Search AI Products and News

Large Language Models (LLMs) have achieved significant progress in complex reasoning tasks by combining task prompts with large-scale reinforcement learning (RL), as demonstrated by models like Deepseek-R1-Zero, which directly apply reinforcement learning to base models, showcasing strong reasoning capabilities. However, this success is difficult to replicate across different base model families, especially within the Llama series. This raises a core question: what factors lead to inconsistent performance of different base models during reinforcement learning? How does reinforcement learning perform in

Recently, a speech synthesis model called Qwen-TTS has made new progress, with its latest version update completed through the Qwen API, bringing users a richer speech synthesis experience. In this update, Qwen-TTS added support for three Chinese dialects: Beijing dialect, Shanghai dialect, and Sichuan dialect, further expanding its application scenarios. The model is trained on a large-scale corpus of more than 3 million hours, achieving naturalness and expressiveness at a human level. Qwen-TTS can not only accurately

Welcome to the AIbase [AI Daily Report] section! Spend three minutes a day to learn about the latest AI events, helping you understand AI industry trends and innovative AI product applications. For more AI news, visit: https://www.aibase.com/zh1. Baidu officially releases the WENXIN Large Model 4.5 series and fully opens it to the public, featuring ten new models with various parameter configurations. These models are trained and inferred using the PaddlePaddle framework, achieving a FLOPs utilization rate of 47%, and perform well in multi-modal text tasks.

Recently, the Qwen VLo multimodal large model was officially released, achieving significant advancements in image content understanding and generation, offering users a brand-new visual creation experience. According to the introduction, Qwen VLo has been comprehensively upgraded based on the advantages of the original Qwen-VL series models. The model not only can accurately understand the "world", but also can perform high-quality re-creation based on understanding, truly achieving the transition from perception to generation. Users can now access Qwen Chat (chat.qwen.ai)

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.

A comprehensive review of MyBodyType.net's AI Body Type Calculator, comparing seven body type classification systems and differences between free and paid versions, revealing the accuracy and practical value of AI-based body analysis to help users decide if this body assessment service is worth investing in.

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