Bibliographic record
Abstract
The resurgence of interest in Artificial Intelligence and advances in several fronts of AI, machine learning with neural network in particular, have made us think again about the nature of intelligence, and the existence of a generic model that may be able to capture what human beings have in their mind about the world to empower them to present all kinds of intelligent behaviors. In this paper, we present Constrained Object Hierarchies (COHs) as such a generic model of the world and intelligence. COHs extend the well-known object-oriented paradigm by adding identity constraints, trigger constraints, goal constraints, and some primary methods that can be used by capable beings to accomplish various intelligence, such as deduction, induction, analogy, recognition, construction, learning and many others. In the paper we will first argue the need for such a generic model of the world and intelligence, and then present the generic model in detail, including its important constructs, the primary methods capable beings can use, as well as how different intelligent behaviors can be implemented and achieved with this generic model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".