In this current time, natural language processing and language are the center of the world’s imagination. AI technology has been applied to different knowledge applications, and businesses have begun to use and capitalize on language data. As AI is used to optimize businesses and reduce costs, there’s a great opportunity to create profound business ROI, leading to career advancement for those who can capitalize on these shifts.
In a recent AI + LLM webinar training with Lilt CEO Spence Green, we discussed the potential of AI in the field of localization and enterprise language. Throughout the webinar, Green discussed the history of large language models, the current state of the industry, and how businesses can embrace AI to achieve significant cost savings and growth.
This webinar was based on two customer questions: is generative AI another hype cycle and how to reduce a budget for language by 50%. The answer? AI technology is not just a hype cycle, but rather a technology shift that can create massive companies and profound changes in the way we work. By capitalizing on this shift, those with language and natural language processing skills can benefit from career advancement.
Whether you watch the 45-minute session or read this summary, you will walk away with a concrete foundation in AI translation to help you transform the way you execute localization. Below are three insightful takeaways from the webinar that we will dive deeper into:
• AI has the potential to revolutionize the field of enterprise language.
• Localization departments can leverage AI for cost optimization and growth.
• Generative AI is an opportunity for career advancement in the language field.
Green began the webinar by discussing the history of AI and Natural Language Processing (NPL). He noted that the field has its roots in the early days of computing when businesses began to realize the need to translate their products and services into different languages. However, the process of localization was initially very time-consuming and expensive and could be improved. You can find the slides to the webinar here.
In recent years, there have been three breakthroughs in AI and machine learning to make localization much more efficient. AI-powered tools can now automate many of the tasks involved in localization, such as translation, editing, and quality assurance. This has led to significant cost savings for businesses and has also made it possible to localize products and services into a wider range of languages.
"AI and language sit right at the center of [cost optimization and growth]. And I think that there's just so much work to do to change the way that businesses operate in the way that they use and capitalize on language data."
The application of AI in language is not just a technology hype cycle, but a significant shift that can create massive companies and profound changes in the way we work — especially during these times when businesses are looking to optimize costs and leverage AI. Green emphasized that despite the apprehension around AI, its advancements hold the potential to enhance and transform content creation for the better. Breakthroughs in generative pre-training also gave rise to In-Context Learning, which present a wide variety of solutions for AI applications.
“Large language models enable a computer or a machine to learn a very broad and very general language capability,” explained Green. He further elaborated that these models could be used to predict the next word in a sequence, mimicking human reasoning and enabling a wide range of applications from text generation to question answering.
“The way these large language models work makes it seem like they are able to do things that they weren't trained to do,” Green stated. He argued that the ability of these models to perform tasks they were not specifically trained for has led to an explosion of applications in various fields.
Green then dived into a case study in which AI translation can optimize ROI. When faced with a directive from management to reduce the budget for the localization department by 50% using AI, Green proposed three different strategies.
The first approach suggested reducing the number of translated languages. The second approach focused on leveraging AI to cut costs while maintaining language diversity. However, Green believed the third option held the most promise – using AI to reduce costs significantly while increasing reach.
“Number one is on goal. There are no changes to operations with minimum risk. The second recommendation is also on goal. We use AI more, but up the risk because we're making some pretty big changes to our workflow. And then there's the third solution—and I think this is really the possibility of AI," Green explained.
Green believed that the third option was the most promising, which involved transforming the business with AI, reducing costs significantly, and increasing reach by 25%. This approach, he argued, not only meets the budget reduction goal but also drives business growth.
Generative AI presents a significant opportunity for career advancement in the language field. He argued that the applications of AI in the field are not a threat to jobs but rather a tool for transformation and progress. With more opportunities for AI to automate and optimize many steps in the language localization workflow, more jobs are going to look different in the future than they do now.
He drew parallels to previous technology shifts and how initial trivial applications pave the way for transformative possibilities. By harnessing the opportunities presented by AI, language professionals can showcase profound business ROI, leading to accomplishments and career advancement. In short, embracing AI in the language field is a win-win scenario for both businesses and professionals.
How AI Can Speed Up Your Localization
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