Maps - Revision history

CUA: /* Mapping 'objects of interest and necessity' */

2025-09-18T10:21:28Z

Mapping 'objects of interest and necessity'

← Older revision		Revision as of 12:21, 18 September 2025
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	[16] Vladan Joler and Matteo Pasquinelli, ''The Nooscope Manifested: AI as Instrument of Knowledge Extractivism'', 2020, https://fritz.ai/nooscope/.		[16] Vladan Joler and Matteo Pasquinelli, ''The Nooscope Manifested: AI as Instrument of Knowledge Extractivism'', 2020, https://fritz.ai/nooscope/.

			== Guestbook ==
			<eplite id="Objects_of_interest_and_necessity_maps" height="600px" width="1000px" />


	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

NicolasMaleve: /* An organisational plane */

2025-09-01T20:25:36Z

An organisational plane

← Older revision		Revision as of 22:25, 1 September 2025
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	In mainstream visual culture the organisation is structured as a relation between a user and a corporate service. For example, users use Open AI's DALL-E to generate images, and may also share them afterwards on social media platforms like Meta's Instagram. In this case, the social organisation is more or less controlled by the corporations who typically allow little interaction between their many users. For instance, DALL-E does not have a feature that allows one to build on or reuse the prompt of other users, or of users to share their experiences and insights with generative AI image creation. Social interaction between users only occurs when they share their images on platforms such as, say, Instagram. Rarely are users involved in the social, legal, technical or other conditions for making and sharing AI generated images, and they have little to say about how these platforms are governed, moderated and censored.		In mainstream visual culture the organisation is structured as a relation between a user and a corporate service. For example, users use Open AI's DALL-E to generate images, and may also share them afterwards on social media platforms like Meta's Instagram. In this case, the social organisation is more or less controlled by the corporations who typically allow little interaction between their many users. For instance, DALL-E does not have a feature that allows one to build on or reuse the prompt of other users, or of users to share their experiences and insights with generative AI image creation. Social interaction between users only occurs when they share their images on platforms such as, say, Instagram. Rarely are users involved in the social, legal, technical or other conditions for making and sharing AI generated images, and they have little to say about how these platforms are governed, moderated and censored.

	Conversely, on the platforms for generating and sharing images in more autonomous AI users and communities are deeply involved in the conditions for AI image generation. LAION, again, is a good example of this. It is run by a non-commercial organisation or 'team' of around 20 members, led by Christoph Shumann, but their many projects involve a wider community of AI specialists, professionals and researchers. They collaborate on principles of access and openness, and their attempt to 'democratise' AI stands in contrast to the policies of Big Tech AI corporations. In many ways, LAION resembles what the anthropologist Chris Kelty has also labelled a 'recursive publics' – a community that care for and self-maintain the means of its own existence.[3]		Conversely, on the platforms for generating and sharing images in more autonomous AI users and communities are deeply involved in the conditions for AI image generation. [[LAION]], again, is a good example of this. It is run by a non-commercial organisation or 'team' of around 20 members, led by Christoph Shumann, but their many projects involve a wider community of AI specialists, professionals and researchers. They collaborate on principles of access and openness, and their attempt to 'democratise' AI stands in contrast to the policies of Big Tech AI corporations. In many ways, LAION resembles what the anthropologist Chris Kelty has also labelled a 'recursive publics' – a community that care for and self-maintain the means of its own existence.[3]

	However, such openness is not to be taken for granted, as also noted in debates around LAION.[4] There are many platforms in the ecology of autonomous AI ( see also [[CivitAI]] and [[Hugging Face]]) that easily become valuable resources. The datasets, models, communities, and expertise they offer may therefore also 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 in the organisation of autonomous AI there are dependencies on not only communities, but often also on corporate collaboration and venture capital.		However, such openness is not to be taken for granted, as also noted in debates around LAION.[4] There are many platforms in the ecology of autonomous AI ( see also [[CivitAI]] and [[Hugging Face]]) that easily become valuable resources. The datasets, models, communities, and expertise they offer may therefore also 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 in the organisation of autonomous AI there are dependencies on not only communities, but often also on corporate collaboration and venture capital.

NicolasMaleve: /* A material plane (GPU infrastructure) */

2025-08-27T14:07:37Z

A material plane (GPU infrastructure)

← Older revision		Revision as of 16:07, 27 August 2025
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	The mainstream commercial services are set up as what one might call a 'client-server' relation. The users of DALL-E or similar services access a main server (or a 'stack' of servers). Users have little control of the conditions for generating models and images (say, the way models are reused or their climate impact) as this happens elsewhere, in 'the cloud'.		The mainstream commercial services are set up as what one might call a 'client-server' relation. The users of DALL-E or similar services access a main server (or a 'stack' of servers). Users have little control of the conditions for generating models and images (say, the way models are reused or their climate impact) as this happens elsewhere, in 'the cloud'.

