Nanadini Thakur

Nanadini Thakur

How Small AI Models Learn From Big Ones

AI model distillation showing a large teacher model training a smaller student model

AI Model Distillation 2026 explains how powerful but heavy AI models teach smaller, faster models to deliver similar performance with far fewer resources. Instead of running massive AI systems everywhere, organizations now rely on distilled models that are efficient, cost-effective,…

How AI Protects Data on Devices

Edge AI privacy showing on-device artificial intelligence processing data securely without cloud sharing

Edge AI Privacy 2026 focuses on how artificial intelligence processes data directly on devices instead of sending it to cloud servers. By keeping data local, edge AI significantly improves privacy, security, and speed while reducing dependency on centralized systems. As…

How AI Learns Without Sharing Data

Alt text: Federated learning use cases in healthcare finance and mobile devices

Federated Learning Explained 2026 describes a privacy-first approach to artificial intelligence where AI models learn from data without ever collecting or centralizing it. Instead of sending sensitive data to servers, federated learning allows AI systems to train directly on local…