MIT scientists developed the "Earth Intelligence Engine," combining a GAN-based generative AI model with a physics-based flood model to generate realistic future flood satellite imagery. Tested on Houston using Hurricane Harvey data, the physics-reinforced method reduces AI hallucinations and accurately maps flood extents pixel by pixel, helping communities visualize risks and make evacuation decisions.

India's data center colocation market is projected to grow at 24.68% CAGR from 2023 to 2029, with investments exceeding $100 billion by 2027. Driven by AI, ML, and generative AI adoption, states like Maharashtra and Tamil Nadu lead growth. Major players including Reliance, Google, and Airtel are building AI-ready infrastructure, positioning India as a global digital hub.

IIT Madras, AI4Bharat, and Sarvam AI have launched IndicVoices, a 12,000-hour multilingual speech dataset covering 22 Indian languages and 208 districts. Accompanied by IndicASR, the first ASR model supporting all 22 official Indian languages, the initiative is open-sourced under CC-BY-4.0, offering a global blueprint for multilingual speech data collection and advancing inclusive AI development.

The Reserve Bank Innovation Hub (RBIH) has developed MuleHunter.AI, an AI/ML-powered tool to detect mule accounts used in financial fraud. Analysing 19 behavioural patterns across banking data, it outperforms traditional rule-based detection methods. Successfully piloted with two public sector banks, it aims for wider rollout to secure India's digital financial ecosystem.

India's 2024 AI landscape saw six breakthrough models: BharatGen's e-vikrAI for e-commerce, Sarvam-1 supporting 10 Indian languages, NVIDIA's Nemotron-4-Mini-Hindi-4B, AI4Bharat's Chitralekha for video transcreation, Everest 1.0 covering 35 languages, and Surya OCR for document processing. These models integrate local languages and cultural context, setting global AI benchmarks.