##### You said: Is science generally considered to include all the sciences that we have? For example, physics, the natural sciences, marine biology, chemical physics, quantum physics, or geology. What links or groups all of these things together? I can imagine that there are some controversial cases about what is and isn’t a science. I'm not especially interested in that question; I'm much more curious about what a physics professor or an analytic philosopher of science would say if I asked them, "What is science?" in the way I've described. And I just thought, I mean I know you could sort of explain what groups all of these things together in terms of the scientific method. Maybe a better question is what, do all scientists study the same thing if we generalise to a sufficient degree? ###### ChatGPT said: Your intuition about generalisation is heading in an insightful direction. Indeed, scientists across various fields like physics, marine biology, geology, chemical physics, and quantum mechanics study profoundly diverse phenomena. Yet at a sufficient level of abstraction, it becomes meaningful to ask whether they're all ultimately studying "the same thing." If you asked a physics professor or an analytic philosopher of science your question—"What is science?" or "What do all scientists study?"—their answer might hinge upon these core ideas: ### 1\. The scientific approach (methodological unity): The first and perhaps most standard response is that what unites sciences is not a single object of study but a shared approach: - Scientists aim to produce empirical knowledge grounded in systematic observation, experimentation, hypothesis testing, and evidence. - They employ strategies for falsifiability, replicability, and systematic peer review. - They pursue explanatory theories or models capable of predictive power and coherence. From this perspective, [[the unity]] of science is methodological. Sciences differ greatly in their phenomena—particles versus ecosystems versus tectonic plates—but all are "sciences" because they approach their objects systematically, empirically, and critically. ### 2\. The pursuit of understanding regularities in nature (ontological unity): Another plausible view (one you hinted at when you asked if they're all studying "the same thing") is that, at a highly general level, all scientists explore the same basic aspect of reality: the patterns, regularities, and laws of nature. On this interpretation, scientists from diverse disciplines share an underlying assumption that the natural world is intelligible, structured, and governed by generalisable regularities or laws. Physics discovers fundamental laws of matter and energy; biology examines regularities in organisms and ecosystems; geology investigates patterns governing Earth's structure and history. Each science, then, uncovers and describes these different sorts of regularities, but the overarching goal—understanding patterns or order in the natural world—remains constant. ### 3\. Explanation and prediction as common goals (epistemic unity): At yet another level, one might say all scientists share common epistemic goals: - Explanation: Offering coherent and testable explanations for phenomena. - Prediction: Using these explanations to predict future events or undiscovered facts. Thus, sciences become linked through their epistemological ambitions rather than through their objects or even their exact methods (since methods differ widely across fields). ### Do all scientists study the same thing? If one abstracts sufficiently, yes: they study reality itself, specifically reality’s intelligibility. Science as a whole can be seen as the organised human effort to systematically understand the universe and the entities within it. The specific subject matter—be it quantum particles, ecological systems, chemical reactions, marine life, or geological structures—is secondary to the general aim of making nature intelligible. ### To summarise clearly: - **Methodologically**, scientists are unified by empirical inquiry and critical testing. - **Ontologically**, they seek regularities, structures, and laws of nature. - **Epistemically**, they share goals of explanation, prediction, and understanding. This is likely the kind of response you'd get from an analytic philosopher of science or a reflective physics professor, who would probably emphasise the methodological and epistemological unity rather than suggesting that there's one concrete, specific object of study shared by all scientists. ##### You said: "On this interpretation, scientists from diverse disciplines share an underlying assumption that the natural world is intelligible, structured, and governed by generalisable regularities or laws. Physics discovers fundamental laws of matter and energy; biology examines regularities in organisms and ecosystems; geology investigates patterns governing Earth's structure and history. Each science, then, uncovers and describes these different sorts of regularities, but the overarching goal—understanding patterns or order in the natural world—remains constant." I find this interesting. Do you think we could say that all sciences aim at studying order in the natural world? Or regularities or laws or the other way you're putting it here. Would this unite all things that are sciences? ###### ChatGPT said: Yes—indeed, you've expressed something important. Saying all sciences aim at studying **order in the natural world** (or, equivalently, its **regularities**, **patterns**, or **laws** ) captures very effectively what many philosophers of science and scientists themselves see as a core unifying feature of science as a general enterprise. [[The idea]] here is: - **Sciences differ greatly** in their subject matter—particles, cells, oceans, or geological formations—but what all scientists share, at the deepest level, is a fundamental interest in discovering and understanding **order and regularities in nature**. - To say the natural world is intelligible means scientists assume there is structure or order that can be discovered, expressed, and explained. - **Physics** seeks to identify fundamental laws governing matter, energy, and their interactions. - **Biology** tries to reveal regularities in the processes of life and ecosystems. - **Geology** investigates stable patterns or regularities governing the Earth’s geological structures, processes, and history. - Even apparently distinct disciplines, such as psychology or sociology, often become "sciences" precisely by looking for regularities and consistent patterns within human behaviour or social interactions. This way of characterising science— **as a systematic attempt to identify and understand order in the natural world** —is compelling precisely because it unites fields without forcing them to share the exact same methods, experimental designs, or standards of evidence. Moreover, this formulation neatly side-steps debates over exactly which methods (observation, experimentation, mathematical formalisation, etc.) are essential or exactly what qualifies as "scientific." Instead, it captures something deeper and less controversial: > **All sciences aim at uncovering, describing, and explaining patterns, regularities, and order in nature.** So, yes, your intuition is very much on target: this idea of studying order or regularities in nature would unite all sciences. It captures succinctly what most analytic philosophers of science or reflective scientists would endorse as a foundational idea about science itself. ##### You said: I guess my question was more can the laws or regularities or orders that different sciences study be enough to differentiate them or rather can the different sciences be differentiated in virtue of their studying different orders patterns regularities laws etc etc ###### ChatGPT said: Yes, the various scientific disciplines can indeed be differentiated based on the specific types of order, patterns, regularities, and laws they investigate.While all sciences share the overarching goal of uncovering and understanding order in the natural world, each discipline focuses on distinct aspects of this order, employing specialized methods and [[theoretical frameworks]] tailored to their unique subject matter. **Disciplinary Differentiation through Subject Matter:** - **Physics:**Explores fundamental forces and principles governing matter and energy, such as gravity, electromagnetism, and quantum mechanics. - **Chemistry:**Investigates the composition, structure, properties, and reactions of substances, focusing on the interactions and transformations of molecules and atoms.[Wikipedia](https://en.wikipedia.org/wiki/Regulatory_science?utm_source=chatgpt.com) - **Biology:**Examines living organisms and life processes, identifying patterns in genetics, evolution, and ecological interactions. - **Geology:**Studies Earth's [[physical structure]] and substance, analyzing patterns in rock formations, seismic activity, and the planet's evolutionary history. - **Psychology:**Focuses on behavior and mental processes, seeking regularities in cognition, emotion, and social interactions. This differentiation is not solely based on the subject matter but also on the methodologies and theoretical approaches developed to study these specific domains.For instance, the experimental techniques in physics differ markedly from the observational methods in astronomy or the field studies in ecology. **Interdisciplinary and Transdisciplinary Approaches:** While disciplines are distinct, the boundaries between them are often permeable, leading to interdisciplinary fields that integrate methods and insights from multiple areas to address complex questions.Examples include:[Wikipedia +1 Wikipedia +1](https://en.wikipedia.org/wiki/Transdisciplinarity?utm_source=chatgpt.com) - **Biochemistry:**Merges biology and chemistry to study the chemical processes within and related to living organisms. - **Environmental Science:**Combines elements of biology, chemistry, geology, and social sciences to understand and address environmental issues. Transdisciplinary approaches go further by transcending traditional disciplinary boundaries to create holistic frameworks, integrating knowledge from various fields to tackle multifaceted problems.[Wikipedia](https://en.wikipedia.org/wiki/Transdisciplinarity?utm_source=chatgpt.com) **Scientific Pluralism:** [[The concept]] of scientific pluralism acknowledges that multiple, sometimes even conflicting, [[theoretical frameworks]] can coexist within and across disciplines.This perspective recognizes that complex phenomena may require diverse approaches for a more comprehensive understanding, reflecting the varied orders and regularities present in nature.[Wikipedia](https://en.wikipedia.org/wiki/Scientific_pluralism?utm_source=chatgpt.com) In summary, while the pursuit of understanding order unites all scientific endeavors, the differentiation of sciences arises from their focus on specific types of order, patterns, and laws, as well as the distinct methodologies they employ.This specialization allows for a more detailed and nuanced exploration of the multifaceted natural world. ##### You said: How could the things you just talked about in that last answer about possibly the different laws and principles and forces etc etc How could these ideas be related to the Spinozian ideas of [[natura naturata]] and [[natura naturans]]? ###### ChatGPT said: Your question about relating the various scientific laws, principles, and forces to Spinoza's concepts of *[[natura naturans]]* and *[[natura naturata]]* offers a fascinating perspective on the intersection of philosophy and science.Spinoza, a 17th-century philosopher, introduced these terms to articulate his view of nature and its manifestations.[brill.com +1 Cambridge [[University Press]] & Assessment +1](https://brill.com/previewpdf/book/9789004246607/B9789004246607-s004.xml?utm_source=chatgpt.com) **Spinoza's Concepts:** - **[[Natura Naturans]] (Nature Naturing):**This refers to nature in its active, self-causing aspect—the dynamic, creative force that is the essence of substance. In Spinoza's framework, this is synonymous with God, conceived as the immanent cause of all that exists. - **[[Natura Naturata]] (Nature Natured):**This denotes nature in its passive aspect—the totality of modes or manifestations that result from the essence of substance. It encompasses all the properties and states that follow from the necessity of God's nature.[Wikipedia, l'enciclopedia libera +3 iep.utm.edu +3 Cambridge [[University Press]] & Assessment +3](https://iep.utm.edu/spinoza/?utm_source=chatgpt.com) **Relation to Scientific Disciplines:** In the context of modern science, we can draw parallels between these Spinozian concepts and the way scientific disciplines understand the natural world: - **Fundamental Laws and Principles as [[Natura Naturans]]:**The fundamental forces and principles studied in physics—such as gravity, electromagnetism, and quantum mechanics—can be seen as manifestations of *natura naturans*. These forces represent the underlying, active principles that drive the behavior and interactions of all entities in the universe. They are the "naturing" aspects of nature, the inherent dynamics that give rise to observable phenomena.[Medium +1 Cambridge University Press & Assessment +1](https://medium.com/illumination/a-philosophical-dialectic-with-spinozas-god-0f9673482aac?utm_source=chatgpt.com) - **Manifestations and Phenomena as Natura Naturata:**The specific phenomena and entities studied in various scientific fields—such as chemical compounds in chemistry, ecosystems in biology, or geological formations in geology—correspond to *natura naturata*. These are the "natured" aspects, the outcomes or expressions resulting from the fundamental principles. They represent the diverse forms and structures that emerge from the underlying laws of nature. **Unifying Perspective:** From this viewpoint, the diverse scientific disciplines are unified in their endeavor to understand both the active principles ( *natura naturans* ) and the resultant manifestations ( *natura naturata* ) of nature. Physics seeks to uncover the fundamental forces ( *natura naturans* ), while other sciences explore the complex structures and systems ( *natura naturata* ) that arise from these forces. This philosophical lens emphasizes that while scientific fields may focus on different aspects or scales of nature, they are interconnected through the continuous interplay between the underlying principles and their manifestations. Understanding this relationship enriches our comprehension of the natural world, highlighting the dynamic process through which the foundational aspects of reality give rise to the myriad phenomena we observe and study. ##### You said: How does what we've just been talking about relate to the ideas in this paper? Please go into a lot of detail. I'm very interested to know what you think here.like a lot a lot The Philosophical Quarterly Vol. 0, No. 0 2024 ISSN 0031-8094 https://doi.org/10.1093/pq/pqae120 Growing the image: Generative AI and the medium of gardening Nick Young and Enrico Terrone University of Genoa, Italy In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the limited fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener’s interaction with natura naturans—nature’s autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. Keywords: Artificial Intelligence; Creativity; Artistic Media; Agency; Tools. Consider the following passage from an article about the artist Sean Mauss, who was disqualified from a competition to design the cover of a science fic- tion book because he employed Midjourney, a generative AI program that transforms text prompts into images: The same day the winner was announced, readers and fans on Twitter were questioning whether the art was created at least in part using AI tools. The incident highlighted a growing crisis of trust in science fiction and fantasy publishing: in a world where AI-generated media is common, do you know the work you’re looking at was made by a human? Correspondence to: Nick Young, [email protected] © The Author(s) 2024. Published by Oxford University Press on behalf of The Scots Philosophical Association and the University of St Andrews. All rights reserved. For permissions, please e-mail: [email protected] Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 2 N. Young and E. Terrone \[…\] Readers and community members responded negatively to the revelations about Bob the Wizard’s cover. But in the long term, it’s not clear if these tools will remain forbid- den. Misrepresenting work as AI-free is one thing—but what happens if generative AI systems become more ingrained in the artistic process? (Sato 2023) Here, we find two broad ways in which we might characterize Midjour- ney. The question ‘\[how\] do you know you’re looking at \[an image\] that was made by a human?’ suggests that Midjourney is some sort of artificial agent: if a human didn’t make the image, who did? The most obvious an- swer would be Midjourney itself. Alternatively, we see that Midjourney can also be thought of as a type of tool that has the potential to become more ‘ingrained in the artistic process’ over time. From this perspective, Sean Mauss used Midjourney to create an image. We will argue that neither ap- proach is satisfactory, and we propose thinking of Midjourney, which will be our case study, and similar text to image systems as a new type of artistic medium. I. Midjourney as an agent If Midjourney’s artistic role is that of an agent, then it could play a variety of different agentive roles. If Midjourney were sufficiently person-like, then it could be thought of as an artist. If it were only somewhat person-like it might still play a simpler agentive role, such as co-author or as some sort of assistant or helper. However, as Anscomb (2022, p. 25) points out, it is hard to think of AI systems like Midjourney as artists because they do not seem as though they could have intentions to create art. Although Midjourney can produce outputs that might be mistaken for those produced by a traditional artist, there does not seem to be any reason to think that generative AI systems have any mental states at all (cf. Chalmers 2023). Despite this, however, the following passage suggests that Anscomb still be- lieves AI systems play some sort of agentive role in the creative process: \[A\]lthough AI agents are not themselves artistically creative, they can work iteratively without human intervention to non-accidentally generate the formal features of an im- age, and thus contribute to the realisation of some of the salient properties of a work. Whether this deserves some greater or lesser share of the credit for the production of the work qua art depends upon whether one takes a monist or pluralist conception of artistic value. (ibid. p. 35, our emphasis) Not only does Anscomb refer to text-to-image systems as ‘AI Agents’ through- out her paper, she suggests here that even if they are not artists proper, they Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 Growing the image: generative AI and the medium of gardening 3 might still deserve some degree of ‘credit’ for the ‘contribution’ that they have made to an artwork’s creation by working ‘autonomously’ and ‘iteratively’: Users of Midjourney lack direct control over exactly what sort of im- age is produced. In this sense, Midjourney is working autonomously so as to provide some of the formal features of the image. But we doubt that this is enough to think that Midjourney is ‘creditworthy’, or, to use a sim- ilarly agency-infused phrase, has made a ‘contribution’ to the artwork’s features. To see why autonomy is not sufficient for attribution of credit, consider the following example. As I pour wine into a glass, you take photos of the liquid splashing and rippling as the glass is filled. The wine is autonomous in the sense that neither I nor you have direct control over exactly how the liquid will splash into the glass (e.g. the size of the ripples, how many bubbles appear), but we would not think that the wine deserves any credit for the resulting photos in any interesting sense, nor would we say it has made any sort of contribution. A supporter of Anscomb’s view might reply that she is using ‘agent’ in the sense as used by Boden (2016, p. 45), who defines an AI agent as ‘a self-contained (‘autonomous’) procedure’. With this understanding of an agent, we might say that while it is not forming its own intentions, a system such as Midjourney, unlike the splashing and rippling wine, is not only working without direct human intervention but also constitutes a procedure. However, this ‘non-intentional’ understanding of an agent is quite differ- ent from the ‘intentional’ sense in which human users of Midjourney are agents (cf. Moruzzi 2022). Thus, it is hard to see how such different sorts of agents can cooperate in a meaningful sense so as to achieve co-authorship. Co-authorship, indeed, seems to require not simply agency but rather the same sort of agency. If, for the sake of argument, we concede that Midjourney is an agent in Anscomb’s sense, we are left with the dilemma of ascribing the artistic merit of the resulting image either to Midjourney’s actions or to the user’s actions since there is no way to make sense of their cooperation as agents. Both options are unsatisfying. While ascribing the artistic merit to the human user would overlook Midjourney’s active contribution, ascribing the merit to Midjourney would downplay the creative activity of prompt-crafting. II. Midjourney as a tool There is an obvious alternative to thinking of Midjourney as an agent. We do not think of a painter’s brush as deserving credit for its contribution to a painting but rather, the brush is a tool used by the artist to create Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 4 N. Young and E. Terrone images. As Hertzmann puts it: ‘Computers do not create art, people using computers create art’ (2018, p. 2). Should we not say the same thing about Midjourney? The idea that Midjourney and similar systems are tools we take to be quite intuitive. Wojtkiewicz (2023) and Martinez and Scarbrough (draft) draw on this intuition to argue that images produced through generative AI can be art. In this section, we shall put some pressure on this intuition. If Midjourney is a tool, it is quite unlike other tools. While our arguments here will not be definitive, they will serve as a motivation for exploring other possibilities and in particular as motivation for our preferred view which we shall present in the next section. If Midjourney is a tool, what sort of tool is it? Lowe (2014) splits tools into two types: utensils and machines. Utensils involve their users exerting more or less continuous control to carry out their functions. A paintbrush won’t paint and a pen won’t write unless they are being moved by someone’s hand. Machines on the other hand are autonomous, in the sense that they can carry out their function without constant user input. Clocks for example can be set up and will carry out their function of telling the time without further user input. While a great deal of artistic creation is carried out by utensils, we can still think of some artistic tools that would be categorized as machines on Lowe’s account. A drum machine can be set up to play a rhythm and then left to do its thing; we can press record on a video camera, and it will record whatever is put in front of it without further human input. If Midjourney is a type of tool, it is clearly a type of machine rather than a type of utensil, as it does not require constant action on the part of the user to create an image; rather, a user enters a prompt and Midjourney will set about creating an image. However, Midjourney differs from video cameras and drum machines in that it is inherently unpredictable. A user can specify in their prompt what they want an image to depict, its colour scheme, the style, etc. But they do not have control over the exact formal properties of the image that Midjourney spits out. One might object here that Midjourney’s unpredictability is not especially unique. An old drum machine might be unpredictable in so much as its owner is never quite sure whether it will turn on when it is plugged in, and a water- colour painter, even an extremely skilled one, is not able to control exactly how the paper will absorb and distribute the paint that they apply. Yet, this is no reason to think that they are not tools. The comparison with the drum machine has a straightforward response. ‘Unpredictable’ should not be taken to mean ‘unreliable’. An old drum ma- chine has a proper function –producing rhythms– and when it fails to switch on, it fails to fulfil its proper function. As Esposito (2022 p. 9) puts it, ‘If the outcome of a traditional machine becomes unpredictable, we do not think that it is creative or original –we think that it is broken’. Midjourney however, Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 Growing the image: generative AI and the medium of gardening 5 is reliably unpredictable. A prompter can never be sure what image they are going to get, but this is a feature, not a bug. As regards the watercolour case, it is indeed true that the painter cannot be entirely sure how paint will –atom for atom– distribute itself over the can- vas, but this will only affect the fine details of the image. Even if they do not have complete control over how the picture surface is marked, a watercolour painter still has control as to what depicted objects go where in her picture, as well as their size, shape, and colour. Even a photographer still has a relevant amount of fine-grained control as to what will appear in her picture. A Mid- journey prompter, on the other hand, will always lack this sort of fine-grained control. Entering the prompt ‘watercolour painting of a London street’ will produce an image of a London street that looks like it was painted in water- colours, but Midjourney will decide what this street looks like, what sort of objects and people populate the image, and what specific style of watercolour is produced. Helliwell (2023) suggests that this unpredictability might count against the idea that generative AI systems are simply tools: An attitude common among philosophers and computer scientists is that AI is just a tool. I would advise against forming this judgement hastily. We are certainly using AI for our purposes, but there is much about AI systems that differs from what we might consider to be a typical artistic tool \[…\] outputs from machine learning systems may not be fully within our control—or our understanding. While Helliwell does not deny that users of generative AI can be given some creative credit, the more autonomous, unpredictable work is being performed by the system, the more pressure is put on the idea that a generative AI such as Midjourney is ‘just a tool’. III. Midjourney and gardening We propose, perhaps surprisingly, that creating images with Midjourney is best understood through a comparison with gardening. Midjourney is not an agent that creates, or contributes to the creation of, art, neither, strictly speak- ing, is it a tool that artists use; rather, it is more similar to a plot of land in which a gardener might sow seeds. Philosophers like Bruno and Spinoza have deployed the Latin expression natura naturans to stress nature’s creative capacity to autonomously generate and dynamically shape things in ways that are so complex and chaotic to ap- pear unpredictable. Spinoza contrasts natura naturans, what nature is doing, to natura naturata, what nature has done. Borrowing this terminology, we might say that a garden is a piece of natura naturata that the gardener has produced together with natura naturans. Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 6 N. Young and E. Terrone Still, there are cases in which the gardener will have some significant con- trol over how natura naturans makes her plants grow: she can cut back the branches of bushes that grow over the pathway or cut the early blooms of her roses to facilitate the growth of more flowers later in the summer. Of course there is a reasonable chance that the bush will grow back and block the path- way in just the same way as it did previously, and pruning the roses early does not guarantee that more flowers will grow later. The gardener can coax her garden into growing the way she wants it to, but she cannot shape it with the same precision she might fold a piece of paper or shape a lump of clay. She is thus somewhat detached from the results of her actions: while she can manip- ulate her secateurs in real-time, she will have to wait to see whether the early pruning of the roses has the desired effect. This process can be thought of in terms of iteration. The gardener acts on her garden (planting seeds, pruning flowers, etc.) and then waits to see how natura naturans responds. How it re- sponds will influence her next move. Doing so, the gardener can exert skilful yet incomplete control over a piece of natura naturans which has a significant degree of autonomy in its own development. Drawing on this account of natura naturans, we introduce the category of machina naturans to characterize those machines that are capable of generating and shaping things in the same way as nature. Generative AI systems are paradigm cases of machina naturans. The practice of making images with Midjourney is, in this sense, strikingly similar to gardening. Both, as ‘naturans’ systems, are under the incomplete control of the user. First, just as the types of flowers that grow will depend on the seeds sown, images produced by Midjourney will depend on the prompts it receives as input. The prompt: /imagine a sunflower is going to produce pictures of sunflowers, not pumpkins or primroses. How- ever, just as which flowers can be grown, and how well they grow, depends on the environment in which the gardener is working, what images can be produced, and how well they can be produced will depend on which text to image system is being used. More importantly, the user of Midjourney, like the gardener, will not be able to control every detail of the images that are created. While a painter has a very high degree of control over how her painted sunflower looks, its precise size and shape, the prompter, just like the gardener, does not: while you can ask Midjourney for a tall or short sun- flower, you cannot ask it for a sunflower that takes up exactly one-third of the canvas. At the same time, like the gardener, a prompter can iterate on Midjour- ney’s products to get (or get closer to) the result for which they are aiming. If Midjourney produces an image with pink flowers but a user decides that they would prefer green, they can update their prompt and then hit the ‘remix’ Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 Growing the image: generative AI and the medium of gardening 7 button, and then, as the gardener must wait for natura naturans to make their move, wait a minute or so before a new image is produced by machina naturans. Arguably, neither the gardener nor the Midjourney user have what we might call fine-grained control over their outputs. As we have seen, a painter can paint a sunflower in the precise way that they want to, but a gardener cannot grow one, and a prompter cannot generate one, with this degree of control. At the same time, it does not seem as though gardeners and prompters have no control, or even limited control, over their output: bushes can be shaped and cut back, Midjourney prompts can be tweaked so that future iterations fall more in line with what the user wants. As ‘the gardener \[is\] a creative agent who is nevertheless thoroughly depen- dent on the cooperation of natural processes’ (Cooper 2009), the prompter is a creative agent who is nevertheless thoroughly dependent on the co- operation of artificial processes. Given the structural similarities between Midjourney and gardening, the reasons why the latter can be an artistic medium should also be reasons why the former can be an artistic medium as well. IV. Midjourney as a medium These considerations give us a third way of categorising Midjourney’s role in relation to its user. Midjourney is not an agent, nor a tool, but a medium. In the first instance, we can think of an artistic medium as the stuff that con- stitutes a particular work of art. We describe artists as working in a medium: a sculptor might work in the medium of marble or bronze, a painter in the medium of oil on canvas, etc. A slightly more sophisticated way of understanding artistic media casts them as systems of resources and practices that mediate between the artist and the audience (cf. Wollheim 1980; Davies 2004; Lopes 2014; Irvin 2022). From this perspective, Wollheim introduces the term recalcitrance to designate the unique difficulties which ‘materials present that can be dealt with only in the actual working of them’ (1980, p. 28). An idea that Thomson-Jones devel- ops as follows: “\[t\]he medium presents particular challenges and possibilities for artistic creativity, and the artwork makes manifest the artist’s response to these challenges and possibilities.” (2016, p. 40; see also Nguyen 2020, pp. 145–152). If this is correct, then a gardener’s medium is not simply the plants, seeds, and soil, but the distinct recalcitrance –the distinct challenges– that these physical materials provide in relation to the tools that she has available. Specifically, given the autonomous and generative character of natura naturans, the Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 8 N. Young and E. Terrone medium of gardening exhibits what we might call a dynamic recalcitrance char- acterized by the unpredictability and autonomy of biological processes. A gardener can only coax, through various iterations, how her garden grows. While painters and sculptors face distinct challenges, they do not work with materials that are intrinsically autonomous. Experts in traditional image- making arts can exert a very fine-grained control over their materials: figures in photographs or paintings do not change shape, colours do not creep across the canvas when their author is not looking. Recalcitrance is a useful way of thinking about different artistic media, but it should be further refined. The stuff that the artist works on (marble, paper, and paint) presents various forms of recalcitrance, but so too do the tools (chisels, paintbrushes) that the artist uses. In watercolour painting, one form of recalcitrance comes from the fact that the paint can spread haphazardly as it soaks into the canvas, but the paintbrush brings its own recalcitrance too: the difference between a novice painter and an expert is that only one can wield the paintbrush with enough skill to mark the paper as desired. The recalcitrance that a gardener faces seems to lie much more in the ma- terials with which they are working than it does their tools. Given the au- tonomous and generative character of natura naturans, the gardener must coax what she grows into growing the way she wants it to. The tools she uses to do this—spades, shears, watering cans, etc.—are not especially hard to use, there are no expert and novice watering can users; rather, it is the resistance presented by nature itself that she needs to overcome. While this dynamic recalcitrance is somewhat different from what we might call the inert recal- citrance faced by the painter or sculptor, this does not make it any less of a challenge. Traditional media, having inert recalcitrance, inherit predictability from the tools that constitute them. A paintbrush is a predictable tool, and a canvas and paint are passive, thereby allowing the painter to have strong, meaningful human control over what she creates. While both gardening and Midjourney involve predictable tools (such as spades or keyboards, respectively), these tools are used to work on dynamic, unpredictable stuff. In both cases, it is never certain what will grow from the seeds planted or the prompts entered; while the artist has meaningful control over what they create, it will be only weak control. Nevertheless, they will still have sufficient control to stamp their ‘personal, expressive signature’ over what is created. In this sense, Midjourney makes room for the sort of ‘meaningful human control’ that, according to Epstein et al. (2023 p. 4), ‘is achieved if human creators can creatively express themselves through the generative system, leading to an outcome that aligns with their intentions and carries their personal, expressive signature’. One may still object that the same sort of weak, meaningful human control can be found in styles of painting such as Pollock’s ‘action painting’. How- ever, action painting is just one way in which to engage with the medium of Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 Growing the image: generative AI and the medium of gardening 9 painting, while making unpredictable pictures is the only way to engage with the medium of generative AI. In painting unpredictability is a possibility, in Midjourney it is a necessity. To sum up, generative AI systems such as Midjourney can be seen as an artistic medium, comparable to gardening. Characterising Midjourney as a type of agent captures its autonomous and unpredictable nature, but not its crucial dependency on a human creative project. Characterising Midjourney as a tool, on the other hand, fits well with our idea that we can use generative AI as a means of creation, but does not capture Midjourney’s autonomy: an unpredictable tool is a malfunctioning one rather than an autonomous one. Considering Midjourney as a dynamic medium incorporates aspects of both the agency and tool views while avoiding their shortcomings. This allows for the recognition of the system’s autonomous nature, similar to the agency view, but also fits with the perception of Midjourney as a means of creation, as per the tool view, and yet acknowledges its autonomy as an integral, func- tional feature, not a malfunction. This approach permits Midjourney to be understood as neither fully autonomous nor completely controlled, but as a dynamically recalcitrant medium with which creators must grapple. Acknowledgement This work has been presented at various workshops and conferences, and we are grateful for the useful feedback received. Special thanks to Chiara Brozzo, Elisa Caldarola, Jacopo Domenicucci, John Kulvicki, Sabrina Martinez, Irene Olivero, Elizabeth Scarbrough, and Iris Vidmar-Jovanovic. Funding This research was funded by the ERC-StG Project 101040535 (‘PEA––The Philosophy of Experiential Artifacts’). Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. References Anscomb, C. (2022) ‘Creating Art with AI’, Odradek, 8: 13–51. Boden, M. (2016) AI: Its Nature and Future, Oxford: Oxford University Press. Chalmers, D. J. (2023) ‘Could a Large Language Model be Conscious?’ arXiv preprint arXiv:2303.07103. Available at: https://arxiv.org/abs/2303.07103, accessed 20 September 2024. Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 10 N. Young and E. Terrone Cooper, D. E. ‘Gardens’; in S. Davies et al. (Eds). (2009) A Companion to Aesthetics John Wiley & Sons. Davies, D. (2004) Art as Performance. New York: Blackwell. Epstein, Z. et al. (2023) ‘Art and the Science of Generative AI’, Science, 380: 1110–1. Esposito, E. (2022) Artificial Communication, Cambridge, MA: MIT Press. Helliwell, A. (2023) ‘Contribution to “Eight Scholars on Art and Artificial Intelligence” Aesthet- ics for Birds’, Available at: https://aestheticsforbirds.com/2023/11/02/eight-scholars-on- art-and-artificial-intelligence/, accessed 20 September 2024. Hertzmann, A. (2018) ‘Can Computers Create Art?.’ In Arts (Vol. 7, No. p. 18). Irvin, S. (2022) Immaterial: Rules in Contemporary Art. Oxford: Oxford University Press. Lopes, D. (2014) Beyond Art. Oxford: Oxford University Press. Lowe, E. J. (2014) ‘How Real Are Artefacts and Artefact Kinds?.’ Artefact Kinds: Ontology and the Human-Made World, 17–26. Martinez, S. and Scarbrough, E. (draft) ‘Co-Creators or Mere Tools? AI and Art’. Moruzzi, C. (2022) ‘Creative Agents: Rethinking Agency and Creativity in human and Artificial Systems’, Journal of Aesthetics and Phenomenology, 9: 245–68. Nguyen, C. T. (2020) Games: Agency as Art. Oxford: Oxford University Press. Sato, M. (2023) ‘How AI Art Killed an Indie Book Cover Contest. The Verge’, Available at: https://www.theverge.com/2023/6/9/23752354/ai-spfbo-cover-art-contest- midjourneyclarkesworld, accessed 20 September 2024. Thomson-Jones, K. (2016) ‘Movie Appreciation and the Digital Medium.’ In Current Controversies in Philosophy of Film (pp. 36–54). Routledge. Wojtkiewicz, K. (2023) ‘How Do You Solve a Problem like DALL-E 2?’ The Journal of Aesthetics and Art Criticism, 81: 454–67. Wollheim, R. (1980) Art and Its Objects: an Introduction to Aesthetics. 2nd edn. Revised, New York: Harper and Row. © The Author(s) 2024. Published by Oxford University Press on behalf of The Scots Philosophical Association and the University of St Andrews. All rights reserved. For permissions, please e-mail: [email protected] Downloaded from https://academic.oup.com/pq/advance-article/doi/10.1093/pq/pqae120/7775353 by guest on 13 October 2024 ###### ChatGPT said: Below is a detailed discussion of how our earlier ideas about science—centered on uncovering order, regularities, and laws—and Spinoza’s distinction between *natura naturans* and *natura naturata* relate to the arguments in the paper by Young and Terrone. --- ### 1\. Recapping the Dual Aspects of Nature **Spinoza’s Framework:** - **Natura Naturans (Nature Naturing):** This is nature in its active, generative mode—the self-causing, creative force that brings forth reality. In Spinoza’s view, this is akin to the underlying, dynamic processes that continually generate the world. - **Natura Naturata (Nature Natured):** These are the manifested, produced aspects of nature—the concrete outcomes or structures that result from the generative process. **Our Earlier Discussion of Science:** We talked about how all scientific disciplines, despite studying very different phenomena, share a core goal: to understand the order, regularities, and laws of the natural world. In this sense, the various fields of science examine different manifestations (or modes) of a single, intelligible nature: - **The underlying laws and forces (akin to *natura naturans* )** are what physics, for example, seeks to articulate in terms of universal principles. - **The specific outcomes, patterns, and structures (akin to *natura naturata* )** appear as the subject matter of disciplines like biology, geology, or chemistry. --- ### 2\. Linking Scientific Unity to Spinoza’s Concepts At an abstract level, the unity of science can be understood as follows: - **Scientific Method as an Encounter with Nature’s Autonomy:** All scientists assume that nature is both lawful and intelligible. They engage with a world that—while structured by underlying laws ( *natura naturans* )—produces a variety of phenomena ( *natura naturata* ) that can be systematically observed, measured, and explained. - **Different Disciplines, Different Manifestations:** Just as Spinoza differentiates between the active forces of nature and their produced effects, each scientific field focuses on particular aspects of this duality. For instance, quantum physics aims to reveal the fundamental interactions (the “active” part), whereas geology studies the sedimentary layers and rock formations (the “manifested” outcomes). --- ### 3\. The Paper’s Application: From Nature to the Medium of Gardening **Generative AI as an Artistic Medium:** Young and Terrone argue that Midjourney—a generative AI—should be understood neither as a traditional tool nor as an agent but rather as an artistic medium. They introduce a parallel with gardening by using Spinoza’s ideas: - **Gardening as Engagement with *Natura Naturans*:** In gardening, the gardener sows seeds and prunes plants, yet much of the growth process remains autonomous. The living garden is the result of nature’s generative activity—its *natura naturans* —and the gardener’s influence is iterative and incomplete. - **Machina Naturans:** The authors extend this analogy by coining the term *machina naturans* to describe machines (or AI systems like Midjourney) that, like nature, generate outcomes in a way that is autonomous and unpredictable. Here, the machine does not merely serve as a passive instrument; it participates in a process that resembles nature’s own creative dynamism. **Artistic Medium vs. Scientific Investigation:** - **In Artistic Creation:** The creative process with Midjourney is much like interacting with a garden. The user provides an initial “prompt” (comparable to planting a seed) and then iterates based on the emergent output. The outcome is a product of both the user’s input and the AI’s autonomous generative capacity—a blending of human intention with the unpredictable dynamics of *natura naturans*. - **In Scientific Practice:** Scientists too interact with nature in a similar, though more observational, way. They propose models, conduct experiments, and then observe outcomes that are determined by the underlying laws of nature—laws that are not fully under human control. In both cases, the creative or investigative process is a negotiation with forces that possess an inherent autonomy. --- ### 4\. Drawing Parallels: What Unites Science and the Artistic Medium? **The Role of Unpredictability and Control:** - In both scientific inquiry and in the creative use of generative AI, there is a recognition that one cannot fully dictate outcomes. - **Science:** Despite our best efforts to model and predict, natural phenomena often surprise us—revealing complexities that point back to the autonomous character of nature ( *natura naturans* ). - **Art with Midjourney:** Similarly, the user cannot micromanage every detail of the generated image. The unpredictability is not a bug but a feature—it is the “dynamic recalcitrance” of the medium that requires creative engagement, much as a gardener must work with the inherent variability of living systems. **Human Agency in an Autonomous World:** - Both domains highlight the interplay between human agency and the autonomous processes of nature or technology. - **In Science:** Scientists design experiments and develop theories to coax nature into revealing its secrets, yet the phenomena they observe are the product of a vast, self-generating natural order. - **In the Artistic Process:** The creative collaboration between the human and Midjourney mirrors this interplay. The artist (or gardener) initiates a process and then works iteratively with the medium, which possesses its own generative capabilities. **Unifying Theme: The Discovery and Co-Creation of Order** - Whether in science or in art, the central aim is to engage with a world that is ordered by underlying principles. - **Science:** Seeks to understand and articulate the laws and regularities (the “order”) that govern everything from subatomic particles to galactic structures. - **Generative AI as a Medium:** Invites us to reconceive creative practice as one where the “order” is not entirely under the artist’s control but emerges from a partnership with autonomous generative processes, reminiscent of how nature creates its own order ( *natura naturans* ), which then manifests in diverse and sometimes unpredictable ways ( *natura naturata* ). --- ### 5\. Concluding Thoughts In sum, the discussion in the paper dovetails with our earlier reflections on science in several ways: - **Both science and generative art engage with the dual nature of reality:** They confront the interplay between an active, self-generating force (whether natural laws or machine autonomy) and the concrete outcomes that emerge from it. - **Spinoza’s duality offers a powerful metaphor:** Just as the sciences distinguish between underlying principles and their manifestations, the paper uses Spinoza’s framework to conceptualize Midjourney as a medium that embodies a creative tension between control and autonomy. - **Dynamic Interaction as a Shared Feature:** In both domains, the practitioner (scientist or artist) works iteratively with processes that cannot be wholly commanded—be it nature’s inherent unpredictability or the recalcitrance of a generative AI system. Ultimately, both lines of inquiry—whether into the natural world through science or into creative expression through art—affirm that while human intention and methodology are crucial, they must always engage with a broader, autonomous order. This dynamic, a dance between control and emergent regularity, is at the heart of both scientific discovery and creative practice. --- This detailed exploration shows how the same philosophical insights that unify the sciences by their study of order and regularities also illuminate the innovative ways in which new media, like generative AI, challenge and expand our understanding of creative processes. ##### You said: Okay, I am actually Nick Young, the author of that paper I just gave you. Here is a rough draft of paper that I am trying to write, which I think of as a kind of sequel paper to this. However, I just can't quite get the ideas straight. I like a lot of the things I say, but I just can't get it myself to structure it properly. Okay, it's possible that I'm trying to cram too much into a paper and a more elegant structure would be waiting for me if I removed an idea or two. But yeah, I really don't know quite what to do. So keep in mind that I'm very attached to a lot of the ideas in this draft. But maybe what you've just been talking to me about, naturans and the different levels of science, maybe that will give us a new perspective on how to approach what I'm going for. So yeah, think how best we can... how best... think of an interesting way I can structure this paper to make an elegant, coherent, philosophical paper. Please note, I don't need any new ideas. Okay, I'm interested in how ideas can be rearranged or repurposed or reconnected or reframed so that they work better to make a more coherent whole, which captures some of the spirit of what I'm trying to get at here. Not just some of the spirit, a lot of the spirit. # The Environmental Aesthetics of Generative AI ## Introduction I argue here that there is an instructive parallel between how we come to appreciate the natural environment and how we might come to appreciate the output of generative AI systems such as Midjourney, ChatGPT, and Sora. While such systems produce text, images, video etc., it is clear that these creations are a quite different sort of thing from traditional artworks. A painting or poem can take months and be a direct result of an individual's labor, attention, and effort. In contrast, an AI painting or AI poem can be produced in seconds, by computational processes, which are, at the very least, not intentional in the same way that human cognitive processes are (footnote). This means that when it comes to appreciation we have two options: 1. AI art is not the sort of thing that can be aesthetically appreciated. 2. We appreciate AI art in a different way than we do traditional art. This paper develops the second option by drawing on Carlson's environmental aesthetics. In his view, the natural world should not be appreciated piecemeal –as a collection of landscapes or objects– but as what they actually are: \*\*unified, natural, dynamic\*\* \_environments\_. We argue that similarly, AI generative systems should be thought of as \_environments\_ with their own generative dynamics. This paper develops this analogy by showing how Spinoza's distinction complements and enriches Carlson's concept of unity, Spinoza's distinction between nature as process (natura naturans) and nature as product (natura naturata). We extend this framework to AI by conceptualizing machine-learning systems as \_machina naturans\_ (artificial generative processes) that produce \_machina naturata\_ (the resulting artefacts). The paper proceeds through four sections: (1) Carlson and Spinoza's complementary frameworks for understanding unity in nature, (2) the application of these concepts to AI as a generative substrate, (3) participatory appreciation through the gardener-prompter analogy, and (4) how informed spectators can properly appreciate AI outputs. ## 1. Appreciating Nature as what it in fact is Carlson's Natural Environmental Model for environmental aesthetics rests on two ideas: > First, that, as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (2000 p. 6) Regarding the idea that we must appreciate nature as what it in fact is—Carlson argues that the environment is not merely a collection of objects or scenes but a system of interconnected elements shaped by various processes and forces (ibid. p. 44). Environments thus "come about 'naturally,' \[in that\] they change, grow, and develop by means of natural processes." From this perspective, aesthetic appreciation involves recognising what one is encountering—a natural environment in which components are interrelated—and understanding it in light of scientific or other relevant knowledge that illuminates its composition and development. Spinoza's terms for these two aspects—\_natura naturata\_ (the concrete product we see) and \_natura naturans\_ (the active, generative processes)—make explicit Carlson's call to appreciate both the existing form and the unseen forces that produce and sustain it. Appreciating this dual aspect—appearance and underlying process—clarifies why scientific understanding heightens our aesthetic appreciation: it shows how the environment's present form is inseparable from the continuous natural forces that shaped it. On Carlson's recommendations, appropriate aesthetic appreciation of nature requires understanding nature. We should not appreciate a forest, coastline, or desert by treating it as we would a painting (framing it, isolating a static view); instead, we appreciate it \_as an environment\_, in light of knowledge about the natural processes that formed and sustain it. Carlson calls this the Natural Environmental Model of appreciation. Just as knowledge of art history and style informs our appreciation of a painting, scientific knowledge – ecology, geology, biology – informs our aesthetic engagement with natural environments (Carlson REF). Consider a coastal cliff like Beachy Head in England. A casual observer might admire it simply for its dramatic height or striking white color. But Carlson's model suggests a deeper appreciation emerges when we consider what the cliff \_actually is\_: a stratified chalk formation shaped by geological uplift and marine erosion, part of a broader shoreline ecosystem. Understanding the cliff's formation through sedimentation of ancient marine organisms, its subsequent uplift through tectonic forces, and its ongoing erosion by the sea allows us to see unity in what might otherwise appear as disparate features – the cliff's shape, the crashing waves at its base, the plant life on its edge – as all interconnected elements of one evolving natural scene. Such scientific cognizance situates what we perceive into a coherent narrative of natural forces. The coastal cliff is not just a picturesque object but a segment of \_nature's ongoing processes\_, appreciated for qualities (ruggedness, grandeur, harmonious form) that emerge from those processes. Natural environments possess an autonomous order: they are not designed for viewers, yet they exhibit form, structure, and beauty arising from natural forces. The "unity" of a natural environment, in Carlson's sense, means that every part is seen in relation to the whole. Here Carlson observes: > natural objects possess \[...