The problem of AI ideology and - more broadly - whether we need AI and if so, what type of AI we need illustrates why we need critical perspectives on AI. Pragmatically, Wright defines it as "the ability of actors to accomplish things in the world."
AI – Humans vs. Machines
Artificial Intelligence (AI)
When Humans and Machines Might Have to Coexist
In the future, artificial intelligence will cause various challenges, and people will have to learn to live with machines and robots. However, the EU has much less influence in the actual development and expansion of artificial intelligence.
Digital Humanism: Epistemological, Ontological and Praxiological
Foundations
Second, the essential characteristics of the individual social actor are reduced to those of the human body. Third, the essential characteristics of the human body are reduced to those of its physical substrate.
An Alternative Rationalisation of Creative AI by De-Familiarising
An alternative rationalization of creative AI by defamiliarizing creativity: Towards an intelligibility on its own terms.
In his paper, Searle argued that AI in its development at the time could only be 'weak', whereby 'the main value of the computer in the study of the mind is that it gives us a very powerful tool'. Or that creativity entails uniquely human experiences, such as 'the need for experience and suffering' (Miller 2019, 16) or self-actualization, where creativity is 'about people claiming not to be machines' and 'revealing what it means to be a conscious, emotional human being' (Sautoy 2019, 283). Edward Branigan (2006) proposes anthropomorphism as an 'analytical category' to measure 'the degree to which a camera is used to simulate some feature of human embodiment', whose analysis then relates the qualities of the camera to 'a typical way of looking at the human, or moving (or thinking and feeling), and to what degree' (37).
Speed is therefore actually about space, or its breakdown, à la Paul Virilio (1991) who calls speed 'a primal dimension that defies all temporal and physical measurements' (18), and which leads directly to 'the crisis of the whole', whereby the substantive, homogeneous and continuous makes way for the fractional, heterogeneous and discontinuous. Stephen Hawking expressed to the BBC that 'the development of full artificial intelligence could spell the end of the human race' (Cellan-Jones 2014, n.p.). For these reasons – to ward off our fears and harness AI for improvement – the need to continually push for deeper understanding of AI is also correspondingly clear.
4 This is a neologism coined by Vertov, roughly referring to 'the quality of the cinema eye', as noted by the editor and translator of Kino-eye (Vertov 1984).
Post-Humanism, Mutual Aid
It's not just that facial recognition seems to work less well for people of color, it's that it performs what Simone Browne calls "digital epidermalization": "exercising the power cast by the disembodied gaze of certain surveillance technologies." For him, the moral concept of the "I" is projected onto events in the world (Nietzsche 1998). People, according to Barad, are part of the continuous reconfiguration of the world produced by these devices.
Humans (like other parts of nature) are of the world, not in the world, and certainly not outside the beholder. Care exists in the shadow of the kind of detachment and abstraction validated by AI. For many of the problems where AI is being used to single out those who deserve it.
This means that one acts 'as if' the intra-actions of a material-discursive apparatus could be determined by worrying about the consequential meanings that are produced.
Discourses and Myths About AI
The Language Labyrinth: Constructive Critique on the Terminology Used
Depending on the context of the discussion, some aspects of the matter should be explained using explanations, metaphors and analogies emphasizing the relevant technical characteristics and implications. Therefore, powerful metaphors push myths of the unlimited potential of (computer) technology, the supremacy of computation over human reasoning (Weizenbaum 1976) or the leading role of the 'digital sublime' in the transformation of society (Mosco 2004). Interestingly, so far none of the currently available methods seem to promise a path to AGI.
These networks are inspired by the workings of the human brain and its network of neurons; However, the model of a neuron used is very simplified. Commonly used ANNs are usually relatively simple, both in terms of how the biochemical properties of the neurons are modeled, but also in the complexity of the networks themselves. This difference in orders of magnitude makes for a huge difference in functionality, let alone understanding them as models of the human brain.
Therefore, the attribution of authorship is a very sensitive and consequent issue that paints a differentiated picture of the consequences of careless use of terms.
AI Ethics Needs Good Data
The chapter will therefore provide an overview and critique of AI ethics, before presenting a conceptual analysis of good data in the context of AI. We conceived the notion of "Good Data" to move beyond the critique of the digital (in which we have participated and continue to do so) to (re)imagining and articulating a more optimistic vision of the future with data and, in particular, how digital technologies and data, including but not limited to AI, can be used for political, more broadly, economic and 10 9) purposes. A good data approach interrogates these situations and broader factors that are often missing from AI ethics initiatives.
