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Record W3168957553 · doi:10.21810/jicw.v4i1.2566

Why HAL 9000 is not the future of intelligence analysis

2021· article· en· W3168957553 on OpenAlexvenueno aff
Giangiuseppe Pili

Bibliographic record

VenueThe Journal of Intelligence Conflict and Warfare · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsIntelligence analysisFunction (biology)Action (physics)Position (finance)Business intelligenceProcess (computing)Human intelligenceIntelligence cycleCore (optical fiber)MissileRisk analysis (engineering)Computer scienceOperations researchPolitical scienceArtificial intelligenceComputer securityMilitary intelligenceKnowledge managementBusinessEngineeringLawTelecommunications

Abstract

fetched live from OpenAlex

Intelligence analysis is a core function of the intelligence process, and its goal is to synthesize reliable information to assist decision-makers to take a course of action toward an uncertain future. There is no escape from uncertainty, friction, and the fog of war. Since the dawn of human history, the present moment has been experienced as unpredictable, and the challenge of determining the right future through sound decisions has always existed. Investing in new technology, continually touted as the answer for analytic troubles, seems far less difficult in the short run than trying to find consensus about a long-term vision. It is easier to develop a nuclear missile, for example, than to give a universal definition of peace, and this is what the history of the XX century was all about. While intelligence analysis is still a necessary tool for decision-makers, it is unclear who or what will perform this function in the future. Though the solution cannot be only technological, the current trajectory tells a different story whereby the human analysts are removed from their central position to make way for Artificial Intelligence.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.331
Teacher spread0.291 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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