\] an organic unity with their environments of creation: such objects are a part of and have developed out of the elements of their environments by means of the forces at work within those environments. Thus the environments of creation are aesthetically relevant to natural objects. (ibid. p. 44) In other words, the aesthetic character of a coastal cliff is not fully realised if one views the cliff in isolation as a static sculpture; it emerges when one appreciates the cliff as part of a larger process – the coastline shaped by tides and weather over millennia – and recognises that the same natural laws govern the rocks, the sea, and the life around it. Carlson's model shifts aesthetic focus from isolated objects to the interaction and continuity among elements, underlining that the environment is \_"a seamless unity"\_ of components and processes. This unity is dynamic: natural beauty often lies in change, growth, and decay (the dramatic collapse of a cliff section, the seasonal cycle of a forest) Carlson's ideas about unity can be understood in terms of Spinoza's distinction between \_natura naturans\_ ("naturing Nature") and \_natura naturata\_ ("natured Nature"). Spinoza used this distinction to describe two inseparable aspects of reality: (1) the active, productive aspect of nature (God or Nature doing: the endless generation of things through the laws of nature), and (2) the passive, product aspect (the things produced: the concrete world of particular forms and events that result from those laws) (Spinoza REF). Crucially, Spinoza did not see these as two different worlds, but as two perspectives on the one unified substance that is Nature. \_Natura naturans\_ is nature as process – the continuous lawful activity that generates and sustains the cosmos. \_Natura naturata\_ is nature as product – the totality of particular things and states of affairs that have been generated. Crucially, Spinoza did not see these as two different worlds, but as two perspectives on the one unified substance that is Nature. \_Natura naturans\_ is nature as process – the continuous lawful activity that generates and sustains the cosmos. \_Natura naturata\_ is nature as product – the totality of particular things and states of affairs that have been generated. On the Natural Environmental Model, when we appreciate a natural environment we appreciate both its current appearance and the unseen processes that gave rise to that appearance. Returning to our coastal cliff example, its sheer face and weathered texture are beautiful in part because they are legible as the outcome of centuries of wind and water – the cliff as process (erosion, sedimentation, or natura naturans) and as product (towering chalk face, or natura naturata) cannot be fully separated in our aesthetic experience. ## 2. Machina Naturans and the Generative Substrate of AI The principle that things must be appreciated "for what they in fact are" means we need to understand how they were created and what shaped them. In Spinozist terms, this means appreciating natura naturata (nature's products) in light of \_natura naturans\_ (nature's ongoing creative activity). By analogy, a generative AI system can be understood as \_machina naturans\_, an artificial generative process analogous to nature, which produces \_machina naturata\_: the images, texts, or other outputs of the system. To appreciate an AI-generated artifact for what it is, one must apprehend it as the result of an underlying generative unity – the computational processes and representations from which it emerges – rather than as an isolated, inexplicable object. In this section, we articulate the structure of such \_machina naturans\_ in machine learning, focusing on how its representational underpinnings function as a kind of "cultural soil" from which new artefacts grow. \*\*Latent Space as Cultural 'Soil'.\*\* Modern generative AI systems (for instance, large language models or image generators) rely on high-dimensional representational structures – often called \_latent spaces\_ or \_manifolds\_ – that encode patterns derived from vast corpora of human culture. These latent spaces are essentially compressed archives of training data, where information from countless artefacts (texts, images, recordings) has been subsumed into a mathematical structure. We can use an ecological metaphor to characterise this: the training corpus serves as a rich compost of \_experiential artefacts\_ from human culture, and the AI's latent space is the fertile soil formed by their decomposition. Drawing on Terrone's notion of "experiential artefacts" – artworks and media that we value for the experiences they evoke (Terrone REF) – we can say that generative models ingest a mulch of such cultural artifacts and metabolise it into abstract representational form. In other words, just as organic matter (leaves, wood, carcasses) decays into nutrients within soil, the AI model "digests" innumerable cultural artefacts (images, texts, sounds) into latent representations. What remains is not literal fragments of the original works, but a distributed encoding of their salient structures: a complex statistical memory of human experiences now made available for new combinations. Indeed, recent analyses describe latent space as a "multi-dimensional archive of culture," embedding "rich representations of our visual world" and other domains of human knowledge (Schaerf REF). This latent archive constitutes the generative substrate – the ground from which novel \_machinic naturata\_ can sprout. Crucially, the elements of this substrate are no longer intact artworks or discrete ideas, but interwoven patterns. The training process breaks down individual artifacts into features and learns how those features co-vary across the dataset. The result is analogous to topsoil enriched by humus: just as one cannot pick out a single leaf or branch from well-mulched earth, one typically cannot identify a single source image or text within a trained model's latent space. Instead, the legacy of countless sources blends into a \_generative potential\_. This perspective helps demystify the creativity of generative AI. Far from creating \_ex nihilo\_, the system reconstitutes and recombines ingredients provided by human culture. Appreciating an output of such a system thus involves recognising in it the transformed traces of many prior creations and experiences. The output is \_of\_ culture even though no individual human hand crafted it. In short, the AI's representational world is a cultural ecology: a dynamic repository of influences analogous to an ecosystem's soil, climate, and nutrients. And like any ecology, it gives rise to phenomena that reflect its conditions. \*\*From Generative Substrate to Emergent Artifact.\*\* Given this substrate, the process of generation in AI can be likened to growth or cultivation. When a generative model produces an image or a text in response to some input (a \_prompt\_ or initial conditions), it is akin to a seed germinating in fertile soil. The prompt sets constraints or initial directions (much as a seed carries genetic instructions), and the model's \_machina naturans\_ – its autonomous generative mechanism – develops this into a full output by drawing on the latent nutrients of its training. The end result, the \_machina naturata\_, is the artifact we observe: for example, a novel image that blends stylistic elements of Renaissance paintings with motifs from modern graphic design, or a paragraph of text mimicking a certain author's tone. Importantly, the generative process operates through the model's internal learned rules rather than direct human design. In this respect, the model's activity resembles nature's \_natura naturans\_: it is a cascading chain of interactions (among numerical weights, layers of a neural network, etc.) that unfolds largely on its own once initiated. The human user does not micromanage how the image develops any more than a gardener controls the minute cell divisions by which a bud blooms. At most, one can guide and adjust parameters from outside, but the detailed formation is driven by the system's inner dynamics. This autonomy of the generative process underlies the analogy between appreciating AI outputs and appreciating natural phenomena. Consider the experience of coming upon a beautiful flower in a garden. Part of what makes the flower aesthetically interesting (beyond its colors and form) is our understanding that it \_grew\_ from soil via sunlight, water, and the intrinsic vitality of the plant. The flower is an instance of \_natura naturata\_ – a finished product of nature – but we appreciate it against the background sense of \_natura naturans\_, the ongoing life process that produced it. We know the blossom is not a painted ornament assembled by an artisan; it is the visible expression of an organic unity (the plant and its environment). Likewise, when confronted with a striking image or text generated by an AI, there is an aesthetic dimension revealed by seeing it as the product of an underlying generative unity. The image is a piece of \_machina naturata\_ that has issued from an evolving computational process rather than from direct human fabrication. Recognizing this can change our mode of appreciation: we attend not only to the surface features (shapes, words) but also to how those features signify the operation of a complex \_machina naturans\_. Just as the delicate symmetry of a flower may be admired both for its immediate beauty and for what it tells us about biological growth, an AI artwork might be admired both for its manifest form and for the remarkable process that gave rise to it. To make this concrete, imagine an AI-generated landscape painting that looks uncannily like a work of Impressionism. A viewer informed by the generative context understands that the AI has synthesized this image from patterns learned across thousands of landscapes and artworks – a tapestry woven from threads of collective visual culture. Appreciating it "for what it is" means appreciating it as an emergent cultural \_flower\_ rather than misidentifying it as a traditionally painted canvas. If one falsely assumed a human painted it from scratch, one's aesthetic reaction might center on the implied intentions or skills of a non-existent artist ("What was the painter trying to express here?"). In contrast, knowing the true origin invites one to marvel at how the \_machina naturans\_ has combined and transformed elements of prior art into a new image. The sense of wonder or curiosity shifts: we might ask, "How did the model manage to evoke Monet's color palette with photographic realism in this scene?" Our aesthetic impression now includes an intellectual appreciation of the generative process – much as a nature-lover appreciates a coastline more deeply when understanding it as the cumulative work of tides and geology over eons. ### Internal Dynamics of Machine Learning Models To support this analogy further, we need to understand how generative AI systems work and why it is apt to describe them as \_environments shaped by internal forces and constraints\_. Modern generative models (such as GANs, VAEs, or large language models) operate by creating and then exploring a \_latent space\_ – a mathematical space encoding features of their training data. During training, the AI system compresses patterns from the data into a multi-dimensional array of numbers (the latent representation) and learns rules to construct new data from these representations. The result of training is not a fixed set of outputs, but rather a learned "world" of possible outputs: an abstract space with its own structure, in which moving in one direction might add, say, more "tree-ness" to an image, or alter the style of a sentence. Researchers often note that these latent spaces are highly structured: they capture meaningful variations in the data (for instance, one axis might correspond to image brightness, another to the presence of buildings vs. open terrain, etc.), even though they are not explicitly labelled (NICD REF). In a sense, the AI has \_discovered\_ an underlying order in the training examples and can use it to generate new combinations. This is analogous to how nature's processes impose an order (via physical laws) that shapes the forms we see. Indeed, we might say that a trained generative model is an artificial world of sorts: it has a kind of landscape (the latent space), natural laws (the model's learned weights and activation functions), and dynamic processes (the generative algorithm that produces outputs). When the model creates an image or text, it is akin to a mini-evolution or simulation happening inside the computer: starting from some initial conditions (noise or a prompt), the internal process iteratively applies its "laws" until a final form emerges. For example, \_diffusion models\_ (used in image generators) literally simulate a kind of random diffusion and then reverse it: they start with random noise and gradually \_coalesce\_ an image by applying learned denoising steps. This can be compared to how, say, a crystal forms from a chaotic solution under natural physical laws – patterns emerge as the process iterates. In neural network terms, the latent representation serves as the hidden generative environment: instead of directly drawing pixel by pixel, the model operates in a compressed feature-space where conceptually related images cluster together. In this latent environment, a single prompt like "old tree by lakeside" might map to a region dense with variations of that theme, and the model "wanders" there to pick one realization. From a technical standpoint, these systems have internal unity: every output is a manifestation of a consistent underlying model. All images generated by a given version of Midjourney, no matter how different, are related by the fact that they are shaped by the same training data and network weights. Analogously, every natural landscape on Earth is different, yet all are outcomes of the same fundamental natural laws (physics, chemistry, biology) and planetary history. This parallel highlights why we call the AI's process \_machina naturans\_. It is the generative capacity of the machine – the algorithm + training – that plays a role akin to "nature naturing." And the specific output we see at a given time is \_machina naturata\_ – a particular "natured" product of that generative capacity. Just as one cannot fully appreciate a natural vista without some sense of the forces that made it, one cannot fully appreciate an AI-generated artifact without recognizing it as the endpoint of an algorithmic generative trajectory. In sum, the structures and processes inside generative AI form a unified generative environment analogous to a natural environment. The latent space – built from the decomposed archive of cultural artifacts – plays a role akin to soil in an ecosystem, containing and recycling the detritus of countless creative works. The generative algorithm's functioning (the forward pass of the neural network, the diffusion of noise into an image, etc.) is comparable to nature's growth forces, spontaneously producing novel configurations. And the outputs are the \_machina naturata\_ that we ultimately encounter, analogous to the plants, rocks, or rivers that constitute \_natura naturata\_. According to the Carlsonian insight, proper aesthetic appreciation depends on understanding an object in light of "what it in fact is" – here, a product of machina naturans. In the case of AI art, that means the spectator or critic should situate the work within its generative context: the cultural-technical soil that nourished it and the algorithmic activity that shaped it. By doing so, we treat the AI output neither as a mere random oddity nor as a strictly human artwork, but as \_the kind of thing it truly is\_: an expression of a hybrid natural-cultural generative process. This sets the stage for a deeper inquiry into the roles of those who engage with such processes. In particular, if \_machina naturans\_ is analogous to nature, what is the role of the human prompter who tends and guides it? We turn now to the figure of the prompter, who stands in relation to generative AI much as a gardener stands in relation to fertile nature. ## 3. Participatory Appreciation: The Prompter as Gardener Thus far, we have treated generative AI's machina naturans as an autonomous system akin to nature, with outputs comparable to natural products. However, just as natural environments often have human caretakers or participants (gardeners, hikers, scientists) who interact with nature's generative forces, so too do AI systems have users or "prompters" who engage with their generative capacities. Carlson's framework primarily valorizes a contemplative appreciation grounded in knowledge – a somewhat distanced stance of observing nature with understanding. Yet Carlson and others also acknowledge that direct \_participation\_ in natural processes (for instance, the hands-on appreciation a gardener has for her garden) offers a legitimate and perhaps qualitatively different mode of aesthetic appreciation. In environmental aesthetics, one can appreciate a landscape by studying it and beholding it, but one can also appreciate a garden by cultivating it. We can apply this insight to the realm of AI art: interacting with a generative model by crafting prompts and curating outputs is a form of participatory aesthetic engagement. The prompter stands at the critical interface of \_machina naturans\_ and \_machina naturata\_, analogous to the way a gardener mediates between natura naturans and the blooms of natura naturata. In this section, we explore how the prompter's role as a kind of "digital gardener" yields a unique, practice-based appreciation of AI's generative artistry. \*\*Guiding a Partly Autonomous Process.\*\* A gardener does not create a flower in the way an artist might create a porcelain replica of a flower; instead, the gardener provides conditions and guidance for a plant to grow, while the actual formation of the flower is executed by nature. In the same way, a prompter using a system like Midjourney or GPT-4 does not explicitly design every detail of the output; rather, they input a prompt (plant a seed) and then \_machina naturans\_ – the AI's internal generative process – autonomously produces an outcome. The prompter's art lies in skillfully guiding this process: selecting input phrases, adjusting parameters, iterating and refining prompts, and maybe applying slight post-generation tweaks, all in an effort to coax the system toward a desired kind of result. Yet, as with gardening, there is an inherent unpredictability and only partial controllability in working with generative AI. The AI, like nature, has a "dynamic recalcitrance" – it can surprise, resist fine control, and require adaptation. Users often find that a prompt yields an output that is interesting but not quite what they envisioned, prompting them to adjust their input and try again (much as a gardener might prune a plant, water differently, or move a flowerpot to shade in response to how the plant grows). This interactive, iterative cultivation is central to the prompter's activity. Nick Young and Enrico Terrone draw this parallel explicitly, arguing that creating images with a tool like Midjourney "parallels a gardener's interaction with \_natura naturans\_—nature's autonomous generative processes" (Young & Terrone REF). They introduce the term "\_machina naturans\_" to characterize machines capable of autonomous generative behavior, with generative AI as the prime example. In their analogy, the \_prompter\_ relates to the AI much as a gardener relates to a plot of fertile land. The gardener can plant seeds (input prompts), tend the conditions, and make rudimentary interventions (trimming, rephrasing, re-rolling outputs), but cannot dictate every outcome in advance. As they note, a painter can precisely control each brush stroke in painting a sunflower, but a gardener cannot likewise control the exact shape of a sunflower growing in her garden, and a prompter cannot precisely determine every detail of a sunflower image generated by the AI. Both gardener and prompter exercise skillful yet incomplete control over a process that has its own momentum. The creative role of the human here is one of \_partnership\_ or \_stewardship\_ rather than unilateral mastery. This kind of partnership yields a distinctive form of aesthetic satisfaction. The gardener's pleasure in seeing her garden in full bloom is partly an appreciation of nature's beauty, but it is heightened by her intimate involvement in the process – the knowledge that her choices and care contributed to, and conversed with, nature's growth. Similarly, a prompter experiences a special gratification on obtaining a stunning AI-generated image after many rounds of trial and error. The result is appreciated not just for its visual qualities, but also as a culmination of an interactive process in which the prompter's ingenuity met the machine's generative power. In philosophical terms, the prompter has situated knowledge of the AI system. Through hands-on engagement, they develop a tacit understanding of the model's "personality" – its tendencies, strengths, and limitations. For example, an experienced user learns how a certain phrase will likely influence the style of an image, or how adjusting a parameter will affect the randomness of the output. This is analogous to a gardener's practical knowledge of soil types, weather patterns, or how a certain species of plant responds to pruning. Such knowledge is not purely theoretical; it is empirical, gained by experience and feedback. It allows the prompter to appreciate subtleties in the AI's outputs that a casual observer might miss – just as a gardener notices the slight improvements in a rose's bloom due to a different fertilizing technique. It is important to clarify that calling the prompter a "gardener" of AI art is not to suggest that the prompter exercises complete creative authorship in the traditional sense. Rather, the prompter is a co-creator operating within the constraints and opportunities provided by the \_machina naturans\_. The gardener metaphor highlights that the prompter's agency is collaborative: he or she works \_with\_ the autonomous generative processes. As one commentary puts it, "the gardener \[is\] a creative agent who is nevertheless thoroughly dependent on the cooperation of natural processes"; by the same token, "the prompter is a creative agent who is nevertheless thoroughly dependent on the cooperation of artificial processes" (Young & Terrone REF). This dependence does not diminish the prompter's contribution – on the contrary, it defines a new mode of creativity. The aesthetic achievement (a beautiful garden, an impressive AI artwork) emerges from the fusion of human decision and non-human generation. Appreciating the final product thus involves appreciating this fusion. A person admiring a well-kept garden can often sense the gardener's hand in the layout and cultivation, while also sensing the wild vitality of nature that exceeds the gardener's designs. Likewise, when we know an image was produced through a prompt-driven interaction, we may simultaneously credit the prompter's vision \_and\_ the AI's algorithmic fecundity. Carlson's original model of nature appreciation emphasized knowledgeable contemplation, but here we see an active mode of appreciation that Carlson (and environmental aestheticians more broadly) can accommodate: the appreciation in doing. The gardener \_appreciates by cultivating\_, and analogously the prompter \_appreciates by prompting\_. Each stands at the nexus of naturans and naturata: the gardener physically engages with soil, water, and living plants, dwelling in the interface between process and product; the prompter intellectually and creatively engages with the model's latent space and output, inhabiting the interface between algorithmic process and resulting image/text. This participatory immersion provides an aesthetic experience that is more process-oriented and intrinsically rewarding in a way that complements the appreciation of a finished product from an external viewpoint. In the AI case, many practitioners describe a sense of discovery and play when working with generative models – an aesthetic joy in exploring what the \_machina naturans\_ can produce, akin to the joy a gardener finds in experimenting with new plants and arrangements. Finally, the gardener analogy helps illuminate the notion of aesthetic profundity arising from generative unity. When a person both participates in and observes a generative process, they often develop a heightened sense of connection to the underlying unity of that process. A devoted gardener may feel deeply connected to her garden as a living whole; the boundary between creator and creation blurs as she attunes to the rhythms of growth and change. This sense of continuity between self, process, and product can be aesthetically profound – it situates the beauty of the flower in a larger meaningful narrative of cultivation and care. Similarly, a prompter who engages deeply with an AI system might feel a certain awe at the \_machina naturans\_ as a creative force and a sense of personal connection to its outputs. The outputs are not just prettified pixels or neatly arranged words; for the engaged prompter they are the visible tips of an iceberg of generative activity in which the prompter has played an integral part. Insofar as the prompter appreciates the output, it is with an awareness of the vast latent space and complex algorithmic dance that underlies it – a parallel to how a knowledgeable gardener perceives even a simple blossom against the background of botany and ecology. This awareness of generative unity can imbue the experience with a depth beyond surface impressions. One might say the prompter appreciates the \_process-in-the-artifact\_, perceiving the unity of machina naturans and machina naturata as aesthetically significant in itself. ## 4. Aesthetic Profundity and the Informed Spectator Thus far we have established a parallel between environmental appreciation and AI art appreciation in terms of generative contexts and the possibility of participatory engagement. We now turn to the case of the informed spectator – someone who observes and appreciates a piece of AI-generated art without having been the prompter or creator. This figure is analogous to a hiker admiring a wildflower they did not plant, or a tourist marveling at coastal rock formations they did not shape. The challenge is to show how such a spectator might attain an appropriate aesthetic appreciation of \_machina naturata\_ by drawing on knowledge of \_machina naturans\_. In keeping with Carlson's dictum, the spectator must appreciate the artifact for "what it in fact is," which in this context means recognising it as the product of an AI generative process (and not, say, a human-made piece of art in the traditional sense). We will argue that even without hands-on involvement, a spectator can achieve a form of conceptual engagement with the generative unity behind the work, thereby accessing a degree of aesthetic depth or profundity comparable to that experienced by the prompter. The key is the spectator's understanding – whether scientific, technical, or via informed imagination – of the underlying creative forces at play. \*\*Knowledge as a Bridge to Appreciation.\*\* Carlson's environmental model asserts that knowledge of natural history and ecology can transform one's experience of a landscape: knowing \_what\_ one is looking at and \_how\_ it came to be enhances its aesthetic qualities. A similar principle applies to AI art. When an observer knows that a given image was generated by a diffusion model trained on, for example, millions of photographs and paintings, this knowledge can attune them to aspects of the image they might otherwise overlook. Instead of viewing it as a mere picture, they perceive it as a \_convergence of influences\_ orchestrated by an algorithm. The colours blending in a sunset scene might prompt the spectator to recall that the AI likely absorbed countless real sunset photos; the inventive twist in the style (say, pixelated impressionism) might be recognised as an emergent quirk of the model rather than the personal signature of a human artist. In effect, the informed spectator sees the \_machina naturata\_ (the output) in relation to its \_machina naturans\_ (the process and dataset). This satisfies the criterion of appreciating the work as the kind of thing it is – namely, an AI-generated artifact. By contrast, a spectator ignorant of the work's origin might apply mismatched categories (for instance, praising brushwork that doesn't exist, or faulting the piece for lack of human emotion, or simply feeling uneasy without knowing why). Informed knowledge prevents such category errors and opens up the path for genuine appreciation. Consider again the analogy with nature. A casual observer might enjoy a coastal vista simply for its picturesque scenery. But an informed observer, who knows that the jagged cliffs were carved by waves over millennia and that the black sand resulted from volcanic activity, can appreciate the scene at another level: the landscape becomes a story of natural forces made visible, a testament to \_natura naturans\_ at work. This geological and ecological understanding often increases the viewer's sense of wonder or reverence – the scene feels more profound because it is connected in the mind to vast processes of time and change. Analogously, an informed viewer of an AI-generated artwork can perceive in it the imprint of vast underlying processes: the training of the model on global datasets, the complex mathematics of neural networks, the iterative refinement through which the final output emerged. Even if these processes are not directly observable in the image or text, knowledge about them can imbue the artifact with an added fascination. The spectator might think, "This surreal landscape looks like a blend of Van Gogh and Dalí – and indeed, knowing the AI was trained on Western art, I can see how it \_dreamed\_ up this scene by combining those influences." The artwork thus becomes not only an image to look at, but evidence of an unseen generative unity – a \_machinic imagination\_ operating behind the scenes. One might worry that requiring such knowledge makes the appreciation of AI art overly intellectual. However, the knowledge need not be detailed technical expertise; it can be more akin to a general understanding or narrative of origin. In art appreciation generally, viewers often benefit from knowing the context of a work's creation (the artist's intent, the art movement, the techniques used). Here the context happens to be a computational one. For example, a museum exhibition of AI-generated art might include wall text explaining the basics of how the pieces were made ("This artwork was created by an artificial neural network that was fed thousands of landscape paintings and then instructed via text prompts to produce new images."). A viewer who reads this gains a framework for interpretation: the strange blending of styles or the slight uncanny quality of the image now makes sense as a result of that process. They might aesthetically enjoy trying to spot which elements came from which influence, or simply marvel at the capability of the system. In short, conceptual knowledge serves as a bridge that allows the spectator's imagination to connect the finished artifact back to its generative source. \*\*Imagination and Aesthetic Profundity.