Difference between revisions of "NN Cluster"
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1. Critical Machine Learning and Technical Practice (Winnie Soon): Through examining various big datasets, tinkering with code libraries, writing computer code and creating software artworks, this project/study aims to develop an aesthetic and critical inquiry method to understand, analyze and reflect upon the material infrastructure and processes of machine learning, as well as examining their implications to wider computational culture. | 1. Critical Machine Learning and Technical Practice (Winnie Soon): Through examining various big datasets, tinkering with code libraries, writing computer code and creating software artworks, this project/study aims to develop an aesthetic and critical inquiry method to understand, analyze and reflect upon the material infrastructure and processes of machine learning, as well as examining their implications to wider computational culture. | ||
2. Unerasable text (Winnie Soon): This is an art project working with the big Chinese dataset of the Social Media, Weibo (around 10m posts), through utilizing natural language processing and recurrent neural network to inquire the automated practice of internet censorship. | 2. Unerasable text (Winnie Soon): This is an art project working with the big Chinese dataset of the Social Media, Weibo (around 10m posts), through utilizing natural language processing and recurrent neural network to inquire the automated practice of internet censorship. | ||
3. Posthuman curating/and Affect Data Bodies (Magda Tyzlik-Carver) - Posthuman curating is a project that investigates the relation betwen affect data and bodies in contermporary culture increasingly organised by practices of automation, machine learning, neural networks and user participation in social media and increasing use of affective features employed for computation. Recognising how practice of curating has been automated and distributed across human and nonhuman actors (Tyzlik-Carver 2016, 2018) the focus of this investigation is on neural networks as an organising principle in organising information and data, be it digital, biological or analogue. Posthuman curating as part of NN Cluster proposes a series of seminars on the subject where scholars, professionals and artists are invited to present their practices of data curating with the use of machine learning technologies. | 3. Posthuman curating/and Affect Data Bodies (Magda Tyzlik-Carver) - Posthuman curating is a project that investigates the relation betwen affect data and bodies in contermporary culture increasingly organised by practices of automation, machine learning, neural networks and user participation in social media and increasing use of affective features employed for computation. Recognising how practice of curating has been automated and distributed across human and nonhuman actors (Tyzlik-Carver 2016, 2018) the focus of this investigation is on neural networks as an organising principle in organising information and data, be it digital, biological or analogue. Posthuman curating as part of NN Cluster proposes a series of seminars on the subject where scholars, professionals and artists are invited to present their practices of data curating with the use of machine learning technologies. | ||
4. **Procedural Generation as mode of production** (Pablo Velasco): Artificial Intelligence (AI) techniques are progressively used to sort complex tasks among big data sets. The AI branch of Procedural Generation (PG) works in the opposite way: it exploits randomness and permutations to generate large amounts of data from a relatively small input. This study observes examples of PG among the entretainment industry and digital cultures, to unravel how PG techniques are used to generate a perception of "infinity" through computation. The study is interested in citically engaging the outsourcing of design and production activities to computation, and in researching the ways in which randomness is operationalised through AI, and how this rearticulates notions of agency, accountability, and logics of rationality. | 4. **Procedural Generation as mode of production** (Pablo Velasco): Artificial Intelligence (AI) techniques are progressively used to sort complex tasks among big data sets. The AI branch of Procedural Generation (PG) works in the opposite way: it exploits randomness and permutations to generate large amounts of data from a relatively small input. This study observes examples of PG among the entretainment industry and digital cultures, to unravel how PG techniques are used to generate a perception of "infinity" through computation. The study is interested in citically engaging the outsourcing of design and production activities to computation, and in researching the ways in which randomness is operationalised through AI, and how this rearticulates notions of agency, accountability, and logics of rationality. | ||
Revision as of 15:26, 12 April 2019
https://etherpad.net/p/nncluster
NN Cluster refers to one of the many machine learning techniques called neural network and clustering. NN Cluster is an interdisciplinary initiative by early career reserchers from Aarhus University in critical technical practices, posthumanities, software studies, artistic practice and curating. Founded by Winnie Soon, Magda Tyzlik-Carver and Pablo Velasco the goal of NN Cluster is to diversify ai practices by introducing, testing, developing, disseminating, non-heterogenous practices of working with and researching ai and ml in the context of arts and humanities. While there is an extensive interest in researching AI/ML, and some concerns about the diversity of inputs that populate/feed its dynamics, a need for a non-heterogenous/interdisciplinary/crtical approach is essential.
Projects
A number of different projects are a part of this cluster including:
1. Critical Machine Learning and Technical Practice (Winnie Soon): Through examining various big datasets, tinkering with code libraries, writing computer code and creating software artworks, this project/study aims to develop an aesthetic and critical inquiry method to understand, analyze and reflect upon the material infrastructure and processes of machine learning, as well as examining their implications to wider computational culture.
2. Unerasable text (Winnie Soon): This is an art project working with the big Chinese dataset of the Social Media, Weibo (around 10m posts), through utilizing natural language processing and recurrent neural network to inquire the automated practice of internet censorship.
3. Posthuman curating/and Affect Data Bodies (Magda Tyzlik-Carver) - Posthuman curating is a project that investigates the relation betwen affect data and bodies in contermporary culture increasingly organised by practices of automation, machine learning, neural networks and user participation in social media and increasing use of affective features employed for computation. Recognising how practice of curating has been automated and distributed across human and nonhuman actors (Tyzlik-Carver 2016, 2018) the focus of this investigation is on neural networks as an organising principle in organising information and data, be it digital, biological or analogue. Posthuman curating as part of NN Cluster proposes a series of seminars on the subject where scholars, professionals and artists are invited to present their practices of data curating with the use of machine learning technologies.
4. **Procedural Generation as mode of production** (Pablo Velasco): Artificial Intelligence (AI) techniques are progressively used to sort complex tasks among big data sets. The AI branch of Procedural Generation (PG) works in the opposite way: it exploits randomness and permutations to generate large amounts of data from a relatively small input. This study observes examples of PG among the entretainment industry and digital cultures, to unravel how PG techniques are used to generate a perception of "infinity" through computation. The study is interested in citically engaging the outsourcing of design and production activities to computation, and in researching the ways in which randomness is operationalised through AI, and how this rearticulates notions of agency, accountability, and logics of rationality.