Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like dermatology, radiology, and cardiology, the need for precise segmentation—assigning a class to every pixel in a medical image—is acute. Yet, the main obstacle remains: the scarcity of large, expertly…
Large Reasoning Models (LRMs) have rapidly advanced, exhibiting impressive performance in complex problem-solving tasks across domains like mathematics, coding, and scientific reasoning. However, current evaluation approaches primarily focus on single-question testing, which reveals significant limitations. This article introduces REST (Reasoning Evaluation through Simultaneous Testing) — a novel multi-problem stress-testing framework designed to push LRMs beyond isolated problem-solving…
Micromobility solutions—such as delivery robots, mobility scooters, and electric wheelchairs—are rapidly transforming short-distance urban travel. Despite their growing popularity as flexible, eco-friendly transport alternatives, most micromobility devices still rely heavily on human control. This dependence limits operational efficiency and raises safety concerns, especially in complex, crowded city environments filled with dynamic obstacles like pedestrians and…
The importance of memory in AI agents cannot be overstated. As artificial intelligence matures from simple statistical models to autonomous agents, the ability to remember, learn, and adapt becomes a foundational capability. Memory distinguishes basic reactive bots from truly interactive, context-aware digital entities capable of supporting nuanced, humanlike interactions and decision-making. Why Is Memory Vital in AI…
Robotic grasping is a cornerstone task for automation and manipulation, critical in domains spanning from industrial picking to service and humanoid robotics. Despite decades of research, achieving robust, general-purpose 6-degree-of-freedom (6-DOF) grasping remains a challenging open problem. Recently, NVIDIA unveiled GraspGen, a novel diffusion-based grasp generation framework that promises to bring state-of-the-art (SOTA) performance with unprecedented…
The discipline of epigraphy, focused on studying texts inscribed on durable materials like stone and metal, provides critical firsthand evidence for understanding the Roman world. The field faces numerous challenges including fragmentary inscriptions, uncertain dating, diverse geographical provenance, widespread use of abbreviations, and a large and rapidly growing corpus of over 176,000 Latin inscriptions, with…
In this tutorial, we build a GPU‑capable local LLM stack that unifies Ollama and LangChain. We install the required libraries, launch the Ollama server, pull a model, and wrap it in a custom LangChain LLM, allowing us to control temperature, token limits, and context. We add a Retrieval-Augmented Generation layer that ingests PDFs or text,…
Advancements in artificial intelligence are rapidly closing the gap between digital reasoning and real-world interaction. At the forefront of this progress is embodied AI—the field focused on enabling robots to perceive, reason, and act effectively in physical environments. As industries look to automate complex spatial and temporal tasks—from household assistance to logistics—having AI systems that…
Large Language Models (LLMs) have revolutionized many areas of natural language processing, but they still face critical limitations when dealing with up-to-date facts, domain-specific information, or complex multi-hop reasoning. Retrieval-Augmented Generation (RAG) approaches aim to address these gaps by allowing language models to retrieve and integrate information from external sources. However, most existing graph-based RAG…
LLMs have demonstrated exceptional performance across multiple tasks by utilizing few-shot inference, also known as in-context learning (ICL). The main problem lies in selecting the most representative demonstrations from large training datasets. Early methods selected demonstrations based on relevance using similarity scores between each example and the input question. Current methods suggest using additional selection…