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INTRODUCTION
The advent of Artificial Intelligence (AI) has precipitated a paradigm shift in the creative
landscape, catalyzing a convergence of technology and artistic expression. This
exploration delves into the profound impact of AI technologies on various creative
domains, unraveling the intricate tapestry of their influence in fields such as music, art,
cinema, and beyond. As we navigate the intersection of AI and creativity, this
examination seeks to illuminate the transformative potential, challenges, and ethical
considerations underpinning this dynamic relationship.
At its core, AI represents a technological frontier that has begun to redefine the boundaries
of creativity. Chapter 1 lays the groundwork by providing clear definitions of AI and
creativity, establishing a comprehensive understanding of the terms within the context of
artistic endeavors. This foundational exploration is essential for comprehending the
subsequent chapters, which delve into the nuanced ways in which AI is reshaping and
augmenting traditional creative paradigms.
Chapter 2 directs its focus toward the specific manifestations of AI prevalent in creative
industries, particularly on generative AI. As a driving force behind machine-generated
content, generative AI holds significant promise for revolutionizing the creative process.
This chapter scrutinizes the technical intricacies of these AIs and investigates their
applications across diverse creative fields, ranging from music composition to visual art
and cinematic storytelling. By unveiling the role of generative AI in these contexts, we
aim to understand how these technologies are becoming integral collaborators in the
creative process.
Chapter 3 navigates the ethical considerations and challenges that arise with integrating
AI into creative endeavors. It addresses the evolving landscape of copyright, exploring
the implications of machine-generated content and the ethical quandaries surrounding
authorship. This chapter aims to provide a nuanced perspective on the ethical
considerations that accompany the coexistence of human creativity and machine-
generated art.
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The fourth and final chapter presents an empirical project where seven creatives actively
engaged with AI in their respective fields were interviewed. The culmination of these
interviews has been transformed into a podcast, disseminated on platforms such as
Spotify, Amazon Music, and Google Podcasts, accompanied by an Instagram profile
dedicated to the podcast. The project seeks to scrutinize how AI is dynamically shaping
creative realms, validating or challenging the observations made in Chapter 2 through the
lived experiences and insights of the interviewees.
As we embark on this exploration, the thesis aims to contribute to the evolving discourse
surrounding AI and creativity, offering insights into the transformative potential and
challenges posed by these technologies in the realms of artistic expression and innovation.
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CHAPTER 1
Introduction to Artificial Intelligence and
Creativity
The primary objective of emerging technologies is generally to simplify, enhance
precision, expedite, or reduce costs in a particular process. In certain instances, they
even grant us the ability to undertake tasks or generate creations that were once deemed
unattainable. Among the scientific methodologies making remarkable strides for
practical applications in recent years, AI stands out prominently. AI techniques
empower machines to execute tasks that traditionally demand a level of intelligence
resembling humans. Thanks to recent advancements in high-performance computing and
expanded data storage capabilities, AI technologies have gained significant
empowerment, leading to their widespread adoption across a myriad of applications
including the creative fields.
AI stands as a swiftly progressing yet often misconstrued technological frontier.
Varying definitions of AI, ranging from its portrayal as a mere tool to that of an
intelligent entity, create conflicting viewpoints regarding its potential and perils. The
perplexity surrounding AI's essence finds its roots not only in its novelty but also in its
character. Now that AI is used as a creative tool, the discussions and skepticism are at
their peak. This chapter aims to clarify what AI is, how it works, and its history but also
analyzes the sub-branch of this discipline. We will then introduce the topic of creativity
and the relationship between the two main focuses of this thesis which will be explored
in more detail in the next chapter.
1.1. Artificial Intelligence
First of all, AI systems are becoming increasingly pervasive and intertwining with our
daily lives. However, the growing prevalence of commonplace AI-driven applications,
though seemingly familiar, can obscure the intricate complexity underpinning their
operation. The reason why AI has multiple definitions comes from the fact that it is a
multifaceted discipline that has significantly contributed to the advancement of
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computer science over the years. It draws insights from an array of disciplines including
philosophy, neurology, mathematics, psychology, cybernetics, cognitive sciences, etc.
