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Recently, Baidu officially launched its 2026 AIDU Program, an elite recruitment project aimed at cultivating future AI technology leaders. This program has once again become a focal point in the tech community. Baidu hosted an AIDU Program • DeepTalk Open Day event in their campus, attracting many young people who are passionate about AI technology to participate. In this year's AIDU Program, there has been a significant expansion in job recruitment. The scale of recruitment has increased by over 60%, covering 23 core businesses and 11 research directions of Baidu. Positions include large model algorithms and large model infrastructure.

In the fierce competition for talent, vivo recently announced the launch of a global talent recruitment program named 'Blue Star'. This plan focuses on high-tech fields such as chips and artificial intelligence large models. The purpose of this program is to attract the top doctoral students from around the world to further promote vivo's technological innovation in research and development. The 'Blue Star Program' is regarded as the core talent strategy of vivo. It plans to recruit about 100 experts. These positions cover several technical fields, including virtual reality (XR), artificial intelligence large models, image processing, chip design, etc.

ByteDance recently announced the official launch of its "Top Seed" program for recruiting top AI talent from the class of 2026. The program aims to recruit approximately 30 outstanding doctoral students. This initiative focuses on cutting-edge artificial intelligence, encompassing research areas such as large language models, machine learning algorithms and systems, multi-modal generation and understanding, and speech processing. ByteDance hopes to attract young talents with strong potential and passion in the field of large language model research. Unlike previous recruitment plans, this year's "Top Seed" program emphasizes no restrictions on academic background.

Recently, at Baidu Create Developer Conference, Baidu founder Robin Li announced a series of significant AI development plans and initiatives, actively embracing the booming era of AI applications and providing comprehensive support for developers. The Baidu Search Open Platform officially launched the "AI Open Program" (sai.baidu.com), aiming to provide users with more comprehensive AI services by establishing diverse content and service distribution mechanisms. This program will target various forms such as intelligent agents, H5, mini-programs, and independent Apps.

At the 2025 SenseTime Technology Exchange Day in Beijing, SenseTime officially announced a comprehensive upgrade to its SenseCore 2.0 large-scale AI infrastructure. As a leader in AI infrastructure, SenseCore 2.0 aims to provide businesses with agile, flexible, and reliable full-stack AI infrastructure services, driving the efficient implementation and large-scale application of large-scale models at an optimal price-performance ratio.

The non-profit organization Digital Promise recently announced the launch of its "Responsible Design AI Product Certification" program. This initiative aims to assist school leaders in selecting AI-powered educational technology tools that meet student learning and safety requirements. With the rapid growth of AI in education, schools face an increasing number of choices, making the selection of safe and effective tools a critical challenge. The certification program specifically assesses several key aspects of AI educational tools, including data security and fairness.

OpenAI has announced the launch of its 'OpenAI Pioneers Program', aimed at improving the current scoring system for AI models to create evaluation standards more aligned with real-world applications. With the rapid advancement of AI across various industries, understanding and enhancing AI's performance in real-world scenarios is crucial. OpenAI states that focusing on domain-specific evaluation metrics will more effectively reflect real-world performance and help teams assess model performance in high-stakes environments.

DeepSeek AI, a prominent Chinese artificial intelligence research lab, following its powerful open-source language model DeepSeek-R1, has achieved another significant breakthrough in the field of Large Language Models (LLMs). Recently, DeepSeek AI officially launched an innovative technology called Self-Principled Critique Tuning (SPCT), aimed at building more general-purpose and scalable AI reward models.

With the rapid development of artificial intelligence, large models and algorithms have become core drivers of innovation across various industries. To regulate technological applications and encourage compliant development, provinces and cities across China have rolled out large model algorithm registration reward and subsidy policies, with the highest subsidy reaching 50 million yuan. This is a summary of the national policies for 2025. I. Policy Background and Significance: Large model registration and algorithm registration are crucial national initiatives to regulate generative AI technologies and algorithm recommendation services. Through registration, companies are required to disclose technical details, ensuring...

Researchers from DeepSeek and Tsinghua University recently published a new paper exploring scaling methods for reward model inference, seemingly advancing DeepSeek R2. Reinforcement learning is widely used in the large-scale post-training phase of large language models, but faces the challenge of obtaining accurate reward signals for these models. The researchers found that using pointwise generative reward modeling (GRM) improves model adaptability and scalability during inference. To this end, they propose Self-Principle Calibration Tuning (SPCT) learning.

On March 11th, the Tongyi Lab team announced the open-sourcing of the R1-Omni model, marking a significant advancement in multimodal model development. This model integrates reinforcement learning with verifiable reward (RLVR) methods, focusing on improving reasoning capabilities and generalization performance in multimodal emotion recognition tasks. R1-Omni's training is divided into two stages. In the cold-start phase, the team fine-tuned the model using a combined dataset containing 580 video clips sourced from Explainable Multimodal Emotion...