Summary of An Early-stage Workflow Proposal For the Generation Of Safe and Dependable Ai Classifiers, by Hans Dermot Doran et al.
An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers
by Hans Dermot Doran, Suzana Veljanovska
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed workflow aims to generate and execute qualifiable safe AI models by defining a transparent, complete, yet adaptable and lightweight process. The paper’s contribution is an extended ONNX model description-based workflow, which ensures the stability required for functional safety developments while accommodating adaptability in the rapidly progressing domain of AI research. The authors present a use case as one foundation of this work, expecting it to be extended by other third-party use-cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to create and use artificial intelligence models safely and reliably. It proposes a workflow that combines transparency, completeness, and adaptability to ensure the AI models are safe and dependable. The idea is based on an extended description of ONNX (Open Neural Network Exchange) models, which allows for flexibility in a rapidly changing field. |
Keywords
» Artificial intelligence » Neural network
