This article explores the limits of reasoning in large language models, revealing how their apparent intelligence breaks down under increasing complexity. Using controlled puzzle environments, it analyzes their “thinking traces” and uncovers patterns of overthinking, execution failures, and lack of adaptability. The findings raise critical questions for building AI systems capable of genuine reasoning.

This article offers a deep dive into the seminal paper Attention Is All You Need, which introduced the Transformer architecture. It explores the limitations of recurrent models, the mechanics of self-attention, training strategies, and the Transformer’s groundbreaking performance on machine translation tasks. The article also highlights the architecture’s enduring legacy as the foundation for modern NLP systems like BERT and GPT.

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

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 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

Explore how Diffusion Models are revolutionizing generative AI, from their mathematical foundations to applications in image and audio.

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

SEDD redefines generative AI with human-like reasoning, enabling faster, high-quality text and code through discrete diffusion models.

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

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

A detailed guide on regularization techniques (L1, L2, Elastic Net, Dropout, Early Stopping) to prevent overfitting in machine learning mode

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

Boost your model's accuracy with bagging! Learn how ensemble techniques can stabilize predictions and improve performance.

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

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!

In this article I share a experimentation framework I work with in my daily job. It uses MLFlow and Hydra to facilitate hypothesis testing.

Recent years have birthed a dangerous usage of the technology offered by neural networks, the deep fakes. We'll explore them in this article

Let's explore Generative Adversarial Networks (GANs), one of the pioneers of Gen AI that was capable of producing photorealistic images.

In this article, we will explore the Mixture-of-Experts (MoE) and discuss the idea behind the gating mechanism used by the Sparse MoE.