 101 100 32 105 110 32 116 104 101 32 114 101 115 101 97 114 99 104 32 112 97 112 101 114 32 40 80 97 114 107 32 101 116 32 97 108 44 32 50 48 49 57 41 44 32 71 97 117 71 97 110 115 32 116 114 97 105 110 115 32 111 110 32 52 49 44 48 48 48 32 70 108 105 99 107 114 32 108 97 110 100 115 99 97 112 101 115 32 97 110 100 32 102 111 117 114 32 111 116 104 101 114 32 100 97 116 97 115 101 116 115 32 97 108 111 110 103 32 119 105 116 104 32 118 97 108 105 100 97 116 105 111 110 32 115 101 116 115 44 32 101 110 97 98 108 101 32 105 116 32 116 111 32 117 110 100 101 114 115 116 97 110 100 32 116 104 101 32 114 101 108 97 116 105 111 110 115 104 105 112 32 98 101 116 119 101 101 110 32 49 56 48 32 110 97 116 117 114 97 108 32 97 110 100 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 111 98 106 101 99 116 115 44 32 97 110 100 32 112 114 111 100 117 99 101 32 97 99 99 117 114 97 116 101 32 114 101 115 117 108 116 32 115 117 99 104 32 97 115 32 114 101 102 108 101 99 116 105

The digital image classification process often includes image segmentation. Image segmentation intends to simplify the input image into machine-processable abstract information. Machine vision can detect the surrounding object by justifying the edges and shapes found in the 2D input image. In reverse, the machine can transform a segmentation map into a realistic 2D image that complies with human perception. NVIDIA GauGAN1 is a deep learning model base on GANs, which allows the machine to produce a photorealistic painting. As stated in the research paper (Park et al., 2019), GauGAN trains on 41,000 Flickr landscapes and four other datasets along with validation sets, enable it to understand the relationship between 180 natural and built environment elements. The training process allows GauGAN to generate accurate painting results such as reflection on the water surface and plants transformation according to seasonal change. Such an artificial intelligence model is harmless to humans; it is merely a tool to spark creativity and expand one’s imagination.

1

<Segmentation map by GauGAN Figure 2.04 (GauGAN, 2021)

See the NVIDIA GauGAN interface at appendices p. 179.

Painting by GauGAN Figure 2.03 (GauGAN, 2021)

650

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e756” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </73 110 116 101 114 97 99 116 105 118 101 32 50 68 32 105 109 97 103 101 32 103 101 110 101 114 97 116 101 32 116 104 114 111 117 103 104 32 71 97 117 71 65 78 44 32 114 101 97 100 97 98 108 101 32 98 121 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 101 32 105 110 112 117 116 32 105 109 97 103 101 32 105 115 32 109 101 114 101 108 121 32 99 111 110 115 105 115 116 105 110 103 32 115 101 103 109 101 110 116 97 116 105 111 110 115 32 111 102 32 99 111 108 111 117 114 115 46 32 73 116 32 100 111 101 115 32 110 111 116 32 112 111 115 115 101 115 115 32 97 110 121 32 115 101 110 115 101 32 111 102 32 100 101 112 116 104 46 32 72 111 119 101 118 101 114 44 32 104 117 109 97 110 32 118 105 115 105 111 110 32 99 97 110 32 112 101 114 99 101 105 118 101 32 97 32 51 68 32 111 117 116 112 117 116 32 105 109 97 103 101 47 32 118 105 100 101 111 46 32 65 32 115 101 110 115 101 32 111 102 32 100 101 112 116 104 32 105 115 32 103 101 110 101 114 97 116 101 100 32 116 104 114 111 117 103 104 32 116 104 101 32 112 101 114 115 112 101 99 116 105 118 101 32 97 108 116 101 114 97 116 105 111 110 46 32} ] } ] } ] }

A machine-vision exclusive interactive 2D image.

The input image is merely consisting segmentations of colours. It does not possess any sense of depth. However, human vision can perceive a 3D output image/ video. A sense of depth is generated through the perspective alteration.

Place the machine eye over the image to see the hidden layer.

< Download Artivive at Google play store

< Download Artivive at Apple app store

Interactive landscape image Figure 2.05 (Leong, 2021) Source: Author

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< Segmentation map

Second dimension II Figure 2.06 (Leong, 2021) Source: Author

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Data collection

A google reCAPTCHA Figure 2.07 (A google reCAPTCHA, n.d.)

According to Moravec’s paradox, it argues that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility” (Moravec, 1988: 15). The machine is working on a different set of principles than humans. A high volume of labelled images is needed to allow the machine vision system to detect and recognise the surrounding environment. Data labelling is a labour-intensive work that required multilateral collaboration between nations.

For example, the ‘select all image with a bus’ results in the captcha test (figure 2.07) are used as training inputs for the autonomous car besides proving one as human. Labeling training images became a paid job in Amazon Mechanical Turk to train Amazon AI. Humans become slaves to machines.

2016, Clive Humby coined “data is the new oil” as if data is the currency that one needs to conquer the digital world.

“Our thirst for data, like our thirst for oil, is historically imperialist and colonist, and tightly tied to capitalist networks of exploitation” (Bridel, 2018: 246). Garbage in, garbage out; data contributors need to be mindful of what they provided to control the output quality. The input data should be relevant, transparent, and holistic. Mining and collecting data without a proper post-collection tool will only contribute to another stack of global trash. The collection of big data is not a problem for companies. Companies often do not know how to utilise big data and responses to the data analysing output (Ross, Beath and Quaadgras, 2013). After all, wise data is needed. Data collectors and users need to be conscious of engaging their data with the proper analysis tool to benefit from it.

“Data is the new oil”

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Data democracy and bias

COMPAS Figure 2.08 (Propublica, 2016)

Humans need to ensure that the training data does not inherit human biases, which leads to AI biases.

Humans are the main character and the most influential factor in data collection. Biases, either conscious or unconscious are rooted deeply within humans throughout history. Humans need to ensure that the training data does not inherit human biases, which leads to AI biases. Besides, there is no “raw data” (Greenfield, 2017: 177). A process of editing and curation is already happening through human bodily senses and perception (Greenfield, 2017: 177). Artificial intelligence is thought to be fairer compared to human regulators and is used in predictive policing. “Data is very effective. However, the data-driven approach ignores several factors that humans think matter a great deal”(Broussard, 2019: 118). The United States court had used Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) to access the re-offend rate among the criminals. However, the system contributes to unreliable result in the forecast of violent crime, having a true prediction rate as low as 20 percent (Angwin, Mattu and Kirchne, 2016). The unconscious dispositions and desires that humans tend to overlook will only amplify in the algorithm. Hence, the artificial intelligence system shall not be deployed in any social relevant sector “because social decisions are about more than just calculations” (Broussard, 2019: 119).

Seeing the freedom of network connection, do the general public have complete access to real information and data protection rights? With varying social, economic, and political status, is it becoming a “dystopian in which the best data goes to the people who can afford to pay the most for it” (Broussard, 2019: 146)? Society needs little sister rather than Big Brother (Ratti and Claudel, 2016). Data contribution should come from all sides to obtain a desirable outcome.

For instance, all autonomous vehicle companies should share their training data to ensure the machine trains in all possible conditions and ready to serve humanity. Through an open and transparent dataset, it drives an extensive benefit for the entire nation.

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Consequences of data mining While current computer power can handle the programming units compare to decades ago, the environmental impacts and energy consumption to train an intelligence model have brought to light. A research paper from the University of Massachusetts Amherst (Strubell, Ganesh and McCallum, 2019) states that estimated carbon dioxide emission from training an artificial intelligence model is five times more than the lifetime of a car emission includes fuel. Bridle (2018: 247) warned that “the debt we have already accrued will take centuries to dissipate, and we have not come close as yet to experience its worst, inevitable effects.” The house to the data, the data farm consumed over 3% of the global electricity even at the dawn of artificial intelligence development. In 2018, 21 Google data centres used 10,104,295 MWh of electricity, which can power 6 billion average home and 4,170 million gallons of water (Google, 2019). However, the big tech company is participating in combat climate change. In 2017, Google announced itself as a net-zero company claiming that its infrastructures will power solely by renewable energy by 2030 (Hern, 2020). Google is using a machine-learning algorithm to ensure its building efficiency through the study of building management pattern. As the world is shifting towards a data-driven era, humans need to be aware that data collecting is not an invisible process.

Machine learning energy control graph Figure 2.09 (DeepMind, 2016)

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“In the past decade, we celebrated the decadent museum and the gallery. Now we have the data centre” (Young, 2019: 9). Server farms are mushrooming around the hinterland and showing a transition in the architecture landscape.

Architects need to be conscious of this phenomenon as it questions the purpose of architecture and the need to re-evaluate the architect’s position in the new architectural landscape (Young, 2019: 13). Humans are no longer the predominant consideration in the design but the hardware that accommodate data. “The countryside is now the site where the more radical, modern components of our civilisation are taking place”, said Rem Koolhaas in the promotional video for his exhibition Countryside, The Future (Guggenheim Museum, 2020). Unlike working with the urban condition, architects need to be extra mindful of the natural context when building in the countryside. The countryside is the last fresh canvas that humans cannot afford to ruin.

The countryside is the last fresh canvas that humans cannot afford to ruin.

Future countryside in Blade Runner 2049 Figure 2.10 (Villeneuve, 2017)

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A human-machine environment The Amazon fulfilment centres is a controlled human-machine environment. It is a testing ground to observe the interaction between the autonomous agent and human. In the WIRED business conference interview with Kiva Systems founder and CEO Mick Mountz (FORA.tv, 2011), Mountz explains that the RDUs move in a Manhattan-like trajectory, across the ‘local street’ and the ‘highway‘ with the machine algorithm at the right speed that does not congest the collecting deport. Collaboration between human pickers and machine are needed to develop smooth working rhythms. Once the items are picked up, the RDU returns the shelf to the nearest open slot, which may differ from the original position. Through this process, the warehouse is continuously reconfiguring itself (Young, 2019: 52).

Can the Amazon fulfilment centre environment become the case study for the future city, where the autonomous fleets move around the city alongside its human users?

Amazon Fulfillment Centre Figure 2.11 (Amazon Fulfillment Centre, 2020)

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‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 73 110 32 116 104 105 115 32 101 110 118 105 114 111 110 109 101 110 116 44 32 104 117 109 97 110 32 105 115 32 108 105 107 101 32 116 104 101 32 115 97 102 97 114 105 32 99 97 114 32 116 104 97 116 32 110 101 101 100 115 32 116 111 32 103 105 118 101 32 119 97 121 32 116 111 32 116 104 101 32 97 110 105 109 97 108 115 32 40 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 115 41 32 119 104 101 110 32 119 111 110 100 101 114 105 110 103 32 97 114 111 117 110 100 32 116 104 101 32 115 97 118 97 110 110 97 46 32 73 110 32 116 104 101 32 67 104 97 110 110 101 108 32 52 32 100 111 99 117 109 101 110 116 97 114 121 32 40 67 104 97 110 110 101 108 32 52 32 68 111 99 117 109 101 110 116 97 114 121 44 32 50 48 49 57 41 44 32 105 116 32 100 105 115 112 108 97 121 115 32 116 104 101 32 119 97 114 101 104 111 117 115 101 32 97 115 32 97 32 110 111 110 45 104 117 109 97 110 32 102 114 105 101 110 100 108 121 32 97 114 101 97 44 32 111 110 108 121 32 116 104 101 32 97 117 116 104 111 114 105 122 101 100 32 119 111 114 107 101 114 32 99 97 110 32 101 110 116 101 114 32 116 104 101 32 -30 -128 -100 99 97 103 101 -30 -128 -99 32 119 104 101 114 101 32 116 104 101 32 114 111 98 111 116 32 114 111 97 109 115 46 32 85 112 111 110 32 116 104 101 32 114 101 112 111 114 116 32 102 114 111 109 32 116 104 101 32 115 121 115 116 101 109 44 32 116 104 101 32 114 111 98 111 116 105 99 32 114 101 115 112 111 110 100 101 114 32 101 110 116 101 114 115 32 116 104 101 32 -30 -128 -100 99 97 103 101 -30 -128 -99 32 119 105 116 104 32 97 32 115 97 102 101 116 121 32 118 101 115 116 32 116 104 97 116 32 99 97 110 32 114 101 108 101 97 115 101 32 97 32 114 97 100 105 111 32 102 114 101 113 117 101 110 99 121 32 116 111 32 105 110 102 111 114 109 32 104 105 115 32 101 120 105 115 116 101 110 99 101 32 116 111 32 116 104 101 32 82 68 85 115 46 32 87 104 105 108 101 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 97 108 108 111 119 115 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 115 32 116 111 32 109 111 118 101 32 97 114 111 117 110 100 32 116 104 101 32 102 117 108 102 105 108 108 109 101 110 116 32 99 101 110 116 114 101 32 119 105 116 104 32 103 105 118 101 110 32 105 110 115 116 114 117 99 116 105 111 110 115 44 32 104 117 109 97 110 115 32 98 101 99 111 109 101 32 116 104 101 32 115 101 99 111 110 100 97 114 121 32 108 97 121 101 114 32 105 110 32 116 104 101 32 115 116 114 117 99 116 117 114 101 100 32 101 110 118 105 114 111 110 109 101 110 116 44 32 119 104 111 32 110 101 101 100 32 116 111 32 114 101 97 99 116 32 116 111 32 116 104 101 32 110 101 101 100 115 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 46 32 73 116 32 105 115 32 97 32 110 101 119 32 102 111 114 109 32 111 102 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 116 121 112 111 108 111 103 121 32 116 104 97 116 32 114 117 110 32 98 121 32 109 97 99 104 105 110 101 32 97 108 103 111 114 105 116 104 109 44 32 97 32 115 112 97 99 101 32 116 104 97 116 32 105 115 32 115 116 114 97 110 103 101 32 116 111 32 104 117 109 97 110 32

In the new machine landscape, the human is like a safari car that needs to give way to the animals when wandering around the savanna. The Channel 4 documentary (2019) displayed the warehouse as a non-human friendly area. Only the authorised workers can enter the ‘cage’ where the robot roams.

Upon receiving a report from the system, the robotic responder enters the machinic ground with a safety vest that can release a radio frequency detectable by the RDUs. Humans become the second layer in this structured environment. They need to react to the needs of machine vision. The fulfilment centre becomes a new form of architectural typology run by a machine algorithm, a space that is strange to human orientation.

Human picker at Amazon Fulfilment Centre Figure 2.12 (Human Picker at Amazon Fulfilment Centre, 2020)

On the other hand, architecture needs to comply with machine vision’s requirements.

Mesrie (2021) mentions in the interview1 that shiny and glass surfaces, moist, fog and the presence of transparent liquids can be seen as nemesis of the machine vision system.

As the machines’ sensors are sensitive to glare and excessive light, the distribution of skylight and windows need to be considered carefully according to the programme and robot territory. Highly reflective or lightabsorbing material should be excluded within the machinic landscape to ensure the safeness of both agents.

The fulfilment centre might be a human exclusion zone that displays the picture of consumers’ collective desires and impulsive quests for fulfilment yet encrypted and reflects in a new machine language human cannot read (Young, 2019: 53). Automation becomes a big part of this architectural setting that architects could not ignore.

Rather than surrendering the place to the technology, architects shall find a way to proliferate the built environment with the logistic pattern that machines offer and bring the human experience back to the scene.

1

See full interview with Mesrie at appendices p. 177.

Robotic responder at Amazon Fulfilment Centre II Figure 2.13 (Techxplore, 2020)

The fulfilment centre becomes a new form of architectural typology run by a machine algorithm, a space that is strange to human orientation.

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e772” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 32 104 117 109 97 110 45 109 97 99 104 105 110 101 32 101 110 118 105 114 111 110 109 101 110 116 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 73 110 32 83 101 97 116 116 108 101 44 32 65 109 97 122 111 110 32 115 101 116 32 117 112 32 105 116 115 32 112 104 121 115 105 99 97 108 32 103 114 111 99 101 114 121 32 115 104 111 112 32 116 104 97 116 32 112 114 111 118 105 100 101 32 97 32 100 105 102 102 101 114 101 110 116 32 115 104 111 112 112 105 110 103 32 101 120 112 101 114 105 101 110 99 101 46 32 65 109 97 122 111 110 32 71 111 44 32 119 105 116 104 32 116 104 101 32 112 114 101 115 101 110 99 101 32 111 102 32 99 97 109 101 114 97 115 32 97 110 100 32 115 101 110 115 111 114 115 32 97 99 114 111 115 115 32 116 104 101 32 99 101 105 108 105 110 103 32 97 110 100 32 116 104 101 32 81 82 32 99 111 100 101 32 117 112 111 110 32 99 104 101 99 107 32 105 110 44 32 105 116 32 97 108 108 111 119 115 32 115 104 111 112 112 101 114 115 32 116 111 32 101 120 105 116 32 116 104 101 32 115 104 111 112 32 119 105 116 104 111 117 116 32 103 111 105 110 103 32 116 104 114 111 117 103 104 32 116 104 101 32 112 97 121 109 101 110 116 32 99 111 117 110 116 101 114 46 32 65 116 32 104 101 114 101 44 32 115 104 111 112 112 101 114 115 32 97 114 101 32 108 105 107 101 32 116 104 101 32 112 105 99 107 101 114 115 32 97 116 32 116 104 101 32 102 117 108 102 105 108 109 101 110 116 32 99 101 110 116 114 101 46 32 84 104 101 32 112 104 121 115 105 99 97 108 32 112 97 121 109 101 110 116 32 97 114 101 97 32 119 104 105 99 104 32 110 111 114 109 97 108 108 121 32 116 97 107 101 32 117 112 32 115 105 103 110 105 102 105 99 97 110 116 32 115 112 97 99 101 32 105 110 32 116 104 101 32 115 116 111 114 101 32 105 115 32 114 101 112 108 97 99 101 100 32 98 121 32 116 104 101 32 100 105 103 105 116 97 108 32 116 117 114 110 115 116 105 108 101 115 32 101 110 103 97 103 101 100 32 119 105 116 104 32 115 101 110 115 111 114 115 32 102 111 114 32 99 104 101 99 107 111 117 116 32 112 114 111 99 101 115 115 46 32 87 104 105 108 101 32 116 104 101 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 97 110 100 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 32 115 111 102 116 119 97 114 101 32 104 97 118 101 32 115 117 99 99 101 115 115 102 117 108 108 121 32 112 114 111 118 105 100 101 100 32 97 32 115 111 108 117 116 105 111 110 32 102 111 114 32 116 104 101 32 116 105 109 101 45 99 111 110 115 117 109 105 110 103 32 99 104 101 99 107 111 117 116 32 112 114 111 99 101 115 115 44 32 105 116 32 97 108 115 111 32 99 104 97 110 103 101 115 32 116 104 101 32 115 104 111 112 112 101 114 -30 -128 -103 115 32 109 111 118 105 110 103 32 112 97 116 116 101 114 110 115 32 119 105 116 104 105 110 32 116 104 101 32 115 112 97 99 101 46 32 83 104 111 112 112 101 114 115 32 110 111 32 108 111 110 103 101 114 32 110 101 101 100 32 116 111 32 103 111 32 116 111 32 116 104 101 32 101 110 100 32 111 102 32 116 104 101 32 115 104 111 112 112 105 110 103 32 102 108 111 111 114 32 97 102 116 101 114 32 112 105 99 107 105 110 103 32 117 112 32 116 104 101 105 114 32 105 116 101 109 115 32 102 114 111 109 32 116 104 101 32 102 114 111 110 116 32 114 111 119 32 110 101 97 114 32 116 104 101 32 101 110 116 114 97

In Seattle, Amazon set up its physical grocery shop that provides a different shopping experience. Amazon Go consists of cameras and sensors across the ceiling.

A QR code is assigned to shoppers upon check-in to activate the machine vision system across the store, allows shoppers to complete the purchasing process without going through the payment counter. There, shoppers are like the pickers at the fulfilment centre.

The conventional payment area that takes up significant space in the store can be replaced by digital turnstiles for virtual checkout. While the machine vision and machine learning software have successfully provided a solution for the time-consuming checkout process, it also changes the shopper’s moving patterns within the space. Shoppers no longer need to go around the shopping floor to make payment after picking up their items from the front row near the entrance. Can a new spatial layout be developed based on this movement route and purchasing algorithm?

Diagram 1.1 (Leong, 2021)

Diagram 1.2 (Amazon.com: Amazon Go Grocery: Amazon Go, n.d.)

