The article explores how China is aggressively advancing its geopolitical influence by releasing powerful, open-source AI models, making high-tier AI capabilities freely and globally accessible. Contrasts this with the proprietary, closed-source approaches dominated by US tech giants. The piece analyzes India's strategic position within this shifting, multipolar AI ecosystem, arguing that India must carefully balance its infrastructure dependencies, leverage open-source breakthroughs for localized economic growth, and craft its own independent AI policies to avoid being caught in a digital duopoly between the US and China.

AI Governance and Board-Level Risk Oversight in the Age of Autonomous Enterprises: A Framework for Financial Resilience and Regulatory Accountability Manas Pandey CEO, MS Risktec Solutions Abstract The rapid integration of Artificial Intelligence (AI) into enterprise decision-making has fundamentally transformed corporate governance, financial risk management, and regulatory accountability. Boards and executive leadership teams are increasingly required to supervise AI-driven systems that influence lending decisions, fraud monitoring, customer profiling, algorithmic trading, operational resilience, and strategic planning. However, traditional governance frameworks were not designed to address autonomous, continuously learning systems capable of creating systemic risks at unprecedented scale and speed. This paper examines the emerging role of board-level AI governance in strengthening enterprise resilience and proposes a structured governance model integrating Enterprise Risk Management (ERM), AI governance, cybersecurity oversight, and regulatory compliance. The paper argues that AI risk can no longer be treated as a purely technological issue and must instead be embedded within corporate governance structures, audit mechanisms, and strategic decision-making processes. Drawing from evolving global regulatory frameworks—including the European Union AI Act[4], the Digital Personal Data Protection Act (India)[8], Basel operational risk principles[5], and ESG governance expectations—the paper introduces an integrated governance framework called Autonomous Intelligence Management Systems (AIMS). The framework emphasises explainability, accountability, fairness, resilience, and continuous monitoring of AI systems. The study concludes that organisations adopting board-driven AI governance structures are likely to achieve superior risk-adjusted performance, regulatory preparedness, and stakeholder trust.

This article explores why conversational AI alone may not be sufficient to solve India’s travel complexity. As AI-native travel interfaces become mainstream, the underlying challenge increasingly shifts toward multimodal orchestration across fragmented transport systems, especially across Tier 2 and Tier 3 India where direct connectivity remains limited.And the long-term opportunity in Indian travel AI may lie not only in building better conversational interfaces — but in solving the orchestration infrastructure underneath them.

Telugu LLM Labs, led by researchers from LlamaIndex, is advancing NLP for Telugu — a language with 100M+ speakers historically underrepresented in AI. The initiative creates open datasets in both native and Romanized Telugu scripts and fine-tunes LLMs like Llama 2, Mistral, and TinyLlama, setting a precedent for other regional Indian languages in AI development.

AI4Bharat launches BhasaAnuvaad, the largest speech translation dataset for Indian languages, covering 44,400 hours of audio across 13 languages including Hindi, Tamil, Telugu, and Bengali. It tackles India-specific challenges like code-switching and dialectal diversity. A synthetic benchmark, Indic-Spontaneous-Synth, is also introduced to test real-world translation model robustness.

Cosmopedia v0.1, hosted on HuggingFace, is the largest open synthetic dataset with 30 million samples and 25 billion tokens, generated by Mixtral 7b. It includes textbooks, blog posts, stories, and WikiHow articles across eight dataset splits. Designed to democratize AI research, it supports NLP, model training, and scalable AI development with rich metadata and diverse content.

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.