RAIDO: Reliable AI and Data Optimization

RAIDO是一个强大的框架解决方案，旨在开发可信赖的绿色人工智能。值得信赖的人工智能专注于确保人工智能系统的可靠性、安全性以及公正的优化和部署，特别是在医疗保健、农业、能源和机器人等关键应用中。另一方面，绿色人工智能涉及开发和部署节能和环境可持续的人工智能技术，从而减少对环境的影响并改善资源管理。RAIDO提供了一系列自动化数据管理和丰富方法，包括数字孪生和扩散模型，以创建高质量、代表性、无偏和合规的训练数据。它还提供各种数据和计算效率高的模型和工具，以创建节能的绿色人工智能，例如少射和零射学习、数据集和模型搜索、数据和模型蒸馏以及持续学习。为了确保优化的人工智能模型和数据处理过程的透明度、可解释性和可靠性，RAIDO使用了各种XAI方法、分散的区块链、基于反馈的强化学习、新型kpi和可视化技术。此外，创新的人工智能编排器优化了相关的任务和流程，在开发和部署期间减少了模型的总体能耗和环境足迹。RAIDO强调动态接口的开发，支持适当的人工智能范例（中央、分布式、动态、混合），并能够无缝地适应使用情况的需求。此外，RAIDO将通过智能电网、基于计算机视觉的智能农业、医疗保健和机器人等四个关键应用领域的实际演示进行评估，展示显着的社会和市场影响。

RAIDO is a powerful framework solution designed to develop trustworthy and green artificial intelligence (AI). Trustworthy AI focuses on ensuring the reliability, safety, and unbiased optimization and deployment of AI systems, particularly in critical applications such as healthcare, farming, energy, and robotics. On the other hand, Green AI involves the development and deployment of energy-efficient and environmentally sustainable AI technologies, leading to reduced environmental impact and improved resource management. RAIDO provides an array of automated data curation and enrichment methods, including digital twins and diffusion models, to create high-quality, representative, unbiased, and compliant training data. It also offers various data- and compute-efficient models and tools to create energy efficient Green AI, such as few- and zero-shot learning, dataset and model search, data and model distillation, and continual learning. To ensure the transparency, explainability, and reliability of the optimized AI models and data handling processes, RAIDO uses various XAI methods, decentralized blockchain, feedback-based reinforcement learning, novel KPIs, and visualization techniques. Additionally, the innovative AI orchestrator optimizes related tasks and processes, reducing the overall energy consumption and environmental footprint of the models during both development and deployment. RAIDO emphasizes the development of dynamic interfaces that support the appropriate AI paradigms (central, distributed, dynamic, hybrid) and enable seamless adaptation to the needs of the use situation. Furthermore, RAIDO will be evaluated through four real-life demonstrators in key application domains, such as smart grids, computer vision-based smart farming, healthcare, and robotics, showcasing notable societal and market impact.