Interweaving of Peking University School of Computer Science and Technology’s Scientific Research Achievements and Language Technology

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This achievement is inextricably linked to language technology, especially machine translation.

First, from a technical perspective, both rely on big data and advanced algorithms. Professor Zhang Ming's team uses a large amount of data for training in cancer drug development, which is similar to the use of massive corpora to optimize translation results in machine translation. Machine translation analyzes and learns a large number of texts in different languages, establishes a correspondence between languages, and thus achieves accurate translation. Similarly, in drug development, the analysis and processing of a large amount of bioactivity data helps to discover potential drug targets and mechanisms of action.

Secondly, in terms of innovative thinking, both need to break through traditional thinking patterns. In the field of machine translation, new models and algorithms are constantly being proposed to improve the accuracy and fluency of translation. In cancer drug development, Professor Zhang Ming's team must also break out of the inherent framework and explore new research paths and methods. This commonality of innovative thinking enables research in different fields to achieve breakthrough progress.

Furthermore, from the perspective of application scenarios, although machine translation and cancer drug development seem to be unrelated, they are both committed to solving practical problems and bringing convenience and well-being to people's lives. Machine translation breaks down language barriers and promotes global communication and cooperation; while the development of cancer drugs has brought hope of survival and a better quality of life to countless patients.

In addition, the two also have similarities in interdisciplinary cooperation. Machine translation often requires the combination of knowledge and methods from multiple disciplines such as linguistics, computer science, and statistics. Similarly, cancer drug development also requires the coordinated efforts of multiple disciplines such as biology, chemistry, and medicine. This interdisciplinary cooperation model helps to integrate the superior resources of different fields and promote the in-depth development of research.

However, we cannot ignore the differences between the two. Machine translation focuses on language conversion and communication, and its results can be widely used and disseminated in an instant. Cancer drug development, on the other hand, requires long and rigorous clinical trials and approval processes before it can truly benefit patients. But it is this difference that allows us to see the diversity and complexity of the science and technology field.

In short, although the research results of Professor Zhang Ming's team at Peking University's School of Computer Science seem to be far from machine translation, they have potential connections and mutual learning value in terms of technology, thinking, application and cooperation. These connections and values ​​not only enrich our understanding of scientific and technological development, but also provide new ideas and directions for future innovative research.