	Autonomous AI distinguishes itself from mainstream AI in the ~~decentral~~ organisation of processing power. Firstly, people who generate images or develop LoRAs with Stable Diffusion can use their own GPU. Often a simple laptop will work, but individuals and communities involved with autonomous AI image creation may also have expensive GPUs with high processing capability (built for gaming). Secondly, there is a decentralised network that connects the community's GPUs. That is, using the so-called [[Stable Horde]] (or AI Horde), the community can directly access each other's GPUs in a peer-to-peer manner. Granting others access to one's GPU is rewarded with [[currencies]] that in turn can be used to skip the line when waiting to access other members' GPUs. This social organisation of a material infrastructure allows the community to generate images almost with the same speed as commercial services.		Autonomous AI distinguishes itself from mainstream AI in the decentralised organisation of processing power. Firstly, people who generate images or develop LoRAs with Stable Diffusion can use their own GPU. Often a simple laptop will work, but individuals and communities involved with autonomous AI image creation may also have expensive GPUs with high processing capability (built for gaming). Secondly, there is a decentralised network that connects the community's GPUs. That is, using the so-called [[Stable Horde]] (or AI Horde), the community can directly access each other's GPUs in a peer-to-peer manner. Granting others access to one's GPU is rewarded with [[currencies]] that in turn can be used to skip the line when waiting to access other members' GPUs. This social organisation of a material infrastructure allows the community to generate images almost with the same speed as commercial services.

	To be dependent on the distribution of resources, rather than a centralised resource (e.g., a platform in 'the cloud'), points to how dependencies are often deliberately chosen in autonomous AI. One chooses to be dependent on a community because, for instance, one wants to reduce the consumption of hardware, because it is more cost-effective than one's own GPU, because one cannot afford the commercial services, or simply because one prefers this type of organisation of labour (separated from capital) that offers an alternative to Big Tech. That is, simply because one wants to be autonomous.		To be dependent on the distribution of resources, rather than a centralised resource (e.g., a platform in 'the cloud'), points to how dependencies are often deliberately chosen in autonomous AI. One chooses to be dependent on a community because, for instance, one wants to reduce the consumption of hardware, because it is more cost-effective than one's own GPU, because one cannot afford the commercial services, or simply because one prefers this type of organisation of labour (separated from capital) that offers an alternative to Big Tech. That is, simply because one wants to be autonomous.

NicolasMaleve at 14:02, 27 August 2025

2025-08-27T14:02:17Z

← Older revision		Revision as of 16:02, 27 August 2025
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	==== Latent space ====		==== Latent space ====
	[[Latent space]] is a highly abstract space consisting of compressed representations of images and texts. A key object is the [[Variational Autoencoder, VAE\|Variational Autoencoder (VAE)]] that makes the image-texts available to different kinds of operations – whose results are then decoded back into images. An important operation happening in the latent space is the training of an algorithm. In [[diffusion\|diffusion-based]] algorithms, the algorithm is trained by learning to apply noise to an image and then reconstruct an image, from complete or random noise (this process is discussed more in-~~dept~~ in the entry on [[diffusion]]).		[[Latent space]] is a highly abstract space consisting of compressed representations of images and texts. A key object is the [[Variational Autoencoder, VAE\|Variational Autoencoder (VAE)]] that makes the image-texts available to different kinds of operations – whose results are then decoded back into images. An important operation happening in the latent space is the training of an algorithm. In [[diffusion\|diffusion-based]] algorithms, the algorithm is trained by learning to apply noise to an image and then reconstruct an image, from complete or random noise (this process is discussed more in-depth in the entry on [[diffusion]]).

