BArch (Hons) Dissertation: The Inception of Artificial Intelligence in Architecture by Eddie Bennet

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r c h i t e c t u r a l

n t e l l i g e n c e

The Inception of Artificial Intelligence in Architecture

By Eddie Bennet - UP850769

Artificial Intelligence in Architecture: Does artificial intelligence hold the ability to generate new conceptual designs through evolutionary computation and what implications will this have on architecture and architects alike? By UP850769

A dissertation submitted in partial fulfilment of the requirements for the degree of Bachelor of Architecture at University of Portsmouth January 2020

Word Count: [5499]

ACKNOWLEDGEMENTS

n writing this dissertation, I have received a great deal of assistance and guidance to help get me to what I achieved. I would first like to thank my lecturer and tutor, Dr Antonino Di Raimo, whose expert experience and understand in the topic guided me throughout this dissertation. You were able to provide me with the tools and content I needed to complete the dissertation. I would also like to acknowledge Benjamin Ennemoser, the designer of House Y(amnitski), who I contacted, via email, about his work. I want to thank you for staying in contact and providing me with an excerpt of your unreleased essay on House Y(amnitski), you have provided me great insight on the topic, as well as, your project. Also, I wish to thank the University of Portsmouth for allowing me to use your extensive range of resources, which gave me crucial information needed.

TABLE OF CONTENTS

Acknowledgements

Table Of Contents

List Of Figures

List Of Table

Abstract

1. Introduction 1.1. Background Knowledge of AI 1.2. Research Focus 1.3. Main Dissertation Hypothesis

14 15 20 21

23 24

Architectural Design 2.1. Nature of Design, A Short Overview

3. Literature Review 3.1. AI In Architecture 3.1.1. Introduction And Aims 3.1.2. Methods and Techniques 3.2. Case Study From Literature Review Relevance

27 28 29 31 33

4. AI Architectural Examination 4.1. AI Approach 4.2. Implementation Of AI To Design 4.2.1. AI Design Process Framework 4.3. The Dilemma With AI In Design

35 36 37 40 41

5. Discussion 5.1. Deep Learning Vs Evolutionary Computation

43 46

51 53 56

Case Studies 6.1. Ada - Intelligence Space 6.2. House (Y)amnitski

7. Conclusion

8. Bibliography

Figure References

10. Appendix

LIST OF FIGURES

Figure 1:

Deep Blue Vs Garry Kasparov

Figure 2:

Deep Neural Network

Figure 3:

MacLeamy Curve

Figure 4:

DNN Discovering Building Blocks

Figure 5:

Building Block Graph

Figure 6:

Building Block Subgraph

Figure 7:

Livability And Sleepability

Figure 8:

Machine Learning Architectural Classification

Figure 9:

Hill-Climbing Process Graph

Figure 10a:

Search Space For Hill-Climbing

Figure 10b:

Creative Search Space For Hill-Climbing

Figure 11:

Ada Live Human Interaction

Figure 12:

Adaâ&#x20AC;&#x2122;s Mean Floor Occupancy To Different Stimulations

Figure 13:

House (Y)amnitski Back Facade

Figure 14:

House (Y)amnitski 2D AI-Generated Facade

Figure 15:

House (Y)amnitski Form Development

Figure 16a:

House (Y)amnitski Front Facade

Figure 16b:

Modern California Front Facade

LIST OF TABLES Table 1:

Evolutionary Computation Target Solutions vs Outputs

ABSTRACT

n a continuously developing world where artificial intelligence (AI) is being delved into, it is yet to make a significant difference. With this, it undoubtedly can be seen as a fundamental instrument for the future, although whether it will be successful, while still intelligent, is still in question. This dissertation will examine which role AI could play on the architectural design process as well as architects themselves. Further analysing critical approaches of AI that include: ‘data mining’, ‘machine learning’ and ‘evolutionary computation’ which, as a modern approach, will tie all three methods together and therefore, attempt to describe how a knowledge-based generative design system could work. After this short overview of AI, the dissertation tries to question whether the idea of using knowledge-based generative design system could to create new conceptual design solutions. Subsequently, whether this AI system will be able to convey a successful resolution to an architectural brief, while also be judged against a human designer, in terms of successfulness and significance. From studied research, it is evident that AI can ‘analyse and understand’ the language of architecture and also recognise different styles belonging to different architects. Alongside this, AI also has the intelligence to generate essential building forms, which then can be merged into a new design; as seen by research from As, Pal and Basu (2018). This dissertation argues that AI could be used in the architectural design process by emphasising the work of some architects who are already using it. This being said, the future will deliver more power and storage; therefore, knowledge to AI systems giving them the capability to be able to reach new potentials. However, there is still great emphasise, as of yet, on the need of the architects’ creativity and mindset in the design process, which cannot be replaced by an AI system.

INTRODUCTION

1. INTRODUCTION

Due to the high technical level of this argument, it is crucial to know that words in bold throughout this dissertation refer to the appendix. To fully understand the argument which has been presented, the appendix is much needed and important to review.

1.1 Background of Artificial Intelligence (AI)

he concept of AI derives from human intelligence; where is intelligence is the process involved in thinking (Colman, 1990). AI first emerged with the birth of computers, around 1940 to 1950, due to AI needing computational power to be effective. The earliest roots date back to McCulloch and Pitts; who in 1943 described mathematical models of neurons in the brain, based on detailed analysis of the original biological models (Warwick, 2012). One of, if not, the most significant forerunners in the field of AI is Alan Turing, who in 1950 wrote a seminal paper whereby he attempted to answer the question ‘Can a machine think?’ (Warwick, 2012). This, at the time, was a revolutionary question and should always be highly regarded. Following this, he then came up with an applicable test to test the intelligence of a machine, known as the Turing Test. Shortly after Turing, Marvin Minsky and Dean Edmonds built what was considered the first AI computer; based on the ‘Network of Neurons’ model produced by McCulloch and Pitts (Warwick, 2012). In 1956, at the instigation John McCathy, along with Minsky and Shannon, the first workshop in the field of AI was celebrated at Dartmouth College in America (Warwick, 2012). It was here the classical foundation of AI was first paved. In the 1960s there became to have a heavy emphasis on making machines able to understand and communicate in human language; primarily driven

