STAR is a joint effort of AI and digital manufacturing experts towards enabling the deployment of standard-based secure, safe reliable and trusted human centric AI systems in real-life manufacturing environments. STAR researches, develops, validates and make available to the AI and Industry4.0 communities novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. The project’s results will be fully integrated into existing EU-wide Industry 4.0 and AI initiatives (notably EFFRA and AI4EU), as a means of enabling researchers and the European industry to deploy and fully leverage advanced AI solutions in manufacturing lines. To address the challenges of ethical, trusted, and secure AI systems STAR carries out leading-edge AI research and innovation activities in the following areas:
- Explainable AI: STAR researches and will provide a library of explainable AI (XAI) techniques for manufacturing use cases such as Quality4.0 and human robot collaboration.
- Active Learning (AL) and Simulated Reality (SR) for Fast, Safe and Efficient On-Line Learning and Knowledge Acquisition: STAR researches AI systems that operate in dynamic manufacturing environments, while acquiring knowledge in a fast and safe matter. Specifically, STAR researches advanced and efficient forms of Reinforcement Learning (RL), including: (i) Active Learning (AL) approaches that enable robots and other AI systems to query human experts about their next course of action; and (ii) Simulated Reality (SR) approaches as a means of enabling agents to simulate the outcomes of their next action before actually taking it.
- Human Centric Digital Twins for Simulation of Safe and Trusted AI Applications with the Human-in-the Loop: STAR researches and will provide advanced Simulation and Digital Twins solutions for AI-based “human in the loop” processes, including human robot collaboration.
- Security for AI systems: STAR will research, implement and validate solutions for securing AI systems in manufacturing, including technologies that address attacks at both the training (i.e., poisoning) and the operational (i.e., evasion) phase of Deep Neural Networks (DNNs).
To find more about STAR, visit our website: www.star-ai.eu
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