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Record W4297047343 · doi:10.1007/978-981-19-1983-1_1

Genealogy of Artificial Beings: From Ancient Automata to Modern Robotics

2022· book-chapter· en· W4297047343 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsRoboticsArtificial intelligenceAutomatonWeavingPeriod (music)Field (mathematics)EngineeringComputer scienceRobotArtMathematicsAestheticsMechanical engineering

Abstract

fetched live from OpenAlex

This chapter is an extensive overview of the history of automata and robotics from the Hellenistic period, which saw the birth of science and technology, and during which lived the founders of modern engineering, to today. Contemporary robotics is actually a very young field. It was preceded by a 2000-years period in which highly sophisticated automata were built for very different purposes—to entertain, to impress or to amaze—at different times. You will see that the methods and techniques that were used to build these automata, and that largely contributed to the development of robotics, were at times imported from unexpected fields—astronomy, music, weaving, jewellery; and that the impulse that drove automata makers to build their artificial beings was far from rational, but rather rooted in the age-old mythical desire to simulate, and even to realize, an entity from inert materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.027
GPT teacher head0.221
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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