by Turing’s ideas of intelligence, rather than allowing a computer to be intelligent in its own way (Warwick, 2012). However, from this rose, Joseph Wisenbaum’s ELIZA, which became one of the best English speaking computer programmes (Warwick, 2012). ELIZA was able to hold conversations convincingly enough to fool users into thinking they were talking to a human. When, in fact, ELIZA mostly gave canned responses, using a few basic grammar rules, although, did mimic some conversational qualities of a human (Warwick, 2012). But ultimately suggesting the system did not completely understand the said conversation, therefore, not giving a real comprehensible answer. In the 1980s, due to the limitations of the computation power in the 1970s, AI development slackened. It was now realised that for AI to perform in the way in which it was intended; computer systems needed high memory and processing power to store vast information and complete tasks (Warwick, 2012). The field of AI now became an interest for philosophers, at which point the philosopher: John Searle emerged in the debate with his ‘Chinese Room’ argument. This was the idea that a computer cannot ‘understand’ the symbol in which it is communicating in, and therefore the machine cannot be described as purely thinking in the sense of symbol manipulation, it’s just simulating intelligent behaviour (Warwick, 2012). The revival of AI started when many researchers continued to develop AI systems from a practical point of view (Warwick, 2012) which saw the introduction of the Expert Systems. Meanwhile, parallel and consequent to the development of AI, robotics in AI was questioned. In respect, a new paradigm arose, 17

which was the belief that for AI to have real human intelligence it must have a body in order to perceive, move and sense the world like a human (Warwick, 2012). In modern-day AI has been used in expansive areas, for instance, the financial systems and the military (Forbes, 2018) which caused an enormous growth in its application to the real world. Soon showing AI being able to replace for human operation and in some cases perform better. Deep Blue who, on May 11th 1997 was developed, as the first chess-playing computer, that later beat a human chess champion: Garry Kasparov [Figure 1] (Warwick, 2012). This AI system, among others, prove the ability for a machine to ‘learn’ and not necessarily need to replicate exact human behaviour. This succession was not due to newly invented technology but instead furthering the limits of what the technology available can achieve. It was Moore’s law that said the speed and memory capacity of a computer doubles every two years (Warwick, 2012), meaning earlier problems are being rapidly overcome. Figure 1: Deep Blue vs Garry Kasparov (Finley, 2012).

1.2 Research Focus

1.3 Main Dissertation Hypothesis

Now holding a better understanding of AI, it is clear to see its potential, especially where AI can store vast information, reproduce this information and give a solution best fitted to its current knowledge. In conjunction with also ‘learning’ from previous solutions, increasing its awareness. From an overview of the contemporary debate on AI in architecture, it is precisely this system of producing solutions while learning and adapting to conditions, which is what is suggested to be implemented in the architectural design process. Specifically, the preliminary creative stage where in some cases the correct, or even proper, design solution is not yet figured; allowing AI to be used as an instrument in which to help discover this design solution. The focus is on generative design in a knowledge-based architectural system; it is then whether if this system is successful or not, which will determine if AI will cause a change in the architectural field.

This dissertation assumes that where an AI system has specific based knowledge and the correct approach in which to tackle the issues it could, in turn, work in a way that will give new solutions and in the case of architecture: generate new conceptual designs. However, the question that is presented is whether these designs could be as successful as a human designer. It is theorised that as of now and current technology, an AI system would not be as successful as a human designer, due to computational power and its inherent artificiality. With that said further in the future, it can be argued that AI will have to ability to be successfully creative and prove to replace the role of the architect in some cases; however, much still remains to be done

This poses great value on not only the field of architecture and architects but also, the urban landscape. The change in process will cause a change in the result, and it is this change in the result that will see changes in our urban landscape. It is here where new styles or architectural concepts will appear and alter how areas are expressed architecturally, further altering society. As Richard Rogers claimed, “The idea is that we have a responsibility to society. That gives us a role as architects not just to the client but also to the passer-by and society as a whole.” (Dezeen, 2013). 20

ARCHITECTURAL DESIGN

2. ARCHITECTURAL DESIGN

o understand how, and why, the integration of AI could be found in architectural design, one must understand what architecture is deemed to be. Useful to this is what Cedric Price rudimentary said on architecture: “Architecture is what architects do” (Fisher, 2015). This may seem obvious, but society struggles to understand what exactly architecture is. However, one thing that will remain constant is the role of the architect; that being the set of intellectual and practical activities the architects perform, which creates the field of architecture.

2.1 Nature of Design, a Short Overview

transformation design, is developed through designbased research; these are actions from initial research which leads to a successful solution (Onal, 2018). Onal (2018) using Aura, Katainen, & Suoranta 2002 study (as cited by Onal, 2018), suggested that this design dimension creates an inspired cognitive design solution based on research concepts through the architect’s knowledge; where the architect uses design research as a tool to achieve an initial relation to the previous researched design, also known to be called precedents. ‘Action’ shows the architectural design as a long creative process, but a process that is not linear but, disordered, allowing the concept of trial and error.

Design is the articulation of selected architectural requirements that lead to a final solution. Solution meaning; building, object or built environment. Gokce Ketizmen Onal (2018) describes design as three dimensions: “3 A’s of Reflexive Design Thinking” (Onal, 2018), these three ‘A’s’ being: architect, actions and artefact (Onal, 2018).

Finally, artefact, being the final of the three A’s, can be distinguished as the reflection of the actions occurred from the design research (Onal, 2018). The artefact is considered the identifiable consequence of the architectural design process (Onal, 2018); it is this tangible artefact that can be judged and quantified in terms of being a ‘successful design’.

Onal (2018) states the first of the three A’s as ‘architect’. Onal (2018), alongside research from (Conway & Roenisch, 2015) and (Schon, 1983), suggests that an architect acts not only as the designer but also as an agent between the client and construction team. Even describing the architect as having the role of researching theories and techniques, but further having the ability to fabricate new theories and techniques (Onal, 2018), ultimately explaining the role of an architect in as being more than just a designer.

Onal (2018) perfectly describes the influential role in which the ‘architect’ plays in designing. From this, it is clear that an AI system would need to be able to seamlessly repeat the way an architect works in order to be an intelligence, and successful, design system. This ‘A’ can be considered most useful to this dissertation. Important to note is the ‘action’ dimension is key to designing intelligently, while also being able to represent the ‘artefact’. It is these aspects which makes intelligence and success in design.

