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Record W2968329815 · doi:10.1109/mprv.2019.2922907

Towards Interpersonal Assistants: Next-Generation Conversational Agents

2019· article· en· W2968329815 on OpenAlexaff
Inseok Hwang, Youngki Lee, Chungkuk Yoo, Chulhong Min, Dongsun Yim, John Kim

Bibliographic record

VenueIEEE Pervasive Computing · 2019
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsInterpersonal communicationComputer scienceKey (lock)Human–computer interactionUbiquitous computingNatural (archaeology)Natural language generationKnowledge managementNatural languageArtificial intelligencePsychologyComputer securityCommunication

Abstract

fetched live from OpenAlex

We propose novel interpersonal assistants, a next-generation conversational agent which is always-on, unobtrusively serving natural human-to-human conversations. We deepen the motivation and design insights with real practices in language delays and parent–child conflicts. We then present a common platform initiative to effectively support rapid development of interpersonal assistant applications, with a highlight on the key functional element of turn isolations and technical insights on microstructural dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.075
GPT teacher head0.302
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations27
Published2019
Admission routes1
Has abstractyes

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