Over the past decade, a growing number of artists and critical practitioners have become engaged with algorithms. This artistic engagement has resulted in algorithmic theatre, bot art, and algorithmic media and performance art of various kinds that thematise the dissemination and deployment of algorithms in everyday life. Especially striking is the high volume of artistic engagements with facial recognition algorithms, trading algorithms and search engine algorithms over the past few years.The fact that these three types of algorithms have garnered more responses than other types of algorithms suggests that they form a popular subject of artistic critique. This critique addresses several significant, supra-individual anxieties of our decade: socio- political uncertainty and polarisation, the global economic crisis and cycles of recession, and the centralisation and corporatisation of access to online information. However, the constituents of these anxieties — which seem to be central to our experience of algorithmic culture — are rarely interrogated. They, therefore, merit closer attention.This book uses prominent artistic representations of facial recognition algorithms, trading algorithms, and search algorithms as the entry point into an exploration of the constituents of the anxieties braided around these algorithms. It proposes that the work of Søren Kierkegaard—one of the first theorists of anxiety—helps us to investigate and critically analyse the constituents of ‘algorithmic anxiety’.
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‘Non art is more art than art art’, Allan Kaprow claimed in 1972. He concluded: ‘Artists of the world, drop out! You have nothing to lose but your professions.’ Are we ready to move on? From the 6th until the 9th of June 2024, an international group of art workers gathered at Floating Berlin to explore this question during the Konteksty Postartistic Congress.
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Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