For instance, philosophy has yielded insights into learning and reasoning theories,
delving into the idea that the operation of physical systems mirrors the workings of the
mind. From the realm of mathematics, emerged the theory of computation, decision
theory, logical reasoning, and the realm of probabilities. Psychology, in turn, has
introduced investigative methods to explore the intricacies of the human mind,
systematically giving rise to subsequent conceptual frameworks. Linguistics has
contributed models that elucidate the structures and meanings inherent in natural
languages. Importantly, computer science has provided the essential programming
techniques that have transformed AI from a concept into a tangible reality. This
collaborative interplay of diverse disciplines underscores the multidimensional nature of
AI's development. Each field has offered unique intellectual contributions, collectively
propelling AI's evolution and solidifying its significance in modern technological
landscapes. Also, rather than being conceived as a solitary intangible asset, AI presents
itself as both a product and a process innovation. It converges with customary intangible
assets like software, patents, and know-how, while also intersecting with other
intangibles propelled by the internet. This amalgamation extends to encompass input
data garnered from expansive sources such as big data and IoT sensors
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.
To unravel the intricacies of this technology, a preliminary understanding of its core
tenets is beneficial. We embark on this journey by first elucidating the scientific
domains at play. As we can see in Fig. 1, the main field is the realm of computer science,
encompassing all facets associated with computer design and utilization. Contained
within this realm is the domain of data science, an arena dedicated to crafting systems
and models capable of distilling pertinent insights from vast datasets. At the heart of this
expansive field lies AI, focused on empowering machines to execute cognitive and
intelligent actions akin to those performed by the human brain. As we see in Fig. 1 AI
contains the branch of Machine Learning that focuses on the use of data and algorithms to
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“IoT sensors are pieces of hardware that detect changes in an environment and collect data. They’re the
pieces of an IoT ecosystem that bridge the digital world to the physical world. IoT sensors may detect
things like temperature, pressure, and motion, and if they are connected to a network, they share data with
the network.” https://www.zipitwireless.com/blog/what-are-iot-sensors-types-uses-and-examples
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imitate the way that humans learn. Deep Learning is also a branch of AI, but in this
case, it is focused on developing computer systems similar to the human brain. We will
analyze AI and the two branches mentioned later on in this chapter.
Fig. 1 - the positioning of AI 1
An incisive examination of concrete AI implementations promptly reveals a diverse
spectrum of technical attributes, objectives, and scopes. This multiplicity underscores
the contextual subjectivity inherent in the term "AI," as it takes on various meanings for
different individuals and contexts.
The present chapter thus assumes the role of a compass, guiding us towards unraveling
the query that resonates: "What truly defines AI?”.
1.1.1 Definitions, elements, and factors of Artificial Intelligence
Before diving into an explanation of AI, it is crucial to contemplate its underlying
essence. To grasp this, we must first dissect the concept of "intelligence." According to
the theory of multiple intelligences, expounded in the Official Textbooks for Huawei
“ICT Academy's Artificial Intelligence Technology”
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, human intelligence can be
categorized into seven distinct types: Linguistic, Logical-Mathematical, Spatial, -
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Huawei Technologies Co., Ltd., 2023
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Kinesthetic, Musical, Interpersonal, and Intrapersonal intelligence, plus Naturalist
Intelligence:
a) Linguistic Intelligence: it encompasses the adeptness to effectively articulate one's
thoughts through spoken or written language, comprehend others' words and texts,
and adeptly navigate the intricacies of phonology, semantics, and grammar. This
skill set extends to managing verbal thinking and decoding the nuanced
connotations of linguistic expressions. Individuals endowed with robust linguistic
intelligence may excel in roles such as politicians, activists, hosts, attorneys, public
speakers, editors, writers, journalists, and educators.
b) Logical-Mathematical Intelligence: it denotes the ability to calculate, quantify,
reason, summarize, classify, and perform intricate mathematical operations. This
attitude is characterized by a sensitivity to abstract concepts, such as logical patterns,
relationships, statements, and functions. Individuals possessing strong logical-
mathematical intelligence are well-suited for careers in scientific research,
accounting, statistics, engineering, computer software development, and similar
fields.
c) Spatial Intelligence: it involves the capacity to accurately perceive visual space
and the objects within it and represent these visual perceptions through paintings
and graphs. Those with heightened spatial intelligence exhibit keen sensitivity to
visual aspects such as color, line, shape, and form. Professions well-suited for
individuals with strong spatial intelligence include interior designers, architects,
photographers, painters, pilots, and more.
d) Bodily-Kinesthetic Intelligence: it pertains to the skill of utilizing one's entire body
to convey thoughts and emotions, and using hands and tools to create products or
manipulate objects. This intelligence encompasses specific physical abilities such as
balance, coordination, agility, strength, flexibility, speed, and tactile acumen. Career
avenues for those with robust bodily-kinesthetic intelligence encompass athletes, actors,
dancers, surgeons, jewelers, mechanics, and others.
