Maps

Mapping 'objects of interest and necessity'

If one considers generative AI as an object, there is also a world of ‘para objects’, surrounding AI and shaping its reception and interpretation in the form of maps or diagrams of AI. They are drawn by both amateurs and professionals who need to represent processes that are otherwise sealed off in technical systems, but more generally reflect a need for abstraction – a need for conceptual models of how generative AI functions. However, as Alfred Korzybski famously put it, one should not confuse the map with the territory: the map is not how reality is, but a representation of reality [reference].

Following on from this, mapping the objects of interest in autonomous AI image creation is not to be understood as a map of what it 'really is'. Rather, it is a map of encounters of objects; encounters that can be documented and catalogued, but also positioned in a spatial dimension – representing a 'guided tour', and an experience of what objects are called, how they look, how they connect to other objects, communities or underlying infrastructures (see also Objects of interest and necessity). Perhaps, the map can even be used by others to navigate autonomous generative AI and create their own experiences. But, importantly to note, what is particular about the map of this catalogue of objects of interest and necessity, is that it purely maps autonomous and decentralised generative AI. It is therefore not to be considered a plane that necessarily reflects what happens in, for instance, Open AI og Google's generative AI systems. The map is in other words not a map of what is otherwise concealed in generative AI. In fact, we know very little of how to navigate proprietary systems, and one might speculate if there even exists a map of relations and dependencies.

Perhaps because of this lack of view, maps and cartographies are not just shaping the reception and interpretation of genrative AI, but can also be regarded as objects of interest in themselves and intrinsic parts of AI’s existence: generative AI depends on an abundance of cartography to build, shape, navigate, and also negotiate and criticise its being in the world. There seems to be an inbuilt need to 'map the territory', and the collection of cartopgraphies and maps is therefore also what makes AI a reality.

A map of autonomous and decentralised AI image generation

To enter the world of autonomous AI image generation a map of how the territories of ‘pixel space’ from ‘latent space’ – the objects you see, from those who cannot be seen – is fundamental. In a pixel space, you would find both the images that are generated by a prompt (a textual input), as well as the many images that are used to compile the textually annotated data set used for training the image generation models (the foundation models).

Latent space is more complicated to explain. It is, in a sense, a purely computational space that relies on models that can encode images with noise (using a 'Variational Autoencoder', VAE), and learn how to de-code them back into images, thereby giving the model the ability to generate new images using image diffusion. It therefore also deeply depends on both the prompt and the dataset in pixel space – to generate images, and to build or train models for image generation.

A diagram of AI image generation separating 'pixel space' from 'latent space' - what you see, and what cannot be seen (by Nicolas Maleve)

Apart from pixel space and latent space, there is also a territory of objects that can be seen, but you typically (as a user) do not. For instance, in Stable Diffusion you find LAION, a non-profit organization that uses the software Clip to scrape the internet for textually annotated images to generate a free and open-source data set for training models in latent space. You would also find communities who contribute to LAION, or who refine the models of latent space using so-called LoRAs, but also models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand) – often with both specialised knowledge of the properties of the foundational diffusion models, and of the visual culture they feed into (for instance manga or gaming). These communities are also organized on different platforms, such as CivitAI or Hugging Face, where communities can exhibit their specialised image creations or share their LoRAs, often with the involvement of different tokens or virtual currencies. What is important to realise when navigating this territory, is that behind every dataset, model, software, and platform lies also a specific community.

This map reflects the separation of pixel space from latent space, and adds a third layer of objects that are visible, but not seen by typical users. Underneath the three layers one finds a second plane of material infrastructures (such as processing power and electricity), and one can potentially also add more planes, such as for instance regulation or governance of AI (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)

A map of this territory would therefore necessarily contain technical objects (such as models, software, and platforms) that are deeply intertwined with communities that care for, negotiate, and maintain the means of their own existence (what the anthropologist Chris Kelty has also labelled a 'recursive publics'), but which may potentially also (at the same time) be subject to value extraction. Hugging Face is a prime example of this - a community hub as well as a $4.5 billion company with investments from Amazon, IBM, Google, Intel, and many more; as well as collaborations with Meta and Amazon Web Services. This indicates that there are other dependencies on not only communities, but also on corporate collaboration and venture capital.

