Video large language models (VLLMs) have emerged as transformative tools for analyzing video content. These models excel in multimodal reasoning, integrating visual and textual data to interpret and respond to complex video scenarios. Their applications range from question-answering about videos to summarization and video description. With their capacity to process large-scale inputs and provide detailed… →

The advent of LLMs has propelled advancements in AI for decades. One such advanced application of LLMs is Agents, which replicate human reasoning remarkably. An agent is a system that can perform complicated tasks by following a reasoning process similar to humans: think (solution to the problem), collect (context from past information), analyze(the situations and… →

Image and video generation has undergone a remarkable transformation, evolving from a seemingly impossible challenge to a task nearly solved by commercial tools like Stable Diffusion and Sora. This progress is largely driven by Multihead Attention (MHA) in transformer architectures, which excel in scaling capabilities. However, this advancement comes with significant computational challenges. The quadratic… →

Multimodal language models (LMMs) are a transformative technology that blends natural language processing with visual data interpretation. Their applications extend to multilingual virtual assistants, cross-cultural information retrieval, and content understanding. By combining linguistic comprehension and image analysis, LMMs promise enhanced accessibility to digital tools, especially in linguistically diverse and visually rich contexts. However, their effectiveness… →

Transformer-based Large Language Models (LLMs) face significant challenges in efficiently processing long sequences due to the quadratic complexity of the self-attention mechanism. This will increase their computational and memory demands exponentially with sequence length, so scaling up these models to realistic applications like multi-document summarization, retrieval-based reasoning, or even fine-grained code analysis at the repository… →

Generative drug design offers a transformative approach to developing compounds that target pathogenic proteins, enabling exploration within the vast chemical space and fostering the discovery of novel therapeutic agents. Unlike traditional methods, such as high-throughput or virtual screening that rely on predefined molecular libraries with limited diversity, generative models can create entirely new molecules with… →

In today’s world, you’ve probably heard the term “Machine Learning” more than once. It’s a big topic, and if you’re new to it, all the technical words might feel confusing. Let’s start with the basics and make it easy to understand. Machine Learning, a subset of Artificial Intelligence, has emerged as a transformative force, empowering… →