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e774” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </51 68 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 65 32 115 112 97 99 101 32 119 105 116 104 32 100 101 112 116 104 32 97 110 100 32 100 105 115 116 97 110 99 101 46 32} ] } ] } ] }

< Image generate through photogrammetry

3D

C H APTE R T H REE

A s p a ce with d ep th and dis tanc e .

Third dimension I Figure 3.01 (Leong, 2021) Source: Author

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e776” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 118 111 108 117 109 101 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 73 116 32 105 115 32 115 105 103 110 105 102 105 99 97 110 116 32 102 111 114 32 109 97 99 104 105 110 101 32 116 111 32 115 101 101 32 105 110 32 116 104 114 101 101 32 100 105 109 101 110 115 105 111 110 97 108 44 32 97 115 32 104 111 119 32 104 117 109 97 110 32 119 105 116 104 32 98 105 110 111 99 117 108 97 114 32 118 105 115 105 111 110 32 104 97 118 101 32 112 101 114 99 101 105 118 101 100 32 116 104 101 32 119 111 114 108 100 46 32 84 104 114 111 117 103 104 32 115 116 117 100 121 32 111 102 32 115 104 97 100 111 119 44 32 99 111 108 111 117 114 32 99 104 97 110 103 101 44 32 108 105 103 104 116 32 114 101 102 108 101 99 116 105 111 110 32 116 111 32 104 97 118 101 32 100 101 112 116 104 32 112 101 114 99 101 112 116 105 111 110 46 32 71 111 111 103 108 101 32 117 115 101 115 32 112 104 111 116 111 103 114 97 109 109 101 116 114 121 32 97 110 100 32 99 111 109 112 117 116 101 114 32 118 105 115 105 111 110 32 97 108 103 111 114 105 116 104 109 32 116 111 32 99 114 101 97 116 101 32 97 32 100 105 103 105 116 97 108 32 97 114 99 104 105 118 101 32 111 102 32 116 104 101 32 69 97 114 116 104 32 102 111 114 32 116 104 101 32 112 97 115 116 32 116 101 110 32 121 101 97 114 115 46 32 85 115 105 110 103 32 99 97 109 101 114 97 115 32 116 111 32 116 97 107 101 32 97 101 114 105 97 108 32 112 104 111 116 111 103 114 97 112 104 115 32 97 99 114 111 115 115 32 100 105 102 102 101 114 101 110 116 32 112 101 114 115 112 101 99 116 105 118 101 115 44 32 111 118 101 114 108 97 112 112 105 110 103 32 112 104 111 116 111 103 114 97 112 104 115 32 97 114 101 32 99 111 108 108 101 99 116 101 100 32 116 111 32 111 98 116 97 105 110 32 116 104 101 32 100 101 112 116 104 32 105 110 102 111 114 109 97 116 105 111 110 46 32 65 102 116 101 114 32 97 110 97 108 121 115 105 110 103 32 116 104 101 32 100 101 112 116 104 32 100 97 116 97 44 32 97 32 109 101 115 104 32 109 111 100 101 108 32 105 115 32 112 114 111 100 117 99 101 100 32 97 110 100 32 119 114 97 112 32 119 105 116 104 32 116 104 101 32 50 68 32 105 109 97 103 101 115 46 32 84 101 110 32 111 102 32 109 105 108 108 105 111 110 115 32 111 102 32 105 109 97 103 101 115 32 97 114 101 32 110 101 101 100 101 100 32 116 111 32 99 114 101 97 116 101 32 97 32 100 105 103 105 116 97 108 32 69 97 114 116 104 32 97 114 99 104 105 118 101 46 32 71 111 111 103 108 101 32 115 116 114 101 101 116 32 118 105 101 119 44 32 104 111 119 101 118 101 114 44 32 117 115 101 115 32 111 110 108 121 32 50 68 32 105 109 97 103 101 115 32 116 111 32 99 114 101 97 116 101 32 97 32 51 68 32 101 102 102 101 99 116 46 32 73 116 32 105 115 32 100 111 110 101 32 116 104 114 111 117 103 104 32 115 116 105 116 99 104 105 110 103 32 117 112 32 116 104 101 32 50 68 32 105 109 97 103 101 115 32 105 110 32 97 32 51 54 48 45 100 101 103 114 101 101 32 112 97 110 111 114 97 109 105 99 32 118 105 101 119 32 117 115 105 110 103 32 105 109 97 103 101 32 112 114 111 99 101 115 115 105 110 103 32 116 101 99 104 110 111 108 111 103 121 46 32 10 10 68 101 112 116 104 32 99 97 110 32 98 101 32 97 99 104 105 101 118 101 100 32 116 104 114 111 117 103 104 32 111 98 116 97 105 110 105 110 103 32 116 104 101 32 100

< Output of LiDAR scanning

Depth

The extrusion of a two-dimensional surface leads to a three-dimensional space. It is where depth present.

It is significant for the machine to perceive the third dimension because that is how the world has created for humans. Google uses photogrammetry and computer vision algorithm to create a digital Earth archive for the past ten years. It uses cameras to take aerial photographs across the perspectives, then overlaps the photos to obtain the depth information and create a mesh model of the Earth. Tens of millions of imageries are needed to completely capture the Earth (Nat and Friends, 2017). Google street view, however, uses only 2D images to create a 3D effect.

It is achieved by stitching up the 2D images in a 360-degree panoramic view using image processing technology.

Depth perception can be achieved through the study of shadow, colour change, and light reflection through human vision. Nonetheless, the machine understands depth by calculating the distance from the origin to the scanning object and the time of flight. Infrared light is commonly used as a measuring element because of its constant speed and invisibility from the human eye. Light detecting and ranging (LiDAR) is the cutting-edge technology for detail site analysis and reconstruction. Most autonomous vehicles use LiDAR with other machine vision system to detect the surrounding environment without understanding the world.

Third dimension II Figure 3.02 (Leong, 2021) Source: Author

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e778” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 118 111 108 117 109 101 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 73 110 32 67 104 105 110 97 44 32 102 97 99 105 97 108 32 114 101 99 111 103 110 105 116 105 111 110 32 112 108 97 121 32 97 110 32 105 109 112 111 114 116 97 110 116 32 114 111 108 101 32 105 110 32 99 105 116 105 122 101 110 -30 -128 -103 115 32 100 97 105 108 121 32 108 105 102 101 44 32 119 105 116 104 32 116 104 101 32 101 118 101 114 45 112 114 101 115 101 110 116 32 109 97 99 104 105 110 101 32 101 121 101 115 46 32 66 101 115 105 100 101 115 32 104 97 118 105 110 103 32 102 97 99 101 32 114 101 99 111 103 110 105 116 105 111 110 32 105 110 32 116 104 101 32 97 105 114 112 111 114 116 32 97 110 100 32 116 111 32 117 110 108 111 99 107 32 111 110 101 115 -30 -128 -103 32 115 109 97 114 116 112 104 111 110 101 44 32 105 116 32 104 97 115 32 98 101 101 110 32 119 105 100 101 108 121 32 97 100 111 112 116 101 100 32 105 110 32 99 111 109 109 101 114 99 105 97 108 32 97 114 101 97 32 97 110 100 32 104 117 109 97 110 32 98 101 104 97 118 105 111 117 114 32 99 111 110 116 114 111 108 46 32 51 68 32 102 97 99 105 97 108 32 114 101 99 111 103 110 105 116 105 111 110 32 115 121 115 116 101 109 32 115 117 112 101 114 115 101 100 101 115 32 50 68 32 98 105 111 109 101 116 114 105 99 32 116 101 99 104 110 111 108 111 103 121 32 105 110 32 111 98 116 97 105 110 105 110 103 32 97 32 109 111 114 101 32 97 99 99 117 114 97 116 101 32 114 101 115 117 108 116 32 117 110 100 101 114 32 109 97 110 105 112 117 108 97 116 105 110 103 32 102 97 99 116 111 114 115 46 32 73 110 102 114 97 114 101 100 32 99 97 109 101 114 97 32 105 115 32 117 115 101 100 32 116 111 32 111 98 116 97 105 110 32 97 99 99 117 114 97 116 101 32 102 97 99 105 97 108 32 100 97 116 97 32 115 117 99 104 32 97 115 32 102 101 97 116 117 114 101 115 32 108 97 110 100 109 97 114 107 44 32 115 104 97 112 101 32 97 110 100 32 116 101 120 116 117 114 101 32 116 104 114 111 117 103 104 32 115 116 114 117 99 116 117 114 97 108 32 115 99 97 110 110 105 110 103 46 32} ] } ] } ] }

In China, the ever-present machine eyes allow facial recognition to adopt in commercial areas and human behaviour control.

3D facial recognition system supersedes 2D biometric technology in obtaining a more accurate result under manipulating factors.

The infrared camera is used to obtain accurate facial data such as features landmark, shape and texture through structural scanning. Facial recognition can be fully utilised as a universal digital identity card once the technology matures in the coming years (Thiagarajan, 2019). The change in identification mode will directly affect the building entrance design and ceiling configuration to embed machine devices within the architecture.

Toilet paper dispensor Figure 3.03 (BBC, 2017)

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e780” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

</79 98 106 101 99 116 32 114 101 99 111 110 115 116 114 117 99 116 105

{

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‘language’:’en’, ‘orientation’:’Up’

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A view of the melting ice in the gallery space Figure 3.04 (ScanLAB projects, n.d.)

<3D scan of Arctic Ice Floe I Figure 3.06 (ScanLAB projects, n.d.)

Object reconstruction and inspection The advancement of 3D technology is likely to change human perception and understanding of the world (Carpo, 2017: 122).

<3D scan of Arctic Ice Floe II Figure 3.07 (ScanLAB projects, n.d.)

Two-dimensional data allows object classification and detection, while three-dimensional data contributed to object replication, reconstruction, and inspection. ScanLAB utilises terrestrial LiDAR scanning to capture the built environment and rediscovering the hidden history of the heritage. The result from the 3D scanning presents a new form of storytelling to humans. Viewers are allowed to explore the site that was once unreachable. Besides, ScanLAB manages to capture the valuable image of the Arctic ice floes (figure 3.06 & 3.07) through LiDAR scanner and underwater sonar during the Climate Impact Tour expeditions. The captured data turns into a more engaging object. A physical 3D model (figure 3.04) is presented to provoke the urgent climate change issue among the public. The frozen saltwater 3D model is a replica of the original ice floe, created through the CNC mould with information gathered from the 3D scanning. The data collected from the scanning also provided visual imagery and simulation for the researchers in the Arctic region, allowing words to replace with interactive images. As Carpo (2017: 130) stated, “three-dimensional models have replaced text and images as our tools of choice for the notation and replication, representation and quantification of the physical world around us: born verbal, then gone visual, knowledge can now be recorded and transmitted in a new spatial format.” The advancement of 3D technology is likely to change human perception and understanding of the world (Carpo, 2017: 122).

Artcic ice floe Figure 3.05 (Arctic ice floe, n.d.)

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e782” headers = {‘Ocp-Apim-Subscription-Key’: sub} {

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{

111 110 103 63 32

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 68 111 32 121 111 117 32 101 118 101 114 32 101 110 99 111 117 110 116 101 114 32 115 105 116 117 97 116 105 111 110 32 119 104 101 114 101 32 121 111 117 114 32 103 111 111 103 108 101 32 109 97 112 32 98 114 105 110 103 32 121 111 117 32 116 111 32 97 32 100 101 97 100 32 99 111 114 110 101 114 32 119 104 101 110 32 105 116 32 115 101 101 109 115 32 116 111 32 98 101 32 111 110 32 116 104 101 32 114 105 103 104 116 32 112 108 97 99 101 32 111 110 32 121 111 117 114 32 115 109 97 114 116 32 100 101 118 105 99 101 115 63 32 84 104 101 32 109 97 99 104 105 110 101 32 100 111 101 115 32 110 111 116 32 97 108 119 97 121 115 32 119 111 114 107 32 111 117 116 32 112 101 114 102 101 99 116 108 121 46 32 73 110 32 116 104 101 32 80 111 115 116 99 97 114 100 32 102 114 111 109 32 71 111 111 103 108 101 32 69 97 114 116 104 32 99 114 101 97 116 101 100 32 98 121 32 67 108 101 109 101 110 116 32 86 97 108 108 97 32 40 86 97 108 108 97 44 32 110 46 100 46 41 44 32 105 116 32 99 97 112 116 117 114 101 115 32 116 104 101 32 101 114 114 111 114 32 116 104 97 116 32 102 111 117 110 100 32 105 110 32 116 104 101 32 51 68 32 71 111 111 103 108 101 32 69 97 114 116 104 46 32 84 97 107 105 110 103 32 97 32 118 105 101 119 32 105 110 116 111 32 71 111 111 103 108 101 32 77 97 112 115 44 32 114 97 116 104 101 114 32 116 111 32 115 97 121 32 116 104 97 116 32 116 104 101 114 101 32 105 115 32 109 105 115 116 97 107 101 32 105 110 32 116 104 101 32 100 97 116 97 32 97 110 97 108 121 115 105 115 32 112 114 111 99 101 115 115 44 32 105 116 32 99 97 110 32 98 101 32 105 110 116 101 114 112 114 101 116 101 100 32 97 115 32 97 32 100 105 102 102 101 114 101 110 116 32 105 110 32 119 97 121 32 111 102 32 -30 -128 -100 115 101 101 105 110 103 -30 -128 -99 32 98 101 116 119 101 101 110 32 109 97 99 104 105 110 101 32 97 110 100 32 104 117 109 97 110 46 32 -30 -128 -100 77 111 115 116 32 111 102 32 116 104 101 32 116 105 109 101 32 116 104 105 115 32 100 111 117 98 108 105 110 103 32 111 102 32 115 112 97 99 101 115 32 105 110 32 71 111 111 103 108 101 32 69 97 114 116 104 32 103 111 101 115 32 117 110 110 111 116 105 99 101 100 44 32 98 117 116 32 115 111 109 101 116 105 109 101 115 32 116 104 101 32 116 119 111 32 115 112 97 99 101 115 32 97 114 101 32 115 111 32 100 105 102 102 101 114 101 110 116 44 32 116 104 97 116 32 116 104 105 110 103 115 32 108 111 111 107 32 115 116 114 97 110 103 101 44 32 118 101 114 116 105 103 105 110 111 117 115 44 32 111 114 32 112 108 97 105 110 32 119 114 111 110 103 46 32 66 117 116 32 116 104 101 121 32 97 114 101 32 110 111 116 32 119 114 111 110 103 46 -30 -128 -99 32 40 86 97 108 108 97 44 32 50 48 49 50 41 46 32 72 111 119 101 118 101 114 44 32 105 116 32 105 115 32 110 111 116 32 97 32 112 101 114 109 97 110 101 110 116 32 105 109 97 103 101 32 97 115 32 71 111 111 103 108 101 32 99 111 110 115 116 97 110 116 108 121 32 117 112 100 97 116 101 32 116 104 101 105 114 32 109 97 112 32 97 110 100 32 97 108 103 111 114 105 116 104 109 32 111 102 32 109 97 112 32 109 97 107 105 110 103 46 32 84 104 101 114 101 32 105 115 32 97 108 119

Glitch on Google Earth Figure 3.09 (Glitch on Google Earth, 2019)

Images by machine The machine does not always work out perfectly due to the disparity in human vision and machine vision. It does not inherently understand the logics of space and materials as humans do. The Postcard from Google Earth (figure 3.09) created by Clement Valla (Valla, n.d.) captures the error found in the 3D Google Earth. Taking a view into Google Maps, rather than say that there is a mistake in the data analysis process, it can be interpreted as a different way of ‘seeing’ in the machine realm. “Most of the time this doubling of spaces in Google Earth goes unnoticed, but sometimes the two spaces are so different, that things look strange, vertiginous, or plain wrong. But they are not wrong.” (Valla, 2012).

However, it is not a permanent image as Google constantly updates their map and algorithm of mapmaking. Data consumers are urged to be critical about what they perceive and not merely accept and consume the given data.

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Surveillance: the different perception There are around 700 million CCTV cameras worldwide, at least 700 million extra eyes staring from a third-person perspective as if a modern Panopticon. The Chinese government developed a mass surveillance system, Skynet1, that allows instant human tracking with its immense logging details.

With this, the government generates a social credit system that monitors its citizen’s behaviour.

While surveillance technology helps detect delinquent conduct, it is also misused to conduct public shaming. Photos of citizens wearing sleepwear in public are captured by the street camera and released on social media as the government claims it as uncivilised behaviour (Qin, 2020). Is this the kind of massive surveillance that is needed to control human behaviour and public safety? Or is the mass-surveillance done to follow the trend, not for its effectiveness; and like any other automation system, it enables human to shift the burden of responsibility and blame onto the machine (Bridle, 2018: 180).

1

Currently, China has the largest video surveillance in the world. The Skynet project has over 350 million cameras across the county, and planned to expand over 600 millions camera by 2022 (VICE News, 2019).

<CCTV display in Beijing, China Figure 3.11 (Sabrié , n.d.)

CCTV cameras Figure 3.10 (Dickinson, 2019)

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Israel on Google Maps Figure 3.12 (Google Maps,2020a)

Marcoule Nuclear Site, France on Google Maps Figure 3.13 (Google Maps,2020b)

Minami Torishima Airport, Japan on Google Maps Figure 3.14 (Google Maps,2020c)

Jeannette Island, Russia on Google Maps Figure 3.15 (Google Maps,2020d)

Today, human privacy comes under threat while having the convenience to access information with a few clicks. The map has become a method to navigate in a fourdimension space, generating a view of boundless space from a third-person perspective.

Google street view also transforms the “travel-on-map” experience into a more engaging one, narrowing the gap between the traveller and the place. Google street view cars and trekkers that manoeuvre around the public space to collect the imagery data sets becoming nothing new to society. Subsequently, it brings privacy issue to the light with the transparency of things under the machine eye. Objects can be censored from the digital world through data post-processing (figure 3.12-3.15) such as blurring, pixelation, oversaturate white, and blackout. The process is commonly executed for highly secretive spaces such as government building and military site. However, the reason for Google to blur out an ordinary British residential building (figure 3.16) on Google street view (figure 3.17) remains unknown. The action brings up the question of cyber privacy among the public and how the built environment can protect human from mass-surveillance.

Unblurred image of the house Figure 3.16 (Evening Gazette, n.d.)

House blurred out on google street Figure 3.17 (Google Maps, 2020e)

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“def_edges_lines "results": [ { "confidence": 0.20158538222312927, "label": "Michelle_Szeyee" }, { "confidence": 0.044169820845127106, "label": "Kaiyi_Wong" }, { "confidence": 0.03976121544837952, "label": "Gul_Kacmax_Erk" }, { "confidence": 0.037657905369997025, "label": "Kamen_Lee" }, { "confidence": 0.031581733375787735, "label": "Chee_Yan_Wong" }, { "confidence": 0.027566682547330856, "label": "LeeMei_Liew" }, { "confidence": 0.021069753915071487, "label": "PeiLeng_Tee" }, { "confidence": 0.0207058172672987, "label": "Don_Rickles" }, { "confidence": 0.017841791734099388, "label": "Jayden_chong" }, { "confidence": 0.017642010003328323, "label": "WeeKee" },

Facebook tag funtion Figure 3.18 (Leong, 2021) Source: Author

In 2014, Facebook introduced DeepFace, a deep learning algorithm that trains on four million images from its users to recognise faces on the social platform. It is a system that reaches 97.25% of face recognition accuracy, only slightly less the 97.53% of average human face recognition ability (Chowdhry, 2014). Through a small action of uploading photos, individual needs to bear the consequences of exposing their privacy to the public and providing their data to the social media company. Many might not take it as a big deal, but it is like an invisible monitoring system that forms human’s digital archive and a new cultural repository from a third-person perspective. It is an irreversible action.

