It Is Time for a UMI Moment for India

Why does it matter?

I asked ChatGPT to explain the concept of AI in Hindi and the response was as shown below.

कृत्रिम बुद्धिमत्ता (Artificial Intelligence, AI) क्या है?
कृत्रिम बुद्धिमत्ता (AI) कंप्यूटर विज्ञान की वह शाखा है जो मशीनों को इंसानों की तरह सोचने, सीखने और निर्णय लेने में सक्षम बनाती है। यह बड़े डेटा, एल्गोरिदम और गणितीय मॉडल का उपयोग करके कार्य करती है।
AI के मुख्य घटक (Components of AI)
मशीन लर्निंग (Machine Learning, ML)
यह AI की एक शाखा है जो कंप्यूटर को डेटा से सीखने और भविष्यवाणी करने में मदद करती है।
उदाहरण: बैंक में धोखाधड़ी का पता लगाना, ई-कॉमर्स में प्रोडक्ट सुझाव देना।
डीप लर्निंग (Deep Learning)
यह मशीन लर्निंग का उन्नत रूप है, जो न्यूरल नेटवर्क (Neural Networks) का उपयोग करता है और इंसानी दिमाग की नकल करता है।
उदाहरण: चेहरे की पहचान (Face Recognition), स्वायत्त वाहन (Self-driving cars)।
नैचुरल लैंग्वेज प्रोसेसिंग (NLP, Natural Language Processing)
यह मशीन को मानव भाषा को समझने, विश्लेषण करने और प्रतिक्रिया देने की क्षमता प्रदान करता है।
उदाहरण: चैटबॉट, वर्चुअल असिस्टेंट (जैसे सिरी, एलेक्सा)।
कंप्यूटर विज़न (Computer Vision)
यह तकनीक मशीन को छवियों और वीडियो को पहचानने और विश्लेषण करने में मदद करती है।
उदाहरण: मेडिकल इमेजिंग में कैंसर की पहचान, ऑटोमैटिक नंबर प्लेट पहचान।
रोबोटिक्स (Robotics)
यह फिजिकल मशीनों (रोबोट्स) को AI के माध्यम से स्वायत्तता प्रदान करता है।
उदाहरण: इंडस्ट्रियल रोबोट, हेल्थकेयर रोबोट।
AI के उपयोग (Applications of AI)
बैंकिंग और वित्त (Banking and Finance): फ्रॉड डिटेक्शन, जोखिम प्रबंधन।
हेल्थकेयर: बीमारी की पहचान, दवा खोज।
ऑटोमेशन: फैक्ट्री में स्वचालित मशीनें।
ग्राहक सेवा: चैटबॉट्स, वर्चुअल असिस्टेंट।
क्या AI इंसानों की जगह ले सकता है?
AI कई कामों को आसान बना सकता है, लेकिन यह अभी भी इंसानी रचनात्मकता, भावनाओं और तर्कशक्ति की बराबरी नहीं कर सकता। इसका सही उपयोग इंसानों के साथ मिलकर काम करने में है, न कि उन्हें बदलने में।

What would you say is right or wrong with the above paragraph?

While the use of Hindi language is appropriate, unfortunately it is more of Hinglish than Hindi, that is English language words and terms written using Hindi alphabets (Barakhadi). While this makes it easy to read for any individual capable of reading Hindi, it does not add much in terms of knowledge. I tried the translator model "Mayura" on the Sarvam.ai platform as well, which yielded similar results. To help you comprehend the complexity of the language ecosystem, have a look at the below metric.

Key points about Indian languages:

Total languages and dialects: over 19,500
Recognized languages (with 10,000+ speakers): 121
Officially recognized languages by the government: 22

India at crossroads

The rapid evolution of artificial intelligence has placed India at a crossroads, with recent debates centering on whether the country should prioritize foundational large language models (LLMs), agentic AI systems, or specialized distilled vertical models.

