Augmented Creativity

A U G M E N T E D C R E AT I V I T Y L A L I N

K E Y V A N

T H E S I S

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

ABSTRACT Computers are autonomous utilitarian machines which can work in wellstructured areas of problem-solving as a tool. However, within an architectural domain, a dialogue between designers and computers remains the same in an age that machines are getting smarter and they can learn.

Augmented Creativity is a project which presents a reflection on the spatial aspects of sound alongside an examination of responsive and collaborative design. It proposes a framework and an interactive installation for participative design by implementing machine learning techniques to build a memory, a fauxconscious with the use of sound as a spatial event, an auditive experience.

In this approach, design aspects such as the nature of creativity, the muse in the machines, the theory of memory, sound as a material phenomenon, the methods and methodologies in machine learning techniques such as recursive neural networks discussed to design a space which is not just active but also collaborative.

The workflow associates computer creativity, interactivity, new media art with the architectural design process, challenging traditional methods and workflows, prioritising process over product and the interaction.

K E Y W O R D S

Advanced Interaction Machine Learning Participatory Design Creative Behaviour Performance Art

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I N T R O D U C T I O N

The Nature of Creativity ─ 08 Theories on Creativity ─ 14 "Hello World!" ─ 17

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C R E A T I V I T Y I N A R T I F I C I A L L I F E

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I N T E R A C T I V E I N S T A L L A T I O N

Participatory Design ─ 34 Virtual Experiments ─ 37 Early Design Proposals ─ 43 Final Workflow ─ 44 User Experiments ─ 45 Results ─ 47

24 ─ Computational Creativity 28 ─ Approaches in Machine Learning 29 ─ Previous Computational Models

50 C O N C L U S I O N

50 ─ Conclusion 51 ─ Appendix 55 ─ Glossary 63 ─ References

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

I N T R O D U C T I O N

T H E N AT U R E OF C R E AT I V I T Y In a world that the enhanced technologies reveal an ability to exist in solely virtual worlds, the struggle between virtual and physical aspects of ourselves is imminent. Machines are getting smarter, and they can learn, how can computers be more involved in the design process? What is the potential for a computer to compose a creative work?

This opening chapter will provide a general discussion on the concept of creativity while creating a base to the further examination of the relationships between human creativity and the capacity of replicating the creative behaviour with machines.

K E Y W O R D S

T H E C R E AT I V E M I N D

A S P EC TS O F C R E AT I V I T Y THE ART AND SCIENCE

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"I begin with an idea, and then it becomes something else."

Pablo Picasso W H A T I S C R E A T I V I T Y ?

The history of the term creativity is a broad ranged topic to be introduced as a part of this thesis. It is a paradox and a puzzle which artists, designers, architects even cannot fully explain how they compose novel ideas. They often talk about intuition; however, they cannot confirm how it works. If 'creativity' is a reminiscent word that can have different meanings to different people, it is important to open up this term with modern scientific theories, and then to focus on the interpretation that this thesis addresses.

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“Is creativity the result of a unique mechanism or is it just a part of normal problem-solving?”

The generation of novel and valuable ideas are forming the basis of creativity which is not just precisely about art or design. The creativity is, at its core, about ideas and how we develop, understand, and communicate them. As a process, it involves critical thinking as well as original insights and fresh ideas. It is not just about having an “a-ha” moment; it is about

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setting ourselves up for that time of spark, then knowing what to do when it happens. Learning creativity, therefore, does not mean starting from scratch, it means enhancing the creative intelligence that already exists within us.

As it is declared in the second edition of Margret Boden's book, The Creative Mind: Myths and Mechanisms, creativity at the core is a combination of familiar ideas in unfamiliar ways.

She also states the fact that there are other cases which involve exploration and transformation of conceptual spaces in people's mind. Boden adopts a view of computational concepts and theories as a crucial element to enhance the psychology behind the creative behaviour while using them as sources of reinforcement for her research. According to Freud, the creative process can be described as a defence mechanism to protect the mind against neurosis and leading it to produce a socially acceptable source of entertainment and pleasure for the public. The 'unconscious' plays a significant role in the act of creation. Hence, this statement

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

P A B L O

P I C A S S O

Portrait of Woman in Red Blouse

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F O R M

O F

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Guernica by Picasso, Westworld Tv Series, "Earth is not flat."(Left to right)

A U G M E N T E D C R E AT I V I T Y

L A L I N K E Y VA N

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

adds another dimension which requires being examined. What are the social aspects of creativity? When does an original idea become innovative? Moreover, most importantly, can creative behaviour be learned? If so, what are the stages of the creative process?

F R A M E O F F O C U S A N D N O V E L T Y P A R A D O X

Creation or creative drive is not a foreign topic to philosophers or theologists in all ages. Two thousand years ago, they argued the ex nihilo, creating out of nothing. They claimed that the universe was created not only by God but also, out of God. In this case, the world appeared to have new properties which also God does not have. To solve this paradox, medieval monotheist theologians proposed the idea of metaphysics - an immaterial God to create material universe. Today, some philosophers have decided in the two conclusions cannot be possible at all: God is a noncreator or the creator of nature by some means shares the same properties because if there is no essential distinction between the creator and the creation, there is nothing new. Briefly, the paradox of the novelty still exists. The creation of the universe might be problematic as explaining human creativity scientifically in just one paper.

Therefore for this research, it is enough to discuss certain frames of focus on creative behaviour to have a better approach replicating it with machines.

'The bath, the bed, and the bus: this trio summarises what creative people have told us about how they came by their ideas."

MARGARET A. BODEN

Before discussing what makes an idea novel, the differences between imagination, creativity and innovation must be resolved. The main difference between these three elements is the frame of focus which will gain importance in the later chapters.

Imagination is at basis about picturing what is unreal or impossible. Creativity uses imagination to make unfamiliar combinations for familiar ideas.

Lastly, innovation practices creativity and imagination to improve existing systems and ideas. Novelty paradox lies under these three types of creative behaviours with a name - social aspects. If creative ideas are originated from other ideas, then it is hard to see whether the outcomes are novel.

If they do not then it is hard to figure where they are coming from at all. In this stage, the reconstruction of the memory will help to understand how this paradox will resolve. The new idea which composed of other ideas will not be the same of the initial or the other new ideas because the question of how did the new idea constructed will matter more than the collection of original ideas. In this way, the new idea, though originating from earlier ones, is different from all

of them. As in their book of Computers and Creativity, Derek Partridge and Jon Rowe state that learning is one of the social aspects of creativity that is revealed by the computational modelling as a component of creative behaviour rather than the idea of becoming or (becoming more) creative.

Thus, creative ideas then are new.

On the other hand, it is not possible to say every new creative idea is novel. There is a distinction between psychological creativity and historical creativity. Psychological creativity, which will be referred as P-creativity later on, includes surprising and valuable ideas which are new to the person who comes up with it. For instance, children come up with ideas which are new to them, even though these ideas have already been in textbooks. Someone who comes up with a bright idea is not necessarily less creative just because someone else had it before them. However, in order, a brilliant idea counts as a novel; first, it should be accepted by the society. So historical creativity which will be referred as P-creativity, later on, must involve with an idea which has arisen for the first time in human history.

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O F

Before discussing scientific theories on creativity, expanding the three forms of creativity is necessary because each way is corresponding to a different type of 'surprise'. The first one as stated before involves unfamiliar combinations to familiar ideas.

Making and appreciating the novel combination requires a rich store of background knowledge in one's mind and many ways of moving around with it. The combinations may or may not have been caused by some random process. For instance, the drama tv series called Westworld(page 12, bottom left) is a combination of the western and the sci-fi concepts which are connecting to each other on the screen with having a 3d printed AI in 13

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a Western theme park.

The second one concerns the exploration of conceptual spaces which are structured styles of thought that are affected by or borrowed from one's culture and peers. The idea of 'earth is flat.' can be given as an example(page 12, bottom right). Exploratory creativity is relevant because it does enable someone to see potential that they never thought before.

The ladder form of creativity involves to realise the limitations of the conceptual spaces and transforming them into new styles such as cubism and its impact on the art. The painting Guernica by Pablo Picasso(page 12, top) depicts this new style with an abandoned perspective, open forms,

THESIS

blending background into the foreground and showing objects from various angles. The impossible looking idea can come about only if the creator changes the preexisting style in some way, it must be tweaked so that thought is now possible which previously were inconceivable.

