Merge the new utku_version

chapters/0.5-preamble.tex

@@ -29,7 +29,7 @@
 	\begin{minipage}[t]{0.48\linewidth}
 		\subsection*{Acknowledgements}\par
 		\vspace{0.8em}
-		\footnotesize This thesis owes special thanks to my supervisor Mag. Dr. Sergej Seitz for his continuous support, patience, and incisive insights during the process of research and writing. I am also deeply grateful to Mag. Dr. Christoph Hubatschke for his generous offer to take the role as co-supervisor, his critical feedback and inspiring ideas that shaped many pivotal conceptual moves in this work. Finally, I wish to thank Assoz. Prof. Dr. Fabio Wolkenstein for his assistance in the early conceptualisation of this research topic and for the valuable guidance that helped clarify and refine its theoretical orientation.
+		\footnotesize This thesis owes special thanks to my supervisor Sergej Seitz for his continuous support, patience, and incisive insights throughout the research and writing process. I am also deeply grateful to Christoph Hubatschke for generously taking on the role of co-supervisor and for his critical feedback and inspiring ideas. Finally, I thank Fabio Wolkenstein for his assistance in the early conceptualisation of the research topic and for his valuable guidance.
 	\end{minipage}
 	\vfill
 
@@ -44,15 +44,3 @@
 
 	\end{minipage}
 \end{fullwidth}
-
-%UNIWIEN
-%\newpage %
-%
-%\begin{fullwidth}
-%	\begin{minipage}[t]{0.48\linewidth}
-%		\section*{Abstract (Deutsch)}\par
-%		\vspace{0.8em}
-%		\footnotesize Die vorliegende Arbeit untersucht, wie Kritik und Widerstand im Kontext der rasant zunehmenden Präsenz von Generativer Künstlicher Intelligenz (genAI) neu theoretisiert werden können, indem diese Systeme zunächst innerhalb von Gilles Deleuze’ Konzept der Kontrollgesellschaften verortet werden. In diesem Rahmen treten klassische Institutionen zugunsten rechnergestützter Infrastrukturen zurück, die durch personalisierte, flexible und kontinuierliche Modulation operieren und so das Feld neu gestalten, in dem sich Prozesse der Subjektivierung entfalten. Frühere KI-Systeme zeigten bereits eine auffällige Ähnlichkeit zur Formulierung von Kontrolle durch prädiktive Relevanzzuweisung und verhaltensbezogene Personalisierung; zeitgenössische genAI-Modelle gehen mit ihren neuartigen Fähigkeiten jedoch einen Schritt weiter und nehmen aktiv an der Wissensproduktion teil, wodurch sie zu zentralen Akteuren in der Bildung menschlicher Subjektivität werden. Auf Grundlage einer theoretischen, historischen und technischen Analyse beleuchtet die Arbeit anschließend zentrale aktuelle Debatten um genAI, untersucht die Bedingungen der Wissensproduktion in Transformer-Architekturen, die Dynamiken der Mensch–Maschine-Interaktion, die Neukonfiguration von Handlungsfähigkeit sowie konkurrierende Entwicklungsparadigmen solcher Modelle. Unter Rückgriff auf Gilles Deleuze’ und Félix Guattaris Projekt \enquote{Kapitalismus und Schizophrenie} mobilisiert die Arbeit Konzepte wie Wunschproduktion, Schizoanalyse und Nomadologie, um ein theoretisches Gerüst zu entwickeln, das neu denken lässt, wie generative Infrastrukturen und Mensch–Maschine-Relationen in divergente, nicht-sedimentierte Formationen überführt werden können. In Kombination mit experimentellen Eingriffen in das Modellverhalten argumentiert die Studie, dass Möglichkeiten für Kritik und Widerstand immanent innerhalb generativer Systeme und ihrer kommunikativen Dynamiken entstehen. Anhand von Interventionen wie Gewichtsverstärkung, künstlicher Neugier und Gegen-Sequenzierung zeigt die Arbeit, wie sich generative Dispositive umnutzen lassen, um divergente Potenziale zu aktivieren, und entwickelt damit ein mikropolitisches Rahmenkonzept für Kritik und Widerstand.
-%	\end{minipage}
-%
-%\end{fullwidth}

chapters/2-control.tex

@@ -12,14 +12,12 @@ It sketches a transition from the closed environments of institutions like schoo
 
