Summary of Key Points
AI for Science (AI4S) is evolving from a tool that merely helps scientists calculate faster to a core partner in reshaping the scientific discovery process. It has already been integrated into real research scenarios such as literature review, experiment design, and instrument operation, significantly reducing research time (for example, literature review tasks that used to take weeks can now be completed in 20 minutes). However, it is still in its early stages, with challenges including inconsistent data standards and the lack of scientific reasoning capabilities in AI systems. The goal of AI is not to replace scientists but to free them from repetitive tasks. Future competition will focus on the coordinated development of computing power, data, and platforms. China is gradually establishing a comprehensive system for AI-driven research.
1. The Evolution of AI4S from a “Calculator” to a “Research Partner”
In the past, AI was used in research as an accelerator—helping physicists with complex calculations or biologists with sequencing data analysis. Now, its role has changed: it is actively participating in the entire research process. For instance, scientists used to spend weeks reviewing dozens of papers; with AI, this task can now be completed in 20 minutes. AI can also generate preliminary experimental reports, increasing efficiency by 5-10 times. During the experiment design phase, students can use AI to discuss various options, leading to more comprehensive approaches. In the words of experts, research cycles are shifting from years to days—a sign that AI is beginning to understand the underlying processes of scientific research.
2. Specific Applications of AI in Real Research Scenarios
AI has been implemented in several research areas:
- The ScienceOne platform at the Chinese Academy of Sciences: Covers hundreds of research scenarios, with AI assistance throughout the process from literature review to experiment validation.
- **Tsinghua University’s “Dark Laboratory”: Combines large models, automated equipment, and data to handle repetitive experimental tasks. Scientists only need to decide what experiments to conduct and how to analyze the results.
- Shenzhen’s Owl·Lingjian system: Enables precision instruments to operate autonomously—without needing to modify the instrument interfaces. AI can simulate human operations, automating sample processing and data analysis. The system has increased the independence of certain tasks from 33% to 80%, effectively providing a “fully automated assistant” for laboratories.
3. Major Barriers: Inconsistent Data Standards and Lack of Scientific Reasoning
The development of AI4S is hindered by two main issues:
- Inconsistent data standards: Different research fields use various types of data (e.g., EEG or calcium imaging), which makes it difficult for AI to process effectively. The Brainμ model from the Zhiyuan Research Institute has addressed this by translating different neural signals into a common format, allowing AI to understand and analyze them.
- Lack of scientific reasoning: While AI can search for information and write reports, it cannot yet make logical deductions like scientists. For example, it may know that a drug can treat a disease but cannot explain why. Experts are training AI models to adopt scientific thinking methods, such as incorporating material analysis and protein research reasoning processes, to improve the reliability of its outputs.
4. The Role of AI: To Reduce Burden, Not to Take Over Jobs
Many worry that AI will replace scientists, but experts emphasize that its purpose is to liberate them from repetitive tasks. AI excels at handling repetitive, standardized tasks (e.g., operating instruments, organizing literature, and running simulations), while scientists’ core value lies in creativity and judgment (e.g., designing new experiments, proposing hypotheses, and interpreting results). As Professor Yu Li said, AI is a component of the research infrastructure that frees scientists to focus on creative aspects of their work.
5. The New Competitive Landscape: Collaborative Development of Computing Power, Data, and Platforms
The competition in AI4S is no longer about which model is the most advanced but about the coordinated development of computing power, data, and platforms. For example, the Shanghai Artificial Intelligence Laboratory, in collaboration with companies, has developed a “Super-Intelligent Integrated Computing Architecture” that has significantly improved efficiency in drug research and material design. Another example is the “Super-Intelligent Integrated Platform,” which controls everything from chips to scheduling, showing promising results in areas like new drug development and photolithography simulation. This indicates that China is building a complete ecosystem for AI-driven research, moving AI4S from the laboratory to practical applications.
In Conclusion
AI4S is not just a fancy “black technology” but a tool that is transforming how research is conducted. It makes research faster and more efficient, yet scientific discoveries ultimately rely on human intelligence to overcome unknowns. The future of research will be a combination of human creativity and AI efficiency.