	To continue our mapping, it is important to note that the latent space is nurtured by various sources. In the process of model training, [[Dataset\|datasets]] play a crucial role. Many of the datasets that are used to train models are made by 'scraping' the internet, while others are built on repositories like Instagram, flickr, or Getty images. [https://storage.googleapis.com/openimages/web/index.html Open Images] and [https://www.image-net.org ImageNet] are commonly used as the backbone of visually training generative AI, built on web-pages, but corporate organisations like Meta and Google also offer open source datasets, as do e.g., research institutions and others. Contrary to common belief, there is not just one dataset used to make a model work, but multiple models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand), 'upscale' images of low resolution or 'refine' the details in the image. [[LoRA\|LoRAs]] trained on users own curated datasets are also often used in AI imaging with Stable Diffusion. The latent space is therefore an interpretation of a large pool of visual and textual resources, external to it.		To continue our mapping, it is important to note that the latent space is nurtured by various sources. In the process of model training, [[Dataset\|datasets]] play a crucial role. Many of the datasets that are used to train models are made by 'scraping' the internet, while others are built on repositories like Instagram, flickr, or Getty images. [https://storage.googleapis.com/openimages/web/index.html Open Images] and [https://www.image-net.org ImageNet] are commonly used as the backbone of visually training generative AI, built on web-pages, but corporate organisations like Meta and Google also offer open source datasets, as do e.g., research institutions and others. Contrary to common belief, there is not just one dataset used to make a model work, but multiple models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand), 'upscale' images of low resolution or 'refine' the details in the image. [[LoRA\|LoRAs]] trained on users own curated datasets are also often used in AI imaging with Stable Diffusion. The latent space is therefore an interpretation of a large pool of visual and textual resources, external to it.

CUA: /* Latent space */

2025-08-27T12:50:40Z

Latent space

← Older revision		Revision as of 14:50, 27 August 2025
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	To continue our mapping, it is important to note that the latent space is nurtured by various sources. In the process of model training, [[Dataset\|datasets]] play a crucial role. Many of the datasets that are used to train models are made by 'scraping' the internet, while others are built on repositories like Instagram, flickr, or Getty images. [https://storage.googleapis.com/openimages/web/index.html Open Images] and [https://www.image-net.org ImageNet] are commonly used as the backbone of visually training generative AI, built on web-pages, but corporate organisations like Meta and Google also offer open source datasets, as do e.g., research institutions and others. Contrary to common belief, there is not just one dataset used to make a model work, but multiple models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand), 'upscale' images of low resolution or 'refine' the details in the image. [[LoRA\|LoRAs]] trained on users own curated datasets are also often used in AI imaging with Stable Diffusion. The latent space is therefore an interpretation of a large pool of visual and textual resources, external to it.		To continue our mapping, it is important to note that the latent space is nurtured by various sources. In the process of model training, [[Dataset\|datasets]] play a crucial role. Many of the datasets that are used to train models are made by 'scraping' the internet, while others are built on repositories like Instagram, flickr, or Getty images. [https://storage.googleapis.com/openimages/web/index.html Open Images] and [https://www.image-net.org ImageNet] are commonly used as the backbone of visually training generative AI, built on web-pages, but corporate organisations like Meta and Google also offer open source datasets, as do e.g., research institutions and others. Contrary to common belief, there is not just one dataset used to make a model work, but multiple models and datasets to, for instance, reconstruct missing facial or other bodily details (such as too many fingers on one hand), 'upscale' images of low resolution or 'refine' the details in the image. [[LoRA\|LoRAs]] trained on users own curated datasets are also often used in AI imaging with Stable Diffusion. The latent space is therefore an interpretation of a large pool of visual and textual resources, external to it.

	When it comes to autonomous AI imaging, there is typically an organisation and a community behind each dataset and training. [[LAION]] (Large-scale-Artificial Intelligence Open Network) is a good example of this, and a very important one. It is a non-profit community organisation that develops and offers free models and datasets. Stable Diffusion was trained on datasets created by LAION, using [https://commoncrawl.org Common Crawl] (another non-profit organisation that has built a repository of 250 billion web pages) and [[Clip\|CLIP]] (OpenAI's neural network which learns visual concepts from natural language supervision) to compile an extensive record of links to images with 'alt text' (a descriptive text for non-text content, for increased accessibility) – that is a useful set of annotated images, to be used for model training. We begin to see that a model's dependencies have large organisational, social and technical ramifications.		When it comes to autonomous AI imaging, there is typically an organisation and a community behind each dataset and training. [[LAION]] (Large-scale-Artificial Intelligence Open Network) is a good example of this, and a very important one. It is a non-profit community organisation that develops and offers free models and datasets. Stable Diffusion was trained on datasets created by LAION, using [https://commoncrawl.org Common Crawl] (another non-profit organisation that has built a repository of 250 billion web pages) and [[Clip\|CLIP]] (OpenAI's neural network which learns visual concepts from natural language supervision) to compile an extensive record of links to images with 'alt text' (a descriptive text for non-text content, created by 'web masters' for increased accessibility) – that is a useful set of annotated images, to be used for model training. We begin to see that a model's dependencies have large organisational, social and technical ramifications.