Facebook post bar Figure 3.19 (Leong, 2021) Source: Author

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>human_face_regconition_failed >ocr_failed} ] } ] } ] }

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Antisurveillance

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‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 77 97 99 104 105 110 101 32 118 105 115 105 111 110 32 104 97 115 32 105 110 118 111 108 118 101 100 32 105 110 32 115 116 111 114 105 110 103 32 109 101 109 111 114 121 32 111 102 32 116 104 101 32 99 101 110 116 117 114 121 44 32 116 117 114 110 105 110 103 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 105 110 116 111 32 97 32 100 105 103 105 116 97 108 32 97 114 99 104 105 118 101 32 97 99 114 111 115 115 32 116 104 101 32 99 108 111 117 100 32 97 110 100 32 115 111 99 105 97 108 32 109 101 100 105 97 32 112 108 97 116 102 111 114 109 46 32 65 32 108 111 116 32 111 102 32 116 104 101 32 97 114 99 104 105 118 101 115 32 97 112 112 101 97 114 32 97 115 32 116 119 111 45 100 105 109 101 110 115 105 111 110 97 108 32 112 104 111 116 111 103 114 97 112 104 115 32 111 114 32 118 105 100 101 111 115 46 32 49 53 116 104 32 65 112 114 105 108 32 50 48 49 57 44 32 97 32 116 114 97 103 105 99 32 102 105 114 101 32 116 111 111 107 32 97 119 97 121 32 112 97 114 116 32 111 102 32 116 104 101 32 104 105 115 116 111 114 105 99 97 108 32 109 101 100 105 101 118 97 108 32 67 97 116 104 111 108 105 99 32 99 97 116 104 101 100 114 97 108 44 32 78 111 116 114 101 45 68 97 109 101 46 32 84 104 101 32 97 118 97 105 108 97 98 108 101 32 116 119 111 45 100 105 109 101 110 115 105 111 110 97 108 32 114 101 115 111 117 114 99 101 115 32 105 110 99 108 117 100 105 110 103 32 112 97 105 110 116 105 110 103 44 32 112 104 111 116 111 103 114 97 112 104 121 32 97 110 100 32 118 105 100 101 111 32 97 114 101 32 110 111 116 32 101 110 111 117 103 104 32 116 111 32 114 101 115 101 109 98 108 101 32 116 104 101 32 100 101 116 97 105 108 32 99 111 110 115 116 114 117 99 116 105 111 110 32 111 102 32 116 104 101 32 49 51 116 104 32 99 101 110 116 117 114 121 32 97 114 99 104 105 116 101 99 116 117 114 101 32 102 111 114 32 116 104 101 32 114 101 99 111 110 115 116 114 117 99 116 105 111 110 32 112 114 111 99 101 115 115 46 32 70 111 114 116 117 110 97 116 101 108 121 44 32 116 104 101 32 108 97 116 101 32 86 97 115 115 97 114 32 97 114 116 32 104 105 115 116 111 114 105 97 110 44 32 65 110 100 114 101 119 32 84 97 108 108 111 110 32 112 101 114 102 111 114 109 101 100 32 97 32 99 111 109 112 108 101 116 101 32 51 68 32 115 99 97 110 110 105 110 103 32 97 110 100 32 116 111 111 107 32 115 112 104 101 114 105 99 97 108 32 112 97 110 111 114 97 109 105 99 32 112 104 111 116 111 103 114 97 112 104 32 97 116 32 53 48 32 100 105 102 102 101 114 101 110 116 32 108 111 99 97 116 105 111 110 115 32 111 102 32 116 104 101 32 98 117 105 108 100 105 110 103 44 32 99 111 108 108 101 99 116 105 110 103 32 109 111 114 101 32 116 104 97 110 32 111 110 101 32 98 105 108 108 105 111 110 32 112 111 105 110 116 115 32 111 102 32 100 97 116 97 32 119 104 105 99 104 32 98 101 99 111 109 101 32 116 104 101 32 111 110 108 121 32 51 68 32 100 105 103 105 116 97 108 32 97 114 99 104 105 118 101 32 111 102 32 78 111 116 114 101 45 68 97 109 101 32 40 72 97 114 116 105 103 97 110 44 32 50 48 49 57 41 46 32 84 97 108 108 111 110 -30 -128 -103 115 32 51 68 32 115 99 97 110 32 98 101

Building restoration The machine vision system is used in capturing memory and history, turning the built environment into a digital archive. “The consequent three-dimensional visualisation allows the past to be reconsidered, re-thought and re-discovered, bringing history into a more proximate layer of time” (Mesrie, 2021)1. 15th April 2019, a tragic fire took away part of the historical medieval Catholic cathedral, Notre-Dame. The available twodimensional resources, including painting, photography, and video are not enough to resemble the detail construction of the 13th-century architecture for the reconstruction process. Fortunately, the late Vassar art historian, Andrew Tallon, performed a complete 3D scanning (figure 3.23) and took a series of spherical panoramic photographs at 50 locations of the building. Tallon’s 3D scan consists of more than one billion data points, became the only 3D digital archive of Notre-Dame (Hartigan, 2019). It is a significant contribution to the reconstruction of the historical building, either to create a replica of the cathedral or to study the existing structure of the building.

1

See full interview with Mesrie at appendices p. 177. .

<3D scan of Notre-Dame by Andrew Tallon Figure 3.23 (Tallon , n.d.)

“...bringing history into a more proximate layer of time”

Notre-Dame cathedral on April 17, 2019 Figure 3.22 (Mossot, 2019)

490

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e794” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </66 117 105 108 100 105 110 103 32 105 110 115 112 101 99 116 105 111 110 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 101 32 51 68 32 108 97 115 101 114 32 115 99 97 110 110 101 114 32 99 97 110 32 98 101 32 117 115 101 100 32 105 110 32 116 104 101 32 115 105 116 101 32 97 110 100 32 108 97 110 100 32 115 117 114 118 101 121 105 110 103 32 116 111 32 111 98 116 97 105 110 32 104 105 103 104 32 114 101 115 111 108 117 116 105 111 110 32 97 110 100 32 104 105 103 104 108 121 32 97 99 99 117 114 97 116 101 32 100 97 116 97 46 32 84 104 114 111 117 103 104 32 116 104 101 32 51 68 32 109 97 99 104 105 110 101 32 101 121 101 44 32 116 104 101 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 105 115 32 99 97 112 116 117 114 101 32 100 105 102 102 101 114 101 110 116 108 121 32 119 105 116 104 32 116 104 101 32 112 114 101 115 101 110 99 101 32 111 102 32 100 101 112 116 104 32 119 105 116 104 105 110 32 116 104 101 32 105 109 97 103 101 114 121 46 32 76 105 107 101 119 105 115 101 44 32 98 117 105 108 100 105 110 103 32 105 110 115 112 101 99 116 105 111 110 32 105 115 32 110 101 101 100 101 100 32 100 117 114 105 110 103 32 116 104 101 32 99 111 110 115 116 114 117 99 116 105 111 110 32 97 110 100 32 112 111 115 116 45 99 111 110 115 116 114 117 99 116 105 111 110 32 116 111 32 102 111 108 108 111 119 32 117 112 32 119 105 116 104 32 116 104 101 32 99 111 110 115 116 114 117 99 116 105 111 110 32 112 114 111 103 114 101 115 115 32 97 110 100 32 116 104 101 32 98 117 105 108 100 105 110 103 32 115 116 97 110 100 97 114 100 46 32 65 110 100 32 98 117 105 108 100 105 110 103 32 109 97 105 110 116 101 110 97 110 99 101 115 32 97 114 101 32 110 101 101 100 101 100 32 114 101 103 117 108 97 114 108 121 46 32 67 111 110 115 116 114 117 99 116 105 111 110 32 115 105 116 101 32 105 115 32 111 102 116 101 110 32 100 97 110 103 101 114 111 117 115 32 97 110 100 32 117 110 102 97 118 111 117 114 97 98 108 101 32 102 111 114 32 104 117 109 97 110 44 32 116 104 101 32 105 110 110 111 118 97 116 105 111 110 32 111 102 32 66 111 115 116 111 110 32 68 121 110 97 109 105 99 32 114 111 98 111 116 -30 -128 -103 115 32 83 112 111 116 32 116 104 97 116 32 99 111 109 101 115 32 119 105 116 104 32 115 101 110 115 111 114 115 32 97 108 108 111 119 32 105 116 32 116 111 32 112 101 114 102 111 114 109 32 115 105 116 101 32 105 110 115 112 101 99 116 105 111 110 115 46 32 65 116 32 116 104 101 32 101 110 100 32 111 102 32 50 48 50 48 44 32 83 112 111 116 32 104 97 115 32 105 116 115 32 102 105 114 115 116 32 100 117 116 121 32 97 116 32 116 104 101 32 114 101 97 108 45 108 105 102 101 32 116 101 115 116 98 101 100 44 32 66 97 116 116 101 114 115 101 97 32 82 111 111 102 32 71 97 114 100 101 110 115 32 100 101 118 101 108 111 112 109 101 110 116 32 105 110 32 76 111 110 100 111 110 32 98 121 32 70 111 115 116 101 114 32 43 32 80 97 114 116 110 101 114 115 46 32 87 105 116 104 32 116 104 101 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 116 101 99 104 110 111 108 111 103 121 32 97 110 100 32 116 104 101 32 112 114 101 45 97 115 115 105 103 110 101 100 32 114 111 117 116 101 32 102 111 114 32 116 104 101 32 114 111 98 111 116 44 32 116 104 101 32

Building inspection

Spot on site Figure 3.24 (Dezeen, 2020)

The 3D laser scanner can be used in the site and land surveying to obtain high resolution and highly accurate data. Likewise, building inspections are needed during the construction and post-construction to follow up with the construction progress and the building standard. But, the construction site is often dangerous and unfavourable for humans. The innovation of the Boston Dynamic robot’s Spot that comes with sensors can replace human’s role in site inspections. At the end of 2020, Spot had its first duty at the real-life testbed, Battersea Roof Gardens development in London by Foster + Partners.

With the machine vision technology and the pre-assigned route for the robot, the site inspection result from Spot is told by Martha Tsigkari, partner of Foster + Partners to be “invaluable not only in terms of the consistency but also the large amount of high-quality data collected” (Foster + Partners, 2020). Architects can borrow the machine eye to observe the site transformation at the same location. The collected construction data also allows architects to detect error and make design changes within a short amount of time, which is hardly achieved by manual inspection.

1

<Result of 3D scan (speculative) Figure 3.21 (Leong, 2021) Souce: Author

Since the machine can now see the architecture, can architects utilise the given data from the robot to design a built environment that is understandable by the machine, allow it to move around the space by reading guideline engraves on the built environment?

Taking the concept of bee’s vision that can see in the ultraviolet spectrum in flowers, which is unobservable by humans.

Can the same concept apply to machine vision, in which a parallel world1 is created exclusively for machine vision but beyond the human visual spectrum?

Refer 4D: The autonomous vehicle testbed (p. 107) for parallel world elaboration.

690

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e796” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </52 68 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 65 32 115 112 97 99 101 32 119 105 116 104 32 116 104 101 32 112 114 101 115 101 110 99 101 32 111 102 32 116 105 109 101 32 97 110 100 32 115 112 101 101 100 46 32 73 116 32 105 115 32 97 32 99 111 108 108 97 98 111 114 97 116 105 111 110 32 98 101 116 119 101 101 110 32 104 117 109 97 110 32 97 110 100 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 46} ] } ] } ] }

< Autonomous vehicle real-time detection

4D

C H A PTE R FOUR

A s p a ce with th e p res e n ce of tim e and s pe e d.

I t is a co lla b o r a tio n b etw ee n hum an and m ac hine v is ion.

Fourth dimension Figure 4.01 (Leong, 2021) Source: Author

890

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e798” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 50 48 49 54 44 32 102 111 108 108 111 119 105 110 103 32 116 104 101 32 108 97 117 110 99 104 32 111 102 32 78 101 117 114 97 108 105 110 107 32 116 104 97 116 32 114 101 115 101 97 114 99 104 101 115 32 111 110 32 116 104 101 32 114 101 108 97 116 105 111 110 115 104 105 112 32 98 101 116 119 101 101 110 32 98 105 111 108 111 103 105 99 97 108 32 97 110 100 32 97 114 116 105 102 105 99 105 97 108 32 98 114 97 105 110 44 32 69 108 111 110 32 77 117 115 107 32 107 101 101 112 115 32 104 105 109 115 101 108 102 32 105 110 118 111 108 118 101 32 105 110 32 116 104 101 32 97 117 116 111 109 111 98 105 108 101 32 115 101 99 116 111 114 32 119 105 116 104 32 84 101 115 108 97 32 99 97 114 32 97 117 116 111 112 105 108 111 116 32 97 110 100 32 84 104 101 32 66 111 114 105 110 103 32 67 111 109 112 97 110 121 32 116 117 110 110 101 108 46 32 84 104 101 32 108 101 118 101 108 32 51 32 97 117 116 111 110 111 109 111 117 115 32 84 101 115 108 97 32 99 97 114 32 115 116 105 108 108 32 114 101 113 117 105 114 101 32 100 114 105 118 101 114 32 97 116 116 101 110 116 105 111 110 32 97 116 32 97 108 108 32 116 105 109 101 46 32 87 105 116 104 32 116 104 101 32 112 114 101 115 101 110 99 101 32 111 102 32 99 97 109 101 114 97 32 115 101 110 115 111 114 115 44 32 114 97 100 97 114 32 97 110 100 32 108 105 100 97 114 32 116 104 97 116 32 99 111 110 110 101 99 116 32 116 111 32 116 104 101 32 99 111 109 112 117 116 101 114 32 105 110 116 101 108 108 105 103 101 110 99 101 32 115 121 115 116 101 109 44 32 97 110 32 97 117 116 111 110 111 109 111 117 115 32 99 97 114 32 97 98 108 101 32 116 111 32 112 101 114 102 111 114 109 32 105 110 32 97 32 99 111 110 116 114 111 108 108 101 100 32 101 110 118 105 114 111 110 109 101 110 116 32 101 97 115 105 108 121 46 32 84 104 101 32 99 117 114 114 101 110 116 32 116 101 99 104 110 111 108 111 103 121 32 97 108 108 111 119 115 32 104 97 110 100 45 111 102 102 32 100 114 105 118 105 110 103 32 111 110 32 116 104 101 32 109 111 116 111 114 119 97 121 115 44 32 98 117 116 32 97 100 118 97 110 99 101 109 101 110 116 32 105 115 32 110 101 101 100 101 100 32 116 111 32 98 101 32 119 105 100 101 108 121 32 117 115 101 100 32 105 110 32 97 32 104 105 103 104 45 99 111 109 112 108 101 120 105 116 121 32 117 114 98 97 110 32 101 110 118 105 114 111 110 109 101 110 116 32 40 77 117 115 107 44 32 50 48 49 54 41 46 32 84 104 101 32 99 104 97 108 108 101 110 103 101 115 32 116 104 97 116 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 115 32 102 97 99 101 32 110 111 119 97 100 97 121 115 32 105 115 32 110 111 116 32 116 104 101 32 112 104 121 115 105 99 97 108 32 111 98 115 116 97 99 108 101 115 32 111 110 32 116 104 101 32 114 111 97 100 32 98 117 116 32 99 111 110 115 116 97 110 116 32 109 111 118 105 110 103 32 111 98 106 101 99 116 115 32 97 110 100 32 116 104 101 32 117 110 112 114 101 100 105 99 116 97 98 108 101 32 104 117 109 97 110 32 98 101 104 97 118 105 111 117 114 115 32 97 110 100 32 105 110 116 117 105 116 105 111 110 46 32 10 10 70 114 111 109 32 97 110 32 97 117 116 111 110 111 109 111 117 115 32

Autonomous agent In 2016, Elon Musk launched Tesla Autopilot and The Boring Company tunnel. The Tesla level 3 autonomous vehicle still requires driver attention at all times. It can simply perform in a controlled environment with the current sophisticated machine vision technology and the computer intelligence system. As Musk (2016) states, hands-off autonomous driving can be executed on the motorways, but not in a high-complexity urban environment. Through sensors and mapping technology, autonomous vehicles can react faster and accurately in most situations than a human response. The challenges for autonomous vehicle are no longer the physical obstacles on the road, but to detect constant moving objects and unpredictable human behaviours and cues.

From an autonomous vehicle’s point of view, any moving elements can obstruct the programme and affect the riding experience. The autonomous vehicle is situating in a “dynamic multivariate decision space” (Greenfield, 2017, 184). For instance, birds in the sky, tyre debris on the ground, moving pedestrians, fog and other unstructured situations. They are uncommon for machines to make a proper reaction. Autonomous vehicle companies like Tesla, Waymo and Uber have a fleet of users on the road to dedicate real-life training footages to the training dataset, yet, still insufficient to guarantee rider safety. Theoretically, for a human to pass a driving test demands 20 hours of practice, 40 hours of driving lessons, and perhaps more than ten years of riding experiences. With that, autonomous vehicles need at least a 275million miles (around twentyfive years) of “driving experience” and as many useful input training footages to be a safe piece of technology on the road.

Autonomous driving Figure 4.02 (Leong, 2021) Source: Author

0010

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7100” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 83 105 109 105 108 97 114 32 116 111 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 44 32 116 104 101 32 82 111 111 109 98 97 32 114 111 98 111 116 32 118 97 99 117 117 109 32 99 108 101 97 110 101 114 32 105 115 32 100 101 115 105 103 110 101 100 32 116 111 32 100 101 116 101 99 116 32 116 104 101 32 115 117 114 114 111 117 110 100 105 110 103 32 118 105 97 32 105 116 115 32 115 101 110 115 111 114 115 46 32 50 48 49 53 44 32 97 32 83 111 117 116 104 32 75 111 114 101 97 32 108 97 100 121 32 119 97 115 32 116 97 107 105 110 103 32 97 32 98 114 101 97 107 32 102 114 111 109 32 104 101 114 32 100 97 105 108 121 32 104 111 117 115 101 104 111 108 100 32 99 104 111 114 101 115 44 32 108 97 121 105 110 103 32 111 110 32 116 104 101 32 102 108 111 111 114 32 119 104 105 108 101 32 97 108 108 111 119 105 110 103 32 104 101 114 32 114 111 98 111 116 32 99 108 101 97 110 101 114 32 116 111 32 99 108 101 97 110 32 117 112 32 116 104 101 32 104 111 117 115 101 46 32 68 101 115 112 105 116 101 32 116 104 101 32 114 111 98 111 116 32 99 108 101 97 110 101 114 32 98 101 105 110 103 32 97 98 108 101 32 116 111 32 100 101 116 101 99 116 32 100 117 115 116 32 97 110 100 32 97 118 111 105 100 32 105 116 115 101 108 102 32 102 114 111 109 32 102 97 108 108 105 110 103 32 100 111 119 110 32 116 104 101 32 101 100 103 101 44 32 105 116 32 99 97 110 110 111 116 32 100 105 102 102 101 114 101 110 116 105 97 116 101 32 105 116 115 32 111 119 110 101 114 32 104 97 105 114 32 102 114 111 109 32 116 104 101 32 114 101 115 116 32 111 102 32 116 104 101 32 100 117 115 116 46 32 84 104 101 32 112 101 97 99 101 102 117 108 32 115 108 117 109 98 101 114 32 116 117 114 110 115 32 105 110 116 111 32 97 110 32 105 109 97 103 101 114 121 32 111 102 32 116 104 101 32 100 121 115 116 111 112 105 97 110 32 102 117 116 117 114 101 46 32 10 10 50 48 49 52 44 32 73 97 110 32 71 111 111 100 102 101 108 108 111 119 32 99 114 101 97 116 101 100 32 103 101 110 101 114 97 116 105 118 101 32 97 100 118 101 114 115 97 114 105 97 108 32 110 101 116 119 111 114 107 32 40 71 65 78 115 41 44 32 97 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 32 102 114 97 109 101 119 111 114 107 32 116 104 97 116 32 97 108 108 111 119 115 32 116 104 101 32 109 97 99 104 105 110 101 32 116 111 32 103 101 110 101 114 97 116 101 32 110 101 119 32 111 117 116 112 117 116 115 32 102 114 111 109 32 116 104 101 32 103 105 118 101 110 32 116 114 97 105 110 105 110 103 32 100 97 116 97 32 98 121 32 115 101 116 116 105 110 103 32 116 104 101 32 110 101 117 114 97 108 32 110 101 116 119 111 114 107 115 32 105 110 32 97 32 99 111 109 112 101 116 105 116 105 118 101 32 115 99 101 110 97 114 105 111 32 99 111 110 115 105 115 116 105 110 103 32 111 102 32 97 32 103 101 110 101 114 97 116 111 114 32 97 110 100 32 100 105 115 99 114 105 109 105 110 97 116 111 114 46 32 82 101 108 101 118 97 110 116 32 115 101 99 116 111 114 115 32 99 97 110 32 117 116 105 108 105 115 101 32 71 65 78 115 32 116 111 32 103 101 110 101 114 97 116 101 32 101 110 100 108 101 115 115 32 112 111

Roomba and the owner Figure 4.03 (Leong, 2021) Source: Author

Roomba incident Figure 4.04 (Yonhap, 2015)