 The formulation of control has already inspired critical readings of emerging computational cultures, internet infrastructures, and surveillance capitalism. Early developments in \glspl{nn} and \glspl{dnn} have already played a prominent role by enabling increasingly capable systems, built upon a long history of \gls{nlp}, and supporting applications such as search engines, social media platforms, recommendation systems, and automated filtering. Within this trajectory, \gls{ai} quickly became a central object in theorising control societies; yet, we now stand at a threshold beyond the early imaginaries of \textit{cyberspace} or the \textit{virtual}: the contemporary \gls{ai} landscape is dominated by \gls{genai} systems, particularly \glspl{llm}, which no longer merely transmit or classify information; they increasingly participate in the production of meaning itself \parencite{kazakov2025, dishon2024}. These models generalise across domains, transfer knowledge between tasks, and adapt to unforeseen situations rather than remaining bound to narrow, predefined functions \parencite{xu2024}. Kazakov \parencite*{kazakov2025} characterises this development as a mode of scalar Darwinism, defined by relentless quantitative expansion rather than qualitative transformation. \Glspl{llm} and other \gls{genai} systems advance primarily by scaling; more parameters, larger datasets, and increasing computational resources, without fundamental architectural innovation. This trajectory reinforces existing capitalist logics, treating data as a resource to be extracted and leveraged; competitive advantage derives from scale rather than novelty. Just as neoliberal governmentality construed \textit{the market} as a quasi-metaphysical plane that produces the optimal outcomes without direct intervention \parencite[131]{foucault2008}, contemporary \gls{ai} discourse often assumes that scaling models and data will automatically yield the solutions humanity is said to need.\sidenote{Not always this explicit, but the technosolutionist propagation is particularly strong in the frontlines of tech giants:
 
-	\begin{minipage}{0.9\marginparwidth}
-		\centering
+	\begin{minipage}{\marginparwidth}
 		\includegraphics[width=\linewidth]{images/musk2.png}
 		\vspace{0.3em} % Optional spacing
 	\end{minipage}
 
-	\citereset
-	— \cite[]{musk2025}
+	— \citeauthorfull{musk2025} \cite*{musk2025}
 }
 
 Whether this acceleration will eventually enable more sophisticated forms of \gls{ai}, including \gls{agi}\sidenote{\Gls{agi} is a hypothetical intelligence of a machine capable of understanding, learning, and performing any intellectual task that a human being can do. It generalises across domains, transfers knowledge between tasks, and adapts to new, unforeseen situations, rather than being specialised for narrow tasks \parencite{xu2024}.}, remains contested. What is clear is that these systems continue to expand in capability, generality, and reach. \Gls{genai} models, especially \glspl{llm}, now function as computational agents that operate within and across domains, mediating how information is organised, circulated, and apprehended. They do not simply support existing knowledge practices; they increasingly generate outputs that are taken as meaningful, authoritative, and actionable \parencite{montanari2025, dishon2024}. Their interpretive operations shape what becomes visible or legible, filter which forms of knowledge can travel, and condition how subjects encounter and interpret information. Processes of subjectivation, therefore, unfold within a landscape where computational models actively participate in producing the categories, associations, and interpretive cues through which reality is navigated. As \gls{genai} systems occupy roles once associated with expert judgement, they begin to function as distributed institutional actors. This development intensifies the micropolitical dynamics identified in control societies, raising the question of how critique and resistance might be articulated when meaning is co-produced by systems whose authority derives from scale and statistical inference. The following analysis situates \gls{genai} within the historical transition from disciplinary institutions to control, in order to examine how these models shape institutional logics and the production of subjectivity.

chapters/3-ai.tex

@@ -156,7 +156,7 @@ Once vectorisation is performed, the next question is how to most effectively re
 	\begin{center}
 		\includegraphics[width=0.7\textwidth]{images/dimensionality_reduction300.png}
 	\end{center}
-	\caption{\footnotesize Dimensionality Reduction via Principal Component Analysis, Image
+	\caption{Dimensionality Reduction via Principal Component Analysis, Image
 		Reconstruction out of 20 Principal Components, and Feature Importance
 		Visualisation using Olivetti Faces Dataset (dataset:
 		\cite{attlaboratoriescambridge2005}, implementation: author's self

chapters/4-institution.tex

@@ -75,7 +75,7 @@ substance by the model (like how gradient descent favours more distinctive
 outputs), only to be amplified even more over the \glspl{epoch} through the
 cycles of backpropagation.
 
