独家专访印尼高等教育与科技部副部长蒋黛兰：AI时代 何以为人？

Core Viewpoint - The rise of artificial intelligence (AI) presents both challenges and opportunities for humanity, necessitating a reevaluation of human roles and skills in the workforce [1][3][9]. Group 1: AI's Dual Nature - AI is described as a double-edged sword, capable of increasing unemployment while also creating new job opportunities [1][3]. - The potential for AI to enhance cybersecurity and improve information quality is acknowledged, alongside its risks [1][3]. Group 2: Education and Skill Development - Emphasis on the need for educational reform to cultivate AI literacy and practical skills that align with industry demands [2][3]. - The importance of teaching students to critically evaluate AI-generated recommendations is highlighted, as this skill is essential across various sectors [4][5]. - The focus should shift from traditional knowledge acquisition to developing curiosity and the ability to analyze anomalies, which are uniquely human skills [10][11]. Group 3: Future Workforce Dynamics - Jobs that rely on human interaction are predicted to become more valuable as AI cannot effectively replace human employees in these roles [5][6]. - The World Economic Forum forecasts that by 2030, 29% of workers will need to adapt their skills due to AI advancements [6]. Group 4: Global Collaboration and AI Development - Collaboration among countries, particularly between Indonesia and China, is deemed essential for advancing AI technology and education [7][8]. - The Global AI Innovation Index 2025 indicates that China and the US lead in AI development, emphasizing the need for global cooperation to bridge technological gaps [7][8]. Group 5: Human Advantages in the AI Era - Human curiosity and the ability to ask "why" are identified as critical skills that will remain superior to AI capabilities in the foreseeable future [10][11]. - The focus on anomaly detection and analysis is crucial for future success, as most human cognitive activities will revolve around handling exceptions in data [11].