China's DeepSeek R1 LLM achieving superior performance at 30% of ChatGPT's parameter count has intensified scrutiny of India's current trajectory. By employing knowledge distillation (transferring capabilities from larger models) and vertical specialization, DeepSeek demonstrated that scale alone doesn't guarantee dominance. This aligns with Bessemer Venture Partners' analysis of vertical AI unlocking $1.2 trillion in enterprise value by 2030 through industry-specific optimization.

In my humble opinion, India's unique linguistic diversity, resource constraints, and sector-specific challenges position distilled vertical AI, that is smaller, domain-focused models trained via knowledge transfer from larger systems, as the most viable path forward. By combining computational efficiency with deep cultural and linguistic customization, this approach could enable India to leapfrog infrastructural limitations while addressing critical gaps in healthcare, agriculture, education, and governance. On one side, proponents of foundational LLMs argue that homegrown systems like Sarvam AI's two-billion parameter model and Krutrim's Indic-language focused efforts represent essential sovereignty in an AI-dominated future. Conversely, advocates for agentic AI emphasize autonomous systems that execute complex workflows, particularly for India's understaffed public sector, to be the need of the hour. However, both approaches face significant hurdles:

Computational costs: training a foundation-scale model requires around $100 million in GPU infrastructure, a prohibitive sum for most Indian startups and a very challenging, politically sensitive sum for the government.
Data scarcity: only 0.1% of global AI training data represents Indian languages, with many dialects lacking standardized scripts.
Relevance gaps: general-purpose LLMs often fail on localized tasks. A ChatGPT query about dry season resistant crops for Maharashtra's black cotton (regur) soil yields generic advice unrelated to regional farming practices.

Vertical AI and knowledge distillation: conceptual foundations

Vertical specialization of open-source models through knowledge distillation

The process of distilling open-source base models into vertical, domain-specific systems involves a structured methodology to preserve generalized capabilities while injecting specialized knowledge. I am attempting to provide a glimpse into the workflow through the lens of India's healthcare and agricultural sectors, where linguistic diversity and resource constraints demand optimized solutions. I chose healthcare and agriculture sectors as the government datasets and methodologies are well documented. I will continue to dig further into financial, manufacturing and other sectors as well.

This distillation methodology enables India to build vertically specialized models that achieve around 90% of a market leading GPT model's diagnostic accuracy on regional cases while requiring significantly less inference cost, which is critical for scaling AI solutions across 700 million regional language speakers. The technical stack's open-source foundation ensures customization without vendor lock-in, though challenges remain in standardizing evaluation metrics for India's linguistic and cultural diversity.

Strategic framework for selecting foundational models

India's approach to selecting large language models for knowledge distillation into vertical, domain-specific systems hinges on a multi-dimensional evaluation framework that balances technical capabilities, linguistic relevance, and socio-economic priorities. Drawing insights from global best practices and India's unique requirements, the selection process prioritizes models that enable efficient knowledge transfer while addressing the nation's linguistic diversity and sector-specific challenges.

