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Recently, Keda GuoChuang announced on its interactive platform that with the rapid development of artificial intelligence technology, its launched 'Nebula Model' has been fully integrated and adapted to the DeepSeek-R1 and V3 models. This initiative marks an important progress in Keda GuoChuang's exploration of large models in the industry and vertical fields, achieving practical applications across multiple industrial scenarios. Keda GuoChuang's Nebula Model is not just a technical upgrade but also provides users with enhanced capabilities by integrating several domestic open-source large models such as DeepSeek and Tongyikianwen.

Today, iFlytek officially launched the Starfire V4.0 version at its grand press conference, marking another milestone after the Android app downloads surpassed 131 million. Since its launch in late June last year, the Starfire app has rapidly covered major app stores such as AppStore, Huawei, Xiaomi, OPPO, vivo, and Tencent Myapp, becoming the first domestic large-scale model application to fully cover both Android and iOS markets. It supports PC, mini-programs, and H5, showcasing its powerful features and wide range of applications.

During the Huawei Developer Conference 2024 (HDC2024), Huawei Music and iFLYTEK Co., Ltd. held a comprehensive deep cooperation signing ceremony in Dongguan. This cooperation marks a joint effort to promote the construction of a new ecosystem geared towards the all-scenario era, based on the development direction of HarmonyOS.

Air India has successfully deployed the generative AI virtual assistant 'Maharaja', becoming the first airline in the world to adopt this technology. 'Maharaja' manages a wide range of customer queries across 1,300 domains, including flight status and baggage allowances. It processes over 6,000 queries daily, successfully answering more than 80% of questions, thereby enhancing customer service efficiency. The new AI chatbot will reduce manual intervention, allowing human agents to handle more complex interactions.

The research team utilized AI to simulate a system that directs drone traffic through automated decision-making, significantly reducing the occurrence of accidents by employing collision avoidance algorithms and strategy conflict resolution methods. By integrating 'noise sensors' and a 'fuzzy reasoning system', the system can automatically decide to prevent collisions while considering various variables such as 'rogue drones' straying from their designated flight paths. The results are very promising.