Latest news with #Bhashini
Time of India
14 hours ago
- Business
- Time of India
₹500cr Maha Agri-AI Policy Roll Out In 4 Phsases
1 2 3 Nagpur: Taking its ambitious Agri-AI vision a step forward, the Maharashtra govt has finalised a four-phase rollout plan under its ₹500-crore Maha Agri-AI Policy, aimed at transforming farming with the help of artificial intelligence (AI) and emerging technologies. The move signals a shift from paper to ground. According to the final policy document prepared by the state agriculture department, the first phase — spread over three months — will focus on setting up the core institutions, including a high-level steering committee, a technical review panel, and a full-time AI and Agritech Innovation Centre to oversee the mission. In the next phase, expected to begin within a year, pilot projects will be launched in select districts to test AI tools like drones, geospatial systems, multilingual chatbots, and advisory platforms. Startups and research bodies will get access to anonymised farm data through a digital 'sandbox' to simulate and refine real-world solutions. Specialised AI labs will also be set up in State Agriculture Universities (SAUs) to develop region-specific innovations. Anil Tekade, a farmer from Katol, expressed his happiness over the project with caution. "AI is undoubtedly the future, and the govt's move is truly commendable. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Hasta 8 meses de protección Seresto Encuéntralo Undo We are all happy to see such progress. However, the real challenge lies in ensuring that a farmer working in the field can fully understand and use this technology. The key is to educate even the last person on the ground so that the benefits truly reach everyone and the actual purpose behind the initiative is fulfilled. That, I believe, will be the real test for the administration." Phase III, planned for 2026, will focus on expanding successful pilots across the state and integrating the platforms with national digital systems such as AgriStack and Bhashini. Phase IV, beginning in 2028, will involve reviewing the results, refining the policy, and replicating the models in other rural sectors like horticulture and livestock. Of the total Rs500 crore outlay, Rs350 crore was earmarked for on-ground AI projects, including incubation and scale-up funding. Another Rs50 crore will go towards training farmers and agriculture officers in using new technologies. An equal amount will be used to build core infrastructure such as traceability tools, AI-powered advisory systems, and satellite-based crop monitoring platforms. A separate Rs20 crore is reserved for global conferences, investor summits, and hackathons to attract innovation and partnerships. Startups, private companies, SAUs, Krishi Vigyan Kendras (KVKs), and other research bodies will be able to submit proposals online. Ideas will be categorised into two streams — early-stage innovations needing incubation, and mature solutions ready for scale-up. A technical committee will evaluate each project and recommend the level of support. Farmers adopting approved technologies may also receive direct benefit transfers (DBT) for devices like sensors or digital tools. Experts say that it's encouraging to see the state moving beyond just vision documents. "If implemented with proper field feedback and farmer involvement, this phased approach could make Maharashtra a model for AI in farming. The key will be in handholding farmers through this shift," an expert said. With the launch plan now in place, the govt aims to turn Maharashtra into a leading hub for AI-powered, farmer-first innovation — one that could eventually be replicated across India. Infographics Ambitious Rs 500-crore Maha Agri-AI Policy Intro Maharashtra Govt's ambitious ₹500-crore Maha Agri-AI Policy aims to transform farming using artificial intelligence and emerging technologies. Here's a breakdown of the policy's phases and key components - Phase I (3 months): Setting up core institutions, including a steering committee and AI Innovation Centre - Phase II (within a year): Launching pilot projects in select districts to test AI tools like drones and chatbots - Phase III (2026): Expanding successful pilots and integrating with national digital systems - Phase IV (2028): Reviewing results and replicating models in other rural sectors The policy includes funding for on-ground projects, training, and infrastructure development, with a focus on supporting startups and farmers SALIENT FEATURES * Pilot projects to test AI tools like drones, chatbots, and geospatial systems in select districts. *Digital sandbox to offer anonymised farm data for startups to simulate real-world agricultural solutions. * Specialised AI labs to be set up in State Agriculture Universities for region-specific innovations. * ₹350 crore allocated for ground-level AI projects, including tech incubation and scaling up solutions. * Farmers and officers to get training; ₹50 crore earmarked for capacity-building and tech education. *Phase-wise rollout to integrate with national systems and expand into livestock, horticulture sectors. BREAK-UP OF FUNDS ALLOCATION (₹ crore) AI & Agritech Innovation Centre setup..................30 Digital Public Infrastructure for Agri-AI..................50 • ADeX, Sandbox, Cloud....................................10 • VISTAAR (Advisory Platform)...........................10 • Innovation Centres at SAUs..............................20 • Remote Sensing & Geospatial Engine...............5 • Traceability & Certification Platform....................5 Financial support for AI-based agri projects..........350 Capacity building (farmers & staff).........................50 Global AI Conference, Summit & Hackathons.......20 Total...............................................................₹500 crore
The Print
a day ago
- Business
- The Print
Panchayati Raj min signs MoU with Bhashini Division, websites to be available in multiple languages
Speaking after the MoU was signed, Minister of State for Panchayati Raj SP Singh Baghel said it will play a role in strengthening the unity of India. Bhashini is an AI-powered language translation platform developed by the Ministry of Electronics and Information Technology (MeitY) under its National Language Translation Mission. It aims to help people translate content into various Indian languages. New Delhi, Jun 19 (PTI) The Panchayati Raj Ministry on Thursday signed a Memorandum of Understanding (MoU) with the Digital India Bhashini Division (DIBD), which will enable the Ministry's online platforms to be available in all Indian regional languages. 'It will also bring Panchayat representatives out of the inferiority complex of not knowing English. It will also reduce language-related tension and bridge language-based divides,' he said. The MoS also said that no politician gives speeches in English while seeking votes, as it remains the language of the elite. Panchayati Raj Secretary Vivek Bharadwaj said while language has often created gaps among people, with Bhashini, the gaps will be bridged. This MoU establishes a partnership between the Ministry and MeitY's DIBD to integrate Bhashini into MoPR's platforms and workflows, the Panchayati Raj Ministry said in a statement. The MoU is valid for three years and covers joint efforts on various technical processes needed for successful integration and multilingual outreach. Since its initial integration with eGramSwaraj (August 2024), the Bhashini plugin has delivered over 15.6 crore language interface hits, and has maintained a consistent daily average of 5.17 lakh hits. Through this agreement, MoPR and DIBD aim to build a model where AI is inclusive, language is no longer a barrier, and Panchayats become the true nodal point of digital transformation, according to the press note. As the next steps, the two institutions will work on expanding language coverage, enabling voice-based services and conducting joint training and awareness campaigns to scale adoption across rural India. PTI AO AMJ AMJ This report is auto-generated from PTI news service. ThePrint holds no responsibility for its content.