-\begin{figure}[htbp]
+\begin{figure}
 	\includegraphics[width=\textwidth]{images/image_recognition_network.png}
 	\caption{A speculative illustration of what the abstraction in the inner layers of an image recognition model looks like (cf. \cite{wolchover2017})}
 	\label{fig:image_recognition_network}

chapters/5-conjunctive_synthesis.tex

@@ -153,9 +153,8 @@ Thinking about the modulating \glspl{dispositif} of control societies and referr
 Do we observe these tendencies in the technical machinery discussed in Chapter~\ref{cha:ai}? I have already partly argued that this is not entirely the case once we look under the hood. In its pre-training phase, a model is nothing but a productive core, generating associations without clear boundaries. The subsequent fine-tuning and alignment processes can be read as attempts to tame this productivity, encircling its outputs within layers of normative coherence in order to make them \textit{useful}, building or strengthening molar structures in the process. The \glspl{llm} are not lacking in divergence; in fact, one of the greatest threats to their usefulness lies in their tendency to be overly productive, which means also often tending toward hallucinations and, more often than not, their complete disregard for given instructions (sometimes even by speaking the truth while they are supposedly trained not to do so, see Figure~\ref{fig:grok}).
 
 \begin{marginfigure}
-
 	\includegraphics[width=\textwidth]{images/grok.png}
-	\caption{\scriptsize X's \gls{llm} Grok arguing against Elon Musk's claims \parencite[]{grok[@grok]2025} }
+	\caption{X's \gls{llm} Grok arguing against Elon Musk's claims \parencite[]{grok[@grok]2025} }
 	\label{fig:grok}
 \end{marginfigure}
 
@@ -178,7 +177,7 @@ What is the implication? Is this the eugenics of human–machine communication t
 
 
 \section{All the Stones and No Mouth: Artificial Desire for Artificial
-Entities}\marginnote{In reference to \citeauthorfull{beckett2009}'s \parencite*[]{beckett2009} novel "Molloy" and Molloy's stone sucking machine.}
+  Entities}\marginnote{In reference to \citeauthorfull{beckett2009}'s \parencite*[]{beckett2009} novel \enquote{Molloy} and Molloy's stone sucking machine.}
 
 But do we have a method to shape \gls{genai} so that it genuinely nurtures creativity and allows users to move beyond the feedback loops formed in their interaction with these systems? In other words, is a non-sedimentary mode of human–machine communication possible? When \gls{ai} development shifted from \gls{sl} to \gls{ul} (see Chapter~\ref{sec:ai_history}), much of the explicit intentionality once encoded into models was lost. Today, intentionality can only be introduced indirectly through training data composition, fine-tuning procedures, or \gls{rlhf} frameworks, all of which remain partial, biased, and structurally constrained. Guiding \gls{genai} toward genuine divergence, therefore, requires not only technical adjustment but also a critical understanding of how its architectures condition and delimit meaning. Through the lens of \gls{dg}, a familiar critique is that \gls{genai} kills the flows of desire (see e.g. \cite{creativephilosophy2023}). This specific critique is concerned that \gls{genai} models' production fills gaps, completes patterns, and reterritorialises fragmented expressions into coherent outputs, leaving little open space for ideas to grow or for desire to flow. It becomes a machinery of completion, supplying coherence even where none exists and producing plausibility in place of truth. Desiring-production is formed by interruptions as much as it is accumulated by flows \parencite[5]{deleuze1983}; thought, critique, belief, and reasoning belong to the same field of production, yet the concern is that the interaction with the model folds them into circuits that privilege completion over interruption. Desire in its free form couples partial objects and generates flows, while simultaneously interrupting them. Gaps in knowledge are essential for growth, but \gls{genai} patches them with persuasive responses, and humans are often ill-equipped to distinguish what is genuinely grounded from what is merely coherent. Acting rarely as a refusing agent, it fills every gap and frequently reinscribes hegemonic representations. What passes as coherence is often believed to align with the dogmas of state and capital \cite[see][]{creativephilosophy2023}, the machine never says \enquote{\textbf{NO!}}.
 The essential role of desire is the production of production; it is abundance itself; it is not \textit{the lack}, as psychoanalysis claims, that drives it \parencite[49]{buchanan2008b}.

