IEEE International Symposium on Multimedia / 2020

Implications of embedded artificial intelligence - machine learning on safety of machinery

S. Anastasi, M. Madonna, L. Monica

Computer VisionFoundation ModelsReinforcement Learning

Abstract The Artificial Intelligence (AI) and the Machine Learning (ML) is a rapidly evolving technology and up until recently has not been a subject of machinery safety. The purpose of this work is to evaluate how embedded artificial intelligence – machine learning can affect the safety of machinery and machinery systems in the development of their applications. This work can be useful to machinery designers to develop their particular applications as it describes how the new hazards, associated with embedded AI – ML, should be considered within the framework of the risk assessment process. The proposed study underlines the new dimension of complexity linked to artificial intelligence and machine learning that could lead to a revision of European legislation in terms of the introduction and/or modification of essential health and safety requirements (EHSR) in the Machinery Directive, in order to guarantee safety levels at least equivalent to those currently achieved.

25 citations1 influential

Full paper

Read the original paper

Source page

A direct open-access PDF is not available in the database yet. Use the source page or learning resources below to open the complete paper from the publisher or index.

Learning resources

Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code Semantic Scholar paper pageSemantic Scholar YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: Implications of embedded artificial intelligence - machine learning on safety of machinery
Authors: S. Anastasi, M. Madonna, L. Monica
Year: 2020
Venue: IEEE International Symposium on Multimedia
Categories: Computer Vision, Foundation Models, Reinforcement Learning
Citations: 25
Paper URL: https://www.semanticscholar.org/paper/9b8cab3600ad35695bd999a540147034bf4160d8
Open PDF: Not available

Abstract:
Abstract The Artificial Intelligence (AI) and the Machine Learning (ML) is a rapidly evolving technology and up until recently has not been a subject of machinery safety. The purpose of this work is to evaluate how embedded artificial intelligence – machine learning can affect the safety of machinery and machinery systems in the development of their applications. This work can be useful to machinery designers to develop their particular applications as it describes how the new hazards, associated with embedded AI – ML, should be considered within the framework of the risk assessment process. The proposed study underlines the new dimension of complexity linked to artificial intelligence and machine learning that could lead to a revision of European legislation in terms of the introduction and/or modification of essential health and safety requirements (EHSR) in the Machinery Directive, in order to guarantee safety levels at least equivalent to those currently achieved.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Implications%20of%20embedded%20artificial%20intelligence%20-%20machine%20learning%20on%20safety%20of%20machinery)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Implications%20of%20embedded%20artificial%20intelligence%20-%20machine%20learning%20on%20safety%20of%20machinery)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/9b8cab3600ad35695bd999a540147034bf4160d8)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Implications%20of%20embedded%20artificial%20intelligence%20-%20machine%20learning%20on%20safety%20of%20machinery+paper+explained)