Innovations beyond the State-of-the-Art (SoTA) in TALON are summarised as follows:
[R1] An automated AI E2C orchestrator enabling zero-touch deployment and autonomous runtime management of AI workloads.
[R2] Four slices for i) automatic UATV coordination, ii) I5.0 automation and planning, iii) AR/VR for training and maintenance, and iv) HRC, that in turn proved different modular usages and deployment of the TALON orchestrator.
[R3] A Mobility manager and social-aware caching mechanisms were empowered by data-driven policies derived from pods resource utilisation and energy usage only when allocated for specific tasks.
[R4] The Automatic off-loading mechanism as part of the TALON Edge-to-Cloud (E2C) Orchestrator, enables real-time delegation of computational workloads between edge nodes and the cloud.
[R5] AI self-healing, self-recovery, and self-correcting mechanisms to ensure resilient and zero-touch operation across the Edge-to-Cloud (E2C) continuum.
[R6] An AI models repository that enables reusability.
[R7] Optimized few-shot learning models that reduce the data exchanging overhead.
[R8] Federated learning deployments were optimized, focused on minimizing information exchange while maximizing model utility across both distributed and centralized scenarios.
[R9] Optimum joint AI and data placement policies were developed.
[R10] The overall AI energy footprint was significantly reduced through the implementation of smart pricing policies.
[R11] Decentralized and hierarchical blockchain-based mechanisms were implemented to accommodate the requirements of TALON pilots and further optimise the mechanisms of the decentralised ledger.
[R12] Novel KPIs were designed to measure the performance improvements of AI-enabled systems in alignment with business objectives derived from end user requirements.
[R13] A data-driven real-time AI optimizer was finalized and validated during piloting, significantly enhancing E2C deployment, management, and resource reuse.
[R14] Simulation mechanisms that enable fast learning were implemented by means of an AI-based synthetic data generation module.
[R15] An Explainable AI (XAI) framework has been designed and implemented to provide semantics, enabling human understanding of the employed AI models.
[R16] An innovative theoretical framework that quantifies the performance of AI algorithms was delivered.
[R17] Extensive benchmarking and execution of multiple AI pipelines tailored for specific learning tasks were conducted to quantify and optimize AI algorithms operations.
[R18] LIME-compatible local explanation schemes were seamlessly integrated into the TALON Dashboard, fostering transparent, locally-interpretable decision-making within all four industrial pilots.
[R19] Counterfactual-based explainability mechanisms were fully implemented and operationalized.
[R20] A digital model was defined to fully align with the final data schema of the Nakamura2 CNC machine in Use case 2.