L O V E L A C E Q U E S T I O N S

After all, the information is given previously; the very idea remains still: 'computers cannot create because they can do only what they are programmed to do.'. This argument published by very first programmer Lady Ada Lovelace and her colleague Charles Babbage. The statement above used as a dismissive no by many people to respond four Lovelace questions which this thesis mostly moved around them.

"Can computational ideas help to understand how human creativity is possible?"

"Could computers (now or in the future) ever do things which at least appear to be creative?"

"Could a computer ever seem to recognise creativity?" (For instance, in poems written by humans poets.)

"Could computers ever really be creative whose originality does not depend on the human programmer?"

Although this thesis is not purely about answering these questions, in the following chapters they will be used to understand better the interaction between architect as a programmer, why certain elements are utilised in the workflow to compose a participatory design.

L A L I N K E Y VA N

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

THEORIES ON C R E AT I V I T Y The chapter Theories on Creativity shall review psychological theories of the creative process to frame an explanation and a base to the proposed computational model.

K E Y W O R D S

KOESTLER

P O I N C A R E ' S 4 S TAG E M O D E L WEISBERG'S CRITICISM

Traditional beliefs about human creativity are often influenced by the paradoxical mechanism of the concept: inspiration, poetic muse, and they are profoundly pessimistic about the capacity of science to explain it.

The inspirational approach comprehends creativity as a divine and mysterious power.

The romantic view, on the other hand, is less extreme. It states cre-

''A poet is holy and never be able to compose until he has become no longer him."

P L ATO

ative minds are gifted people with a particular talent, insights or intuition.

Until now, the explanations are mostly vague and fundamentally unanalysable. From a psychological view, insight is the name of the question rather than the answer. One of the first published reaction to romanticism comes from the book The Act of Creation by A. Koestler, 1975.

However, Koestler's description has no detail, and it is pretty generic. This thesis picks up where Koestler left it.

The primary concern is the first Lovelace question, an artificial intelligence and computational concepts help to understand better the process of a creative idea. Another theory about creativity is written by the French mathematician Poincare. As he suggests in Mathematical Creation(1924, chapter 7) there are four stages to be considered. Preparation which is the stage of concentrated work and accumulating data, incubation which is the time corresponds to digesting all

the information knowledge, illumination which is the moment of finding a solution to the problem, verification which is the further period to justify and correct the results. However, Weisberg claims that incubation and illumination stages that represented in the model of Poincare have no experimental evidence. Later he analysed the experiments conducted by Olton, Read and Bruce or Perkins in the 1960s on the problem-solving, the writing poems, the conscious efforts of remembering. His conclusions on the final stage of his career were that the creative thinking employs the same mechanisms as usual problem-solving techniques: a person may become more creative with a significant amount of background knowledge because it is easier to recognise patterns in the base data by committing to solve problems. This argument indicates that innovative behaviour may be domain dependent. Hence, creative thinking does require special mechanisms.

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"The moment of truth, the sudden emergence of a new insight is an act of intuition. Such intuitions give the appearance of miraculous flashes or short-circuit of reasoning. In fact, they may be likened to an immersed chain, of which only the beginning and the end are visible above the surface of consciousness.

The diver vanishes at the one end, guided by invisible links."

ARTHUR KOESTLER

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S TAT E O F A R T:

"HELLO WORLD!" It is known the fact that computers are utilitarian machines, they understand the world through 1 or 0, yes or no. This argument is the reason why they can work in well-struc-

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tured areas of problem-solving or they can be used as tools to aid designers through the creative phase. However, for a computer, forming original work by itself is an enormous challenge because it requires a machine which can learn. Today, computer scientists mainly focus on the usage of artificial intelligence in business, medicine, fabrication rather than architecture, design, art which requires creative notion. Therefore, communication and dialogue between architect and computer remain the same, it does not change into a partnership and collaboration.The following chapter will showcase current applications in design and architecture domain while discussing the rules for computational creativity.

K E Y W O R D S

CO M P U TAT I O N A L C R E AT I V I T Y

C U R R E N T A P P L I C AT I O N S

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H A R O L D

C O H E N

Computer(AARON) Generated Art

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A U G M E N T E D C R E AT I V I T Y

In a world that machines are getting smarter and they can learn, how can computers be more involved in the design process? What is the potential for a device to compose a creative work? Can machines help us conceive design possibilities and solutions beyond human creative capacity and limitations?

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When programming a soft intelligence, the computer is expected to solve problems, to represent knowledge, to have a common sense and social intelligence, to learn, to perceive motion and manipulation and lastly to be able to have creative drive. The chatbots, automation in production, medical diagnosing software already have covered most of the steps. However computational creativity has its sub necessities. Tho goals of simulating creative behaviour with a computer can be summarised into three major accomplishments: constructing a program that is capable of human creativity, understanding better human creativity and formulating an algorithmic perspective on creative behaviour, designing programs that can enhance human creativity without necessarily being creative themselves. This paper mostly moves around the ladder two.

Newell, Shaw and Simon as artificial intelligence researchers defined computational creativity in similar ways to Boden's book The Creative Mind.

They stated that the output from any programme should be useful or novel regarding P-Creativity or H-Creativity, it should often demand to reject old ideas that humanity previously accepted, it should come from clarifying an initially vague problem. They have also proposed few strategies to

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“Current procedures can be automated, thus speeding up and reducing the cost of existing practices; existing methods can be altered to fit within the specifications and constitution of a machine, where only those issues are considered that are supposedly machine-compatible; the design process, considered as evolutionary, can be presented to a machine, also considered evolutionary, and a mutual training, resilience, and growth can be developed.�

NEGROPONTE, 1969

compose a novel work such as placing a familiar object to a superficially unrelated or distant topic, adding an unexpected feature to an existing concept, combining two irrelevant scenarios into the same narration and lastly using an iconic idea from one

domain in an incongruous area. As it seems, computational creativity is a multidisciplinary endeavour such as architecture that is positioned in a milieu where the fields of artificial intelligence, cognitive psychology, philosophy, and art intersect.

L A L I N K E Y VA N

H A R R Y

B E R T O I A

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Cover of the 1962 “Man’s Creative Mind” issue of IBM’s THINK magazine, with cover art

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1 G O O G L E

3

D E E P

7 M I N D

An algorithm composes digital visuals while continuously evolving them.

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8

4

C LOT H G R A S P P O I N T D E T E C T I O N

A machine learning algorithm which uses point detection to automate certain tasks.

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8

A P P

9 G O O G L E A I C U LT U R E E X P E R I M E N T S

A machine learning algorithm which is developed to identify shapes and forms.

An ongoing AI project to classify or to build relationships between existing artefacts.

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D R E A M

A neural network which is designed to recognise patterns and recompose drawings.

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P R I S M A

A mobile application which recognises artists style and applies them as filters onto the photographs.

Q U I C K , D R AW !

D E E P

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L I Q U I D S E LV E S

An artificial intelligence developed to play the strategy game Go.

T H E

S P R I N G

S P Y R E

A sound installation which uses machine learning algorithms.

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E - DAV I D

P E R F O R M AT I V E E C O LO G I E S

A robot which can draw with the understanding of brush strokes and line weights.

An interactive installation which uses computer vision to change the experience for the observer.

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A A R O N

S U N S P R I N G

A machine which can compose paintings which are not surreal with a peculiar perception of colour.

A science fiction movie which is written by a machine learning algorithm.

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C R E A T I V I T Y I N A R T I F I C I A L L I F E

C O M P U TAT I O N A L C R E AT I V I T Y Creativity requires the hidden combination of unconscious ideas. There are four phases of creativity within which conscious and unconscious mind work figure to varying extents: preparation, incubation, illumination and verification. Similarly, the machine learning techniques which is a subset of artificial intelligence uses a similar approach to the training process of the computer. In the computational creativity chapter, artificial intelligence and neural network terms will be examined briefly to be able to propose a method to use in the interactive demonstration.

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MACHINE LEARNING

RECURRENT NEURAL NETWORKS HYPOTHESIS

W H A T I S A R T I F I C I A L I N T E L L I G E N C E ?

