Group 1 - AI for Science (AI4S) is driving automation in research, freeing researchers from tasks like literature analysis and data analysis to focus on more creative work [1] - AI4S lowers the barriers to entry in scientific research, enabling a diverse range of new entities, including startups and industry leaders, to engage in high-level research activities [1] - AI4S aims to address the imbalance between massive research investments and limited scientific discoveries, alleviating the issue of insufficient scientific productivity [1] Group 2 - AI4S is being applied in cutting-edge scientific fields, such as material discovery, where Google's DeepMind has successfully predicted millions of new stable crystal structures [2] - In the pharmaceutical sector, AI4S services are being fully utilized, with companies offering AI protein design platforms and automated laboratories [2] - AI is expected to significantly enhance drug development efficiency by conducting virtual clinical trials, allowing for pre-screening of suitable patients using digital twin models [2] Group 3 - The integration of commercial and scientific sectors is becoming increasingly important, as those who can commercialize new scientific discoveries will gain a competitive edge [3] - AI4S-generated research outcomes require a profitable commercial mechanism and market environment to incentivize companies to invest in original innovation [3] - The challenge for AI4S industrialization lies in ensuring that research outcomes can generate revenue, motivating companies to allocate more resources for innovation [3]