An Innovative Industry Program in A New Era of Multimedia with Generative AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ACM Multimedia 2024 industry program offers a unique platform for fostering collaboration between academia and industry. This year's program features a diverse range of industry keynotes, expert talks, seminars, and demonstrations, showcasing the latest advancements in multimedia technology. Renowned experts from industry and academia will share their insights on topics such as generative AI, automotive design, computer vision, spatial experience, healthcare, and more. Attendees will have the opportunity to network with industry leaders, learn about cutting-edge technologies, and explore potential collaborations. The industry program highlights the growing importance of multimedia technology in various domains and demonstrates the innovative ways in which AI and other emerging technologies are transforming industries. By participating in this program, attendees can gain valuable knowledge, expand their professional networks, and contribute to the advancement of the field.
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.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it