	(Read more on [[latent space]] in its own entry)		(Read more on [[latent space]] in its own entry, as well as [[Variational Autoencoder, VAE]])
	[[File:Latent space map.jpg\|alt=A diagram of latent space in AI imaging\|none\|thumb\|640x640px\|The map reflects the separation of pixel space from latent space; i.e., what is seen by users from the more abstract space of models and computation, i.e., latent space. It particularly emphasises the objects involved in model training (the stacking of latent spaces), but also how latent space is dependent on and array of organisational, technical, textual and visual resources (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco)]]		[[File:Latent space map.jpg\|alt=A diagram of latent space in AI imaging\|none\|thumb\|640x640px\|The map reflects the separation of pixel space from latent space; i.e., what is seen by users from the more abstract space of models and computation, i.e., latent space. It particularly emphasises the objects involved in model training (the stacking of latent spaces), but also how latent space is dependent on and array of organisational, technical, textual and visual resources (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco)]]
	==== Pixel space ====		==== Pixel space ====

CUA at 11:53, 27 August 2025

2025-08-27T11:53:43Z

← Older revision		Revision as of 13:53, 27 August 2025
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	[12] Marcus Lu, “Mapped: The Number of AI Startups by Country,” ''Visual Capitalist'', May 6, 2024, https://www.visualcapitalist.com/mapped-the-number-of-ai-startups-by-country/.		[12] Marcus Lu, “Mapped: The Number of AI Startups by Country,” ''Visual Capitalist'', May 6, 2024, https://www.visualcapitalist.com/mapped-the-number-of-ai-startups-by-country/.

	[13] “Cartography of Generative AI,” ''Estampa'', accessed August 11, 2025, ~~<nowiki>~~https://tallerestampa.com/en/estampa/cartography-of-generative-ai/~~</nowiki>~~.		[13] “Cartography of Generative AI,” ''Estampa'', accessed August 11, 2025, https://tallerestampa.com/en/estampa/cartography-of-generative-ai/.

	[14] Celia Lury, ''Problem Spaces: How and Why Methodology Matters'' (Cambridge, UK; Medford, MA: Polity, 2021).		[14] Celia Lury, ''Problem Spaces: How and Why Methodology Matters'' (Cambridge, UK; Medford, MA: Polity, 2021).

	[15] Rachel Adams, “Can Artificial Intelligence Be Decolonized?,” ''Area'' 53, no. 1 (2021): 6–13, ~~<nowiki>~~https://doi.org/10.1080/03080188.2020.1840225~~</nowiki>~~.		[15] Rachel Adams, “Can Artificial Intelligence Be Decolonized?,” ''Area'' 53, no. 1 (2021): 6–13, https://doi.org/10.1080/03080188.2020.1840225.

	[16] Vladan Joler and Matteo Pasquinelli, ''The Nooscope Manifested: AI as Instrument of Knowledge Extractivism'', 2020, ~~<nowiki>~~https://fritz.ai/nooscope/~~</nowiki>~~.		[16] Vladan Joler and Matteo Pasquinelli, ''The Nooscope Manifested: AI as Instrument of Knowledge Extractivism'', 2020, https://fritz.ai/nooscope/.



	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

CUA at 11:49, 27 August 2025

2025-08-27T11:49:34Z

CUA: /* Mapping the many different planes and dependencies of generative AI */

2025-08-27T11:42:53Z

Mapping the many different planes and dependencies of generative AI

← Older revision		Revision as of 13:42, 27 August 2025
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	What is particular about the maps of this catalogue of objects of interest and necessity, is that they purely attempt to map autonomous generative AI imaing, serving as a map for a guided tour and experience of autonomous AI. However, both Hugging Face' dependency on 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 maps – all are equally important. For instance, The [https://artificialintelligenceact.eu EU AI Act] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, the extraction of resources, as well as a technical organisation of knowledge. The dependencies on capital should not be forgotten either.		What is particular about the maps of this catalogue of objects of interest and necessity, is that they purely attempt to map autonomous generative AI imaing, serving as a map for a guided tour and experience of autonomous AI. However, both Hugging Face' dependency on 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 maps – all are equally important. For instance, The [https://artificialintelligenceact.eu EU AI Act] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, the extraction of resources, as well as a technical organisation of knowledge. The dependencies on capital should not be forgotten either.

	[[File:Many planes map.jpg\|alt=A diagram of the many planes of AI imaging\|thumb]]		[[File:Many planes map.jpg\|alt=A diagram of the many planes of AI imaging\|none\|thumb\|640x640px\|\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)]]

			[[File:Map_objects_and_planes.jpg\|none\|thumb\|640x640px\|A sketch map showing the same as above from collaborative workshop (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco) ]]

	In mapping the objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.		In mapping the objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.

