AI in Action | Chapter 1

AI and Human Collaboration

We live in an AI-driven content transformation era that is changing how we create, manage, and localize content.

The rise of AI in content creation has led to misconceptions, conflicting information, and an overall lack of clarity.

The truth is AI and human intelligence work best together.

AI technologies are fundamentally transforming translation workflows. Find out how.

Data annotation isn’t just a checkbox task. It requires a skilled workforce and the ability to think beyond the label.

Two case studies demonstrating our commitment to the strengths of AI and human intelligence to achieve streamlined processes.

The rise of AI in content creation and localization has led to misconceptions, conflicting information, and an overall lack of clarity. Here are some common myths and the reality behind them.

Reality: AI is not a threat but a tool that will enhance translators’ capabilities, making them more efficient and effective. Localization, with its requirements of domain expertise, cultural sensitivity, and consistency with the brand voice, is a task that human translators and reviewers excel at. AI is here to support them, not replace them.

Still, AI could replace low-skilled jobs, as it could automate repetitive tasks and translate high-resource languages or content types where quality is not of the highest importance. This would free translators and experts to focus on higher-level cognitive tasks requiring creativity, problem-solving, and human interaction.

Reality: AI can generate and translate content that sounds human. However, it creates content based on existing data sets, making its output derivative and often repetitive. AI is unable to think creatively, understand emotions, or possess sentience. It cannot consistently replicate the human ability to understand context, humor, or cultural references.

Reality: Humans are needed to set parameters, train models, and ensure AI functions ethically and accurately. Human oversight is also crucial for identifying and correcting inaccuracies and biases in AI-generated and translated content. For certain languages and content types, AI’s performance is not consistent enough to perform localization tasks independently.

Reality: The performance of AI models varies greatly based on the source and target languages, as well as the content type. For example, most LLMs can easily rewrite an English-Spanish sentence with simple grammar, but a specialized legal translation from English to Hungarian might not go well. Continuous model testing and evaluation, as well as careful and well-researched application of localization solutions, are key to avoiding quality degradation when applying AI.

“Welocalize has built a state-of-the-art AI platform, enabling rapid deployment of tailored solutions like custom MT, AIQE, LLM-based revisions, and error detection for each client. Achieving top-notch localization means matching the right AI models and human expertise to the specific content and program needs. Our approach ensures client-specific solutions, while maintaining high-volume capacity, enterprise-grade engineering standards, and efficient turnaround times.”

Mikaela Grace, Head of AI/ML Engineering, Welocalize