Mining News Data for Peripheral Culture Training in AGI
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 computing applications edge closer toward developing human-like ability, the requirement for culture aware human-machine communication is becoming paramount. Ever evolving natural language is full of abstraction, pop-culture reference, and metaphor. Rigid language understanding implementations in Artificial Intelligence (AI) systems like voice assistants often lead to misunderstanding in command execution, degrading the user experience and accuracy of AI applications. Further, the inability for AI to have an understanding of the data in reference to itself limits its ability to fully comprehend conversation. Theorized Artificial General Intelligence (AGI) aids this problem through enabling cognitive data processing. This paper presents a novel method of training an AGI system through a peripheral interface using online news articles to increase its understanding of data in reference to current societal culture while heightening its overall awareness.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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