Artificial intelligence(often called AI) is the intelligent behaviour by computers which can mimic cognitive functions that humans do such as problem-solving, learning, being self-aware. With today's technology, machines can understand successfully human speech (such as Amazon's personal assistant Alexa), compete in high-level strategic games (such as Google DeepMind), drive autonomously(Tesla Cars), simulate and interpret complex data. Another approach which uses artificial intelligence as a tool is

Artificial Life which is a research domain where the systems and processes in nature are examined by researchers with the simulations, computer models and robotics.

Software, hardware or biochemical based approaches such as evolutionary algorithms, multi-agent systems, swarm intelligence allow studying living systems by researchers. Although Artificial Intelligence might seem useful in automation, medicine, military when it comes to the creative domain, it brings up many questions and challenges to the architects and designers. (page 25-26).

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

Industrials & Manufacturing

Business Intelligence

Productivity

Customer Management

Security & Risk

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Enterprise AI Companies HR & Talent

Engineering

Presented by

B2B Sales & Marketing

Data Science

Digital Commerce

Finance & Operations

Consumer Marketing

A I

S T A R T U P

I N D U S T R I E S

Distribution by Countries(Left), Distribution by Domains(Right)

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A U G M E N T E D C R E AT I V I T Y

Since AI is the broadest term which applies any technique that enables computers to mimic human intelligence using logic, if-then rules and decision trees algorithms, understanding about two types of artificial intelligence which are general and narrow are required to follow up this research with a hypothesis.

General AI as a term, is given for the generic idea of behaving exactly like a human. Today, it is only possible to talk about General AI in movies or scripted narratives. With the advances in computing power, it is expected to observe evolution in artificial intelligence as well. The prospect of what might become are gathered into four types for the AI. The first category, 'purely reactive' can perceive its environment and acts accordingly without having any idea of a wider word or memories such as IBM's DeepBlue or Google's Alpha Go. The second type, 'limited memory' can learn arbitrarily from the past information, plus what it just learned such as self-driving cars, chatbots or personal assistants.

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How does a neural network work? (Left), AI Pionners(Right).

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

Further up on the AI evolutionary ladder, 'theory of mind' can understand thoughts, emotions, motives such as iconic movie characters C-3PO and R2-D2 from Star Wars universe. Lastly, there is 'self-aware' type, the future generation of machines such as Eva in the 2015 movie Ex Machina.

On the other hand, the Narrow type of an AI reflects a more realistic point of view wherein specifically directed to a domain to perform a certain task such as managing the Big Data, finding patterns, means in industries.

U N D E R S T A N D I N G T H E M A C H I N E L E A R N I N G

One of the subsets of a narrow artificial intelligence is machine learning.

With the use of machine learning, it is possible for machines to guide themselves without human intervention or explicitly programmed. Similarly to the Poincare's theory on creativity, machine learning has five steps: collecting data, preparing the dataset, training a model, evaluating the model, improving performances. Machine learning, which will be referred later on as ML, works with three types of learning systems to learn from the collected data and make predictions.

As an addition to four stage model, ML can use the learned data to come up with better predictions for future patterns.

ognition in computer science, ML tasks fit into one of the three learning types: supervised, unsupervised, reinforcement learning.

Supervised Learning is a form of learning which depends on the database where the actual tag(or tags) is indicated. For instance, detecting pictures of buildings and bridges can be taught with a database with images tagged as ''building" or "bridge" and supervised learning algorithms. When the computer learns the difference, the ML algorithm can classify new data and predict labels on previously unseen images.

Unsupervised Learning is another type of learning where there is no need for labelling. However, it highly depends on a database with a vast of data of every aspect of an object. For example, it is possible to feed the algorithm with every cat video on Youtube without labelling them as cats, after the training, the model will give an estimate on the new visuals such as 80% cat.

Reinforcement Learning, on the other hand, works completely different.

Let's assume that the model will learn how to play chess. ML will only receive information about whether the game is lost or won. After playing many times, the programme will start to learn which moves can lead itself to the winning.

Originally as a branch of pattern rec-

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H O P F I E L D

N E T W O R K ( H N )

Is a network where every neuron is connected to every other neuron; it is an entirely entangled plate of spaghetti as even all the nodes function as everything. Each node is input before training, then hidden during training and output afterwards. These networks are often called associative memory because the converge to the most similar state as the input.

F E E N E U ( F F P E R

D F O R W A R D R A L N E T W O R K S O R F F N N ) A N D C E P T R O N S ( P )

Are very straightforward, they feed information from the front to the back (input and output, respectively).

Neural networks are often described as having layers, where each layer consists of either input, hidden or output cells in parallel.

THESIS

L A L I N K E Y VA N

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

3

R E S T R I C T E D B O L T Z M A N N M A C H I N E S ( R B M )

Are a better usable because they are more restricted? They do not trigger-happily connect every neuron to every other neuron but only connect every different group of neurons to every other group, so no input neurons are directly linked to other input neurons, and no hidden to hidden connections are made either..

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R E C U R R E N T N E U R A L N E T W O R K S ( R N N )

Have connections between passes, connections through time. Neurons are fed information not just from the previous layer but also from themselves from the previous pass. This argument means that the order in which is necessary to feed the input and train the network matters: feeding it “milk” and then “cookies” may yield different results compared to feeding it “cookies” and then “milk”.

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L O N G / S H O R T T E R M M E M O R Y ( L S T M )

Networks try to combat the vanishing / exploding gradient problem by introducing gates and an explicitly defined memory cell. These are inspired mostly by circuitry, not so much biology. Each neuron has a memory cell and three gates: input, output and forget. LSTMs have been shown to be able to learn complex sequences, such as writing like Shakespeare or composing primitive music.

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Architectures of the most used methods in Deep Neural Networks

I N F O G R A P H I C S O N D E E P N E U R A L N E T W O R K S

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31 THESIS

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

L A L I N K E Y VA N

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C R E A T I V I T Y I N A R T I F I C I A L L I F E

PA R T I C I PAT O RY DESIGN 33 As shown previously, the similarity between psychological, creative behaviour and computational creativity is inevitable. With the composing algorithms and learning types correctly according to perform specific tasks, teaching a machine to learn can be taught.

Consequently, this thesis hypothesises that computers can learn creative behaviour using RNN(Recurrent Neural Networks) machine learning techniques while building an LSTM(long short-term memory). Moreover, as an objective, this can be demonstrated with an interactive installation where the sound becomes a spatial event, a material phenomenon, an auditive phenomenon to explore the invisible public space. As a result, the designer, the user and space can collaborate and compose a participatory design. Despite the fact that collaborative design often involves the creation and observation of novel design processes, intended to move beyond conventional technical roles, participatory design techniques aim to involve the end-users in the development of that technology and design.

K E Y W O R D S

CO L L A B O R AT I O N

ROLE OF THE ARCHITECT LONG SHORT TERM MEMORY I N T E R AC T I V E S O U N D I N S TA L L AT I O N

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L A L I N K E Y VA N

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U N T I T L E D

Collage above represents how movement can turn into a sound.

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I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

C A L L A N D R E S P O N S E

The interactivity of this installation works almost the same way of a jazz improvisation "Call and Response". In music, a call and intervention is a succession of two distinct phrases usually played by different musicians, where the second phrase is heard as a direct commentary on or response to the first. Here, in the beginning, the user activates the space by moving around with a direct sound feedback. After ten seconds, the generated sound caused by the movement of the user will be saved into the database folder of the recurrent neural network in order the computer can respond with a next generation of the user's call. After the user hears the generated chords, he/she may respond to the machine with improvising his/her movements in the space to participate in the collaborative design. At this point, it is also important to state the fact that the designer's first calibrations such as the visible space layout, the sound and space relationships and the decision of the body part as a controller, are extremely important. According to the feedback from the overall sound and the user's preferences, the designer can make adjustments or additions to the overall interaction.

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W H AT

I S

M I D I ?

MIDI is a set of pure data or in other terms instructions. It contains a list of events and messages to a device how to generate a specific sound such as polyphonic key pressure, control change, pitch wheel, note on or off. The best way to describe MIDI is to explain what MIDI is not. MIDI is not music, does not contain actual sounds and lastly, it is not a digital sound format such as MP3 or FLAC. The importance of using MIDI files in the database for the experiments is the fact that MIDI files are not restricted with a specific instrument, and the pitch for vocal ranges can easily be changed.

