Menneske og drømmemaskine. Posthumane myteskabelser om AI hos Amalie Smith
Bibliographic record
Abstract
SummaryIn the present article, I analyse the process of making the artwork Machine Learning I II III (2018) by Danish artist Amalie Smith to examine how it conceptualizes a posthuman myth about computer vision and AI. Smith’s artwork investigates the phenomenon of computer vision through using a convolutional neural network to represent what this network is thought to “see”. The artwork is an aesthetic manifestation of the invisible operations of the machine learning algorithm in a form that is visible to humans, and thereby it engages aesthetic speculations about how computer vision works. The artist provides an insight into how machine learning algorithms interpret images in a way that is radically different from humans, but at the same time greatly affects human reality because of the algorithmic culture that permeates contemporary societies. I read Machine Learning I II III with posthuman thinkers Rosi Braidotti, Donna Haraway and N. Katherine Hayles to show how Smith imagines a co-creative relationship between human and technology that neglects a myth about “machine” and “human” as distinct and isolated categories; a myth where symbols, human and algorithmic intelligence, weaving, and written discourse intertwine to make the artwork. In this way, Machine Learning I II III moves towards a posthuman myth of computer vision and AI where it is impossible to unentangle human and technological forces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".