Net-in-AI: A Computing-Power Networking Framework with Adaptability, Flexibility, and Profitability for Ubiquitous AI
Bibliographic record
Abstract
Along with the unprecedented development of artificial intelligence (AI), a considerable number of intelligent applications are universally recognized to significantly facilitate the evolution of anthropogenic activities. The abundant AI computing power is one of the main pillars to fuel the booming of ubiquitous AI applications. As the computing power proliferates to a multitude of network edges, even end devices, the networking function bridges the gap, on the one hand, among ends-edges-clouds, on the other hand, between the multiple AI computing power and the heterogeneous AI requirements. The emerging new opportunities have spawned the deep integration between computing and networking. However, the complete development of the integrated system is under-addressed, including adaptability, flexibility, and profitability. In this article, we propose a computing-power networking framework for ubiquitous AI by establishing Networking in AI computing-power pool, denoted as Net-in-AI. We design the framework to enable the adaptability for computing-power users, the flexibility for networking, and the profitability for computing-power providers. We then formulate a computing-networking resource allocation problem, with the joint perspective of these three aspects. Experimental results prove the superior performance of the proposed framework in comparison to the current popular schemes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".