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e) Musical Intelligence: it involves the capacity to discern pitch, tone, melody,
rhythm, and timbre. Individuals with heightened musical intelligence display
heightened sensitivity to these musical elements and often excel in performing,
composing, and analyzing music. Potential vocational paths for those with strong
musical intelligence encompass singers, composers, conductors, music critics, and
piano tuners, among others.
f) Interpersonal Intelligence: it embodies the ability to understand and effectively
interact with others. Those with pronounced interpersonal intelligence can adeptly
discern the moods and temperaments of others, empathize with their feelings and
emotions, decode the intricate dynamics of diverse interpersonal relationships, and
respond appropriately. Suitable professions for individuals rich in interpersonal
intelligence span politicians, diplomats, leaders, psychologists, PR officers,
salespeople, and more.
g) Intrapersonal Intelligence: it revolves around self-awareness, encompassing the
ability to understand oneself and subsequently act in alignment with this self-
knowledge. Those with robust intrapersonal intelligence can identify their strengths,
weaknesses, inner inclinations, moods, intentions, temperaments, and self-esteem,
often thriving in independent thought. Suitable career avenues for such individuals
include philosophers, politicians, thinkers, psychologists, and more.
h) Naturalist Intelligence: it refers to proficiency in observing the various facets of
nature, identifying and categorizing objects, and discerning between natural and
artificial systems.
The term “artificial” “can closely approximate their counterparts, and sometimes they
can be advantageous when they do not have any maintenance requirements”
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After this
analysis, we can say that AI represents a novel realm of technological science,
investigating and cultivating theories, methodologies, technologies, and application
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“Artificial Intelligence Theory, Models, and Applications”, P Kaliraj, T. Devi, New York, Auerbach
Publications, 2021
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systems to simulate, enhance, and elevate human intelligence. AI is designed to enable
machines to reason akin to humans, thereby imbuing them with a form of intelligence.
Nagy Zsolt, author of “Artificial Intelligence and Machine Learning Fundamentals:
Develop Real-World Applications Powered by the Latest AI Advances”
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explains that
humans have five basic senses simply divided into visual, auditory, kinesthetic, olfactory,
and gustatory, and based on these senses, intelligent machines are created to act like
humans:
• Listening and Speaking: using a speech recognition system, AI can collect
information. Using speech synthesis, it can turn internal data into understandable
sounds.
Speech recognition and speech synthesis techniques deal with the recognition
and construction of sounds humans emit or that humans can understand.
Imagine you are on a trip to a country where you don't speak the local language.
You can speak into the microphone of your phone, expect it to "understand"
what you say, and then translate it into the other language. The same can happen in
reverse with the locals speaking and AI translating the sounds into a language you
understand. Speech recognition and speech synthesis make this possible.
• Understanding Language: we can understand natural language by processing it.
This field is called Natural Language Processing, or NLP for short. When it
comes to natural language processing, we tend to learn languages based on
statistical learning.
• Remembering Things: we need to represent things we know about the world.
This is where creating knowledge bases and hierarchical representations called
ontologies comes into play.
• Thinking: the AI system has to be an expert in a certain domain by using an
expert system, which can be based on mathematical logic in a deterministic way,
as well as in a non-deterministic way.
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“Artificial Intelligence and Machine Learning Fundamentals: Develop Real-World ApplicationsPowered
by the Latest AI Advances”, Nagy Zsolt, Packt Publishing, 2018
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The knowledge base of an expert system is represented using different
techniques. As the problem domain grows, we create hierarchical ontologies. We
can replicate this structure by modeling the network on the building blocks of the
brain. These building blocks are called neurons, and the network itself is called a
neural network.
There is another key term you need to connect to neural networks: deep learning.
Learning is deep because it goes beyond pattern recognition and categorization.
Learning is imprinted into the neural structure of the network. One special deep
learning task, for instance, is object recognition using computer vision.
• Seeing: we have to interact with the real world through our senses. We have
only touched upon auditory senses so far, regarding speech recognition and
synthesis. What if we had to see things? Then, we would have to create computer
vision techniques to learn about our environment. After all, recognizing faces is
useful, and most humans are experts at that.
Computer vision depends on image processing. Although image processing is
not directly an AI discipline, it is a required discipline for AI.
• Moving: moving and touching are natural to us humans, but they are very
complex tasks for computers. Moving is handled by robotics, which is based on
control theory, where you create a feedback loop and control the movement of
your object based on the feedback gathered. Interestingly enough, control theory
has applications in other fields that have absolutely nothing to do with moving
objects in space. This is because the feedback loops required those modeled in
economics.
AI is then based on these senses to create machines that can recreate humans’ complex
acts and processes.
After this brief contextualization, we are ready to answer the question “What is AI?”. As
we saw earlier, there are several reasons why it is difficult to arrive at a unified
definition. First, many subfields of AI research foreground different capabilities
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