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On Future Development of Autonomous Systems: A Report of the Plenary Panel at IEEE ICAS’21

2021· article· en· W3202994115 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
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsDefence Research and Development CanadaUniversity of TorontoConcordia UniversityUniversity of Calgary
Fundersnot available
KeywordsCognitive computingIntelligent decision support systemArtificial intelligenceComputer scienceRoboticsComputational intelligencePanel discussionArtificial general intelligenceField (mathematics)Big dataCognitionData scienceCognitive scienceRobotPsychology

Abstract

fetched live from OpenAlex

Autonomous Systems (AS) are perceived as the most advanced intelligent systems evolved from those of reflexive, imperative, and adaptive intelligence. A plenary panel on “Future Development of Autonomous Systems” is organized at the inaugural IEEE ICAS’21. This paper reports the panel discussions about the-state-of-the-art and paradigms of AS, the basic research on theoretical foundations and mathematical means of AS, and challenges to the future development of AS. As an emerging and increasingly demanded field, AS provide an unprecedented approach to contemporary intelligent industries including deep machine learning, highly intelligent robotics, cognitive computers, general AI technologies, and industrial applications enabled by transdisciplinary advances in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.273

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 head0.029
GPT teacher head0.235
Teacher spread0.206 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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