CUA at 11:41, 27 August 2025

2025-08-27T11:41:50Z

← Older revision		Revision as of 13:41, 27 August 2025
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	A useful annotated and categorised dataset - be it for a foundation model or a LoRA – typically involves specialised knowledge of both the technical requirements of model training (latent space) and the aesthetics and cultural values of visual culture itself (pixel space). For instance, of common visual conventions such as realism, beauty, horror, and also (in the making of LoRAs) of more specialised conventions such as ,say, a visual style that an artist wants to generate (see e.g. the generated images of Danish Hiphop by Kristoffer Ørum[2]).		A useful annotated and categorised dataset - be it for a foundation model or a LoRA – typically involves specialised knowledge of both the technical requirements of model training (latent space) and the aesthetics and cultural values of visual culture itself (pixel space). For instance, of common visual conventions such as realism, beauty, horror, and also (in the making of LoRAs) of more specialised conventions such as ,say, a visual style that an artist wants to generate (see e.g. the generated images of Danish Hiphop by Kristoffer Ørum[2]).
	[[File:Pixel space map.jpg\|none\|thumb\|640x640px\|alt=A diagram of pixel space in AI imaging\|A map of AI image generation separating 'pixel space' from 'latent space', but particularly emphasising the objects of pixel space, operated by the users. Pixel space is the home of both conventional visual culture and a more specialised visual culture. Conventionally, image generation will involve simple 'prompt' interfaces, and models will be built on accessible image, scraped from archives on the Internet, for instance. The specialised visual culture takes advantage of the openness of Stable Diffusion to, for instance, generate specific manga or gaming images with advanced settings and parameters. Often, users build and share their own models, too, so-called <nowiki>''</nowiki>LoRAs'. (by Christian Ulrik Andersen, Nicolas ~~Maleve~~, and Pablo Velasco)]]		[[File:Pixel space map.jpg\|none\|thumb\|640x640px\|alt=A diagram of pixel space in AI imaging\|A map of AI image generation separating 'pixel space' from 'latent space', but particularly emphasising the objects of pixel space, operated by the users. Pixel space is the home of both conventional visual culture and a more specialised visual culture. Conventionally, image generation will involve simple 'prompt' interfaces, and models will be built on accessible image, scraped from archives on the Internet, for instance. The specialised visual culture takes advantage of the openness of Stable Diffusion to, for instance, generate specific manga or gaming images with advanced settings and parameters. Often, users build and share their own models, too, so-called <nowiki>''</nowiki>LoRAs'. (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco)]]

	==== An organisational plane ====		==== An organisational plane ====
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	However, such openness is not to be taken for granted, as also noted in debates around LAION.[4] There are many platforms in the ecology of autonomous AI ( see also [[CivitAI]] and [[Hugging Face]]) that easily become valuable resources. The datasets, models, communities, and expertise they offer may therefore also 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 in the organisation of autonomous AI there are dependencies on not only communities, but often also on corporate collaboration and venture capital.		However, such openness is not to be taken for granted, as also noted in debates around LAION.[4] There are many platforms in the ecology of autonomous AI ( see also [[CivitAI]] and [[Hugging Face]]) that easily become valuable resources. The datasets, models, communities, and expertise they offer may therefore also 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 in the organisation of autonomous AI there are dependencies on not only communities, but often also on corporate collaboration and venture capital.

	[[File:Organisation map.jpg\|alt=A diagram of the organisational plane in AI imaging\|none\|thumb\|640x640px\|The map reflects the organisation of AI image generation. In conventional visual culture, users will access cloud-based services like OpenAI's DALL-E, and may also share their images on social media. In autonomous AI visual culture, the platforms are more democratic in the sense that moodels or datasets are freely available for training LoRAs or other development. Users also share their own models, datasets, images, and knowledge between on dedicated platforms, like CivitAI or Hugging Face. Many of the organisations from conventional visual culture (like Meta, who owns Instagram) also invest in the platforms of autonomous AI, and openness is not to be taken for granted (by Christian Ulrik Andersen, Nicolas ~~Maleve~~, and Pablo Velasco).]]		[[File:Organisation map.jpg\|alt=A diagram of the organisational plane in AI imaging\|none\|thumb\|640x640px\|The map reflects the organisation of AI image generation. In conventional visual culture, users will access cloud-based services like OpenAI's DALL-E, and may also share their images on social media. In autonomous AI visual culture, the platforms are more democratic in the sense that moodels or datasets are freely available for training LoRAs or other development. Users also share their own models, datasets, images, and knowledge between on dedicated platforms, like CivitAI or Hugging Face. Many of the organisations from conventional visual culture (like Meta, who owns Instagram) also invest in the platforms of autonomous AI, and openness is not to be taken for granted (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco).]]
	==== A material plane (GPU infrastructure) ====		==== A material plane (GPU infrastructure) ====
	Just like the objects of autonomous AI depend on a social organisation (and also on capital and labour, see [[currencies]]), they also depend on a material infrastructure – and are, so to speak, always suspended between many different planes. First of all, on hardware and specifically the [[GPU\|GPUs]] that are needed to generate images as well as the models behind. Like in the social organisation of AI image generation, infrastructures too are organised differently.		Just like the objects of autonomous AI depend on a social organisation (and also on capital and labour, see [[currencies]]), they also depend on a material infrastructure – and are, so to speak, always suspended between many different planes. First of all, on hardware and specifically the [[GPU\|GPUs]] that are needed to generate images as well as the models behind. Like in the social organisation of AI image generation, infrastructures too are organised differently.
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	At this material plane, there are many other dependencies. For instance, energy consumption, the use of expensive minerals for producing hardware, or the exploitation of labour in the production of hardware.		At this material plane, there are many other dependencies. For instance, energy consumption, the use of expensive minerals for producing hardware, or the exploitation of labour in the production of hardware.