AI image generation is (and not least) always also dependent on material infrastructures. First of all on hardware and specifically GPUs that are needed to both generate images as well as develop and refine the diffusion models (e.g. developing LoRAs). GPUs can be found in personal computers, but very often individuals and communities will invest in expensive GPUs with high processing capability. Subsequently, people who generate images or develop LoRAs with Stable Diffusion can have their own GPU (built into their computer or specifically acquired), but they can also benefit from a distributed network, allowing them to access other people’s GPUs, using the so-called Stable Horde. This points to how autonomous AI image generation not only depends on, but often also chooses dependencies. For example, to be dependent on the distributed resources of a community, rather than a centralised resource (e.g., a platform in 'the cloud'). At this material plane, there are also other dependencies that one can choose. For instance, energy. Both generating images and tracing models require a massive energy consumption, and the question of how much and from which source (green or black) may also be a key factor in choosing Stable Horde over one's own GPU, or a platform. The labour and minerals that go into the production of required hardware can be considered another dependency on this material plane.

Mapping other planes of AI

What is particular about the map of this catalogue of objects of interest and necessity, is that it purely maps autonomous and decentralised generative AI. Both Hugging Face' dependency in venture capital and Stable Diffusion's dependency on hardware and infrastructure point to the fact that there are several planes that are not captured in the above map of this catalogue, but which are equally important – in that they point to a number of other dependencies in other territories that can be explored and mapped. One might also add, a plane of governance and regulation. For instance, The EU AI Act or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on. The anthropologists and AI researcher Gertraud Koch has drawn a map (or diagram) of all the planes that she identifies in generative AI.

[Gertraud's map + further commentary]

There are, as such, many other maps that each in their own way build conceptual models of generative AI. There are for instance maps of the corporate plane of AI, such as entrepreneur, investor and pod cast host Matt Turck’s “ultimate annual market map of the data/AI industry”. Since 2012 Matt Turck has documented the ecosystem of AI not just to identify key corporate actors, but also developments of trends in business. Comparing the 2012 version with the most recent map from 2024, one can see how the corporate landscape moves from 'Big Data' to 'AI', and also how the division of companies dealing with infrastructure, data analytics, applications, data sources, and open source becomes fine grained over the years, and forking out into, for instance, applications in health, finance and agriculture; or how privacy and security become of increased concern in the business of infrastructures.

The corporate landscape of Big Data in 2012, by Matt Turck and Shivon Zilis.

Critical cartography in the mapping of AI

In mapping AI there are also 'counter maps' or 'critical cartography'. Conventional world maps are built on set principles of, for instance, North facing up, and Europe at the centre. The map is therefore not just a map for navigation, but also a map of more abstract imaginaries and histories originating in colonial times, where maps was the outset of Europe and an intrinsic part of the conquest of territories. In this sense, a map always also reflects hierarchies of power and control that can be inverted or exposed (for instance by turning the map upside down, letting the south be a point of departure). Counter-mapping technological territories would, following this logic, involve what the French research and design group Bureau d´Études has called "maps of contemporary political, social and economic systems that allow people to inform, reposition and empower themselves." They are maps that reveal underlying structures of social, political or economic dependencies to expose what ought to be of common interest, or the hidden grounds on which a commons rests. Félix Guattari and Gilles Deleuze' notion of 'deterritorialization' can be useful, here, as a way to conceptualise the practices that expose and mutate the social, material, financial, political, or other organisation of relations and dependencies. The aim is ultimately not only to destroy this 'territory' of relations and dependencies, but ultimately a 'reterritorialization' – a reconfiguration of the relations and dependencies.