Similar to the autonomous vehicle, the Roomba robot vacuum cleaner can detect its surrounding via sensors. In 2015, a South Korean lady was attacked by her robot cleaner while laying on the floor to take a break from her daily household chores. Despite the robot cleaner being able to detect dust and avoid itself from falling down the edge, it cannot differentiate its owner hair from the rest of the dust. The lady’s hair was locked on to the robot cleaner, becoming a victim in the machine-human environment. The peaceful slumber turns into a reality of the dystopian future where machines may be harmful to human beings. <Roomba’s vision (speculative) Figure 4.05 (Leong, 2021) Surce: Author

2010

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7102” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 97 117 116 111 110 111 109 111 117 115 32 97 103 101 110 116 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 83 105 109 105 108 97 114 32 116 111 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 44 32 116 104 101 32 82 111 111 109 98 97 32 114 111 98 111 116 32 118 97 99 117 117 109 32 99 108 101 97 110 101 114 32 105 115 32 100 101 115 105 103 110 101 100 32 116 111 32 100 101 116 101 99 116 32 116 104 101 32 115 117 114 114 111 117 110 100 105 110 103 32 118 105 97 32 105 116 115 32 115 101 110 115 111 114 115 46 32 50 48 49 53 44 32 97 32 83 111 117 116 104 32 75 111 114 101 97 32 108 97 100 121 32 119 97 115 32 116 97 107 105 110 103 32 97 32 98 114 101 97 107 32 102 114 111 109 32 104 101 114 32 100 97 105 108 121 32 104 111 117 115 101 104 111 108 100 32 99 104 111 114 101 115 44 32 108 97 121 105 110 103 32 111 110 32 116 104 101 32 102 108 111 111 114 32 119 104 105 108 101 32 97 108 108 111 119 105 110 103 32 104 101 114 32 114 111 98 111 116 32 99 108 101 97 110 101 114 32 116 111 32 99 108 101 97 110 32 117 112 32 116 104 101 32 104 111 117 115 101 46 32 68 101 115 112 105 116 101 32 116 104 101 32 114 111 98 111 116 32 99 108 101 97 110 101 114 32 98 101 105 110 103 32 97 98 108 101 32 116 111 32 100 101 116 101 99 116 32 100 117 115 116 32 97 110 100 32 97 118 111 105 100 32 105 116 115 101 108 102 32 102 114 111 109 32 102 97 108 108 105 110 103 32 100 111 119 110 32 116 104 101 32 101 100 103 101 44 32 105 116 32 99 97 110 110 111 116 32 100 105 102 102 101 114 101 110 116 105 97 116 101 32 105 116 115 32 111 119 110 101 114 32 104 97 105 114 32 102 114 111 109 32 116 104 101 32 114 101 115 116 32 111 102 32 116 104 101 32 100 117 115 116 46 32 84 104 101 32 112 101 97 99 101 102 117 108 32 115 108 117 109 98 101 114 32 116 117 114 110 115 32 105 110 116 111 32 97 110 32 105 109 97 103 101 114 121 32 111 102 32 116 104 101 32 100 121 115 116 111 112 105 97 110 32 102 117 116 117 114 101 46 32 10 10 50 48 49 52 44 32 73 97 110 32 71 111 111 100 102 101 108 108 111 119 32 99 114 101 97 116 101 100 32 103 101 110 101 114 97 116 105 118 101 32 97 100 118 101 114 115 97 114 105 97 108 32 110 101 116 119 111 114 107 32 40 71 65 78 115 41 44 32 97 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 32 102 114 97 109 101 119 111 114 107 32 116 104 97 116 32 97 108 108 111 119 115 32 116 104 101 32 109 97 99 104 105 110 101 32 116 111 32 103 101 110 101 114 97 116 101 32 110 101 119 32 111 117 116 112 117 116 115 32 102 114 111 109 32 116 104 101 32 103 105 118 101 110 32 116 114 97 105 110 105 110 103 32 100 97 116 97 32 98 121 32 115 101 116 116 105 110 103 32 116 104 101 32 110 101 117 114 97 108 32 110 101 116 119 111 114 107 115 32 105 110 32 97 32 99 111 109 112 101 116 105 116 105 118 101 32 115 99 101 110 97 114 105 111 32 99 111 110 115 105 115 116 105 110 103 32 111 102 32 97 32 103 101 110 101 114 97 116 111 114 32 97 110 100 32 100 105 115 99 114 105 109 105 110 97 116 111 114 46 32 82 101 108 101 118 97 110 116 32 115 101 99 116 111 114 115 32 99 97 110 32 117 116 105 108 105 115 101 32 71 65 78 115 32 116 111 32 103 101 110 101 114 97 116 101 32 101 110 100 108 101 115 115 32 112 111

In 2014, Ian Goodfellow created GANs, a machine learning framework that allows the machine to generate new outputs from the given training data by setting the ANN in a competitive scenario consisting of a generator and a discriminator. The designer of the autonomous vehicle system cannot possibly foresee all road circumstances. GANs can be used to generate endless driving footages as the training dataset, which are difficult to capture in real-life footage.

Consciousness and intuition are what differentiate humans from autonomous agents.

<Driver Monitoring Systems (DMS): Machine vision Figure 4.07 (NXP, 2019)

This transition period to the autonomous driving era can be the most dangerous as the environment is not ready for the autonomous vehicle. The autonomous vehicle is operating in a shared environment and requires humans attention. Still, the autonomous vehicle needs to understand its role as a driver.

Through the footage of the internal and external driving environment, an analysis of the driver behaviour in relation to the external environment can be carried out via deep learning and reinforcement learning.

However, human drivers do more than controlling the wheel in the public transport environment. They need to attend to the needs of the passengers and react to ethical problems. Therefore, it is difficult to achieve full autonomous due to the nature of driving, as “driving is many jobs simultaneously” (Broussard, 2019: 133). Consciousness and intuition are what differentiate humans from autonomous agents.

Driver Figure 4.06 (NXP, 2019)

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7104” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 116 101 115 116 98 101 100 batch_size = 32 img_height = 297 img_width = 210 train_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, K-city validation_split=0.2, subset=”training”, seed=123, image_size=(img_height, img_width), batch_size=batch_size) } class_names = train_ds.class_names print(class_names) [‘k-city’, ‘Mcity’, ‘trafiic_light’] { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 112 114 111 118 105 100 101 32 8220 118 101 104 105 99 108 101 32 116 111 32 101 118 101 114 121 116 104 105 110 103 8221 32 118 105 114 116 117 97 108 32 99 111 109 109 117 110 105 99 97 116 105 111 110 32 10 117 108 116 114 97 45 104 105 103 104 32 115 112 101 101 100 32 100 97 116 97 32 99 111 108 108 101 99 116 105 111 Mcity 110 10 115 121 110 99 104 114 111 110 105 122 97 116 105 111 110 32 111 102 32 97 115 115 101 115 115 109 101 110 116 32 115 121 115 116 101 109 32 97 110 100 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101’} ] } ] } ] }

The autonomous vehicle testbed A highly controlled environment

Fake city Figure 4.08 (Start up, 2020)

Rural road Figure 4.09 (Start up, 2020) K-city Figure 4.10 (Google Maps, 2021)

MCity Figure 4.11 (Google Maps, 2021)

Fake 2D facades Figure 4.12 (Google My Maps, n.d.)

This is not enough to train the autonomous vehicle.

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7106” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </84 104 101 32 10 114 101 97 108 45 108 105 102 101 32 10 101 110 118 105 114 111 110 109 101 110 116 {

‘language’:’en’, ‘orientation’:’Up’

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The real-life environment A few countries move the autonomous vehicle testbed to public roads to conduct testing in the real-life environments. In July 2019, the country that is the readiest for autonomous transportation, the Netherlands, announced the Experimental Law on Self-driving vehicles. The law allows autonomous vehicles to be tested on public roads under strict conditions and approval by various safety authorities (Government of the Netherlands, 2019). In the same year, Singapore Land Transport Authority announced half of its land to be the testbed. The plan opens up 1000km of public roads to test autonomous vehicle in expected and unexpected conditions with public acknowledgement (Abdullah, 2019). “The car of the future is being designed today. It is important that we prepare our roads and make our legislation future-proof to accommodate such transport,” said the Netherlands Minister (Government of the Netherlands, 2019). Opening the public roads to autonomous vehicles gives a direct interaction between the sentience agent and the autonomous agent, whereby the safety of the human remains the highest priority.

<Testbed under ultraviolet scanner (speculation) Diagram 4.2 (Leong, 2021) Source: Author

In 2016, the death of Joshua D. Brown in his Tesla autopilot self-driving features was the first fatal autonomous accident. The catastrophe is caused by the sensor failure in picking up the white reflective truck on a sunny day.

In 2018, Elaine Herzberg was killed by the autonomous Uber car because the system could not recognise her as a jaywalking pedestrian without a crosswalk as a background.

According to Richmond (2019), autonomous driving remains an unsafe operation as the crashes rate per million miles driven on public roads for autonomous vehicle (9.1) is almost 2.5 times more than human drivers (4.1). As Broussard (2019: 144) says, death is a feature but not a bug in autonomous vehicles. Both controlled testbed and public testbed are needed to prepare the autonomous agent for the real world. The idea of a parallel world that was suggested in chapter 3 can be applied here. The parallel world may be similar to the concept of digital twins, in which a replica digital profile is generated to capture and reflect on the real-time data behaviours of a physical object (Deloitte, n.d.). Diagram 4.1 shows the autonomous vehicle testbed can connect to an existing city that is occupied with sensors to reflect real-time information such as human movement and other changes. The information can then encode into the invisible layer of the testbed that is only detectable by the machine vision system (diagram 4.2). The parallel world proposes a safe and efficient autonomous testbed.

Parallel world Diagram 4.1 (Leong, 2021) Source: Author

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Trolley problem The trolley problem is a philosophical exercise. It is a discussion on the absence of a standard solution to scenarios that involve moral intuitions, questioning what to sacrifice and to save at a critical juncture.

Imagine the autonomous vehicle is directing to a crowd of people, and it needs to choose between killing hundreds or only sacrifice one driver; what should the autonomous vehicle designer programmes in the algorithms? If it programmes to save the larger group, will the users be willing to ride the autonomous vehicle? However, it should not be a problem and a decision made solely by the designer. Machine ethic is a subjective issue that requires a collective answer from the public, and there will always be disagreement throughout the process. “You can teach algorithm to recognise a table readily enough…but how do you teach it to recognise poverty” (Greenfield, 2017: 198)? Machines see things differently compare to humans who are made up of flesh and blood. As Broussard (2019: 129) states, the core problem is sentience, and there is no way to program theory of mind. The consciousness and awareness that humans possess are the missing value of autonomous vehicles, which is the reason that Broussard insisted on “human-assistance systems” and “human-centred design” instead of fully automation (Broussard, 2019).

“humanassistance systems” & “humancentred design”

Driver making decision Figure 4.13 (Leong, 2021) Source: Author

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Real-time tracking In this fast-moving generation, real-time data is needed to generate accurate results. Simultaneous Localization and Mapping (SLAM) is used in the autonomous vehicle way-finding system.

It discovers and maps real-time surrounding changes, overlapping them with the existing map to create a more accurate navigation result; double-checking becoming a crucial step, especially in the autonomous vehicle because “the consequences of failure here can all too easily be fatal” (Greenfield, 2017: 186). However, it is almost impossible to collect the entire dataset of the ever-growing world and translate it to a detail algorithmic library. Greenfield (2017:186) suggests that the machine neural network system shall be able to perform unsupervised feature learning, detecting surrounding objects and making judgments on what is salient without human intervention, instantaneously and inductively. Object clustering can be done to classify objects into a bigger group that conceptualises the complex world.

Furthermore, real-time data needs a tremendous number of collectors to make it effective.

Way-finding application such as Waze allows its users to upload and exchange real-time on-the-road data to the interface. Data sourcing from the public might be the fastest way to collect data because that is where the system gets the highest number of feedbacks. The public subsequently becomes the eyes for the application. Using the most found tools on the street, the urban vehicles, MIT senseable city lab’s urban sensing project (figure 4.15) manages to collect hyper-local data such as air quality, traffic condition and road qualities (MIT senseable city lab, 2021). The marriage of inexpensive machine vision technologies and the local vehicles provides a desirable data outcome, in which it becomes the key for future smart city development.

Urban Sensing data (Manhattan) Figure 4.14 (Urban Sensing data (Manhattan), 2021)

Urban Sensing New York city The number of trips needed to scan half of the scannable street segments

1, 179

The percentage of trips needed to scan half of the segments

0.3%

Taxi collecting urban data in Manhattan Figure 4.15 (MIT News, 2019)

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Senseable city, as proposed by Carlo Ratti, is a city that makes sense of its inhabitants, unlike a smart city that pays attention to its technology (Ratti and Claudel, 2016). In the senseable city, the city is working as a giant clockwork which formed an “urban scale circuit board, a computer in the air, driven by the objective of efficiency” (Ratti and Claudel, 2016: 28). It engages with the micro details of the built environment, which then convert into data points. However, society is yet to understand the true purpose of the machine vision system because, by far, they are merely a targeted object consumed by machine vision. The top-down system practiced in the recent years have led to “concentration of power in fewer hands, and the concentration of understanding in fewer heads” (Bridle, 2018: 120).

What if the public becomes an agent of data collection?

Senseable city What if the public becomes an agent of data collection?

Senseable City Diagram 4.3 (Leong, 2021) Source: (Senseable City Lab, 2021)

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Architects need to consider the impact of the autonomous vehicle from a broader perspective and rethink how it can reshape the entire urban fabric in preparation for an autonomous transportation future. “The full potential of driverless cars cannot be realised without implementing significant changes to the urban fabric” (Jaimes, 2016). The road infrastructure today is designed for human users to navigate around. The traffic light system that is designed to control the traffic flow and give signs to human users in the last 150 years shall be replaced.

With the increase of vehicles on the road, the traffic light zone becomes a congestion point during peak hours due to the frequent speed transitions under human control. Senseable city lab works together with three other research organisations, developed a “slot-based intersection” (Tachet et al., 2016) that could replace traditional traffic lights and enhance traffic patterns. The system provides a seamless traffic flow at the intersection area by assigning each vehicle with safe access time slots based on a carefully designed scheduling algorithm (Tachet et al., 2016). Besides, road infrastructure as stop signage, wayfinding signage, and others that dedicated to the human driver can be eliminated and replaced with minimum physical infrastructure. The road width can also be reduced following accurate vehicle-to-vehicle communication1. The senseable city will be a city that fully utilises the collected data to empower humans and machines.

1

Vehicle-to-vehicle (V2V) communication is a wireless communication system that allows vehicles to exchange informations such as speed, location, and heading.

Light traffic Figure 4.16 (Light traffic, 2021)

Smart street Figure 4.17 (Big, 2010)

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‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 8220 85 114 98 97 110 32 112 108 97 110 110 105 110 103 32 105 115 32 100 101 102 105 110 101 100 32 98 121 32 99 97 114 32 99 117 108 116 117 114 101 44 32 97 110 100 32 116 104 101 32 114 101 115 117 108 116 105 110 103 32 117 114 98 97 110 32 115 121 115 116 101 109 115 32 112 114 101 115 101 110 116 32 102 101 119 32 116 114 97 110 115 112 111 114 116 97 116 105 111 110 32 97 108 116 101 114 110 97 116 105 118 101 115 8221 32 40 82 97 116 116 105 32 97 110 100 32 67 108 97 117 100 101 108 44 32 50 48 49 54 44 32 112 112 46 57 55 41 46 32 84 114 97 102 102 105 99 32 99 111 110 103 101 115 116 105 111 110 115 32 114 101 109 97 105 110 32 97 32 100 105 108 101 109 109 97 32 102 111 114 32 116 104 101 32 114 111 97 100 32 117 115 101 114 32 97 110 100 32 116 104 101 32 101 110 118 105 114 111 110 109 101 110 116 46 32 65 110 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 99 111 117 108 100 32 116 114 97 110 115 102 111 114 109 32 105 110 116 111 32 112 117 98 108 105 99 32 116 114 97 110 115 112 111 114 116 97 116 105 111 110 32 116 111 32 112 114 111 118 105 100 101 32 97 32 110 101 119 32 109 111 98 105 108 105 116 121 32 112 108 97 110 46 32 85 115 105 110 103 32 98 105 103 32 100 97 116 97 32 97 110 100 32 114 101 97 108 45 116 105 109 101 32 100 97 116 97 32 97 110 97 108 121 116 105 99 32 116 104 114 111 117 103 104 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 44 32 101 110 97 98 108 101 115 32 116 104 101 32 99 97 114 112 111 111 108 105 110 103 32 97 112 112 108 105 99 97 116 105 111 110 32 115 121 115 116 101 109 32 116 111 32 97 110 97 108 121 115 101 32 116 104 101 32 117 114 98 97 110 32 109 111 98 105 108 105 116 121 32 100 101 109 97 110 100 115 32 97 110 100 32 116 104 101 32 109 97 116 99 104 105 110 103 32 114 105 100 101 115 44 32 119 104 105 99 104 32 100 101 99 114 101 97 115 101 32 116 104 101 32 111 118 101 114 97 108 108 32 116 114 97 102 102 105 99 32 97 110 100 32 116 104 101 32 99 105 116 121 32 118 101 104 105 99 108 101 32 101 109 105 115 115 105 111 110 115 32 112 101 114 32 112 101 114 115 111 110 32 40 77 73 84 32 83 101 110 115 101 97 98 108 101 32 67 105 116 121 32 76 97 98 44 32 50 48 49 55 41 46 32 32 65 117 116 111 110 111 109 111 117 115 32 115 104 97 114 105 110 103 32 109 111 98 105 108 105 116 121 32 109 97 121 32 114 101 115 104 117 102 102 108 101 32 116 104 101 32 117 114 98 97 110 32 108 97 121 111 117 116 44 32 98 114 105 110 103 32 112 114 105 111 114 105 116 121 32 98 97 99 107 32 116 111 32 116 104 101 32 112 101 100 101 115 116 114 105 97 110 32 112 97 116 104 32 97 110 100 32 112 117 98 108 105 99 32 115 112 97 99 101 115 46 32 10 10 87 104 105 108 101 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 115 121 115 116 101 109 32 105 115 32 8220 115 109 97 114 116 8221 32 101 110 111 117 103 104 32 116 111 32 99 97 114 114 121 32 111 117 116 32 105 116 115 32 116 97 115 107 44 32 97 114 101 32 116 104 101 32 101 120 105 115 116 105 110 103 32 105 110 102 114 97 115 116 114 117 99

Redesign the urban fabric

As autonomous vehicle technology is becoming more mature, are the existing road infrastructures ready to accommodate the smart technologies?

“Previous attempts to reorient urban planning away from automobiles failed – not for lack of effort or sophistication but because the car was still firmly entrenched in daily life and culture” (Ratti and Claudel, 2016: 103). In the Audi Future Urban Award proposal, Bjarke Ingels Group (Jordana, 2010) introduces a new traffic movement pattern in an elastic urban space. The proposal suggests that autonomous vehicles no longer drive on a fix road network but react to the surrounding changes through machine sensors and other reactive technologies.

Tarmac pavements transform into a reprogrammable digital smart surface with sensors, re-animating a familiar city (Jordana, 2010). This flexible dynamic city runs by big data may be a replacement for the current static city, creating new chemistry between sentient agents and intelligent agents.

Smart tile surface Figure 4.18 (BIG, 2010)

A new chemistry between sentient agents and intelligent agents.

