India’s TCS gets TPG to fund half of $2B AI data center project

India's semiconductor industry faces criticism for focusing on manufacturing chips for foreign clients rather than prioritizing domestic needs. Experts argue that this approach undermines the potential for local growth and innovation in the sector. The sentiment reflects concerns about the long-term sustainability of India's chip manufacturing ambitions.

India's AI mission focuses on addressing the needs of its 800 million people rather than just developing chatbots. Experts warn that the AI divide could surpass the existing digital divide, highlighting the importance of building foundational models that cater to the diverse population. This initiative aims to ensure that advancements in AI technology benefit all segments of society.

The SAS Academy for Data & AI Excellence is actively training India's workforce to adapt to the emerging Generative AI (GenAI) era. This initiative aims to equip professionals with the necessary skills to thrive in a rapidly evolving technological landscape, thereby enhancing India's position in the global AI market.

India's technology minister announced that the country's chipmaking capabilities are expected to match those of major global producers by 2032. This ambitious timeline reflects the government's commitment to enhancing domestic manufacturing in the technology sector.

India generates 20% of global data but stores only 3% of it locally. This discrepancy is being addressed by two significant forces that aim to enhance the country's data storage capabilities. The ongoing data centre boom in India is expected to bridge this gap, facilitating better data management and storage solutions.

Rice is a vital staple food globally, significantly impacting international trade, economic growth, and nutrition. Major Asian countries, including China, India, Pakistan, Thailand, Vietnam, and Indonesia, contribute to rice cultivation and utilize various rice types, such as basmati and jasmine. Ensuring the quality of rice grains is crucial for trade and reputation, but manual classification is labor-intensive and error-prone. This article explores the use of Convolutional Neural Networks (CNN) and explainable AI methods like LIME and SHAP to automate rice grain classification effectively.

Princeton Digital Group (PDG) has announced a $700 million investment to establish a data center in South Korea, marking its entry into the country as its seventh market in Asia Pacific. The initial phase will involve a 48 MW facility in Incheon, part of a larger plan to develop a total of 500 MW for AI and cloud services in the region.