Hindustan Times
6 days ago
- Business
- Hindustan Times
How language LLMs will lead the India's AI leap
The next great power struggle in technology won't be about speed or scale, it'll be about whose language AI speaks. Because trust in technology begins with something deeply human: being understood. You trust a doctor who speaks your language. You trust a banker who understands your context. So why would you trust an algorithm that doesn't know who you are, where you're from, or what your words mean? This question is being asked by governments, developers, and communities across the Global South who have seen how powerful large language models (LLMs) can be—and how irrelevant they often are to people who don't speak English or live in Silicon Valley. In India, the response until now has been BharatGPT. This is a collaboration between startups like government-backed platforms like Bhashini, and academic institutions such as the IITs. Its aim is not to chase ChatGPT on global benchmarks. Instead, it hopes to solve problems at home—helping citizens navigate government forms in Hindi, automating railway queries in Tamil, or enabling voice assistants in other regional languages. CoRover has already deployed multilingual chatbots in sectors like railways, insurance, and banking. The value here isn't just in automation. It's in comprehension. This isn't unique to India. In South Africa, Lelapa AI is working on InkubaLM, a small language model trained in African languages. In Latin America, a consortium is building LatAm GPT, rooted in Spanish, Portuguese, and indigenous dialects. Each of these projects is a rebellion: against invisibility, against standardization, against a worldview where the technology speaks only in one accent. What's driving this shift? 'Current large language models do not adequately represent the linguistic, cultural, or civic realities of many regions,' says Shrinath V, a Bengaluru-based product coach and Google for Startups mentor. 'As governments begin exploring AI-powered delivery of public services, from education and legal aid to citizen support, they recognize the need for models that reflect local languages, data, and social context. Regional LLMs are being positioned to fill that gap,' he explains. Manoj Menon, founder of the Singapore-based research firm Twimbit, is on the same page as Shrinath: 'With AI there are several nuances that come into play—how we train them to be contextually relevant for our local, national needs.' At the heart of it lies something more political: digital sovereignty. Shrinath breaks it down and says, 'Data sovereignty is no longer an abstract idea. Countries don't want to depend on models trained on data they don't control. Indigenous models are a way to retain that control.' It boils down to geopolitical leverage. Nations that build their own models won't just protect cultural identity—they'll shape trade, diplomacy, and security doctrines in the AI era. 'This is a reasonable argument,' says Menon. 'How we interpret a particular subject or issue depends completely on the context. Hence geo-politics is a significant input. Also the ability to train based on local issues and context.' Viewed through this lens, the shift underway towards frugal AI is more radical than most people realise. These are models that don't need massive GPUs or high-speed internet. They're lean, nimble, and context-rich. Think of it like this: if ChatGPT is a Tesla on a six-lane highway, BharatGPT is a motorbike designed for rough, narrow roads. Not as flashy. But it gets where it needs to go. 'Most countries will want a say in shaping how AI is adopted, governed, and deployed within a sovereign context,' points out Shrinath. This matters because AI is starting to mediate access to public services—healthcare, legal advice, welfare. And in that context, a model that doesn't understand a citizen's language isn't just ineffective. It's dangerous. It can mislead, it can exclude and it can fail silently. So yes, Silicon Valley still leads the headlines. But away from the noise, something deeper is unfolding. A shift in who gets to define intelligence, in whose language it speaks and in whose image it is built. Regional AI, says Menon, 'won't go head-on with what is built in Silicon Valley. They will complement it and their opportunity will help AI be more relevant locally.' These regional AI efforts don't seek applause, they seek agency. They aren't chasing scale, they're chasing significance instead. This revolution is not being televised, it's being trained.
Mint
13-06-2025
- Business
- Mint
Innovating India's Banking Future: Finnovate Hackathon Paves the Way for Language-Driven Financial Inclusion
The Indian finance and banking sector is experiencing a phenomenal change. Accelerated digitisation, the emergence of fintech start-ups, and increasing customer expectations are revolutionising the way the financial sector works. With this change comes an unprecedented set of challenges—especially in a nation as linguistically and digitally diverse as India. Language remains one of the biggest barriers to financial inclusion for all today. Millions of Indians are struggling to access the banking system effectively due to the dominance of English and limited regional language support on digital channels. At the same time, the industry is being required to make things simpler, more personalised, and automated with document-intensive processes such as lending and KYC. These are not tech gaps—these are inclusion and growth barriers. Enter Finnovate Hackathon, a collaborative initiative designed to address precisely these issues. Finnovate Hackathon is a unique platform for fintech and banking startups and Independent Software Vendors (ISVs) where they can innovate, cooperate, and create high-impact ideas to address