Engagement with Artificial Intelligence through Natural Interaction Models
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.
Bibliographic record
Abstract
As Artificial Intelligence (AI) systems become more ubiquitous, what user experience design paradigms will be used by humans to impart their needs and intents to an AI system, in order to engage in a more social interaction? In our work, we look mainly at expression and creativity based systems, where the AI both attempts to model or understand/assist in processes of human expression and creativity. We therefore have designed and implemented a prototype system with more natural interaction modes for engagement with AI as well as other human computer interaction (HCI) where a more open natural communication stream is beneficial. Our proposed conversational agent system makes use of the affective signals from the gestural behaviour of the user and the semantic information from the speech input in order to generate a personalised, human-like conversation that is expressed in the visual and conversational output of the 3D virtual avatar system. We describe our system and two application spaces we are using it in – a care advisor / assistant for the elderly and an interactive creative assistant for uses to produce art forms.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it