Fraunhofer FOKUS is the leader of the working package "Asset Management Bundles Methods and System Designs". In this working package, management and sharing methods are defined and prototypically implemented for the assets. The assets mentioned are industrial data as well as AI models and analyses based on these data.
The XMANAI project is working to provide the tools to navigate the Artificial Intelligence (AI)’s “transparency paradox”, designing, developing and deploying a novel Explainable AI Platform powered by explainable AI models that inspire trust, augment human cognition and solve concrete manufacturing problems with value-based explanations.
XMANAI is one of the few European projects focusing on eXplainable Artificial Intelligence (XAI) methodology. However, XMANAI doesn't use only XAI to analyze deeper and efficient the data but also introduces many other novel methodologies to provide better data insights. Have you ever listen about Graphical Neural Networks?
The field of explainable AI is thriving with interesting solutions, showing the potential to address almost any task in any given setting. This outburst of methods and models comes in response to interpretability being identified as one of the key factors for AI solutions to be trusted and widely deployed.
- Overview requirements
- XMANAI partner spotlight – Athena Research Center
- Industrial Assets Provenance in XMANAI
- XMANAI partner spotlight – Innovalia Association
- The Draft Catalogue of XMANAI XAI Models
- XMANAI´s Evaluation Framework
- XMANAI partner spotlight – Suite5
- XMANAI Reference Architecture: Perspectives
- Security Aspects of Industrial Data Management
- XMANAI partner spotlight – AiDEAS
- What is the XMANAI Minimum Viable Product (MVP)?
- XAI in Manufacturing
- Data Asset Management in XMANAI
- XMANAI partner spotlight – Tyris AI
- XMANAI partner spotlight – Politecnico di Milano
- Education role in AI technology implementation in industry
- XMANAI partner spotlight – Fraunhofer FOKUS
- AI Requirements for Manufacturing in the XMANAI Project
- Have you ever heard about Graphical Neural Networks?
- A brief overview of XAI Landscape