some of the most pressing issues in the Indian financial ecosystem. Organised by Mint, sponsored by Bhashini, and chaired by Kyndryl, the hackathon aims to encourage experiential, scalable, and technology-based solutions to five key problem statements that are the most urgent operational and customer experience challenges of banks in the current times. The hackathon's themes are rooted in real-world friction points: Breaking Language Barriers: Participants are tasked with leveraging AI-powered translation and conversational interfaces to make banking services accessible in multiple Indian languages. Participants are tasked with leveraging AI-powered translation and conversational interfaces to make banking services accessible in multiple Indian languages. Digital Transformation: Innovators will work on enhancing OCR and multilingual translation capabilities to streamline document processing—a common roadblock in areas like loan applications and onboarding. Innovators will work on enhancing OCR and multilingual translation capabilities to streamline document processing—a common roadblock in areas like loan applications and onboarding. Personalised Customer Experience: Teams are expected to develop multi-lingual virtual assistants that can integrate seamlessly with core banking systems, offering personalised support to users in their preferred language. Teams are expected to develop multi-lingual virtual assistants that can integrate seamlessly with core banking systems, offering personalised support to users in their preferred language. Multilingual Product Promotion & Day-to-Day Activities: Addressing everyday use cases, this problem statement calls for AI-driven solutions that break language barriers in marketing and daily banking interactions. Addressing everyday use cases, this problem statement calls for AI-driven solutions that break language barriers in marketing and daily banking interactions. Unified Lending Interface (ULI): By digitising and applying OCR to loan documents, participants can contribute to a more efficient and user-friendly lending experience. By digitising and applying OCR to loan documents, participants can contribute to a more efficient and user-friendly lending experience. Financial Services LLM: Another challenge involves developing a specialised Large Language Model (LLM) trained on financial terminology, with the potential to revolutionise customer interactions, analytics, and backend operations. Each of these themes reflects the current gaps in India's banking infrastructure—gaps that, if addressed effectively, could unlock the next wave of digital banking growth and financial inclusion. Finnovate isn't your average hackathon open to all developers or students. It specifically invites: Independent Software Vendors (ISVs) operating in the fintech and banking domains. operating in the fintech and banking domains. Startup teams with experience in BFSI. with experience in BFSI. Banking and Financial Institution representatives looking to co-develop solutions. looking to co-develop solutions. Technology Experts specialising in AI, machine learning, and OCR. Participants must form a team of 2 to 4 members. The first registrant becomes the team leader, who then invites others. Importantly, all team members must be employed by a startup or ISV operating in the BFSI space, students and unemployed individuals are not eligible. Cross-company teams are permitted, allowing diverse collaboration across the sector. However, participants must choose one of the problem statements, and that track must remain consistent for all team members. The participants are required to follow a well-defined code of conduct: All the work should be original and done within the hackathon period. Pre-written, third-party, or AI-written code will result in disqualification. Projects should be in on time; late submissions will be at the organisers' discretion. Participants consent to uphold principles of respect and integrity, thereby fostering a positive and inclusive atmosphere consistently. The assessment of an individual's employment status may occur at any time, with inconsistencies potentially leading to the disqualification of a team. Such safeguards are designed to maintain the event's professionalism, focus, and adherence to its fundamental objectives. As a global leader in mission-critical enterprise technology services, Kyndryl is no stranger to complexity. In India, Kyndryl supports major BFSI institutions—including both small finance and large-scale banks—on their digital transformation journeys. Their involvement in Finnovate reflects a strong belief in building agile, inclusive, and customer-centric solutions through collaboration. The term "Kyndryl" summarises this spirit, "Kyn" for affiliation and kinship, and "-dryl" borrowed from tendrils, thus reminding one of new growth. Finnovate is an extension of these principles; it is a program designed to foster new ideas, encourage closer affiliation of fintech with traditional systems, and spur growth of the banking ecosystem in a direction that includes everyone. India's financial sector is at a crossroads. On one hand, we have cutting-edge AI and digital capabilities; on the other, we have fundamental issues of accessibility, language, and outdated manual processes. Competitions like the Finnovate Hackathon are not just competitions but drivers of change. They bring together the industry's best minds to break through barriers and solve challenges that have far-reaching real-world consequences. With its cutting-edge focus on multilingual solutions, document digitisation, and smart customer service, Finnovate has the potential to be a trendsetter for a more inclusive and smart banking experience—one in which language, accessibility, and innovation harmonise with each other.