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EXPERIMENTS Finding a good workflow, it is important to understand 37

software's capacities and variables on hand. This chapter explains the process and the experimentation part behind the Augmented Creativity while mentioning earlier designs, advantages and drawbacks.

K E Y W O R D S

INTERACTION

TENSORFLOW W E K I N ATO R

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THESIS

Recognising face locations to learn how to map the FM Sytnts with using Webcam

A U G M E N T E D C R E AT I V I T Y

V I R T U A L E X P E R I M E N T S

Virtual experiments ran to optimise variables and control them to achieve best results with the computer power which is used for this research. The general aim of the virtual experiments is to assure for a user an invisible user experience.

The workflow brings many different software and hardware components together.

1- Max MSP is used to extract location values from the captured skeleton of the user with the help of a Kinect sensor.

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2- Wekinator uses a machine learning algorithm to transform x,y,z locations into a sound.

3- Max Msp saves the sound file into the database.

4- Recurrent Neural Networks generates next sequences.

For the architecture of the neural network, Bob Sturms character based model - ABC notation, Doug Eck's blues improvisation and lastly Nicolas Boulanger-Lewandowski's polyphonic music generation researchers have been investigated. The lack of understanding the time signature, the se-

lection of coherent chords and the differences between a long hold and two beats within the same duration, created a challenge. However, since the main goal is to explore the possibilities of interaction between architect, computer and user, being able to pass the Turing Test was not the main concern unlike many researchers going on about computational creativity.

L A L I N K E Y VA N

T E N S O R F L O W

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Coding sample of an RNN(Top), Architecture of an RNN(Bottom)

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THESIS

R E S U LT S 1

2 41

3

4

5

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1 I N I T I A L

T E S T

Purpose of this experiment is to create a music/sound generation with using basic Rnn methods. Dependencies: Numpy, Pandas, Msgpack, Glob, Tensorflow, Tqdm, Python-Midi Since the duration is kept too short to prevent any crashes in the first runtime, there is no compositional element detected in the generated chords such as verse, chorus and tempo.

2 D U R AT I O N

Length of the songs in the data set and complexity have an impact on training time. It also makes the machine learn different patterns.

3 M O N O P H O N I C

42 TO

P O LY

This step failed every runtime due to the computational power. The given result ran with a very lower learning rate (0.1) and a 100 of hidden layers. The machine can understand; briefly, the concept of polyphony, however, can not generate compositions.

MacBook Pro(Early, 2015) with a 3,1 GHz Intel Core i7 Processor, 16 GB RAM, Iris Graphics 6100 1536 MB.

4 G E N R E

Creating a particular dataset helps to understand better the style of the music. However, in this case, the problem is avoiding overfitting which means learning specific parts of specific pieces instead of the overall patterns.

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5 L E A R N I N G

R AT E

To prevent the overfit, dropout method has been implemented to the code. It essentially means randomly removing some part of the hidden nodes from each layer during every training batch.

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DESIGN PROPOSALS O B J E C T

A S

A N

I N S T R U M E N T

The first design proposal investigates the portability aspect of the instrument. Using different textures and materials on the surfaces might create different effects on the each note such as the musical instrument accordion. The user plays while computer listens then, the computer will give an auditive response, and the cycle continues. The major disadvantage of this design besides computational power is the learning how to play it since the user will need time and practice to master it to become a part of the collaborative effort.

43 B O D Y

A S

A N

I N S T R U M E N T

The second proposal designed as a wearable. Using capacitive touch sensors will allow the body to use itself as a tool to create sounds such as clapping, grasping, snapping. It is easier to fabricate, no need for the user manual(intuition will work just fine), plus, the wearable can be used with other as well to enhance the physical awareness. Technical difficulties such as the necessity to be connected to a computer all times was a drawback for this program.

S P A C E

A S

A N

I N S T R U M E N T

The final design came out the idea of immersing sound and space as a whole experience. Here, the sound becomes a spatial event, a material phenomenon and it activates the space. Another interesting fact is the workflow bringing two types of learning together. With the correct power station or cloud, it is possible to have a real-time feedback with multiple users.

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L A L I N K E Y VA N

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FINAL WORKFLOW K I W E K M A T E N S

N E C T I N A T O R X M S P O R F L O W

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USER EXPERIENCE 45

W E K I N A T O R

UI / X - Body Recognition(Top), UX - Hand Recognition(Bottom).

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P E R F O R M A N C E S

46

Firas Safiyedden, Burak Paksoy, Elena Janeva(From top to bottom).

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Initial experiments were about smoothing the connection in the workflow to create invisible UI(user interface) with a natural UX (user experience) which was highly challenging due to the software, hardware and OSX compatibility. In the beginning, the aim was, detecting skeleton and train a supervised learning to entwine body positions with specific oscillator libraries in Processing 3. Kinect skeleton tracking in MacOs is not available to run with Processing.

To solve this issue mentioned above, a combination of Delicode_Ni Mate and MaxMSP is used. MaxMSP directed the received information to Wekinator then, again MaxMSP is utilised to listen to Fm synths and drum beats coming from the speakers and transform them into MIDI format.

Running many different applications at the same time is another challenge concerning the automation process and CPU. One of the interesting things to observe in the performances is the aggressive dialogues in between the user and the computer. When the user does not wait to listen to the whole generated composition, but

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he/she decides to react immediately or to dance while listening. In the end, the collection memories or dialogues gathered to compose a one-minute performance.

F U R T H E R D E V E L O P M E N T S

Investigation body language, auditive experience and the dialogue between users and the evolving environment is a particularly interesting topic. It makes the designer or architect question their changing role and the capacity to intervene and act as a catalyst. The interactive installation creates a conversational environment which enables to observe and be observed by machines to the three most important participants of the design process: the architect, the computer and lastly the user. The same workflow with different sensors (such as Kinect2 or Leap Motion) or changing space dynamics in real-time, can create a possibility to observe how body language changes if there is another user in this conversational environment.

THESIS

L A L I N K E Y VA N

F U R T H E R

D E V E L O P M E N T S

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Manimuplation Space(Left), Multiuser Detection(Right).

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THESIS

L A L I N K E Y VA N

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

CONCLUSION In this paper, it is presented a workflow and a method for responsive and collaborative design alongside the spatial aspects of sound. The system aims to compose and find alternative relationships between designer and space by implementing machine learning techniques in an interactive installation. Such as supervised learning techniques allow the designer has more control over the results while unsupervised machine learning techniques might flourish new ideas or new perspectives while generating sound with the learnings from the user/designer. Changing variables such as data sets and learning rate, have a higher impact of the composing innovative responses. New application domains enabled by this work range from the materiality of sound design to temporary structure designs. At the core of this research lies our motivation to advance the invention and practice of novel workflows and tools involving machine learning techniques, interaction design and sound as a spatial event. This research will enable a shift the overall approach to design process while challenging the designers to produce specific models of computational learning techniques according to the needs.

