# thought - [type:: note] - different levels of bracket and intervention. # Prompt to rewrite part of [[The Environmental Aesthetics of Generative AI]] [type:: prompt] type:: draft [source:: [[environmental aesthetics of generative ai]]] - I am happy with this part of a paper I am writing until it gets to the sentence "- That is, generative ai systems can be considered another type of naturans system: *[[machina naturans]]*, allowing for instructive similarities between in the way that they are appreciated." Then [[the structure]] gets a little muddled. I would like you to write out each sentence verbatim as a bullet point, but put the bullet points in the most logical order to plan the structuree of this part of the paper. - The key idea is that [[generative system]] like midjourney and LLMs can be considered a [[machina naturans]] system, which is basically a made system like an llm that is similarly generative in the style of spinoza's natura naturans. - I would like some technical detail about how they are trained, manifolds etc., but it is all in service of showing why LLMs/midjourney are naturans systems. ## 3 [[Generative Environments]] We suggest that Carlson's recommendations also hold for the [[aesthetic appreciation]] of generative AI: we should appreciate these system for what they are, and we should appreciate them in light of our [[scientific knowledge]] of what they are. Moreover, we shall suggest in a moment that Carlson's [[Environmental Model]] ([[the naturans]] model) can in fact be adapted so as to provide at least the starting point for an aesthetics of generative AI. We can begin to see this by considering the following remarks from Dario Argento, the CEO of Anthropic (my italics). > I think one useful way to think about neural networks is that we don't program and we don't make them. We kind of, we grow them. You know, we have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on, and they sort of, you know, it starts off with some kind of random, you know, random things and it grows. And it's almost like the objective that we train for is this light. > > And so we create the scaffold that it grows on and we create the, you know, the light that it grows towards. But the thing that we actually create, it's this almost biological, you know, entity or organism that we're studying. And so it's very, very different from any kind of regular [[software engineering]], because at the end of the day, we end up with this artifact that can do all these amazing things. It can, you know, write essays and translate and, you know, understand images." > > ([[Lex Fridman]], [[Dario Amodei]]: Anthropic CEO on Claude, AGI & the Future of AI & Humanity | [[Lex Fridman Podcast]] #452) my emphasis.  Our contention is that generative AI can be approached in a way that parallels Carlson’s environmental model. - That is, generative ai systems can be considered another type of naturans system: *machina naturans*, allowing for instructive similarities between in the way that they are appreciated. In neural network training, an architecture is designed, a loss objective is specified, and then the system “develops” through iterative exposure to data. - This leads to emergent capabilities that are not individually coded by designers. - The analogy to a “growing” system highlights how these models evolve in complexity and capability over time, as though they were living entities shaped by training forces. - Rather than having a single function, generative AI systems can serve multiple roles. - One user might prompt a text-based model for language tutoring, another for creative writing or code debugging, and yet another for summarising research articles. - This open-ended flexibility is better captured by thinking of the system as an evolving environment than as a finite tool with a single purpose. - 3.2 Machina Naturans - Drawing from the notion of natura naturans, we can refer to generative AI as machina naturans. - This term signals that these systems display a self-directed or emergent aspect; they produce content in ways that cannot be reduced simply to direct instructions. - Internally, generative AI relies on latent spaces: high-dimensional, learned representations in which data are mapped and associated. - These spaces resemble a conceptual “terrain” shaped by the model’s training set. - A text-to-image system, for instance, learns an extensive “manifold” of possible images. - Depending on the prompt and the underlying latent space, different regions of this manifold are activated, producing varied, sometimes surprising outputs. - One might compare the training data to “soil,” whose composition influences what “blooms” in the model’s outputs. - If certain art historical styles are prevalent in the dataset, the model will “grow” images reflecting those styles more readily. - If an entire region of possible imagery was scarcely included in the dataset, prompting for those styles or subjects might lead to incongruous or incomplete results. - Even adjusting parameters like temperature or sampling strategies can be likened to modifying the “soil conditions,” altering how easily certain outputs are “encouraged.” - In short, the generative system behaves as a dynamic environment whose emergent forms reflect the interplay among data distributions, model architecture, and user prompts. - 3.3 Navigating an Internal Terrain - Once trained, a generative model can be explored through prompts, akin to navigating a digital environment. - A user might discover that requests for certain artistic styles or textual formulations produce outcomes that cluster around particular “regions” of the latent space. - Some of these regions might be more easily reached; others might require intricate prompting or model fine-tuning. - The system’s “terrain” can therefore appear surprisingly vast and varied. - Seeing generative AI as an evolving system—rather than a static artefact or single-function tool—underscores the importance of knowledge for aesthetic appreciation. - One can certainly enjoy an AI-generated image or poem on first glance, but deeper engagement benefits from some sense of how the “soil” was formed and how a user’s input interacts with the system’s latent space. - This conceptual move parallels Carlson’s argument that appreciating a forest or reef is enriched by understanding the ecological forces driving its structure. - shaped by evolving forces and conditions. - In Carlson’s words, environments are shaped by “various forces, random and otherwise,” and appreciation requires an account that “makes this order visible and intelligible.” - Generative AI is likewise formed by “forces,” such as training data distributions, model architectures, and parameter tuning. - While those forces are cultural, algorithmic, or mathematical rather than natural, they shape what outputs the model can produce. - To consider a generative model as an “environment” is to see it not as a static artefact or a single-purpose machine, but as a dynamic system whose “terrain” can be navigated by users through prompts or queries. - Much like a natural environment that allows multiple pathways of exploration, an AI system—once trained—can generate a multitude of textual or visual creations, driven partly by user interaction and partly by its learned internal structures. - 2.3 Spinoza’s Notion of Natura Naturans - - Although Carlson is concerned with natural rather than artificial systems, the philosophical image of continuous, partly autonomous production captures something about how generative AI operates. - In generative models, outputs often emerge from learned patterns rather than explicit programming that pre-specifies every aspect. - The system “generates” rather than merely retrieves. - This sense of an ongoing, partly self-directed process is reminiscent of how Spinoza depicts nature’s productive activity. - With an AI system, user prompts supply new contexts or “seeds,” but the internal generative routines autonomously create novel configurations. - Hence, just as natura naturans emphasises nature’s active production of phenomena, generative AI might be regarded as a “machina naturans,” a machine that continually produces outputs shaped by internal processes and interactions with users. - 3. What Generative Systems Are - 3.1 An Evolving, Grown Process - Some researchers describe large models as “grown” rather than strictly programmed, invoking a biological analogy. # Meeting with group about asa, bsa submissions ### Arianna - harmony of solution vs harmony of action - she thinks the harmony of solution is better. sometihng something feedback ### Elsa - decided to define pioneering as provoking socially rejected. -