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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".