Guest Editorial of the Special Section on Generative Artificial Intelligence With Applications on Consumer Electronics
Bibliographic record
Abstract
Consumer electronics are electronic equipment intended for everyday use, and they constitute a part of the wider electronics industry including devices and services used for entertainment, communications and recreation. In practice, consumer electronics use digital technologies to enhance performance in real-world applications, such as AI-generated content, chatbot, online retailing, automatic driving systems, fashion and apparel industry, etc., where the information in these applications usually generate a significant amount of high-quality data for creation of digital content, Semantic Comprehension or data generation and augmentation, etc. Recently, Generative Artificial Intelligence (GAI) has been gaining significant attention from society. For example, ChatGPT is a language model developed by OpenAI for building chatbot, which can efficiently understand and respond to human language in a logical and meaningful way. In addition, DALL-E-2 is another state-of-the-art GAI model, which is capable of creating unique and high-quality images from textual descriptions in a few minutes. In general, GAI techniques, as opposed to being created by human authors, is to automate the creation of large amounts of content such as images, music, and natural language, etc. Therefore, how to develop robust models for generative AI in this field of consumer electronics is of great importance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".