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Tech company Anthropic has quietly launched a blog project called 'Claude Explains' to showcase the content creation capabilities of its AI model, Claude. The content of the blog is primarily generated by the Claude AI model and then edited and enhanced by a team of human experts, covering various technical topics. It is reported that the content of the 'Claude Explains' blog is not just automatically generated by AI, but also goes through a series of rigorous editing processes.

On May 8th, OpenAI o4-mini launched with Reinforcement Fine-Tuning. The combination of these two technologies completely transforms the cost structure and technical threshold for AI specialization, enabling enterprises to rapidly turn general-purpose AI into expert systems in specific fields using only a small amount of training data. The leap from general intelligence to expert-level AI is highlighted by the Reinforcement Fine-Tuning technology at the core of this release. Unlike traditional supervised fine-tuning, RFT is based on reinforcement learning algorithms, driven by reward-based training.

Meta has released its latest open-source AI model, Llama 4, marking another significant advancement in the field of artificial intelligence. Llama 4 comes in two versions, Scout and Maverick, designed to enhance AI model capabilities and performance. Meta states that Llama 4 is a multimodal large language model capable of processing various data types, including text, images, video, and audio, and can freely convert between these formats. Notably, the Llama 4 series is the first...

News is spreading in the AI field that Jie Junyuan, a former AI expert from ByteDance, has officially joined Chixun Intelligent, an embodied AI startup, as the head of its embodied intelligence department. This change not only injects strong momentum into Chixun Intelligent's technical team but also paves the way for the future development of embodied AI. Jie Junyuan is a highly respected figure in the field of artificial intelligence. He graduated from the University of Science and Technology of China and received his doctorate from the University of Washington. He has published papers at several top academic conferences, and these papers...

Sakana AI's newly developed AI Scientist-v2 model has accomplished a jaw-dropping feat—it independently authored a complete scientific paper, from conception to completion, without any human intervention. Even more astonishing, this paper successfully passed the double-blind peer review of the ICLR2025 workshop, fooling the discerning eyes of human experts. This AI-generated paper achieved fully end-to-end autonomous creation; Sakana AI confirms that the entire process was completed without any human modification.

DeepSeek launched an official account on Zhihu and published a technical article, "DeepSeek-V3/R1 Inference System Overview," detailing its model inference system optimization and cost-profit margin information for the first time, marking the end of the highly anticipated DeepSeek Open Source Week. The article highlights two key optimization goals for the DeepSeek-V3/R1 inference system: higher throughput and lower latency. To achieve these goals, DeepSeek employs large-scale cross-node expert parallelism (EP) technology, although...

DeepSeek announced its second-day product of the open-source week: the first open-source EP communication library for MoE models, enabling full-stack optimization for mixed-expert model training and inference. DeepEP is a high-efficiency communication library specifically designed for Mixture-of-Experts (MoE) and Expert Parallelism (EP). It aims to provide high-throughput and low-latency many-to-many GPU kernel communication, commonly known as MoE routing and aggregation. DeepEP not only supports low-precision operations such as FP8 but also integrates with the DeepSeek-V3 paper.

A recent study released by the University of Munich, the Munich Machine Learning Center, and Adobe Research shows that 12 top AI language models, including GPT-4o, Gemini1.5Pro, and Llama-3.3-70B, face significant performance degradation in long-text conceptual reasoning tasks. Despite these models supporting context processing of at least 128,000 tokens, their ability to establish deep logical connections still has fundamental limitations. The research team developed NOLIMA (No Text Matching).

Today, iFLYTEK officially launched its latest research and development achievement, the Starfire simultaneous translation voice model, marking the debut of the first domestic large model with end-to-end speech simultaneous translation capabilities. This innovative technology has significantly improved the translation performance across all scenes compared to iFLYTEK's previous translation technologies, and has greatly shortened the end-to-end response time.

Recently, Hu Han, former chief researcher of the visual computing group at Microsoft Research Asia, officially joined Tencent, where he will be responsible for the development of the Hunyuan multimodal large model. This news has attracted widespread attention in the industry, and Hu Han's addition is believed to inject new vitality into Tencent's artificial intelligence initiatives. Hu Han obtained his bachelor's degree from Tsinghua University in 2008 and his Ph.D. in 2014, studying under the renowned professor Zhou Jie. His doctoral thesis was awarded the Excellent Doctoral Dissertation Award by the Chinese Association for Artificial Intelligence in 2016, showcasing his academic achievements.

According to a report by Late Auto, BYD is accelerating its research and development in the field of artificial intelligence. Nio Pengcheng, the former chief technology expert of Zero One Technology, joined BYD's Advanced Technology Research Center in November 2024. He will primarily be responsible for work related to AI supercomputing and may be involved in the development of AI applications. With nearly 12 years of rich experience in AI computing, Nio Pengcheng brings new vitality to BYD's technological advancements. To speed up the pace of technology research and development, BYD has recruited experienced professionals from large model companies.

Today, OPPO announced that it has successfully applied the Expert Mixture of Experts (MoE) architecture to mobile devices for the first time globally. This groundbreaking advancement significantly enhances AI processing efficiency, opening new pathways for the further development of terminal AI technologies and laying the foundation for the deep integration of AI and mobile hardware. As AI technology rapidly advances, an increasing number of AI tasks need to be completed on terminal devices. However, large AI models often require powerful computing capabilities, which poses a huge challenge for mobile devices with limited hardware resources. This breakthrough aims to overcome this limitation.