Most of the notable LLMs are proficient in widely-spoken languages such as English but do not encompass the linguistic diversity necessary to effectively cater to global cultural and regional nuances.
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Developing home-grown LLMs is a significant technological advancement with its own merits. It sets a precedent for everyone to participate in the digital transformation, benefiting both by increasing customer reach and enabling businesses to connect and serve diverse customer bases globally.
AI presents appealing use cases across various applications, addressing cognitive overload, providing easy access to information, and enhancing customer experience.
LLMs trained in diverse linguistic backgrounds cover all three aspects, offering quick and easy access to information. This accessibility can assist local communities in resolving their inquiries promptly.
While we have discussed the advantages of building such models, it is crucial to acknowledge that developing these models requires access to data in local languages. Although challenging initially, it is achievable and can benefit local communities through efficient data labeling processes.
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Furthermore, the development of LLMs necessitates high-performance computing infrastructure, such as GPUs and cloud computing services, which can be costly and require financial support from sponsors or partners.
For any nation to succeed, it is essential to develop cost-effective and energy-efficient chips for building the next generation of AI models. This requires increased funding for research and development to foster collaboration between academia, industry, and government.
Data is no longer the new oil; the focus now is on processing large datasets efficiently, highlighting the need for energy-efficient chips.
In addition to software, funding research and development in cutting-edge technology and hardware self-sufficiency is essential for developing models trained in local languages. Large models heavily rely on data centers, demanding substantial power and emphasizing the need for power-efficient chips.
Inclusivity is key in including everyone in this technological breakthrough, ensuring that diverse cultures are promoted through LLMs trained in local languages.
While some may believe that LLMs could negatively impact employment, they also provide job opportunities for technology developers and participants in the technology stack value chain.
Breaking down language barriers for non-English speakers through technology can lead to significant improvements in their lives, opening doors to opportunities and active participation in the digital world.
Access to knowledge is crucial, and digitalization has played a significant role in leveling the playing field. Applications powered by AI chatbots in conversational languages can help bridge the gap in providing support and guidance to customers in various sectors.
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For instance, in agriculture, LLMs can assist farmers without language barriers, providing guidance on best practices for irrigation and efficient water use.
In healthcare, understanding complex domain-specific terms in insurance policies can be challenging. Implementing chatbots that educate communities in their language can help bridge this gap.
Inclusive AI models that support diverse languages help bridge the digital divide, offering opportunities to all through technological advancements. This inclusivity emphasizes fair access to marginalized groups and ethical considerations in providing access to local communities.
Vidhi Chugh, an AI strategist and digital transformation leader, works at the intersection of product, sciences, and engineering to build scalable machine learning systems. She aims to democratize machine learning and make it accessible to all in this transformative era.
