Tata-ASML deal: How significant is it for India's semiconductor push? India's Tata Electronics has signed a deal with the Dutch technology giant ASML (Advanced Semiconductor Materials Lithography) to build India's first front-end semiconductor fabrication plant as New Delhi pushes to develop a domestic semiconductor manufacturing base. Front-end manufacturing refers to the building of microscopic circuits onto a blank silicon wafer using specialised lithographic machines. ASML is a pioneer of lithographic technology used in the mass production of microchips across the world. Semiconductor chips power modern technology and are critical for everything from smartphones and cars to artificial intelligence systems and defence technology.

Indian Prime Minister Narendra Modi takes a group photo with leaders of artificial intelligence companies at the AI Impact Summit in New Delhi on Thursday. But analysts said the country was unlikely to have a "DeepSeek moment" -- the sort of boom China had last year with a high-performance, low-cost chatbot -- any time soon. Still, building custom AI tools could bring benefits to the world's most populous nation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

World leaders discuss AI future at India's global summit in New Delhi The fourth, and most high-profile, day of a global artificial intelligence summit in India is under way with world leaders such as United Nations chief Antonio Guterres and French President Emmanuel Macron taking the floor to discuss how to handle the fast-advancing technology that is prompting investment enthusiasm and deep concern in equal measure. The huge gathering in New Delhi is the fourth in a series of international AI meetings that have been taking place since 2023 in France, South Korea and the United Kingdom. Job disruption, child safety and regulations have topped the agenda of this year's edition. The UN chief called on tech tycoons to support a $3bn global fund to ensure open access to the fast-advancing technology for all. The French president also spoke of needing deep involvement: "The message I have come to convey is what is that we are determined to continue to shape the rules of the game, and to do with our allies such as India," Macron said. "Europe is not blindly focused on regulation - Europe is a space for innovation and investment, but it is a safe space."

Commuters walk along a street on the eve of the India AI Impact Summit 2026 in New Delhi on Sunday. New Delhi - A global artificial intelligence summit kicks off in New Delhi on Monday with big issues on the agenda, from job disruption to child safety, but some attendees warn the broad focus could diminish the chance of concrete commitments from world leaders. While frenzied demand for generative AI has turbocharged profits for many tech companies, anxiety is growing over the risks that it poses to society and the environment. Prime Minister Narendra Modi will on Monday afternoon inaugurate the five-day AI Impact Summit, which aims to declare a shared roadmap for global AI governance and collaboration. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

India plans AI'data city' on staggering scale Information technology minister for India's Andhra Pradesh state, Nara Lokesh, speaks during an interview in New Delhi in January. New Delhi - As India races to narrow the artificial intelligence gap with the United States and China, it is planning a vast new data city to power digital growth on a staggering scale, the man spearheading the project says. The AI revolution is here, no second thoughts about it, said Nara Lokesh, information technology minister for Andhra Pradesh state, which is positioning the city of Visakhapatnam as a cornerstone of India's AI push. And as a nation ... we have taken a stand that we've got to embrace it, he said ahead of an international AI summit this week in New Delhi. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Air pollution has become a significant health risk in developing countries. While governments routinely publish air-quality index (AQI) data to track pollution, these values fail to capture the local reality, as sensors are often very sparse. In this paper, we address this gap by predicting AQI in 1 km^2 neighborhoods, using the example of AirDelhi dataset. Using Spatio-temporal GNNs we surpass existing works by 71.654 MSE a 79% reduction, even on unseen coordinates. New insights about AQI such as the existence of strong repetitive short-term patterns and changing spatial relations are also discovered. The code is available on GitHub.

Tensions continue to mount as India and Pakistan traded accusations and attacks across their frontier in Kashmir overnight. New Delhi and Islamabad accused one another on Friday of launching drone attacks as well as "numerous ceasefire violations" over the Line of Control (LoC) in the disputed territory. The ongoing hostilities have provoked further calls for restraint as the risk of an escalation between the two nuclear powers grows. Pakistan launched "multiple attacks" using drones and other munitions along India's western border on Thursday night and early Friday, the Indian army said, claiming it had repelled the attacks and responded forcefully, although it did not provide details. Islamabad has denied any cross-border attacks and instead accused Indian forces of sending drones into Pakistani territory, killing at least two civilians.

When Elon Musk met Narendra Modi in Washington DC in February, the SpaceX and Tesla chief presented India's prime minister with a gift and introduced him to his family. Modi described the meeting as "very good". Modi was in the United States to see President Donald Trump. In Modi's meeting with Musk, the two talked about collaborating in the fields of artificial intelligence (AI), space exploration, innovation and sustainable development, according to India's Ministry of External Affairs. But almost a month later, Musk's social media platform X has filed a lawsuit against the Indian government, alleging that New Delhi is unlawfully censoring content online. The lawsuit comes as Musk edges closer to launching both Starlink and Tesla in India.

Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi's existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1, 2018, to Nov 1, 2020. Applying established definitions of hotspots to this data, we found the existence of additional 189 hidden hotspots apart from confirming 660 hotspots detected by the public network. Using predictive techniques like Space-Time Kriging, we identified hidden hotspots with 95% precision and 88% recall with 50% sensor failure rate, and with 98% precision and 95% recall with 50% missing sensors. The projected results of our predictive models were further compiled into policy recommendations for public authorities. Additionally, we developed a Gaussian Plume Dispersion Model to understand the mechanistic underpinnings of hotspot formation, incorporating an emissions inventory derived from local sources. Our mechanistic model is able to explain 65% of observed transient hotspots. Our findings underscore the importance of integrating data-driven predictive models with physics-based mechanistic models for scalable and robust air pollution management in resource-constrained settings.

Large ground-truth datasets and recent advances in deep learning techniques have been useful for layout detection. However, because of the restricted layout diversity of these datasets, training on them requires a sizable number of annotated instances, which is both expensive and time-consuming. As a result, differences between the source and target domains may significantly impact how well these models function. To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain. In this research, we introduced a synthetic document dataset called RanLayNet, enriched with automatically assigned labels denoting spatial positions, ranges, and types of layout elements. The primary aim of this endeavor is to develop a versatile dataset capable of training models with robustness and adaptability to diverse document formats. Through empirical experimentation, we demonstrate that a deep layout identification model trained on our dataset exhibits enhanced performance compared to a model trained solely on actual documents. Moreover, we conduct a comparative analysis by fine-tuning inference models using both PubLayNet and IIIT-AR-13K datasets on the Doclaynet dataset. Our findings emphasize that models enriched with our dataset are optimal for tasks such as achieving 0.398 and 0.588 mAP95 score in the scientific document domain for the TABLE class.