To respond to the National Science Popularization Day and promote the popularization of scientific knowledge among teenagers, Associate Professor Wenjian Liu, Associate Dean of the School of Data Science at City University of Macau, was recently invited to attend the "Local Experts Enter Campuses" series of science popularization activities hosted by the Association for Promotion of Science & Technology of Macau and sponsored by the Macau Foundation. He delivered a special lecture titled Dancing with Artificial Intelligence: Growth and Success in the AI Era to over 250 teachers and students of ESCOLA DOS MORADORES DE MACAU.

The lecture aimed to help students gain an in-depth understanding of the current development status and future trends of artificial intelligence (AI), and master the growth path and key capabilities in the AI era. Associate Professor Wenjian Liu guided teachers and students to sort out and clarify the definition of AI. Subsequently, he shared content around three core topics, including the development of AI, Gen-AI and AGI, and self-growth in the AI era.

In response to the learning problems and challenges faced by students amid the development of AI, he summarized and put forward constructive suggestions, arguing that AI is a tool for improving efficiency and an assistant for cognition, rather than a competitor. The unique advantages of humans in terms of tacit knowledge and value judgment can complement AI’s efficiency advantages in data processing and pattern discovery. Logical ability is an important skill that current students must develop, and human-AI collaboration is the key to unlocking potential for future development.

Focusing on the path to growth and success in the AI era, Associate Professor Wenjian Liu further analyzed the growth logic of the AI era from four dimensions: cognition, methodology, capabilities, and collaboration. First, cognitive transformation: it is necessary to shift from "mastering knowledge" to "solving complex problems", from "accumulating the quantity of knowledge" to "building a structured and transferable knowledge system", and from "knowledge receiver" to "AI collaboration partner". Second, methodology reconstruction: it is essential to build a self-driven learning system of "demand identification (finding real needs in scenarios) – resource integration (AI tools + Multi-channel) – practical verification (output driving input) – feedback iteration (dynamically adjusting the path)" to achieve a shift from "passive input" to "active exploration". Third, capability focus: focus on developing core capabilities that are difficult for AI to replace, such as logical thinking, cross-boundary integration, psychological insight, and cross-cultural communication and expression. Fourth, he further explained through the "Human Cognition and AI Capabilities" four-quadrant diagram that humans have unique advantages in fields such as tacit knowledge and value judgment, and need to complement AI’s efficiency advantages in data processing and pattern discovery to collaboratively explore unknown areas.

This lecture was rich in content and forward-looking, further enhancing students’ interest in and understanding of cutting-edge technology. The School of Data Science at City University of Macau has always been committed to promoting the in-depth integration of scientific research and education, actively fulfilling its social responsibilities, and supporting the development of local science popularization undertakings. In the future, the School will continue to strengthen cooperation with various sectors to cultivate more future talents with scientific literacy and innovative capabilities for Macau.