The Post-bit Human Universe: An Experiment on the Evolutionary History of Human-Posthuman Relations
Bibliographic record
Abstract
This paper outlines the authors' most recent artistic experimentation on the evolutionary history of human-posthuman relations, the Post-bit Human Universe (PBHU) exhibition at the Guangdong Museum of Art (GDMoA) in 2021–2022. PBHU is a multimodal project that began in 2015 and continues to the present day. This project depicts a narrative path of an evolutionary universe of post-bit humans from the start of the Anthropocene to the conjecture of the post-Anthropocene through the use of a variety of artistic approaches and expressions in collaboration with our multifunctional Artificial Intelligence programme. It composes of multi-modal works. In contrast to a conventional strategy for creating digital art that relies on generic machine learning-related algorithms and large-scale datasets to generate and provide creative contexts, this project explores the possibility of a human-machine hybrid creator as a synergistic symbiosis of biological and artificial intelligence. It places a greater emphasis and concentration on critical reflection and contemplation on the process of synergistic creation between human artists and the artificial intelligence programme (AP). AP gives itself a voice by utilising the narrative content that they have been iteratively trained to generate as an interface and medium of communication with biological intelligence, which permeates each work in PBHU. In addition, the project offers a profound reflection and examination of the potential crisis precipitated by the current technomania caused by functionalism, technicism, and technocentrism: the dissipation and disintegration of the independence and heterogeneity of human intelligence and thought. From now on, the validity of physical existence is eroding, and digital existence is increasingly becoming the only credential for the legitimacy identity of organisms.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".