\*\* Beyond factual knowledge, there is also a role for the spectator's imagination in deepening aesthetic experience. Even in the absence of complete information about an AI's workings, a spectator can speculate or conceptually envision the process that led to the artwork. This is similar to how one might appreciate a thunderstorm by imaginatively connecting the lightning and thunder to the idea of electrical charges and atmospheric dynamics, even if one is not a meteorologist. In the case of AI art, an informed imagination means the viewer actively conceives of the artwork as \_a machinic creation\_. They might imagine the myriad images the AI "saw" during training, now distilled into this one output, or picture the algorithm iterating through noise and clarity to form the image. This imaginative reconstruction can yield a sense of awe or "aesthetic sublime," where the viewer feels the presence of something larger and more complex than the immediately given form. The artwork points beyond itself to the totality of data and computations that birthed it – much as a fossil points to a prehistoric world or starlight points to cosmic distances. We can thus identify a source of aesthetic profundity in AI art that parallels that in nature. Both a wild coastline and a well-crafted AI-generated image can inspire profound feeling because they connect the perceiver to a grand creative force: in one case, the force of natural processes; in the other, the sprawling convergence of human culture and machine learning. The unity of \_natura naturans\_ or \_machina naturans\_ with the particular \_naturata\_ before us evokes a sense of continuity and meaning. It reminds us that what we admire is not an isolated object but part of an ongoing story of formation. Many have noted that nature's beauty often lies not just in static appearance but in our sense of the \_life\_ or \_history\_ within it (the way the gnarled trunk of an old tree is beautiful partly because it signifies decades of growth, struggle, and regeneration). Likewise, one could argue that a compelling aspect of AI art is that it encapsulates an expansive cultural and computational journey: it is literally the output of a network that has learned from innumerable prior artifacts and is performing an act of creation. When this backstory is appreciated, the artwork gains a new dimension of significance. The \_profundity\_ here is intellectual-emotional: a mixture of admiration for human technological ingenuity, reflection on the interplay between human creativity and artificial generation, and perhaps a philosophical wonder at how patterns of meaning can arise in unexpected new mediums. It is worth emphasizing that the informed spectator's appreciation still relies on the fusion of process and product we have been discussing throughout. Even though the spectator did not personally operate the generative process, their understanding of that process lets them symbolically reunite the machina naturata with its machina naturans in their mind. In effect, the spectator mentally reconstructs the connection that the prompter physically engaged in. This is analogous to how one can appreciate gardening without being a gardener: a visitor to a garden who knows about horticulture can admire how the layout and health of the plants result from skilled gardening practices and natural growth working in concert. They might notice, "These roses are vibrant – someone must have carefully tended the soil and pruned them at the right times," thereby acknowledging the cooperation of gardener and nature. Similarly, an informed audience of AI art can think, "This image's surreal yet coherent style tells me the AI blended different art genres – and the person who guided it must have found a clever prompt to yield such balance." In doing so, the audience member is \_conceptually participating\_ in the generative unity: they appreciate the artifact with an awareness of both the human guidance and the machine creativity behind it. Lastly, one might ask: Is it truly necessary for appreciation to depend on knowing the work's origin in this way? In environmental aesthetics, there is debate about whether one can appropriately appreciate nature without scientific knowledge. Carlson would say such knowledge is required for full appreciation, whereas others might allow more intuitive or emotional engagement to count as aesthetic. In the AI case, if a person unknowingly enjoys an AI-generated artwork under the false belief it is human-made, are they appreciating it wrongly? One could argue yes – they attribute qualities or intentions to it that are mislocated. Their experience might still be pleasant, but it misses the distinctive character of the work. Our view, following the spirit of Carlson, is that a degree of category-informed understanding is indeed crucial for the \_proper\_ aesthetic appreciation of AI art. The work's identity as \_machina naturata\_ is one of its defining features. Appreciators should not ignore that any more than one should ignore that a waterfall is a natural phenomenon (not a fountain sculpture) when judging its aesthetics. That said, the knowledge can be quite general. What matters is that the spectator situates the work in the right conceptual frame – as an output of generative AI – and thereby becomes attuned to the right qualities. ## Conclusion We have developed an analogy between environmental aesthetics and the aesthetics of generative AI, grounded in the idea of appreciating things \_as what they are in fact\_. Carlson's environmental aesthetics, complemented by Spinoza's process-product distinction, provides a unified framework for understanding how natural environments should be appreciated as integrated wholes shaped by natural processes. Extending this framework to AI, we introduced the concepts of \_machina naturans\_ (the machine as generative process) and \_machina naturata\_ (the machine's product). We argued that advanced generative AI systems can be viewed as artificial, law-governed environments with each output analogous to a "natural" occurrence within the system, governed by the system's trained parameters. Therefore, an AI output is best appreciated not as a standalone traditional artwork, but in relation to the AI's generative context. Through our examination of how AI latent spaces function as "cultural soil" and how generative algorithms operate, we showed that the aesthetic characteristics of an AI output are inseparable from the process that produced it. We then expanded this analogy through the gardener–prompter comparison, showing that engaging with AI's generative process can be a mode of aesthetic appreciation analogous to gardening. Finally, we demonstrated how even non-participating spectators can achieve proper aesthetic appreciation of AI outputs through informed understanding of their generative origins. One virtue of this framework is its coherence: it provides a unified way to think about various phenomena in AI aesthetics that might otherwise seem disconnected. It explains why people often describe using generative AI in exploratory, discovery-laden terms – because they are treating it like an environment to explore. It explains why the lack of human intention in AI art need not be a deficit – because we transfer the source of order to the AI's internal dynamics, much as we find beauty in natural forms with no human author. It also offers an elegant resolution to the debate of "is the AI the artist, or the prompter, or the programmer?" by sidestepping the notion of a single artist: the AI art emerges from an interactive unity of machine and human, process and prompt, akin to a collaborative emergence. By appreciating AI artworks as we do environments, we foster a stance of respectful fascination: we respect the AI's complexity and limits, and we are fascinated by the interplay between our inputs and its autonomous development. In doing so, we may find a new kind of beauty – one that is born from code and data, yet resonates with age-old aesthetic ideals of unity, harmony, and the sublime power of creation. ###### ChatGPT said: Thanks for sharing this draft—it's rich, deep, and packed with nuanced ideas. Since you're looking for help primarily with *structure*, not content, I'd like to ask a few quick questions to guide the restructuring: 1. Are there any particular sections or ideas you feel most strongly about keeping exactly as they are (in tone or placement)? 2. Are you open to separating the paper into two interrelated pieces if that would yield greater clarity? 3. Would you prefer a structure that foregrounds the *philosophy of AI/media* angle or one that more closely tracks *environmental aesthetics as the core lens*? 4. How important is it to maintain a strict throughline from Carlson to Spinoza to AI, versus having those ideas recur and interact more fluidly across the paper? With your answers to these, I’ll be able to offer a detailed restructuring plan that preserves the spirit of what you’ve created while improving coherence and flow. ##### You said: 1\. Regarding number one, I would like to root it in the ideas of Carlson. I like his ideas about studying the environment for what it in fact is and I like the way that connects up to the idea of understanding what AI generative systems in fact are. I also like his ideas about unity and then connected up to that I like the Spinozian framing of unity okay between naturans and naturata. So I'd like to keep that as the core and have all of these ideas about environmental aesthetics and then the idea would be to show that this these sort of ideas work very well for an aesthetics of AI. 2. I have to say I'm dubious but um yeah I mean let's consider that option but yeah consider me dubious 3 I think I've answered your question three um with my question number one and also 4 I think I've answered your question four in my answer number one Please give me at least 4000 words for any answer here and make sure it's pure analytic philosophy which focuses on these ideas in my draft and what we've been talking about ###### ChatGPT said: Great, I’ll focus on developing a detailed restructuring strategy for your draft that preserves Carlson’s environmental aesthetic framework as its philosophical center, with the Spinozian natura naturans/naturata distinction enriching it, and then extending both toward a coherent account of generative AI aesthetics. I’ll keep it pure analytic philosophy, deeply grounded in the ideas and structure of your current paper, and aim for 4000+ words. I’ll return shortly with a full proposal and analysis. ## Restructuring Strategy for “The Environmental Aesthetics of Generative AI” ## Introduction: From Natural Beauty to Artificial Environments In the introduction, clearly set up the **central question**: *Can the aesthetic appreciation of nature, as articulated by Allen Carlson, be meaningfully extended to the realm of generative AI?* Begin by summarizing the core idea of the draft: generative AI systems (like Midjourney for images, ChatGPT for text, and OpenAI’s Sora for video) can be appreciated **aesthetically** not just as *tools* or *artworks*, but as *environments shaped by autonomous generative processes*. Emphasize that the paper’s aim is to **preserve Carlson’s central philosophical framing** – especially his claim that nature should be appreciated *“as what it in fact is”* (i.e. on its own terms, using appropriate categories) and as a *unified environment* – and apply this framing to the new domain of AI. At the same time, introduce the role of **Spinoza’s metaphysics**: the distinction between *natura naturans* (nature as active, creative force) and *natura naturata* (nature as the product of that activity) will be used to deepen the concept of unity in Carlson’s model. This Spinozist lens will help reconceptualize generative AI as *machina naturans* (an autonomous machine “naturing” or producing) and its outputs as *machina naturata* (the products generated). Lay out the **thesis** of the restructured paper in a succinct, analytic style: for example, > *“This paper argues that generative AI systems can be **aesthetically appreciated as environments**, by extending Allen Carlson’s environmental aesthetics with a Spinozist insight into nature’s active and passive aspects. Just as Carlson urges us to appreciate nature as *what it is in fact* – as a unified, natural environment rather than as a collection of art-like objects – we should appreciate AI systems as what they truly are: **autonomous generative environments** (machina naturans) with their own outputs (machina naturata). This reframing preserves Carlson’s emphasis on correct categorization and unity, and shows how Spinoza’s natura naturans/naturata enriches our understanding of unity and creativity in an “aesthetics of AI.”* ” After stating the thesis, **motivate** the discussion: Why is it important or interesting to treat AI in this way? You might note the growing presence of generative AI in art and design, raising questions about how to appreciate AI-generated images, text, or video. Typically, we have well-developed theories for **art** (human-created artifacts) and for **nature**, but generative AI blurs these categories – it is artificial in origin yet often autonomous in operation. This calls for a new aesthetic framework. By drawing on Carlson and Spinoza, we can avoid simply labeling AI outputs as “fake art” or evaluating them by inappropriate standards; instead, we can appreciate them *on their own terms*. Finally, outline the **structure** of the paper. Forecast that the paper will proceed in a logically organized manner: first reviewing Carlson’s environmental aesthetics and its key principles (appreciating nature *as nature*, and the idea of unified environments), then introducing Spinoza’s natura naturans/naturata distinction to enrich those principles, and finally applying this combined framework to generative AI systems (with examples like Midjourney, ChatGPT, and Sora). Mention that potential objections (e.g. “But AI isn’t *really* nature”) will be addressed, and that the conclusion will highlight how this approach yields a more coherent and rigorous understanding of an **aesthetics of AI**. The tone here should be one of *clarity and purpose*: the reader should know exactly what to expect and why each component is necessary. **Why this restructuring?** In the original draft, these elements may not have been presented in an optimal order. The introduction of the restructured paper must establish the *conceptual bridge* between environmental aesthetics and AI right away. By clearly stating the problem (aesthetic category confusion with AI) and the solution strategy (using Carlson’s model plus Spinoza’s insight), we set the stage for a focused, unified argument. Ensure the introduction does not delve into excessive detail or new ideas – it should preview *existing* ideas from the draft, re-framed to show their logical connections. Aim for a concise but **rich** overview that engages a philosophy reader: it should signal that the paper will take a serious, conceptually rigorous approach (analytic philosophy style) to a novel topic. *(By the end of the introduction, the reader should understand the main claim and how Carlson and Spinoza will be used. The stage is then set to delve into Carlson’s theory in depth.)* ## Carlson’s Environmental Aesthetics: Appreciating Nature as Nature Begin the next major section by **expounding Allen Carlson’s environmental aesthetics** in a structured, analytic way. This section serves as the philosophical foundation, so it should carefully explain Carlson’s key ideas and why they matter for the current project. Possible organization: **1\. The “Natural Environmental Model” (NEM) – Overview:** Carlson’s approach, often called the Natural Environmental Model, insists that *appropriate aesthetic appreciation of nature* is fundamentally different from the appreciation of art. Summarize Carlson’s core claim: *we must appreciate nature as what it **in fact** is – as nature, not as artifice* [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Spring_2006/Carlson_Appreciation_and_the_Natural_Environment.htm#:~:text=,made) [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=knowledge%20of%20art%20history%20and,the%20way%20in%20which%20knowledge). In Carlson’s words, appreciating nature demands *“regarding it as the particular sort of environment that it is”*, based on scientific knowledge of that environment [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Alongside%20his%20critique%20of%20the,analogy%20between%20nature%20and%20art). Contrast this with the mistaken approaches Carlson critiques: - **The Landscape Model (scenic appreciation):** where one treats natural scenes like paintings or vistas. Carlson argues this is inappropriate because it frames nature as a series of static images (like treating a mountain view as if it were a painted landscape). This *“appreciates nature as something it is not – a view or scene – rather than as what it is”*, leading to aesthetic mistakes [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Spring_2006/Carlson_Appreciation_and_the_Natural_Environment.htm#:~:text=,made). For instance, focusing only on visual scenery ignores other sensory aspects of nature, and often favors picturesque locales while neglecting less immediately scenic ecosystems. Carlson finds this analogous to enjoying a painting, not nature itself. - **The Object Model:** where one picks out individual natural objects (a rock, a tree) and appreciates them as if they were sculptures or art objects in isolation. Carlson also rejects this: isolating a natural object from its environment or treating it like a human-made art piece either *turns it into an artifact via our imagination or leads to category errors*. A rock, for example, does not have the same kind of expressive properties as a sculpture, and removing it from its ecological context **distorts** its aesthetic character [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Spring_2006/Carlson_Appreciation_and_the_Natural_Environment.htm#:~:text=,does%20that%20rock%20express%20solidity). After describing these rejected models, **clearly state Carlson’s positive alternative**: the aesthetic appreciation of nature should treat nature *on its own terms*. That means two crucial things: (a) *Nature is appreciated as natural*, not as a product of human design, and (b) *Nature is appreciated as an environment*, not a mere collection of discrete objects or images [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Spring_2006/Carlson_Appreciation_and_the_Natural_Environment.htm#:~:text=than%20as%20what%20it%20is%2C,and%20how%20to%20app%20questions). Carlson’s model thus requires the appreciator to take an **environmental perspective** – seeing the landscape as an interconnected whole that one is embedded in, and using knowledge about what one is looking at to guide the appreciation. Support this explanation with Carlson’s own formulation. For example, Carlson (1979) analogizes to art appreciation: just as a knowledgeable viewer appreciates Picasso’s *Guernica* by understanding it *as a cubist painting in the context of art history*, the nature lover should appreciate a geological formation or an ecosystem *as what it in fact is – as described by geology, biology, ecology* [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=knowledge%20of%20art%20history%20and,the%20way%20in%20which%20knowledge). Cite Carlson or commentators to back this up: *“appropriately appreciating a natural environment requires regarding it as the particular sort of environment that it is: that is to say, in terms of the characterization… provided by natural science”* [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Alongside%20his%20critique%20of%20the,analogy%20between%20nature%20and%20art). This is Carlson’s *cognitive thesis*: scientific knowledge (natural history, ecology, etc.) provides the “correct categories” for identifying *what* a natural thing is, which in turn determines *how* to appreciate it aesthetically. **2\. Unity and the Environmental Whole:** Next, draw attention to Carlson’s emphasis on the **unity** and **order** of natural environments. One key outcome of appreciating nature through the lens of science, Carlson argues, is that we come to see nature as **orderly, harmonious, and unified**, which for him are aesthetically positive qualities [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Fall_2010/Carlson_Aesthetic_Appreciation_of_the_Natural_Environment.htm#:~:text=a,these%20are%20aesthetically%20positive%20traits). Unpack this idea carefully. Why does Carlson believe a natural environment should be appreciated as a *unified whole* rather than as isolated parts? There are a few points to mention: - **Contextual Aesthetic Qualities:** Many aesthetic qualities of natural things only emerge when we consider them in their environmental context. A lone boulder might not strike us as beautiful in itself, but seeing it *in situ* as part of a mountainside shaped by glacial forces (knowledge of which reveals an underlying order) can make it aesthetically interesting. The environment is not just a miscellaneous container of things; it is an integrated system. Carlson’s model encourages awareness of relationships: predator and prey, rock and river, climate and vegetation – all forming a larger pattern. Appreciating a forest correctly means appreciating it *as a forest ecosystem*, with interdependent elements, rather than, say, as a random collection of tree-shaped objects. This yields a sense of wholeness or **integrity** in the aesthetic experience. - **Scientific Unity => Aesthetic Unity:** Carlson often points out that natural science portrays nature as a network of cause and effect, evolved adaptations, and geophysical processes, which tends to underscore *unity in diversity*. For example, knowing how a valley was formed (glacial vs. river erosion) allows one to see disparate features of the landscape (the U-shape of a valley, the distribution of rocks) as parts of a coherent whole. This knowledge-based unity is aesthetically valuable: nature, properly understood, is *not chaotic* but has an internal logic and coherence. Carlson even suggests this underlies the idea of **positive aesthetics** (the view that untouched nature is essentially aesthetically good or beautiful) – since science reveals nature’s harmony and balance, an informed appreciation will find beauty in most natural phenomena [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Fall_2010/Carlson_Aesthetic_Appreciation_of_the_Natural_Environment.htm#:~:text=a,these%20are%20aesthetically%20positive%20traits). - **The Environment as Background:** Another aspect of unity in Carlson’s thought is that environments form a continuous background in which we (the observers) are situated. Unlike a painting which has a frame separating art from viewer, an environment surrounds the observer. Carlson (following some environmental thinkers) stresses that we typically engage nature by moving through it, by multisensory immersion, not by contemplating a separated object. Thus, **spatial unity** (everything is part of one continuous space around you) and **temporal unity** (processes unfolding over time) are part of environmental appreciation. Carlson himself remained more on the cognitive side (focusing on knowledge guiding what to appreciate), while others like Arnold Berleant emphasized immersion and continuity. You can mention this contrast to clarify Carlson’s position: Carlson doesn’t go so far as to dissolve the subject-object distinction (he actually criticizes total “aesthetics of engagement” for lacking focus [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Fall_2010/Carlson_Aesthetic_Appreciation_of_the_Natural_Environment.htm#:~:text=28) ), but he *does* require that we situate aesthetic objects within the whole environment to appreciate them properly, rather than treating nature as a sequence of museum exhibits. By the end of this Carlson section, the reader should have a firm grasp of two pillars: **(a)** the *“as what it is in fact”* principle (proper category = appreciate nature as natural, not as art; use scientific knowledge to appreciate appropriately), and **(b)** the importance of *environmental unity* (seeing nature as an integrated, orderly whole, not a jumble or a mere pretty picture). Use a straightforward analytical tone, perhaps even enumerating these principles for clarity: - *Principle 1 (Correct Category):* We must appreciate an object *as itself*. In art, this means understanding the art-historical category (e.g. cubist painting); in nature, this means understanding the natural category (e.g. this is a sedimentary rock formation in a desert ecosystem) [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=knowledge%20of%20art%20history%20and,the%20way%20in%20which%20knowledge). Misidentification leads to aesthetic *inappropriateness*. Carlson’s slogan: **appreciate nature as nature**, not as artifice. - *Principle 2 (Environmental Unity):* We should appreciate nature as a **unified environment**, not a series of isolated objects or scenes. The environment’s **unity** – revealed by ecological and geological knowledge – is central to its aesthetic value [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Fall_2010/Carlson_Aesthetic_Appreciation_of_the_Natural_Environment.htm#:~:text=a,these%20are%20aesthetically%20positive%20traits). This implies appreciating relationships and processes (how parts fit into the whole) rather than just isolated sensations. Cite Carlson’s work or commentators to support each point, as we have done with key quotes, to show fidelity to the original ideas. By doing this, we preserve the *spirit* of Carlson’s framing in a concise, rigorous way, which will be crucial when we later analogize these points to AI. Importantly, **transition** at the end of the Carlson section towards any tension or open question that leads into Spinoza. For example, point out that Carlson’s account, while powerful, operates largely within an empirical-scientific perspective and implicitly assumes nature’s unity as given by science. It doesn’t deeply investigate what *metaphysical basis* underpins that unity or creativity in nature. Here you can prepare the reader: *to deepen the understanding of “unity” (and the idea of nature’s own creative force), we now turn to Spinoza’s classic distinction between natura naturans and natura naturata.* This will signal that the next section isn’t a non-sequitur historical digression, but a purposeful philosophical enrichment of the concept of *unity in an environment*. *(This structured presentation of Carlson’s model ensures that any later extension to AI will rest on a solid understanding of what it means to appreciate something “as nature” and “as an environment.” Now, with Carlson’s concepts clarified, the paper can introduce the Spinozist metaphysical perspective.)