
Jadavpur University's library for visually challenged turns ‘barrier-free'
In a stride toward inclusive education, Jadavpur University has taken a major leap forward for its visually challenged students.
While the university had already established its Accessible Library back in 2018 with institutional funding, this year it has transformed into a more advanced and inclusive space—what many now proudly call a 'Barrier-Free Library.'
Previously, the library depended heavily on audiobooks. However, the time-consuming process of producing these made it difficult to keep up with students' academic demands. This transformative step came with the support of the Rotary Foundation and includes a range of imported assistive instruments aimed at enhancing learning experiences for students with visual challenges.
One of the most remarkable upgrades is the introduction of high-end technological aids such as two embossers, an OCR (Optical Character Recognition) camera, Braille eMotion tools, and low vision cameras. The library has also been physically expanded, with rooms being extended to accommodate more students. Additionally, e-books are now being developed in accessible formats, making them usable for both visually challenged and sighted students.
Subhadip Mondol, a postgraduate student of Bengali and member of the university's Forum for Students with Disabilities (FSD JU) emphasised the 'life-changing' impact of these developments. 'This helped the students who are disabled and gave them a chance to get an exposure in their education with the help of this advanced technology,' he shared.
Echoing Subhadip's sentiments, Monojit Ram, a PhD scholar in Bengali from the university, said, 'This accessible library was there in this university, but the advanced equipment which has been brought recently has made the library more friendly for visually challenged people and have never made them be in the back row of the education.'
As this inclusive model continues to grow, its users believe it stands as a beacon of what can be achieved when institutions prioritise accessibility, empathy, and innovation.
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India Today
26-05-2025
- India Today
Indian AI startup launches Sarvam-M model: What is it, why is everyone talking about it
India's homegrown AI startup Sarvam has launched its newest language model, Sarvam-M, which is making waves in the tech community for both good and not-so-good reasons. The model is being praised for its focus on Indian languages, maths, and programming tasks, but it is also facing criticism for not being 'good enough.' The drama surrounding the AI company has sparked even more interest. If you have questions too, here's a breakdown of what the Sarvam-M model is, why it matters, and why the AI company is facing exactly is Sarvam-M?Sarvam-M is a large language model, or LLM, developed by Indian startup Sarvam AI. These types of models are trained to understand and generate human-like text, and they power tools like chatbots, translation software, and educational apps. Sarvam-M is based on a smaller model called Mistral Small and has been expanded into a much larger system with 24 billion parameters, which are basically the knobs and dials that help it process language and learn from simple terms, Sarvam-M is like a very smart AI assistant that can handle a wide range of tasks, from answering complex math questions to understanding and responding in Indian languages like Hindi, Bengali, Gujarati, and more. What makes it different is that it has been built with India in mind, supporting 10 local languages and offering strong performance in tasks involving both language and was it built?advertisement Sarvam-M was trained using a three-step process:Supervised Fine-Tuning (SFT): This stage involved feeding the model high-quality questions and answers to help it learn. The team made sure the responses were relevant, less biased, and culturally appropriate. This helped the model get good at both everyday conversations and more complex problem-solving Learning with Verifiable Rewards (RLVR): In this step, Sarvam-M was further improved using data related to instructions, programming, and mathematics. It was taught to follow instructions better and think more logically using feedback loops and carefully designed Optimisation: This final stage involved making the model run faster and more efficiently. Techniques like FP8 quantisation (a way of simplifying data without losing accuracy) and better decoding methods helped improve the model's speed and performance, though there were still some issues with handling high can Sarvam-M do?The model has been built to power various real-world applications. It can be used for:Conversational AI, which will basically mean that it can power chatbots and virtual assistantsMachine translation, where it could be used to translate between English and Indian languagesEducation, considering its ability to solve maths problems, answer science questions, or even helping students prepare for competitive exams like JEE. In fact, one of the Sarvam team members shared results showing that Sarvam-M's "Think" mode correctly answered several JEE Advanced-level questions in Hindi, which can be a major step in making such tools useful for Indian does it compare with other models?advertisementSarvam-M has shown impressive results in certain areas. In a test that combined math with romanised Indian languages, the model achieved over 86 per cent improvement, beating out some other well-known models. It performed better than Meta's Llama-4 Scout on many benchmarks and was on par with much larger models like Llama-3.3 70B and Google's Gemma 3 it did slightly underperform in English knowledge tests, with about 1 per cent lower accuracy compared to others. Still, the model stands out for its Indian language skills and reasoning why the backlash?Despite all the technical achievements, the model didn't get the warm welcome one might expect. On Hugging Face, a platform where developers can download and test AI models, Sarvam-M was downloaded only 334 times in the first two days. Some critics saw this as a sign of Das, an investor at Menlo Ventures, called the response 'embarrassing,' saying there's little interest in this kind of work. He compared it to a different model created by two Korean college students, which got nearly 200,000 downloads quickly. advertisementThis sparked a debate. Supporters of Sarvam-M, including Aashay Sachdeva from the company, defended the model, highlighting its benchmark results and customisation process. He even posted proof of the model's performance on social media. Another user, who works at AI4Bharat, added that the real achievement was not just the model, but the method used to train it. He said it sets a strong foundation for other Indian developers to build on. advertisementMeanwhile, Sarvam's co-founder, Vivek Raghavan, called Sarvam-M a 'stepping stone' toward building India's own AI systems. The company is one of the few chosen under the Indian government's IndiaAI Mission to develop a sovereign LLM for the founder Sridhar Vembu also urged people not to focus only on instant success. He said that most products take time to find their place, and praised Sarvam for their efforts. 'Keep fighting the good fight,' he encouraged.