“(Driver)less is more” BIG’s proposal for Audi Future Urban Award Figure 4.19 (BIG, 2010)

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7118” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 8220 85 114 98 97 110 32 112 108 97 110 110 105 110 103 32 105 115 32 100 101 102 105 110 101 100 32 98 121 32 99 97 114 32 99 117 108 116 117 114 101 44 32 97 110 100 32 116 104 101 32 114 101 115 117 108 116 105 110 103 32 117 114 98 97 110 32 115 121 115 116 101 109 115 32 112 114 101 115 101 110 116 32 102 101 119 32 116 114 97 110 115 112 111 114 116 97 116 105 111 110 32 97 108 116 101 114 110 97 116 105 118 101 115 8221 32 40 82 97 116 116 105 32 97 110 100 32 67 108 97 117 100 101 108 44 32 50 48 49 54 44 32 112 112 46 57 55 41 46 32 84 114 97 102 102 105 99 32 99 111 110 103 101 115 116 105 111 110 115 32 114 101 109 97 105 110 32 97 32 100 105 108 101 109 109 97 32 102 111 114 32 116 104 101 32 114 111 97 100 32 117 115 101 114 32 97 110 100 32 116 104 101 32 101 110 118 105 114 111 110 109 101 110 116 46 32 65 110 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 99 111 117 108 100 32 116 114 97 110 115 102 111 114 109 32 105 110 116 111 32 112 117 98 108 105 99 32 116 114 97 110 115 112 111 114 116 97 116 105 111 110 32 116 111 32 112 114 111 118 105 100 101 32 97 32 110 101 119 32 109 111 98 105 108 105 116 121 32 112 108 97 110 46 32 85 115 105 110 103 32 98 105 103 32 100 97 116 97 32 97 110 100 32 114 101 97 108 45 116 105 109 101 32 100 97 116 97 32 97 110 97 108 121 116 105 99 32 116 104 114 111 117 103 104 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 44 32 101 110 97 98 108 101 115 32 116 104 101 32 99 97 114 112 111 111 108 105 110 103 32 97 112 112 108 105 99 97 116 105 111 110 32 115 121 115 116 101 109 32 116 111 32 97 110 97 108 121 115 101 32 116 104 101 32 117 114 98 97 110 32 109 111 98 105 108 105 116 121 32 100 101 109 97 110 100 115 32 97 110 100 32 116 104 101 32 109 97 116 99 104 105 110 103 32 114 105 100 101 115 44 32 119 104 105 99 104 32 100 101 99 114 101 97 115 101 32 116 104 101 32 111 118 101 114 97 108 108 32 116 114 97 102 102 105 99 32 97 110 100 32 116 104 101 32 99 105 116 121 32 118 101 104 105 99 108 101 32 101 109 105 115 115 105 111 110 115 32 112 101 114 32 112 101 114 115 111 110 32 40 77 73 84 32 83 101 110 115 101 97 98 108 101 32 67 105 116 121 32 76 97 98 44 32 50 48 49 55 41 46 32 32 65 117 116 111 110 111 109 111 117 115 32 115 104 97 114 105 110 103 32 109 111 98 105 108 105 116 121 32 109 97 121 32 114 101 115 104 117 102 102 108 101 32 116 104 101 32 117 114 98 97 110 32 108 97 121 111 117 116 44 32 98 114 105 110 103 32 112 114 105 111 114 105 116 121 32 98 97 99 107 32 116 111 32 116 104 101 32 112 101 100 101 115 116 114 105 97 110 32 112 97 116 104 32 97 110 100 32 112 117 98 108 105 99 32 115 112 97 99 101 115 46 32 10 10 87 104 105 108 101 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 115 121 115 116 101 109 32 105 115 32 8220 115 109 97 114 116 8221 32 101 110 111 117 103 104 32 116 111 32 99 97 114 114 121 32 111 117 116 32 105 116 115 32 116 97 115 107 44 32 97 114 101 32 116 104 101 32 101 120 105 115 116 105 110 103 32 105 110 102 114 97 115 116 114 117 99

Current Boulevard Périphérique Figure 4.20 (Abaca, n.d.)

Mobility plan

New Deal Paris I Figure 4.21 (CRA, 2019)

New Deal Paris II Figure 4.22 (CRA, 2019)

“Urban planning is defined by car culture, and the resulting urban systems present few transportation alternatives” (Ratti and Claudel, 2016: 97).

Traffic congestions remain a dilemma for the road user and the environment. An autonomous vehicle could transform into public transportation to provide a new mobility plan. Through big data and real-time data analysis, MIT senseable city lab link the urban mobility demands with the matching rides for a carpooling application system, which decreases the overall traffic and the city vehicle emissions per person (MIT Senseable City Lab, 2017). Besides, Carlo Ratti Associati suggests in the New Paris Deal (Figure 4.21 & 4.22) proposal a new road layout for the congested Boulevard Périphérique.

The proposal states different transportation modes that reshuffle the urban road layout, bringing priority back to the pedestrian path and public spaces.

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7120” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </8220 67 105 116 121 32 76 111 103 103 105 110 103 8221 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 83 69 78 83 69 97 98 108 101 32 99 105 116 121 44 32 97 115 32 112 114 111 112 111 115 101 100 32 98 121 32 67 97 114 108 111 32 82 97 116 116 105 44 32 105 115 32 97 32 99 105 116 121 32 116 104 97 116 32 109 97 107 101 32 115 101 110 115 101 32 111 102 32 105 116 115 32 105 110 104 97 98 105 116 97 110 116 115 44 32 117 110 108 105 107 101 32 115 109 97 114 116 32 99 105 116 121 32 119 104 105 99 104 32 112 97 121 32 97 116 116 101 110 116 105 111 110 32 111 110 32 105 116 115 32 116 101 99 104 110 111 108 111 103 121 46 32 73 110 32 116 104 101 32 83 69 78 83 69 97 98 108 101 32 99 105 116 121 44 32 105 116 32 116 97 107 101 115 32 97 32 99 108 111 115 101 114 32 108 111 111 107 32 105 110 116 111 32 116 104 101 32 100 101 116 97 105 108 115 32 111 102 32 116 104 101 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 44 32 119 104 105 99 104 32 99 111 110 118 101 114 116 32 105 110 116 111 32 100 97 116 97 32 112 111 105 110 116 115 32 111 102 32 116 104 101 32 103 105 97 110 116 32 99 108 111 99 107 119 111 114 107 46 32 83 111 99 105 101 116 121 32 104 97 115 32 114 101 99 111 103 110 105 122 101 100 32 116 104 101 32 110 101 103 97 116 105 118 101 32 105 109 112 97 99 116 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 116 101 99 104 110 111 108 111 103 121 32 97 110 100 32 103 111 32 97 103 97 105 110 115 116 32 105 116 32 100 117 101 32 116 111 32 116 104 101 32 108 97 99 107 32 111 102 32 112 117 98 108 105 99 32 105 110 118 111 108 118 101 109 101 110 116 32 105 110 32 116 104 101 32 112 114 111 99 101 115 115 46 32 84 104 101 121 32 97 114 101 32 109 101 114 101 108 121 32 97 32 116 97 114 103 101 116 101 100 32 111 98 106 101 99 116 32 119 104 111 32 103 101 116 32 112 117 110 105 115 104 32 98 121 32 116 104 101 32 116 101 99 104 110 111 108 111 103 121 46 32 32 84 104 101 32 116 111 112 45 100 111 119 110 32 115 121 115 116 101 109 32 116 104 97 116 32 115 111 99 105 101 116 121 32 104 97 100 32 112 114 97 99 116 105 99 105 110 103 32 102 111 114 32 116 104 101 32 112 97 115 116 32 121 101 97 114 115 32 108 101 97 100 32 116 111 32 -30 -128 -100 99 111 110 99 101 110 116 114 97 116 105 111 110 32 111 102 32 112 111 119 101 114 32 105 110 32 102 101 119 101 114 32 104 97 110 100 115 44 32 97 110 100 32 116 104 101 32 99 111 110 99 101 110 116 114 97 116 105 111 110 32 111 102 32 117 110 100 101 114 115 116 97 110 100 105 110 103 32 105 110 32 102 101 119 101 114 32 104 101 97 100 115 -30 -128 -99 32 40 66 114 105 100 108 101 44 32 50 48 49 56 44 32 112 112 46 32 49 50 48 41 46 32 87 104 97 116 32 105 102 32 112 117 98 108 105 99 32 98 101 99 111 109 101 32 97 110 32 97 103 101 110 116 32 111 102 32 100 97 116 97 32 99 111 108 108 101 99 116 105 111 110 63 10 10 84 104 101 114 101 32 105 115 32 97 32 108 97 99 107 32 111 102 32 112 117 98 108 105 99 32 97 119 97 114 101 110 101 115 115 32 105 110 32 116 104 101 32 101 110 118 105 114 111 110 109 101 110 116 97 108 32 99 104 97 110 103 101 115 32 116 104 97 116 32 115 108 111 119 108 121 32 116 97 107 105 110 103 32 111 118 101 114 32 116 104 101 32 100 97 105 108 121 32 108 105 102 101 32 99 111 109

“City Logging”

The general public shall be involved in the senseable city data collection. Instead of taking big moves to achieve the result, the application of crowdsensing1 proposes a time and energy-saving solution. It brings in the bottom-up system rather than the top-down system to include people in the built environment design. Crowdsensing can empower individuals and provide meaning to their inhabitation, transforming “life-logging” to “city-logging” (Ratti and Claudel, 2016). The citizens see their shadow across the city, forming a tight-knit community. It is a shift of mass data collection from the big-tech companies to data agglomeration. The city becomes more than just highrise steel structures that frame the road-network; it is a “mosaic portrait of the city” (Ratti and Claudel, 2016: 56) compose by the citizens.

1

Crowdsensing is a process of data collection through IoT by a large group of users, upload to a common Information and Communications Technology (ICT) platform for the benefits of entire community.

City logging Figure 4.23 (Leong, 2021) Source: Author

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7122” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </53 68 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 65 32 115 112 97 99 101 32 119 105 116 104 32 97 32 110 101 119 32 118 105 115 117 97 108 32 99 117 108 116 117 114 101 46 32 84 104 101 32 109 97 99 104 105 110 101 32 115 116 97 114 116 115 32 116 111 32 116 104 105 110 107 32 97 110 100 32 112 101 114 99 101 105 118 101 46 32 10’} ] } ] } ] }

< procedural generation

5D

C H A PTE R FIVE

A sp a c e w i th a n e w v is u a l cu ltu re. T h e mac hine s tar ts to think and pe rc e iv e .

Fifth dimension Figure 5.01 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

011000000000000000000000111000000000001001000000000001000000000000000000000000000 <types> 011111011111111111111111111111111111111110011111101111110111010011111100000000000 <types> 000011111011111100000001111000000011111111111111111111111011111111111110001111111 ocr_url = base_url + “ocr” 111100011111011111100000001111000000001111111111111111000001011011111110110111111 111111000011111011111110000101111100000001111111111111111100001111111111111111001 100100111000011111011111110000001111100000001111111111111011100001111111111111111 sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7124” 111111111111000011111011111111111111111100000001110000000000000000000000000000000 headers = {‘Ocp-Apim-Subscription-Key’: sub} 001111111111111110011111011111110000000111000000001110000000000000000001000000000 { 000001101111111111110011111011111110000000111100000001111001100000011111100101110 </84 104 101 32 105 110 118 105 115 105 98 108 101 32 101 121 101 000000111111110000111000011111011111110000001111100000001111001101000011111111111 100000000100011110010111000011111011111111111111111000000011111011111110100111111 { 111100100011100001100110001000011111011111110000001111000000001111111111110100111 ‘language’:’en’, 111111111111101111001000000001100011111011111111000001111000000001111111111111111 ‘orientation’:’Up’ 111111111111101100000001000000001000011111011111000000000111100000001111111001110

{translated_ 8-bit sign integer: ‘ 72 117 109 97 110 115 32 97 114 101111111111100101000000000000000001000011111011111000000000111100000001110000001 101 32 101 110 116 101 114 105 110 103 32 116 104 101 32 -30 -128 -100 100 011101111100010000100110001100110000000000111111011111000000000111100000011111011 105 103 105 116 97 108 32 100 105 109 101 110 115 105 111 110 -30 -128 -99 111111100111001011110110111111111000000000011111111011111000000000111100000011111 32 40 82 97 116 116 105 32 97 110 100 32 67 108 97 117 100 101 108 44 32 50 111111111011101000000000001111111111000000001110011111011111000000000111111111111 48 49 54 41 44 32 97 32 -30 -128 -100 115 112 97 99 101 32 111 102 32 102 111101111111111001111111100001111111111000000001000011111011111000000000111111111 108 111 119 115 -30 -128 -99 32 98 101 121 111 110 100 32 97 110 121 32 102 111111111111111111101111111000001111111111000001011000011110011111111101111111111 111 114 109 32 111 102 32 118 105 115 105 111 110 32 105 110 32 116 104 101 111111111011111111111110100001001111111111111001001111000001111011111111111111111 32 116 119 101 110 116 121 45 102 105 114 115 116 32 99 101 110 116 117 114 111111111111011110001111111001111011111111111111100101011110011111011111111111111 121 46 32 32 84 104 101 32 105 110 116 101 114 110 101 116 32 101 120 112 111111101111111110011001111100000001111010011111101000010001111011111011111111111 108 111 100 101 100 32 105 110 116 111 32 101 118 101 114 121 32 98 105 116 111111111111111111111111001000000010011101001111111111111110001111011111011111111 32 111 102 32 115 111 99 105 101 116 121 44 32 102 114 111 109 32 99 105 111111111001111000111110011110011110110000111111111111110000000001111011111011111 118 105 108 32 116 111 32 99 111 109 109 101 114 99 105 97 108 32 97 110 111111111111101111011111100011000111011000000011111111001111000010001100011111011 100 32 115 111 99 105 97 108 32 97 99 116 105 118 105 116 105 101 115 44 111111011111111111011000000000011000000000000010001000011111111110110011000011111 32 121 101 116 32 105 116 32 105 115 32 105 110 118 105 115 105 98 108 101 011111100001001100110000000110000011000000000000010000000001001111111110111000011 46 32 84 104 101 114 101 32 97 114 101 32 98 121 32 110 111 32 109 101 97 111011101001111111100110000000110000011000000000000010000000000001111111110011000 110 115 32 116 104 101 32 116 101 99 104 110 111 108 111 103 121 32 119 97 011111011101001111101100000000000100000011000000000000000000000000001111111110011 110 116 115 32 104 117 109 97 110 115 32 116 111 32 117 110 100 101 114 115 110011111011111101111101100000000000000000011000000000000000000000000001111111110 116 97 110 100 32 105 116 32 111 114 32 116 104 101 32 98 97 99 107 103 114 011110011111011111101111101100000000000000000011000000000000000000010000001111111 111 117 110 100 32 102 111 114 99 101 115 32 116 104 97 116 32 107 101 101 110011111011111011101001111101100000000000001000011000000000000000000011010001111 112 32 105 116 32 114 117 110 110 105 110 103 32 40 66 114 105 100 108 101 111110011110011111011101001111101100000000000001000011000000000000000100011010001 44 32 50 48 49 57 99 41 46 32 85 110 108 105 107 101 32 116 104 101 32 116 111111110011000011111011101011111101000000000000001000011000000000000000100011010 114 97 100 105 116 105 111 110 97 108 32 109 97 105 108 105 110 103 32 115 001111111110011000011111011101011111101000000000000001000011000000000000000000011 121 115 116 101 109 32 116 104 97 116 32 112 111 115 116 99 111 100 101 32 010001111111110011000011111011101001111101111111101111000010111000110000010000001 105 115 32 97 116 116 97 99 104 101 100 32 97 110 100 32 115 101 110 100 32 011110001110111110011000011111011111111011101111111111111111111111111111111111111 116 111 32 97 32 112 104 121 115 105 99 97 108 32 104 111 109 101 59 32 116 111111111000000110000001111011111011111110011101000000000000001000001111111111111 104 101 32 71 109 97 105 108 32 100 111 101 115 32 116 104 101 32 115 97 111111111111000111111111111111011111011111111111111000000000000000000011000111100 109 101 32 102 117 110 99 116 105 111 110 44 32 98 117 116 32 105 110 118 000000000001001111101111110011100011111011111111111111111001010100011011111000000 105 115 105 98 108 121 44 32 119 105 116 104 105 110 32 97 32 115 112 108 000000000000000000000011110000011100011111011111111111111111111111111111111110111 105 116 32 115 101 99 111 110 100 46 32 79 110 108 121 32 119 104 101 110 111111111011111100000001111101110111000011111011111111111111111111111111111111110 32 111 110 101 32 115 116 97 114 116 115 32 116 111 32 117 110 100 101 114 111111111111111111111000111111111111111100011111011111111111111111111111111111111 115 116 97 110 100 32 104 111 119 32 105 110 116 101 114 110 101 116 32 119 111111111111111111111111111111111111111111100011111111111111111111111111111111000 111 114 107 44 32 111 110 101 32 99 97 110 32 98 101 32 105 110 32 99 111 011100111111111111111111111111111111111111111100011101111011110000001110001111010 110 116 114 111 108 32 111 102 32 105 116 46 32 72 117 109 97 110 115 32 000001111001111111110111111111111111111111111111110001111001011110000011111000000 110 101 101 100 32 116 111 32 100 101 118 101 108 111 112 32 97 32 115 121 000001111111111110011000000101111100111110010011111111111111011111011100001110000 109 98 105 111 116 105 99 32 114 101 108 97 116 105 111 110 115 104 105 112 011111111000111111000011110011000111000011110011111011110111111010000101010010000 32 119 105 116 104 32 105 110 116 101 114 110 101 116 32 98 117 116 32 110 000111100111110001111010000000001100000000000000000000000000001 111 116 32 111 110 108 121 32 116 111 32 111 98 116 97 105 110 32 105 110 102 111 114 109 97 116 105 111 110 32 102 114 111 109 32 105 116 59 32 117 110 100 101 114 115 116 97 110 100 32 116 104 97 116 32 116 104 101 114 101 32 105 115 32 97 108 119 97 121 115 32 109 111 114 101 32 116 104 97 110 32 119 104 97 116 32 105 115 32 115 104 111 119 32 111 110 32 116 104 101 32 115 117 114 102 97 99 101 46 32 79 110 32 116 104 101 32 111 116 104 101 114 32 104 97 110 100 44 32 119 105 116 104 32 116 104 101 32 114 97 105

The invisible eye Humans are entering the “digital dimension” (Ratti and Claudel, 2016), a “space of flows” beyond any form of vision in the twenty-first century.

The internet exploded into every bit of society, from civil to commercial and social activities, yet invisible. The technology has no means for humans to understand it or the background forces that keep it running (Bridle, 2019c). On the other hand, with the rise of intelligent agents in image interpretation system such as licence-plate readers, Young (2019: 22) argues that it is an emerging of a new visual culture which is “paradoxically largely invisible to human eyes”. Most of the events are made by machine for the machine without the human in the loop.

And those who notice the invisible images hardly understand it. The visual culture consists of the unseen eyes that are constantly active: the suggestion bars appear after Google search, the next Netflix series suggestion, the friend recommendation on Facebook and others. People need to realise that “[artefacts] and systems we interact with daily collect, store and process information about us, or are activated by our movement and transactions” (Shepard, 2011: 20). It is a system that watches closely into the detail of one’s daily life in the digital dimension, linking it to the individual historical data and make an accurate suggestion or prediction base on personal preferences.

Motorola 68030 Figure 5.02 (Morlinghaus, n.d.)

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Thinking machine

With intelligent machines burgeoned in recent years, the question now becomes, can the machines think creatively within themselves? It might not suggest so through the winning of Deepblue, the IBM supercomputer, in the 1997 chess tournament against Garry Kasparov. As Bridle (2018: 149) claims Deepblue winning as a complete brute-force of machine learning that involved “a massively parallel architecture of 14,000 custom-designed chess chips, capable of analysing 200 million boards position per second”. It suggests here that if there is enough power to keep the computer system going, the computer will win the game. “Kasparov was not outthought, merely outgunned” (Bridle, 2018: 149).

On the contrary, move 37 played by Alpha Go, a computer programme with a deep learning model generated by Google DeepMind suggested something different – the machine might think creatively. In 2016, Alpha Go achieved victory against the 18-time world Go1 champion. In the game documentary (DeepMind, 2020), Fan Hui, the European Go champion commented move 37 as a non-human move and have never seen a human played a similar move before. To develop an indecipherable strategy for this intuitive game, the computer programme does not solely get inputs of millions of Go moves made by expert Go players. It carries out training with itself millions of times more. The winning of the DeepMind, however, is unreadable by a human. In such a situation, there is no way to quantify machine intelligence and only winning against human does (Bridle, 2018: 149). The machine will not explain itself to humans; humans need to play with it to understand the machine’s mind.

From both games, the advancement in machine learning is observable and giving a hint of sentience machine. However, everything is still under human control until the day machine develops sentience and consciousness within itself.

1

Go is a ancient board game invented in China. It is an abstract strategy board game in which the player who gains more territory on the board wins the game. There are nearly 130,000 possible next moves in Go, whereas there are only 400 possible next moves in Chess.

