Empowering Extreme Automation via Zero-Touch Operations and GPU Parallelization
Bibliographic record
Abstract
The extream automation model attracts increasingly more manufacturing enterprises to deploy their services and applications on the emerging automation infrastructure that come with extreme range of new requirements. These include smart collaborative factories, personalized services with dramatic improvements in customer- experience, massive capacity, imperceptible latency, ultra-high reliability, global webscale reach, and support for massive machine-tomachine communication. The ultimate challenge is to have an infrastructure with a scalable performance. Straight forward thinking may think of scalable performance in terms of adding additional processing capabilities to a manufacturing problem set or a simulation. Because more parallelization means more communication and data movement between the independent services and tasks, the result often is even more communications between them. The benefits of such collective communication include: Cross-domain IT automation; Information, Analytics and Data Transparency; DevOps Integration; and Digital Cognitive Systems. The authors argue that all the above benefits cannot be achieved for a harmonized and effective extreme automation environment without the enforcement and the availability of following two notions: (1) Zero-Touch Provisioning (ZTP), where ZTP is the feature that allows the devices to be provisioned and configured automatically, eliminating most of the manual labor involved with a collective communication; and (2) Parallelization of GPUs based on the use general-purpose computing on graphics processing Units (GPGPU).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".