This interdisciplinary course examines the notion of “truth” in the era of Artificial Intelligence (AI). It delves into the philosophical discourse on truth (e.g., correspondence, coherence, consensus, and pragmatism theories) and examines the scientific principles underlying logical reasoning. Students are challenged to reassess their assumptions about truth, as they analyse AI’s transformative power to learn and reason. They explore its implications across diverse disciplines (e.g., law, medicine, science, and humanities) with real-life cases. This foundational knowledge equips students with a good grasp of the complexities underlying truth, enhances their critical thinking and analytical skills to navigate advanced topics in AI.

This course examines Machine Learning (ML) and Artificial Intelligence (AI) within the context of natural and cultural learning processes. Beginning with information processing in primitive organisms, it traces the evolution of learning through neural systems, human societies, and modern computers. As foundational material, the course covers classic theories of learning and knowledge acquisition from cognitive science, philosophy, and psychology. Four main approaches to ML (supervised, unsupervised, reinforcement, and deep learning) are explored through applications such as medical diagnosis, cybersecurity, autonomous vehicles, and chatbots. Hands-on experiments with generative AI provide practical experience, as students reflect on human and machine learning processes.

This interdisciplinary course examines the evolving relationship between humans and machines in the age of advanced technology and AI. Drawing from phenomena in philosophy, psychology, behavioural science, sociology, and biology, it explores theories of human dignity, including consciousness, empathy, creativity, and progress. Students will analyse how these ideas apply to real-world examples in case studies covering law, medicine, and entertainment, alongside up-to-date case studies of human–machine competition, collaboration, and everyday usage. The course thus emphasises broader social, ethical, and biological perspectives, equipping students with analytical skills to critically navigate the complexities of technological change and humanity’s position within.

This course examines the risks and the role of governance in the use of AI, with a particular focus on Singapore’s approach to balancing technological advancement with ethical and regulatory concerns. Students will explore key AI safety challenges, including lack of transparency, bias, privacy vulnerabilities, cybersecurity threats, autonomous weaponisation, goal misalignment, and overreliance. Through inter-disciplinary perspectives, governance frameworks, and technical approaches to AI safety, students will critically assess trade-offs in policy decisions. Case studies will provide insights into aligning AI with human values, thereby allowing students to develop responsible AI strategies and contribute meaningfully to ethical AI deployment in Singapore and beyond.

This course examines how Singapore harnesses specific AI strategies (e.g. based on generative, predictive, embodied and emotion AI paradigms) to efficiently and effectively address healthcare needs by (i) improving the use of reliable health information by the public and health professionals to facilitate education and research; (ii) enhancing health technology for early detection, accurate and rapid diagnosis, and the discovery of new, safe and effective treatments; and (iii) integrating healthcare management and support for monitoring patients’ health condition and emotions. The prospects, limitation and possible mitigation of these AI technologies, and their governance, ethics and regulatory framework will be discussed.