Blockchain-based Edge Intelligence Enabled by AI Large Models for Future Internet of Things
Bibliographic record
Abstract
In recent years, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has become a key factor in the development of modern communication systems. Through the deep integration of AI and IoT, the intelligence level of the network has been greatly improved, achieving higher data transmission speed, lower latency, and higher reliability to meet the growing communication needs. Especially in the context of intelligence networking and edge computing, large-scale language models (LLM) such as the Generative Pretrained Transformer (GPT) series have extended the capabilities to handle complex tasks and predict user intentions, programming, and planning. These capabilities provide new possibilities for optimizing communication systems, reducing semantic communication costs, and customizing services according to user preferences. This article explores the application of blockchain and AI large models in the edge intelligence of the Internet of Things, and proposes a distributed, non-tamperable knowledge and learning achievement recording system based on blockchain technology and AI large models. The system is designed to automatically generate code to train new models in a privacy-preserving manner. Experimental results show that the system can accurately understand user needs, efficiently execute, and create high-performance artificial intelligence models at minimal cost on edge servers.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".