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.

Can AI chatbots comfort us—or make us dependent? A study explores ChatGPT's emotional impact and the ethics of affective design.

Discover how neural networks power modern AI, from deep learning to generative models, shaping the future of technology and innovation.

SuperGPQA evaluates LLMs across 285 disciplines with 26,529 questions, testing their reasoning and knowledge beyond traditional fields.

Optimizing AI reasoning with adaptive test-time computation using recurrent depth transformers for smarter, efficient problem-solving.

AI is not just replacing jobs; it's empowering 10x professionals, and amplifying their impact in marketing, recruitment, and beyond.

Active Inheritance curates synthetic data to control LLM behavior, preventing AI model collapse and improving diversity, safety, and bias.

DeepSeek R1 enhances AI reasoning with reinforcement learning and distillation, achieving top-tier performance while maintaining efficiency

A comprehensive review of essential benchmarks and metrics for evaluating Large Language Models, from accuracy to fairness and conversationa

Orca 2: A smaller AI model that rivals larger ones by mastering task-specific reasoning, achieving high performance with less computation.

A clustering-based method balances web-scraped datasets, improving AI model performance by ensuring diverse and uniform data representation.

AI Scientist automates research, generating ideas, running experiments, and writing papers, challenging AI's role in novel scientific discov

Models could spread bias and discrimination if you don't know what they have learned. Here we show a technique to prevent it.

Explore the hidden biases in LLMs and their impact. The opinions of which sector of society are reflected in them?

Discover how NLP tools can identify and analyze contentious topics.

Dive into the world of Retrieval-Augmented Generation! See how RAG transforms AI responses by blending retrieval with generation.

Computers model text with vectors. Using Word2Vec, FastText, and Transformers, they understand and generate context-aware text. Learn how!

Are political discourses consistent? Is a judgment fair? In this article, we'll explore argumentation mining, an AI field to analyze debates