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APPENDIX activate_this = '/Users/lalinkeyvan/tensor/bin/activate_this.py' execfile(activate_this, dict(__file__=activate_this))

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import numpy as np import pandas as pd import msgpack import glob import tensorflow as tf import threading import pygame from tensorflow.python.ops import control_flow_ops from tqdm import tqdm ################################################### # In order for this code to work, you need to place this file in the same # directory as the midi_manipulation.py file and the correct directory i mport midi_manipulation def printit(): threading.Timer(50, printit).start() print "Hello,World!" def get_songs(path): files = glob.glob('{}/*.mid*'.format(path)) songs = [] for f in tqdm(files): try: song = np.array(midi_manipulation.midiToNoteStateMatrix(f)) if np.array(song).shape[0] > 50: songs.append(song) except Exception as e: raise e return songs

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songs = get_songs('Test') # These songs have already been converted from midi to msgpack print ("{} songs processed".format(len(songs))) ################################################### # HyperParameters # First, let's take a look at the hyperparameters of our model: lowest_note = midi_manipulation.lowerBound # the index of the lowest note on the piano roll highest_note = midi_manipulation.upperBound # the index of the highest note on the piano roll note_range = highest_note - lowest_note # the note range num_timesteps = 100 # This is the number of timesteps that we will create at a time n_visible = 2 * note_range * num_timesteps # This is the size of the visible layer. n_hidden = 100 # This is the size of the hidden layer num_epochs = 200 # The number of training epochs that we are going to run. For each epoch we go through the entire data set. batch_size = 100 # The number of training examples that we are going to send through the RBM at a time. lr = tf.constant(20, tf.float32) # The learning rate of our model # Variables: # Next, let's look at the variables we're going to use:

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x = tf.placeholder(tf.float32, [None, n_visible], name="x") # The placeholder variable that holds our data W = tf.Variable(tf.random_normal([n_visible, n_hidden], 0.01), name="W") # The weight matrix that stores the edge weights bh = tf.Variable(tf.zeros([1, n_hidden], tf.float32, name="bh")) # The bias vector for the hidden layer bv = tf.Variable(tf.zeros([1, n_visible], tf.float32, name="bv")) # The bias vector for the visible layer # Helper functions. # This function lets us easily sample from a vector of probabilities def sample(probs): # Takes in a vector of probabilities, and returns a random vector of 0s and 1s sampled from the input vector return tf.floor(probs + tf.random_uniform(tf.shape(probs), 0, 1)) # This function runs the Gibbs chain. We will call this function in two places: # - When we define the training update step # - When we sample our music segments from the trained RBM def gibbs_sample(k): # Runs a k-step gibbs chain to sample from the probability distribution of the RBM defined by W, bh, bv def gibbs_step(count, k, xk): # Runs a single gibbs step. The visible values are initialized to xk hk = sample(tf.sigmoid(tf.matmul(xk, W) + bh)) # Propagate the visible values to sample the hidden values xk = sample(tf.sigmoid(tf.matmul(hk, tf.transpose(W)) + bv)) # Propagate the hidden values to sample the visible values

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THESIS

return count + 1, k, xk # Run gibbs steps for k iterations ct = tf.constant(0) # counter [_, _, x_sample] = control_flow_ops.while_loop(lambda count, num_iter, *args: count < num_iter, gibbs_step, [ct, tf.constant(k), x]) #[_, _, x_sample] = control_flow_ops.While(lambda count, num_iter, *args: count < num_iter, # gibbs_step, [ct, tf.constant(k), x], 1, False) # This is not strictly necessary in this implementation, but if you want to adapt this code to use one of TensorFlow's # optimizers, you need this in order to stop tensorflow from propagating gradients back through the gibbs step x_sample = tf.stop_gradient(x_sample) return x_sample

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# Training Update Code # Now we implement the contrastive divergence algorithm. First, we get the samples of x and h from the probability distribution # The sample of x x_sample = gibbs_sample(1) # The sample of the hidden nodes, starting from the visible state of x h = sample(tf.sigmoid(tf.matmul(x, W) + bh)) # The sample of the hidden nodes, starting from the visible state of x_sample h_sample = sample(tf.sigmoid(tf.matmul(x_sample, W) + bh)) # Next, we update the values of W, bh, and bv, based on the difference between the samples that we drew and the original values size_bt = tf.cast(tf.shape(x)[0], tf.float32) W_adder = tf.multiply(lr / size_bt, tf.subtract(tf.matmul(tf.transpose(x), h), tf.matmul(tf.transpose(x_sample), h_sample))) bv_adder = tf.multiply(lr / size_bt, tf.reduce_sum(tf.subtract(x, x_sample), 0, True)) bh_adder = tf.multiply(lr / size_bt, tf.reduce_sum(tf.subtract(h, h_sample), 0, True)) # When we do sess.run(updt), TensorFlow will run all 3 update steps updt = [W.assign_add(W_adder), bv.assign_add(bv_adder), bh.assign_add(bh_adder)] # Run the graph! # Now it is time to start a session and execute the chart! with tf.Session() as sess: # First, we train the model # initialize the variables of the model init = tf.global_variables_initializer() sess.run(init) # Run through all of the training data num_epochs of times for epoch in tqdm(range(num_epochs)): for song in songs: # The songs are stored in a time x notes format. The size of each song is timesteps_in_song x 2*note_range # Here we reshape the songs so that each training example is a vector with num_timesteps x 2*note_range elements song = np.array(song)

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song = song[:int(np.floor(song.shape[0] / num_timesteps) * num_timesteps)] song = np.reshape(song, [song.shape[0] / num_timesteps, song.shape[1] * num_timesteps]) # Train the RBM on batch_size examples at a time for i in range(1, len(song), batch_size): tr_x = song[i:i + batch_size] sess.run(updt, feed_dict={x: tr_x}) # Now the model is fully trained, so let's make some music! # Run a gibbs chain where the visible nodes are initialized to 0 sample = gibbs_sample(1).eval(session=sess, feed_dict={x: np.zeros((2, n_visible))}) for i in range(sample.shape[0]): if not any(sample[i, :]): continue # Here we reshape the vector to be time x notes, and then save the vector as a midi file S = np.reshape(sample[i, :], (num_timesteps, 2 * note_range)) midi_manipulation.noteStateMatrixToMidi(S, "generated_chord_{}".format(i)) def play_music(music_file): clock = pygame.time.Clock() try: pygame.mixer.music.load(music_file) print "Music file %s loaded!" % music_file except pygame.error: print "File %s not foung!"% (music_file, pygame.get.error()) return pygame.mixer.music.play() while pygame.mixer.music.get_busy(): clock.tick(30) midi_file = '/Users/lalinkeyvan/Library/Mobile Documents/com~apple~CloudDocs/Iaac/2nd Year/3rd Term/Thesis/Music_Generator_Demo-master/generated_chord_1.mid' freq = 44100 # audio CD quality bitsize = -16 # unsigned 16 bit channels = 2 # 1 is mono, 2 is stereo buffer = 1024 # number of samples pygame.mixer.init(freq, bitsize, channels, buffer) pygame.mixer.music.set_volume(0.8) try: play_music(midi_file) except KeyboardInterrupt: # if user hits Ctrl/C then exit # (works only in console mode) pygame.mixer.music.fadeout(1000) pygame.mixer.music.stop() raise SystemExit printit()

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THESIS

GLOSSARY AI, Computer Science, "An ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximise its chance of success at some goal.â&#x20AC;?

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AI Build, Company, Ai Buildâ&#x201E;˘ is a London based company that is focusing on improving the way we make and experience our built environment., http://3dp.ai-build.com.

Algorithm, Computer Science, A self-contained step-by-step set of operations to be performed.

AlphaGo, Computer Program, A computer program developed by Google DeepMind in London to play the board game Go. https://en.wikipedia.org/wiki/AlphaGo Artificial Consciousness, "A field related to artificial intelligence and cognitive robotics. The theory of artificial consciousness aims to define that which would have to be synthesised were consciousness to be found in an engineered artefact", https://en.wikipedia.org/wiki/Artificial_consciousness Artificial Imagination, Computer Science, Also called Synthetic imagination or machine imagination is defined as the artificial simulation of human imagination by general or special purpose computers or artificial neural networks.

Autoencoder, ANN, "An artificial neural network used for unsupervised learning of efficient coding.[The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data. Typically it is used for unlabelled feature extraction or unsu-

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pervised learning.” https://en.wikipedia.org/wiki/Autoencoder Backpropagation, Computer Science, "an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction with an optimisation method such as gradient descent. It calculates the gradient of a loss function concerning all the weights in the network so that the gradient is fed to the optimisation method which in turn uses it to update the weights, in an attempt to minimise the loss function.”

Central Processing Unit (CPU) "the electronic circuitry within a computer that carries out the instructions of a computer program by performing the basic arithmetic, logical, control and input/output (I/O) operations specified by the instructions.”

Chinese Room Argument, Computer Science, "The Chinese room argument holds that a program cannot give a computer a "mind", "understanding" or "consciousness",[a] regardless of how intelligently or human-like the program may make the computer behave.” https://en.wikipedia.org/wiki/Chinese_room Cluster Analysis, Algorithm, The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

Convolutional Neural Networks (CNN), Computer Science, A type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organisation of the animal visual cortex.