Action, 24

focusing

creative

generation

and 25

LITERATURE REVIEW

3. LITERATURE REVIEW

3.1.1 Introduction and Aims As, Pal and Basu (2018) aim tackle the concept of creative architectural design using DNN through a series of â&#x20AC;&#x2DC;building blocksâ&#x20AC;&#x2122;; which are not put together pre-determined but instead allows DNN to discover them from the design data [Figure 4] (As, Pal and Basu, 2018). As, Pal and Basu (2018) indicate that DNN should be able to learn rules which have been applied in previous architectural works.

Figure 2: Deep Neural Network [Primary Source].

3.1 AI in Architecture

A 28

mong the thousands of papers that deal with AI, I am focusing on those in which involve creativity explored by deep learning and machine learning. This is due to previous AI studies surrounding creativity continuously using deep learning as a way of dealing with creativity, opposing to how this dissertation targets evolutionary computation as a means to reach creativity. This dissertation will review the article written by Imdat As, Siddharth Pal and Prithwish Basu who explore the ideas of using deep learning AI to create a conceptual design; comprehensively using deep neural network (DNN) [Figure 2] to extract designs and generative adversarial networks (GANs) to generate and represent the obtained design.

Through understanding the MacLeamy curve [Figure 3], As, Pal and Basu (2018) state human designers seem to make changes to the design around the construction documents stage and through AI, this could be improved. It was suggested that AI could present changes at the design development stage, which would, in turn, be much more cost-effective As, Pal and Basu (2018).

Figure 3: Macleamy Curve (As, Pal and Basu, 2018).

9.1.2 Methods and Techniques To test their theories, As, Pal and Basu (2018) had DNN to represent architecture into graphs, where nodes and edges represented a graph [Figure 5]. The nodes being rooms and the attribute of such room like the type, area, volume and perimeter and the edges being adjacencies between rooms like doors, open connection or vertical connection like stairs, elevators or ramps (As, Pal and Basu, 2018). Each graph was a data set of the building as a whole, then was broken down into identifiable essential building blocks: subgraphs, which captures areas of the building; like the kitchen, living room, bedroom [Figure 6]. The data sets evaluated the successfulness of the architectural building based on the comfort of living: ‘livability’, and the comfort of the house in terms of the bedroom: ‘sleepability’ [Figure 7]. It is this livability and ‘sleepability’ which create the new ‘function-driven’ compositions (As, Pal and Basu, 2018). However, as DNN cannot represent the design, using GAN allows for AI to fashion a representation of the DNN compositions (As, Pal and Basu, 2018).

Figure 4: DNN Discovering Building Blocks (As, Pal and Basu, 2018).

9.2 Case Study Relevance

Figure 5: Building Block Graph (As, Pal and Basu, 2018).

from

Literature

Review

The significance of this literature review from As, Pal and Basu (2018) has proved that AI can be used to generate creative design decisions at an architectural level. It is this concept which is fundamental to what this dissertation aims to challenge: AI having the ability to create innovative knowledge-based design solutions. This literature review, in a way, lays the foundations of AI generative, creative designs; however, As, Pal and Basu (2018) use DNN as a way of generating design solutions and this dissertation proposed the use of evolutionary computation as an alternative way of creating such design solutions. Further examining the relation machine learning has alongside evolutionary computation.

Figure 7: Livability And Sleepability (As, Pal and Basu, 2018). Figure 6: Building Block Subgraph (As, Pal and Basu, 2018). 32

AI ARCHITECTURAL EXAMINATION

4. AI ARCHITECTURAL EXAMINATION

4.1 AI Approach

4.2 Implementation of AI to Design

hroughout the development of AI, there have been an arsenal of approaches to show AI abilities; however, in the field of architecture, not all these approaches would be regarded as appropriate. This dissertation will focus on the use of classical approaches of ‘Data Mining’ and ‘Machine Learning’, while also looking at the modern approach of ‘Evolutionary Computation and Genetic Algorithms’ to tie all three methods together. By exploring data mining, it will allow for the AI system to investigate previous architectural work, as Onal (2018) explained as the ‘action’ process in design. While AI creativity can be accounted for through the use of machine learning and evolutionary computation.

When designing, design-based research and the use of existing theories and techniques are needed to reach a solution. It is this initial stage where the architect’s knowledge and design research is critical in order to achieve successful action (Onal, 2018). Thus it is clear to say the operation of an architect here is to gain and store knowledge from existing architectural information; therefore, such a process could be achieved through using an AI data mining system. The current computational power and memory gives the data mining system the ability to be more successful than a human, but also due to the human brain not having the same computational capability. Furthermore, a data mining architectural system would only store knowledge-based specifically on the architectural field, meaning that it would be ideally suited to the field of architecture. For a human, this would be impossible, as you cannot survive in the world without basic common knowledge; implying how an AI data mining architectural system would operate more efficiently than a human. Further to this, Yuji Yoshimura et al (2018) wrote a paper on ‘Classification for Architectural Design through the Eye of AI’, where it was stated that AI could visually measure and understand similarities and differences between architectural projects by different architects [Figure 8] (Yoshimura et al, 2018). From this, it can be seen that using a dataset from data mining, to pull specific architectural works and analyse them for the developmental purpose would be useful in an AI machine learning system.

The process of evolutionary computation approach can be applied to machine learning; where machine learning and evolutionary computation can work together to create solutions that can learn from themselves, known as ‘Input-to-Output Mapping’ (Warwick, 2012). The fundamental idea of this is to come up with an appropriate form of a function, where, in essence, a function from set A to set B assigns a unique element for the solution (Warwick, 2012).

The design dimension labelled as ‘action’ (Onal, 36

2018) is where machine learning in conjunction with evolutionary computation, should be considered. It would, conceptually, work by having a series of target solutions, as described by client requirements. Then, subsequently using data mining as a design-based knowledge system together with genetic algorithms, through evolutionary computation, a series of solutions will be provided. The genetic algorithms would then produce a succession of iterations until the target solutions are conclusively met. During this process, slight mutations to the solution, in this case, mutations based upon previous design knowledge through the data mining system, which would be the cause for a change in the final design solution. The mutations in the solution will give a ‘creative’ aspect to the AI system, which argues the case of AI being considered creative; which is the essential requirement of design. Machine learning would be applied during this process so that precise solutions, in relation to the adjoining condition, can take advantage of another successful solution, in order to ‘learn’. When a ‘creative’ mutation takes place, if such mutation is successful, it will be rewarded or, if unsuccessful, punished (Munakata, 2008). If rewarded, the mutated solution will be used more often, developing the AI system as well as the solution; in time, causing the said solution to be its own established technique. In turn, meaning it will fabricate new techniques, which Onal (2018) described as a design requirement of the ‘architect’.