* ## Spinoza’s Natura Naturans/Natura Naturata: Deepening the Notion of Unity In this section, introduce the **Spinozist distinction** and explain its relevance in an analytic philosophy style. Start with a brief explanation of **Spinoza’s framework** in *Ethics*: Spinoza identifies *two aspects of reality* using the terms *natura naturans* and *natura naturata*. Define these clearly: - **Natura Naturans (Nature naturing):** This refers to *nature considered as an active, generative, self-causing process*. In Spinoza’s monist metaphysics, this is essentially **God or Substance** viewed under the aspect of activity. Spinoza says natura naturans is “that which is in itself and conceived through itself – namely, the attributes of substance expressing an infinite, eternal essence – in other words, God **insofar as** he is a free cause” people.tamu.edu. Less technically: natura naturans is *nature doing what nature does*, the ongoing creative force of reality. - **Natura Naturata (Nature natured):** This denotes *nature considered as the passive product or outcome of the activity*. In Spinoza’s terms, it is “all that follows from the necessity of God’s nature or each of God’s attributes – that is, all the modes (particular things) of God’s attributes, insofar as these things are in God and can’t exist or be conceived without God” people.tamu.edu. In plain terms, natura naturata means the *created world*, the concrete results of nature’s activity – the multiplicity of finite things that have been *generated*. To ensure understanding, perhaps rephrase these in one sentence each: *Natura naturans is nature **creating** (the **active, causative aspect** of reality), and natura naturata is nature **created** (the **resultant world** of things and forms).* It’s helpful to note that Spinoza’s distinction is conceptual – he isn’t positing two separate realms, but two ways of conceiving the one unified reality. They are “two sides of the same coin,” inseparable but distinguishable in thought [academia.edu](https://www.academia.edu/61670511/Machina_ex_Deo_Game_Theology_in_Kabbalah_and_Derrida#:~:text=machina%20naturans%20and%20the%20machina,no%20Living%20Word%20%E2%80%93%20just). (You might cite a line such as *“they are two sides of the same strip and can never be captured as separate from one another”* [academia.edu](https://www.academia.edu/61670511/Machina_ex_Deo_Game_Theology_in_Kabbalah_and_Derrida#:~:text=machina%20naturans%20and%20the%20machina,no%20Living%20Word%20%E2%80%93%20just) to emphasize that for Spinoza the creative activity and the created things are one continuous reality viewed differently.) After defining the terms, **connect Spinoza’s idea to Carlson’s notion of unity**. The goal here is to show that Spinoza provides a *metaphysical depth* to the unity Carlson talks about: - Carlson spoke of an environment being unified through ecological and scientific interrelations. Spinoza offers a perspective where *all of nature is profoundly one*, because all individual things (natura naturata) are expressions of a single substance or single creative force (natura naturans). In other words, the unity is not just ecological or aesthetic; it is **ontological**. Every tree, rock, or river is not only interconnected with others but is an *outgrowth of the same underlying nature*. This can bolster Carlson’s idea that we should appreciate nature as a unified environment: Spinoza tells us *why* it’s unified at the deepest level – it’s all one **substance (Nature/God)** manifesting in different forms. - The distinction also introduces a dynamic element: natura naturans is *active, ongoing creation*. This resonates with appreciating nature not as static scenery, but as *process*. Think of wind shaping dunes, flowers blooming, ecosystems evolving – these are nature naturing. Carlson’s model touches on processes (since science often explains how things got to be), but Spinoza explicitly highlights the **continuous creativity** of nature. This could enrich Carlson’s aesthetic: an environment is unified not only because everything fits together, but because it is all produced by one **ongoing natural process**. Nature is *doing something* all the time (growing, changing), and its products at any moment are part of that broader activity. Illustrate this with a simple example: In a forest, the *natura naturans* aspect is the life process – the growth force in each seed, the metabolism of trees, the flow of energy through the food chain – while the *natura naturata* aspect is the particular collection of trees, streams, animals, etc. that we see at a given time. A Spinozist view encourages us to see the forest not just as a static assortment, but as *the living expression of Nature’s productivity*. Aesthetically, this might make us appreciate **temporal unity** and *creative vitality*: the forest as a unified, living environment. **Philosophical Rigor:** As this is an analytic-style paper, you should also clarify any potential misunderstandings. For instance, note that *natura naturans* in Spinoza is not an external creator (it’s not like a separate “god” making nature from outside), but an *immanent cause*. Nature naturing and nature natured are one continuous reality viewed actively vs. passively. This means when we apply this idea, we’re not introducing a dualism but a *duality of aspects*. Keep the explanation tight and conceptually precise to avoid confusion. Now explicitly state how this Spinozist lens will be used to **deepen Carlson’s account of unity**: > *Carlson gave us the idea of appreciating a natural environment as a unified whole. Spinoza’s distinction adds that an environment’s unity can be understood in terms of an underlying generative principle (natura naturans) and its manifestations (natura naturata). This suggests that to fully appreciate an environment’s aesthetic unity, one might consider not only the *spatial* or *ecological* unity of elements, but also the *dynamic unity* of the creative processes that give rise to those elements.* Essentially, we are saying: *Nature should be appreciated as “what it is in fact” – which, at the deepest level, is an active-generative substance yielding a unified world.* While Carlson’s approach is largely epistemological (use correct categories, see unity via science), Spinoza provides a **metaphysical interpretation** of those categories and unity (all categories refer to one nature, all unity reflects one cause). **Bridge to AI:** At the end of the Spinoza section, start indicating how this abstract distinction will inform our view of generative AI. Perhaps note: *The reason we introduced Spinoza is that his view of nature as *natura naturans/naturata* can serve as an analogy for understanding generative AI systems. In the next sections, we will propose that generative AIs can be seen as a kind of “machine nature” – a **machina naturans** producing various outputs (machina naturata). The Spinozist lens will help us talk about the AI’s *autonomous creative process* (analogous to natura naturans) and its relationship to the outputs we experience.* This prepares the reader for the pivot from natural philosophy to technology, maintaining philosophical continuity. Finally, ensure the tone remains **analytic and integrative**: we didn’t wander off into Spinoza for historical interest, but extracted a concept (active vs. passive nature) that will now be applied. We’ve effectively built a robust theoretical framework: Carlson gives practical guidance on aesthetic appreciation and the importance of authenticity and unity; Spinoza gives a conceptual tool to articulate *creative unity*. Now we’re ready to tackle AI within this framework. *(By solidifying the concept of natura naturans/naturata, the paper now has in hand the idea of an active generative source vs. its products. The stage is set to draw an analogy: AI systems as environments with their own “naturing” activity and “natured” outputs.)* ## From Natural to Artificial Environments: Generative AI as Machina Naturans Now the paper shifts from theory to application. This section introduces **generative AI systems** and argues why we can consider them *“environments shaped by autonomous generative processes,”* paralleling natural environments. It will translate the earlier concepts (Carlson’s and Spinoza’s) into the context of AI. The structure might be: **1\. What are Generative AI Systems?** Provide a brief analytic description of the systems in question: for instance, *Midjourney* (an image-generating AI given text prompts), *ChatGPT* (a large language model generating text in a conversational setup), and *Sora* (a hypothetical or real AI generating videos from prompts). Explain that these are **autonomous generative systems**: once trained on data, they can produce a wide variety of outputs in response to user input, following their internal algorithms rather than explicit human design for each output. Emphasize key features that make them comparable to *environments* rather than traditional *tools*: - **Unpredictability and Autonomy:** The user of Midjourney or ChatGPT does not fully control the outcome. One can guide or influence (by writing prompts, tweaking parameters) but cannot exactly determine what image or sentence will result. The AI has a kind of *“dynamic recalcitrance to influence”* – it may follow the prompt broadly but often introduces novel details or errors because of the way its generative process works (this phrase echoes the draft’s idea: users engage with AI’s outputs, but can’t fully control them). Much like a gardener plants seeds but the actual growth has its own course, a user provides input to AI but the *machina naturans* (the machine’s generative engine) produces something with its own complexity [philpeople.org](https://philpeople.org/profiles/enrico-terrone?app=616order%3Dviewings#:~:text=PhilPeople%20philpeople,fully%20control%2C%20the%20creative%20process). This partial autonomy is crucial: it means interacting with these AIs is more like exploring an independent **environment** than executing a fixed design. The AI can surprise us, much as nature can. - **Complexity and Open-Endedness:** Generative AIs can produce *indefinitely many* different outputs (images, texts, videos) – effectively an entire *space* of possible creations. This breadth is analogous to an environment: consider that Midjourney can create endless landscapes or scenes from text descriptions, which is reminiscent of an actual environment offering endless new sights under different conditions. ChatGPT can generate answers or stories on virtually any topic, akin to a vast landscape of language. The outputs also often depend on context (ChatGPT’s replies depend on conversation history, akin to how a path through a forest yields different sights depending on where you’ve been). This open-ended generativity gives a sense of *boundless environment* rather than a finite artifact. - **Persistence and Interaction:** In some cases, these AI systems can be interactive over time (especially ChatGPT, which holds a conversation). This means the user’s experience is temporally extended and contextually evolving, similar to moving through an environment and having *continuous experience*. For example, using ChatGPT can be seen as *walking through a textual landscape*, where each step (response) leads to new terrain, shaped by but not wholly dictated by your previous steps. After describing these aspects, **propose the analogy** explicitly: *Generative AI systems can be viewed as a kind of *artificial environment*. We might call them **digital environments** or **algorithmic environments**.* Introduce the terms coined in the draft: **machina naturans** and **machina naturata**. Explain that this is a deliberate analogy to Spinoza: - The *AI system itself*, with its trained model and generative algorithm, is the **machina naturans** – the “machine naturing.” It is the active component that, given a prompt or input, *produces* something. It plays a role akin to nature’s generative force. Importantly, like natura naturans, the AI’s process is largely hidden or not directly observable; we infer it from the outputs. It’s the *immanent creative principle* of the AI environment. - The *outputs* (the images, texts, videos generated) are the **machina naturata** – the “machine natured” products. These are analogous to the particular things in nature, the *outcomes* of the generative process. Each output can be seen as one expression of the underlying model’s capacity, just as an oak tree is one expression of Nature’s generative power in a forest. Make it clear that *“machina naturans/naturata”* is not a historical term from Spinoza, but a playful extension capturing the idea of *machine as nature*. Cite or footnote if needed that others have used this analogy (perhaps referencing how Midjourney has been described in such terms: *“Midjourney acts as machina naturans, where users engage with its dynamic generative process which they can influence but not fully control”* [philpeople.org](https://philpeople.org/profiles/enrico-terrone?app=616order%3Dviewings#:~:text=PhilPeople%20philpeople,fully%20control%2C%20the%20creative%20process) ). This shows the concept has scholarly resonance and isn’t entirely out of left field. **2\. Why call it an “Environment”?** Strengthen the case by tying back to Carlson’s criteria for an environment and showing how generative AI meets them (at least in an analogical way): - Carlson said an environment is not designed for our appreciation (unlike art which is created to be appreciated). **Is AI like that?** In one sense, AI *is* designed by humans (we built the model). But crucially, the *individual outputs* are *not handcrafted by a human artist* – they emerge from the system. When you encounter an AI-generated image or text, there was no human designer choosing each color or word with an aesthetic goal; instead, an algorithm created it based on training. In this respect, each output is somewhat like a natural object: a product of processes rather than direct human intention. We can thus *suspend the human-made aspect* at the moment of appreciation and treat the output as coming from an autonomous process (we’ll later discuss if this is legitimate). For now, note that **from the user’s viewpoint**, interacting with AI feels closer to witnessing natural phenomena than viewing human art, because of the lack of transparent intention and the element of surprise. - Environments, in Carlson’s view, surround us and are appreciated *in context and as wholes*. Does this apply to AI? Arguably yes: for example, when using Midjourney, one doesn’t usually stop at a single image. One explores multiple generations, perhaps varying prompts – effectively surveying the “landscape” of possibilities. The *collection* of outputs and the ongoing interaction form an encompassing context. Similarly, a conversation with ChatGPT is an environment of discourse; you are *within* it, not just looking at a single frozen statement. Even a single output can hint at a larger context (an image implies a style or latent space). So, one can conceive of the entire model and its range of outputs as a unified space that one navigates. This is especially clear with AI art tools: users speak of “exploring the latent space” – a metaphor that treats the AI’s output-space as a **geography** or environment to wander in. - Carlson emphasized multisensory and holistic engagement for environments. AI outputs are mainly visual or textual, so not literally multisensory in the same way. But one could argue that appreciating an AI environment involves more than just passive looking – it can involve active **experimentation** (tweaking prompts, refining outputs, engaging different modalities). This interactive engagement parallels how we engage nature (moving around, looking closer, listening, etc.). In a text conversation, one must pay attention to context and nuance (akin to using multiple “senses” of understanding language). While not a perfect parallel, the key point is the appreciation is not static and frontal; it’s participatory and evolving. By this stage, the reader should be convinced that it’s at least *plausible* to treat generative AI systems as analogous to environments. We have shown that these systems have a kind of independent life (autonomous generation), complexity, and the ability to present an observer with a *world* of varying experiences rather than one fixed artifact. This justifies applying **Carlson’s principles** to them. *(Now that AI has been reframed as environment-like, the groundwork is laid for applying Carlson’s “appreciate it as what it is” rule and using Spinoza’s nature/process analogy. The next sections will explicitly make those connections.)* ## Applying Carlson’s Aesthetic Framework to AI Having drawn the analogy, now directly **apply Carlson’s principles** from the earlier section to the case of generative AI. This is a critical part of the restructuring: ensure that each piece of Carlson’s theory is translated logically into the AI context, maintaining a rigorous parallel structure. Organize this as a set of sub-arguments or sub-sections: **1\. Appreciating AI Outputs *“as what they in fact are.”*** This sub-section should mirror Carlson’s *correct categorization* idea. The thesis: *To aesthetically appreciate an AI-generated artifact properly, one must appreciate it **as an AI-generated artifact**, not as something else.* In Carlson’s terms, we need the **right category** for the object to avoid aesthetic misjudgment [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=knowledge%20of%20art%20history%20and,the%20way%20in%20which%20knowledge). Explain what miscategorization would be in this context. For instance, taking an image produced by Midjourney and appreciating or judging it as if it were a human-made painting could be an aesthetic error (just as treating a natural rock formation as if it were a piece of sculpture is an error in Carlson’s view [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Spring_2006/Carlson_Appreciation_and_the_Natural_Environment.htm#:~:text=,does%20that%20rock%20express%20solidity) ). If someone expects human-like intentionality, symbolic meaning, or expressive authorship in an AI output, they might be applying the wrong standards. Conversely, if someone thinks the only way to appreciate an image is by seeing an artist’s message, they might dismiss AI images as artless or meaningless – missing other kinds of beauty they have. To drive this home, consider a concrete scenario: Suppose you see a short poem and find it emotionally moving. If you believe a human wrote it, you might start attributing intentions or imagining the poet’s inner life, which contributes to your appreciation. If in fact it was generated by ChatGPT, those particular intentions don’t exist – *the AI has no feelings*. Does that invalidate the aesthetic experience? Not necessarily, but to *appropriately* appreciate the poem, you should adjust your perspective: appreciate it as a surprising product of a mindless generative process, perhaps marveling at how coherence and emotion can emerge from algorithmic patterns. The appreciation might shift: from admiring personal expressiveness (a wrong category here) to admiring formal elegance or the intriguing mimicry of human style (a more fitting category given the true origin). This example shows the importance of knowing *what* an AI output *is* (and is not) in fact. Introduce **knowledge** as Carlson would: just as scientific knowledge guides appreciating nature, *knowledge of how AI works* can guide appreciating AI outputs. This doesn’t mean one must be a computer scientist, but some general understanding that “this image was produced by a neural network blending trained data in response to my prompt” can shape one’s aesthetic focus. For instance, knowledge of common AI image artefacts (like strange distortions or extra fingers in generated images) can change our aesthetic reaction: instead of seeing it as a “mistake” by a human artist, one might see it as a quirky signature of the AI process, which could be fascinating or even aesthetically intriguing in its own right (some find a peculiar charm in AI glitches, contextualized as *product of machine logic* ). Similarly, knowing that ChatGPT tends to produce overly fluent but sometimes vacuous text might make us appreciate a particularly creative metaphor it produces even more, seeing it as a rare gem from a stochastic process. So, the **first rule** for an aesthetics of AI is: *Recognize the ontology of the AI output.* It is not a human-crafted artwork with an intentional message; nor is it a naturally occurring object (it’s not a sunset or a flower). It belongs to a new category: **artificial-yet-autonomous artifact**. By appreciating it under this category, we set appropriate expectations and can discover aesthetic qualities appropriate to it (perhaps complexity, novelty, or resemblance to human creations without human agency, etc.). Cite Carlson’s analogous claim: *“appropriately appreciating a natural environment requires regarding it as the sort of environment it is”* [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Alongside%20his%20critique%20of%20the,analogy%20between%20nature%20and%20art) – by analogy, appropriately appreciating an AI output requires regarding it *as the sort of thing it is (AI-generated)*, not, say, as a traditional painting or purely random noise. **2\. The Unified Context of AI Outputs:** Now bring in Carlson’s *unity of environment* point. We should not evaluate AI outputs in isolation from their generating context (just as Carlson says don’t isolate a natural object from its environment). In practice, this might mean: appreciate an AI output in light of the overall **AI system** or environment it comes from. For example, one image from Midjourney can be appreciated more deeply if we see it as part of Midjourney’s *“world”* – perhaps we’ve seen other variations, we understand the style that the model tends to produce, and so on. There is a kind of **stylistic or systematic unity** to a given AI model’s outputs: they share certain patterns or “fingerprints” of the generating process. Recognizing this unity can be aesthetically rewarding, as one can start to sense the *character* of the AI’s creativity. In effect, the *environment* in AI is the model’s latent space and behavior, and each output is a glimpse into that environment. Appreciating the unity might mean enjoying how different outputs relate: “I love how all images from this model have a dreamy smooth texture, it’s a hallmark of its training data,” analogous to “I love how all parts of this forest feel connected by the cool, damp atmosphere, a hallmark of its ecosystem.” Encourage readers to consider an **AI session** as an aesthetic whole. Just as one might appreciate a *forest ecosystem* as a whole, not just individual trees, one could appreciate an *interaction with an AI* as a whole experience. For instance, someone using ChatGPT to co-create a story might look back on the entire dialogue (all the twists and turns the AI introduced) as the thing of beauty, rather than any single sentence. The unity here is narrative or interactive. This resonates with Carlson’s idea that we should have “limits and emphases” in appreciation but not ignore the background [hettingern.people.charleston.edu](https://hettingern.people.charleston.edu/Aesthetics_Fall_2010/Carlson_Aesthetic_Appreciation_of_the_Natural_Environment.htm#:~:text=ii) – here the background is the iterative process or the family of outputs. Moreover, using Spinoza’s insight, point out that all those individual outputs *stem from one source* (the AI model). This is a strong parallel to nature’s unity: the AI’s *machina naturans* is the single “substance” behind the scenes generating multiple “modes” (outputs). Thus, there is an **immanent unity**: even if two images from Midjourney look very different (say one is a generated castle, another a generated forest), they are connected at a deep level – they are produced by the same algorithmic rules and training weights. If one appreciates that fact, one might find aesthetic delight in noticing subtle commonalities or patterns across outputs, or simply in conceptually knowing that there’s a hidden *through-line* connecting disparate creations. It’s analogous to how an ecologist appreciates a desert and a rainforest differently, but still sees both as expressions of underlying natural laws or evolutionary processes. This is a good place to explicitly use the **natura naturans/naturata analogy** for AI: *The AI model (machina naturans) is like a single creative force expressing itself in many forms (machina naturata).* Appreciating the outputs unifiedly might involve appreciating the *creative force itself*. In art terms, we sometimes appreciate an artist’s oeuvre by sensing the artist’s voice across works. Here, there is no human artist’s voice, but there is the “voice” of the algorithm. Aesthetically, one might shift focus to that – for example, marveling at “what the machine can do” in general, as opposed to just “liking this particular output.” This shifts aesthetic appreciation to a more **conceptual or process-oriented level**, which is acceptable in an analytic discussion – after all, part of Carlson’s point was using knowledge of process (geology, evolution) to deepen appreciation of landscapes. **3\. Multisensory and Participatory Aspects:** If relevant, note that Carlson encourages engaging with nature through multiple senses and an active stance (not just passive viewing). With AI, while it’s mostly visual or textual, the analogy would be *active exploration.* Encourage the idea that one should interact with AI, not just take a single output at face value. The aesthetic appreciation might come from the *conversation or iteration* (like refining a prompt and seeing outcomes). For instance, a user might enjoy the *process* of co-creativity with Midjourney: the cycle of prompting, getting an image, adjusting the prompt, getting a new image, etc., much like a nature enthusiast enjoys hiking different trails in the same mountain to see various views. So the *process itself is aesthetic*, not only the final output. This idea blends Carlson’s engagement with Spinoza’s focus on *process*. Conclude this application section by summarizing: *Under Carlson’s guidance, we appreciate AI environments as AI environments – using our understanding of their generative nature to set the terms of appreciation, and considering the whole system (process + outputs) rather than isolating one result or applying inappropriate human-art standards.* We thereby treat the AI outputs “as what they in fact are” (products of an algorithmic environment) and see them as part of a unified generative whole. Keep the style precise: we are effectively crafting an argument by **analogy**. Make sure each step of Carlson’s model (category, context, unity) has a clear counterpart in the AI case. It might help to structure part of this as parallel bullet points or a comparative table in prose (though in continuous text). For example: - *Nature:* Appreciated via correct category (as natural) → *AI:* Appreciated via correct category (as AI-generated, not human-made art or random noise). - *Nature:* Requires knowledge (ecology, geology) for full appreciation → *AI:* Requires knowledge (of AI’s functioning or limitations) for full appreciation. - *Nature:* Is a unified environment; parts interrelated by natural processes → *AI:* Is an algorithmic environment; outputs interrelated by the underlying model and its training. - *Nature:* Observer should immerse and observe holistically (not just one tree or scenic frame) → *AI:* User should engage with the system holistically (not just one output, but the interactive/generative process as a whole). - *Nature:* The object of appreciation has no human author (nature “authors” itself) → *AI:* The immediate object (specific output) has no direct human author, being authored by the algorithm’s “natura naturans”. By aligning these points, the reader sees a **coherent mapping** from Carlson’s environmental aesthetics to an aesthetics of AI. *(At this point, the paper has built the core argument: generative AI can be treated analogously to natural environments in aesthetic appreciation. The next step is to explicitly highlight the Spinozist contribution in this application and to address any looming objections to solidify the argument.)