Model Context Protocol (MCP) vs Function Calling: A Deep Dive into AI Integration Architectures [English]

The blog post explores the Model Context Protocol (MCP) and Function Calling, two methods for integrating Large Language Models (LLMs) with external systems. It highlights their architectural differences, use cases, and security features, emphasizing MCP’s robustness for complex integrations and Function Calling’s flexibility for dynamic task execution.

Smart Shelves to Self-Checkout: How AI is Reshaping the Retail Experience in 2025

In 2025, AI-powered retail has transformed shopping experiences, offering personalized promotions and smart checkout systems. Consumers enjoy tailored recommendations, while businesses leverage dynamic pricing and automation. However, this evolution raises important questions about data privacy and the future roles of retail workers, necessitating adaptation and upskilling across the industry.

U.S. lawmakers claim DeepSeek uses “tens of thousands of chips” and urge Nvidia to provide sales information (11:48) – 20250417 – Instant Financial News [Chinese (Traditional)]

A bipartisan U.S. House committee has raised concerns about DeepSeek, claiming it poses a significant threat to national security. The committee urges NVIDIA to disclose sales information regarding chips used by DeepSeek, which allegedly has ties to the Chinese government and may bypass U.S. export controls.

AI-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms [English]

A noise-adapted AI model for single-lead electrocardiograms (ECGs) effectively predicts heart failure risk, offering a scalable strategy for risk stratification. This study analyzed data from diverse cohorts, demonstrating that AI-ECG significantly improves risk assessment compared to traditional methods, highlighting its potential in community-based health monitoring.

Beyond Algorithms: Navigating the Ethical Landscape of AI in 2025

In 2025, ethical considerations in AI development are essential, not optional. As AI systems permeate daily life, robust frameworks, transparency, and human-centered approaches are crucial. Addressing algorithmic bias, data privacy, and explainability will shape responsible AI deployment, ensuring technology aligns with societal values and serves the common good.

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