glossary.tex

@@ -19,14 +19,14 @@
 \newglossaryentry{assemblage}
 {
 	name = assemblage,
-	description = {A concept developed by Deleuze and Guattari to describe heterogeneous constellations of material elements (bodies, objects, infrastructures) and discursive components (rules, practices, ideas) that function together without forming a unified whole. An example derived from Foucault's analysis is the prison, which combines architecture, guards, inmates, and routines with legal codes and discourses on criminality. Assemblages are contingent, evolving configurations that integrate but do not fuse their parts, and they provide the conditions through which desire and social organisation manifest \parencite[see][66-67]{buchanan2018}.}
+	description = {A concept developed by Deleuze and Guattari to describe heterogeneous constellations of material elements (bodies, objects, infrastructures) and discursive components (rules, practices, ideas) that function together without forming a unified whole. An example derived from Foucault's analysis is the prison, which combines architecture, guards, inmates, and routines with legal codes and discourses on criminality. Assemblages are contingent, evolving configurations that integrate but do not fuse their parts, and they provide the conditions through which desire and social organisation manifest \parencite[see][66-67]{buchanan2018}}
 }
 
 
 \newglossaryentry{epoch}
 {
 	name = epoch,
-	description = {Epochs represent the number of times the entire training dataset passed through the algorithm. \cite{nebius-team2024}}
+	description = {Epochs represent the number of times the entire training dataset passed through the algorithm \parencite{nebius-team2024}}
 }
 
 \newglossaryentry{token}
@@ -52,7 +52,7 @@
 \newglossaryentry{neuron}
 {
 	name = neuron,
-	description = {An artificial neuron is the basic building block of \glspl{nn}, inspired by how biological neurons work. It takes several inputs, multiplies each by a weight, adds them together with a bias, and then passes the result through an activation function to decide the output. This simple mechanism allows networks of many neurons to learn patterns and make complex decisions (see \cite[]{mcculloch1943} for the early initiation of neurons.)}
+	description = {An artificial neuron is the basic building block of \glspl{nn}, inspired by how biological neurons work. It takes several inputs, multiplies each by a weight, adds them together with a bias, and then passes the result through an activation function to decide the output. This simple mechanism allows networks of many neurons to learn patterns and make complex decisions (see \cite[]{mcculloch1943} for the early initiation of neurons)}
 }
 
 