Figure 8: Machine Learning Architectural Classification (Yoshimura et al, 2018).

4.2.1 AI Design Process Framework

4.3 The Dilemma with AI in Design

Where AI is being used as the fundamental process of the design, a new set of rules or framework will need to be executed; this section will attempt to explain how that framework works.

Vital to know is that the design process is very different from that of problem-solving, which is how AI works. The design process is non-linear (Sönmez, 2017), very much going back and forth through different stages of design and making small, but significant changes throughout. Whereas with an AI system, being problem-solving, thinks of the process as very much a linear approach (Sönmez, 2017), whereby each step follows the next until you reach the end solution.

Framework: 1. The client will input data into the AI system about his/her project requirements; including the preferred style samples and architects, in company with spatial criteria and restrictions. These requirements will become conditions. 2. Through data mining, previous architectural projects and techniques will be analysed, after being narrowed down based upon clients input conditions. Once the system has finalised the design-based research, it will set a series of target solutions, based on previous work, best suited the conditions. 3. Through evolutionary computation, the AI system will create a run of solutions to the conditions and perform a series of iterations; where it will mutate the solutions designed until the target solutions are succeeded. 4. If successful, the AI system will evaluate what it has been achieved; based on what solutions have been used. If the said solution is successful, it will be used more often, and vice versa if unsuccessful.

An architectural designer primarily designs with experience and understanding of architectural spaces. This is where an AI system would massively lack. Now the argument of robotics is bought into question, whether an AI system would need a ‘body’ to perceive the world (Warwick, 2012), and architectural spaces, in the same way in which an architect does. Without this basic understanding, which requires a lot of memory and power in AI, an AI system cannot be regarded as a successful designer. This indispensable understanding should be considered as an essential part of designing intelligently and successfully.

5. Finally, the computer algorithms used will be realised through an architectural known software which can understand input data and perform the relating output; for instance through a product akin to grasshopper (Su & Yan, 2014). Achieved using a machine learning plugin called LunchBoxML and an evolutionary computation plugin called Galapagos (Shan, 2014).

DISCUSSION

5. DISCUSSION

ndispensable to this dissertation, the literature review uses the study of As, Pal and Basu (2018), while also exploring the study of Yoshimura et al (2018), and it can be possible for AI to generate a conceptual knowledge-based design. This dissertation, however, challenges the ideas of using deep learning as a method of generative design and uses evolutionary computation as a driver and elevates the successfulness of this approach. With today’s vast increase of computational power, evolutionary computation is in a position to become the new deep learning system, as this dissertation indicates. As, Pal and Basu (2018) first explain function as an essential driver in architectural design and the intent to generate a form directly from purpose and utility. Further, describing how “the advent of machine learning systems, and as seen in our deep learning approach, form indeed can literally follow function.” (As, Pal and Basu, 2018). Illuminating how function can mean more than just utility of designed spaces, it can be “poetic and uplifting” (As, Pal and Basu, 2018).

“I do not do function”. It is essential to understand that this minimalistic style was enhancing space and not genuinely giving a personal identity to the design, which can be considered a non-creative design. As, Pal and Basu (2018) used DNN and GAN to create this AI-generated functional form, and is a beneficial start. But for AI systems to be more successful, than architects, it would need to follow a more contemporary, parametric, unthought-of conceptual design in which suiting today’s, and future, architectural styles. This debated successfulness could be due to the simplicity in which they measured architecture: only analysing the essential building blocks, or spaces (As, Pal and Basu, 2018). Even to the basic way that As, Pal and Basu (2018) only measured the space and not the more fine-grained detail, such as walls and corridors (As, Pal and Basu, 2018). Analysing the building in the way of form would have allowed for a less functional, and more conceptual design; like in the House (Y)amnitski, which is later explored as case study research.

It can be observed from As, Pal and Basu (2018) to how it is currently possible to use machine learning in a way to generate conceptual design, however, only in terms of function as explained (As, Pal and Basu, 2018). This once was a widespread belief in the field of architecture through, in 1896, Louis Sullivan stating that “form follows function” and the idea that beauty of the building stems from the practical, functional uses. However, in modern-day, these ideas have been described ‘Depression Modern’ (As, Pal and Basu, 2018) and it was Peter Eisenman that said: 44

5.1 Deep Learning vs Evolutionary Computation Looking at the way in which a human designer works is, first by looking at previous architectural works and evaluating the relation to the current project, while performing a series of iterations, back and forth, until reaching a successful design. This is how evolutionary computation works by performing iterations of mutations until the final solution is achieved, known as the hill-climbing process [Figure 9] (Miikkulainen, 2019). DNN works in a, more systematic way, which is still not completely clear due to ‘the hidden layer’; the layer between the input layer and output layer where the output activation function occurs [Figure 1]. This hidden layer has been seen as a vital issue always throughout DNN and makes it is hard to understand the route the system took to reach its final solution. Whereas this stage is precise in evolutionary computation (Munakata, 2008); and beneficial in the way that if an issue occurs in the solution, it can be easier to understand how and where these problems happened and therefore easier to solve.

Figure 9: Hill-Climbing Process Graph (Rawat, n.a.). Risto Miikkulainen (2019) found that current machine learning approaches also use the hill-climbing process, identical to that of the evolutionary computation process (Mikkulainen, 2019). Whereas, DNN uses a gradient that is computer-based (Schmidhuber, 2015) and follows a nature-oriented approach; Mikkulainen (2019) described it as not knowing “where to start and which hill to climb” (Mikkulainen, 2019). DNN has achieved extraordinary accomplishments; all be it with large networks and datasets while using high computational power. However, with design, there are lots of possible solutions, and when using DNN, it would result in many likely restarts in the algorithm (Mikkulainen, 2019), which is suboptimal. 47

Though using the hill-climbing process in creative design creates more challenges [Figure 10] (Mikkulainen, 2019) however, evolutionary computation, through population-based research (Mikkulainen, 2019), has the capability to execute multiple parallel searches, meaning it can explore lots of possible solutions at once as stated in Deb et al 2000 study (as cited by Mikkulainen). Meaning where space is misleading or an incorrect, evolutionary computation algorithms will find that successful solution; due to working in different dimensions, suggesting if the algorithm gets stuck in one dimension, it will figure a solution in another dimension (Mikkulainen, 2019).

Figure 10a: Search Space for Hill-Climbing (Miikkulainen, 2019).