Utilising the opportunities of info-graphics in mapping can be a powerful tool. At the plane of financial dependencies, one can map, as Matt Turck, the corporate landscape of AI, but one can also draw a different map that reveals how the territory of 'startups' does not compare to a geographical map of land and continents. Strikingly, The United States is double the size of Europe and Asia, whereas there are whole countries and continents that are missing (such as Russia and Africa). This map thereby not only reflects the number of startups, but also how venture capital is dependent on other planes, such as politics and the organisation of capital, or infrastructural gaps. In Africa, for instance, the AI divide is very much also a 'digital divide', as argued by Cameroonian AI researcher Jean-Louis Fendji.

Numbers of newly funded AI startups per country // MISSING SOURCE

Counter-mapping the organisation of relations and dependencies is also prevalent in the works of the Barcelona-based artist collective Estampa, which exposes how generative AI depends on different planes (venture capital, energy consumption, a supply chain of minerals, human labour, as well as other infrastructures (such as the internet, which is 'scraped' for images or other media, using e.g. software like Clip).

Taller Estampa, map of generative AI, 2024

Epistemic mapping of AI

Maps of AI often also address how AI functions as what Celia Lury has called an 'epistemic infrastructure'. That is, AI is an apparatus that builds on knowledge, creates knowledge, but also shapes what knowledge is and we consider to be knowledge. To Lury, the question of 'methods' here becomes central - not as a neutral, 'objective' stance, as one typically regards good methodology in science, but as a cultural and social practice that help articulate the questions we ask and what we consider to be a problem in the first place. When one for, instance, criticises the social, racial or other biases in generative AI (such as all doctors being white males in generative AI image creation), we are not just dealing with bias in the dataset that can be fixed with 'negative prompts' or other technical means. Rather, AI is fundamentally – in its very construction and infrastructure – based in a Eurocentric history of modernity and knowledge production. For instance, as pointed out by Rachel Adams, AI belongs to a genealogy of intelligence, where one also ought to ask, whose intelligence and understanding of knowledge is modelled within the technology?

There are several attempts to map this territory in the plane of knowledge production, and its many social, material, political or other relations and dependencies. Sharing the concerns of Lury, Adams, and others, a key feature is that they too seek to abstract and challenge the organisation of relations and dependencies. Vladan Joler and Matteo Pasquinelli's 'Nooscope' is one example of this. In their understanding AI belongs to a much longer history of knowledge instruments ('nooscopes', from the Greek skopein ‘to examine, look’ and noos ‘knowledge’) that would also include optical instruments, but which in AI is a form of knowledge magnification of patterns and statistical correlations in data. The nooscope map is an abstraction of how AI functions as "Instrument of Knowledge Extractivism". It is therefore not a map of 'intelligence' and logical reasoning, but rather of a "regime of visibility and intelligibility" whose aim is the automation of labour, and of how this aim rests on (as other capitalist extractions of value in modernity) a division of labour – between humans and technology, between for instance historical biases in the selection and labelling of data, and their formalisation in sensors, databases and metadata. The map refers to how selection, labelling and other laborious tasks in the training of models is done by "ghost workers" thereby referring to a broader geo-politics and body-politics of AI where human labour is often done by subjects of the Global South (although they might oppose being referred to as 'ghosts').

A map of AI as an instrument of knowledge by Vladan Joler and Matteo Pasquinelli (2020)

genealogy of intelligence: whose con-

ception of intelligence is modelled within technology

, but also with the infrastructure of a more fundamental epistemic bias.

that methods are practices that articulate as much as capture a social problem

for seeing and also being seen

Intro: Celia Lury - AI as an 'epistemic infrastructure' >> "epistemic maps"

Examples:

Atlas of AI

Joler and Pasquinelli

Not sure how to fit this map (a map of the process of producing a pony image) in this entry. Perhaps another entry?

Map of genereting pony image