Privacy-preserving AI brings together methods that enable training and using machine learning models without directly sharing raw data.
Core technologies include Federated Learning (FL), Differential Privacy (DP), secure computation (homomorphic encryption and secure multi‑party computation), and hardware trusted execution environments.
This...

Artificial intelligence unsettles long-standing assumptions in intellectual property (IP).
This article synthesizes current doctrine and practice on (1) human authorship and the copyrightability of AI-assisted works; (2) lawful acquisition and use of training data, including EU text-and-data mining (TDM) exceptions and opt-outs; (3) licensing...

As variable renewable energy (VRE) rises, utilities must operate closer to limits without compromising reliability.
This article presents a production-oriented framework for applying AI across the grid lifecycle.
We detail (i) grid digital twins that fuse SCADA/PMU telemetry with physics-based models for contingency and “what-if” analysis; (ii)...

AI is reshaping how governments deliver services and manage registers.
This article maps high-value uses—proactive, event-driven benefits and permits; risk-based inspections; fraud detection; and intelligent queueing—inside a governance frame that embeds privacy-by-design and human oversight.
We align with current guidance (OECD digital...

Space missions increasingly depend on AI—from real-time, radiation-tolerant inference onboard satellites to ground-based analytics that fuse multi-sensor constellations with in-situ and economic data.
This article provides a systems view of “Space-AI”: (1) constraints of computing in orbit; (2) onboard edge models for prioritization, compression,...