The research presented throughout this dissertation clearly showed the power and influence that DNN had on creating a design based solutions, but evolutionary computation gives us an insight into the importance it could have in the future and how effective it could be. An exciting area to explore for this would be the combination of evolutionary computation with neural networks, rather than genetic algorithms.

Figure 10b: Creative Search Space for Hill-Climbing (Miikkulainen, 2019).

CASE STUDIES

6. Case Studies

his section will analyse case-studies where AI has been used in real-world architectural situations and the relative successfulness.

6.1 Ada – Intelligent Space Ada is an AI system embedded within architectural space, often described as an “inside-out robot” (Eng, 2003). This system is an interactive space developed for the Swiss national exhibition in 2002 by a team lead by P. Verschure, a neuro-informatic scientist. Ada had visitors immersed in its environment [Figure 11], where Ada herself created sensory stimulations. Ada used a neural network, being the largest neural network ever created, based on a Distrusted Adapted Control which follows classical and operant conditioning (Eng, 2005). She was designed in a way so that she could give an appearance of sentience and have a level of logic and unity with the users of the buildings. Ada did this through the use of visual, audio and tactile input sensors; giving a way to express herself, creating this sensory stimulation (Eng, 2003). Ada was expressed as an artificial organism (Eng, 2003) and used four basic human behavioural functions (Eng, 2003). Where she was able to track and then identify more “interesting” users, based on their level of interactions. Ada also tries to guide users to form a group and rewards these groups by playing games and giving visual and audio cues throughout the space (Eng, 2003).

Figure 11: Ada Live Human Interaction (Eng, K., Mintz, M., Verschure, P. F. M. J. (2005). 52

The level of successfulness of Ada was very much dependent on how convinced users were that Ada was an artificial ‘organism’. It was noted that the system was able to learn to make cues and influence positions and behaviours of users [Figure 12] (Eng, 2005). Therefore, it can be said that Ada was a successful machine in being able to influence behaviours and creating relationships between the users and space. Although Ada is not a system designed by AI, which has been the critical discussion throughout this dissertation; she nevertheless remains a project where the use of AI has been introduced in architecture. Therefore, considered as another branch of AI in architecture, and it should be considered and reviewed when discussing AI and architecture in the same bracket.

Figure 12: Ada’s Mean Floor Occupancy To Different Stimulations (Eng, K., Mintz, M., Verschure, P. F. M. J. (2005).

6.2 House (Y)amnitski Looking towards a more recent project with a strong influence on this dissertation: the House (Y)amnitksi [Figure 13]. This project was AI-generated through the architect Benjamin Ennemoser using machine learning. Ennemoser is an architect and researcher established in Los Angeles, California; his work is placed in the field of computer science; focusing on AI, machine learning and digital design (Ennemoser, 2019). The Yamnitski family ordered house (Y)amnitski as a two-story modern house in Marina del Rey, California. The AI system was given style samples from which to learn, based upon work from Frank Lloyd Wright and John Lautner while sampling current estate houses in the area (Ennemoser, 2019). Ennemoser (2019) called this a “messy dataset” as it was used to account for the disorganised and diverse dataset that the machine learning system was presented. After the machine produced, what it considered, a modern California house façade [Figure 14], Ennemoser trained it to provide not only 2D images but 3D models (Ennemoser, 2019). The result simulated principles that of ‘the modern house’ while also representing a new architectural concept.

Figure 13: House (Y)amnitski Back Facade (Ennemoser, 2019).

This project is currently focusing on the methodology of design and the generation of the design based on machine learning techniques. Although said to be realised at the beginning of 2021 (Ennemoser, 2019).

Figure 14: House (Y)amnitski 2D AI-Generated Facade (Ennemoser, 2019).

and ultimately Ennemoser (“in-press”) describes the machine learning system as envisioned its own California house. Figure 15: House (Y)amnitski Form Development (Ennemoser, 2019). This study is the closest representation of the ideas which have been researched throughout this dissertation. It first must be noted that the question of an AI system having the ability to generate a new conceptual form has been seen to be possible. Further, in terms of this project discovering a unique solution, as Onal (2018) states: being able to fabricate new techniques has been successful, which is described as a critical aspect of design. AI has clearly explored a design of this nature as an unthought-of style, therefore suggesting how it is possible to train an AI to detect and transfer architectural concepts (Ennemoser, “in-press”). Accordingly, AI can design a new conceptual form based on previous work fabricating new techniques, and is, therefore, on some part, a successful designer.

Conversely comparing this design against an established California modern home [Figure 16], it is clearly, different in terms of materiality; that being said you can see similarities in the nature of design, with a large central glass window and balcony area. Suggesting how the samples used in the AI system influenced the generated designed. It yet must be stated that the architect contributed to the machine learning design by inputting dataset samples (Ennemoser, “in-press”), and it is this contribution that influences the final design solution. However, the contribution acts in the same way as precedents,

(B. Ennemoser, Personal Communication, December 30, 2019) stated that he partly generated the 3D datasets by himself because there was difficulty in finding enough datasets for such a computational task, he continued to say that the challenge was how and where to find the datasets. In terms of successfulness it can be said that the established California modern home would be chosen over the two more often than not; which would suggest that the human designer is more successful than the AI generative design. Yet, the term ‘successfulness’ is subjective, and it down to the eye of the beholder to make that judgement.

Figure 16a: House (Y)amnitski Front Facade (Ennemoser, 2019).

Figure 16b: Modern California Front Facade (Contemporist, (2017).

CONCLUSION

7. CONCLUSION

7 Conclusion

his dissertation proposed an idea of an AI system with the ability to generate a new design, centred around a generative knowledgebased system; whether this system was able to create a design following a set of conditions.

Following the research of this dissertation, the question should not have been whether an AI system can design because, with the correct given inputs and approach, it can. The question should have been how successful a machine is at generating designs and if such design can surpass that of an architect. Successfulness is however down to preference of style, as the machine can design a successful, and usable, architectural space but in a new form of its own. Currently, through the paper on machine learning by As, Pal and Basu (2018) suggested it could be very successful, if not better than architects, at developing functionalbased designs or minimalistic designs; though this is very limiting. Through the case of House (Y)amnitski, it shows machine learning can create a new style altogether. As, Pal and Basu’s (2018) paper suggests the successfulness of functional design in AI, while also, more excitingly, House (Y)amnitski exploring the potential for future design forms through AI systems.

