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Category: robotics/AI – Page 150

Introduction to MCP: The Ultimate Guide to Model Context Protocol for AI Assistants

The Model Context Protocol (MCP) is an open standard (open-sourced by Anthropic) that defines a unified way to connect AI assistants (LLMs) with external data sources and tools. Think of MCP as a USB-C port for AI applications – a universal interface that allows any AI assistant to plug into any compatible data source or service. By standardizing how context is provided to AI models, MCP breaks down data silos and enables seamless, context-rich interactions across diverse systems.

In practical terms, MCP enhances an AI assistant’s capabilities by giving it controlled access to up-to-date information and services beyond its built-in knowledge. Instead of operating with a fixed prompt or static training data, an MCP-enabled assistant can fetch real-time data, use private knowledge bases, or perform actions on external tools. This helps overcome limitations like the model’s knowledge cutoff and fixed context window. It is observed that simply “stuffing” all relevant text into an LLM’s prompt can hit context length limits, slow responses, and become costly. MCP’s on-demand retrieval of pertinent information keeps the AI’s context focused and fresh, allowing it to incorporate current data and update or modify external information when permitted.

Another way MCP improves AI integration is by unifying the development pattern. Before MCP, connecting an AI to external data often meant using bespoke integrations or framework-specific plugins. This fragmented approach forced developers to re-implement the same tool multiple times for different AI systems. MCP eliminates this redundancy by providing one standardized protocol. An MCP-compliant server (tool integration) can work with any MCP-compliant client (AI application). In short, MCP lets you “write once, use anywhere” when adding new data sources or capabilities to AI assistants. It brings consistent discovery and usage of tools and improved security. All these benefits make MCP a powerful foundation for building more capable and extensible AI assistant applications.

SourceNet: A Deep‐Learning‐Based Method for Determining Earthquake Source Parameters

ABSTRACT. Seismic waves carry rich information about earthquake sources and the Earth’s medium. However, the process of extracting earthquake source parameters from seismic waves using traditional methods is complex and time consuming. In this study, we present a deep‐learning‐based method for automatic determination of earthquake source parameters. Considering the principle of calculating source parameters, the input of the deep neural network (SourceNet) includes not only the seismic waveform, but also the amplitude, epicenter distance, and station information. The utilization of multimodal data significantly improves the accuracy of determining earthquake source parameters. The test results using the real seismic data in the Sichuan–Yunnan region show that the earthquake source parameters obtained by SourceNet are in good agreement with the manual results and have higher computational efficiency. We apply the trained SourceNet to the seismic activities in the Changning area and further verify the reliability of the method by comparing our estimates of stress drops with those reported in previous studies of this area. The average time for SourceNet to calculate the source parameters of an earthquake is less than 0.1 s, which can be used for real‐time automatic determination of source parameters.

Using OpenUSD for Modular and Scalable Robotic Simulation and Development

The world of robotics is undergoing a significant transformation, driven by rapid advancements in physical AI. This evolution is accelerating the time to market for new robotic solutions, enhancing confidence in their safety capabilities, and contributing to the powering of physical AI in factories and warehouses.

Announced at GTC, Newton is an open-source, extensible physics engine developed by NVIDIA, Google DeepMind, and Disney Research to advance robot learning and development.

NVIDIA Cosmos launched as a world foundation model (WFM) platform under an open model license to accelerate physical AI development of autonomous machines such as autonomous vehicles and robots.

Super-resolution imaging technology reveals inner workings of living cells

A breakthrough in imaging technology promises to transform our understanding of the inner workings of living cells, and provide insights into a wide range of diseases.

The study, recently published in the journal Nature Communications, unveils an innovative approach that combines super-resolution imaging with and to reveal and dynamics. It was led by researchers from Peking University, Ningbo Eastern Institute of Technology and the University of Technology Sydney.

“It’s like taking an airplane over a city at night and watching all the live interactions,” said UTS Distinguished Professor Dayong Jin. “This cutting-edge will open new doors in the quest to understand the intricate world within our cells.”

When AI builds AI: The next great inventors might not be human

In the paper accompanying the launch of R1, DeepSeek explained how it took advantage of techniques such as synthetic data generation, distillation, and machine-driven reinforcement learning to produce a model that exceeded the current state-of-the-art. Each of these approaches can be explained another way as harnessing the capabilities of an existing AI model to assist in the training of a more advanced version.

DeepSeek is far from alone in using these AI techniques to advance AI. Mark Zuckerberg predicts that the mid-level engineers at https://fortune.com/company/facebook/” class=””>Meta may soon be replaced by AI counterparts, and that Llama 3 (his company’s LLM) “helps us experiment and iterate faster, building capabilities we want to refine and expand in Llama 4.” https://fortune.com/company/nvidia/” class=””>Nvidia CEO Jensen Huang has spoken at length about creating virtual environments in which AI systems supervise the training of robotic systems: “We can create multiple different multiverses, allowing robots to learn in parallel, possibly learning in 100,000 different ways at the same time.”

This isn’t quite yet the singularity, when intelligent machines autonomously self-replicate, but it is something new and potentially profound. Even amidst such dizzying progress in AI models, though, it’s not uncommon to hear some observers talk about the potential slowing of what’s called the “scaling laws”—the observed principles that AI models increase in performance in direct relationship to the quantity of data, power, and compute applied to them. The release from DeepSeek, and several subsequent announcements from other companies, suggests that arguments of the scaling laws’ demise may be greatly exaggerated. In fact, innovations in AI development are leading to entirely new vectors for scaling—all enabled by AI itself. Progress isn’t slowing down, it’s speeding up—thanks to AI.

The Power Of AI In Your Workflow: Copilot Explained | Satya Nadella

Satya Nadella, CEO of Microsoft, shares the groundbreaking potential of AI Copilot — a powerful tool that’s transforming how we work. From streamlining everyday tasks to revolutionizing healthcare workflows, AI Copilot is designed to seamlessly integrate with the tools we already use, like Teams, Word, and Excel.

Satya Nadella explains how AI Copilot is helping doctors prepare for high-stakes meetings, automatically generating agendas, summaries, and even PowerPoint presentations. Plus, see how it empowers professionals to gather the latest insights, collaborate with teams, and create smarter workflows with ease.

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Cocoa extract fails to prevent age-related vision loss, clinical trial finds

Brigham and Women’s Hospital-led research reports no significant long-term benefit of cocoa flavanol supplementation in preventing age-related macular degeneration (AMD). The paper is published in the journal JAMA Ophthalmology.

AMD is a progressive retinal disease and the most common cause of severe vision loss in adults over age 50. AMD damages the macula, the central part of the retina responsible for sharp, detailed vision. While peripheral sight is typically preserved, central vision loss can impair reading, driving, facial recognition, and other quality of life tasks. Abnormalities of blood flow in the eye are associated with the occurrence of AMD.

Cocoa flavanols are a group of naturally occurring plant compounds classified as flavonoids, found primarily in the cocoa bean. These bioactive compounds have been studied for their vascular effects, including improved endothelial function and enhanced nitric oxide production, which contribute to vasodilation and circulatory health. Previous trials have shown that moderate intake of may , improve lipid profiles, and reduce markers of inflammation, suggesting a role in mitigating cardiovascular and related vascular conditions.