AI lifecycle zero-touch orchestration within the edge-to-cloud continuum for industry 5.0
The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that wer...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/405057 |
| Acceso en línea: | https://hdl.handle.net/2117/405057 https://dx.doi.org/10.3390/systems12020048 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Digital twins (Computer simulation) Industries Industry 4.0 Human-centered AI Manufacturing Model orchestration Digital twin Intel·ligència artificial Rèpliques digitals (Simulació per ordinador) Indústries Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems. |
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