The following interview took place on November 19, 2025 between the Localization Institute and Dr. Peng Wang. This conversation provides additional context for the Multilingual AI Master Class, which was launched in May 2025. A video recording of this interview is also available.

1. Why was the Multilingual AI Master Class launched?

The Localization Institute prioritizes innovative technology in its training sessions. In this mindset, I launched the Machine Translation Master Class in 2020. The class helped bridge the gap between technical implementation and business strategy. It was highly valued by participants.

However, since ChatGPT was introduced in 2022, the scope of machine translation alone has become insufficient to capture the rapid advancements in AI. As a result, Large Language Models (LLMs) have transformed the field. Within the language industry, both LLM agents and automation agents help convert linguistic intelligence into practical workflows. Meanwhile, the landscape of plays who navigate transformation in multilingual AI has also been evolving quickly. To address this broader and rapidly changing ecosystem, the class was updated and relaunched in May 2025 as the Multilingual AI Master Class.

This new class focuses on the principles and implementation of enterprise-level AI applications. It covers topics of agentic AI, LLMs, prompt engineering, retrieval augmented generation (RAG), and knowledge graphs. It prioritizes AI risk management in practice, including AI quality, security, and data analytics.

2. What are the key objectives of this class?

Although the purpose is to teach technology and AI, the underlying considerations of this class put humans first. That is, examining the ways in which machine intelligence augments human efforts.

To begin, this class aims to empower participants with relevant AI knowledge and skills. It will help them build a strategic mindset to balance productivity, cost, quality, and risk concerning enterprise-level AI. With this foundational knowledge, participants can (1) verify if the AI information they are exposed to is correct and relevant, and (2) know how and when to use which technology, for example, LLM-driven agents or automation agents.

However, doing is more important than simply being aware of something and discussing it. I believe conventional roles in the language industry will begin heading in a new direction, to adopt new roles similar to indie developers.

In software development, an indie developer refers to an individual or small team capable of creating robust and effective technical solutions. Given the current accessibility of technology, multilingual AI stakeholders can apply this approach to address their own business challenges. These solutions are typically project-based, excelling in workflow orchestration and targeting specific problems. These solutions may be light weight. However, they are very flexible and can be easily customized to address the variety and specificity of real-life business problems.

In the language industry, indie developers not only develop or use technological tools, but also identify and create new tasks, roles, and strategies that will add value to the implementation of these tools.

The transition in this new direction is propelled by both internal and external factors:

1. Internal Motivation: Multilingual AI stakeholders are beginning to redefine their professional value and skillsets​.
2. External Guidance: The industry is providing wider exposure to new technologies, knowledge, and diverse market input.

The internal force plays a significant role to push forward the frontier, as stakeholders have the relevant knowledge concerning business needs and what they can contribute. This class aims to help participants to realize their potential in AI implementation.

3. What are the characteristics of this class?

The course design is dynamic, driven by both participants’ feedback and real-world business scenarios. There are four core features, which address some of the issues in human learning regarding artificial intelligence.

1. Differentiating AI and Non-AI Automation: This class helps participants effectively differentiate between AI and non-AI automation tools. While AI offers powerful solutions, it is not always the best fit for every business case due to its intrinsic limitations. In many situations, conventional tools, such as non-AI agents and structured database queries, provide a more cost-effective and efficient solution. In this class, participants will learn how to leverage both types of tools to maximize machine power.

2. Grasping AI Algorithms: This class prioritizes core AI concepts over surface-level tools. The fundamental principles of AI come before innovative use of AI tools. This common knowledge is classical and applicable regardless of which tool is chosen and how a tool updates. It will support users’ understanding and implementation of AI workflows, and keep up to date as new AI innovations become available.

3. Augmenting Human Efforts: When discussing AI implementation, we focus on those aspects that can best augment human efforts, rather than solely enhancing LLM and other model capability. By understanding the whole picture of the AI landscape, participants should be able to design technical solutions, as well as identify new roles and tasks that they can add value to AI workflows.

4. Emphasizing Data and Analytics: Data is a central, hidden theme throughout the class. As a valuable asset, multilingual data can train AI models, build up and monitor model performance, and ensure security and intention alignments. This class covers critical areas such as AI Quality, Security, and Data Analytics.