Core Viewpoint - The integration of AI in scientific research, termed AI4S, is transforming traditional research methodologies, enhancing efficiency while raising concerns about the quality and depth of scientific outcomes [1][3][13]. Group 1: AI4S Development and Impact - DeepSeek's latest model, DeepSeekMath-V2, demonstrates significant advancements in AI's reasoning capabilities, potentially revolutionizing scientific research [1]. - AI4S has led to exponential improvements in research efficiency across various fields, such as life sciences, materials science, and environmental science, with notable examples including Alpha Fold2, which reduced protein structure prediction time from decades to days [2][3]. - Countries like the U.S. and EU are accelerating their AI4S strategies, with the U.S. enhancing its strategic position through executive orders and the EU launching the "AI Continental Action Plan" [2]. Group 2: National and Local Policies - China is actively promoting AI4S through various policies, including the "Artificial Intelligence Empowering Scientific Research" initiative and local government plans in cities like Shanghai and Beijing [3][8]. - The emphasis on AI4S in national strategies aims to explore new research paradigms and accelerate significant scientific discoveries [3]. Group 3: Challenges in AI4S Implementation - Despite the rapid development of AI4S, challenges such as data isolation, high costs of quality data acquisition, and a shortage of interdisciplinary talent persist [9][10][12]. - The lack of high-quality, AI-ready datasets is a critical barrier to the effective application of AI in scientific research, with significant costs associated with data collection and annotation [9][10]. - The verification of AI-generated predictions remains a bottleneck, with a significant gap between AI's predictive capabilities and human validation processes [10][11]. Group 4: New Research Ecosystem - The shift towards AI4S necessitates a new research ecosystem that includes the cultivation of interdisciplinary talent and the establishment of collaborative platforms [13][14]. - Initiatives like the "double mentor system" at Peking University aim to bridge the gap between AI technology and scientific inquiry, fostering a new generation of researchers [14]. - The transition from traditional research models to platform-based approaches is essential for integrating diverse expertise and enhancing collaborative innovation [15][16]. Group 5: Ethical Considerations and Future Directions - The ethical implications of AI in research, including algorithm transparency and data privacy, require robust governance frameworks to ensure responsible use [17]. - The future of AI4S lies in creating a synergistic environment where data, tools, talent, and models evolve together, maximizing the potential of AI in scientific discovery [17].