Interpreter's Memory

We’ve noticed something important: many professionals are feeling anxious in the face of rapid AI developments. Many are curious, but feel overwhelmed. 

One question keeps coming up in conversations with colleagues and peers:
“If I want to learn AI, where should I start?”

Or they are thinking:

◼ Do I need to code?
◼ What’s the difference between training a model and prompting one?
◼ How does any of this relate to localization of language work?

So we’ve decided to address that question head-on. This series will offer simple, approachable explanations and starting points—especially for those coming from language backgrounds.

We hope it helps more people build confidence, find direction, and feel empowered in this fast-evolving space.

Concepts that we help you to clarify:

Even before diving into the basic concepts like fine-tuning and prompt engineering, I believe we need a shared foundation.

【Neural Networks vs. Transformer】→ What are they? How did we get from one to the other? Why does it matter for language work?

No worry, you don’t need to understand them like an engineer or go deep into math. Having basic awareness of what sets these two architectures apart is crucial for anyone in the language industry to participate in tech conversations and to understand where your skills fit in the AI-driven workflow.

【Automation vs. Augmentation】→ Is AI here to replace us—or support us? Understanding this distinction helps us position ourselves in the AI era.

In Augmentation, the human leads. The machine is there to support, suggest, and respond. And they adapt to human’s workflow, rather than humans adjusting their habits to serve machines.

【Human-in-the-loop vs. Human-at-the-heart】→ When people say HITL, what they are talking about? Why “human-at-the-heart”, not “human-in-the-loop”?

In a truly human-centered workflow, the human is indispensable. When we center the human, machines don’t dictate the flow. They support the human’s thinking and knowledge growth, and streamline retrieval and knowledge management

Before anything else, ethics framework.

【Thinking Ethically About AI in Language Work】→ Dimensions + Timeline 

Once we begin asking who leads, who decides, and who is affected—we need a framework to think clearly. To grasp AI ethics in a practical and structured way, it’s helpful to consider two key aspects: 

◼ Dimensions (Fairness, responsibility, transparency, privacy) 

◼ Timeline  (Before and after deployment)

Finally, prompt engineering.

【Prompt engineering vs. Fine-tuning】→ Differences, and their impact on our industry.

Before we bring in prompt engineering, we’ve been slowly introducing Neural Networks and Transformers, Automation vs. Augmentation, human-in-the-loop vs. human-at-the-heart, and AI ethics framework.

Only now am I starting to talk about the most talked-about term. That order is intentional.

Our perspective is that we don’t need to start with buzz words.

We can start with understanding and professionalism, with how systems relate to humans. Understanding the fundamentals gives us a quieter kind of confidence—so when we do encounter these concepts, we know where we stand, and where our work fits in.

At the end, lens to look at AI’s impact on TILM

【How should we evaluate AI’s impact on TILM?】→ Impact varies by task, domain, language pair and direction. If AI performs well in one area—what does that tell us?

This isn’t about optimism or pessimism. It’s about staying grounded and making space for real assessment. And hopefully, helping more people in our field find where they stand.