However, in both cases a fundamental stage was human input creating a dataset, therefore is it real AI creativity or human creativity projected upon them? It also must be stated that the way in which an architect can perceive space and modestly be in space is undoubtedly advantageous in the process of design. Something which an AI system does not have the ability to do; this understanding of a space is essential in creating a successful praised architectural project. Therefore, the need for AI to have a ‘body’ in which to interact and perceive the world in the same way as an architect would be needed; but would it truly have the ability to recognise the space in the same way entirely? Improbable. This, however, can only be judged when such technology is present. Another vital aspect to the role of an architect is what Onal (2018) described as not only being a designer but also being a design-based researcher, even further, an agent between the client and constructor (Onal, 2018). This final feature is what a machine would struggle to achieve, without going into the argument of the Turing Test and whether AI can communicate in the way of a human. Machine learning in design through AI is an energetic

field in which to investigate, even more so when linking and creating concepts that apply to real-life situations. AI can fill gaps of unknown or unthoughtof solutions in the profession of architecture, but also in the profession of design development all over. This dissertation could help guide future research on the subject, and with time and growth, AI could reach new potentials and present new findings. However, as of now, single-handedly an AI system will not be able to generate a more successful design than that of a human designer; mainly due to the need of humannature in design. It can, nonetheless, be used as a tool in which to guide; but not used individually in which to control.

8. BIBLIOGRAPHY As, I., Pal, S., Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi.org/10.1177/1478077118800982 Brownlee, J. (2019). A Gentle Introduction to Generative Adversarial Networks (GANs). Retrieved from https:// machinelearningmastery.com/what-are-generativeadversarial-networks-gans/ Colman, A (1990). Chapter 7: Aspect of Intelligence. In Open University. Ilona Roth (Ed.), The Open University’s Introduction to psychology, Volume 1. Publisher: L. Erlbaum Associates. Conway, H., & Roenisch, R. (2005). Understanding Architecture: An introduction to architecture and architectural history. New York: Routledge. https://doi.org/10.4324/9780203973196 Donges, N. (2019). 4 Reasons Why Deep Learning and Neural Networks Aren’t Always the Right Choice. Retrieved from https:// builtin.com/data-science/disadvantages-neural-networks Dowe, D., & Oppy, G. (2003). The Stanford Encyclopedia of Philosophy: Turing Test. Retrieved from https://plato.stanford. edu/entries/turing-test/ Eng, K., Babler, A., Bernardet, U., Blanchard, M., Costa, M., Delbriick, T., Douglas, R. J., … Verschure, P. F. M. J. (2003). Ada – Intelligent Space: An Artifial Creature for the Swiss Expo.02. Eng, K., Mintz, M., Verschure, P. F. M. J. (2005). An Interactive Space That Learns to Influence Human Behaviour. IEEE Transaction on System, Man, and Cybernetics – Part A: Systems and Humans, Vol. 35, No. 1. Eng, K., Mintz, M., Verschure, P. F. M. J. (2005). Collective Human Behavior in Interactive Spaces. IEEE International Conference on Robotics and Automation.

Ennemoser, B. (2019). About. Retrieved from https://www. benjamin-ennemoser.com/about/ Ennemoser, B. (2019). House (Y)amnitski. Retrieved from https:// www.benjamin-ennemoser.com/projects/house-yamnitski/ Ennemoser, B. (2019, October 21). House (Y)amnitski - A modern House generated by AI and Machine Learning [Video File]. Retrieved from https://vimeo.com/367869314 Ennemoser, B. (“in-press”). “Excerpt” Tracing the Latent Space Machine Learning and Data Curation as Computational Design. In B.E. Ingrid Hufnagel UIBK (Ed.), [UN]TIMELY ARCHITECTURE. Publisher: [transcript] in the Architecture and Design series. Fairs, M. (2013). “We have a responsibility to society” says Richard Rogers. Retrieved from https://www.dezeen.com/2013/07/16/ we-have-a-responsibility-to-society-says-richard-rogers/ Feraud, R., Clerot, F. (2002). A Methodology to Explain Neural Network Classification. Publisher: Elsevier Science Ltd. Fisher, S. (2015). The Stanford Encyclopedia of W of Architecture. Retrieved from https://plato.stanford.edu/entries/architecture/ Fogel, D. B. (2006). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence [eBook]. https://dx.doi. org/10.1002/0471749214 Frazer, J. (1995). An Evolutionary Architecture [Online Book]. Retrieved from https://issuu.com/aaschool/docs/anevolutionary-architecture-webocr Hackearth. (2018). [Infographic] Applications of Artificial Intelligence (AI) in business [Blog]. Retrieved from https://www. hackerearth.com/blog/developers/applications-of-artificialintelligence/ Miikkulainen, R. (2019). Creative AI Through Evolutionary

Computation. Texas: The University of Texas at Austin and Cognizant Technology Solutions. Munakata, T. (2008). Fundamentals of The New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More. London: Springer.

Routledge Yoshimura, Y., Cai, B., Wang, Z., Ratti, C. (2018). Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence. Massachusetts: SENSEable City Laboratory, Massachusetts Institute of Technology.

Onal, G. K. (2018). 3 A’s of Reflexive Design Thinking in Architecture. International Journal of Social Science Studies, 6(11), 49-56. Rawat, U. (n.a.). Introduction to Hill Climbing | Artificial Intelligence. Retrieved from https://www.geeksforgeeks.org/ introduction-hill-climbing-artificial-intelligence/ Schon, D. (1983). The Reflective Practitioner: How professionals think in action. London: Temple Smith. Schmidhuber, J. (2014). Deep learning in neural networks: An overview (Neural Networks, 61:85–117). https://dx.org/10.1016/j. neunet.2014.09.003 Shan, R. (2014). Integrating Genetic Algorithm with Rhinoceros and Grasshopper in Whole Building Energy Simulation. Paper Presented at Grand Renewable Energy 2014. Retrieved from https://www.researchgate.net/publication/267980610_ Integrating_Genetic_Algorithm_with_Rhinoceros_and_ Grasshopper_in_Whole_Building_Energy_Simulation Sönmez, N. O. (2017). A Review of the Use of Examples for Automating Architectural Design Tasks. Computer-Aided Design. Retrieved from https://www.journals.elsevier.com/computeraided-design Su, Z., Yan, W. (2014). Improving Genetic Algorithm for Design Optimisation using Architectural Domain Knowledge. Texas: A&M University. Warwick, K. (2012). Artificial Intelligence: The Basics. New York:

