In the dazzling world of AI, where tech giants are creating jaw-dropping language models, there’s a whisper we’re not hearing - the voice of smaller languages. As we marvel at the latest chatbot or AI writer, languages spoken by millions are slowly fading into silence. It’s time we turned up the volume on linguistic diversity in our AI endeavors.
The Overlooked Gems of Northeast India
Imagine a tapestry of cultures, each thread a unique language, weaving stories passed down through generations. This is Northeast India, home to over 220 languages, each a treasure trove of knowledge and identity. Languages like Bodo, Khasi, and Mizo aren’t just words; they’re the heartbeat of communities, the keepers of ancient wisdom.
But in our rush towards a global language, we’re unknowingly unraveling this tapestry. As younger generations pivot towards more “economically viable” languages, these linguistic gems are losing their sparkle. It’s not just words we’re losing; it’s entire worldviews, unique ways of understanding our planet and ourselves.
The AI Revolution: A Double-Edged Sword
The AI revolution promises to break down language barriers, but ironically, it might be building new ones. Large Language Models (LLMs) are incredible, but they’re also resource-hungry beasts, typically trained on widely spoken languages. This creates a technological divide, leaving smaller languages in the digital dark ages.
Enter the Humble Heroes: Small Language Models (SLMs)
But there’s hope on the horizon. Small Language Models (SLMs) are the underdogs in the AI world, capable of big impact with smaller datasets. They’re like the local artisans of the AI world - specialized, efficient, and deeply connected to the languages they serve.
Imagine an SLM trained on Manipuri poetry, preserving the nuances of emotion and cultural context that a larger, more generalized model might miss. Or an SLM helping to digitize and translate ancient Naga folk tales, ensuring these stories live on in the digital age.
A Call to Action: Embracing Linguistic Diversity in AI
It’s time we expanded our AI vision beyond the languages of Silicon Valley and embraced the rich linguistic diversity of our world. Here’s how we can start:
- Invest in data collection for smaller languages
- Develop and promote SLMs for regional languages
- Collaborate with linguistic experts and local communities
- Integrate these languages into educational tech and digital platforms
By doing so, we’re not just preserving languages; we’re ensuring that diverse perspectives and knowledge systems have a place in our AI-driven future.
In the end, true artificial intelligence should reflect the beautiful complexity of human intelligence - in all its linguistic glory. Let’s make sure our AI speaks not just in ones and zeros, but in the myriad voices of humanity.
