India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. India's Prime Minister Narendra Modi takes a group photo with AI company leaders at the AI Impact Summit in New Delhi on Feb. 19, 2026. The world's largest-ever AI summit took place in India this week, with hundreds of thousands of people, including world leaders and CEOs of AI companies, descending upon New Delhi for five days. It was the fourth in a series of summits that were initially designed as a place for governments to coordinate global action in the face of threats from advanced AI. But the India summit, like one in Paris before it, functioned more as a trade fair and an advertisement for the host nation's AI prowess than a venue for meaningful international diplomacy.

Tech platforms would have to remove intimate images which have been shared without consent within 48 hours, under a proposed UK law. The government said tackling intimate image abuse should be treated with the same severity as child sexual abuse material (CSAM) and terrorist content. Failure to abide by the rules could result in companies being fined up to 10% of their global sales or have their services blocked in the UK. Janaya Walker, interim director of the End Violence Against Women Coalition, said the welcome and powerful move... rightly places the responsibility on tech companies to act. The proposals are being made through an amendment to the Crime and Policing Bill, which is making its way through the House of Lords.

Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Campaigners fear Narendra Modi could use AI to increase state surveillance and sway elections. Silicon Valley tech billionaires will land in Delhi this week for an AI summit hosted by India's prime minister, Narendra Modi, where leaders of the global south will wrestle for control over the fast-developing technology. During the week-long AI Impact Summit, attended by thousands of tech executives, government officials and AI safety experts, tech companies valued at trillions of dollars will rub along with leaders of countries such as Kenya and Indonesia, where average wages dip well below $1,000 a month. Amid a push to speed up AI adoption across the globe, Sundar Pichai, Sam Altman and Dario Amodei, the heads of Google, OpenAI and Anthropic, will all be there.

The tech bros might show more humility in Delhi - but will they make AI any safer? Those who shout the loudest about artificial intelligence tend to be in the West, notably the US and Europe. So it's significant that a gathering of powerful leaders is being held in the Global South, a region of the world that runs the risk of being left behind in the AI race. Tech bosses, politicians, scientists, academics and campaigners are meeting at the AI Impact Summit in India this week for top-level discussions about what the world should be doing to try to marshal the AI revolution in the right direction. At last year's AI Action Summit, as it was then known, an ugly power struggle broke out between some Western countries over who should be in charge.

Reddit's human content wins amid the AI flood For Ines Tan there's one particular site she turns to again and again for advice - and that's Reddit. Tan, who works in communications, regularly jumps on the site for skincare advice, to view reactions to shows she watches, such as The Traitors, and for help planning her upcoming wedding in May. It's a very empathetic place, she says of Reddit. For my wedding, I've found help emotionally, logistically and inspiration-wise. Tan believes people are consulting the online discussion platform more as they're craving human interaction in the world of increasing AI slop.

Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretable deep learning framework for short-term precipitation prediction in four major Indian cities: Bengaluru, Mumbai, Delhi, and Kolkata, spanning diverse climate zones. We implemented a hybrid Time-Distributed CNN-ConvLSTM (Convolutional Neural Network-Long Short-Term Memory) architecture, trained on multi-decadal ERA5 reanalysis data. The architecture was optimized for each city with a different number of convolutional filters: Bengaluru (32), Mumbai and Delhi (64), and Kolkata (128). The models achieved root mean square error (RMSE) values of 0.21 mm/day (Bengaluru), 0.52 mm/day (Mumbai), 0.48 mm/day (Delhi), and 1.80 mm/day (Kolkata). Through interpretability analysis using permutation importance, Gradient-weighted Class Activation Mapping (Grad-CAM), temporal occlusion, and counterfactual perturbation, we identified distinct patterns in the model's behavior. The model relied on city-specific variables, with prediction horizons ranging from one day for Bengaluru to five days for Kolkata. This study demonstrates how explainable AI (xAI) can provide accurate forecasts and transparent insights into precipitation patterns in diverse urban environments.

Air pollution causes about 1.6 million premature deaths each year in India, yet decision makers struggle to turn dispersed data into decisions. Existing tools require expertise and provide static dashboards, leaving key policy questions unresolved. We present VayuChat, a conversational system that answers natural language questions on air quality, meteorology, and policy programs, and responds with both executable Python code and interactive visualizations. VayuChat integrates data from Central Pollution Control Board (CPCB) monitoring stations, state-level demographics, and National Clean Air Programme (NCAP) funding records into a unified interface powered by large language models. Our live demonstration will show how users can perform complex environmental analytics through simple conversations, making data science accessible to policymakers, researchers, and citizens. The platform is publicly deployed at https://huggingface.co/spaces/SustainabilityLabIITGN/ VayuChat. For further information check out video uploaded on https://www.youtube.com/watch?v=d6rklL05cs4.

Air pollution remains a critical environmental and public health concern in Indian megacities such as Delhi, Kolkata, and Mumbai, where sudden spikes in pollutant levels challenge timely intervention. Accurate Air Quality Index (AQI) forecasting is difficult due to the coexistence of linear trends, seasonal variations, and volatile nonlinear patterns. This paper proposes a hybrid forecasting framework that integrates LOESS decomposition, ARIMA modeling, and a multi-scale CNN-BiLSTM network with a residual-gated attention mechanism. The LOESS step separates the AQI series into trend, seasonal, and residual components, with ARIMA modeling the smooth components and the proposed deep learning module capturing multi-scale volatility in the residuals. Model hyperparameters are tuned via the Unified Adaptive Multi-Stage Metaheuristic Optimizer (UAMMO), combining multiple optimization strategies for efficient convergence. Experiments on 2021-2023 AQI datasets from the Central Pollution Control Board show that the proposed method consistently outperforms statistical, deep learning, and hybrid baselines across PM2.5, O3, CO, and NOx in three major cities, achieving up to 5-8% lower MSE and higher R^2 scores (>0.94) for all pollutants. These results demonstrate the framework's robustness, sensitivity to sudden pollution events, and applicability to urban air quality management.

Urban air pollution poses significant risks to public health, environmental sustainability, and policy planning. Effective air quality management requires predictive tools that can integrate diverse datasets and communicate complex spatial and temporal pollution patterns. There is a gap in interactive tools with seamless integration of forecasting and visualization of spatial distributions of air pollutant concentrations. We present CityAQVis, an interactive machine learning ML sandbox tool designed to predict and visualize pollutant concentrations at the ground level using multi-source data, which includes satellite observations, meteorological parameters, population density, elevation, and nighttime lights. While traditional air quality visualization tools often lack forecasting capabilities, CityAQVis enables users to build and compare predictive models, visualizing the model outputs and offering insights into pollution dynamics at the ground level. The pilot implementation of the tool is tested through case studies predicting nitrogen dioxide (NO2) concentrations in metropolitan regions, highlighting its adaptability to various pollutants. Through an intuitive graphical user interface (GUI), the user can perform comparative visualizations of the spatial distribution of surface-level pollutant concentration in two different urban scenarios. Our results highlight the potential of ML-driven visual analytics to improve situational awareness and support data-driven decision-making in air quality management.

Zubaida starts her day at eight in the morning, sorting discarded plastics, glass, and chemicals with her bare hands, to collect items she can sell. With waste-segregation centers in this part of East Delhi currently shut down, she and other waste-pickers from the Seemapuri slum work outside by a dusty road through the hottest hours of the day, under the blazing sun. There is no fan or shade. With Delhi's heat wave season here, they are constantly exposed to intense high temperatures. On June 11, the India Meteorological Department (IMD) issued a red alert for Delhi, warning of a high risk of heat illness and heat stroke.