The grand challenge of predictive empirical abstract knowledge
Bibliographic record
Abstract
We survey ongoing work at the University of Alberta on an experience-oriented approach to artificial intelligence (AI) based an reinforcement learning ideas. We seek to ground world knowledge in a minimal ontology of just the signals passing back and forth between the AI agent and its environment at a fine temporal scale. The challenge is to connect these low-level signals to higherlevel representations in such a way that the knowledge remains grounded and autonomously verifiable. The mathematical ideas of temporally abstract options, option models, and temporal-difference networks can be applied to begin to address this challenge. This has been illustrated in several simple computational worlds, and we seek now to extend the approach to a physically realized robot. A recent theoretical development is the extension of simple temporal-difference methods to offpolicy forms using function approximation; this should enable, for the first time, efficient and reliable intraoption learning, bringing the goal of predictive empirical abstract knowledge closer to achievement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".