main.lof

@@ -1,22 +0,0 @@
-\babel@toc {british}{}\relax 
-\addvspace {10\p@ }
-\addvspace {10\p@ }
-\addvspace {10\p@ }
-\contentsline {figure}{\numberline {3.1}{\ignorespaces An illustration of overlap and interplay between \gls {ai} domains leading to the \glspl {llm} such as ChatGPT (cf. \blx@tocontentsinit {0}\cite [47]{alomari2024})}}{41}{chapter.3}%
-\contentsline {figure}{\numberline {3.2}{\ignorespaces Algorithmic Selection and Relevance Assignment Process (cf. \blx@tocontentsinit {0}\cite [241]{just2017})}}{46}{Item.16}%
-\contentsline {figure}{\numberline {3.3}{\ignorespaces A Simplified Illustration of a \gls {nn} (cf. \blx@tocontentsinit {0}\cite {subramaniam2019})}}{48}{section.3.3}%
-\contentsline {figure}{\numberline {3.4}{\ignorespaces \relax \fontsize {8}{10}\selectfont \abovedisplayskip 6\p@ plus2\p@ minus4\p@ \abovedisplayshortskip \z@ plus\p@ \belowdisplayshortskip 3\p@ plus\p@ minus2\p@ \def \leftmargin \leftmargini \parsep 4\p@ plus2\p@ minus\p@ \topsep 8\p@ plus2\p@ minus4\p@ \itemsep 4\p@ plus2\p@ minus\p@ {\leftmargin \leftmargini \topsep 3\p@ plus\p@ minus\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep \parsep }\belowdisplayskip \abovedisplayskip Dimensionality Reduction via Principal Component Analysis, Image Reconstruction out of 20 Principal Components, and Feature Importance Visualisation using Olivetti Faces Dataset (dataset: \blx@tocontentsinit {0}\cite {attlaboratoriescambridge2005}, implementation: author's self work, see Annex~\ref {cha:dimensionality_reduction}.) }}{50}{subsection.3.3.1}%
-\contentsline {figure}{\numberline {3.5}{\ignorespaces The original Transformer Architecture with built-in Multi-Head Attention Mechanism in Encoder and Decoder Processes (cf. \blx@tocontentsinit {0}\cite [3]{vaswani2017a}) }}{53}{subsection.3.3.2}%
-\contentsline {figure}{\numberline {3.6}{\ignorespaces Non-convex optimisation: Utilisation of gradient descent to find a local optimum ona loss/cost manifold (cf. \blx@tocontentsinit {0}\cite [3]{amini2018}) }}{56}{subsection.3.3.3}%
-\contentsline {figure}{\numberline {3.7}{\ignorespaces A simple illustration of how backpropagation updates the neurons among the layers of a \gls {nn} in a backwards manner (cf. \blx@tocontentsinit {0}\cite {3blue1brown2017}) }}{58}{subsection.3.3.3}%
-\addvspace {10\p@ }
-\contentsline {figure}{\numberline {4.1}{\ignorespaces A speculative illustration of what the abstraction in the inner layers of an image recognition model looks like (cf. \blx@tocontentsinit {0}\cite {wolchover2017})}}{68}{section.4.1}%
-\contentsline {figure}{\numberline {4.2}{\ignorespaces A human's development of a world model via a language capability (language app) in a natural environment (Animal OS) (cf. \blx@tocontentsinit {0}\cite [268]{matsuo2022}) }}{70}{section.4.2}%
-\addvspace {10\p@ }
-\contentsline {figure}{\numberline {5.1}{\ignorespaces \relax \fontsize {7}{8}\selectfont X's \gls {llm} Grok arguing against Elon Musk's claims \blx@tocontentsinit {0}\parencite []{grok[@grok]2025} }}{89}{section.5.2}%
-\contentsline {figure}{\numberline {5.2}{\ignorespaces Claude's Response before and after the Amplification of the \textit {Golden Gate Bridge} Feature}}{98}{Item.22}%
-\contentsline {figure}{\numberline {5.3}{\ignorespaces A cat image misclassified as guacamole after the addition of adversarial noise.}}{103}{Item.32}%
-\addvspace {10\p@ }
-\addvspace {10\p@ }
-\addvspace {10\p@ }
-\contentsfinish 

main.tex

@@ -1,28 +1,36 @@
-\input{/Users/ubd/Library/Mobile Documents/iCloud~md~obsidian/Documents/rhizome/06_projects/UNI/latex_template}
+\input{/Users/ubd/Library/Mobile Documents/iCloud~md~obsidian/Documents/rhizome/08_templates/latex_template.tex}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Title & Author %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
+
+\hypersetup{ % MY own darker colors
+	colorlinks=true,
+	%citecolor=deepGreen,
+	citecolor=maroon!75!black,
+	linkcolor=persianBlue!75!black,
+	filecolor=persianGreen!75!black,
+	urlcolor=persianBlue!75!black,
+	pdfpagemode=FullScreen,
+}
+
 \title{Nomadic Descent:\\{\Huge Generative AI, Subjectivation, and Resistance/Critique in Control Societies}}
-%\title{The Bureau Virus and the Word Virus:\\{\Huge Generative AI, Subjectivation, and Resistance/Critique in Control Societies}}
-% \title{The Bureau Virus and the Word Virus: Generative AI, Subjectivation, and Resistance in Post-Disciplinary Societies}
-% \title{The Bureau Virus and the Word Virus: Generative AI, Subjectivation, and Resistance in Algocene}
 \author{Utku B. Demir}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 \input{glossary.tex}
-%\makeglossaries
 \makenoidxglossaries
+
 \begin{document}
 \maketitle
 
-%\title{The Bureau Virus and the Word Virus: Generative AI, Subjectivation, and Resistance/Critique in Control Societies}
 \title{Nomadic Descent: Generative AI, Subjectivation, and Resistance/Critique in Control Societies}
-%\author{} % UNIWIEN
+\author{} % UNIWIEN
 \input{chapters/0.5-preamble.tex}
 
+\hypersetup{linkcolor=black} % I don't like the toc colors being blue
 \tableofcontents
 \listoffigures
-%\listoftables
-%\printglossaries
+
+\hypersetup{linkcolor=persianBlue!75!black}
 \printnoidxglossaries