26 專題探討 • TOPIC INSIGHT 澳大新語 • 2024 UMAGAZINE 30 a deeper understanding of the brain, so I consult neuroscience experts at the hospital and study the anatomy of neurons to understand how various brain regions affect language ability. The key lies in identifying problems and finding solutions, rather than confining yourself to a specific field. In today’s world, boundaries between disciplines are like lines in the sand. When the next wave of new knowledge arrives, these lines will be erased. Prof Leong: I initially trained in chemical engineering at university. During my doctoral studies, my interest shifted to drug delivery, so I started to engage in interdisciplinary research spanning chemistry, biology, and medicine. When it comes to developing technologies to treat human diseases, collaboration with scientists from various disciplines is essential. I consider myself a multidisciplinarian—I am passionate about leveraging knowledge from different fields to advance medical development. Therefore, in the era of digital technology, it is crucial for students to receive interdisciplinary training and learn to collaborate with teams from different disciplines. Question: In research innovation, should universities prioritise market demand or the interests of their research teams? Prof Maloberti: University research teams should maintain close connections with industry partners to avoid unnecessary pitfalls. This is especially important in the microelectronics field, where companies often need something that is immediately useful, rather than just viable in the near future. I once dedicated a great deal of effort to a project I believed was excellent, only to discover that the partner company did not need such a product. This experience taught me a valuable lesson: regular communication new horizon of information and knowledge. Prof Leong: Biomedical sciences have been greatly influenced by AI. In particular, AI has enhanced our productivity and the ability to analyse data that are imperceptible to the human eye. For example, in biomedical sciences, you need to deal with massive amounts of data when conducting singlecell analyses such as single-cell RNA sequencing. In this case, machine learning algorithms can help you analyse such data. Prof Cai: Most traditional economic forecasts in social sciences research lack accuracy, primarily for two reasons. First, researchers often base predictions on historical data. Second, data collected through sampling methods do not represent the whole picture, and it is also difficult for researchers to obtain comprehensive datasets. However, the future application of cloud computing and real-time big data analysis will enhance the relevance of research outcomes by reflecting more comprehensive data sets, thereby improving the representativeness of the research. This will be a trend in future research methodologies. Consequently, it is essential to train students to proficiently use AI technologies rather than be outpaced by them.’ Question: As emerging interdisciplinary fields thrive, traditional disciplinary boundaries are blurring. Do you view this development as positive? Prof Wang: Each discipline has its own inherent thinking patterns and limitations. Without thinking outside the box, it is difficult to innovate. For example, a person’s gradual loss of language ability could result from neurological disorders or simply ageing. To understand the cause, I need 蔡昉教授 Prof Cai Fang 梁錦榮教授 Prof Leong Kam Weng
RkJQdWJsaXNoZXIy NzM0NzM2