This article explores Constitutional AI, a framework developed by Anthropic to train AI systems that are helpful, harmless, and non-evasive—without relying on human labels for harmfulness. By guiding models through critique–revision loops and reinforcement learning from AI-generated feedback, this method offers a scalable, transparent alternative to RLHF and advances the field of AI alignment and self-supervised safety

Foundation models like GPT-3 and CLIP are reshaping AI by enabling general-purpose systems trained on massive, unlabelled data. This article explores their key concepts—emergence and homogenization—their capabilities across language, vision, and more, and the risks they pose, from bias to environmental impact. Based on the Stanford report, it highlights why foundation models are powerful, unpredictable, and demand responsible development.

This article explores the groundbreaking findings of Kaplan et al. on scaling laws for neural language models. It explains how model performance improves predictably with increased model size, dataset size, and compute budget, highlighting power-law relationships. The piece discusses implications for efficient AI training, optimal resource allocation, overfitting avoidance, and future research directions.

Convolutional Neural Networks have revolutionized artificial intelligence by enabling machines to process visual data with remarkable accuracy. Inspired by the visual cortex, CNNs evolved from early models like LeNet-5 to powerful architectures such as AlexNet, VGG, ResNet, and DenseNet. This article explores CNN core concepts, key innovations, real-world applications, and future trends, highlighting their enduring impact on AI.

This article explores how AI is transforming science and medicine in 2025. From breakthroughs in protein engineering and brain mapping to outperforming doctors in clinical diagnosis, AI is becoming an active research partner and clinical assistant. It highlights key findings from Stanford’s AI Index Report, including the rise of virtual labs, predictive healthcare models, AI scribes, and the importance of ethical, inclusive, and regulated deployment.

This article explores TabPFN, a transformer-based foundation model designed for small tabular datasets. Trained on millions of synthetic datasets generated via structural causal models, TabPFN learns to predict labels through in-context learning. It outperforms traditional methods like CatBoost and XGBoost in both speed and accuracy, while offering robustness, interpretability, and fine-tuning capabilities. A breakthrough in tabular ML, it redefines what's possible on structu