Artificial intelligence (AI) is a branch of computer science that enables machines to perform tasks requiring human intelligence. This article covers how AI works, main types, real-world examples, limitations, and history.

Data engineering is the foundational backbone of artificial intelligence systems. This guide for data professionals covers the complete lifecycle of data engineering for AI, from ingestion and architecture to feature engineering, generative AI integration, compliance, and career development.

A data warehouse is a centralized repository that stores structured historical data from multiple sources, optimized for complex queries and business intelligence. This guide covers the three primary types: Enterprise Data Warehouses (EDW), Data Marts, and Operational Data Stores (ODS), along with modern cloud, hybrid, and lakehouse architectures, helping you choose the right data warehouse for your needs.

A year after announcing ambitious AI plans at NVIDIA GTC Paris, France’s AI infrastructure is coming online: AI agents are in production, startups are deploying applications, and the ecosystem is developing models and platforms tailored to local languages and European requirements. Key developments include Mistral’s new data center, open model collaborations via NVIDIA Nemotron, and enterprise AI adoption across healthcare, telecom, automotive, energy, and cosmetics industries.

This paper presents a real-time closed-loop robotic orientation control pipeline for precision visual inspection, using an admittance-based framework that unifies operator input and perception-driven surface alignment. The end-effector is modeled as a virtual sphere in a viscous medium, creating a mass-damper system for compliant motion. Validated on a 6-DOF manipulator with a mean orientation error of 0.4°.

This study evaluates four action spaces (pose increment, pose velocity, joint position increment, and joint velocity) in vision-based picking and pushing tasks. Training in simulation and deploying via sim-to-real transfer shows that joint velocity action space is best for smoothness and task success, with practical guidance for choosing action spaces.

DREAM-Chunk is a test-time scaling method that augments chunking-based policies with a lightweight latent world model, without additional policy fine-tuning. It samples multiple candidate action chunks, rolls out predicted latent futures, and selects actions from the chunk whose predicted state best matches observed rollout, improving robustness under stochastic dynamics. Validated on Kinetix benchmark and multiple robot platforms.