\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