This report contains an overview of the integration of ethics into the first six months of the AI-PROFICIENT project (taking into account also an update from information retrieved after site visits done in November and December 2021). The overview includes a literature review of the current and past work upon Industrial AI ethics, a survey of related guides and principles, and observations regarding the state of the discipline and its particular character relative to other fields of AI ethics.
This report incorporates a specification of the demonstrator to be constructed per use case. This specification has been reviewed and includes a structured and unified approach to the specification of all use cases, with a common information regarding Gap Analysis, Stakeholders, Data Sources, Ethical issues, and a High Level design including Use Case and Sequence Diagrams, as well as a flowchart showing the expected contributions from different partners and the link to appropriate task activities within development work packages WP2-WP4.
This report sets out the dissemination and communication strategy as well as the plan to raise awareness, share knowledge, attract potential stakeholders in the context of the AI-PROFICIENT project, through various means, including the AI-PROFICIENT website, the use of Social Media, the distribution of communication material, publications in scientific and industrial journals, participation in events and organization of dedicated workshops with potential end-users and main outreach events.
This document will help you understand the essential elements of the AI-PROFICIENT identity. It explains how to use the identity and serves as a source of inspiration for you to continue building a strong brand people love to be a part of.
The aim of this document is to provide the architecture of the AI-PROFICIENT platform following the “ethics by design approach”.
D3.1: AI-PROFICIENT hybrid models and digital twins first version (state of the art, designing and specification)
This report presents the state of the art of hybrid modelling and digital twins, covering the techniques from first principles modelling to fully data based surrogate models and digital twins based on these approaches. On that basis, designing of the approach for selected use cases and the specifications in each case are described.
This report incorporates the description of the end user’s requirements related to the use cases in terms of the feedback management and the description for each one to manage it for a reinforcement learning approach.
The aim of this document is to provide the design and specification of AI-PROFICIENT platform middleware and interface towards smart components and edge AI.
- Using advanced AI technologies to improve manufacturing
- Technologies that Drive Smart Manufacturing in 2022
- 4 technology trends that will define the future of manufacturing
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