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Designing [with] Machines

2020· article· en· W4387860167 on OpenAlexaff
Maria Yablonina, Nicolas Kubail Kalousdian, Achim Menges

Bibliographic record

VenueACADIA quarterly · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The aim of this research is to investigate the potential of a design and fabrication workflow that is centered around the development of task- and site-specific robotic systems for in-situ architectural making: Designing [with] Machines (D[w]M). The project proposes an alternative strategy to the established logic of design for production, in which design decisions are a function of affordances and limitations of available fabrication equipment. D[w]M engages the designer to define their own parameter ranges for the fabrication process through simultaneous development of fabrication machines and complimentary material, and architectural systems. In addition to affording more flexibility, D[w]M offers an opportunity to develop robotic fabrication systems uniquely tailored for deployment on sites that are not suited for conventional robotic equipment. In this paper, D[w]M workflow is outlined in the description of a task- and site-specific robotic system for additive fabrication of a tensile filament-wound object in an in-situ environment. Specifically, the presented project investigates design opportunities afforded by cooperative operation of multiple mobile single-axis robots deployed along linear structural elements of the given site. In utilizing column and beam elements as machine locomotion substrates, the system contributes them to the robotic assembly as parts of the in-situ digital fabrication machine.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.380

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)0.0000.000

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 head0.045
GPT teacher head0.328
Teacher spread0.282 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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