Rules of Emergence — Generating and Curating Creativity

This Bachelor’s thesis discusses the relationship between generating and curating in the context of artistic activity. In this context, generating refers to processes and systems used in generative art and generative design. Curating refers to the traditional profession of producing exhibitions, and to the contemporary definition of curating as a universal act of selection and evaluation. The objective of this thesis is to introduce the processes and methods used in generating and curating, and to expose the creative potential emerging from the combination of these practices. The research analyses and compares contemporary discourses of generating and curating, and presents examples of modern generative and curatorial practices. A joint framework is proposed which illustrates the interconnection of generating and curating. Theories of creativity by Deleuze and Boden & Wiggings are accommodated in the framework to demonstrate the potential of the synthesis for emergent outcomes. Despite the apparent discreteness of generating and curating, they in fact share many characteristics, both practical and conceptual. They both require the definition of a rule, which determines the curatorial or generative process. In generative design or art, this rule is an algorithm or some other formalisation of an action, in curating the rule is the selection criteria of the collection. Both in generating and curating, the agent creates the process instead of designing directly the product. Generating requires curating in evaluating and selecting the outcomes, as curating depends upon generating in forming the collection according to the selective rules. Deleuze’s concepts of ‘virtual’ and ‘actual’ capture the emergent properties of generating and curating: the rules define the ‘virtual’ cloud of possible outcomes, from which the perceptible products are actualised. Thus, generating and curating both supervene on and contain each other.Tämä opinnäytetutkielma tarkastelee generoinnin ja kuratoinnin suhdetta taiteellisen toiminnan näkökulmasta. Generoinnilla tässä yhteydessä tarkoitetaan generatiivisessa suunnittelussa ja generatiivisessa taiteessa käytettäviä synnyttäviä järjestelmiä ja prosesseja. Kuratoinnilla puolestaan tarkoitetaan sekä perinteisessä museokontekstissa tapahtuvaa näyttelyiden koostamista, että yleistynyttä määritelmää kuratoinnista universaalina arvioinnin ja valikoinnin prosessina. Tämän tutkielman tavoitteena on esitellä lukijalle generoinnin ja kuratoinnin prosesseja ja käytäntöjä, sekä osoittaa näiden konseptien yhdistämisestä kumpuavat luovat mahdollisuudet. Tutkielmassa analysoidaan generoinnin ja kuratoinnin nykydiskursseja, sekä esitetään esimerkkejä moderneista generoinnin ja kuratoinnin praktiikoista. Tutkielmassa ehdotetaan näiden diskurssien pohjalta luotua viitekehystä, joka havainnollistaa generoinnin ja kuratoinnin yhtymäkohtia. Viitekehykseen liitetään myös Deleuzin sekä Bodenin ja Wigginsin teorioita luovuudesta, joilla todistetaan synteesin potentiaalia emergentteihin ratkaisuihin. Generointi ja kuratointi jakavat monia ominaisuuksia sekä konseptuaalisella että käytännöllisellä tasolla. Molemmat edellyttävät säännön määrittelemistä, jonka mukaan kuratoiva tai generoiva prosessi muodostetaan. Generatiivisessa suunnittelussa tämä sääntö on algoritmi tai muu toimenpiteen formalisointi, kuratoinnissa kokoelman valintakriteerit. Sekä generoinnissa että kuratoinnissa tekijä suunnittelee prosessin, sen sijaan että suunnittelisi valmiin lopputuloksen. Generointi vaatii kuratointia lopputulosten arvioinnissa ja valinnassa, samoin kuin kuratointi edellyttää kokoelman muodostavan säännön generatiivista toteutusta. Deleuzen käsitteet ”virtuaalisesta” ja ”aktuaalisesta” kuvaavat generoinnin ja kuratoinnin emergenttejä ominaisuuksia: säännöt määrittelevät ”virtuaalisen” joukon mahdollisia lopputuloksia, joista havaittavat tuotteet aktualisoituvat. Generoinnin ja kuratoinnin voi ajatella sekä sisältävän että mahdollistavan toisensa

See me, feel me, touch me, heal me: working with affect, emotion, and creation of transformative energies as a feminist curatorial practice

This research presents the gap contemporary curatorial discourses have in terms of feminist theory and work, as well as the gap principal contemporary discourses on feminisms and curating have in terms of discursive curatorial practices and independent curatorship. I argue, that the current discussions on feminisms and curating are narrowed down by governing art historical approaches, in which focus remains on representation instead of curatorial practice. Focusing primarily on exhibitions presenting art by feminist and/or women artists, the critique remains in the ways exhibitions are framed in terms of art historical narratives within museum institutions. The paradigm of feminist curating needs to be shifted to the realm of the curatorial, in order to extend the discussion to discursive feminist curatorial practices and the actual potential of feminist curatorial work with art. Within the curatorial, curating is seen beyond exhibition-making as a discursive practice with art, artists, spaces and audiences. Drawing from curatorial theory, affect theory, and feminist new materialist theory, I present a model for a feminist curatorial practice based on a process of thinking with art, and aiming at creating transformative energies through affective encounters with artworks. The practice relies on the political potential of affect, and engages the notion of affective transformation as an essential part of feminist work with contemporary art. Curating is discussed in relation to independent curatorhip, with reflection on my own practice. I analyse current discourses in the fields of contemporary curating, and curating and feminist thought, and present current views on feminist affect and new materialist theory. I discuss the topics through reflection on selected artistic and curatorial practices, exhibition projects, and two group exhibitions I have curated during the research process

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Methodological Rationale for the Taxonomy of the PO.EX Digital Archive

The PO.EX Digital Archive aims to create a digital representation of large corpus of intermedia literary works produced by Portuguese authors since the 1960s. In the process of remediating these works for the current digital networked environment we address metadata issues in a way that satisfies both our material and textual analysis of intermediality, and also the interoperability requirements of current information systems. The creation of a taxonomy for organizing and classifying a diverse array of materials such as those that constitute the digital archive of Portuguese Experimental Poetry (which includes Performance, Digital, Concrete, Spatial, Sound, Video, and Visual poetry) is a challenging task for the present researchers. The purpose of this article is to offer a brief rationale for our decisions, and to explain and illustrate our classification system.info:eu-repo/semantics/publishedVersio

GitTables: A Large-Scale Corpus of Relational Tables

The success of deep learning has sparked interest in improving relational table tasks, like data preparation and search, with table representation models trained on large table corpora. Existing table corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database tables. To train and evaluate high-capacity models for applications beyond the Web, we need resources with tables that resemble relational database tables. Here we introduce GitTables, a corpus of 1M relational tables extracted from GitHub. Our continuing curation aims at growing the corpus to at least 10M tables. Analyses of GitTables show that its structure, content, and topical coverage differ significantly from existing table corpora. We annotate table columns in GitTables with semantic types, hierarchical relations and descriptions from Schema.org and DBpedia. The evaluation of our annotation pipeline on the T2Dv2 benchmark illustrates that our approach provides results on par with human annotations. We present three applications of GitTables, demonstrating its value for learned semantic type detection models, schema completion methods, and benchmarks for table-to-KG matching, data search, and preparation. We make the corpus and code available at https://gittables.github.io

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems