Unlocking India's Digital Potential: Role Of AI, Generative AI In Vernacular Languages For Users
AI language models offer exciting possibilities for improved language production, enhanced translation, and augmented content creation.
By Rahul Prasad
Technology is changing how we talk and connect with each passing day. In India, where there are many different cultures and languages, more people are using the Internet than ever before. In the past, it was hard for people from different regions to communicate because of language differences. But now, with the help of artificial intelligence (AI), businesses are making it easier for people to chat in their languages.
Generative AI is also making it easier to search for things online in India. This technology is helping people learn, communicate, and express themselves better, making the Internet more inclusive and personalised for everyone.
Generative AI is working together with language science to do some amazing things. When it's combined with language models and deep learning, generative AI can understand the details of human language and create content that makes sense and fits the situation. For example:
Machine Translation is making it easier to translate languages and communicate across different languages.
Natural Language Processing: Improving how computers understand and respond to what we say.
Content Development: Helping to create rich and relevant content quickly and easily.
Application Of Generative AI
Generative AI is making waves in the world of linguistics, offering some fantastic applications. Here’s a quick look at how it’s changing the game:
Learning
Summarisation: AI can condense long-form content into bite-sized pieces in any language. Think of international news, summarised and translated into a regional language.
Interactive Reading: AI-powered chatbots can personalise learning by adapting learning pace, and answering students’ questions. Imagine you're reading a book and have a question, and instead of searching through the pages, a reader can simply "chat" with the book to get the answer as per the age and language proficiency of the reader.
Communication
Contextual Translations: AI translations are more relevant and contextual. Now, family members can chat seamlessly across continents and generations. Picture a grandson in America chatting with his grandparents in their native language.
Tone and Personalisation: AI helps tailor tones, add wishes, poetry, and taglines. It’s like having a personal assistant for your greetings, whether for family, co-workers, or elders.
Customer Support: With LLMs improving language understanding, AI agents can now effectively handle customer queries in multiple languages. They can manage follow-up questions, make corrections until the user is satisfied, and assist with tasks like scheduling and arranging meetings, providing a comprehensive support experience.
Expression
Personalised Content: AI allows users to customise content in their regional languages. From “Good Morning” to “Happy Durga Puja” in Bengali, it’s all about making connections more personal and culturally relevant.
Enabling Scalable Marketing: Brands are leveraging Generative AI to automate the creation of marketing content in multiple languages. By generating regional promotional materials and social media posts efficiently, the brand reduces the need for repetitive manual work. This approach cuts marketing costs by easing the burden on human teams, enabling them to focus on strategic initiatives while ensuring consistent and engaging content across all platforms.
Generative AI is not just a tool; it’s a bridge connecting people through language, making communication smoother, more intuitive, and a lot more fun.
Challenges Of Generative AI In Linguistics
Generative AI is a game-changer, but it faces some hurdles, especially with vernacular languages:
Data Requirement: AI needs a ton of structured and truthful datasets in regional languages, capturing all the dialects and cultural nuances. Preparing the dataset is time-consuming. Poor-quality or incomplete datasets can lead to errors and misrepresentations, confusing the training process and impacting the reliability of the AI’s output.
Training: Training AI models is expensive due to the need for powerful processing units and expert knowledge. It requires significant resources to ensure models are effective and fair.
Hallucination: AI systems sometimes generate inaccurate or fabricated information. This can result in misleading or incorrect responses, similar to a friend who might tell entertaining but false stories.
Generative AI is amazing, but it’s still learning the ropes with our diverse languages.
Way Forward
Generative AI isn’t here to replace humans but to assist them. The challenges mentioned earlier have opened doors for third-party organisations to harness AI, making it easier for people to communicate in their native languages.
A recent Salesforce survey found that 75per cent of users are automating tasks with generative AI, even using it for work communications. As these numbers rise, generative AI will become more accessible, allowing anyone to train large language models (LLMs) on their hardware at a reasonable cost. These models will also run on everyday devices like smartphones, providing the best responses based on user data without needing to upload to a business server.
AI language models offer exciting possibilities for improved language production, enhanced translation, augmented content creation, language learning, accessibility and inclusivity, automated customer support, creative writing aids, and many more with thoughtful implementation. In summary, AI language models are invaluable tools for enhancing communication across various fields.
(The author is the Co-founder and CTO, Bobble AI)
Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.
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