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Opera has officially announced the release of its new AI-driven proxy browser, Opera Neon, and opened an Alpha version waiting list. This browser redefines the way users interact with the web by integrating innovative features such as intelligent chatting, task automation, and content creation through its unique proxy function. AIbase has compiled the latest highlights of Opera Neon and its far-reaching impact on future browsing experiences. Global First Proxy Browser: Redefining the Web Experience Opera Neon is positioned as

Baidu has officially released Xinxiang, a mobile application featuring multi-agent collaboration. The Android version is already available, with the iOS version expected soon. Positioned as a general-purpose super AI agent, the app aims to intelligently address complex user needs in daily life.

A new Gartner report predicts that by 2027, enterprises will utilize task-specific AI models three times more frequently than general-purpose large language models. While acknowledging the strong language processing capabilities of general-purpose models, the report highlights their decreased accuracy in tasks requiring deep understanding of specific business domains. Consequently, businesses are increasingly focusing on customized AI models to meet their unique needs. Image note: Image generated by AI, image licensing provided by Midjourney.

A groundbreaking achievement in quantum computing has been reached with the successful fine-tuning of a billion-parameter AI model on Origin Quantum's Wukong, marking a significant milestone in the development of quantum-enhanced AI.

The Arc Prize Foundation, responsible for maintaining and managing the competition, last week revised its cost estimate for OpenAI's upcoming o3 inference AI model with a staggering adjustment—from an initial estimate of $3,000 per ARC-AGI task to $30,000. This price correction reveals that the operational costs of today's most complex AI models may be ten times higher than previously anticipated. While OpenAI has yet to announce an official pricing strategy for o3, or even officially release the model, the Arc Prize...

Recently, Google announced the release of a brand new Vision-Language Model (VLM) called PaliGemma 2 Mix. This model combines image processing and natural language processing capabilities, allowing it to understand visual information and text input simultaneously, generating corresponding outputs as needed. This marks a significant breakthrough in artificial intelligence technology for multi-task processing. PaliGemma 2 Mix boasts powerful features, integrating image description and optical character recognition.

The GLM-PC base model CogAgent-9B under Zhipu AI has now been open-sourced to promote the development of the large model Agent ecosystem. CogAgent-9B is a specialized Agent task model trained based on GLM-4V-9B, capable of predicting the next GUI action based solely on a screenshot input, combining user-specified tasks with historical operations. This model's versatility makes it widely applicable to various GUI interaction scenarios such as personal computers, smartphones, and vehicle systems.

As tech giants like Microsoft, Google, OpenAI, and Anthropic successively launch AI agent products, a technological revolution in AI agent systems is quietly unfolding. What sets these new AI tools apart? How will they change our digital lives? AI agents are essentially intelligent software systems capable of autonomously achieving specific goals. They function like efficient project managers, breaking complex objectives into smaller tasks, allocating necessary resources, and monitoring task progress until completion.

Recently, a research team from Shanghai Jiao Tong University and Harvard University introduced a novel model fine-tuning method — LoRA-Dash. This new approach claims to be more efficient than existing LoRA methods, particularly in the fine-tuning of specific tasks, achieving the same results with an 8 to 16 times reduction in the number of parameters. This is undoubtedly a major breakthrough for fine-tuning tasks that require substantial computational resources. In the context of the rapid development of large-scale language models, the demand for fine-tuning specific tasks is steadily increasing. However, fine-tuning often