Good data is usable and fit for purpose, consensual, fair and transparent (Trenham and Steer 2019). It is unclear what socio-economic structures would enable truly ethical AI with good data – but certainly not the current ones. While at least one fundamental problem of good data may be capitalist political economy (Daly 2016; Benthall 2018), some additional steps to promote positive change can still be taken (Raicu 2018) – or 'better' data.
Like data itself, it is impossible for us to cover everything that is encompassed by 'Good Data' and therefore we cannot offer a 'complete'.
The Social Reconfiguration of Artificial Intelligence: Utility and Feasibility
I argue that existing considerations of AI reconfiguration have focused primarily on utility and largely neglected issues of feasibility. The chapter first discusses the contemporary form of AI called machine learning and its growing importance to the technology industry. Perhaps the most fundamental aspect of the materiality of machine learning is that it requires a lot of data from which to derive patterns (Alpaydin 2014, 1–4).
Left accelerators argue that under capital, the “productive forces of technology” are limited and directed “toward unnecessarily narrow ends” (Williams and Srnicek 2014, 355). So-called "democratized" machine learning does not enable the production of new technology applications beyond predetermined limits. Any reconfiguration of AI would require seizing the data centers that make up the cloud, as well as the energy resources and infrastructures needed to power them.
Last accessed May 5, 2020: https://developers.google.com/machine-learning/data-prep/construct/collect/data-size-quality.
Creating the Technological Saviour
Discourses on AI in Europe
Recent studies of popular and public debates on AI have begun to show the extent of the dominance of this technological-determinist ideology, particularly in the US (Mayer-Schönberger and Cukier 2013). To support the development of the AI strategies summarized here, the EC established two advisory entities: the High Level Expert Group on AI (HLEG); and the European AI Alliance. In the first year after its establishment in June 2018, the HLEG published two key policy documents that form the basis of the latest White Paper on UA, adopted in 2020.
Advances in computing and the increasing availability of data are therefore the main drivers of the current growth of AI' (European Commission 2020, 2). In more evocative terms, the myth of AI's revolutionary character is reinforced by a comparison with the steam and electricity 'revolution'. Myth #2: Creating urgency and societal 'preparation' - AI as inevitable The second of the most compelling myths to emerge from my analysis of EU AI strategies is the myth of the perceived undesirability of AI, constructed through a constant emphasis on its urgency.
Final communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions.
AI Bugs and Failures: How and Why to Render AI-Algorithms More Human?
About 60 years ago, part of the computer science community embarked on an ambitious research program called 'artificial intelligence'. To see the expectations of the experts (in the arts as well as in the sciences), a summary of publications on intelligent and spiritual machines is sufficient. Computer art as a genre followed this normative definition and emphasized the intentions of the artist/scientists.
Random events, or the ability to create randomness in a work of art, was seen as one of the advantages that the computer offers. Reichardt is of the opinion that such an event cannot be duplicated or imitated by computers (Reichardt 1971). The statistical analysis showed that the order of questions had no significant influence on the preference of the subjects.
He refers to Deniz Yilmaz as a failed experiment, as his name as the creator of the robot poet is still in the foreground.
AI Power and Inequalities
Initial Preview: A Critical Examination of the Implications of Ontological Exclusion in AI Protocol.
Primed Prediction: A Critical Examination of the Consequences
The primary purpose of this chapter is to explore the shortcomings of modern applications of Wiener's cybernetic prediction—the theoretical foundation of artificial intelligence (AI)—particularly in terms of the capture technologies that remain ubiquitous as a method of gathering data to power such systems. As Faucher (2013) argues, “the applicability of cybernetics is limited to very local and specific contexts, and in an increasingly complex universe, cybernetics will not necessarily save us” (206). The power of God that defined ontological reality in the first order of simulacra, as well as the power of scientific imagination that defined ontology in the second, was now firmly replaced by a new mode of instantiation—the algorithm.
In her book, Paper Knowledge: Towards a Media History of Documents, Lisa Gitelman (2014) examines the troubled ethics behind digital simulation—the vanishing site of tangible meaning and representation. However, as Galison (1994) notes, critics of Wiener's black box project saw the potential for "eliminating internal states of human purpose, desire, pleasure, and pain in favor of merely observable manifestations" (252). However, such cybernetic prediction is a narrow, self-referential system focused on the past and the future, in which data information plays a privileged role in hiding "reality behind a veil of digital representations designed to take command of life itself" (Faucher 2013, 211).
And, as Hand explains, "The dataverse promises a new descriptive-predictive analysis of pattern and correlation, prioritizing meaning and causation."