Hindustan Times
23-05-2025
- Hindustan Times
Can AI ‘think'? In a few years, this won't be a question any more, says one Indian researcher
Swarat Chaudhuri was obsessed with puzzles as a child, he says. Growing up in Kolkata, he spent the afternoons solving every number pyramid and word jumble he could find in local publications. He then graduated to Bengali translations of the American mathematician Martin Gardner's popular-science books on math, logic and puzzles. Chaudhuri dreamed of a world in which he could solve puzzles for a living. That is, more or less, what he now does, as a researcher and professor of computer science at the University of Texas, Austin. One of the puzzles he's working on is particularly crucial. It is the question of whether artificial intelligence can actually expand the scale of human knowledge. Here's how Chaudhuri, 46, is going about it: At his Trishul lab at the university, he has developed an AI program called COPRA (short for In-Context Prover Agent) that works with large language models (in this case, GPT-4), to prove mathematical theorems. That may not sound like much fun, but here's what waits down the line. As the two systems work together, Chaudhuri's eventual aim is for Copra to propose new math problems of its own, and then work to solve them. This would be a crucial step towards determining whether AI can emulate the curiosity-driven explorative nature of the human mind. It would go some way towards answering questions such as: Could AI eventually co-author a scientific paper? In other words: Can an AI program reach beyond what it knows, in order to not just connect dots in new ways (they are already doing this) but to reach out and gather more dots to add to the matrix? (Dots that we may not have factored in at all.) 'This would mean a big leap, from the AI engines we see around us, which deal in available data and perform somewhat repetitive tasks, to a system that uses a lot more logic and can perform 'superhuman' tasks,' he says. *** That 'superhuman' bit is what interests him, because it would mean newer and faster solutions to some of our most pressing problems. Such programs could potentially alter how we view our world, and navigate it. New maths problems would be just the start. Down the line, he believes, these programs could be collaborators working alongside researchers. 'They could be like curious children stepping out to find things on their own and figure out what works and what doesn't,' he says. With the help of such a program, small start-ups and lone tinkerers could take on giant puzzles such as cleaner energy and urban planning. New answers could emerge to questions such as: How do we move large numbers of people from Point A to Point B and back every day? How do we address the issue of solar cells having such a short lifespan? Or, how can we better manage indoor temperature control, amid the climate crisis? *** Chaudhuri has been at this for a while. In 2017, he and his students created Bayou, an early AI-led tool that could build code based only on text prompts. That early win set the stage for his current Copra system of AI agents. Chaudhuri has now been awarded a prestigious Guggenheim Fellowship, for his body of work and for the projects he is leading on 'open-ended mathematical discovery'. He knew early on, he says, that the path to the world's greatest and gravest puzzles lay through the world of computing. After school, he studied computer science and enrolled at the Indian Institute of Technology (IIT)-Kharagpur. After college, he began studying neural networks — which are types of programs that seek to mimic the dot-connecting capabilities of the human brain, rather than relying on the linear (this-therefore-that) progressions that guide traditional software. At this time, 20 years ago, the idea that a computer could one day sift through options and pick the right one, rather than spit out a ready answer that had been fed to it, was considered outlandish. Today, of course, all LLMs do it. It's how ChatGPT converses; how Midjourney and Sora creates their eerily realistic images and videos. *** Five years from now (if not sooner), Chaudhuri believes it will be as common for AI programs to 'think' in ways that more closely mimic the human brain, and to have superhuman abilities in many areas of human activity. A switch to renewable resources will be vital, to lessen the environmental impact of the server farms and data centres that support such systems, he adds. What's something he believes even this advanced version of AI would struggle to do? Create profound art, Chaudhuri says. For the simple reason that the arts are driven, perhaps more than any other human endeavour, by the artist's lived experience of the world. 'It is unlikely that AI will start writing like Rabindranath Tagore or churning out innovative movie scripts because, to produce something like literature, the constantly shifting inputs from the world and the interaction of the artist with the world are vital,' Chaudhuri says. 'That level of input is a long way away for AI.'