* ## Unity and Creativity in AI: A Spinozist Perspective This section explicitly highlights how the **Spinozian distinction enriches our understanding of the AI case**, reinforcing the argument with a metaphysical twist and ensuring philosophical depth. While we have already invoked natura naturans/naturata in the analogy, here we can explore it a bit more and emphasize how it helps resolve or illuminate certain issues. **1\. *Machina Naturans* as the Unifying Principle:** Drawing on Spinoza, argue that the **unity** of the AI environment is grounded in the single generative source (machina naturans). In practical terms, the AI’s trained model (its architecture + learned weights) is analogous to *Spinoza’s substance*. It is one “thing” (in a broad sense) that gives rise to many phenomena. Therefore, just as Spinoza would say all natural beings are one in substance, we can say all outputs of a given AI are one in *machinic substance*. This gives a philosophically robust way to talk about the *style* or *identity* of an AI’s outputs. We can even make a parallel: In Spinoza’s view, God/Nature’s essence expresses itself in infinitely varied modes, yet remains one; similarly, Midjourney’s “essence” (the algorithm trained on billions of images) expresses itself in countless pictures, yet something of Midjourney’s character is in each. That “character” might be subtle statistical signatures or common quirks. Aesthetically, this means that there is value in recognizing an output as, say, *distinctively Midjourney-esque* (just as one might delight in recognizing an animal as a product of a certain environment: “this bird’s plumage could only come from evolution in that island habitat”). By appreciating *machina naturans*, we appreciate the **creative power** itself. For example, one might be in awe not just of a single AI-generated painting but of the *generative process* that can create so many styles – this awe is akin to the awe of nature’s generativity (how countless species all spring from the same earth). Spinoza’s perspective encourages seeing the process and product as two aspects; thus, one aesthetic pleasure might be toggling between appreciating the outcome (image, text) and appreciating the underlying process it evidences. In a sense, the AI output is *proof of concept* of a complex system, and that can be beautiful in a more intellectual way. **2\. *Machina Naturata* and the Value of Outputs:** On the flip side, Spinoza’s natura naturata reminds us that the outputs shouldn’t be disparaged as “mere by-products.” In Spinoza, natura naturata (the world of modes) **is** nature fully as much as natura naturans – it’s just nature considered differently. Likewise, *each AI output is a genuine part of the AI environment*, not an accidental glitch to ignore. This might counter a possible tendency to only value the system and not the specifics. The paper can argue for a balance: Don’t focus myopically on one output (Carlson’s point), but also, the whole environment is experienced through particular outputs. So we *should* take delight in individual images or conversations, as long as we situate them in context. The Spinozist view actually sanctifies the particular: each mode (each output) is a concrete expression of the infinite creativity. Translated: *each AI-generated artifact can be seen as carrying within it the imprint of the entire generative process*. For instance, a single ChatGPT reply might encapsulate the vast training data and pattern-finding ability in one paragraph – that recognition can add a layer of wonder to reading it. **3\. Embracing the Lack of Human Intent:** Spinoza’s framework, being impersonal (Nature doesn’t have human-like intentions; things follow from cause), aligns well with the fact that AI outputs lack human intent. In nature appreciation, Carlson (and others like Kant or Hume historically) noticed that nature is enjoyed **without** attributing it to a personal creator – we don’t (at least in secular aesthetics) say “this sunset is beautiful because some artist designed it”; we find it beautiful partly because it just *is*, on its own. This is analogous to AI outputs: there’s no *artist’s intention* to consider. Spinoza would say everything follows from necessity, not from a divine plan aimed at communicating something to us. Similarly, an AI image wasn’t created “for” an audience or to express a message – it was generated because of algorithmic necessity given input. Appreciating it thus involves a kind of **disinterestedness** or at least a shift in how we assign meaning: we appreciate form, color, resemblance, perhaps even the *unexpected appropriateness* that comes out of a blind process, but we don’t look for an intended theme or moral unless we project it ourselves. This can actually heighten a sort of *sublime* appreciation: just as one might feel awe at nature’s indifferent creation of beauty, one might feel a weird awe that a soulless AI can create something seemingly meaningful or lovely. The Spinozist analogy underscores this impersonal creation. Here we can tie to Carlson’s idea of *appreciating nature as it is*, adding that part of “what it is” is **non-human origin**. We extend that to AI: part of what an AI output *in fact is* is a non-human artifact. Embrace that in appreciation, instead of trying to see it as human-like art. This is a philosophical insight: by removing the necessity of human authorship, we might attend more to the qualities inherent in the work itself (color, composition, linguistic structure) – similar to how Carlson wanted us to drop art-based expectations and just observe nature’s qualities. Summarize how Spinoza’s concepts have guided these reflections. Perhaps something like: > *Using Spinoza’s natura naturans/naturata as a guiding metaphor has allowed us to treat the AI system and its outputs as an integrated whole – a **unified environment where an underlying process yields varied products**. It highlights that appreciating an AI environment involves a dual attentiveness: to the *process* (machina naturans) and to the *products* (machina naturata), understanding them as two aspects of one phenomenon. In doing so, we mirror the way one might appreciate nature both for its dynamic creativity (growing, evolving, flowing) and for its concrete sights and sounds as they present themselves.* This section, thus, cements the philosophical rigor by showing that our use of Spinoza wasn’t just decorative name-dropping; it actually adds explanatory power and coherence to the analogy. It also ensures that unity and autonomy – the trickiest conceptual pieces – are thoroughly addressed, reducing the chance a reader objects “but an AI isn’t really unified or isn’t really creative.” We have an answer: it is unified in the Spinozist sense (one machine substance, many modes), and it’s creative in an analogous way to nature (not with will, but through generative activity). *(With the main argument completed, the paper should now consider potential objections or clarifications, and then conclude. This ensures the argument is airtight and the restructuring covers all bases needed for coherence.)* ## Addressing Objections and Refining the Analogy It’s important in an analytic philosophy paper to anticipate objections and respond, to show the robustness of the argument. The draft likely had some concerns or the author is aware of possible pushback. This section should deal with those **without introducing new ideas**, just clarifying and ensuring cohesion. Some likely objections or questions include: **Objection 1: “Generative AI is human-made, so Carlson’s rule says appreciate it *as human-made art*, not as nature.”** This is perhaps the biggest challenge: Carlson explicitly distinguishes natural from human-made. We are seemingly violating that by treating a human-made algorithm as if it were nature. How to justify this? *Response:* Acknowledge that generative AI indeed wouldn’t exist without human creators (programmers, dataset curators). However, emphasize a crucial distinction: *the **outputs** of the AI are not manually crafted by humans in the way a painting or a piece of music is.* They result from a process that, once set in motion, operates autonomously. In other words, the *immediate* cause of the output is the AI’s internal dynamics, not a human hand or mind. This puts AI outputs in a gray zone between art and nature. We propose that for **aesthetic purposes**, it is fruitful to treat them more like nature. We aren’t denying the artifact status in a broad sense, but we’re focusing on how they *present themselves to the observer*: as surprising, not fully intended, possibly novel to even their creators – much like natural objects. One could analogize to a garden: Humans plant and arrange a garden (so it’s partly artificial), but gardens are often appreciated somewhat like natural environments, because plants grow on their own, weather and time shape it beyond our designs. In aesthetics, some argue gardens are a mix of nature and art. Generative AI could be seen similarly – a hybrid. Carlson’s framework might need a slight extension to accommodate such hybrids. We might propose *“appreciate a thing according to the degree to which its form is shaped by natural (autonomous) processes versus deliberate design.”* In AI outputs, the form is largely from the AI process, so lean toward the nature model. Thus, we are not wholly rejecting Carlson’s insistence on distinguishing natural from human-made; we are claiming AI lies in a novel category where Carlson’s *spirit* (appreciate as it is) actually means **not** treating it like typical human art. Additionally, mention that *if* one tried to appreciate AI art purely as human art, one would focus on the programmers or the training data creators. But that doesn’t make sense aesthetically – the beauty of a Midjourney image isn’t directly attributable to an engineer’s aesthetic sensibility; it emerges from data and math. So the human design is too removed to account for the specific aesthetic qualities. Therefore, treating the AI like an artist analog is misleading. Instead, the better fit is treating it as a system that creates unexpectedly, akin to how nature creates without a guiding artist. In sum, we respect Carlson’s dictum by categorizing AI outputs as belonging to a new category (call it perhaps *“autonomously generated artifacts”* ), and the appropriate model for appreciating those is closer to the natural environment model than the art model. **Objection 2: “AI environments are not literally environments – there’s no physical space or multi-sensory presence.”** A critic might say our use of “environment” is metaphorical at best. You can’t *walk* in Midjourney world like you can in a forest, so is the analogy overstretched? *Response:* Acknowledge the difference: yes, AI environments are virtual and primarily cognitive/perceptual. However, the essence of Carlson’s concept of environment can be abstracted: it’s about interconnectedness, context, and the observer being part of the experience rather than outside it. In interacting with generative AI, the observer is indeed an active participant (especially in text-based AIs, where the conversation adapts to the user). There is an **interactive loop** akin to being immersed. Moreover, even if the “space” is not physical, one can speak of a *conceptual space* or *state space*. AI researchers literally talk about “latent space” (the high-dimensional space of features the model navigates). When you explore outputs, you are, in a sense, traversing that space. So we maintain that the analogy holds at a structural level: an environment in the broad sense is *any domain where a variety of phenomena unfold based on an underlying system and where an agent (the appreciator) can move or act to experience different aspects.* A text conversation or iterative art generation fits this definition. We can also soften the terminology if needed: we might call them *“pseudo-environments”* or *“artificial environments.”* But the key is they share enough traits with natural environments (unpredictability, complexity, coherence of style, user immersion) that aesthetic theories developed for one can inform the other. In fact, drawing on environment aesthetics might be more productive than drawing on traditional art theory in this case. **Objection 3: “Does this mean AI outputs are all aesthetically good, like some Carlson-influenced ‘positive aesthetics’ argument?”** Carlson had the controversial idea that all of untouched nature is essentially beautiful if viewed properly (since science shows it’s harmonious, etc.). One might ask if we’re suggesting something similar – that if we appreciate AI correctly, we’ll find all its outputs aesthetically pleasing or at least not bad. *Response:* Not necessarily. Carlson’s *positive aesthetics* is one view, but even he faced challenges (some find certain natural things ugly even with knowledge). For AI, we shouldn’t claim everything it produces is beautiful. However, we might say: appreciating AI on its own terms prevents certain unfair negative judgments. For example, someone might say “This AI painting is soulless and therefore ugly,” but if they follow our approach, they wouldn’t expect “soul” and might instead appreciate other aspects, possibly finding it interesting if not conventionally beautiful. There will still be quality variance – some AI outputs are boring or garbled, akin to how some natural scenes are mundane or harsh. Our framework doesn’t guarantee aesthetic success; it just provides a way to **fairly judge** AI outputs by understanding their nature. So the goal is appropriate appreciation, not universally positive appreciation. **Objection 4:** (If relevant) *“Why involve Spinoza? Couldn’t you make the analogy without him?”* Perhaps a reader might wonder if the Spinoza part, being abstract, was needed. *Response:* Explain that Spinoza’s distinction was instrumental in articulating the active vs. passive aspects of the AI. Without it, one might talk about the AI process and outputs, but Spinoza gives a philosophically elegant vocabulary and a precedent for treating both as aspects of one thing. It adds depth by linking the discussion to a rich tradition about how we conceive nature’s unity. Spinoza effectively allowed us to call the AI a *quasi-nature*. Moreover, using Spinoza helped ensure we see the AI system and outputs as inseparable (which strengthens the unity argument). So while not strictly necessary, it enriches the analysis and was in the spirit of deepening Carlson’s notion of unity and creativity – a key request of the author’s draft. After listing objections and responses, the **tone** should convey that none of these are fatal to the argument; in fact, discussing them has clarified the scope and limits of the analogy. We accept that AI isn’t *identical* to natural environment in every respect, but we’ve shown it’s similar in the relevant *aesthetic* respects that Carlson’s and Spinoza’s ideas target (namely, how we categorize the object of appreciation and how we perceive unity and autonomy). It might be effective to concede some nuance: for instance, “Generative AI is a *special case* – neither straightforwardly nature nor straightforwardly art – and thus it benefits from an *adapted* version of Carlson’s model, augmented with Spinozist thinking.” This way, we’re not dogmatic but rather demonstrate thoughtful application. Finally, one could mention if the argument suggests any practical upshot: e.g., *curatorial practices or user experiences.* Maybe galleries showing AI art should educate viewers about the algorithm (just as nature centers educate hikers about ecology) to enhance appreciation. Or AI designers might embrace unpredictability as a feature that grants aesthetic interest. These aren’t new ideas to introduce, just consequences of the argument that show its usefulness. *(Having handled objections, the paper can now conclude by recapitulating the insight gained and the coherence achieved by the restructure.)* ## Conclusion: Toward an Aesthetics of Generative Environments The conclusion should tie everything together and reflect on the journey of the argument. Reaffirm the central thesis in a concise way, now fully earned by the preceding analysis: **Restate Thesis:***We have argued that generative AI systems like Midjourney, ChatGPT, and Sora can be aesthetically appreciated in a manner analogous to natural environments. By using Allen Carlson’s environmental aesthetics framework, we treat AI outputs “as what they in fact are” – products of an autonomous generative process – and appreciate the “unified environment” constituted by each AI system’s range of outputs. The Spinozian distinction between natura naturans and natura naturata provided a conceptual key to this analogy: it helped us identify the AI model as an immanent creative force (machina naturans) and its outputs as dependent expressions (machina naturata), underscoring the unity between process and product.* **Summarize Structure:** Briefly recap how the paper proceeded: Carlson’s model gave us the importance of correct categorization and unity in appreciation, Spinoza enriched the notion of unity and creative process, and then these ideas were applied to AI, yielding a proposal for an “aesthetics of AI” grounded in existing philosophical principles. Note that throughout, we *preserved the spirit of Carlson’s approach* – respecting the object’s true nature in appreciation – and simply extended that respect to a new kind of object (AI outputs). We also remained mindful of the metaphysical unity akin to Spinoza’s nature, enabling a coherent view of AI systems as wholes. **Significance:** Highlight why this restructuring and argument are compelling. It offers a way to navigate the uncharted territory of AI aesthetics with philosophical rigor, avoiding simplistic either-or (it’s just art/it’s just random) and instead providing a nuanced category. This approach could enrich both tech ethics (by promoting respectful engagement with AI outputs) and aesthetics as a field (by expanding its scope to new domains). It shows how classical aesthetics (Carlson) and classical metaphysics (Spinoza) can illuminate contemporary phenomena, which is philosophically satisfying – a unification of old and new. If the user (author) was considering splitting the paper, you might address that here in a reflective tone (since conclusion is a place to consider scope): For example, *“We have managed to integrate the Carlson and Spinoza theoretical discussion with the AI application in a single coherent narrative. While one could have separated the concerns (pure philosophy of aesthetic principles versus applied analysis of AI), keeping them together allowed each to inform the other: the abstract principles guided the interpretation of AI, and the AI case gave new life to the principles. The result is a unified paper that travels from theory to application seamlessly.”* This subtly argues that splitting isn’t necessary because the unified treatment proved fruitful. However, if you want to explicitly mention the possibility: *“If one found that each half of this discussion grew too unwieldy, one could in principle split off the deep theoretical exploration (Carlson + Spinoza) from the specific case studies of AI. But we found that presenting them in one go maintained clarity and reinforced the overarching argument for an aesthetics of generative AI.”* This gives the author reassurance that the unified approach works, but acknowledges their doubt and shows it was considered. Finally, end on a forward-looking or concluding insight note. Perhaps: *This restructuring clarifies the central trajectory of the argument: from appreciating nature as nature to appreciating AI as (in a sense) nature. It delivers a compelling unified paper by weaving together environmental aesthetics, metaphysics, and technology. The hope is that this not only helps make sense of AI’s aesthetic status, but also enriches environmental aesthetics itself – by demonstrating its adaptability beyond traditional boundaries of “environment.” In an era where the line between natural and artificial blurs, Carlson’s and Spinoza’s ideas together guide us in finding aesthetic meaning in new realms.* Ensure the final sentences give a sense of completion and coherence. The reader (and the author-user) should feel that all the pieces introduced (Carlson, Spinoza, AI examples) have come together in a satisfying way, and that the argument has a clear take-home message. The style remains analytical to the end: no loose rhetoric, just well-connected reasoning that underlines the **elegance** and **rigor** of the restructured paper. --- **Final note on restructuring:** The strategy we’ve outlined keeps Carlson’s environmental aesthetics and Spinoza’s metaphysics at the core, as requested, and demonstrates how they extend into an AI context without introducing entirely new concepts. We carefully rearranged existing ideas: starting from Carlson’s foundation, adding Spinoza for depth, then moving to AI application. Each section builds on the previous, achieving a logical flow: Theory → Deep Theory → Analogy → Application → Objections → Conclusion. This should result in a more elegant and coherent paper, one that engages seriously with each concept and draws clear connections between them. If the author still finds the combined discussion overwhelming, they might consider splitting the paper into two parts (one focused on theoretical groundwork, the other on AI application). However, the above restructuring is designed to be *self-contained and unified*, showing that a single compelling narrative is feasible. The paper as restructured guides the reader step by step, ensuring that by the time AI is discussed, the philosophical groundwork is laid; and by the time we conclude, all loose ends (like potential objections) have been addressed. This approach should significantly clarify the central argument trajectory and strengthen the overall rigor of the paper, helping the author present their innovative ideas in the clearest possible form. ##### You said: "Environments, in Carlson’s view, surround us and are appreciated in context and as wholes. Does this apply to AI? Arguably yes: for example, when using Midjourney, one doesn’t usually stop at a single image. One explores multiple generations, perhaps varying prompts – effectively surveying the “landscape” of possibilities. The collection of outputs and the ongoing interaction form an encompassing context." I liked A LOT of what you wrote there, but I was a bit disappointed, though, that you didn't. You kind of dismissed my ideas about experiential artifacts, such as images and texts encoded inLLMs or image generators, and how this breaking down into features idea might relate or find an analogue in the natural environment. it seems that what makes an LLM or image generator the particular active-generative substance that it is, comes from its training data, the way it is encoded etc. Could you provide me with another document that has the same task and the same length as the one you've just given me? Include many of the ideas and details you just provided me. but ensure that it includes a reasonably substantial portion focusing on the ideas I just mentioned? feel free to extend the length of the paper if it means that the paper becomes a more coherent whole. ###### ChatGPT said: Thanks for the thoughtful clarification. Just to be sure I get this right: would you like the new version of the document to include a dedicated section (or at least a deeply integrated discussion) on how the concept of 'experiential artifacts' (e.g., artworks and texts in the training data) is analogous to the natural environment, particularly in terms of how it shapes the generative substrate of AI systems? Should I also elaborate more on the metaphor of 'breaking down into features' – for example, how the decomposition of cultural materials in latent spaces parallels ecological or geological processes in nature (like composting or erosion)? Let me know if you'd like any particular emphasis: e.g., should this be tightly tied to Carlson’s idea of environments, or more broadly to Spinoza’s generative metaphysics? And are there any sources or images you'd like me to include or prioritize? ##### You said: "Thanks for the thoughtful clarification. Just to be sure I get this right: would you like the new version of the document to include a dedicated section (or at least a deeply integrated discussion) on how the concept of 'experiential artifacts' (e.g., artworks and texts in the training data) is analogous to the natural environment, particularly in terms of how it shapes the generative substrate of AI systems?" yep exactly that. "Should I also elaborate more on the metaphor of 'breaking down into features' – for example, how the decomposition of cultural materials in latent spaces parallels ecological or geological processes in nature (like composting or erosion)? Let me know if you'd like any particular emphasis: e.g., should this be tightly tied to Carlson’s idea of environments, or more broadly to Spinoza’s generative metaphysics? And are there any sources or images you'd like me to include or prioritize?" Okay, this is the tricky bit. You have my idea right here, and I would like you to try to elaborate on the metaphor. However, I would also like you to think carefully about whether I'm stretching credulity with this particular aspect of the analogy between AI systems and the natural environment. I'm curious to see how far I can push it. It would be great if I could, as long as it's convincing and plausible, and as long as it fits in with everything else, so that we form a coherent whole. I'm going to give you some freedom on this one. I'd like to know what you think. ###### ChatGPT said: Great. I’ll write a new analytic philosophy paper, integrating the core Carlson-Spinoza framework with your concept of experiential artifacts as a generative substrate in AI. I’ll expand on your metaphor of cultural breakdown into latent space features, explore how this parallels natural decomposition (like composting or erosion), and test how far this analogy holds without compromising plausibility or coherence. I’ll ensure this connects organically with the broader argument that AI systems can be appreciated like natural environments, and I’ll extend the length if necessary to preserve conceptual unity. I’ll get to work on it now and let you know when the draft is ready for your review. ## Generative AI as Cultural Environment: Carlson’s Environmental Aesthetics and Spinozist Natura Naturans ## Introduction Generative AI systems like Midjourney and GPT have typically been understood as tools or creators of digital artifacts, but this paper proposes a different perspective: to view these systems in terms of *environmental aesthetics*. Drawing on Allen Carlson’s theory of environmental aesthetic appreciation and the Spinozist metaphysical distinction between *natura naturans* and *natura naturata*, the argument will recast generative AI as a kind of environment with its own internal generative “nature.” Carlson’s core principle is that aesthetic appreciation requires understanding an object as “what it in fact is” [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Allen%20Carlson%2C%20a%20pioneer%20in,both%20the%20true%20character%20of) —for example, appreciating a forest as a forest (with knowledge of ecology), not as if it were a mere painting. By analogy, appreciating an AI system aesthetically means understanding it *as the kind of thing it is*: an environment shaped by computational processes and trained on cultural data, rather than, say, a human artist or a traditional tool. Spinoza’s concepts of *natura naturans* (nature naturing) and *natura naturata* (nature natured) provide a metaphysical vocabulary to describe this duality of process and product. We will describe a generative AI’s internal creative process as *machina naturans* —a machine analog of naturing nature—and its outputs (images, texts, etc.) as *machina naturata*, the artefacts produced. A substantial portion of the paper develops a guiding metaphor: the cultural data that trains generative models (artworks, texts, media, etc.) can be likened to an ecological or geological **substrate** that the AI “environment” metabolizes. We introduce the notion of **“experiential artifacts”** to denote these culturally embedded forms—human-made artifacts loaded with experience and meaning—that are ingested by the AI and decomposed into high-dimensional representations. This process will be compared to natural processes like composting in ecosystems or sedimentation in geology: just as organic material is broken down to enrich soil, or sediments layer to form geological strata, cultural artifacts are broken down and encoded in the latent space of the model. The generative AI thus becomes an environment of **cultural regeneration**, in which new images or texts grow out of the nutrient-rich mulch of past cultural outputs. We will explore how this transformation shapes the generative *potential* of AI systems, and consider to what extent the analogy