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
AI-ML suffers from a reliability glass-ceiling phenomenon (e.g.~10 -3 error/inference), making it incompatible with safety-criticality.Several orders of magnitude are missing.We explain why, we point to the characteristics of ML that conflict with the assurance objectives assigned to safety-critical developments.Could encapsulation of ML constituents into fault-tolerant architectures, ML development assurance, and software/hardware development assurance, altogether mitigate the gap?We argue that in spite of impressive progress of ML state-of-the-art, the answer is negative.Drawing from Topological Data Analysis (TDA) and set-based non-linear control, we propose to supplement ML point-based specification and verification with volume-based specification and verification to meet 10 -5 err./ inf.levels, as a minimum.We outline the rationale of a new research field we name (Ultra) Reliable Machine Learning, at the confluence of TDA, statistics on manifolds, and ML safety assurance.Some cross-domain safety regulation principles guide the underlying rationale.We illustrate the methodology on image classification.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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