Mint
20-05-2025
- Mint
Why aggregation could be a game changer for niche OTTs
With niche streaming platforms, including international players like Apple TV+ and regional services such as hoichoi and Chaupal, now bundled into popular aggregation services, industry experts see an opportunity for these platforms to move closer to the mainstream in a cluttered OTT economy. Bundling through services like Prime Video Channels has helped many platforms reach beyond their core linguistic audiences. For instance, Apple TV+ is now accessible to users outside its traditional, urban, upmarket base, while names like hoichoi and Chaupal are being discovered by non-Bengali or Punjabi viewers. 'Aggregation has proven instrumental in expanding our footprint beyond core markets," Soumya Mukherjee, chief operating officer of Bengali streaming service hoichoi, said. 'For hoichoi, being part of Amazon Channels has allowed us to tap into new user cohorts, especially in regions where Bengali isn't the primary language. These partnerships enhance discoverability, leading to higher engagement and time spent from previously untapped audiences." Aggregation has the potential to be a strategic catalyst for language platforms like hoichoi, both in terms of visibility and business outcomes, Mukherjee added. Also Read: Trump tariffs promise a horror show for Indian movies, streaming in US Local to global By being part of larger aggregator ecosystems, regional services gain access to a much broader and often more diverse audience segment, many of whom might not have previously engaged with regional-language content directly. According to the Ormax Audience Report 2024, 42.2 million of the 150.6 million SVoD (subscription video-on-demand) audiences have access via B2B subscriptions. Keerat Grewal, head - business development (streaming, TV and brands), Ormax Media emphasized that while aggregation lowers average revenue per user or ARPU, it helps in sampling and reach for niche platforms like Apple TV+ as well as regional platforms seeking to expand their base. Charu Malhotra, co-founder and managing director, Primus Partners, a management consultancy firm, said aggregation platforms use AI-powered personalisation that recommends niche content and also adapts to user patterns. Platforms integrated into aggregator apps have seen up to 35% higher engagement in tier-two and tier-three cities compared to when they operated as standalone apps. Aggregation is not just a content strategy, but also a market-entry and brand-building strategy, she said. Once niche platforms gain visibility and user interest through aggregators, they also witness more direct app downloads and even social media traction. This eventually leads to a dual-revenue model, combining aggregator licensing and direct subscription, according to Malhotra. 'Aggregation by bigger platforms like Prime Video Channels, Tata Play Binge, or Airtel Xstream gives niche players a solid push by offering distribution through a single app," said Mahesh Sharma, president- strategic revenue partnerships at Chaupal, a platform specializing in Punjabi, Haryanvi and Bhojpuri content. In a market like India, where users are selective about the apps they keep, due to limited phone storage, being available through an already installed, trusted app helps. It increases visibility, adds a trust factor, and boosts reach, he said. Also Read: Overseas markets emerge as big opportunity for local streaming platforms as diaspora seeks more regional content Bundled and discovered Aggregation helps break geographical and language boundaries. When a regional or niche platform becomes part of a larger app ecosystem, it reaches non-traditional markets, he added. 'Chaupal may start seeing engagement from cities where Punjabi isn't the main language, simply because users are curious or exploring content across categories. Similarly, Apple TV+ might get discovered in smaller towns where people might not have gone out of their way to subscribe separately," Sharma said. So, while the platform might already have a strong name in its core market, aggregation opens it up to a wider audience, and often leads to increased time spent and engagement as users sample content they wouldn't have otherwise tried, he added. Industry experts emphasize that aggregation can definitely help build brand familiarity, which in turn can lead to direct subscriptions in the long run. However, India remains a price-sensitive market. So, while aggregators give visibility, their impact on conversion depends on whether the niche service can stand out and justify its value. 'Aggregation definitely has the potential to bring niche platforms into the mainstream spotlight," said Kaushik Das, founder and CEO of AAO NXT, an Odia content platform. By being part of a bigger ecosystem, smaller or regional platforms gain more credibility and reach, which can lead to increased direct subscriptions over time. 'It also signals to investors and industry stakeholders that there is a growing appetite for diverse content, potentially driving bigger investments into India's digital entertainment sector," he added. Indian OTT market regional language content OTT