ITEA CLEAR: Comprehensive Learning for Enhanced AI Responsiveness

Many industries need to deal with and process diverse sets of data of various types from multiple sources (e.g., sensors, architectural diagrams, log files). Industries such as manufacturing, telecommunications, and transportation face significant challenges in integrating multimodal data due to the increasing complexity of their digital ecosystems, leading to inefficiencies, delays in decision-making, and higher operational costs. In this regard, the use of multimodal AI techniques has shown great potential and can help solve industrial challenges such as improving services and integrating different information systems. However, they find it difficult to incorporate these systems into their current environments and existing processes, especially because the AI outputs need to be highly dependable. That is where the CLEAR project (Comprehensive Learning for Enhanced AI Responsiveness) comes in. CLEAR is on a mission to tackle the hurdles of integrating multimodal data into AI systems and making their outputs more reliable for industrial applications. By using advanced AI technologies and context awareness, CLEAR aims to boost and capitalize on the capabilities of Large Multimodal Models (LMMs) and Large Language Models (LLMs) towards this goal.