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Record W3000043683 · doi:10.1109/msmc.2018.2889502

Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence

2020· article· en· W3000043683 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

VenueIEEE Systems Man and Cybernetics Magazine · 2020
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of TorontoUniversity of CalgaryUniversity of Manitoba
Fundersnot available
KeywordsCyberneticsHumanityEmbodied cognitionCognitive scienceCognitionCognitive computingCognitive roboticsArtificial intelligenceComputer scienceIntelligent decision support systemField (mathematics)Systems sciencePsychologyNeurosciencePhilosophy

Abstract

fetched live from OpenAlex

Brain-inspired cognitive systems (BCSs) are an emerging field of cybernetics, cognitive science, and system science. BCSs study not only the intelligence science foundations of artificial intelligence (AI) and cognitive systems, but also formal models of the brain embodied by computational intelligence. This article presents the brain and intelligence science foundations of BCS toward hybrid intelligent systems and the symbiotic intelligence of humanity. It explores the transdisciplinary theoretical foundations of system, brain, intelligence, knowledge, cybernetic, and cognitive sciences toward the next generation of knowledge processors beyond classic data processors for autonomous computing systems. A BCS provides an overarching platform for cognitive cybernetics, humanity, and systems to enable emerging hybrid societies shared by humans and intelligent machines.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.000
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 head0.074
GPT teacher head0.283
Teacher spread0.208 · 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