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Recently, Bilibili announced the integration of Qwen3 and Qwen-VL models, and based on this technology, launched InsightAgent. This initiative has significantly improved the efficiency of its commercial platforms, HuaFire and BiDa. During the recent 618 major e-commerce promotion event, Bilibili's commercial platforms leveraged InsightAgent to achieve a significant leap in commercial order conversion and data insight efficiency.

On June 19th, the Tongyi APP has comprehensively upgraded its translation capabilities, covering four core scenarios: text translation, simultaneous interpretation translation, document translation, and image translation, creating the strongest translation complex for individual and professional users. After the upgrade, the translation capabilities support 119 languages and dialects, achieving comprehensive improvements in accuracy, professionalism, and interaction experience. Whether it's cross-border office work, academic reading, or travel, the Tongyi APP can provide a truly all-scenario and all-modal translation solution. The Tongyi APP now supports 119 languages and dialects.

Alibaba Group announced the release of an upgraded version of its flagship AI model Qwen3, which has successfully been adapted to Apple's MLX architecture. This indicates that the launch of Apple Intelligence in the Chinese market may be imminent. According to Alibaba, the new Qwen3 version boasts stronger performance, supporting 119 languages and dialects, further enhancing user experience. In the previous iOS 18.4 update, Apple Intelligence had already started supporting Simplified Chinese, but its generation ...

Recently, the official account of Alibaba's Tongyi Qwen announced the official release and open-sourcing of the full series of 32 Qwen3 MLX quantized models. This move has attracted widespread attention in the AI field. MLX is an open-source machine learning framework specifically designed for deep compatibility with Apple chips, providing efficient capabilities for training and deploying large AI models, and is increasingly favored by many AI developers. Alibaba's actions are seen by insiders as preparation for domestic Apple Intelligence (Apple AI). Previously, multiple authoritative messages had hinted that Alibaba...

Gongbu Group recently announced a comprehensive and in-depth full-stack AI cooperation with Alibaba Cloud. The two parties will jointly create products based on Qwen large model and AI computing power and other advanced technologies to explore innovative end-side intelligent solutions. This move not only marks another significant breakthrough for Gongbu in the smart home field, but also indicates that its future will accelerate its overseas strategy by relying on Alibaba Cloud's global infrastructure. Gongbu and Alibaba Cloud have a long history of deep cooperation, establishing a close partnership since 2017. Currently, Gongbu's IoT system has been fully running on the Alibaba Cloud platform.

Global renowned open-source AI platform Hugging Face recently released the open-weight model contributor ranking. Chinese teams Qwen and DeepSeek successfully ranked in the top 15, showcasing China's technical strength and influence in the global open-source AI field. This ranking honors the teams that provide high-quality model weights to the open-source community, with their models widely applied in academic and industrial innovation. The Qwen team supported by Alibaba Cloud Intelligence Group has gained favor from the community due to the excellent performance of its Qwen3 series models in tasks such as instruction following and code generation.

Recently, Alibaba Cloud officially announced that BYD successfully integrated the Alibaba Qwen large model. This means that in the future within BYD's smart cockpit, users will be able to easily achieve online services such as ticket booking and meal ordering with just one sentence. This new technology will greatly enhance the driving experience and provide more convenient services. This innovation is called Mobile-Agent and adopts a full visual solution. It relies on BYD's open cockpit application ecosystem and combines the visual recognition and reasoning capabilities of Alibaba Qwen-VL to build a multi-modal system.

According to official data, Alibaba's Qwen3 large model has exceeded 12.5 million downloads globally within just one month since its open-source launch. It has drawn significant attention on major AI open-source platforms such as Hugging Face, ModelScope Community, and Ollama. Currently, Qwen3 offers four model versions with sizes of 0.6B, 8B, 30B, and 32B respectively, each exceeding one million downloads on the above-mentioned platforms, demonstrating strong global developer appeal. Particularly on Hugging Face platform, millions of users

In the rapidly developing field of artificial intelligence today, Alibaba officially open-sourced its new Qwen3 Embedding model series on June 6th. Based on the Qwen3 foundation model, this model is optimized for tasks such as text representation, retrieval, and ranking, marking another significant breakthrough for Alibaba in the AI field. Compared to the previous version, Qwen3 Embedding has achieved over a 40% performance improvement in core tasks such as text retrieval, clustering, and classification. This progress not only makes it more competitive

Recently, the official Qwen announced the release of the Qwen3-Embedding series models, as new members of the Qwen model family. These models are specifically designed for text representation, retrieval, and ranking tasks. The Qwen3-Embedding series is trained based on the Qwen3 base model, fully inheriting its significant advantages in multi-language text understanding capabilities. These models have performed excellently in various benchmark tests, especially showcasing outstanding performance in text representation and ranking tasks. The test used MTE

Silicon Valley investor Mary Meeker's latest released AI report reveals that the global AI competition landscape is experiencing profound reshaping. Chinese strength and open source wave are shaking up the ecosystem dominated by top companies like OpenAI, entering a new stage of 'contested supremacy'. The report points out that China's large language models are rapidly catching up with the U.S., with models such as DeepSeek, Alibaba Qwen, and Baidu Wenxin Yiyan showing excellent performance in core tasks such as code generation and mathematical reasoning.

Recently, the Beijing Academy of Artificial Intelligence (BAEI), in collaboration with Shanghai Jiao Tong University and other institutions, officially released a new generation of ultra-long video understanding model - Video-XL-2. The introduction of this model represents a significant breakthrough in the field of open-source ultra-long video understanding technology, injecting new vitality into the development of multimodal large models for understanding long video content. In terms of technical architecture, Video-XL-2 mainly consists of three core components: a visual encoder, the Dynamic Token Synthesis (DTS) module, and a large language model (LLM). The model adopts

Recently, the MiMo-VL multi-modal model developed by Xiaomi Company has taken over the baton from MiMo-7B, showcasing powerful capabilities in multiple fields. The model significantly outperforms the same-size benchmark multi-modal model Qwen2.5-VL-7B in tasks such as general question answering and understanding inference for images, videos, and language, and its performance in GUI Grounding tasks is comparable to that of specialized models, preparing for the arrival of the Agent era.

Recently, the Speech Team of Qwen Laboratory has made a milestone achievement in the field of spatial audio generation and launched the OmniAudio technology. This technology can directly generate FOA (First-order Ambisonics) audio from 360-degree videos, bringing new possibilities to virtual reality and immersive entertainment. Spatial audio, as a technology that simulates real auditory environments, can enhance immersive experiences. However, existing technologies are mostly based on fixed perspective videos and underutilize the spatial information of 360-degree panoramic videos. Traditional video-to-audio generation

Recently, Qwen released CoGenAV, innovating speech recognition technology with the concept of audio-visual synchronization. It effectively addresses the challenge of noise interference in speech recognition. Traditional speech recognition performs poorly in noisy environments, while CoGenAV takes a different approach by learning the temporal alignment relationships among audio-visual-text, building a more robust and generalizable speech representation framework, systematically improving tasks such as speech recognition (VSR/AVSR), speech reconstruction (AVSS/AVSE), and audio-visual synchronization (A').