Microsoft Commits $17.5 Bn to Advance India’s AI Ambition

Microsoft has announced a significant investment of $5.4 billion aimed at enhancing artificial intelligence (AI) and cloud infrastructure in Canada. This initiative will also focus on improving cybersecurity measures and providing training for workers in the AI sector.

Azure DevOps Server has reached general availability, marking Microsoft’s launch of a self-hosted DevOps platform that includes ongoing updates and Git-first enhancements. This release is aimed at providing organizations with a robust tool for managing software development and delivery processes.

President Trump has granted Nvidia permission to sell its H200 artificial intelligence chips to approved customers in China, lifting a previous export ban due to national security concerns. This decision includes a 25% surcharge on sales, marking a significant policy shift in U.S.-China tech relations.

Microsoft is expanding its Azure AI offerings by introducing more model choices through Microsoft Foundry and enhancing hybrid and sovereign deployment options, promoting a flexible approach to AI deployment. This initiative aims to cater to diverse organizational needs and preferences in AI integration.

India is taking steps to regulate artificial intelligence (AI) training conducted by major companies like Google and OpenAI, reflecting a growing concern over the implications of AI technologies on society. This move is part of a broader initiative to ensure that AI development aligns with national interests and ethical standards.

Tata and Intel have formed an alliance to develop a silicon and compute ecosystem in India, focusing on the manufacturing and packaging of Intel products at Tata Electronics' upcoming facilities. This collaboration aims to enhance India's semiconductor capabilities and foster local production.

Google’s Accelerator Programmes are significantly contributing to the growth of India’s AI-native startup ecosystem, providing essential support and resources to emerging companies in the field. This initiative aims to foster innovation and enhance the capabilities of startups leveraging artificial intelligence technologies.

The introduction of Persian-Phi, a 3.8B parameter model, marks a significant advancement in the adaptation of Large Language Models (LLMs) for low-resource languages, specifically Persian. This model utilizes a curriculum learning pipeline that begins with bilingual narratives to align embeddings before extensive training, challenging the notion that large model sizes are necessary for multilingual capabilities.