Background: Large Language Models show promise in psychiatry but are English-centric. Their ability to understand mood states in other languages is unclear, as different languages have their own idioms of distress. Aim: To quantify the ability of language models to faithfully represent phrases (idioms of distress) of four distinct mood states (depression, euthymia, euphoric mania, dysphoric mania) expressed in Indian languages. Methods: We collected 247 unique phrases for the four mood states across 11 Indic languages. We tested seven experimental conditions, comparing k-means clustering performance on: (a) direct embeddings of native and Romanised scripts (using multilingual and Indic-specific models) and (b) embeddings of phrases translated to English and Chinese. Performance was measured using a composite score based on Adjusted Rand Index, Normalised Mutual Information, Homogeneity and Completeness. Results: Direct embedding of Indic languages failed to cluster mood states (Composite Score = 0.002). All translation-based approaches showed significant improvement. High performance was achieved using Gemini-translated English (Composite=0.60) and human-translated English (Composite=0.61) embedded with gemini-001. Surprisingly, human-translated English, further translated into Chinese and embedded with a Chinese model, performed best (Composite = 0.67). Specialised Indic models (IndicBERT and Sarvam-M) performed poorly. Conclusion: Current models cannot meaningfully represent mood states directly from Indic languages, posing a fundamental barrier to their psychiatric application for diagnostic or therapeutic purposes in India. While high-quality translation bridges this gap, reliance on proprietary models or complex translation pipelines is unsustainable. Models must first be built to understand diverse local languages to be effective in global mental health.

What AI doesn't know: we could be creating a global'knowledge collapse' As GenAI becomes the primary way to find information, local and traditional wisdom is being lost. And we are only beginning to realise what we're missing This article was originally published as'Holes in the web' on Aeon.co A few years back, my dad was diagnosed with a tumour on his tongue - which meant we had some choices to weigh up. My family has an interesting dynamic when it comes to medical decisions. While my older sister is a trained doctor in western allopathic medicine, my parents are big believers in traditional remedies. Having grown up in a small town in India, I am accustomed to rituals. My dad had a ritual, too. Every time we visited his home village in southern Tamil Nadu, he'd get a bottle of thick, pungent, herb-infused oil from a vaithiyar, a traditional doctor practising Siddha medicine. It was his way of maintaining his connection with the kind of medicine he had always known and trusted.

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.

Farmers in developing regions like Karnataka, India, face a dual challenge: navigating extreme market and climate volatility while being excluded from the digital revolution due to literacy barriers. This paper presents a novel decision support system that addresses both challenges through a unique synthesis of machine learning and human-computer interaction. We propose a hybrid recommendation engine that integrates two predictive models: a Random Forest classifier to assess agronomic suitability based on soil, climate, and real-time weather data, and a Long Short-Term Memory (LSTM) network to forecast market prices for agronomically viable crops. This integrated approach shifts the paradigm from "what can grow?" to "what is most profitable to grow?", providing a significant advantage in mitigating economic risk. The system is delivered through an end-to-end, voice-based interface in the local Kannada language, leveraging fine-tuned speech recognition and high-fidelity speech synthesis models to ensure accessibility for low-literacy users. Our results show that the Random Forest model achieves 98.5% accuracy in suitability prediction, while the LSTM model forecasts harvest-time prices with a low margin of error. By providing data-driven, economically optimized recommendations through an inclusive interface, this work offers a scalable and impactful solution to enhance the financial resilience of marginalized farming communities.

Data in the real world often has an evolving distribution. Thus, machine learning models trained on such data get outdated over time. This phenomenon is called model drift. Knowledge of this drift serves two purposes: (i) Retain an accurate model and (ii) Discovery of knowledge or insights about change in the relationship between input features and output variable w.r.t. the model. Most existing works focus only on detecting model drift but offer no interpretability. In this work, we take a principled approach to study the problem of interpretable model drift detection from a risk perspective using a feature-interaction aware hypothesis testing framework, which enjoys guarantees on test power. The proposed framework is generic, i.e., it can be adapted to both classification and regression tasks. Experiments on several standard drift detection datasets show that our method is superior to existing interpretable methods (especially on real-world datasets) and on par with state-of-the-art black-box drift detection methods. We also quantitatively and qualitatively study the interpretability aspect including a case study on USENET2 dataset. We find our method focuses on model and drift sensitive features compared to baseline interpretable drift detectors.

By entering the habitats of wild animals, wildlife watchers can engage closely with them. There are some wild animals that are not always safe to approach. Therefore, we suggest this system for observing wildlife. Android phones can be used by users to see live events. Wildlife observers can thus get a close-up view of wild animals by employing this robotic vehicle. The commands are delivered to the system via a Wi-Fi module. As we developed the technology to enable our robot to deal with the challenges of maintaining continuous surveillance of a target, we found that our robot needed to be able to move silently and purposefully when monitoring a natural target without being noticed. After processing the data, the computer sends commands to the motors to turn on. The driver motors, which deliver the essential signal outputs to drive the vehicle movement, are now in charge of driving the motors.

Providing fast and accurate resolution to the student's query is an essential solution provided by Edtech organizations. This is generally provided with a chat-bot like interface to enable students to ask their doubts easily. One preferred format for student queries is images, as it allows students to capture and post questions without typing complex equations and information. However, this format also presents difficulties, as images may contain multiple questions or textual noise that lowers the accuracy of existing single-query answering solutions. In this paper, we propose a method for extracting questions from text or images using a BERT-based deep learning model and compare it to the other rule-based and layout-based methods. Our method aims to improve the accuracy and efficiency of student query resolution in Edtech organizations.

"Getting access to many of our public libraries is so difficult, and after a point people will give up asking for access. That's the case in many of our public-funded educational institutes too," says Arul George Scaria, an associate professor at the National Law School of India University Bengaluru, who studies intellectual-property law. One of the best ways to liberate access to these libraries, he says, is through digitization. Technologist Omshivaprakash H L felt the acute lack of such resources when he needed references for writing Wikipedia articles in Kannada, a southwestern Indian language. Around 2019, he heard that Carl Malamud, who runs Public Resource, a registered US charity, was already archiving books like Gandhi's Hind Swaraj collection on Indian self-rule and works of the Indian government in the public domain.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Technicolor Creative Studios is a creative technology company driven by one purpose: The realization of ambitious and extraordinary ideas. We inspire creative companies across the world to produce their most iconic work. Our award-winning teams of artists and technologists' partner with the creative community across film, television, animation, gaming, brand experience and advertising to bring the universal art of storytelling to audiences everywhere. No idea is too ambitious for us to create to an incredibly high standard. We are looking for a hands-on senior data engineer to join the data management team supporting data warehouse and reporting solutions.