Guest Editorial of the Special Section on Generative Artificial Intelligence With Applications on Consumer Electronics
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
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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