	[[File:Material plane map.jpg\|alt=A diagram of the material plane of AI imaging\|none\|thumb\|640x640px\|The map reflects the material organisation, or infrastructure, of AI image generation. This particularly concerns the use of GPU and the processing power needed to generate images and train models and LoRAs. In conventional visual culture, users will access cloud-based services (like OpenAI's DALL-E, Adobe Firefly or Microsoft Image Creator) to generate images. In this client-server relation, users do not know where the service is (in the 'cloud'). In autonomous AI visual culture, users benefit from each others' GPU's in Stable/AI Horde's peer-to-peer network - exchanging GPU for the currency 'Kudos'. Knowing the location of the GPU is central. Users also train models, for instance on Hugging Face. Here, the infrastructure resembles more that of a platform (by Christian Ulrik Andersen, Nicolas ~~Maleve~~, and Pablo Velasco).]]		[[File:Material plane map.jpg\|alt=A diagram of the material plane of AI imaging\|none\|thumb\|640x640px\|The map reflects the material organisation, or infrastructure, of AI image generation. This particularly concerns the use of GPU and the processing power needed to generate images and train models and LoRAs. In conventional visual culture, users will access cloud-based services (like OpenAI's DALL-E, Adobe Firefly or Microsoft Image Creator) to generate images. In this client-server relation, users do not know where the service is (in the 'cloud'). In autonomous AI visual culture, users benefit from each others' GPU's in Stable/AI Horde's peer-to-peer network - exchanging GPU for the currency 'Kudos'. Knowing the location of the GPU is central. Users also train models, for instance on Hugging Face. Here, the infrastructure resembles more that of a platform (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco).]]
			=== Mapping the many different planes and dependencies of generative AI ===
			What is particular about the maps of this catalogue of objects of interest and necessity, is that they purely attempt to map autonomous generative AI imaing, serving as a map for a guided tour and experience of autonomous AI. However, both Hugging Face' dependency on 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 maps – all are equally important. For instance, The [https://artificialintelligenceact.eu EU AI Act] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, the extraction of resources, as well as a technical organisation of knowledge. The dependencies on capital should not be forgotten either.
	~~[[File:Materiality gpu.jpg\|alt~~=~~A photo~~ of ~~an illustration. On~~ the ~~one side a cloud client server use~~ of ~~resources (open ai~~, ~~adobe~~, ~~microsoft). On the other~~ a ~~peer-to-peer based (Stable Horde)\|none\|thumb\|640x640px\|The~~ map ~~reflects the material organisation, or infrastructure,~~ of AI ~~image generation~~. ~~This particularly concerns the use of GPU~~ and ~~the processing power needed to generate images and train models and LoRAs. In conventional visual culture, users will access cloud-based services (like OpenAI~~'s ~~DALL-E, Adobe Firefly or Microsoft Image Creator)~~ to ~~generate images. In this client-server relation, users do~~ not ~~know where the service is (~~in the ~~'cloud')~~. ~~In autonomous~~ AI ~~visual culture~~, ~~users benefit from each others' GPU's in~~ Stable/AI ~~Horde's peer-~~to~~-peer network - exchanging GPU for the currency 'Kudos'. Knowing the location~~ of ~~the GPU is central~~. ~~Users~~ also ~~train models, for instance~~ on ~~Hugging Face. Here~~, the ~~infrastructure resembles more that~~ of a ~~platform (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)~~.]]

