Bibliographic record
Abstract
Kunstig intelligens er ikke blot én bestemt teori, teknik eller metode, men dækker over en lang række forskellige teknikker til at simulere aspekter af menneskelig kognition på en computer. Forskelligheden i teknikkerne dækker blandt andet over, at de forsøger at efterligne forskellige typer af intelligens, fx sproglig, social eller logisk intelligens. Forskelligheden dækker også over, at vi forsøger at efterligne intelligens på forskellige abstraktionsniveauer gående fra direkte forsøg på at efterligne de atomare neurologiske processer i hjernen helt op til meget abstrakte modeller af vores bevidste, sproglige og logiske tænkning. Fælles for alle teknikkerne er, at det handler om at skabe matematiske modeller af aspekter af kognition, og at få computere til at regne på disse modeller. Desuden handler det om at få kunstig intelligens-systemerne til selv at skabe modeller af deres omverden, som de kan bruge til at ræsonnere om denne omverden. I denne artikel vil vi først give en introduktion til kunstig intelligens og de forskellige hovedparadigmer indenfor området. Dernæst vil vi gå lidt mere i detaljen med, hvordan logiske modeller kan bruges til at skabe ræsonnerende robotter.
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.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".