Bibliographic record
Abstract
In this article, we explore how the history and myths about Artificial Life (AL) inform the pursuit and reception of contemporary AL technologies. First, we show that long before the contemporary fields of robotics and genomics, ancient civilizations attempted to create AL in the magical and religious pursuits of automata and alchemy. Next, we explore four persistent cultural myths surrounding AL—namely, those of Pygmalion, Golem, Frankenstein, and Metropolis. These myths offer several insights into why humanity is both fascinated with and fearful of AL. Thereafter, we distinguish contemporary approaches to AL, including biochemical or “wet” approaches (e.g., artificial organs), electromechanical or “hard” approaches (e.g., robot companions), and software-based or “soft” approaches (e.g., digital voice assistants). We also outline an emerging approach to AL that combines all three of the preceding approaches in pursuit of “transhumanism.” We then map out how the four historical myths surrounding AL shape modern society’s reception of the four contemporary AL pursuits. Doing so reveals the enduring human fears that must be addressed through careful development of ethical guidelines for public policy that ensure human safety, dignity, and morality. We end with two sets of questions for future research: one supportive of AL and one more skeptical and cautious.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".