AI Models Increasingly Trained on Local Languages, Cultures in Asia, AWS Exec Says -- Market Talk

Dow Jones
Jun 17, 2025

0921 GMT - Asia's diverse cultures and languages are increasingly driving the training and deployment of LLMs for local application, says Jaime Vallés, Amazon Web Services' vice president of Asia-Pacific and Japan. This makes generative AI more accessible and useful for enterprises, Vallés says. AWS works with AI Singapore on a family of LLMs trained for Southeast Asian languages and cultures. It has also teamed up with a Malaysian startup to train a generative AI model to understand local nuances like slang and colloquialisms that merge different dialects for use in AI assistants. Malaysian enterprises can utilize the generative AI model to enhance operations and assist underserved audiences in making data-driven decisions, such as helping farmers in rural areas make weather and crop-viability assessments in regional languages, Vallés adds. (kimberley.kao@wsj.com)

 

(END) Dow Jones Newswires

June 17, 2025 05:21 ET (09:21 GMT)

Copyright (c) 2025 Dow Jones & Company, Inc.

At the request of the copyright holder, you need to log in to view this content

Disclaimer: Investing carries risk. This is not financial advice. The above content should not be regarded as an offer, recommendation, or solicitation on acquiring or disposing of any financial products, any associated discussions, comments, or posts by author or other users should not be considered as such either. It is solely for general information purpose only, which does not consider your own investment objectives, financial situations or needs. TTM assumes no responsibility or warranty for the accuracy and completeness of the information, investors should do their own research and may seek professional advice before investing.

Most Discussed

  1. 1
     
     
     
     
  2. 2
     
     
     
     
  3. 3
     
     
     
     
  4. 4
     
     
     
     
  5. 5
     
     
     
     
  6. 6
     
     
     
     
  7. 7
     
     
     
     
  8. 8
     
     
     
     
  9. 9
     
     
     
     
  10. 10