Cognitive Architecture, Computer Science, "Theory about the structure of the human mind. One of the main goals of a cognitive architecture is to summarise the various results of cognitive psychology in a comprehensive computer model. However, the results need to be in a formalised form so far that they can be the basis of a computer program. The formalised models can be used to refine a comprehensive theory of cognition further, and more immediately, as a commercially usable model. Successful cognitive architectures include ACT-R (Adaptive Control of Thought, ACT), SOAR and OpenCog.”, https://en.wikipedia.org/wiki/Cognitive_architecture Cost, Computer Science, "Cost = Generated Output - Actual Output to have better accuracy in AI, the cost must be lowest as it can be.”

Cybernetics, Computer Science, "A transdisciplinary approach for exploring regulatory systems – their structures, constraints, and possibilities.”, https://en.wikipedia.org/wiki/Cybernetics

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Data Mining, Computer Science, "The computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.”

Deep Belief Networks (DBN), Computer Science, "a generative graphical model, or a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.“ Deep Learning, Computer Science, "a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.”, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press."

Donald Hebb, Psychologist, "In the area of neuropsychology, where he sought to understand how the function of neurons contributed to psychological processes such as learning. Father of neuropsychology and neural networks.”,

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The Emotion Machine, Book, "By examining these different forms of mind activity, Minsky says, we can explain why our thought sometimes takes the form of carefully reasoned analysis and at other times turns to emotion. He shows how our minds progress from simple, instinctive kinds of thought to more complex forms, such as consciousness or self-awareness”, https://www.amazon.com/Emotion-Machine-Commonsense-Artificial-Intelligence/dp/0743276647 an Evolutionary architecture, Book, "The role of the architect here, I think, is not so much to design a building or city as to catalyse them: to act that they may evolve.’ – Gordon Pask in his foreword.”

Genetic Algorithm, Computer Science, "metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

Genetic algorithms are commonly used to generate high-quality solutions to optimisation and search problems by relying on bio-inspired operators such as mutation, crossover and selection.”, Gradient, Computer Science, The term is used to explain the rate at which Cost changes concerning weight or biases, Gradient Clipping, Computer Science, "Gradient Clipping is a technique to prevent exploding gradients in very deep networks, typically Recurrent Neural Networks.”,

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Graphics Processing Unit (GPU), "occasionally called visual processing unit (VPU), is a specialised electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to display.”, Hebbian Engrams, Neuroscience, "If the inputs to a system cause the same pattern of activity to occur repeatedly, the set of active elements constituting that pattern will become increasingly strongly inter-associated. That is, each element will tend to turn on every other element and (with negative weights) to put out the items that do not form part of the pattern. To put it another way, the model as a whole will become 'auto-associated'. We may call a learned (auto-associated) pattern an engram.",Neural Networks,"Cells that fire together, wire together."

IPL, Programming Language, "Information Processing Language, is a programming language created by Allen Newel.”

Johari Window, Psychology, "Technique used to help people better understand their relationship with themselves and others, which was created by psychologists. https://en.wikipedia.org/wiki/Johari_window John Searle, Video, "Slusser Professor of Philosophy at the University of California, Berkeley.",Consciousness in Ai, https://www.youtube.com/watch?v=rHKwIYsPXLg John McCarthy, Computer Scientist who discussed “Chinese Room Experiment”, Karl Sims, "A computer graphics artist and researcher, who is best known for using particle systems and artificial life in computer animation.”,Algorithms, http://karlsims.com Ken Robinson, Video, Sir Ken Robinson makes an entertaining and profoundly moving case for creating an education system that nurtures (rather than undermines) creativity., https://www.youtube.com/watch?v=iG9CE55wbtY Keras, Library, "Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.”, https://keras.io/

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Logistic Regression, Mathematics, "a mathematical model used in statistics to estimate (guess) the probability of an event occurring having been given some previous data. Logistic Regression works with binary data, where either the event happens (1) or the event does not happen (0). So given some feature x, it tries to find out whether some event y happens or not. So 'y' can either be 0 or 1. In the case where the event happens, y is given the value 1. If the event does not happen, then y is given the value of 0.”, https://simple.wikipedia.org/wiki/Logistic_Regression Long Short-Term Memory (LSTM), ANN, A recurrent neural network (RNN) architecture (an artificial neural network) proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber to suggest a solution to backpropagation problems. LSTM is a gating unit to forget an input and remember it when it becomes necessary., Machine Learning, Computer Science, A subfield of computer science that gives computers the ability to learn without being explicitly programmed,

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Neural Network, Computer Science, a computational approach which is based on an extensive collection of neural units loosely modelling the way the brain solves problems with large clusters of biological neurons connected by axons., Naive Bayes Classifier, Computer Science, A family of a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions between the features., https://en.wikipedia.org/wiki/Naive_Bayes_classifier Parsing, Computer Science, "In some machine translation and natural language processing systems, written texts in human languages are parsed by a computer program. Human sentences are not easily parsed by programs, as there is substantial ambiguity in the structure of human language, whose usage is to convey meaning (or semantics) amongst a potentially unlimited range of possibilities but only some of which are germane to the particular case. So an utterance "Man bites dog" versus "Dog bites man" is definite on one detail but in another language might appear as "Man dog bites" with a reliance on the larger context to distinguish between those two possibilities if indeed that difference was of concern. It is hard to prepare formal rules to describe informal behaviour even though it is clear that some rules are being followed.”, Perceptron, Algorithm, "the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).”

Principal Component Analysis (PCA), A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

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Recurrent Neural Network (RNN), ANN, "a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behaviour. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition.”, https://en.wikipedia.org/wiki/Recurrent_neural_network Recursive Neural Tensor Network (RNTN), ANN, "A recursive neural network is created by applying the same set of weights recursively over a differentiable graph-like structure, by traversing the structure in topological order. Such networks are typically also trained by the reverse mode of automatic differentiation.They were introduced to learn distributed representations of structure, such as logical terms. A special case of recursive neural networks is the RNN itself whose structure corresponds to a linear chain. Recursive neural networks have been applied to natural language processing.The Recursive Neural Tensor Network uses a tensor-based composition function for all nodes in the tree.”

Reinforced learning, Computer Science, "an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment to maximise some notion of cumulative reward.”

Restricted Boltzmann Machine (RBM), ANN, a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs., https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine Ruairi Glynn, "practices as an installation artist and directs the Interactive Architecture Lab at the Bartlett School of Architecture, University College London.”, "http://www.ruairiglynn.co.uk Self-Organizing Map, ANN, "A type of Artificial Neural Network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretised representation of the input space of the training samples, called a map. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighbourhood function to preserve the topological properties of the input space.”, Strong AI, Computer Science, The appropriately programmed computer with the right inputs and outputs would thereby have a mind in the same sense human beings have minds.

Supervised Learning, Computer Science,

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The machine learning task of inferring a function from labelled training data.

Support Vector Machine (SVM), Computer Science, "supervised learning models with associated learning algorithms that analyse data used for classification and regression analysis.

Given a set of training examples, each marked as belonging to one or the other of two categories; an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.â&#x20AC;?, https://en.wikipedia.org/wiki/Support_vector_machine to Train, Information Science, Finding predictive relationships from data is a critical task.

Unsupervised Learning, Computer Science, The machine learning task of inferring a function to describe hidden structure from unlabeled data.

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Weak AI, Computer Science, A non-sentient artificial intelligence that is focused on one narrow task.

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REFERENCES “Choice of sources can shield extreme bias behind a façade of objectivity.” ~ Noam Chomsky T H E O R E T I C A L

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B O O K S

Minsky, Marvin Lee. The Society of Mind. New York: Simon & Schuster Paperbacks, 2007.

Boden, Margaret A. The creative mind: myths and mechanisms.

London: Routledge, 2005.

McCormack, Jon. Computers and Creativity. Berlin: Springer, 2012.

Bourg, David M., and Glenn Seemann. AI for Game Developers.

Sebastopol CA: O’Reilly, 2004.

Negroponte, Nicholas. The Architecture Machine. Cambridge, MA: M.I.T. Press, 1970.

Carpo, Mario. The Alphabet and The Algorithm. Cambridge, MA: MIT Press, 2011.