9. FIGURE REFERENCES As, I., Pal, S., Basu, P. (2018). Building Block Graph. Retrieved from Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi. org/10.1177/1478077118800982 As, I., Pal, S., Basu, P. (2018). Building Block Subgraph. Retrieved from Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi. org/10.1177/1478077118800982 As, I., Pal, S., Basu, P. (2018). DNN Discovering Building Blocks. Retrieved from Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi. org/10.1177/1478077118800982 As, I., Pal, S., Basu, P. (2018). Livability and Sleepability. Retrieved from Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi. org/10.1177/1478077118800982 As, I., Pal, S., Basu, P. (2018). MacLeamy Curve. Retrieved from Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing 2018, Vol. 16(4) 306–327. https://dx.doi. org/10.1177/1478077118800982

Ennemoser, B. (2019). House Y(amnitski) Back Facade. Retrieved from https://www.benjamin-ennemoser.com/projects/houseyamnitski/ Ennemoser, B. (2019). House Y(amnitski) Form Development. Retrieved from https://www.benjamin-ennemoser.com/ projects/house-yamnitski/ Ennemoser, B. (2019). House Y(amnitski) Front Facade. Retrieved from https://www.benjamin-ennemoser.com/projects/houseyamnitski/ Eng, K., Mintz, M., Verschure, P. F. M. J. (2005). Ada Live Human Interaction. Retrieved from Collective Human Behavior in Interactive Spaces. IEEE International Conference on Robotics and Automation. Eng, K., Mintz, M., Verschure, P. F. M. J. (2005). Ada’s Mean Floor Occupancy to Different Stimulations. Retrieved from Collective Human Behavior in Interactive Spaces. IEEE International Conference on Robotics and Automation. Finley, K. (2012). Deep Blue vs Garry Kasparov. Retrieved from https://www.wired.com/2012/09/deep-blue-computer-bug/ Miikkulainen, R. (2019). Creative Search Space for Hill-Climbing. Retrieved from Creative AI Through Evolutionary Computation. Texas: The University of Texas at Austin and Cognizant Technology Solutions.

Contemporist. (2017). Modern California Front Facade. Retrieved from https://www.contemporist.com/light-inside-new-house-inlos-angeles/modern-house-design-060417-1158-21/

Miikkulainen, R. (2019). Search Space for Hill-Climbing. Retrieved from Creative AI Through Evolutionary Computation. Texas: The University of Texas at Austin and Cognizant Technology

Ennemoser, B. (2019). House Y(amnitski) 2D AI-Generated Facade. Retrieved from https://www.benjamin-ennemoser. com/projects/house-yamnitski/

Rawat, U. (n.a.). Hill-Climbing Process Graph. Retrieved from https://www.geeksforgeeks.org/introduction-hill-climbingartificial-intelligence/

Yoshimura, Y., Cai, B., Wang, Z., Ratti, C. (2018). Machine Learning Architectural Classification. Retrieved from Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence. Massachusetts: SENSEable City Laboratory, Massachusetts Institute of Technology.

10. APPENDIX

List of Definitions

Data Mining

Bucket Brigade: Method of transporting items are passed through a series of â&#x20AC;&#x2DC;stationsâ&#x20AC;&#x2122; and works by passing from one station to the next until reaching the end; like a human chain.

To make a knowledge-based architectural AI system, a key component to achieving this would be data mining, and this is based on the idea, that the way humans operate is by gathering facts, or data, about the world and use that data in real-life situation to make decisions. However, it has been said that the amount of data in the world doubles every two years, which means over 10 years the amount of data increased by 1,000 times. Human brains cannot hold and store this amount of information, which is where AI systems can come in hand. AI systems are perfectly suited to this specific task due to their ability to store vast amounts of data and then create a meaningful relationship between this such data. In an architectural situation, it will mean that it can store architectural works and designs to make a connection between certain design requirements and what has been performed previously to give an example of how to come to a solution. (Warwick, 2012)

Classical Conditioning: Classical conditioning is a psychological learning process where a biologically potent stimulus are paired with previously neutral stimuli. Generative Design: Generative design is an iterative design process that creates a design through the use of a computer program which is done using a series of data inputs which leads to a design solution. Nature-Oriented Approach: Nature is considered to be influenced by genetic inheritance and biological factors. Operant Conditioning: Operant conditioning is a psychological associative learning process whereby the strength of a behaviour is altered through reinforcement and punished to that particular behaviour. Precedents: Using previous architectural projects as inspiration for our own architectural concept/project.

Deep Learning: Deep Neural Network Neural networks, as from the name, is based on the human brain, and the algorithm is designed to recognise and interpret patterns. Neural networks gather sensory data and recognise this data in numerical value which is then interpreted into realworld values and results, but must be translated. Neural networks help cluster and classify data in a way so that it is stored and manageable according to similarities in the input data. (Feraud & Clerot, 2002)

Expert System An ‘Expert System’ is a machine that can reason solutions from a series of input data, from a specific domain, of which the data is input by a human expert. The human expert creates a series of rules for the machine to follow. The rules work as IF (condition) statements and these give THEN (conclusion) statements. However, it may not only be one condition, it may need multiple conditions in order to reach the correct conclusion. The first, and one of the key machines to do this was, ‘MYCIN’ which is a medical system to diagnose blood infections. To help further understand an example of this machine work is this manner would be: IF (sneezing) THEN (flu). It was not always as simple as just IF THEN statements, if a conflict has been introduced through several conditions it may result in more than one conclusion; which was due to the expert rules set. The way the expert system avoided this was by following a set of criterion, which is as follows: 1. Highest priority rule, the conclusion reached from the conditions 2.

