The quest for truly autonomous and adaptable robots has long been a central challenge in artificial intelligence and robotics. Traditional methods, while effective in controlled environments, often struggle with the inherent complexities, uncertainties, and vast variability of the real world. From manufacturing floors to healthcare settings, robots need to perform diverse tasks, adapt to unforeseen changes, and generalize their learned skills to novel situations without extensive re-programming. This demand for greater flexibility and intelligence has driven researchers to explore new paradigms in robot control. Enter NVIDIA Cosmos Policy for Advanced Robot Control , a groundbreaking framework that promises to redefine how robots learn and operate. Leveraging the power of diffusion models, Cosmos Policy offers a novel approach to policy learning, enabling robots to acquire a broad range of skills, generalize across tasks, and perform robustly in dynamic environments. This deep-dive g...

The rapid evolution of text-to-image models has revolutionized digital content creation, enabling users to generate stunning visuals from simple text prompts. From DALL-E to Midjourney and Stable Diffusion, these models represent a pinnacle of generative AI, blending natural language understanding with sophisticated image synthesis. However, behind every breathtaking image lies an intricate and often painstaking training process. Developing these models is not merely about assembling the right architecture; it's about meticulously fine-tuning every aspect of their training design to achieve optimal performance, efficiency, and generalization. This deep dive explores the critical insights gained from systematic ablation studies in the context of text-to-image model training. Drawing lessons from cutting-edge research, including the development of models like PhotoRoom's PRX-1, we'll unpack how specific design choices impact model quality, training speed, and resource consu...

Introduction: The Dawn of Autonomous GPT-OSS Agents The landscape of artificial intelligence is undergoing a profound transformation. While Large Language Models (LLMs) have captivated the world with their ability to generate human-like text, the next frontier lies in empowering these models with true agency – the capacity to understand, plan, execute, and adapt to complex tasks autonomously. This evolution, often termed 'Agentic Reinforcement Learning' (RL), promises to elevate LLMs from sophisticated text generators to intelligent, goal-directed agents capable of interacting with dynamic environments and utilizing external tools. Simultaneously, the rise of GPT-OSS (GPT-Open Source Software) models has democratized access to powerful AI capabilities, fostering innovation and transparency. Projects like Llama, Mistral, and Falcon have put advanced LLM technology into the hands of developers and researchers worldwide. The convergence of Agentic RL with these open-source models ...

The promise of artificial intelligence (AI) agents transforming industrial operations is immense, yet the journey from theoretical breakthroughs to practical, real-world deployment remains fraught with significant challenges. While AI agents have demonstrated remarkable capabilities in controlled environments and game simulations, their application in complex, high-stakes industrial settings demands a level of robustness, reliability, and safety that traditional benchmarks often fail to capture. This is precisely the chasm that AssetOpsBench industrial AI agents aims to bridge, offering a groundbreaking benchmark suite designed to evaluate AI agents in scenarios that closely mirror the intricacies of industrial asset management. Developed by IBM Research and made accessible on Hugging Face, AssetOpsBench represents a pivotal step forward in making industrial AI agents truly viable. It moves beyond abstract metrics, focusing instead on operational efficiency, cost implications, and t...