	~~=== Mapping the many different~~ planes ~~and dependencies of generative AI~~ ===		[[File:Many planes map.jpg\|alt=A diagram of the many planes of AI imaging\|thumb]]
	What is particular about the map of this catalogue of objects of interest and necessity, is that it purely attempts to map autonomous and decentralised generative AI, serving as a map for a guided tour and experience of ~~autonomous AI. However, 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. For instance, The [https://artificialintelligenceact.eu EU~~ AI ~~Act~~] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, or the extraction of resources (such as the mining minerals for hardware).

	In ~~describing our~~ objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.		In mapping the objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.

	[[File:Planes of ai.jpg\|alt=A photo of a map and illustration of material, technical and other planes that AI objects exist between\| none\|thumb\|640x640px\|\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)]]		[[File:Planes of ai.jpg\|alt=A photo of a map and illustration of material, technical and other planes that AI objects exist between\| none\|thumb\|640x640px\|\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)]]

CUA: /* A material plane (GPU infrastructure) */

2025-08-27T11:33:02Z

A material plane (GPU infrastructure)

← Older revision		Revision as of 13:33, 27 August 2025
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	At this material plane, there are many other dependencies. For instance, energy consumption, the use of expensive minerals for producing hardware, or the exploitation of labour in the production of hardware.		At this material plane, there are many other dependencies. For instance, energy consumption, the use of expensive minerals for producing hardware, or the exploitation of labour in the production of hardware.

	[[File:Material plane map.jpg\|alt=A diagram of the material plane of AI imaging\|thumb]]		[[File:Material plane map.jpg\|alt=A diagram of the material plane of AI imaging\|none\|thumb\|640x640px\|The map reflects the material organisation, or infrastructure, of AI image generation. This particularly concerns the use of GPU and the processing power needed to generate images and train models and LoRAs. In conventional visual culture, users will access cloud-based services (like OpenAI's DALL-E, Adobe Firefly or Microsoft Image Creator) to generate images. In this client-server relation, users do not know where the service is (in the 'cloud'). In autonomous AI visual culture, users benefit from each others' GPU's in Stable/AI Horde's peer-to-peer network - exchanging GPU for the currency 'Kudos'. Knowing the location of the GPU is central. Users also train models, for instance on Hugging Face. Here, the infrastructure resembles more that of a platform (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco).]]

← Older revision		Revision as of 13:49, 27 August 2025
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	What is particular about the maps of this catalogue of objects of interest and necessity, is that they purely attempt to map autonomous generative AI imaing, serving as a map for a guided tour and experience of autonomous AI. However, both Hugging Face' dependency on 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 maps – all are equally important. For instance, The [https://artificialintelligenceact.eu EU AI Act] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, the extraction of resources, as well as a technical organisation of knowledge. The dependencies on capital should not be forgotten either.		What is particular about the maps of this catalogue of objects of interest and necessity, is that they purely attempt to map autonomous generative AI imaing, serving as a map for a guided tour and experience of autonomous AI. However, both Hugging Face' dependency on 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 maps – all are equally important. For instance, The [https://artificialintelligenceact.eu EU AI Act] or laws on copyright infringement, which Stable Diffusion (like any other AI ecology) will also depend on, point to a plane of governance and regulation. AI, including Stable Diffusion, also depends on the organisation of human labour, the extraction of resources, as well as a technical organisation of knowledge. The dependencies on capital should not be forgotten either.

	[[File:Many planes map.jpg\|alt=A diagram of the many planes of AI imaging\|none\|thumb\|640x640px\|\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas ~~Maleve~~, and Pablo Velasco)]]		[[File:Many planes map.jpg\|alt=A diagram of the many planes of AI imaging\|none\|thumb\|640x640px\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco)]]

	[[File:Map_objects_and_planes.jpg\|none\|thumb\|640x640px\|A sketch map showing the same as above from collaborative workshop (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco) ]]		[[File:Map_objects_and_planes.jpg\|none\|thumb\|640x640px\|A sketch map showing the same as above from collaborative workshop (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco) ]]

	In mapping the objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.		In mapping the objects of interest and necessity, we attempt to describe how Stable Diffusion and autonomous AI image generation build on dependencies to these different planes, but an overview of the many planes of AI and how it 'stacks' can of course also be the centre of a map in itself. One example of this is Kate Crawford's ''[https://katecrawford.net/atlas Atlas of AI]'', a book that displays different maps (and also images) that link AI to 'Earth' and the exploitation of energy and minerals, or 'Labour' and the workers who do micro tasks ('clicking' tasks) or the workers in Amazon's warehouses. Additionally, Crawford's book has chapters on 'Data', 'Classification', 'Affect', 'State' and 'Power'.

