Ideation processes often require time-consuming analysis and debate. What if we make two LLMs come up with ideas and then make them debate about those ideas? Sounds interesting right? This tutorial exactly shows how to create an AI-powered solution using two LLM agents that collaborate through structured conversation. For achieving this we will be using…
Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack essential entity relationships, diminishing their utility in retrieval-augmented generation (RAG) and other machine-learning tasks. Traditional extraction methods are likely to provide sparse graphs with absent important connections or noisy,…
In this tutorial, we will build an interactive text-to-image generator application accessed through Google Colab and a public link using Hugging Face’s Diffusers library and Gradio. You’ll learn how to transform simple text prompts into detailed images by leveraging the state-of-the-art Stable Diffusion model and GPU acceleration. We’ll walk through setting up the environment, installing…
The field of large language models has long been dominated by autoregressive methods that predict text sequentially from left to right. While these approaches power today’s most capable AI systems, they face fundamental limitations in computational efficiency and bidirectional reasoning. A research team from China has now challenged the assumption that autoregressive modeling is the…
Multimodal AI agents are designed to process and integrate various data types, such as images, text, and videos, to perform tasks in digital and physical environments. They are used in robotics, virtual assistants, and user interface automation, where they need to understand and act based on complex multimodal inputs. These systems aim to bridge verbal…
Multimodal Large Language Models (MLLMs) have gained significant attention for their ability to handle complex tasks involving vision, language, and audio integration. However, they lack the comprehensive alignment beyond basic Supervised Fine-tuning (SFT). Current state-of-the-art models often bypass rigorous alignment stages, leaving crucial aspects like truthfulness, safety, and human preference alignment inadequately addressed. Existing approaches…
Humans possess an innate understanding of physics, expecting objects to behave predictably without abrupt changes in position, shape, or color. This fundamental cognition is observed in infants, primates, birds, and marine mammals, supporting the core knowledge hypothesis, which suggests humans have evolutionarily developed systems for reasoning about objects, space, and agents. While AI surpasses humans…
In the realm of artificial intelligence, enabling Large Language Models (LLMs) to navigate and interact with graphical user interfaces (GUIs) has been a notable challenge. While LLMs are adept at processing textual data, they often encounter difficulties when interpreting visual elements like icons, buttons, and menus. This limitation restricts their effectiveness in tasks that require…
Efficiently handling long contexts has been a longstanding challenge in natural language processing. As large language models expand their capacity to read, comprehend, and generate text, the attention mechanism—central to how they process input—can become a bottleneck. In a typical Transformer architecture, this mechanism compares every token to every other token, resulting in computational costs…
Whole Slide Image (WSI) classification in digital pathology presents several critical challenges due to the immense size and hierarchical nature of WSIs. WSIs contain billions of pixels and hence direct observation is computationally infeasible. Current strategies based on multiple instance learning (MIL) are effective in performance but considerably dependent on large amounts of bag-level annotated…