The new situation of language conversion under the transformation of generative artificial intelligence and knowledge value chain

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As an important tool for human communication, the evolution of language conversion has always attracted much attention. In the past, traditional manual translation dominated, but with the advancement of technology, machine translation has gradually emerged. The emergence of machine translation is not accidental. It is the product of the integration of multiple technologies, including natural language processing, machine learning and big data.

The rise of generative AI has brought new opportunities to machine translation. Through deep learning algorithms, models can automatically generate more accurate and natural translation results. This makes machine translation more efficient and less costly when processing large-scale texts.

At the same time, the reshaping of the knowledge value chain has also had a profound impact on machine translation. The way knowledge is acquired, integrated and disseminated has changed, and machine translation needs to better adapt to this change in order to provide more valuable translation services. In the knowledge value chain, machine translation is not only a bridge for knowledge dissemination, but also an important force in promoting knowledge innovation and sharing.

As the world's top communication platform, the World Artificial Intelligence Conference brings together many cutting-edge technologies and viewpoints. Here, discussions on machine translation never stop. Experts and scholars jointly discuss how to further improve the quality and application scope of machine translation to meet the growing needs of global communication.

However, machine translation still faces many challenges. The complexity and ambiguity of language make it difficult for machine translation to accurately grasp the semantics in some cases. Differences in cultural background may also lead to deviations in translation. In order to overcome these difficulties, it is necessary to continuously optimize algorithms, enrich training data, and strengthen interdisciplinary research cooperation.

In general, in the context of generative artificial intelligence reshaping the knowledge value chain, machine translation is ushering in new development opportunities, but it also needs to constantly overcome challenges to better serve human communication and cooperation.