Highest priority condition

Most recent condition

Longest matching condition to conclusion

5. A most active group, rules are spilt into groups some of which may be extinct (smallpox) Expert systems also use layers, which is defined when 76

two conditions would give two separate conclusions (layer 1), and these two separate conclusions create their conditions which then gives a final conclusion (layer 2). For example: for starting a car, layer 1: IF (start button pressed) THEN (start engine) and IF (gear selection made) THEN (engage gear) layer 2: IF (engine started and gears engaged) THEN (vehicle drive). (Warwick, 2012) Evolutionary Computation and Genetic Algorithms Both ‘machine learning’ and ‘data mining’ were classic AI approaches, now looking forward to a modern approach called ‘evolutionary computing’ which is based on genetic algorithms. To first understand genetic algorithms, understanding genetics in real life would be helpful. Before conception there are 23 chromosomes, both for the egg and sperm, giving a total of 46 chromosomes. In each of the chromosomes, there are thousands, if not millions, of genes which are made up from the mother, and the corresponding, from the father. These make a gene pair for the child, these are called phenotypes. The way a phenotype is created is each gene being given a value (0, 1, 2) which these are called alleles; these alleles make a certain gene pair (00, 10, 11, 20, 21, 22) called genotypes which gives the resembling phenotype. Further, in biology, those who better adapt to the environment will have a higher probability of survival, therefore meaning they will higher have chances of passing on their genes to offspring. Meaning that certain genes, which are successful, will remain while those that aren’t success will extinct, giving a higher chance of getting a successful gene. This is Darwinian evolution. It is this model of natural genetics and 77

evolution process that computer genetic algorithms are based on, it includes concepts like chromosomes, genes, mating or crossover breeding, mutation and evolution; however it must be stated that it borrows these concepts and does not replicate them. First, the genetic algorithm will randomly generate solutions, or “chromosomes”, and then it will perform iterations where it will use fitness values, which are defined as values for the solutions to compare itself against to determine which solution is better. After determining fitness values the solutions which are fit and more likely to “survive” will be carried on, following this it will use the concept of ‘crossover breeding’ so that it solutions will swap parts so that they are not the copies from the step before. “Mutations” of these solutions then occur, whereby small parts of the solution are artificially changed to try and find the optimal solution. (Munakata, 2008) Evolutionary Learning

Computation

Applied

Machine

In this example, x=input – where there can be n amount of inputs, and y=output – where there can be m outputs. Input values range from values of 0 and 1, and each input set will give a certain output/solution (yc), and each solution should match the target solutions set (yt).

Table 1: Evolutionary Computation Target Solutions vs Output

Within this example table, we have achieved a solution that matches the target solution four-times, however, the ideal would be to achieve eight yeses. This example of fitness of the solution table is w1, w2, w3= -1, 0, 1 with w meaning weight. For example, 00 for 0, 01 for 1, 11 for -1 so for example (1, -1, 0) will be 011100. After introducing weights, they go through the genetic algorithm and the solution being a fitness solution. It follows the genetic algorithm with the process of generating random solutions, introducing a new mating pool to create new solutions, crossover breeding of the two mating pools and mutating the solution. This process is called an iteration, and these iterations are repeated until the correct solution, where eight yeses, is achieved. This example shows how evolutionary computing can be applied to machine learning problems. (Munakata, 2008) Generative Adversarial Networks GAN is short for generative adversarial network, and GAN is an approach to generative design which uses neural networks. It is an unsupervised system that uses machine learning to discover regularities and patterns to generate outputs. GANs have the

ability to generate realistic examples across fields and particularly image-to-image translation and generating photorealistic objects/images that fool humans. (Brownlee, 2019) Grasshopper Grasshopper is a plugin for a 3D modelling programme: ‘Rhinoceros’ which is widely used by architects and well known. This plugin is a graphical algorithms editor that can write design scripts, which uses Rhinoceros’ existing 3D design and modelling tools. It works by a user inputting a series of design scripts/algorithms into the grasshopper plugin which will give a set design outcome on the Rhinoceros programme based on the algorithms written. Hill-Climbing Process The hill-climbing process is an iterative process that starts with an arbitrary solution to a problem, then tries to find a better solution to the problem by making incremental changes. It is broken down into five stages: 1. Local maximum: It is a position in which is better than its current state however there is a position that is better than both (global maximum). This state is better because the objective function is higher than the current function [Figure 9]. 2. Global maximum : It is the best possible position due to it being the highest value. 3. 80

Plateau/flat local maximum : It is a flat area of

state where positions have the same value. 4. Ridge : Is area that is higher than surrounding areas but itself has a slope. Acting as special kind of local maximum. 5. Current state : The state that is currently present during the research. 6. Shoulder : The should acts as plateau with an uphill edge. The hill-climbing process ultimately tries to find a sufficiently good solution to the problem, it may not be the ‘global maximum’ solution but it will always find a solution that will be successful. Essentially working by analysing the data against the graph and finding the optimum solution based on inputs. (Rawat, n.a.) Machine Learning Within AI, a key aspect is machine learning, and this approach can be described as computers learning and altering their solutions from previous operations and experiences, changing their ‘behaviour’ in fundamental ways. This is due to working on a rulebased Expert System. However, the way machine learning differs is; if the conclusion reached is a good one the rules that have used will be, in a sense, rewarded and therefore will be more likely used again. This similarly works the same if the conclusion reached is not a good one; the rules used in this algorithm will be punished and therefore, will less likely be used in future situations. In a way, learning; using a Bucket Brigade technique. (Warwick, 2012) 81

Additional to this, it has been seen that the machine can create a new rule by itself, which is performed by allowing small mutations to the rules to better suit conclusions or resolve conflict. It is these mutations that over time will be used, and if the mutation causes a good conclusion, then it will be rewarded, and the chances of it being used again will be increased. Although, the way it learns, and is considered to be learning, is dependent on how much trial and error is allowed for in real-life situation with humans. (Warwick, 2012) Turing Test

of an ‘average’ interrogator is under question. This is due to the fact that the interested participants would be that of Professors of Computer Science, Philosophers and even students of AI, who under all circumstances would not be considered ‘average’. Second, the question that this test poses is: what does the Turing Test truly test? It does not seem to answer the question ‘can a machine think?’ but more ‘can machines appear to think the same as a human’. Although it must be regarded how important this such test has been in the contribution of AI development. (Dowe & Oppy, 2003)

The Turing Test tests the idea being that when conversing with a computer over a period of time would you be able to tell the difference between the conversations with the human or computer? If not then you must credit the computer with having some level of intelligence. The test is as follows: an interrogator faces a keyboard which is attached to a spilt monitor. Behind one half of the monitor is a computer respondent and behind the other half is a human respondent. Both respondents are hidden, and the only interaction is with the keyboard so that tests remains fair. The interrogator then has 5 minutes for which to discuss whatever he/she desires. Following this 5 minutes discussion, the interrogator must decide which unknown entity is human and which is a computer. The aim is for the computer to try and fool the interrogator, not that they are human, but are more human than the human. Turing stated that to pass the Turing Test, the computer needs to fool the average interrogator into making an incorrect decision, at least 30% of the time. However, the idea 82