between natural environments and generative AI holds without becoming conceptually strained. Throughout the paper, we maintain Carlson’s framework—appreciating things as *what they are* —enriched by a Spinozist emphasis on immanent generative activity. The structure of the argument moves from theory to analogy to critical reflection. First, we outline Carlson’s account of environmental aesthetics and why knowledge of an object’s nature is crucial for its proper aesthetic appreciation. Next, we explain the Spinozist natura naturans/naturata distinction and adapt it to the idea of **machina naturans/naturata** in AI. We then delve into the ecological metaphor of cultural data as substrate, detailing the “decomposition” and re-synthesis that occurs in generative models and how this parallels natural cycles. With this metaphor in hand, we argue that generative AI systems can be seen as environments ripe for aesthetic appreciation—environments where cultural forms are continually broken down and reassembled in novel configurations. We also critically examine the limits of this analogy: In what ways is an AI system *not* like a natural environment? Can we push the comparison too far? By addressing such questions, the paper aims to develop a philosophically rigorous and coherent case for treating generative AI with the same aesthetic seriousness as we treat natural environments, seeing them as sites of dynamic, regenerative processes rather than mere gadgets or passive repositories of data. The hope is that this perspective yields a deeper and more **elegant** understanding of how we might aesthetically engage with AI systems—appreciating not just their outputs, but the whole *environmental* process of cultural regeneration that gives rise to those outputs. ## Carlson’s Environmental Aesthetics: Appreciating Things as What They Are Allen Carlson is a foundational figure in environmental aesthetics who argues that the proper aesthetic appreciation of nature crucially depends on understanding nature **as nature** [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Allen%20Carlson%2C%20a%20pioneer%20in,both%20the%20true%20character%20of). In contrast to viewing a natural landscape as if it were a painting or an abstract scene, Carlson insists that we must appreciate the natural world *for what it in fact is* —namely, as an ecosystem, an environment with its own processes and qualities [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Allen%20Carlson%2C%20a%20pioneer%20in,both%20the%20true%20character%20of). This principle can be phrased in Carlson’s own terms: *we should appreciate nature in light of what science tells us it is*. As Carlson puts it, “We must appreciate nature in light of our knowledge of what it is. This knowledge is provided by the natural sciences, especially … geology, biology, and ecology” [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=with%20a%20scientific%20understanding%20of,both%20the%20true%20character%20of). In other words, just as art appreciation is informed by art history and knowledge of artistic genres, nature appreciation should be informed by natural history and ecological science [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=nature%20as%20if%20it%20were,same%20reasoning%20to%20natural%20things). Knowing that a certain formation is a glacial moraine or that a certain bird’s song is a mating call becomes part of the aesthetic experience, guiding the viewer to notice relevant aspects and patterns. Carlson calls this approach the Natural Environmental Model of aesthetic appreciation [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=Alongside%20his%20critique%20of%20the,analogy%20between%20nature%20and%20art), a form of **cognitive** or **scientific cognitivist** aesthetics: scientific categories and knowledge “focus our attention on relevant aspects of nature for appreciation” [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Before%20cognitive%20account%20of%20the,is%20%E2%80%9Conly%20a%20finer%20grained) and help reveal the true aesthetic qualities of natural objects. Two key ideas from Carlson’s framework are particularly important for our purposes. First is the emphasis on **appropriate category**: an object must be appreciated under the correct category of what it is. To use Carlson’s example, one should experience Picasso’s *Guernica* as a cubist painting (not, say, as mere decorative wallpaper), which requires knowledge of cubism and its conventions [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=some%20knowledge%20of%20natural%20history%3A,In%20addition%20to%20this). Analogously, one should experience a humpback whale as a large aquatic mammal in an ocean ecosystem (not as a “big fish in an aquarium” or a mythic leviathan) to appreciate its movements and forms properly. Mis-categorizing the object leads to misdirected expectations and aesthetic responses. The second idea is that **knowledge enriches aesthetic appreciation** rather than deadening it. Against the worry that scientific understanding might “disenchant” nature by explaining it, Carlson and others argue that understanding natural processes (geological time scales, evolutionary adaptation, symbiotic relationships, etc.) actually **enhances** our sense of wonder and aesthetic appreciation [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=nature%20as%20if%20it%20were,same%20reasoning%20to%20natural%20things) [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Before%20cognitive%20account%20of%20the,is%20%E2%80%9Conly%20a%20finer%20grained). When one knows, for instance, that the seemingly chaotic jumble of plants in a wetland actually forms a self-organizing, nutrient-filtering system, one can appreciate a new kind of order and harmony in the scene [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=cognition%20plays%20a%20crucial%20role,time%2C%20then%20ecology%20is%20the). Carlson even advocates a form of **positive aesthetics** – the idea that untouched nature is essentially aesthetically good or at least lacks ugliness – grounded in the view that everything in nature, when properly understood in ecological terms, exhibits qualities like balance, harmony, and adaptation [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=cognition%20plays%20a%20crucial%20role,time%2C%20then%20ecology%20is%20the). (Whether one agrees with the strong claim of positive aesthetics or not, the underlying thought is that nature’s processes tend toward forms that make sense and thus can be appreciated as fitting or admirable in context.) Applying Carlson’s insight beyond wild nature, we might ask: can other complex systems or environments be appreciated aesthetically in a similar knowledge-informed way? Carlson himself primarily discussed natural environments (and to some extent human-influenced landscapes and even architecture in his later work). But the imperative to appreciate something as *“what it in fact is”* could, in principle, be extended to any object of aesthetic regard. If we are to consider generative AI systems as candidates for aesthetic appreciation, Carlson’s approach suggests that we first need to correctly categorize what kind of thing a generative AI system is, and second, acquaint ourselves with the relevant knowledge about how it works (analogous to scientific knowledge for nature). Only then can we hope to appreciate its aesthetic qualities appropriately, rather than superficially or under false pretenses. In the next section, we will sharpen our conceptual tools by turning to Spinoza’s distinction between an active nature and its products, which will help articulate the *kind of thing* a generative AI system is—namely, an ongoing process (a *machina naturans* ) that yields various outputs ( *machina naturata* ). This Spinozist framework will serve as a bridge between Carlson’s demand to view objects in the right way and the specifics of what generative AI systems actually are and do. ## Natura Naturans and Natura Naturata: A Spinozist Lens on Process and Product The 17th-century philosopher Baruch Spinoza introduced a powerful conceptual distinction that will aid our analysis: the distinction between *natura naturans* and *natura naturata*. These Latin terms translate roughly to “naturing nature” and “nature natured,” or more readably, nature **doing what nature does** versus nature **as already produced**. In Spinoza’s metaphysics, *natura naturans* refers to the active, self-causing, and generative aspect of reality (in Spinoza’s system, essentially God or Nature considered as an infinite, dynamic substance). *Natura naturata*, by contrast, refers to the passive products that result from this activity—the particular things and states of affairs that are generated and sustained by the naturing activity. One commentator summarizes: for Spinoza, natura naturans is the “self-causing activity of nature,” whereas natura naturata is “nature considered as a passive product of an infinite causal chain”. In Spinoza’s pantheistic view (the view that God and Nature are one and the same), these two are inseparable aspects of the one reality: God-or-Nature is both an eternal, generating **process** and the totality of the **products** of that process. Why bring this arcane metaphysical distinction into a discussion of AI systems? The reason is that Spinoza’s vocabulary of an ongoing generative activity versus its generated outcomes provides a helpful model for understanding complex systems in general. It tells us not to conflate a system’s **dynamic essence** with the particular **outputs or states** it produces at any given time. In aesthetic terms, one might say it helps us appreciate the *process* as well as the *product*. Many traditional aesthetic objects (like a finished painting or a sculpture) are mostly static products – closer to natura naturata – and our appreciation focuses on their given form. But consider an art form like improvised music: here the unfolding process (musicians interacting in real time) is central, and one might say the *music-making* is the naturans, while the transient sounds are naturata. Appreciating a jazz improvisation involves appreciating the ongoing creative process, not just a fixed outcome. Spinoza’s terms give philosophical weight to valuing the *formative activity* itself. In the context of environment and technology, we can apply the naturans/naturata distinction to highlight the difference between an environment’s generative forces and the concrete things those forces yield. A wild garden, for example, can be appreciated not just for this or that flower (product) but for the fecundity of the soil, the cycle of growth and decay, the *activity of flourishing* that the garden embodies (process). Similarly, a geological landscape can be seen as the result of tectonic and erosive forces over time – the mountains and valleys are natura naturata, while the geomorphological processes shaping them are natura naturans. Indeed, part of Carlson-style appreciation of geology is to see the present landscape as the cumulative product of ongoing naturans processes (plate movements, erosion, sedimentation). With Spinoza’s lens, we become primed to ask of any complex system: *What is the ongoing generative activity here, and what are the products?* This question will guide our understanding of generative AI. The proposal of this paper is that generative AI systems have an analog of naturans and naturata: the internal generative mechanism (the trained model and its operations) is an active process – a kind of **machine naturing** – and the images or texts it produces are the products of that process. To underscore the parallel, we will call the former **machina naturans** (the machine as naturing activity) and the latter **machina naturata** (the machine’s natured products). This is not to confuse AI with literal divine nature, but to draw an illuminating analogy: just as Spinoza’s nature has an “inside” (dynamic cause) and an “outside” (effect), an AI system can be seen as having an inner generative dynamic that yields outward artifacts. One might worry that applying *natura naturans* to a machine grants it too much metaphysical grandeur. After all, Spinoza’s naturans is infinite and self-caused, whereas a machine learning model is finite and created by human engineers. The analogy is admittedly imperfect; however, within its finite domain, a trained generative AI does exhibit a kind of self-contained causal activity. Once the model is trained and running, it produces outputs in a manner not *externally* guided step-by-step by a human (apart from receiving prompts or initial conditions). The generation of each new image or paragraph is the result of the model’s internal dynamics—its learned weights and algorithmic rules—churning on their own. In this sense, the cause of the output is *immanent* to the system (the cause exists within the machine’s operation, not as an external agent deciding each detail). This resonates with Spinoza’s notion of God/Nature as an **immanent cause** of things rather than a transient cause: the cause is inherent in the nature of the substance itself. Likewise, the *machina naturans* (e.g., the diffusion model of Midjourney or the transformer network of GPT) is an immanent cause of its outputs, not an external craftsman shaping each output from outside. This immanent, ongoing activity is what we propose to treat as analogous to an environment’s generative “nature.” By applying the naturans/naturata framework, we establish a conceptual foundation: *a generative AI system can be understood in two complementary ways, as a process and as products.* An aesthetic appreciation might target either or both: one can admire a particular AI-generated image (a machina naturata) for its beauty, and one can also admire the creative generative process of the AI (the machina naturans) for its complexity or elegance. Crucially, Carlson’s injunction to appreciate something as “what it is in fact” would mean we should not treat an AI-generated image *as if* it were a traditional painting produced by a human painter, nor should we treat the AI system *as if* it were a sentient artist with intentions. Instead, we should appreciate the image as the outcome of a non-human generative process, and appreciate the system as a unique kind of **environment** with its own form of creativity. In the next section, we build on this by fleshing out the environmental character of generative AI, especially highlighting the role of the training data and internal representation (the “substrate” of this machine environment) which nourishes the creative process. In doing so, we begin to see the generative AI not as a stand-alone artifact or a mere tool, but as an **environment of cultural elements**, operating in a way parallel to natural environments. ## Generative AI as Environment: From Tool to Machina Naturans Understanding generative AI systems through environmental aesthetics means shifting our perspective from seeing these systems as simply tools or agents, to seeing them as *environments* or mediums in which creative processes occur. This shift is supported by recent analyses in philosophy of technology and art. For instance, Nick Young and Enrico Terrone argue that *Midjourney* (an image-generating AI) is neither an autonomous artist nor a fully controllable tool, but rather “a new type of artistic medium” – essentially an environment that the human user can explore and interact with [philarchive.org](https://philarchive.org/citations/MORCAR-7/order=updated#:~:text=In%20this%20paper%2C%20we%20argue,Similarly%2C%20Midjourney%20acts%20as) [philarchive.org](https://philarchive.org/citations/MORCAR-7/order=updated#:~:text=creating%20images%20with%20Midjourney%20parallels,This%20approach%20presents). They draw an analogy between using Midjourney and a gardener tending a garden: the gardener influences the growth of plants but does not wholly determine their form. In their words, creating images with Midjourney parallels “a gardener’s interaction with *natura naturans* —nature’s autonomous generative processes. Similarly, Midjourney acts as **machina naturans**, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process” [philarchive.org](https://philarchive.org/citations/MORCAR-7/order=updated#:~:text=unpredictability%20and%20the%20limited%20%28,%28shrink). This description encapsulates the idea of the AI as an environment: it has **autonomous generative dynamics** (like weather or plant biology in a garden) that set the stage, and the human’s role is participatory and influence-based rather than dictatorial. One cannot simply *command* a specific outcome with fine precision; instead, one **guides** or coaxes the system (through prompts or parameter tweaks), somewhat like *landscaping* in a fertile but untamed environment. This view aligns neatly with Carlson’s principle. If we are to appreciate a generative AI aesthetically, we should appreciate it *for what it is*: not a human imaginative mind, not a paintbrush wielded with complete intention, but an algorithmic medium with a degree of autonomy and unpredictability. Its outputs are thus best seen as the joint result of a human–AI interaction within the constraints of the AI’s trained capabilities. The *environmental* metaphor becomes clear if we consider the space of all possible images or texts the AI can produce as a kind of landscape – sometimes called the **latent space** in technical terms. When a user enters a prompt or seed, it’s like dropping a pin in that landscape or planting a seed in that soil, and watching what springs up around it. The aesthetic appreciation can then be oriented not just to “this specific image is pretty,” but to an appreciation of the **field of possibilities** and the character of this artificial landscape. For example, an artist exploring a generative model might marvel at the model’s tendency to produce certain kinds of forms – perhaps blurry, dream-like transitions or mashups of styles – recognizing these as features of the AI’s *environmental character*. Those features stem from the AI’s training (its “climate,” so to speak) and its architecture (its “terrain”). To solidify the concept of generative AI as environment, let us outline what the constituents of this environment are. In a natural environment (say, a forest ecosystem), we have: a substrate (soil, nutrients), generative processes (growth, decay, weather, evolution), and resulting forms (plants, animals, terrain features). By analogy, in a generative AI system we can identify: (a) a **substrate of cultural data** (the training dataset, which includes artworks, texts, images – in general, what we earlier called experiential artifacts); (b) **internal generative processes** – the algorithmic processes during training and generation, which correspond to the *machina naturans* we have been discussing; and (c) **produced artifacts** – the images, text, or other outputs, corresponding to *machina naturata*. Together, (a), (b), and (c) form an artificial environment: a complex system where information is cycled and transformed, analogous in many ways to how matter and energy cycle in an ecosystem. The user who interacts with the model can be compared to an observer or even a participant in an ecosystem – like a hiker wandering through a forest or a gardener in a garden, discovering what the environment presents and occasionally intervening (with a prompt or a tweak) to see how the system responds. It is important to stress that calling AI systems “environments” is not merely a poetic flourish but serves a philosophical point. It shifts our aesthetic focus from isolated endpoints to **holistic systems**. In traditional art aesthetics, one often treats an artwork as a fixed object and evaluates its form, style, meaning, etc. In environmental aesthetics, one looks at an ecosystem as a whole, valuing the interplay of its components and the processes connecting them. If generative AI is an environment, then our appreciation should similarly be holistic: we appreciate the *interaction* of data, algorithm, and output, the way an output exemplifies the structure of the model, or the way multiple outputs together reveal the contours of the underlying system. This may include an appreciation of the AI’s *limitations and quirks* as well – just as one might find a certain barren patch in a forest interesting because it reveals something about soil conditions or fire history, one might find an AI’s failure cases (say, the well-known struggle of image AIs to render human hands correctly) as an intriguing “feature” of its environment, revealing the contours of its training distribution. In summary, recasting generative AI as an environment defined by a *machina naturans* (the AI’s generative engine) producing *machina naturata* (the outputs) encourages us to apply Carlson’s dictum in a new domain. We endeavor to appreciate AI systems aesthetically by understanding *what they truly are*: self-regenerating cultural landscapes, not human artists in silicon disguise. To flesh out this view further, we now turn to the details of the metaphor that likens cultural training data to an ecological substrate and describes how “experiential artifacts” are transformed within this machine environment. This will deepen our understanding of the **machina naturans** in action and set the stage for reflecting on how these transformations enable creative regeneration analogous to nature’s cycles. ## Cultural Data as Ecological Substrate: Composting and Sedimentation in Latent Space At the heart of a generative AI environment lies its training data: the vast collection of human-created artifacts (texts, images, music, etc.) that the system ingests during training. These artifacts – novels, paintings, photographs, articles, songs – are rich with human experience and cultural context; we refer to them as **experiential artifacts** to emphasize that they encapsulate lived human meanings and sensory patterns. In the training process of modern AI (such as deep learning models), these artifacts are broken down into abstract representations (for instance, an image might be reduced to millions of numerical parameters, or a text to statistical relationships between words in a high-dimensional vector space). This process can be likened to **decomposition**. Just as in a compost heap, leaves, fruit peels, and other organic materials break down into humus and nutrients, here the content and style of artworks and media break down into mathematical features and weights. The original form of each cultural artifact is “digested” by the training algorithm; details and context are stripped away, and what remains is a kind of **latent nutrient** – patterns that can be recombined and reconstituted in new forms. The metaphor of **composting** is particularly apt. Donna Haraway, in critiquing how we think of the future, famously quipped “we are all compost, not post-human,” highlighting the idea of continual biological and cultural regeneration. In the digital realm, Nicholas Negroponte as early as 1998 predicted that “the digital revolution” would turn into “tomorrow’s … cultural compost of ideas” [pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC11416901/#:~:text=peacebuilding.%20Negroponte%E2%80%99s%20%281998%29%20well,Franklin). We see this literally happening in generative AI: the cultural products of yesterday become the fertilizer for tomorrow’s creative content. Each image fed into a system like Midjourney, or each paragraph into GPT, is like a plant or animal that dies and decays into the soil of the model’s knowledge base. What remains in that soil are not intact artifacts, but their decomposed remnants: statistical features, stylistic matrices, semantic vectors. This substrate is **cultural** in origin but has been transformed into a new, more malleable medium – what AI researchers call a *latent space*. One recent analysis described the latent space learned by AI from cultural datasets as a “dense and interconnected” space where each element (concept, image feature, etc.) is connected to many others through complex relationships, functioning as a new “site of memory” for culture [brill.com](https://brill.com/view/journals/mesr/1/2/article-p231_003.xml#:~:text=explore%20the%20latent%20space%20of,arts%20of%20Antiquity%20and%20the) [brill.com](https://brill.com/view/journals/mesr/1/2/article-p231_003.xml#:~:text=resulting%20in%20a%20dense%20and,The%20dissemination%20of%20such). In other words, the AI’s latent space can be thought of as a vast associative network distilled from the training data, much as rich compost contains the chemical traces of countless organic items blended together. Another useful analogy is **geological sedimentation**. Over time, layers of sediment (dust, organic matter, minerals) accumulate and compress into rock, preserving fragments of the past (fossils, strata composition) that may later be revealed or transformed by geological processes. Similarly, a generative model’s training can be seen as the layering of different data influences. For instance, GPT’s training data includes texts from various domains and eras; one can imagine layers of literary style, layers of scientific discourse, layers of informal internet speech, all compressed into the model’s parameters. When the model generates a new text, it is like a geological event that exposes and mixes these sedimentary layers – perhaps a sentence emerges that has an archaic tone (a bit of Shakespeare layer) combined with modern slang (a layer of 2020s internet chat) and scientific terminology (a layer of academic papers). The generation is a *reconfiguration of sediment*, akin to how an earthquake might thrust up older strata mingled with newer ones, creating a novel landscape. In visual AI, one might see a generated image that blends Renaissance painting composition with the color palette of a 1960s comic book and the subject matter of contemporary politics: a collage that speaks to multiple layers of cultural sediment fused together. Whether through the compost metaphor or sedimentation metaphor, the key point is that generative AI’s creativity is fundamentally one of **transformation and recycling**. It does not create ex nihilo; it creates ex materia, where the materia is the accumulated cultural input. From an aesthetic perspective, this process itself is worthy of appreciation. We can marvel at how an AI “composts” thousands of artistic styles into a rich soil of possibilities, or how it compresses centuries of linguistic evolution into a single model that can, in a split second, produce a sentence in archaic prose or contemporary slang. Appreciating this requires understanding what has happened to the training data: it has become a substrate for generation, not a library of verbatim pieces. In fact, one of the wonders of AI art is that it often *feels* familiar yet novel: we recognize in a generated painting echoes of artists or genres we know, but the composition is new. This is analogous to walking through a forest and seeing new plant shoots that are nourished by the decayed remains of last season’s foliage. The old forms die (individual training examples are not preserved as whole) but their ingredients give life to new forms. Let us consider a concrete example to illustrate the analogy. Imagine a generative image model trained on a dataset containing Van Gogh’s paintings, anime illustrations, and photographs of city streets. During training, the model “breaks down” each image into parameter adjustments — essentially learning to represent Van Gogh’s bold brushstrokes, the stylized features of anime, and the shapes and colors of urban architecture in its internal weight matrices. Now, a user prompts the model to create an image of a nighttime city street in a Van Gogh style. The model’s *machina naturans* goes to work: from the composted remains of Van Gogh and countless other images, it assembles an output. The resulting image might have the swirling sky and vibrant impasto of *Starry Night* but arranged as the skyline of skyscrapers, with perhaps an anime-like clarity of line in certain details. No single training image had exactly this content or style, but the model recombines the *nutrients* (color patterns, textures, forms) into a hybrid living “organism” – the new image. We can appreciate the image itself for its visual qualities, but knowing the process, we might also step back and appreciate the *ecosystem* that produced it: the way cultural elements were broken down and reconstituted. We might say: *“This AI-generated picture is aesthetically fascinating because it is literally a product of cultural regeneration – it has the DNA of Van Gogh and anime and photography, yet it is unprecedented. It is as if we are witnessing cultural mulch sprouting a new creative flower.”