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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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; both teacher heads agree on what is shown here.
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".