Proceedings of the 10th International Symposium on Smart Graphics
Bibliographic record
Abstract
Welcome to the International Symposium on Smart Graphics! It will bring together people from the fields of Computer Graphics, Graphics Design, Cognitive Psychology and Artificial Intelligence, all working on different aspects of computer generated graphics. After a very successful AAAI Spring Symposium on Smart Graphics in 2000 the organizing committee decided to turn the event into a self-contained symposium. Last year's event (Smart Graphics 2001) attracted our target of 30 attendees. This was despite an unfortunate clash with the ACM I3DG conference. This year we aim to increase the number of attendees without creating a conference atmosphere, the number of attendees will be capped at 50. Seeing the Smart Graphics initiative taking off so dynamically, we are expecting a great number of varied and interdisciplinary submissions, and we are looking forward to the 2002 symposium, which is generously hosted by the IBM T.J. Watson Research Center.Advances and breakthroughs in the area of Computer Graphics have made visual media a major ingredient of the modern user interface, and it is likely that graphics will play a dominant role in the way people communicate and interact with computers in the future. Especially the evolution of computing towards more and more pervasive and distributed devices pose new and challenging problems for the effective use of graphics. We believe that intelligent behavior and graphics will provide the technical core of next generation interfaces. But in order to make those interfaces successful, principles and findings from Cognitive Psychology and Graphics Design are equally important to reflect the user's needs and abilities.Until recently there has been very little overlap between the Cognitive Psychology, Computer Graphics, AI and Graphics Design communities. The Smart Graphics Symposium intends to close these gaps. Recent advances in Computer Graphics have allowed AI researchers to integrate graphics in their systems (without being burdened by low-level issues such as image rendering) and graphics acceleration hardware has become affordable and is now available for a broad range of platforms. On the other hand, many AI techniques have matured to the point of being usable by non specialists. Furthermore, these very techniques are likely to be the vehicle by which both principles from Graphics Design and the results of research in cognitive aspects of visual representations, will be integrated in next generation graphical interfaces.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.250 | 0.165 |
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".