Dais
Distributed Artificial Intelligent System, aims at creating intelligence-centered heterogeneous distributed edge computing systems and solutions. The DAIS approach is to develop intelligent, secure, and trustworthy systems. This is for industrial applications to provide comprehensive, cost- and energy-efficient solutions for intelligent, end-to-end secure, trustworthy connectivity and interoperability. The process will bring the Internet of Things and Artificial Intelligence together.
Dais
Distributed Artificial Intelligent System, aims at creating intelligence-centered heterogeneous distributed edge computing systems and solutions. The DAIS approach is to develop intelligent, secure, and trustworthy systems. This is for industrial applications to provide comprehensive, cost- and energy-efficient solutions for intelligent, end-to-end secure, trustworthy connectivity and interoperability. The process will bring the Internet of Things and Artificial Intelligence together.
About the project
The main objective of the DAIS project is to research, promote, and deliver distributed artificial intelligence (AI) systems and their respective architectures. By doing so, the project aims to solve the problems associated with running existing algorithms on decentralized edge devices. In other words, the project seeks to address the challenges of implementing AI systems in highly decentralized environments. This is a significant step forward in the development of more robust and efficient AI solutions.
DAIS project is organized around eight different supply chains. Specifically, five of these supply chains focus on delivering the necessary hardware and software to run industrial-grade AI on different types of networking topologies. On the other hand, the other three supply chains will demonstrate how this pan-European effort can address known AI challenges from various functional areas. These supply chains are designed to tackle the complex issues related to the implementation of AI systems in highly decentralized environments.
By leveraging a robust sector with a solid reputation and an informed society, DAIS will establish the EU as a center for intelligent, secure, and trustworthy systems for industrial applications. This will ensure AI-based products and services comply with European values and are proudly “Made in Europe.”
Edge Intelligence (EI) has emerged as a promising solution to develop intelligent, secure, and energy-efficient systems for industrial applications. The DAIS project is a leading approach that integrates cutting-edge technologies to provide comprehensive solutions for decentralized environments. By bringing together the Internet of Things and Artificial Intelligence, DAIS ensures secure and trustworthy connectivity and interoperability. This drives the advancement of technology and contributing to the growth of the economy.
Tasks
- Providing intelligent processing of data and communication locally at the edge to enable real-time and safety-critical industrial applications.
- Developing industrial-grade secure, safe, and reliable solutions that can cope with cyberattacks and difficult network conditions.
- Providing AI techniques on the edge that match with diverse computing powers contrary to relatively consistent computing power on the cloud. As different AI algorithms have different computing power requirements, it is a big challenge to match an existing algorithm with the certain edge platform.
- Distribute and divide the complex AI operations between the cloud and edge. With edge undertaking early intelligent data processing reducing the bandwidth of data being transmitted to cloud; and building the hardware and software infrastructure to provide for this in Europe.
- Providing data sharing and collaborating solutions on the edge to handle the temporal-spatial diversity of edge data.
- Developing solutions for IoT, i.e. mostly wireless devices with energy- and processing- constraints, in heterogeneous and also hostile/harsh environments.
- Providing re-usable solutions across industrial domains.
- Methodological approach with the Integral Supply Chain, from academic, to system designers and integrators, to component providers, applications and services developers & providers and end users.