Alpha Go Move 37 Figure 5.03 (Leong, 2021) Source: Author

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{translated_ 8-bit sign integer: ‘ 69 118 101 114 121 116 104 105 110 103 32 99 97 110 32 98 101 32 100 101 114 105 118 101 100 32 116 111 32 97 32 115 105 110 103 108 101 32 109 97 116 104 101 109 97 116 105 99 97 108 32 97 98 115 116 114 97 99 116 105 111 110 115 32 111 114 32 97 108 103 111 114 105 116 104 109 32 105 110 32 116 104 101 32 100 105 103 105 116 97 108 32 100 105 109 101 110 115 105 111 110 46 32 -30 -128 -100 73 102 32 119 101 32 116 97 107 101 32 97 32 112 101 101 107 32 105 110 116 111 32 116 104 101 32 105 110 116 101 114 110 97 108 32 119 111 114 107 105 110 103 115 32 111 102 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 115 121 115 116 101 109 115 44 32 119 101 32 102 105 110 100 32 97 32 109 101 110 97 103 101 114 105 101 32 111 102 32 97 98 115 116 114 97 99 116 105 111 110 115 32 116 104 97 116 32 115 101 101 109 32 99 111 109 112 108 101 116 101 108 121 32 97 108 105 101 110 32 116 111 32 104 117 109 97 110 32 112 101 114 99 101 112 116 105 111 110 32 40 89 111 117 110 103 44 32 50 48 49 57 44 32 112 112 46 32 50 54 41 46 -30 -128 -99 32 87 105 116 104 32 71 97 117 71 65 78 32 109 101 110 116 105 111 110 101 100 32 105 110 32 99 104 97 112 116 101 114 32 50 44 32 104 117 109 97 110 -30 -128 -103 115 32 114 111 108 101 32 105 110 32 116 104 101 32 97 114 116 32 99 114 101 97 116 105 111 110 32 105 115 32 110 111 32 108 111 110 103 101 114 32 116 104 101 32 115 97 109 101 58 32 97 99 116 105 111 110 32 111 102 32 105 110 115 112 105 114 97 116 105 111 110 32 99 111 108 108 101 99 116 105 110 103 32 98 101 99 111 109 101 32 104 117 103 101 32 100 97 116 97 32 105 110 112 117 116 115 44 32 116 104 101 32 103 101 115 116 117 114 101 32 111 102 32 115 116 114 111 107 101 32 98 101 99 111 109 101 32 97 32 115 105 110 103 108 101 32 99 111 108 111 117 114 44 32 116 111 117 99 104 105 110 103 32 117 112 32 98 101 99 111 109 101 32 112 111 115 116 45 112 104 111 116 111 115 104 111 112 32 112 114 111 100 117 99 116 105 111 110 46 32 70 117 114 116 104 101 114 109 111 114 101 44 32 116 104 101 32 117 110 115 117 112 101 114 118 105 115 101 100 32 109 97 99 104 105 110 101 32 108 101 97 114 110 105 110 103 44 32 97 32 99 111 109 98 105 110 97 116 105 111 110 32 111 102 32 97 108 103 111 114 105 116 104 109 32 97 110 100 32 71 65 78 115 32 99 114 101 97 116 101 100 32 98 121 32 77 97 114 105 111 32 75 108 105 110 103 101 109 97 110 110 32 116 111 32 103 101 110 101 114 97 116 101 32 104 105 115 32 97 114 116 119 111 114 107 40 115 41 32 -30 -128 -100 77 101 109 111 114 105 101 115 32 111 102 32 80 97 115 115 101 114 98 121 32 73 -30 -128 -99 32 115 117 103 103 101 115 116 32 110 101 119 32 119 97 121 32 111 102 32 118 105 115 117 97 108 32 101 120 112 101 114 105 101 110 99 101 46 32 85 116 105 108 105 115 105 110 103 32 116 104 101 32 115 105 109 105 108 97 114 32 116 101 99 104 110 105 113 117 101 32 97 115 32 71 97 117 71 65 78 44 32 75 108 105 110 103 101 109 97 110 110 32 99 114 101 97 116 101 32 97 32 -30 -128 -104 84 105 110 100 101 114 45 108 105 107 101 -30 -128 -103 32 97 108 103 111 114 105 116 104 109 105 99 32 97 114 116 119 111 114 107 32 116 104 97 116 32 116 114 97 105 110 115 32 111 110 32 104 105 115

Algorithmic world The mathematical abstraction and algorithm are the basic form of objects in the digital dimension. “If we take a peek into the internal workings of machine vision systems, we find a menagerie of abstractions that seem completely alien to human perception (Young, 2019: 26).” With GauGAN, the human’s role in the art creation is no longer the same; the action of inspiration collecting becomes collections of data inputs, the gesture of stroke becomes a single colour, touching up becomes post-photoshop production. Furthermore, Mario Klingemann uses the GANs unsupervised machine learning model to generate his artwork(s) “Memories of Passerby I”, suggesting a new form of art creation. Utilising a similar technique as GauGAN, Klingemann creates a ‘Tinder-like’ algorithmic neural network that trains with his aesthetic preference (portraits from the 17th and 19th centuries) (ArtDip, 2019). The machine can generate infinite portrait paintings as long as it lasts. With sufficient training data, the machine algorithm can pick up on the creator’s preference and generate a new déjà vu image in its distinct vision towards the world. The machine-generated images create confusion among objects humans once familiar with, requiring humans to relearn how to see the world. With this kind of real-time visual experience, images possess a temporarily human-understandable state and turn into a new group of pixels the next second. The event is machine-vision exclusive.

Memories of Passerby I by Mario Klingemann Figure 5.04 (Medium, n.d.)

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{translated_ 8-bit sign integer: ‘ 85 110 108 105 107 101 32 84 114 101 118 111 114 32 80 97 103 108 101 110 32 119 104 111 32 99 97 112 116 117 114 101 32 116 104 101 32 105 110 118 105 115 105 98 108 101 32 102 111 114 99 101 32 116 104 114 111 117 103 104 32 100 105 114 101 99 116 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 44 32 116 117 114 110 105 110 103 32 105 116 32 105 110 116 111 32 97 32 116 119 111 45 100 105 109 101 110 115 105 111 110 97 108 32 112 104 111 116 111 103 114 97 112 104 46 32 84 104 101 32 73 114 105 115 104 32 97 114 116 105 115 116 32 74 111 104 110 32 71 101 114 114 97 114 100 -30 -128 -103 115 32 70 97 114 109 32 105 115 32 97 32 99 111 109 112 108 101 116 101 32 109 97 99 104 105 110 101 32 105 116 115 101 108 102 44 32 117 115 101 32 116 111 32 117 110 118 101 105 108 32 116 104 101 32 100 97 116 97 32 102 97 114 109 32 116 104 97 116 32 71 111 111 103 108 101 32 119 105 115 104 101 115 32 116 111 32 104 105 100 101 32 102 114 111 109 32 104 117 109 97 110 32 101 121 101 46 32 71 101 114 114 97 114 100 32 115 117 114 118 101 121 115 32 116 104 101 32 115 105 116 101 32 102 114 111 109 32 97 105 114 32 97 102 116 101 114 32 97 32 114 101 106 101 99 116 32 116 114 105 112 32 116 111 32 79 107 108 97 104 111 109 97 32 100 97 116 97 32 102 97 114 109 44 32 116 104 101 110 32 109 111 100 101 108 32 105 116 32 105 110 32 97 32 99 111 109 112 117 116 101 114 45 103 101 110 101 114 97 116 101 100 32 105 109 97 103 101 114 121 32 109 111 100 101 108 108 105 110 103 32 115 111 102 116 119 97 114 101 46 32 84 104 111 117 103 104 32 116 104 101 32 51 54 48 45 100 101 103 114 101 101 32 97 110 105 109 97 116 105 111 110 32 97 112 112 101 97 114 115 32 108 105 107 101 32 97 110 121 32 111 116 104 101 114 32 116 114 97 100 105 116 105 111 110 97 108 32 97 110 105 109 97 116 105 111 110 32 119 105 116 104 32 114 101 97 100 97 98 108 101 32 102 114 97 109 101 115 44 32 101 97 99 104 32 102 114 97 109 101 32 105 115 32 100 111 110 101 32 98 121 32 112 114 111 99 101 100 117 114 97 108 32 103 101 110 101 114 97 116 105 111 110 44 32 119 104 105 99 104 32 -30 -128 -100 97 108 103 111 114 105 116 104 109 105 99 32 112 114 111 99 101 100 117 114 101 32 116 97 107 101 115 32 112 114 101 99 101 100 101 110 99 101 32 111 118 101 114 32 97 108 108 32 99 111 110 116 101 110 116 32 97 115 32 116 104 101 32 115 105 109 117 108 97 116 105 111 110 32 105 110 100 101 102 97 116 105 103 97 98 108 121 32 114 111 108 108 115 32 111 110 32 104 101 114 109 101 116 105 99 97 108 108 121 -30 -128 -99 32 40 75 108 101 105 110 109 97 110 44 32 50 48 49 53 41 46 32 10 10 72 117 109 97 110 115 -30 -128 -103 32 99 117 114 105 111 115 105 116 121 32 116 111 119 97 114 100 115 32 116 104 101 32 110 101 119 32 100 105 109 101 110 115 105 111 110 32 99 114 101 97 116 101 115 32 118 105 114 116 117 97 108 32 114 101 97 108 105 116 121 32 40 86 82 41 32 97 110 100 32 97 117 103 109 101 110 116 101 100 32 114 101 97 108 105 116 121 32 40 65 82 41 46 32 84 104 101 32 109 97 99 104 105 110 101 32 117 115 101 115 32 116 111 32 118 105 101 119 32 116 104 101 32 100 105 109 101 110 115 105 111 110 32 98 101 99 111 109 101 32 97 110 32 101 120 116 101 110 115

<National Reconnaissance Office Ground Station Figure 5.06 (GauGAN, 2021)

Unlike Trevor Paglen, who captured the invisible force through direct machine vision, turning it into a two-dimensional photograph (figure 5.05), the Irish artist John Gerrard’s Farm (figure 5.07) is a complete machine itself. The project unveils the data farm that Google wishes to hide from the human eye. Gerrard surveys the site from the air after a rejection ground trip to an Oklahoma data farm, then modelled it in computer-generated imagery modelling software.

Though the 360-degree animation appears like any other traditional animation with readable frames, every frame is procedurally generated, which “algorithmic procedure takes precedence over all content as the simulation indefatigably rolls on hermetically” (Kleinman, 2015).

National Reconnaissance Office Ground Station (ADF-SW) Jornada del Muerto, New Mexico; Distance ~16 Miles Figure 5.05 (Paglen, 2012)

Humans no longer seek comfort within a physical place but mental enjoyment in the virtual world.

Virtual reality (VR) and augmented reality (AR) fulfilled humans’ curiosity towards the new dimension. With VR, the physical place becomes useless besides providing basic shelter to the occupants. Somehow, space returns to its original status – to provide protection, no extra ornament is needed to satisfy human needs. What is leave behind are the abandoned places that once served entertainment and service purposes. Taking the high street as an example, the 2019 pandemic outbreak has further taken away businesses from physical shops and create a new online shoping experience. A new definition of physical space is needed. While humans are immersing themselves in the digital world, the physical space becomes a supporting system, more of it will be dedicated to the infrastructure that keeps the algorithmic world running.

Farm (Pryor Creek, Oklahama) Figure 5.07 (Gerrard, 2015)

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{translated_ 8-bit sign integer: ‘ 72 117 109 97 110 115 32 97 114 101 32 108 105 110 103 101 114 105 110 103 32 111 110 32 116 104 101 32 101 100 103 101 32 111 102 32 116 104 101 32 118 105 115 105 98 108 101 32 97 110 100 32 105 110 118 105 115 105 98 108 101 32 115 112 97 99 101 46 32 76 105 107 101 32 80 97 103 108 101 110 32 97 110 100 32 71 101 114 114 97 114 100 32 119 104 111 32 97 114 101 32 116 114 121 105 110 103 32 116 111 32 114 101 118 101 97 108 32 116 104 101 32 117 110 115 101 101 110 44 32 97 32 -30 -128 -100 118 105 115 105 98 108 101 32 97 101 115 116 104 101 116 105 99 32 102 111 114 32 97 110 32 105 110 118 105 115 105 98 108 101 32 110 101 116 -30 -128 -99 32 40 87 105 103 108 101 121 44 32 50 48 48 49 44 32 112 103 46 32 49 49 49 41 32 105 115 32 109 117 99 104 32 110 101 101 100 101 100 32 105 110 32 99 117 114 114 101 110 116 32 115 111 99 105 101 116 121 46 32 87 105 116 104 32 97 108 108 32 116 104 101 32 101 120 97 109 112 108 101 115 32 103 105 118 101 110 44 32 104 117 109 97 110 115 32 115 101 101 109 32 116 111 32 114 101 97 99 104 32 97 32 103 114 101 121 32 122 111 110 101 32 116 104 97 116 32 111 98 106 101 99 116 32 97 112 112 101 97 114 32 111 110 32 110 97 107 101 100 32 101 121 101 115 32 100 111 32 110 111 116 32 114 101 112 114 101 115 101 110 116 32 114 101 97 108 105 116 121 46 32 70 101 101 108 105 110 103 32 119 105 116 104 105 110 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 100 101 102 105 110 101 100 32 108 101 115 115 101 114 32 98 121 32 116 104 101 32 112 104 121 115 105 99 97 108 32 115 117 98 115 116 97 110 99 101 115 32 40 83 104 101 112 97 114 100 44 32 50 48 49 49 41 46 32 87 105 116 104 32 116 104 101 32 111 109 110 105 112 114 101 115 101 110 116 32 116 101 99 104 110 111 108 111 103 121 32 109 97 99 104 105 110 101 44 32 104 117 109 97 110 115 32 110 111 32 108 111 110 103 101 114 32 99 108 97 105 109 115 32 116 104 101 105 114 32 112 101 114 115 111 110 97 108 32 115 112 97 99 101 32 98 121 32 116 104 101 32 115 111 108 105 100 105 116 121 32 111 102 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 98 117 116 32 101 110 103 97 103 101 32 109 111 114 101 32 119 105 116 104 32 116 104 101 105 114 32 -30 -128 -100 116 101 114 114 105 116 111 114 121 32 109 97 99 104 105 110 101 -30 -128 -99 46 32 65 115 32 83 104 101 112 97 114 100 32 40 50 48 49 49 41 32 99 108 97 105 109 101 100 32 109 111 98 105 108 101 32 112 104 111 110 101 32 97 115 32 97 32 112 101 114 115 111 110 97 108 32 -30 -128 -100 116 101 114 114 105 116 111 114 121 32 109 97 99 104 105 110 101 -30 -128 -99 32 105 110 32 74 97 112 97 110 32 116 104 97 116 32 99 97 112 97 98 108 101 32 111 102 32 109 111 117 108 100 105 110 103 32 97 110 121 32 115 112 97 99 101 32 105 110 116 111 32 111 110 101 -30 -128 -103 115 32 111 119 110 32 112 101 114 115 111 110 97 108 32 112 97 114 97 100 105 115 101 46 32 84 104 97 116 32 105 115 32 97 110 32 105 110 118 105 115 105 98 108 101 32 115 112 97 99 101 32 116 104 97 116 32 112 114 111 118 105 100 101 115 32 97 32 115 101 110 115 101 32 111 102 32 98 101 108 111 110 103 105 110 103 32 97 110 100 32 116 104 101 32 102 101 101 108 105

Too smart city Humans are lingering on the edge of the visible and invisible space. Like Paglen and Gerrard attempt to reveal the unseen, current society needs a “visible aesthetic for an invisible net” (Wigley, 2001: 111).

Humans seem to arrive at a grey zone where objects that appeared on naked eye do not represent reality.

Experience within a built environment are defined lesser by the physical substances (Shepard, 2011).

With the omnipresent technology machine, humans no longer claim their personal space by the solidity of the built environment but engage more with their “territory machine”. As Shepard (2011) claims mobile phone as personal “territory machine” that is capable of moulding any space into one’s paradise. That is an invisible space that provides a sense of belonging and the feeling of being occupied. The machine becomes “a tool for organising space, time, and the boundaries around the body in public space” (Shepard, 2011: 25).

There is a constant flow of invisible information across technology machines that fuels the current society. The invisible outweighs the visible. “Thinking about space in non-visual ways, where formal geometry and material articulation become less relevant than the topologies of networked information systems and their intersection with the socio-spatial practises of daily life” (Shepard, 2011: 20).

<‘Territory machine’ Figure 5.09 (GauGAN, 2021)

In the senseable city, the city describes as a giant computer capable of seeing and remembering. In this too smart city, or name it “sentient” city, the built environment becomes “an adaptive learning entity” (Ratti and Claudel, 2016: 80), possessing a dialogue with the inhabitants instead of a monologue. Question are, do people want the smart chair to disturb the instant moment by reminding themselves it is time to move after thirty minutes? Do one wish to reject the underground turnstile because of the digital bias embedded in the system? How smart does a city need to balance the freedom of its occupants while regulating itself? In this ever-changing posthuman city, designers and architects shall not only focus on the hardware of the built environment but the immaterial software infrastructure and its ability to react with the new urban experiences (Shepard, 2011: 20).

“Thinking about space in nonvisual ways, where formal geometry and material articulation become less relevant than the topologies of networked information systems and their intersection with the socio-spatial practices of daily life” (Shepard, 2011: 20).

‘Territory machine’ Figure 5.08 (Leong, 2021) Source: Author

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{translated_ 8-bit sign integer: ‘ 65 115 32 72 97 114 114 105 115 111 110 32 40 50 48 49 51 58 32 49 53 55 41 32 115 117 103 103 101 115 116 101 100 44 32 116 119 101 110 116 121 45 102 105 114 115 116 45 99 101 110 116 117 114 121 32 97 114 99 104 105 116 101 99 116 115 32 109 97 121 32 99 111 110 99 101 110 116 114 97 116 101 32 111 110 32 99 114 101 97 116 105 110 103 32 8220 102 108 101 120 105 98 108 101 44 32 100 105 118 101 114 115 101 44 32 104 117 109 97 110 101 32 104 97 98 105 116 97 116 115 32 102 111 114 32 101 108 101 99 116 114 111 110 105 99 97 108 108 121 32 115 117 112 112 111 114 116 101 100 32 110 111 109 97 100 105 99 32 111 99 99 117 112 97 116 105 111 110 46 8221 32 87 105 116 104 32 116 104 105 115 44 32 97 114 99 104 105 116 101 99 116 117 114 101 32 100 111 101 115 32 110 111 116 32 111 110 108 121 32 105 110 118 111 108 118 101 32 98 114 105 99 107 115 32 97 110 100 32 99 111 108 117 109 110 32 98 117 116 32 97 32 99 97 114 110 105 118 97 108 32 111 102 32 101 118 101 114 121 32 101 120 112 101 114 105 101 110 99 101 32 119 105 116 104 105 110 32 97 110 32 101 110 118 105 114 111 110 109 101 110 116 46 32 72 97 110 115 32 72 111 108 108 101 105 110 8217 115 32 65 108 108 101 32 115 105 115 116 32 65 114 99 104 105 116 101 107 116 117 114 32 40 69 118 101 114 121 116 104 105 110 103 32 105 115 32 97 114 99 104 105 116 101 99 116 117 114 101 41 32 98 114 105 110 103 115 32 105 110 116 111 32 116 104 101 32 115 99 101 110 101 32 104 101 114 101 44 32 97 114 99 104 105 116 101 99 116 117 114 101 32 100 105 115 115 111 108 118 101 115 32 105 110 116 111 32 100 97 105 108 121 32 108 105 102 101 44 32 98 101 99 111 109 101 32 109 111 109 101 110 116 115 32 111 102 32 116 101 109 112 111 114 97 114 121 32 101 120 112 101 114 105 101 110 99 101 115 46 32 73 116 32 119 105 108 108 32 98 101 32 116 104 101 32 114 101 97 108 105 115 97 116 105 111 110 32 111 102 32 65 114 99 104 105 103 114 97 109 8217 115 32 112 108 117 103 45 105 110 32 99 105 116 121 32 97 110 100 32 105 110 115 116 97 110 116 32 99 105 116 121 46 32 8220 84 104 101 32 119 104 111 108 101 32 99 105 116 121 32 105 115 32 97 32 115 111 114 116 32 111 102 32 111 110 103 111 105 110 103 32 102 101 115 116 105 118 97 108 8230 32 73 32 119 97 110 116 101 100 32 97 32 102 114 101 101 45 102 111 114 45 97 108 108 32 115 111 114 116 32 111 102 32 115 105 116 117 97 116 105 111 110 8230 8221 44 32 115 97 105 100 32 80 101 116 101 114 32 67 111 111 107 32 105 110 32 116 104 101 32 105 110 116 101 114 118 105 101 119 32 119 105 116 104 32 66 76 68 71 66 76 79 71 32 40 77 97 110 97 117 103 104 44 32 50 48 48 57 58 32 51 48 41 46 32 65 114 99 104 105 116 101 99 116 115 32 115 104 111 117 108 100 32 110 111 116 32 99 111 110 102 105 110 101 32 116 104 101 109 115 101 108 118 101 115 32 119 105 116 104 32 97 114 99 104 105 116 101 99 116 117 114 101 32 107 110 111 119 108 101 100 103 101 32 98 117 116 32 109 97 107 101 32 99 114 111 115 115 45 100 105 115 99 105 112 108 105 110 101 32 99 111 108 108 97 98 111 114 97 116 105 111 110 115 32 116 104 97 116 32 119 105 108 108 32 111 112 101 110 32 116 111 32 110 101 119 32 115 112 97 116 105 97 108 32 112 111 115 115 105

As Harrison (2013: 157) suggests, architects of the 21st century may concentrate on creating “flexible, diverse, humane habitats for electronically supported nomadic occupation.”