	[[File:Planes of ai.jpg\|alt=A photo of a map and illustration of material, technical and other planes that AI objects exist between\| none\|thumb\|640x640px\|\|A map of how objects not only relate pixel space to latent space, but how they are always suspended between different planes - not only a technical one, but also an organisational one, a material one, and potentially many others (capital, labour, knowledge, governance, etc.) (by Christian Ulrik Andersen, Nicolas Maleve, and Pablo Velasco)]]

	~~[[File:Map_objects_and_planes.jpg\|none\|thumb\|640x640px\|A sketch map showing the same as above from collaborative workshop (by Christian Ulrik Andersen, Nicolas Malevé, and Pablo Velasco) ]]~~

	Another abstraction of the layered nature of generative AI is found in Gertraud Koch's map of all layers that she and her coauthors connects to "technological activity", which is clearly relevant to AI.[5] On top of a layer of technology (the 'data models and algorithms') one will find other layers that are interdependent, and which contribute to the political and technological qualities of AI. As such, the map is also meant for navigation – to identify starting points for rethinking its concepts or reimagining alternative futures (in their work, particularly in relation to a potential delinking from a colonial past, and reimagining a pluriversality of technology)		Another abstraction of the layered nature of generative AI is found in Gertraud Koch's map of all layers that she and her coauthors connects to "technological activity", which is clearly relevant to AI.[5] On top of a layer of technology (the 'data models and algorithms') one will find other layers that are interdependent, and which contribute to the political and technological qualities of AI. As such, the map is also meant for navigation – to identify starting points for rethinking its concepts or reimagining alternative futures (in their work, particularly in relation to a potential delinking from a colonial past, and reimagining a pluriversality of technology)
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	==== Critical cartography in the mapping of AI ====		==== Critical cartography in the mapping of AI ====
	In mapping AI, there are also 'counter maps' or 'critical cartography'.[8] 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 were 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."[9] 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.[10] 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.		In mapping AI, there are also 'counter maps' or 'critical cartography'.[8] 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 were 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."[9] 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.[10] The aim is 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 AI researcher Jean-Louis Fendji.[11]		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 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 AI researcher Jean-Louis Fendji.[11]

	[[File:Numbers of newly funded AI startups.png\|none\|thumb\|640x640px\|Numbers of newly funded AI startups per country (by Visual Capitalist).[12]]]		[[File:Numbers of newly funded AI startups.png\|none\|thumb\|640x640px\|Numbers of newly funded AI startups per country (by Visual Capitalist).[12]]]

	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). [[File:Taller Estampa, map of generative AI.png\|none\|thumb\|640x640px\|Map of generative AI (by Taller Estampa).[13]]]		Counter-mapping the organisation of relations and dependencies is also prevalent in the works of the Barcelona-based artist collective Estampa, which also 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). [[File:Taller Estampa, map of generative AI.png\|none\|thumb\|640x640px\|Map of generative AI (by Taller Estampa).[13]]]

	==== Epistemic mapping of AI ====		==== Epistemic mapping of AI ====
	Maps of AI often also address how AI functions as what Celia Lury has called an 'epistemic infrastructure'.[14] 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, and one also ought to ask, whose intelligence and understanding of knowledge is modelled within the technology – and whose is left out?[15]		Maps of AI often also address how AI functions as what Celia Lury has called an 'epistemic infrastructure'.[14] That is, AI is an apparatus that builds on knowledge, creates knowledge, but also shapes what knowledge is and what 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 helps 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, and one also ought to ask, whose intelligence and understanding of knowledge is modelled within the technology – and whose is left out?[15]

	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 many of the concerns of Lury and Adams, Vladan Joler and Matteo Pasquinelli's 'Nooscope' is a good example of this.[16] 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 also 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').		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 many of the concerns of Lury and Adams, Vladan Joler and Matteo Pasquinelli's 'Nooscope' is a good example of this.[16] 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 also 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').
	[[File:Nooscope.png\|none\|thumb\|640x640px\|alt=A map of AI as an instrument of knowledge by Vladan Joler and Matteo Pasquinelli (2020)\|A map of AI as an instrument of knowledge (by Vladan Joler and Matteo Pasquinelli). ]]		[[File:Nooscope.png\|none\|thumb\|640x640px\|alt=A map of AI as an instrument of knowledge by Vladan Joler and Matteo Pasquinelli (2020)\|A map of AI as an instrument of knowledge (by Vladan Joler and Matteo Pasquinelli). ]]