Images of Machine Learning Online Symposium

Charu Maithani's picture
Type: 
Symposium
Date: 
June 30, 2021 to July 1, 2021
Location: 
Australia
Subject Fields: 
Art, Art History & Visual Studies, Cultural History / Studies, Digital Humanities

 

Images of Machine Learning

Online Symposium

June 30 & July 1, 2021

 

Free registration and more informationhttps://imagesofml.ai/

 

Large image collections – from Sloan Sky Survey through to ImageNet – and their traversals through and by datasets, algorithmic functions and predictive models have become the operative ‘matter’ of contemporary AI. At the same time, machine learning assemblages have come to re-organise seeing itself as comprising processes of feature detection, convolutional accumulation of image/pixel building blocks and latent spaces.  How have artists, cultural producers and critical AI scholars engaged with the ways in which images and machine learning have come to re-configure each other? What might redeployments of image-oriented machine-learning techniques tell us about the less than predictable sensibilities of AI’s visuality? This online symposium gathers artists and producers, media and STS thinkers together to think about and discuss such issues, collectively questioning how we might come to differently parse images of machine learning.

 

Organised by Anna Munster (University of New South Wales, Sydney, Australia) and Adrian MacKenzie (Australian National University, Canberra Australia) and supported by the Australian Research Council Discovery Project scheme.

Hosted by Media Futures Hub, UNSW.

 

Program

Wednesday, June 30 2021, 3-6pm AEST

Keynote by Anna Ridler, artist and researcher

Automated Dreaming: Using AI in a Creative Practise

 

Panel 1: Images and Machine Learning Practices: critical interventions in art and curation

 

Thursday, July 1 2021, 3-6:30pm AEST

Keynote by Fabian Offert, Assistant Professor, History and Theory of Digital Humanities, UC Santa Barbara 

Latent Deep Space: GANs between Art and Science

 

Panel 2: Images, machine learning and practices of knowledge-production

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