Hindustan Times
13-06-2025
- Business
- Hindustan Times
How language LLMs will lead India's AI leap
The next great power struggle in technology won't be about speed or scale, it'll be about whose language AI speaks. Because trust in technology begins with something deeply human: being understood. You trust a doctor who speaks your language. You trust a banker who understands your context. So why would you trust an algorithm that doesn't know who you are, where you're from, or what your words mean? This question is being asked by governments, developers, and communities across the Global South who have seen how powerful large language models (LLMs) can be—and how irrelevant they often are to people who don't speak English or live in Silicon Valley. In India, the response until now has been BharatGPT. This is a collaboration between startups like government-backed platforms like Bhashini, and academic institutions such as the IITs. Its aim is not to chase ChatGPT on global benchmarks. Instead, it hopes to solve problems at home—helping citizens navigate government forms in Hindi, automating railway queries in Tamil, or enabling voice assistants in other regional languages. CoRover has already deployed multilingual chatbots in sectors like railways, insurance, and banking. The value here isn't just in automation. It's in comprehension. This isn't unique to India. In South Africa, Lelapa AI is working on InkubaLM, a small language model trained in African languages. In Latin America, a consortium is building LatAm GPT, rooted in Spanish, Portuguese, and indigenous dialects. Each of these projects is a rebellion: against invisibility, against standardization, against a worldview where the technology speaks only in one accent. What's driving this shift? 'Current large language models do not adequately represent the linguistic, cultural, or civic realities of many regions,' says Shrinath V, a Bengaluru-based product coach and Google for Startups mentor. 'As governments begin exploring AI-powered delivery of public services, from education and legal aid to citizen support, they recognize the need for models that reflect local languages, data, and social context. Regional LLMs are being positioned to fill that gap,' he explains. Manoj Menon, founder of the Singapore-based research firm Twimbit, is on the same page as Shrinath: 'With AI there are several nuances that come into play — how we train them to be contextually relevant for our local, national needs.' At the heart of it lies something more political: digital sovereignty. Shrinath breaks it down and says, 'Data sovereignty is no longer an abstract idea. Countries don't want to depend on models trained on data they don't control. Indigenous models are a way to retain that control.' It boils down to geopolitical leverage. Nations that build their own models won't just protect cultural identity—they'll shape trade, diplomacy, and security doctrines in the AI era. 'This is a reasonable argument,' says Menon. 'How we interpret a particular subject or issue depends completely on the context. Hence geo-politics is a significant input. Also the ability to train based on local issues and context.' Viewed through this lens, the shift underway towards frugal AI is more radical than most people realise. These are models that don't need massive GPUs or high-speed internet. They're lean, nimble, and context-rich. Think of it like this: if ChatGPT is a Tesla on a six-lane highway, BharatGPT is a motorbike designed for rough, narrow roads. Not as flashy. But it gets where it needs to go. 'Most countries will want a say in shaping how AI is adopted, governed, and deployed within a sovereign context,' points out Shrinath. This matters because AI is starting to mediate access to public services—healthcare, legal advice, welfare. And in that context, a model that doesn't understand a citizen's language isn't just ineffective. It's dangerous. It can mislead, it can exclude and it can fail silently. So yes, Silicon Valley still leads the headlines. But away from the noise, something deeper is unfolding. A shift in who gets to define intelligence, in whose language it speaks and in whose image it is built. Regional AI, says Menon, 'won't go head-on with what is built in Silicon Valley. They will complement it and their opportunity will help AI be more relevant locally.' These regional AI efforts don't seek applause, they seek agency. They aren't chasing scale, they're chasing significance instead. This revolution is not being televised, it's being trained. Get 360° coverage—from daily headlines to 100 year archives.