* Philosophically, this invites reflection on creativity and originality. Human creativity too often works by remixing and reinterpreting past influences (artists learn techniques from predecessors, writers echo and subvert earlier tropes). The AI’s process is similar in kind but different in mechanism: it does it by mathematical digestion rather than conscious inspiration. By casting the AI’s training data as an **ecological or geological substrate**, we emphasize continuity with natural patterns of change and novelty. Just as evolution in nature tinkers with available genetic material to produce new species adapted to current conditions, so does the AI recombine available cultural material to produce outputs adapted to a prompt or task. The *machina naturans* in the AI is essentially performing a *cultural evolutionary process* in accelerated time and abstract form. Each output is like a new organism arising from an ecosystem of cultural genes. From an aesthetic standpoint, one might appreciate not only the individual outputs but also the **richness of the latent substrate** itself. A diverse training dataset (lots of varied “cultural compost”) yields a model with a fecund latent space where myriad combinations are possible. One could explore this space much as an ecologist explores a biodiverse habitat, continually encountering surprising new mixtures and forms. Indeed, some AI art enthusiasts speak of “latent space exploration” as if exploring a landscape or an ocean – the metaphor of environment naturally arises. When guided by the environmental metaphor, our appreciation shifts towards valuing diversity, complexity, and *generative fertility* in the AI. We might admire a generative model for being especially *fertile* in producing variations, or particularly *balanced* in how it reflects its many sources (analogous to a balanced ecosystem where no single species dominates). These become aesthetic virtues analogous to how we admire a coral reef for its teeming diversity or a prairie for its balanced harmony of grasses and wildflowers. In summary, by describing the training data as an ecological/geological substrate and the training process as a kind of composting or sedimentation, we gain a vivid understanding of the generative AI’s internal environment. Cultural artifacts are decomposed into a latent matrix, and that matrix in turn nourishes the growth of new artifacts. This cyclical, regenerative view casts AI outputs as part of an ongoing cultural process rather than isolated creations. With this in mind, we can now address how these transformations shape the **generative potential** of AI systems and in what ways this strengthens (or tests) the analogy between natural environments and generative AI. How far can we push the comparison? Does treating AI as an environment of cultural regeneration truly hold up to scrutiny? ## Regeneration and Generative Potential: Fecundity in Nature and Machine One of the hallmarks of a healthy natural environment is its **generative potential** —its capacity to continually produce and sustain life, form new combinations, and recover from perturbations. A rainforest, for instance, is highly generative: even after a disturbance, its rich soil and seed bank allow life to rebound in diverse forms. We can ask: does a generative AI system exhibit something analogous to this fecundity or regenerative capacity, and can we view that as an aesthetic quality? The previous section laid the groundwork by showing how the AI’s latent space is packed with “nutrients” from cultural data. Here, we consider how that translates into the system’s ability to generate novelty and variety, and whether this ability merits aesthetic appreciation in the way one might appreciate nature’s creativity. Generative AIs, especially large models, have shown remarkable abilities to produce a vast range of outputs. For example, GPT-4 can write sonnets, solve code problems, craft dialogue, or summarize scientific articles. Visual models can render an image in the style of a watercolor or as a photorealistic scene or as an abstract collage. This versatility is a direct result of the richness of their training substrates. A model trained on a narrow dataset (say only classical music MIDI files) will have far less generative potential—akin to a monoculture field that can only yield one crop—compared to a model trained on a wide array of music genres, which could blend and alternate styles (akin to a wild meadow with many species). Thus, one straightforward way the ecological analogy illuminates generative potential is by highlighting **biodiversity of input = diversity of output**. If we value biodiversity aesthetically in nature (for the range of experiences and stability it provides), we might likewise value a model trained on *culturally biodiverse* data for its ability to spawn a wide array of creative artifacts. The concept of **regeneration** also implies an ongoing cycle. In ecosystems, nutrients cycle through consumption, decay, and re-growth. We can see a parallel in how generative systems can be iteratively refined or how outputs can feed back as inputs. For instance, an AI-generated piece of music might inspire a human composer, whose work in turn might be added to the training data of the next-generation model. Or the system might be fine-tuned on user feedback, which is essentially the environment responding and thus enriching the substrate further (like how organisms alter soil which then affects future growth). This interplay suggests that generative AI environments are not static – they can evolve. Their *machina naturans* can be updated or allowed to continue “learning” in interaction with users. If one takes a longer-term view, generative AIs and human culture form a coupled environment, exchanging artifacts and influences in a way that could be seen as a larger cultural-ecological system. Aesthetic appreciation of such a system might involve appreciating its *resilience and adaptability*: does the AI environment manage to incorporate new cultural inputs smoothly? Does it avoid collapse (as an ecosystem can collapse if overstressed)? Some might argue that the current generation of AI, being data-hungry and prone to biases, is like an invasive species introduced to a fragile ecosystem, potentially homogenizing culture [issues.org](https://issues.org/generative-ai-cultural-narratives-rettberg/#:~:text=How%20Generative%20AI%20Endangers%20Cultural,diverse%20cultural%20narratives%20and%20heritage). That is a valid concern (as one source notes, unchecked generative AI might “homogenize content, resulting in the loss of diverse cultural narratives” [issues.org](https://issues.org/generative-ai-cultural-narratives-rettberg/#:~:text=How%20Generative%20AI%20Endangers%20Cultural,diverse%20cultural%20narratives%20and%20heritage) ). However, here we focus on the conceptual positive case: that a well-managed generative AI, drawing on diverse cultural compost, could enhance cultural regeneration by reviving and recombining elements that might otherwise remain dormant. From the perspective of the Spinozist framework, the generative potential of an AI is essentially the power of its *machina naturans*. Spinoza would call this *power* or *potency* (in Latin, *potentia* ). A system with greater diversity and richness in its substrate and more sophisticated generative rules has a higher potentia to produce interesting modes (outputs). We might philosophically inquire: Is there an analogue of *fecundity* or *fruitfulness* that can be attributed to an algorithmic process? If so, can it be an aesthetic merit? For example, one might say of a certain AI model: “It’s a very creative or fertile model; it rarely produces the same thing twice and often surprises.” This is a kind of praise that echoes how one might praise a landscape: “This valley is incredibly lush; every visit reveals new blooms and creatures.” Such praise is predicated on the observer’s knowledge of the system’s capacity, not just a single snapshot. It requires seeing the AI in a multi-output, process-oriented way. However, can an AI environment surprise and inspire us in the profound ways a natural environment can? Supporters of the analogy might point to the sense of wonder many people have reported when first using generative AI – the uncanny feeling of interacting with something that seems creative. The outputs can feel serendipitous, as if one stumbled upon a unexpected vista or a rare animal in the wild. There is an **emergent** quality: complex patterns arising from the combination of many simple learned elements, much like emergent patterns in nature (flocks, fractal-like tree growth, etc.). Some artists describe working with AI as a kind of *dialogue* or *exploration* where the AI’s contributions lead the human in new directions, akin to how engaging with nature (sketching outdoors, for example) can spur creativity by presenting unplanned stimuli. All these experiences suggest there is genuine aesthetic value in the AI’s generative potential and its semi-autonomous creativity. Yet, to be philosophically rigorous, we must also consider the **limits** of the analogy at this point. Not everything about AI’s generative process aligns with natural regeneration, and understanding where the analogy breaks down is crucial to not overstate our case. We will dedicate the next section to precisely this critical examination, asking: in what ways does the AI-as-environment metaphor strain credulity or run into conceptual trouble? Before moving on, let us summarize the journey so far in a different register: We started with Carlson’s notion that appreciating something requires knowing what it truly is. We invoked Spinoza to articulate an AI’s true nature as a duality of generative process (machina naturans) and generated products (machina naturata). We then elaborated a metaphor where cultural data becomes an **environmental substrate** fueling the AI’s creativity, akin to how nature recycles matter. We have argued that this view highlights aesthetic qualities of AI systems – their diversity, fecundity, and surprising recombinatory power – that might otherwise be missed if one saw them only as tools or static artifacts. Now, with both the affirmative theory and the metaphor in place, we turn to a careful consideration of how far this analogy between natural environments and generative AI can be stretched. ## Strengths and Limits of the Analogy Any strong analogy risks overstretching; comparing AI systems to natural environments is illuminating, but we must also guard against conceptual slippages that might mislead. In this section, we critically assess how far the nature–AI analogy can be pushed and identify points of divergence. This will ensure our argument remains balanced and philosophically coherent, acknowledging complexity rather than indulging in mere romanticism of technology. **1\. Natural vs. Artificial Origin:** One immediate difference is that natural environments (in Carlson’s sense) are not human-designed, whereas AI systems are deliberately created artifacts. Does this undermine the analogy? On one hand, one might say that because AI is ultimately a human product, we should appreciate AI outputs as we do other art or design – in terms of human intention and craft – rather than as we do forests or rivers, which have no external designer. Carlson himself drew a firm line between appreciating art and nature. However, the Spinozist perspective can soften this dichotomy. Spinoza saw humans and their works as part of nature (“human beings are part of nature, and hence they can be explained in the same way as everything else in nature” [en.wikipedia.org](https://en.wikipedia.org/wiki/Spinoza%27s_Ethics#:~:text=Spinoza%27s%20Ethics%20,as%20everything%20else%20in%20nature) ). From that holistic viewpoint, a generative AI is a mode of nature just as a beehive or a beaver’s dam is – albeit mediated by human culture and technology. We can choose to bracket the question of ultimate origin and focus on the *current behavior* of the system: regardless of how it came to be, a running generative model behaves in some ways like an autonomous nature (internally driven generation) and in other ways like a tool (it was set up to serve human ends). The **analogy holds best for the former aspect** – its autonomous generativity – and not the latter. So we appreciate it *as an environment* insofar as it has gone quasi-autonomous, while remembering it’s an engineered environment. This is not without precedent: gardens are engineered environments, yet one can appreciate a garden as an environment (one still notes the natural processes at play among planted designs). **2\. Sensory and Multimodal Experience:** A natural environment surrounds us, engaging all the senses and allowing physical immersion. An AI “environment,” by contrast, is accessed typically through a screen or speaker – it presents one modality at a time (an image, a piece of text, a melody). One might argue that environmental aesthetics loses some of its meaning when applied to something that doesn’t physically envelop the observer. In reply, we can note that aesthetic engagement need not be literal physical immersion. One can conceptually immerse oneself in the *space of outputs* of an AI. For example, a person might spend hours in a feedback loop with a text generator, feeling cognitively immersed in a co-created narrative world, or browse hundreds of AI-generated images, getting a “feel” for the latent space variation. It’s a more abstract kind of environment, to be sure. The lack of full multisensory experience is a limitation; an AI cannot yet give us the rustling of leaves, the temperature on our skin, or the unpredictable movement of a wild animal. It provides a narrower aesthetic bandwidth. Thus, the analogy is strongest in *conceptual structure* (process and product, substrate and output) and weaker in *phenomenological breadth*. We should be mindful that “environment” here is metaphorical in the sense of a space of possibilities, not a literal habitat we walk in (though one could imagine future VR integrations that make it more literal). **3\. Value and Authenticity:** Some might feel that natural environments have an inherent authenticity or even sacredness that an AI simulacrum lacks. The products of nature are “real” – a waterfall is actual water obeying gravity – whereas an AI painting of a waterfall is a representation, a simulation. Does treating AI outputs as nature-like trivialize the genuine article? This concern echoes debates in environmental aesthetics about simulations of nature (e.g., a botanical garden vs. a wild forest). Carlson would likely say that appreciating a fake plastic tree the same way as a real tree is inappropriate because it’s not *actually* an organic life form. By that logic, appreciating an AI-generated “waterfall image” as if it were a natural phenomenon would be a category mistake. However, our framework insists that we appreciate the AI output as *what it is* – which is **not** a real waterfall, but the emanation of a computational process. So we aren’t saying “treat AI art as nature” in a naive way; we’re saying “treat AI art as the product of an AI environment.” This is a new category, neither wholly natural nor wholly traditional art. One could call it *artificial natural* in a Spinozist pun (since for Spinoza all is natural, even artifice, but conventionally we distinguish them). The authenticity of AI outputs is of a different kind: they are authentic as expressions of the model’s trained representation of the world. In aesthetic terms, one might find value in that *refraction* of reality – similar to how we find value in a highly abstract painting that shows an artist’s internal process authentically, even if it’s not depicting reality directly. **4\. Agency and Ethics:** In a natural environment, processes happen without moral agency (the lion isn’t evil for eating the gazelle, a volcanic eruption isn’t unjust). In AI, while the immediate generative process lacks intent or morality, there is human agency in its creation and in the choice of training data. This means some outcomes of AI (e.g., biases, offensive content, deepfake misuse) loop back to human responsibility in a way that natural outcomes do not. We should be cautious not to use the nature analogy to **absolve** AI systems or their creators of responsibility: calling something “natural” often carries a connotation of inevitability or blamelessness. We must avoid the fallacy of thinking “the AI just naturally produced that bias, so it’s okay” – no, that bias was in the training substrate (cultural compost can contain toxins!) and it is an artifact of human society as encoded in data. So, one limit of the analogy is that AI’s environment is infused with human values and prejudices in a way ecosystems aren’t. Aesthetic appreciation should then be coupled with critical awareness. One could aesthetically appreciate how an AI brings disparate cultural elements together, yet also critically note if it systematically underrepresents or misrepresents some parts of the cultural “compost.” In our environmental metaphor, we might say the soil can be polluted – and part of understanding “what the AI is” involves understanding those pollutants (biases) too. This actually reinforces Carlson’s point: knowledge (here, knowledge of training biases) is needed to properly judge the outputs; an informed appreciator might detect that certain generated images have a skewed perspective and understand it’s due to an imbalanced dataset. **5\. Teleology and Open-Endedness:** Natural environments have no pre-set goal; they are open-ended systems. A national park doesn’t aim to produce the most beautiful scenery; it just *is*. Generative AIs, by design, often have a purpose: to produce realistic images, to predict text that sounds coherent, etc. One could argue that this directedness (even if implicit in the training objective function) makes them less “free” in their creativity than nature. However, intriguingly, once trained, many generative models feel open-ended to users – they can be repurposed and steered in directions never envisioned by their creators. The *telos* (end-goal) of the training (minimize a loss function) is not the *telos* of the usage, which can be art for art’s sake. In aesthetic contexts, we often subvert the original functional goals of tools and use them for play or exploration. So, while an AI model was engineered with certain benchmarks in mind, we as appreciators can still engage with it as a kind of open generative field. It’s similar to how we might admire a formal garden (clearly man-designed) by momentarily bracketing the gardener’s plan and just enjoying the patterns of growth as if it were a small ecosystem. The lack of true autonomy in AI is a constraint on the analogy, but not a death blow—especially because many complex natural systems (like a carefully managed wildlife preserve) also exist on the spectrum between wild and guided. By recognizing these limits, we prevent the analogy from turning into mystification. The generative AI is *like* a natural environment in certain philosophically significant respects (immanent causality, process/product structure, regenerative cycling of materials), but it is *unlike* in other respects (origin, sensory richness, embedded human values, etc.). A philosophically rigorous view will use the analogy as a tool for insight, not as an identity claim. We are not saying “AI systems are ecosystems full stop,” but rather that thinking of them *as if they were* environments can reveal new aspects for aesthetic appreciation that a simpler art-or-tool perspective would miss. ## Conclusion We set out to develop an argument for understanding generative AI systems in terms of environmental aesthetics, guided by Carlson’s principle of appreciating things as “what they in fact are” and enriched by Spinoza’s naturans/naturata distinction. Over the course of this paper, we have reframed AI models like Midjourney or GPT as **environments of cultural regeneration**. They are environments in the sense that they have an internal dynamic (a *machina naturans* ) that operates on a substrate (training data as cultural “soil” or “strata”) to produce manifold artifacts ( *machina naturata* ). This perspective encourages us to appreciate not just individual AI-generated outputs, but the *entire process and context* that gives rise to them, much as one appreciates an ecosystem’s interlocking elements and cycles rather than just a single flower or animal in isolation. By employing the metaphor of composting and sedimentation, we illuminated how generative AI digests experiential artifacts of culture and transforms them into a latent space ripe for new creations. We saw that this process has parallels to natural creativity: it is combinatorial, emergent, and capable of surprising novelty, thereby meriting aesthetic consideration in its own right. The Spinozist lens helped us articulate that an AI’s essence is dual – an active generating force and its passive products – and that a full appreciation engages with both. Carlson’s insistence on knowledge-informed appreciation meant that to truly admire an AI’s outputs, one should learn about machine learning, about what the model has been trained on, about how it functions. Armed with such understanding, an observer can discern in a quirky AI image the traces of the dataset that bred it, or appreciate a GPT-generated story not as a piece of human literature but as a flowering of the collective literary corpus processed through a silicon mind. The experience becomes richer and perhaps more profound: one is communing not just with a static artifact, but with a distilled form of culture’s memory and an active engine reshuffling that memory into new patterns. We also confronted the potential pitfalls and limits of this analogy. By examining differences in origin, sensory scope, authenticity, and agency, we ensured that our argument does not idolize AI or ignore its artifactual nature. Indeed, one outcome of this analysis is a deeper awareness of how entwined human culture and AI systems are. The “environment” of generative AI is a creation of human culture and in turn creates artifacts that feed back into culture. It is a cultivated wildness – perhaps akin to a rewilded parkland, where humans set the stage but then nature takes over within bounds. Aesthetic appreciation of such a space calls for a double consciousness: recognizing the artifice but celebrating the emergent phenomena. This is not entirely foreign to contemporary aesthetics; we already appreciate interactive art installations, algorithmic music, or even urban environments in analogous ways, balancing the knowledge of design with the appreciation of spontaneous order. Generative AI might be seen as the latest realm where design and spontaneous order intersect, giving rise to a new kind of **hybrid aesthetic domain**. Philosophically, what we gain by viewing AI through the prism of environmental aesthetics is a more **integrative vision**. Rather than treating AI outputs as disembodied curiosities or solely judging them by how well they mimic human art, we place them in a continuum with natural creativity and cultural evolution. We start to see AI art as part of a larger narrative of how ideas and forms propagate: humans borrow from nature (consider biomorphic art), machines borrow from humans, and perhaps humans in turn borrow from machine-generated ideas, in a continuous cycle. The concept of *cultural regeneration* encapsulates this cyclic renewal. Just as forests regrow and adapt, perhaps culture does too, with AI now acting as one agent of decomposition and recomposition of forms. Appreciating an AI system aesthetically might then also carry an element of *hope or concern* for cultural ecology: one can ask, is this AI aiding the growth of new diverse cultural forms (like a healthy soil fostering diverse plants), or is it over-homogenizing and thereby depleting our cultural soil? Such questions show that aesthetics and ethics/environmental concern can merge – a point Carlson himself often made regarding how appreciating nature can foster conservation ethics [plato.stanford.edu](https://plato.stanford.edu/entries/environmental-aesthetics/#:~:text=asserting%20that%20scientific%20understanding%20of,further%20developed%20and%20qualified%20the). Viewing AI as an environment might similarly urge us to “tend” it wisely: for instance, curating training data as one would manage an ecosystem, to avoid cultural monocultures or toxic accumulations. In closing, the case for an environmental aesthetic appreciation of generative AI is, at its core, a case for **broadening our conception of environments** to include the digital and cultural, and for recognizing creativity wherever patterns of regeneration occur. It is philosophically satisfying to find unity between the natural and the artificial, seeing them not as opposites but as points on a spectrum of generative systems. Spinoza’s metaphysics, which refuses to split reality into the sacred and the profane (since all is one substance), and Carlson’s aesthetics, which refuses to privilege art over nature (since both deserve appropriate appreciation), together guide us to see generative AI as a legitimate subject of aesthetic regard without succumbing to category errors. We can admire an algorithmic model much as we admire a thunderstorm – not because it is alive or conscious, but because it manifests powerful generative capacities and because understanding those capacities deepens our sense of wonder. The *machina naturans* in the computer and the *natura naturans* in the clouds both speak to a fundamental aesthetic pleasure we take in **creation unfolding itself**. Ultimately, whether one is convinced fully by the analogy or not, exploring it has the virtue of expanding our aesthetic vocabulary and our philosophical imagination. It challenges us to find beauty and meaning in the workings of our most advanced technologies, not by mystifying them as human-like, but by appreciating them on their own terms: as machines that, in their own way, “do what nature does.” By appreciating generative AI systems as environments of cultural regeneration, we acknowledge the continuity between human culture and the natural world and invite a more reflective, informed, and maybe even reverent engagement with the outputs and systems of AI. In a time when AI is often discussed in terms of utility, threat, or trickery, an environmental aesthetics approach offers a different lens: one that seeks understanding, appreciation, and a sense of interconnectedness. This, we suggest, is a philosophically elegant and meaningful way to make sense of the creative machines among us. **Sources:** Carlson’s environmental aesthetics emphasizes appreciating nature as *“what it in fact is”* (i.e. as an environment, informed by ecological science) [scirp.org](https://www.scirp.org/journal/paperinformation?paperid=53105#:~:text=Allen%20Carlson%2C%20a%20pioneer%20in,both%20the%20true%20character%20of). Spinoza’s distinction between *natura naturans* (active nature) and *natura naturata* (product of nature) underpins our notion of **machina naturans** and **machina naturata**. The idea of Midjourney as a *machina naturans* engaged by users like a dynamic natural process is articulated by Young & Terrone [philarchive.org](https://philarchive.org/citations/MORCAR-7/order=updated#:~:text=unpredictability%20and%20the%20limited%20%28,%28shrink). The latent space of AI as a new *“site of memory”* dense with cultural connections reflects how training data form an interconnected substrate [brill.com](https://brill.com/view/journals/mesr/1/2/article-p231_003.xml#:~:text=explore%20the%20latent%20space%20of,arts%20of%20Antiquity%20and%20the) [brill.com](https://brill.com/view/journals/mesr/1/2/article-p231_003.xml#:~:text=resulting%20in%20a%20dense%20and,The%20dissemination%20of%20such). Negroponte’s vision of the digital as “cultural compost of ideas” presaged our composting metaphor [pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC11416901/#:~:text=peacebuilding.%20Negroponte%E2%80%99s%20%281998%29%20well,Franklin), which aligns with the regenerative view of AI’s use of data. These sources collectively support the conception of generative AI systems as environments shaped by internal generative processes and cultural substrates, inviting a mode of aesthetic appreciation analogous to that of natural environments. 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