Keynote Presentation of ICAC2023
Bibliographic record
Abstract
Smart manufacturing also known as intelligent manufacturing has been recognized as major driving force to transform manufacturing industry. The first international collaboration on Intelligent Manufacturing Systems (IMS) was proposed by Japan with participation of US, Canada, Australia, and European partners in early 90s. In the following years, artificial intelligence (AI) applications in manufacturing were limited to certain focused areas. Since Germany introduced Industry 4.0, other major manufacturing nations such as US, China and Japan proposed programs to promote advanced and smart manufacturing. The recent development of ChatGPT demonstrated the potential of AI technologies. The society started to realize that AI will have profound impact not only on manufacturing industry, but also almost all other sectors of the society. This speech will provide a brief historical review of IMS, the current technological development of intelligent product design and manufacturing, and future perspectives of smart manufacturing in digitalization technologies and industrial metaverse.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.200 | 0.148 |
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".