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Google DeepMind recently published a blog post, officially introducing the new Gemini Robotics On-Device localized robot AI model. The model uses a vision-language-action (VLA) architecture, enabling precise control of physical robots without cloud support. The biggest highlight of the new model is that it runs entirely locally on the robot device, achieving low-latency response capabilities. This feature makes it particularly suitable for environments with unstable network connections, such as critical application scenarios like medical facilities.

Recently, a new general intelligent agent called MiniMax Agent has been officially launched, aiming to provide efficient solutions for complex long-term tasks and has attracted widespread attention in the industry. MiniMax Agent: A breakthrough work of general intelligent agents - MiniMax Agent is an intelligent agent designed specifically to solve long-term complex tasks, featuring expert-level multi-step planning capabilities, flexible task decomposition mechanisms, and end-to-end execution efficiency. Its design concept is to make AI a reliable teammate.

A new study led by Apple researchers has poured cold water on the current large reasoning models (LRMs) that are highly anticipated. The research found that when solving complex tasks, reasoning models designed to simulate thinking processes, such as Claude3.7Thinking and Deepseek-R1, not only failed to show advantages but also exhibited serious problems like insufficient reasoning and performance collapse. This study tested four classic logical puzzles: the Tower of Hanoi, Checkers, River Crossing, and Block World. These puzzles allow precise control over the tasks.

In today's modern workplace, the importance of IT support cannot be overstated. Whenever employees encounter issues such as locked computers or inaccessible applications, they always hope to contact IT support promptly. However, help desk staff are often busy handling other requests, which may result in employees not receiving timely support. Image source note: The image is generated by AI and provided by Midjourney. Andrei Serban was once the product leader of the application and integration team at Rippling, and he...

In the field of artificial intelligence, Meta recently introduced the WebSSL family of models. These models, ranging in size from 300 million to 7 billion parameters, are trained on purely image data and aim to explore the vast potential of language-free visual self-supervised learning (SSL). This new research opens up new possibilities for future multimodal tasks and offers a fresh perspective on understanding how visual representations are learned. Previously, OpenAI's CLIP model was known for its performance in multimodal tasks such as visual question answering (VQA) and document understanding.

At the 2025 China Mobile Cloud Intelligence Computing Conference, Yang Jie, Chairman of China Mobile Communications Corporation, revealed that by 2030, artificial intelligence (AI) tasks are projected to account for 60% of total network traffic. This indicates an increasing reliance on AI technologies in future networks, demanding more advanced compute network infrastructure. Yang Jie pointed out that the "rebound effect" of computing power is gradually emerging, with inference computing power expected to grow four times faster than training computing power by 2030. This shift will drive new demands for computing power services.