Technical evaluation criteria
- Architectural compatibility. Parameter efficiency: models like Meta's Llama-3-70B should be preferred over larger counterparts due to their balance of performance and distillation feasibility. A smaller variant can achieve nearly the same accuracy as a much larger model on Indian language tasks, while significantly reducing training costs. Modular design: models supporting LoRA (Low-Rank Adaptation) enable cost-effective fine-tuning, which is critical for adapting to India's 22 scheduled languages.
- Linguistic performance benchmarks. India employs specialized evaluation metrics: Bhasha-Abhijnaam tests code-mixing comprehension across 18 language pairs, and the ASER Linguistic Depth Index measures morphological richness handling for agglutinative languages like Telugu and Kannada.
Domain-specific alignment
- Healthcare models must demonstrate: ICD-11 code mapping with 95% or better accuracy in linking vernacular symptom descriptions, and drug interaction knowledge integrated with the National Digital Health Mission's formulary database.
- Agricultural models must exhibit: agro-climatic zone awareness across 127 microclimates (differentiating Konkan coast from Vidarbha plateau conditions), and crop ontology mapping that precisely links regional terms, since sorghum in Maharashtra and maize in Rajasthan are referred to using the same term in Hindi.
Ethical and regulatory compliance
- Data sovereignty protocols. Local storage mandate: all training data must reside on NIXI (National Internet Exchange of India) servers. Constitutional AI audits: models undergo caste and gender bias testing using the DALI framework across 8 demographic axes.
- Intellectual property considerations. The draft AI policy outlines an open-source preference, giving significant weightage to open-sourced models in procurement weighting, and derivative model royalties, agreeing on a range for revenue sharing if commercializing distilled models based on foreign LLMs.
Implementation workflow
- Stakeholder-driven selection. A cross-functional committee evaluates models through a model longlist, technical evaluation, a top five, domain testing across healthcare, agriculture and education, stakeholder consensus, and a final model.
- Knowledge transfer optimization. India's distillation stack employs multi-teacher ensembling, combining Krutrim for Indic focus with Med-PaLM for medical expertise, and dynamic weight allocation where language accuracy (BLEU) and domain relevance (ROUGE) determine teacher influence.
Aligning with the infrastructure. The government's Bhashini mission aims to create open-source datasets for 22 scheduled languages via crowdsourced data collection (15,000 hours of transcribed speech from 400 districts, capturing regional accents and vernacular terms), parallel corpus development (2.2 million translated sentence pairs enabling cross-lingual transfer learning), and studio-grade speech data (professional voice artist recordings for high-fidelity text-to-speech in languages like Bhojpuri and Meitei).

This strategic approach enables India to select foundation models that serve as effective knowledge sources while meeting the nation's unique linguistic, sectoral, and ethical requirements. By prioritizing modular open-source architectures and rigorous domain testing, the framework ensures distilled vertical models deliver maximum societal impact per compute rupee spent.

Challenges and mitigations

Low-resource language handling. The oral language prioritization framework addresses scriptless languages through phoneme-based tokenization for certain languages and dialects, community recording drives to build a large speech corpus per endangered language, and recognition of oral tradition limitations, since a significant portion of Indian languages lack standardized writing systems. Code-mixing prevalence means urban users blend languages, requiring hybrid tokenization approaches. Script diversity across Brahmi-derived scripts, Perso-Arabic and romanization demands distinct OCR solutions.
Vendor lock-in prevention. India's AI interoperability standards mandate ONNX format compatibility for all distilled models and API standardization across agriculture, health and education verticals.
Technical barriers. Data sparsity: creating a large corpus of training data for languages with under 1% of speakers requires novel few-shot learning techniques. Model compression: maintaining accuracy when distilling 175B-parameter models to under 10B for low-power rural edge devices. Evaluation metrics: current benchmarks fail to capture culturally nuanced outputs in languages like Santali or Khasi.
Legal and ethical concerns. IP risks: DeepSeek-style distillation from Western models may trigger licensing disputes if commercialized. Bias amplification: transfer learning could inherit gender stereotypes from source models, conflicting with India's constitutional equality principles. Data sovereignty: storing endangered language data on global cloud platforms violates proposed Data Empowerment Act guidelines.
Workforce readiness. Skill gaps: approximately 10% of India's AI researchers specialize in NLP for low-resource languages. Annotation costs: labeling one hour of rarely used dialect speech data leads to higher cost due to scarce linguists. Geographic disparities: over 75% of AI roles cluster in Bengaluru, Hyderabad, Pune and Chennai, while over 90% of vernacular content jobs remain unfilled in Bihar, Chhattisgarh and similar geographies.

Catalyzing employment through domain-specific innovation

Disclaimer: The information in this section is curated using Perplexity Deep Research. I have made every effort to reason with the model and verify the authenticity of the information; however, I apologize in advance for any inaccuracies.