Parisi, Luciana. Contagious Architecture: Computation, Aesthetics, and Space. Cambridge, MA: MIT Press, 2013.

Partridge, Derek, and Jon Rowe.

Computers and Creativity. Oxford: Intellect, 1994.

Gelernter, David Hillel. The Muse in the Machine: Computerising the Poetry of Human Thought. New York: Free Press, 1994.

JONES, JEFF. From Pattern Formation to Material Computation.

Place of Publication Not Identified: SPRINGER INTERNATIONAL PU, 2016.

Minsky, Marvin. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of The Human Mind. New York: Simon & Schuster, 2006.

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Pereira, Francisco Câmara. Creativity and Artificial Intelligence: A Conceptual Blending Approach.

Berlin:Mouton De Gruyter, 2007.

Schank, Roger C & Cleary, Chip (1995) Making Machines Creative.

In: S Smith, T B Ward & R A Finke (eds) The Creative Cognition Approach. MIT Press. 229-247.

THESIS

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“The Nature of Code.” The Nature of Code. Accessed December 06, 2016. http://natureofcode.com/book/ chapter-10-neural-networks/.

Torres, Jordi. First Contact with TensorFlow: Get Started with Deep Learning Programming. Barcelona: Universitat Politècnica De Catalunya, Centro Nacional De Supercomputación, 2016.

Veale, Tony, Kurt Feyartes, and Charles Forceville. Creativity and the Agile Mind: A Multi-disciplinary Study of a Multi-faceted Phenomenon. Berlin: W. De Gruyter, 2013.

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r2d3.us/visual-intro-to-machinelearning-part-1/.

“Creativity and Unpredictability.”

Creativity and Unpredictability.

Accessed December 06, 2016. http://psych.utoronto.ca/users/ reingold/courses/ai/cache/boden. html.

“Machine Learning for Artists.”

Machine Learning for Artists.

Accessed December 06, 2016. https://ml4a.github.io/index/.

“Psychoanalysis and Creativity.”

Psychoanalysis and Creativity.

Accessed December 06, 2016. http://www.freudfile.org/ psychoanalysis/papers_9.html.

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“Sound DataBase.” http://wiki. musicbrainz.org/About.

J O U R N A L S

“Computers at the Dawn of Creativity.” Planet Science: Networld: Technophile. Accessed December 06, 2016. http://psych.utoronto.ca/ users/reingold/courses/ai/cache/ai_ creativity.html.

@protocol96. “Protocol 96 | Codementor: An Introduction to Python Machine Learning with Perceptrons.” Protocol 96. 2016.

Accessed December 05, 2016. https://protocol96.com/feeds/ codementor-an-introduction-topython-machine-learning-withperceptrons/.

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Flaherty, Alice W. “FRONTOTEMPORAL AND DOPAMINERGIC CONTROL OF IDEA GENERATION AND CREATIVE DRIVE.” The Journal of Comparative Neurology. 2005. Accessed December 06, 2016. https://www. ncbi.nlm.nih.gov/pmc/articles/ PMC2571074/.

M A G A Z I N E S

“Creativity: Method or Magic?”

Creativity: Method or Magic?

Accessed December 06, 2016. http://psych.utoronto.ca/users/ reingold/courses/ai/cache/harnad. creativity.html.

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W E B

P A G E S

“A Visual Introduction to Machine Learning.” A Visual Introduction to Machine Learning. Accessed December 06, 2016. http://www.

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P A G E S

Ref:”Artificial Intelligence on Flipboard | Machine Learning, Computer Science and Algorithms.”

Flipboard | Machine Learning, Computer Science and Algorithms.

THESIS

Accessed December 05, 2016. https://flipboard.com/topic/ artificialintelligence.

“Can We Open the Black Box of AI?” Nature.com. Accessed December 06, 2016. http://www.nature.com/news/ can-we-open-the-black-box-ofai-1.20731.

Fiebrink, R. (n.d.). Retrieved May 04, 2017, from http://www.wekinator. org/.

V I D E O S

Etlinger, Susan. “What we do with all this big data?” Talk, TED Talk TED@ IBM, San Francisco, 23 September 2014.

S U P P O R T T H E O R I E S B O O K S

Eagleman, David. Incognito: The Secret Lives of the Brain. New York: Vintage Books, 2012.

Negroponte, Nicholas. Soft Architecture Machines. Cambridge, MA: MIT Press, 1975.

Veale, Tony. Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity.

2012.

MAGAZINES and JOURNALS “ArXiv.org Cs ArXiv:1311.1213.”

[1311.1213] A Big Data Approach to Computational Creativity. Accessed December 06, 2016. https://arxiv. org/abs/1311.1213.

“Precis of “THE CREATIVE MIND: MYTHS AND MECHANISMS” London: Weidenfeld & Nicolson 1990 (Expanded Edn., London:

L A L I N K E Y VA N

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Abacus, 1991.).” Precis of “The Creative Mind: Myths and Mechanisms” Accessed December 06, 2016. http://psych.utoronto.ca/ users/reingold/courses/ai/cache/ bbs.boden.html.

J O U R N A L S

Banavar, Gruduth,IBM.”Learning to Trust Artificial Intelligence Systems.”San Francisco, September 2016.

“The Further Exploits of AARON, Painter.” The Further Exploits of AARON, Painter. Accessed December 06, 2016. http://psych. utoronto.ca/users/reingold/courses/ ai/cache/cohen.html.

Emotions from text: machine learning for text-based emotion prediction.”Vancouver, British Columbia, Canada — October 06 08, 2005.

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P A G E S

“Automation Threatens to Make Graphic Designers Obsolete.” Eye on Design. 2016. Accessed December 06, 2016. http://eyeondesign.aiga. org/automation-threatens-to-makegraphic-designers-obsolete/.

Copeland, Michael. “The Difference Between AI, Machine Learning, and Deep Learning? | NVIDIA Blog.” The Official NVIDIA Blog. 2016.

Accessed December 06, 2016. https://blogs.nvidia.com/ blog/2016/07/29/whats-differenceartificial-intelligence-machinelearning-deep-learning-ai/.

Creativesomething. “The Differences between Imagination, Creativity, and Innovation.” Creative Something.

2015. Accessed December 06, 2016. http://creativesomething.net/ post/119280813066/thedifferences-between-imaginationcreativity.

“IBM Research: Computational Creativity.” IBM Research: Computational Creativity. Accessed December 06, 2016. http://www. research.ibm.com/cognitivecomputing/computational-creativity. shtml.

Mybridge. “Algorithm Top 10 Articles (v.November).” Mybridge for Professionals. 2016. Accessed December 05, 2016. https://medium. mybridge.co/algorithm-top-10articles-v-november-e73cba2fa87e. @OU_com. “15 Scientific Facts About Creativity OnlineUniversities.com.”

OnlineUniversities.com. 2016.

Accessed December 06, 2016. http://www.onlineuniversities. com/15-scientific-facts-aboutcreativity.

“The Ultimate Idea.” Imagination Engines, Inc., Home of the Creativity Machine. Accessed December 06, 2016. http://www.imaginationengines.com/iei_cm.php.

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P A G E S

“A.I. Architecture Intelligence.” Future vvArchitecture Platform. Accessed December 06, 2016. http:// futurearchitectureplatform.org/ news/28/ai-architectureintelligence/.

“Can Robots Be Creative? - Gil Weinberg.” TED-Ed. Accessed December 06, 2016. http://ed.ted. com/lessons/can-robots-be-creativegil-weinberg.

“A Machine Learning Primer For BT Professionals.” A Machine Learning Primer For BT Professionals.

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Searle, John.”Consciousness in Artificial Intelligence.”Talks at Google, SanFrancisco, December 2015.

Burgess, Matt. “Google’s AI Has Written Some Amazingly Mournful Poetry.” WIRED UK. 2016. Accessed December 06, 2016. http://www.

V I D E O S

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wired.co.uk/article/google-artificialintelligence-poetry.

“Experiments.” Art and Cultural Experiments. Accessed December 06, 2016. https://artsexperiments. withgoogle.com/.

“Evolved Virtual Creatures.” Evolved Virtual Creatures. Accessed December 06, 2016. http://www. karlsims.com/evolved-virtualcreatures.html.

