Introduction: Understanding Generalization in Deep Generative Models Deep generative models, including diffusion and flow matching, have shown outstanding performance in synthesizing realistic multi-modal content across images, audio, video, and text. However, the generalization capabilities and underlying mechanisms of these models…
Language modeling plays a foundational role in natural language processing, enabling machines to predict and generate text that resembles human language. These models have evolved significantly, beginning with statistical methods and progressing through neural architectures to today’s large-scale transformer-based systems.…
The Agent-to-Agent (A2A) protocol is a new standard by Google that enables AI agents—regardless of their underlying framework or developer—to communicate and collaborate seamlessly. It works by using standardized messages, agent cards (which describe what an agent can do), and…
Launched in February of this year, the MIT Generative AI Impact Consortium (MGAIC), a presidential initiative led by MIT’s Office of Innovation and Strategy and administered by the MIT Stephen A. Schwarzman College of Computing, issued a call for proposals,…
The Importance of Symbolic Reasoning in World Modeling Understanding how the world works is key to creating AI agents that can adapt to complex situations. While neural network-based models, such as Dreamer, offer flexibility, they require massive amounts of data…
This post is divided into three parts; they are: • Why Attention is Needed • The Attention Operation • Multi-Head Attention (MHA) • Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) Traditional neural networks struggle with long-range dependencies in sequences. Source…
In this tutorial, we’ll build a powerful and interactive Streamlit application that brings together the capabilities of LangChain, the Google Gemini API, and a suite of advanced tools to create a smart AI assistant. Using Streamlit’s intuitive interface, we’ll create…
Cybersecurity has become a significant area of interest in artificial intelligence, driven by the increasing reliance on large software systems and the expanding capabilities of AI tools. As threats evolve in complexity, ensuring the security of software systems has become…
Understanding Subgroup Fairness in Machine Learning ML Evaluating fairness in machine learning often involves examining how models perform across different subgroups defined by attributes such as race, gender, or socioeconomic background. This evaluation is essential in contexts such as healthcare,…
Introduction AI agents are increasingly moving from pure backend automators to visible, collaborative elements within modern applications. However, making agents genuinely interactive—capable of both responding to users and proactively guiding workflows—has long been an engineering headache. Each team ends up…
