\newglossaryentry{Generative}
{
	name =  Generative Artificial Intelligence,
	description = {GenAI description}
}
\newglossaryentry{dispositif}
{
	name = dispositif,
	description = {A dispositif (often translated as “device,” “deployment,” “apparatus,” or “setup,” though sometimes left untranslated) is, in Foucault’s usage, elements of a heterogeneous network of discourses, institutions, architectural arrangements, regulatory rules, technologies, and practices. Rather than locating power in a single structure or person, the dispositif describes how power operates through relations and resistances embedded in everyday formations. It is central to Foucault’s analyses of genealogies, biopower, and governmentality \parencite[see][]{crano2020}}
}


\newglossaryentry{body}
{
	name = body,
	description = {It critical theory, the concept of \textit{body} usually, though not exclusively, taken to mean the human body, but also seen as a surface of social inscription, often thought in its social context. While philosophy since Descartes often mistrusted the body as a source of impulses, Spinoza insisted on asking what a body can do. A major shift came with Merleau-Ponty’s phenomenology, which foregrounded embodied perception, and with feminist theory, beginning with Beauvoir, which exposed the neglect of sexual difference. Later thinkers such as Butler challenged the distinction between natural and cultural bodies, and Haraway reconceived the body as cyborg, blurred with animals and machines. Politically, feminism introduced the notion of “body politics,” while cultural studies analysed the body as a site of media representation and social anxiety. Foucault’s concepts of discipline and biopower remain central, highlighting how bodies are inscribed and governed within regimes of power \parencite[see][98-99]{buchanan2018}.}
}

\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}}
}


\newglossaryentry{epoch}
{
	name = epoch,
	description = {Epochs represent the number of times the entire training dataset passed through the algorithm \parencite{nebius-team2024}}
}

\newglossaryentry{token}
{
	name = token,
	description = {Tokens are the smallest units of text that a model processes; typically words, subwords, or characters in natural language processing tasks \parencite{cser2024}. In current AI systems, a token often corresponds to a single word, and the process of breaking text into tokens is known as tokenization \parencite[59]{jurafsky2009}. For \gls{genai} models, particulary \glspl{llm}, this enables efficient computation across varying text inputs}
}

\newglossaryentry{kernel}
{
	name = kernel,
	description = {In Machine Learning, the Kernel method consists of using a linear classifier to solve a non-linear problem. This is achieved by transforming a linearly inseparable set of data into a linearly separable set \parencite{melanie2024}}
}


\newglossaryentry{loss}
{
	name = loss/cost function,
	description = {A mathematical rule that quantifies the difference between a model’s prediction and the correct outcome in order to \textit{punish} weak predictions and \textit{reward} the correct ones in following processes. For example, if the model predicts “cat” but the true label is “dog,” the loss is high; if it predicts “cat” and the true label is also “cat,” the loss is low. Training minimises this loss so the model improves (see \cite[178]{goodfellow2016})}
}


\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)}
}


\newacronym{agi}{AGI}{Artificial General Intelligence}
%%%%%\newglossaryentry{AGI}
%%%%%{
%%%%%	name = Artificial General Intelligence,
%%%%%	description = {Artificial General Intelligence (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}) .}
%%%%%}
% \newglossaryentry{NN}
% {
% 	name = Artificial Neural Networks,
% 	description = {NN description}
% }

\newacronym{ml}{ML}{Machine Learning}
\newacronym{nlp}{NLP}{Natural Language Processing}
\newacronym{ai}{AI}{Artificial Intelligence}
\newacronym{nn}{NN}{Artificial Neural Network}
\newacronym{aann}{ANN}{Artificial Neural Network}
\newacronym{dl}{DL}{Deep Learning}
\newacronym{rnn}{RNN}{Recurrent Neural Network}
\newacronym{cnn}{CNN}{Convolutional Neural Network}
\newacronym{dnn}{DNN}{Deep Artificial Neural Network}
\newacronym{gan}{GAN}{General Adversarial Network}
\newacronym{drl}{DRL}{Deep Reinforcement Learning}
\newacronym{genai}{genAI}{Generative Artificial Intelligence}
\newacronym{gm}{GM}{Generative Model}
\newacronym{lm}{LM}{Language Model}
\newacronym{llm}{LLM}{Large Language Model}
\newacronym{t2i}{T2IM}{Text to Image Model}
\newacronym{mgm}{MGM}{Multimodal Generative Model}
\newacronym{aigc}{AIGC}{AI Generated Content}
\newacronym{sl}{SL}{Supervised Learning}
\newacronym{ul}{UL}{Unsupervised Learning}
\newacronym{ssl}{SSL}{Self-Supervised Learning}
\newacronym{gofai}{GOFAI}{Good old-fashioned AI}
\newacronym{symai}{symAI}{Symbolic Artificial Intelligence}
\newacronym{rlhf}{RLHF}{Reinforcement Learning from Human Feedback}
\newacronym{pca}{PCA}{Principal Component Analysis}
\newacronym{nr}{NR}{Neuro-Representationalism}
\newacronym{cp}{CoPhe}{Computational Phenomenology}


\newacronym{dg}{D\&G}{Gilles Deleuze \& Felix Guattari}
\newacronym{bwo}{BwO}{Body without Organs}

%TEST
%\newglossaryentry{pos}{name=\glslink{POS}{Part Of Speech},text=Part of Speech,description={``Part of Speech'' Description}}
%\newacronym[description={\glslink{pos}{Part of Speech}}]{POS}{POS}{Part Of Speech}