“Quick, Draw!” Quick, Draw!

Accessed December 06, 2016. https://quickdraw.withgoogle.com/.

@newscientist. “Google’s Neural Networks Invent Their Own Encryption.” New Scientist. Accessed December 06, 2016.- https://www. newscientist.com/article/2110522googles-neural-networks-inventtheir-own-encryption/.

06, 2016. https://vimeo. com/163739005.Ovesdotter Alm, Cecilia. Roth, Dan. Sproat, Richard.” “(50X) Autonomously Folding a Pile of 5 Previously-unseen ...” Accessed December 6, 2016.

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(n.d.). Retrieved May 04, 2017, from https://aiexperiments.withgoogle. com/ Fiebrink, R. (n.d.). Retrieved May 04, 2017, from http://www.wekinator. org/.

T H E

B O O K L E T

I M A G E S

Dothemutation. "VENEZIA 02.13. LA MUTAZIONE." DOTHEMUTATION. January 24, 2014. Accessed September 26, 2017. https://dothemutation. wordpress.com/2013/01/29/venezia-02-13-la-mutazione/.

V I D E O S

Wakefield, Jane. “Robots Face New Test of Creative Abilities.” BBC News. 2014. Accessed December 06, 2016. http://www.bbc.com/ news/technology-30144069.

Wernert, Gregory T., and Thomas Reiner. Music and Emotions: An Investigation into the Musical Representation of the Identified Emotional Content of Poetic Text.

Master’s thesis, 2009. @futurism. “Controversial New AI Can Tell Whether or Not You’re A Criminal.” Futurism. 2016. Accessed December 05, 2016. https:// futurism.com/controversial-new-aican-tell-whether-or-not-youre-acriminal/. @futurism. “Partnership on AI: Tech Giants Unite to Develop Synthetic Intelligence for Humanity.” Futurism.

2016. Accessed December 05, 2016. https://futurism.com/ partnership-on-ai-tech-giants-uniteto-develop-synthetic-intelligencefor-humanity/.

Arstechnicavideos. “Sunspring | A Sci-Fi Short Film Starring Thomas Middleditch.” YouTube. 2016.

Accessed December 06, 2016. http://www.youtube.com/ watch?v=LY7x2Ihqjmc.

“Building “self-aware” Robots.” Hod Lipson: Building “self-aware” Robots | TED Talk | TED.com. Accessed December 06, 2016. http://www. ted.com/talks/hod_lipson_builds_ self_aware_robots.

“Computers Are Learning to Be Creative.” Blaise Agüera Y Arcas: How Computers Are Learning to Be Creative | TED Talk | TED.com.

Accessed December 06, 2016. https://www.ted.com/talks/blaise_ aguera_y_arcas_how_computers_ are_learning_to_be_creative.

“E-David Robot Painting.” Vimeo.

Accessed December 06, 2016. https://vimeo.com/68859229.

“Grasshopper Machine Learning Test.” Vimeo. Accessed December

Liquid Selves. Accessed September 27, 2017. http://www.karlsims.com/ liquid-selves.html.

"Portrait Of Woman In Red Blouse - A Picasso 1930s art wallpaper." Art Wallpapers and Images. Accessed September 27, 2017. http://art.ayay. co.uk/art/picasso/1930s/portrait-ofwoman-in-red-blouse/.

Ge, Linda. "Inside ‘Westworld’s’ Elaborate, Monochromatic Opening Credit Sequence." TheWrap. October 03, 2016. Accessed September 27, 2017. http://www.thewrap.com/inside-westworlds-elaborate-monoc h ro m a t i c - o p e n i n g - c re d i t - s e quence/.

"Image." Flat Earth. Accessed September 27, 2017. http://forums.sherdog.com/threads/conor-fans-arelike-people-who-dont-believe-thee a r t h - i s - f l a t - s t u p i d - a n d - a r ro gant.3516539/.

Wilde, Robert. "Think Turing Was the First? Discover Charles Babbage's Analytical Engine." ThoughtCo. Ac-

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cessed September 27, 2017. https:// www.thoughtco.com/first-computer-charles-babbages-1221836.

"Harold Cohen Art Original Unique AARON Generated Computer Art Signed 1982 22x30 In Black White."

Wilbere ~ For vintage collectibles, antiques, records, china, crystal & more.

Accessed September 28, 2017. h t t p : // w i l b e r e . e c r a t e r . com/p/11570922/harold-cohen-art-original-unique.

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Popova, Maria. "The Creative Architect: Inside Psychology’s Most Ambitious and Influential Study of What Makes a Creative Person." Brain Pickings. January 14, 2017. Accessed September 28, 2017. https://www. brainpickings.org/2016/12/29/ the-creative-architect/.

Byford, Sam. "DeepMind founder Demis Hassabis on how AI will shape the future." The Verge. March 10, 2016.

Accessed September 28, 2017. h t t p s : // w w w . t h e v e r g e . com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai.

RLLberkeley. "(50X) Autonomously folding a pile of 5 previously-unseen towels." YouTube. March 17, 2010.

Accessed September 28, 2017. h t t p s : // w w w . y o u t u b e . c o m / watch?v=gy5g33S0Gzo. DuckRubaDUb. "PICTIONARY WITH AI | Quick, Draw! With Google." YouTube. November 17, 2016. Accessed September 28, 2017. https://www. youtube.com/watch?v=suqv140ckEo. Arcas, Blaise Agüera y. "Cómo las

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computadoras aprenden a ser creativas." Blaise Agüera y Arcas: Cómo las computadoras aprenden a ser creativas | TED Talk. Accessed September 28, 2017. https://www.ted.com/ talks/blaise_aguera_y_arcas_how_ computers_are_learning_to_be_creative/up-next?language=es.

"E-David." EDavid. Accessed September 28, 2017. http://graphics.uni-konstanz.de/eDavid/.

"Harold Cohen's Assisted Artistry."

The New York Times. May 07, 2016.

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"Karl Sims home page." Karl Sims home page. Accessed September 28, 2017. http://www.karlsims.com/.

Sathe, Gopal. "Prisma Review." NDTV Gadgets360.com. May 16, 2017. Accessed September 28, 2017. http:// gadgets.ndtv.com/apps/reviews/ prisma-app-turns-the-most-boringphotos-into-striking-paintings-859151.

"Google explores how machine learning could navigate the history of art."

CDM Create Digital Music. November 15, 2016. Accessed September 28, 2017. http://cdm.link/2016/11/ google-explores-machine-learning-navigate-history-art/.

London, EAVI Goldsmiths University of. "MetaGestureMusic." Meta Gesture Music CD. Accessed September 28, 2017. http://mgm.goldsmithsdigital.com/cd/.

THESIS

"Performative Ecologies."

Ruairi Glynn. November 04, 2015. Accessed September 28, 2017. http:// www.ruairiglynn.co.uk/portfolio/performative-ecologies/.

Nordine, Michael. "Watch ‘Sunspring,’ a Short Sci-Fi Film Written by An Artificial-Intelligence Algorithm." IndieWire. June 09, 2016. Accessed September 28, 2017. http://www.indiewire.com/2016/06/watch-sunspring-sci-fi-artificial-intelligence-ai-written-1201687033/.

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Knight, Will. "The Man with a Plan to Make AI More Human." MIT Technology Review. March 27, 2017. Accessed September 28, 2017. https:// www.technologyreview. com/s/544606/can-this-man-makeai-more-human/.

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L A L I N K E Y VA N

I N S T I T U T E F O R A D VA N C E D A R C H I T E C T U R E O F C ATA LO N I A

com/pin/396598310910702025/.

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Search Code by icon 54 from the Noun Projectw chart by Nirbhay from the Noun Project

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L O G O S

creative thinking by Akin DEMIR from the Noun Project Idea by ProSymbols from the Noun Project "Colored round infographic with four steps." Freepik. Accessed September 28, 2017. http://www.freepik.com/ free-vector/colored-round-infographic-with-four-steps_1134348. htm.

Artificial Intelligence by Oksana Latysheva from the Noun Project

W W W. L A L I N K E Y VA N . C O M /A U G M E N T E D - C R E AT I V I T Y/

A U G M E N T E D

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A U T H O R :

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