The Computer Vision Lab

maleve, nicolas

doi:10.4324/9781003095019-7

Cited by 2 publications

(3 citation statements)

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“…Instead of simply introducing common types of machine learning models mathematically, such as supervised and unsupervised learning, the pedagogical approach, similar to how we introduced the artwork nag in the earlier API section, is to introduce art practice as forms of learning and knowing (Borgdorff, 2010; Sullivan, 1993; Pritchard and Prophet, 2015) in order to open up ways of working with machine learning otherwise. To highlight the scale of datasets, Nicolas Malevé’s artwork entitled 12 Hours of ImageNet (Malevé, 2019a) shows the labelling work that was completed by over 25,000 workers from a crowdsourcing platform called Amazon Mechanical Turk, addressing the labour conditions that are required to annotate and categorise in a short period of time, over 14 million photographs in the ImageNet dataset collected over 2 years. Unlike the common way of visualising data as graphs and charts, the artwork is a durational piece with a coded script to cycle through all the images of the ImageNet dataset at a speed of 90 milliseconds per image, but the screen pauses at random points to enable the observation of some of the images and their categorisation.…”

Section: Learning Algorithmic Modelling Via Critical Artmentioning

confidence: 99%

“…Unlike the common way of visualising data as graphs and charts, the artwork is a durational piece with a coded script to cycle through all the images of the ImageNet dataset at a speed of 90 milliseconds per image, but the screen pauses at random points to enable the observation of some of the images and their categorisation. In this way, some of the key technical concepts of supervised learning can be introduced at the same time, such as training datasets, labelling and discrete classification connected to a concrete application and research object, which in this case is the ImageNet as a large visual database, and further points to some of the matters of concern regarding image sources, labour conditions, labelling and categorisation (Malevé, 2019b).…”

Section: Learning Algorithmic Modelling Via Critical Artmentioning

confidence: 99%

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(De)constructing machines as critical technical practice

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Velasco

2023

Convergence: The International Journal of Research into New Med

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This paper discusses the role of technology under the framework of Critical Technical Practice specifically in the form of constructing artefacts and deconstructing tools in order to produce what Philip Agre would describe as ‘reflexive work of critique’ (Agre, 1997:155). By presenting the activities and methods used in the teaching and shaping of undergraduate courses, this paper aims to show how technical objects, such as data, datasets, application programming interfaces and machine learning models, can be considered as discursive subjects, demonstrating pedagogical understanding across fields. The courses operate in the humanities tradition and take critical technical practice as a didactic approach, insofar as software and data are understood and manipulated on an instrumental level, while encouraging critical engagement and embodied reflection that bridge the technical and social/cultural domains. Within this pedagogical approach, critical is not only understood as a paradigm of rationality or quantitative, data-driven argumentation, but as adopting a critical position – that is, to research and reflect on the social structures and cultural phenomena entangled with digital objects, bodies, tools, methods and software production. By embracing work-in-progress and reflexive exploration, we aim to extend the notion of critical technical practice by unfolding how (de)constructing machines can be achieved beyond thinking of technology as neutral instrumentalisation. The challenge is how to find a balance, not only as researchers but as educators, unfolding aspects of both formality and functionality as well as questioning and understanding technology at a discursive and critical level. We argue that learning technical practice in an educational setting is not an end, but rather a means to question existing technological structures and create further changes in socio-technical systems.

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Section: Learning Algorithmic Modelling Via Critical Artmentioning

confidence: 99%

Section: Learning Algorithmic Modelling Via Critical Artmentioning

confidence: 99%

(De)constructing machines as critical technical practice

Soon

Velasco

2023

Convergence: The International Journal of Research into New Med

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“…The parallel to AI is direct, and problems with idiosyncratic sourcing and labeling of training data have been a constant headache [25,26] . For example, datasets of household objects trained on Flickr images perform poorly on objects from low and middle income countries [27] . In ImageNet, hammerhead sharks are depicted as swimming, trout are trophy catches, and lobster are cooked on a plate [28] .…”

Section: The Hobbesian Alternativementioning

confidence: 99%

Unlearning Descartes: Sentient AI is a Political Problem

Hull

2023

J. Soc. Comput.

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The emergence of Large Language Models (LLMs) has renewed debate about whether Artificial Intelligence (AI) can be conscious or sentient. This paper identifies two approaches to the topic and argues:(1) A "Cartesian" approach treats consciousness, sentience, and personhood as very similar terms, and treats language use as evidence that an entity is conscious. This approach, which has been dominant in AI research, is primarily interested in what consciousness is, and whether an entity possesses it. (2) An alternative "Hobbesian" approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or conscious. This both enables a political disambiguation of language, consciousness, and personhood and allows regulation to proceed in the face of intractable problems in deciding if something "really is" sentient. (3) AI systems should not be treated as conscious, for at least two reasons: (a) treating the system as an origin point tends to mask competing interests in creating it, at the expense of the most vulnerable people involved; and (b) it will tend to hinder efforts at holding someone accountable for the behavior of the systems. A major objective of this paper is accordingly to encourage a shift in thinking. In place of the Cartesian question-is AI sentient?-I propose that we confront the more Hobbesian one: Does it make sense to regulate developments in which AI systems behave as if they were sentient?

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The Computer Vision Lab

Cited by 2 publications

References 0 publications

(De)constructing machines as critical technical practice

(De)constructing machines as critical technical practice

Unlearning Descartes: Sentient AI is a Political Problem

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