Re-imaging the empirical: statistical visualisation in art and science
Re-imaging the empirical is a research project investigating the visual cultures of machine learning (ML). It is led by Professor Anna Munster (UNSW) in partnership with Professor Adrian MacKenzie (ANU) and Dr Kynan Tan (UNSW Postdoctoral Fellow). It has been funded by Australian Research Council, Discovery Project scheme.
For the last few years, we have been interested in the dominant role that images play in many contemporary ML projects and endeavours from AlphaGo through to style transfer. We have been inquiring into how images are used by ML models and techniques as part of a broader re-contouring of what it is to both see and know the empirical world. We use ML and dataset methods in this project drawn from scientific scholarship – specifically the pre-print repository arXiv – to detect vectors and differences across scientific images; images that have themselves been generated by ML research in statistics, physics, mathematics, computer vision and more.
The project has three threads:
• Tracing the genealogies and interrelations of images generated as a result of ML research (see Images of the arXiv: reconfiguring large scientific datasets)
• Developing a new set of conceptual tools for thinking changes in seeing, images, and perception in a culture(s) of ML (see Platform Seeing: Images and their Invisualities)
• Building a visual explorer web application that foregrounds new ways of seeing and perceiving shaped by ML computation.
ImageMesh is both a practical outcome of this project and an ongoing research tool for exploring a large sample of images published in arXiv articles. We encourage you to follow the errant paths these images generate as they compose and are plied by new modes and practices of machinic observation.
