I've always felt that being bilingual isn't just about speaking two languages--it's about THINKING and muttering in whichever language feels more natural depending on the topic and context. For example, I prefer doing math in Chinese because each digit is just one syllable, which makes calculations crisp and efficient. But when it comes to topics like unconscious bias, I automatically switch to English, mainly because that's where I first learned and absorbed those ideas.

It reminds me of programming languages: you can write almost any program in most languages, but you still pick Bash for a quick command-line script and Python for machine learning because each language has its sweet spot. The same logic applies to real-world languages--we pick the one that feels most seamless for a particular subject or situation, based on its inherent features and on the depth of our experience in that language.

This is also why I believe that keeping large language model (LLM) training corpora unbiased and inclusive across all languages and cultures is so powerful. In Ludwig Wittgenstein's words, "The limits of my language mean the limits of my world." By embracing every linguistic nuance, we expand the model's worldview and allow it to learn from the full spectrum of human knowledge. Even if two words from different languages share the same meaning on paper, their embeddings can diverge in an LLM because they carry unique cultural contexts and usage patterns. In my view, this inclusiveness not only creates a more equitable and accurate model--it also enables the LLM to handle a wider variety of tasks and unify the collective intelligence of all people, no matter where they come from.