Healthcare: diagnostics and rural outreach. Vertical AI models tailored to India's public health infrastructure could create a large number of jobs in regional language annotation (training diagnostic models on 22 scheduled languages requires linguists to transcribe 450 or more regional disease terminologies), ASHA worker augmentation (voice-enabled AI assistants for 1.3 million Accredited Social Health Activists would need developers proficient in low-resource speech recognition for languages like Garo and Kodava), and telemedicine infrastructure (deploying distilled models on edge devices for rural clinics could drive demand for biomedical engineers maintaining AI-powered ECG analyzers and ultrasound systems).
Agriculture: precision farming ecosystems. The agricultural sector stands to gain 1.1 million AI-related roles through hyperlocal model training (agronomists and data scientists developing crop-specific models for 127 agro-climatic zones), voice-based market platforms (building voice interfaces for 22 languages in APMC mandis, employing conversational AI designers familiar with dialects like Marwari and Malvi), and drone maintenance networks (federated learning systems for crop health monitoring requiring certified drone technicians trained in AI model updates).
Linguistic infrastructure development. India's language technology mission could catalyze job creation through speech data curation (freelancers collecting conversational speech across 121 mother tongues, prioritizing oral languages like Toda, Kota, Irula and Kurumba), translation workflows (bilingual linguists building parallel corpora between English and low-resource languages), and ethical AI auditing (compliance specialists ensuring distilled models avoid caste and gender biases in languages with complex honorific systems like Bengali and Telugu).
Education: vernacular upskilling hubs. Regional content creation, with instructional designers developing AI courses in various languages and adapting concepts like neural networks into cultural metaphors, and AI tutor deployment, where maintaining personalized learning systems across vernacular-medium schools would require EdTech support staff trained in model fine-tuning.
Technical workforce development. Low-code AI tools: training 1.4 million professionals in platforms like TensorFlow Lite for deploying distilled crop disease models on budget Android devices. Federated learning: upskilling data engineers to manage privacy-preserving AI training across distributed healthcare datasets. Edge optimization: certifying embedded systems developers in model quantization techniques for regional language voice assistants.
Academic-industry pipelines. Diploma courses certifying vertical AI domains such as healthcare NLP and agri-computer vision, targeting ITI graduates. Apprenticeship 2.0 mandating AI modules in MSME sectors, potentially placing millions of apprentices in model monitoring roles. PhD industry tracks with corporate-sponsored doctoral positions for advanced research in distillation techniques for Indic languages.
Policy enablers and market incentives. Proposed state-level regulatory sandboxes include an Andhra Pradesh AgTech Zone waiving a significant portion of GST for startups building distilled models for Telugu-speaking farmers, and a Kerala Health AI Hub offering subsidies per project for Malayalam diagnostic tools meeting NABH standards.

Conclusion

India's AI trajectory need not replicate the West's "bigger is better" paradigm. By strategically leveraging knowledge distillation and vertical specialization, the country can address its unique linguistic and developmental challenges while avoiding the computational arms race. Success will require coordinated efforts across policymakers, academia, and private players. With 65% of India's population reliant on regional languages, distilled vertical models represent not just technical infrastructure, but a vital bridge to inclusive digital participation. While generic large language models have their place, relying on them exclusively could lead to a situation where the benefits of AI are concentrated among a select few, while a huge portion of the Indian population, those speaking regional languages, are left behind.

Much like India's Mars Orbiter Mission, I hope our AI mission transcends the mere pursuit of cost-effectiveness. It's about strategically delivering tangible value to every citizen, leveraging the unique resources we possess. This is a vision rooted in the principle of "for the people, of the people, and by the people." Just as the Mars mission showcased India's ability to achieve remarkable feats with ingenuity and resourcefulness, our AI journey should aim to democratize technology, ensuring that its benefits reach the most remote corners of our nation. By focusing on solutions tailored to our diverse linguistic and cultural landscape, we can create an AI ecosystem that truly empowers every Indian. Jai Hind.

References

← All writing Originally published on Technology Cravings