Architecture should not only involve bricks and columns but becomes a carnival of every experience within an environment. Quoting Hans Hollein (1968), Alle sist Architektur (Everything is architecture), architecture dissolves into daily life, becoming moments of temporary experiences. It will be the realisation of Archigram’s plug-in city and instant city.

“The whole city is a sort of ongoing festival…

I wanted a free-for-all sort of situation…”, said Peter Cook in the interview with BLDGBLOG (Manaugh, 2009: 30). Architects should not confine themselves with architecture knowledge and expand beyond the built statement.

Flexible city Figure 5.10 (Leong, 2021) Source: Author

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‘language’:’en’, ‘orientation’:’Up’

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Society of control

The world is progressing towards a posthuman era where smart materials, sentient systems, and ubiquitous communication networks populate the built environment (Harrison, 2013: 3).

As Deleuze (2007: 7) puts it: “Felix Guattari has imagined a city where one would be able to leave one’s apartment, one’s street, one’s neighbourhood, thanks to one’s (dividual) electronic card that raises a given barrier; but the card could just as easily be rejected on a given day or between certain hours; what counts is not the barrier but the computer that tracks each person’s position–licit or illicit– and effects a universal modulation”.

Deleuze’s society of control reflects the human powerlessness against the high-tech world and surrender the authorship to the machine, becoming a blind follower of their creation. It is no different in the architecture design discipline To design space, architects need to revivify the definition of sentient agent and intelligent agent in life because “life, per se, is now a radically different cultural-biological complex than it was even fifty years ago” (Harrison, 2013: 44).

Society of control Figure 5.11 (Leong, 2021) Source: Author

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7138” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

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What if

Mojo Lens, the augmented wearable technology that allows one to augment the world but still stay connected to real life, turning into a reality soon?

It is a new way of seeing that engages with the fifth dimension. It is a dimension that humans never come across. We need to ask ourselves, do we need a machine vision that can see through the fourth dimension? Do we need a life assistant that constantly reminds us of our daily routine and foresee the future?”

Augmented city Figure 5.12 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7140” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 87 104 97 116 32 105 102 32 97 32 109 97 99 104 105 110 101 32 97 98 108 101 32 116 111 32 112 101 114 99 101 105 118 101 32 97 110 100 32 117 110 100 101 114 115 116 97 110 100 32 105 116 115 32 115 117 114 114 111 117 110 100 105 110 103 115 63 32 65 32 -30 -128 -100 115 101 108 102 45 99 111 110 102 105 103 117 114 105 110 103 32 101 108 101 99 116 114 111 110 105 99 32 101 110 118 105 114 111 110 109 101 110 116 32 40 72 97 114 114 105 115 111 110 44 32 50 48 49 51 58 32 49 53 56 41 -30 -128 -99 32 119 105 108 108 32 98 101 32 100 101 118 101 108 111 112 101 100 32 105 110 32 116 104 101 32 109 97 99 104 105 110 101 32 114 101 97 108 109 46 32 87 105 110 100 111 119 115 32 115 116 97 114 116 32 116 97 108 107 105 110 103 32 119 105 114 101 108 101 115 115 108 121 32 116 111 32 116 104 101 32 99 104 97 105 114 32 110 101 120 116 32 98 121 44 32 98 111 111 107 115 32 116 111 32 116 104 101 32 98 111 111 107 115 104 101 108 118 101 115 44 32 99 111 109 112 117 116 101 114 32 116 111 32 116 104 101 32 115 99 114 101 101 110 32 112 114 111 106 101 99 116 111 114 115 44 32 97 110 100 32 115 111 32 111 110 46 32 84 104 101 32 99 117 115 116 111 109 105 115 101 100 32 115 109 97 114 116 32 115 121 115 116 101 109 32 119 105 108 108 32 111 114 103 97 110 105 115 101 32 116 104 101 32 115 112 97 99 101 32 97 99 99 111 114 100 105 110 103 32 116 111 32 116 104 101 32 111 119 110 101 114 -30 -128 -103 115 32 100 97 105 108 121 32 114 111 117 116 105 110 101 46 32 10 10’} ] } ] } ] }

What if

a machine able to perceive and understand its surroundings? A “self-configuring electronic environment (Harrison, 2013: 158)” will be developed in the machine realm. Windows start talking wirelessly to the chair next by, books to the bookshelves, computer to the screen projectors, and so on. The customised smart system will organise the space according to the owner’s daily routine.

Too-smart home Figure 5.13 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7142” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 87 104 97 116 32 105 102 32 116 104 101 32 97 117 116 111 110 111 109 111 117 115 32 118 101 104 105 99 108 101 32 114 101 97 99 104 101 115 32 108 101 118 101 108 32 53 32 97 117 116 111 110 111 109 121 63 32 32 72 117 109 97 110 115 32 110 111 32 108 111 110 103 101 114 32 110 101 101 100 32 116 111 32 100 114 105 118 101 32 98 121 32 116 104 101 109 115 101 108 118 101 115 46 32 84 104 101 32 109 97 99 104 105 110 101 32 98 101 99 111 109 101 115 32 116 104 101 32 100 114 105 118 101 114 46 32 84 114 97 110 115 112 111 114 116 97 116 105 111 110 32 115 112 97 99 101 32 109 97 121 32 116 117 114 110 32 105 110 116 111 32 97 32 109 101 101 116 105 110 103 44 32 100 105 110 105 110 103 44 32 111 114 32 101 110 116 101 114 116 97 105 110 109 101 110 116 32 112 108 97 99 101 59 32 105 116 32 98 101 99 111 109 101 115 32 97 32 116 114 97 110 115 105 116 105 111 110 32 115 112 97 99 101 32 102 114 111 109 32 111 110 101 32 112 111 105 110 116 32 116 111 32 97 110 111 116 104 101 114 46 32 73 116 32 99 97 110 32 98 101 32 97 110 32 101 118 101 114 45 116 114 97 110 115 102 111 114 109 105 110 103 32 115 112 97 99 101 32 116 104 97 116 32 109 101 114 103 101 115 32 119 105 116 104 32 116 104 101 32 100 101 115 116 105 110 97 116 105 111 110 46 32’} ] } ] } ] }

What if

the autonomous vehicle reaches level 5 autonomy? Humans no longer need to drive by themselves. The machine becomes the driver.

Transportation space may turn into a meeting, dining, or entertainment place; it becomes a transition space from one point to another. It can be an ever-transforming space that merges with the destination.

Autonomous city Figure 5.14 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

111111111111111111111100000000000001100011000111111111111111111111111111111111111 <types> 111111111111111111111111111001000000001100111000000111111111111111111111111111111 <types> 111111111111111111111111111111111000000001100111000000001111111111111111111111110 ocr_url = base_url + “ocr” 011110000111111111111111111111111111110000001100111000000000110111111111111111111 000111111111111111111111111111111111111111111111100111110000000111011000011111111 111111111111111111111101111111111111111111111111111100111110000000011110000000000 sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7144” 000000111111111111111111111111111111111111111111111111100111110000000001110000000 headers = {‘Ocp-Apim-Subscription-Key’: sub} 000000111111100111111111111110111111111111111111111111111100111111000001111111111 { 111111111111111111111111111111111011111111010111111111111111100111111000000000111 </69 112 105 108 111 103 117 101 000000000000000010010000000111111111011111111111111111111111111100111111100111100 111100010000000000000000000000111111111101111111111111111111111111110111111000000 000110100000000000000000000000001111111111110111111111111111111111111110111011100 001101110110100000000001100000111110111111111111111101111111111111111111111111010 000001100110011000000000000011110000110111111111111111111111111111111111111111111 111100001100110001111000000000000001000000111111111111111111111111111111111111111 111111010011100111101110000000000000000000000111111111111111111111111111111111111 110111111111111100110001111000000000000000100000111111111111111111111111111111111 111110111111111111101110000011010110010000000000000111111111111111111111111111111 111111110111111111111100111111011100000000000000000000111111111111111111111111111 111111111111111111111111101110011110110100000000000000000111111111111111111111111 111111111111111111011111111101110001010111001001100000000000111111111111111111111 111111111111111111111111111111101111000110101011110000111111110111111111111111111 111111111111111111111111111111111111110010111001100010001111101100111111111111111 111111111111111111111111011011111011111111111111101110000010000000001111111111111 111111111111111111111111111111111111111111111111110001111110000101111111111111111 111111111111111111111111111111111110111111111111111111111001100011101111111111111 111111111111111111111111111111111111111111111111111111110001001110000101111111111 111111111111111111111111111111111111111111111111111111111111111101011100101111111 111111111111111111111111111111111111111111111111111111111111111111111111010101000 000111111111111111111111111111111111111111111111111111111111111111111001111111101 110011111111111111111111111111111111111111111111111111111111111111111111101011010 101000001111111111111111111111111111111111111110011011111111111111111111111101111 011111000111111111111111111111111111111111111111110111011111111111111111111111101 111111111111111111111111111111111011111111111111111110111011111111111111111111111 101011110111111111111111111111111111111111111111111111110011011111111110110111111 111101011010111111111111111111111111111111111111111111111110011011111111100110111 111101101011010111111111111111111111111111111111111111111111111111111111111110110 111111001101011011100011110111111111111111111111111111111111111111111111111111111 111101111001101111011111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111111111111111111111111 111111111111110000011111111111111111111111111111111111111111111111111111111111111 111110111111111111111111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111101111111111111111111 111111111111111111110111111111111111111111111111111111111111111111111111111111111 111111111111111101111111111111111111111111111111111111111111111111111111111111111 111111111111111111111111111111111111111111111111111111111111111111111111111111111 111111111101111111111111111111111111111111111111111111111111111111111111111111111 111111000111111111111111111111111111111111111111111111111111111

1 O NE

E p ilo g ue

Figure 6.1 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7146” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </69 112 105 108 111 103 117 101 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 85 110 100 101 114 32 116 104 101 32 109 97 99 104 105 110 101 32 101 121 101 44 32 116 104 101 32 116 104 101 115 105 115 32 104 105 103 104 108 105 103 104 116 101 100 32 116 104 101 32 115 111 117 114 99 101 32 111 102 32 97 114 116 105 102 105 99 105 97 108 32 105 110 116 101 108 108 105 103 101 110 99 101 44 32 116 104 101 32 112 114 111 98 108 101 109 32 116 104 97 116 32 101 109 98 101 100 100 101 100 32 119 105 116 104 105 110 32 105 116 32 97 110 100 32 108 101 97 100 32 116 111 32 97 32 100 105 115 99 117 115 115 105 111 110 32 111 102 32 116 104 101 32 102 117 116 117 114 101 32 99 105 116 121 32 119 105 116 104 32 116 104 101 32 99 111 45 104 111 115 116 32 111 102 32 109 97 99 104 105 110 101 115 46 32 73 116 32 100 114 105 118 101 115 32 100 111 119 110 32 116 111 32 97 32 99 111 110 99 108 117 115 105 111 110 32 116 104 97 116 32 104 117 109 97 110 32 119 105 108 108 32 114 101 109 97 105 110 32 97 115 32 116 104 101 32 108 101 97 100 105 110 103 32 114 111 108 101 32 101 118 101 110 32 105 110 32 116 104 101 32 102 105 102 116 104 32 100 105 109 101 110 115 105 111 110 32 116 104 97 116 32 105 109 112 108 97 110 116 101 100 32 119 105 116 104 32 99 111 100 101 115 32 97 110 100 32 97 108 103 111 114 105 116 104 109 115 46 32 79 109 110 105 112 114 101 115 101 110 116 32 100 97 116 97 32 105 115 32 109 101 97 110 105 110 103 108 101 115 115 32 119 105 116 104 111 117 116 32 104 117 109 97 110 32 97 115 32 116 104 101 32 100 97 116 97 32 99 111 108 108 101 99 116 111 114 115 32 97 110 100 32 117 115 101 114 115 46 32 84 104 101 32 119 111 114 108 100 32 105 115 32 110 111 116 32 108 111 111 107 105 110 103 32 102 111 114 32 98 105 103 32 100 97 116 97 32 98 117 116 32 119 105 115 101 32 100 97 116 97 44 32 119 104 105 99 104 32 99 97 110 32 98 101 99 111 109 101 32 97 32 99 111 108 108 101 99 116 105 118 101 32 109 101 109 111 114 121 32 111 102 32 116 104 101 32 103 101 110 101 114 97 116 105 111 110 46 32 68 97 116 97 32 99 101 110 116 114 101 32 98 101 99 111 109 101 115 32 116 104 101 32 110 101 119 32 109 117 115 101 117 109 32 102 111 114 32 116 104 101 32 104 105 115 116 111 114 121 32 111 102 32 104 117 109 97 110 32 97 110 100 32 115 111 99 105 97 108 32 97 110 116 104 114 111 112 111 108 111 103 121 44 32 105 116 32 105 115 32 112 108 97 121 105 110 103 32 97 32 98 105 103 32 114 111 108 101 32 105 110 32 116 104 101 32 110 101 119 32 97 114 99 104 105 116 101 99 116 117 114 97 108 32 108 97 110 100 115 99 97 112 101 46 32 32 32 10 10 65 115 32 104 117 109 97 110 32 98 101 99 111 109 101 115 32 116 104 101 32 112 97 114 101 110 116 115 32 111 102 32 65 73 44 32 105 116 32 117 110 100 111 117 98 116 101 100 108 121 32 105 110 104 101 114 105 116 101 100 32 116 104 101 32 68 78 65 32 111 102 32 104 117 109 97 110 32 105 110 99 108 117 100 105 110 103 32 98 105 97 115 46 32 65 115 32 97 32 99 111 110 116 114 105 98 117 116 111 114 44 32 119 101 32 110 101 101 100 32 116 111 32 98 101 32 119 105 115 101 32 97 98 111 117 116 32 116 104 101 32 105 110 102 111 114 109 97 116 105 111 110 32 103 105 118 101 110 32 116 111 32 116 104 101 32 109 97 99 104 105 110 101 44 32 111 114 32 115 104 97 108

Epilogue

Through 5 spatial dimensions, the thesis highlighted applications of the machine vision system and problems embedded within the machine and artificial intelligence, leading to speculations of the future city where the machines co-exist. Humans will still play a fundamental role in the human-machine environment that consists of codes and algorithms.

The barcode that used as the global identifier can be translated onto the architectural design.

Each building will have a unique label to allow intelligent agents to locate themselves within the urban environment. The city will become a giant clockwork that is readable by machines.

Amazon fulfilment centre suggested a highly-controlled human-machine environment that gives a hint of the future urban environment.

1D

Furthermore, omnipresent data is meaningless without human collectors and users. As a contributor, human needs to be wise about the information given to the machine, or shall it be another intelligent model that tracks down biases? Ubiquitous machine eye puts society on nerves, worrying about the overexposure of their daily life. However, the misuse of data is more threatening than mass data collection. Government or active authorities need to ensure that data falls into the right hands and processes with the owner consent. Data needs to be transparent, from everyone and accessible by anyone. Only wise and effective data can compose a collective memory of this digital epoch. Meanwhile, the data centre becomes the new museum for social and cultural anthropology, forming a new architectural landscape.

2D

As the machine vision system is used in scanning and analysing the built environment, the collected data such as point cloud data, the depth map and 3D scanning may become the fundamental guideline to trigger a new built environment. Advancement is needed in the built environment designs to prepare the world for the autonomous vehicle. It may start from studying the relationship of building materials with the machine visions system. Nowadays, unpredictable factors become autonomous vehicles biggest challenge. The parallel world is suggested as a testbed to train the autonomous vehicle in a real-life environment. However, autonomous vehicles will not replace human drivers overnight. The transition can commence with public transportations to reduce the number of cars on the road. To achieve level 5 autonomy which Musk believe “will happen very quickly”(SHINE, 2020), it does not solely rely on technology advancement; the other factors like road infrastructures and built environments need to be in place. If urban infrastructures need to be updated and renewed every five years, this will be an opportunity to upgrade the existing infrastructure in preparation for the arrival of autonomous agents.

3D &4D

The machine vision system is becoming the ‘smart dust’ as the world is approaching an era of “hard-drive-centred design, LIDAR-centred design or autonomous-car-centred design” (Young, 2019). Architects are confronting a new situation where humans and machines are carrying equal weight in the built environment. Today, architects are responsible for redefining the built environment design through a different visual perspective. Additionally, architecture is about everything. It can be beyond physical boundaries.

5D

What if >>

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7148” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 87 104 97 116 32 105 102 32 116 104 101 32 118 105 115 117 97 108 32 119 111 114 108 100 32 114 101 112 108 97 99 101 100 32 116 104 101 32 112 104 121 115 105 99 97 108 32 119 111 114 108 100 63 32 87 104 97 116 32 119 105 108 108 32 114 101 109 97 105 110 32 105 110 32 116 104 101 32 112 104 121 115 105 99 97 108 32 119 111 114 108 100 32 119 104 101 110 32 105 116 32 98 101 99 111 109 101 115 32 97 32 112 108 97 99 101 32 116 104 97 116 32 104 117 109 97 110 32 111 110 108 121 32 114 101 116 117 114 110 32 116 111 32 119 104 101 110 32 116 104 101 121 32 97 114 101 32 114 101 115 116 105 110 103 63 32 83 104 111 117 108 100 32 116 104 101 32 112 104 121 115 105 99 97 108 32 119 111 114 108 100 32 114 101 116 117 114 110 32 116 111 32 116 104 101 32 115 105 109 112 108 101 115 116 32 102 111 114 109 32 111 102 32 115 104 101 108 116 101 114 44 32 115 117 114 114 101 110 100 101 114 32 116 104 101 32 114 101 115 116 32 111 102 32 116 104 101 32 108 97 110 100 32 98 97 99 107 32 116 111 32 110 97 116 117 114 101 46 32 65 114 99 104 105 116 101 99 116 117 114 101 32 109 101 114 101 108 121 32 115 101 114 118 101 115 32 116 104 101 32 112 117 114 112 111 115 101 32 111 102 32 112 114 111 116 101 99 116 105 111 110 59 32 98 117 105 108 116 32 101 110 118 105 114 111 110 109 101 110 116 32 101 109 98 101 100 100 101 100 32 119 105 116 104 32 105 110 102 114 97 114 101 100 45 100 101 116 101 99 116 97 98 108 101 32 98 97 114 99 111 100 101 115 44 32 98 101 99 111 109 105 110 103 32 103 117 105 100 101 108 105 110 101 32 102 111 114 32 116 104 101 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 46 32 68 97 116 97 32 98 101 99 111 109 101 32 116 114 117 101 32 111 105 108 59 32 105 116 32 105 115 32 116 104 101 32 110 101 119 32 99 117 114 114 101 110 99 121 32 105 110 32 116 104 101 32 102 105 102 116 104 32 100 105 109 101 110 115 105 111 110 46 32 84 104 101 32 100 97 116 97 32 99 101 110 116 114 101 32 119 105 108 108 32 100 111 109 105 110 97 110 116 32 116 104 101 32 97 114 99 104 105 116 101 99 116 117 114 101 32 108 97 110 100 115 99 97 112 101 44 32 119 104 105 99 104 32 98 101 99 111 109 101 115 32 116 104 101 32 98 97 110 97 108 32 109 117 115 101 117 109 32 116 104 97 116 32 115 116 111 114 101 32 116 104 101 32 109 111 115 116 32 105 109 112 111 114 116 97 110 116 32 97 114 99 104 105 118 101 32 111 102 32 116 104 101 32 102 117 116 117 114 101 46 32 32’} ] } ] } ] }

Epilogue What if

the visual world replaced the physical world?

