Chapter 11

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Reading Time: 5.11-5.25
Reading Task: Chapter 11
Summary of the Content:
AI has been widely applied in the translation industry. The author believes that translation can generally be regarded as a relatively fixed response to certain words. Moreover, for translation, the judgment of right and wrong is also very clear. Because translation follows certain rules, and AI is very good at working in accordance with certain rules, it can complete the translation smoothly. The author believes that perhaps one day, the main job of translators will become to create complete translation data for AI to learn from.
Evaluation:
The author's argument mainly revolves around the rule-based advantages of AI translation and the future transformation of the translator's role. The author believes that translation is essentially an activity that follows rules, so AI can achieve efficient translation by learning a large amount of translation data. This view reflects a full trust in the technical capabilities of AI, especially in line with the current development status of translation models based on deep learning.
Meanwhile, the author proposes that translators might become "producers of translation data" in the future. This viewpoint is somewhat forward-looking and echoes the practical demand for AI technology to rely on high-quality training data. Professional translators can enhance the accuracy of AI translation by marking complex contents such as domain terms and cultural metaphors.
However, the author's argument that translation is a relatively fixed answer and the judgment of right and wrong is very clear is questionable. Translation is essentially a creative activity that transcends languages and cultures, rather than simply following rules. For instance, in literary translation, there are often untranslatable aspects, such as puns and culturally loaded words. This requires the translator to carry out re-creation. Even in the translation of technical documents, differences in context, audience and function can lead to multiple legitimate translations of the same original text.
The evaluation of translation quality often involves subjective dimensions as well. For example, in localization translation, there is no absolute standard for whether to retain the cultural characteristics of the source language. It is necessary to balance the customer's needs and the acceptance of the target readers. Even if the grammar is correct, style deviations may still be judged as errors.
Reflection:
AI has indeed demonstrated the potential to replace human labor in standardized translation tasks. Translators need to reposition their own value, such as turning to areas that are difficult for AI to handle or upstream data governance work. High-quality bilingual data will become the core resource of the translation industry in the future. Translators can form competitive advantages by constructing corpora in specific fields.
The author's viewpoint prompted me to reflect on the excessive trust in the capabilities of AI. For instance, machine translation still relies heavily on human intervention when dealing with low-resource languages such as those of ethnic minorities, while the translation of culturally sensitive content such as religious texts requires even more human judgment.

Translation is not only language conversion, but also the transmission of meaning and cultural adaptation. Even if AI can generate grammatically correct translations, it may still lead to misunderstandings due to the lack of cultural empathy.
Practitioners can learn skills such as corpus linguistics and AI training data annotation, transforming from translation executors to AI collaboration experts. Enterprises can establish human-machine collaboration processes, using AI to handle the first draft and check the consistency of terms, while human translators focus on post-translation editing, conducting style refinement and cultural adaptation, thereby enhancing overall efficiency.
The author's discussion reveals the technological impact of AI on the translation industry, but its simplified understanding of the essence of translation may underestimate the core value of human translators. It is more likely to form a human-machine collaborative model in the future. AI is responsible for efficiency improvement, while humans are responsible for creative decision-making and cultural mediation. This reflection is of reference significance for both personal career planning and industry strategy formulation.