What will remain in the physical world when it becomes a place that human only return to when they are resting? Should the physical world return to the simplest form of shelter, surrender the rest of the land back to nature. Architecture merely serves the purpose of protection; built environment embedded with infrared-detectable barcodes, becoming guideline for machine vision.

Data becomes true oil; it is the new currency in the fifth dimension. The data centre will dominate the architectural landscape. It will be the banal museum that stores the most important archive of the future.

Virtual world Figure 6.2 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7150” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </65 114 99 104 105 116 101 99 116 117 114 101 32 114 101 115 112 111 110 115 101 {

‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 87 104 97 116 32 105 102 32 111 110 101 32 110 101 101 100 115 32 97 32 98 114 101 97 107 32 102 114 111 109 32 116 104 101 32 102 105 102 116 104 32 100 105 109 101 110 115 105 111 110 63 32 84 104 101 32 109 97 99 104 105 110 101 32 117 110 114 101 97 100 97 98 108 101 32 112 108 97 99 101 32 98 101 99 111 109 101 115 32 97 32 115 97 102 101 32 98 117 110 107 101 114 32 102 111 114 32 116 104 111 115 101 32 119 104 111 32 119 97 110 116 32 97 110 32 101 115 99 97 112 101 32 102 114 111 109 32 116 104 101 32 104 105 103 104 108 121 32 100 105 103 105 116 97 108 105 115 101 100 32 119 111 114 108 100 32 116 104 97 116 32 105 115 32 111 118 101 114 119 104 101 108 109 101 100 32 119 105 116 104 32 105 110 102 111 114 109 97 116 105 111 110 32 97 110 100 32 100 97 116 97 46 32 73 116 32 105 115 32 97 32 8220 115 119 105 116 99 104 45 111 102 102 8221 32 98 117 116 116 111 110 32 116 111 32 100 101 116 97 99 104 32 102 114 111 109 32 116 104 101 32 119 111 114 108 100 32 116 104 97 116 32 104 117 109 97 110 32 111 110 99 101 32 121 101 97 114 110 115 32 102 111 114 46 32 84 104 101 32 114 101 102 108 101 99 116 105 118 101 32 111 114 32 116 104 101 32 108 105 103 104 116 45 97 98 115 111 114 98 105 110 103 32 118 111 108 117 109 101 32 98 101 99 111 109 101 32 116 104 101 32 111 110 108 121 32 112 108 97 99 101 32 116 111 32 104 105 100 101 32 97 119 97 121 32 102 114 111 109 32 116 104 101 32 109 97 99 104 105 110 101 32 101 121 101 46 32’} ] } ] } ] }

Epilogue What if

one needs a break from the fifth dimension?

The machine unreadable place becomes a safe bunker for those who need an escape from the highly digitalised world and the overwhelming information. It is a “switch-off” button to detach from the world that human once yearning for. The reflective or light-absorbing volume becomes the only place to hide away from the machine eye.

The bunker Figure 6.3 (Leong, 2021) Source: Author

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001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055

<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7152” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </69 112 105 108 111 103 117 101 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 104 101 32 102 117 116 117 114 101 32 99 105 116 121 32 100 101 115 99 114 105 98 101 100 32 105 110 32 116 104 105 115 32 116 104 101 115 105 115 32 100 111 101 115 32 110 111 116 32 112 114 111 118 105 100 101 32 97 32 115 111 108 117 116 105 111 110 44 32 114 97 116 104 101 114 32 115 112 101 99 117 108 97 116 101 44 32 97 110 100 32 112 114 111 109 112 116 32 100 105 115 99 117 115 115 105 111 110 115 32 97 98 111 117 116 32 116 104 101 32 102 117 116 117 114 101 32 119 105 116 104 32 116 104 101 32 112 114 101 115 101 110 99 101 32 111 102 32 109 97 99 104 105 110 101 32 101 121 101 115 32 109 111 114 101 32 116 104 97 110 32 104 117 109 97 110 32 101 121 101 115 46 32 84 104 101 32 114 101 97 108 105 115 97 116 105 111 110 32 111 102 32 116 104 101 32 102 117 116 117 114 101 32 99 105 116 121 32 105 115 32 110 111 116 32 97 32 115 105 110 103 108 101 32 101 102 102 111 114 116 32 102 114 111 109 32 97 114 99 104 105 116 101 99 116 115 32 98 117 116 32 97 32 99 114 111 115 115 45 100 105 115 99 105 112 108 105 110 97 114 121 32 112 114 111 106 101 99 116 32 119 105 116 104 32 116 104 101 32 99 111 109 112 117 116 101 114 32 115 99 105 101 110 116 105 115 116 115 44 32 116 101 99 104 32 103 105 97 110 116 115 44 32 115 111 99 105 97 108 32 115 99 105 101 110 116 105 115 116 32 97 110 100 32 109 111 115 116 32 105 109 112 111 114 116 97 110 116 108 121 32 116 104 101 32 112 117 98 108 105 99 46 32 85 110 100 101 114 115 116 97 110 100 105 110 103 32 109 97 99 104 105 110 101 32 118 105 115 105 111 110 32 119 105 108 108 32 108 101 97 100 32 116 111 32 97 32 98 101 116 116 101 114 32 102 117 116 117 114 101 46 32 65 32 102 117 116 117 114 101 32 99 105 116 121 32 105 115 32 97 32 112 108 97 99 101 32 116 104 97 116 32 109 105 110 103 108 101 115 32 97 114 111 117 110 100 32 100 105 102 102 101 114 101 110 116 32 100 105 109 101 110 115 105 111 110 115 44 32 119 105 116 104 32 116 104 101 32 99 111 101 120 105 115 116 101 110 99 101 32 111 102 32 115 101 110 116 105 101 110 116 32 97 103 101 110 116 115 32 97 110 100 32 105 110 116 101 108 108 105 103 101 110 116 32 97 103 101 110 116 115 46 32’} ] } ] } ] }

Epilogue

In conclusion, the future city described in this thesis does not provide a solution but an agenda. The thesis intents to speculate and prompt urgent discussions among human races regarding the near future that consists of more machines than humans. The realisation of the future city is not an individual effort from architects but a cross-disciplinary collaboration with computer scientists, social scientists, tech giants, and most importantly, the general public.

The future city is a place that mingles around different dimensions, with the coexistence of sentient agents and intelligent agents.

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<types> <types> ocr_url = base_url + “ocr” sub = “98f714r6vb2e193018b28fg1u9b3b0d7e7154” headers = {‘Ocp-Apim-Subscription-Key’: sub} { </69 112 105 108 111 103 117 101 { ‘language’:’en’, ‘orientation’:’Up’

{translated_ 8-bit sign integer: ‘ 84 101 99 104 110 111 108 111 103 121 32 105 115 32 116 104 101 32 97 110 115 119 101 114 44 32 98 117 116 32 119 104 97 116 32 105 115 32 116 104 101 32 113 117 101 115 116 105 111 110 63’} ] } ] } ] }

Technology is the answer, but what is the question?

(Price, 1980)

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Mediography

Bibliography Abbott, E. (1994) Flatland. Champaign, IL: Project Gutenberg.

Berger, J. (1972) Ways Of Seeing. London, England: Penguin Books.

Bridle, J. (2018) New Dark Age. Brooklyn, NY: Verso.

Broussard, M. (2019) Artificial Unintelligence. Massachusetts: MIT press.

Carpo, M. (2017) The Second Digital Turn: Design Beyond Intelligence. Massachusetts: MIT Press.

Deleuze, G. (1992) Postscript on the societies of control. Massachusetts: MIT press.

Greenfield, A. (2017) Radical Technologies. Brooklyn, NY: Verso.

Hollein, H., 1968. Alles Ist Architektur. Bau, 1/2 1968.

Harrison, A. L. (2013) Architectural Theories of the Environment: Posthuman Territory. Oxon: Routledge.

Ratti, C. and Claudel, M. (2016) The City of Tomorrow. New Haven and London: Yale University Press.

Manaugh, G. (2009) The BLDGBLOG Book. California: Chronicle Books.

Moravec, H. (1988) Mind Children, Harvard University Press Price, C. (1979) Technology is the answer, but what was the question?. London: World Microfilms Publications Ltd. Shepard, M. (2011) Sentient city. Massachusetts: MIT Press.

Warwick, K. (2012) Artificial Intelligence. Oxon: Routledge.

Wigley, M. (2001) “Network Fever”, Grey Room 04. Massachusetts: MIT press.

Young, L. (2019) Machine Landscapes: Architectures of the Post-Anthropocene. Architectural Design, (257).

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Mediography

TV and Podcasts Blade Runner 2049 (2017) Directed by D. Villeneuve. [Film]. California: Warner Bros.

Bridle, J. (2019a) New Ways Of Seeing: Machine Vision [podcast]. 26 April. Available at: <https://www.bbc.co.uk/ sounds/play/m0004f3h> [Accessed 21 January 2021].

Bridle, J. (2019b) New Ways Of Seeing: Invisible Network [podcast]. 17 April. Available at: <https://www.bbc. co.uk/sounds/play/m000458l> [Accessed 21 January 2021].

Channel 4 Documentary (2019) Amazon Warehouse is Run by Robots? [video] Available at: <https://www.youtube. com/watch?v=TUx-ljgB-5Q&ab_channel=Channel4Documentary> [Accessed 11 February 2021].

FORA.tv (2011) A Day in the Life of a Kiva Robot. [video] Available at: <https://www.youtube.com/ watch?v=6KRjuuEVEZs&ab_channel=FORA.tv> [Accessed 9 January 2021].

Guggenheim Museum (2020) See Countryside, The Future At The Guggenheim. [video] Available at: <https://www. youtube.com/watch?v=eTbH5RWb66o&feature=emb_logo&ab_channel=GuggenheimMuseum> [Accessed 19 January 2021].

Tw19751 (2012) John Berger/Ways of Seeing, Episode 1(1972) [video] Available at: https://www.youtube.com/ watch?v=0pDE4VX_9Kk [Accessed 30 November 2020].

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Mediography

News, article and Online resources Abdullah, Z. (2019) Entire Western Part Of Singapore To Become Testing Ground For Driverless Vehicles. [online] CNA. Available at: <https://www.channelnewsasia.com/news/singapore/autonomous-vehicles-western-singaporetestbed-12029878> [Accessed 22 January 2021].

Accenture.com (2021) Applied Intelligence Glossary | Accenture. [online] Available at: <https://www.accenture. com/gb-en/insights/applied-intelligence/artificial-intelligence-glossary> [Accessed 21 February 2021].

Amazon.com. (n.d) Amazon.com: Amazon Go Grocery: Amazon Go. [online] Available at: <https://www.amazon. com/b?ie=UTF8&node=20931388011> [Accessed 26 February 2021].

Angwin, J., Mattu, S. and Kirchne, L. (2016) Machine Bias. [online] ProPublica. Available at: <https://www. propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing> [Accessed 19 January 2021].

Appen (2021) AI Glossary | Curated by Data Scientists and ML Experts. [online] Available at: <https://appen.com/ ai-glossary/> [Accessed 21 February 2021].

ArtDip (2019) Mario Klingemann MEMORIES OF PASSERSBY I. [online] Medium. Available at: <https://medium. com/dipchain/mario-klingemann-memories-of-passersby-i-c73f72675743> [Accessed 31 January 2021].

Bridle, J. (2020) Under The Cloud. [online] Jamesbridle.com. Available at: <https://jamesbridle.com/works/underthe-cloud> [Accessed 21 January 2021].

Chowdhry, A. (2014) Facebook’s Deepface Software Can Match Faces With 97.25% Accuracy. [online] Forbes.

Available at: <https://www.forbes.com/sites/amitchowdhry/2014/03/18/facebooks-deepface-software-can-matchfaces-with-97-25-accuracy/?sh=53fa162b54fc> [Accessed 19 January 2021].

Deloitte (n.d) Industry 4.0 and the digital twin. [ebook] Deloitte University Press. Available at: <https://www2. deloitte.com/content/dam/Deloitte/cn/Documents/cip/deloitte-cn-cip-industry-4-0-digital-twin-technologyen-171215.pdf> [Accessed 24 February 2021].

Department for Business, Energy & Industrial Strategy (2019) Regulation for the Fourth Industrial Revolution. [online] GOV.UK. Available at: <https://www.gov.uk/government/publications/regulation-for-the-fourth-industrialrevolution/regulation-for-the-fourth-industrial-revolution> [Accessed 27 February 2021].

Electronic Imaging Materials, Inc. (n.d) You Can Learn to Read Barcode! | The Label Experts. [online] Electronic Imaging Materials. Available at: <https://barcode-labels.com/you-can-learn-read-barcode/> [Accessed 27 February 2021].

Foster + Partners (2020) Foster + Partners collaborates with Boston Dynamics to monitor construction progress with Spot | Foster + Partners. [online] Fosterandpartners.com. Available at: <https://www.fosterandpartners.com/ news/archive/2020/11/foster-partners-collaborates-with-boston-dynamics-to-monitor-construction-progress-withspot/> [Accessed 11 December 2020].

Google (2019) Google Environmental Report 2019. [online] Available at: <https://services.google.com/fh/files/ misc/google_2019-environmental-report.pdf> [Accessed 19 January 2021].

Government of the Netherlands (2019) Green light for Experimental Law for testing self-driving vehicles on public roads. [online] Government.nl. Available at: <https://www.government.nl/latest/news/2019/07/02/green-light-forexperimental-law-for-testing-self-driving-vehicles-on-public-roads> [Accessed 13 December 2020].

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Mediography

News, article and Online resources Hartigan, R. (2019) Historian uses lasers to unlock mysteries of Gothic cathedrals. [online] National Geographic News. Available at: <https://www.nationalgeographic.com/news/2015/06/150622-andrew-tallon-notre-damecathedral-laser-scan-art-history-medieval-gothic/> [Accessed 15 November 2020].

Hern, A. (2020) Facebook And Google Announce Plans To Become Carbon Neutral. [online] the Guardian.

Available at: <https://www.theguardian.com/environment/2020/sep/15/facebook-and-google-announce-plansbecome-carbon-neutral> [Accessed 19 January 2021].

Hyperstealth.com (2019) HyperStealth Biotechnology Corp.. [online] Available at: <http://www.hyperstealth.com/> [Accessed 2 January 2021].

Iriondo, R. (2020) Key Machine Learning Definitions. [online] Medium. Available at: <https://pub.towardsai.net/ key-machine-learning-ml-definitions-43e837ec6add> [Accessed 1 December 2020].

Jaimes, D. (2016) How Driverless Cars Could, Should - and Shouldn’t - Reshape Our Cities. [online] ArchDaily.

Available at: <https://www.archdaily.com/780512/how-driverless-cars-could-should-and-shouldnt-reshape-ourcities> [Accessed 2 January 2021].

Jajal, T. (2019) Distinguishing between Narrow AI, General AI and Super AI. [online] Medium. Available at: <https://medium.com/mapping-out-2050/distinguishing-between-narrow-ai-general-ai-and-super-ai-a4bc44172e22> [Accessed 25 January 2021].

Jordana, S. (2010) BIG’s proposal for the Audi Urban Future Award. [online] ArchDaily. Available at: <https:// www.archdaily.com/77103/bigs-proposal-for-the-audi-urban-future-award> [Accessed 2 February 2021].

Kleinman, A. (2015) John Gerrard’s “Farm” - Features - art-agenda. [online] Art-agenda.com. Available at: <https://www.art-agenda.com/features/237033/john-gerrard-s-farm> [Accessed 31 January 2021].

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Mediography

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Mediography

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Light traffic (2021) [Screenshot]. Available at: < http://senseable.mit.edu/light-traffic/> [Access: 4 January 2021].

Medium (n.d.) Memories of Passerby I by Mario Klingemann. Available at: <https://medium.com/dipchain/mario-klingemann-memories-of-passersby-i-c73f72675743> [Accessed: 13 October 2020].

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Mossot (2019) Notre-Dame cathedral on April 17, 2019. Available at: <https://www.architecturalrecord.com/articles/14024-after-fire-at-notre-dame-questions-remain-about-prevention-cause-and-repairs> [Accessed: 16 October 2020].

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NXP (2019) Driver. Available at: < https://www.nxp.com/video/driver-monitoring-systems-dms-with-s32v234-vision-processor:DRIVER-MONITOR-SYSTEM> [Accessed: 18 November 2020].

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Mediography

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Start Up (2020) [Film] Directed by Park Hye-Ryun. South Korea: Studio Dragon.

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Appendices

Interview with Manuela Mesrie fron ScanLAB project 23 February 2021 What are the challenges ScanLAB faced when you are doing terrestrial laser scanning in the built environment?

There are some elements of the physical world that can be seen as nemesis of the 3D sensors that are not able to provide the same level of fidelity. These can be shiny, glass surfaces, people in movement around the main survey subject, moist, fog, the presence of water and other transparent liquids.

How do you think 3D scanning technologies (machine vision) are changing the way human perceive the world?

Lidar technology can be used outside the realm of architecture and surveying. We have pioneered the use of Lidar technology to approach and develop journalistic stories, scientific research and creative entertainment across TV, film and VR. Bearing witness to events is often the assignment of photography. And we are all too aware how the timing of a shutter release can distort interpretations of those events. Film and moving image add dimension to those difficulties and scanning adds yet more.

Capturing everything, quickly and at extreme resolutions locks evidence securely in a digital world to be examined, hours, months or even decades later. An example is the development of forensics architecture research and discoveries via the use of 3D-scanning technology. The scanner’s ability to document space at meticulous levels of detail far exceeds what is humanly possible with tape measurements and sketchbook. The consequent three-dimensional visualisation allows the past to be reconsidered, re-thought and re-discovered, bringing history into a more proximate layer of time.

ScanLAB works have successfully discovered the history of the invisible cities, how do you think 3D scanning will affect the future built environment?

As answered in the first question, we need to bear in mind that there are quite some nemesis elements to the 3D sensors. If the aim is to reproduce a digital replica of the built environment, such nemesis elements have to be taken into account when confronting choices related to materiality and physicality of the built environment.

Can you speculate on the future of machine vision?

We argue that lidar is the future of photography and much more beyond; it is the underlying technology of driverless vehicles that will soon be the cartographers of our own urban environments.

Its fundamental unit of resolution - the point - it is just an evolution of the pixel soon to be so dense and quantized that the two will eventually be indistinguishable.

Scan data and 3D visualisations of virtual spaces have the unique ability to capture architecture and our world over time and space. The architectural model in its virtual form is now able to hold and store information and details belonging to a very precise moment in history.

The encapsulated time can hold universal information that will remain true in any past or future analysis but the facts of the moment remain.

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Appendices

Understanding machine vision with Generative Adversarial Networks (GANs) Generating image through GauGAN Images are uploaded to GauGAN to generate a segmentation map. The neural networks will then generate a photorealistic image as an output.

This process is to study how the machine interprets objects, mostly in a human’s illegible way.

< QR code to access NVIDIA GauGAN Beta

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Appendices

Dissecting machine vision with Computer Vision Explorer Generating depth image Some of the case studies are lacking in the 3D scan image. With the inaccessibility of a 3D scanner, a photo editing process is included in the research to generate a realistic 3D scan image. Computer vision explorer is used to produce depth analysis which is part of the 3D scan result.

< QR code to access Computer Vision Explorer

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Appendices

Developing visual language Testing visual effect on different dimensions Test 1

Test 2

Test 3

traffic light 99%

person 99%

person 99%person 99% id 38.00 personperson 99% 99% /0.00

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Virtual world Figure 7.1 (Leong, 2021) Source: Author

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Too-smart home Figure 7.2 (Leong, 2021) Source: Author

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Augmented city Figure 7.3 (Leong, 2021) Source: Author

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Society of control Figure 7.4 (Leong, 2021) Source: Author

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Flexible city Figure 7.5 (Leong, 2021) Source: Author

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Autonomous city Figure 7.6 (Leong, 2021) Source: Author

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The bunker Figure 7.7 (Leong, 2021) Source: Author

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The present Figure 7.8 (Leong, 2021) Source: Author

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<types> <types> <types> <types> <types> <types> <types> <types> Private Function View_Closing(ByVal sender As Variant) As Boolean Dim screenAtLogoff As String screenAtLogoff = ThisScreen.GetText2(1, 1, ThisScreen.DisplayRows, ThisScreen.DisplayColumns) If InStr(1, screenAtLogoff, “connection has terminated”) = 0 Then ‘If exit is not found do not allow closing MsgBox “You can’t close this session until you close the application” View_Closing = False Else View_Closing = True End If End Function