Authenticity, creativity, and art in the age of artificial intelligence [Student Editorial]

August 31, 2026 by Moira Prates

Artificial intelligence (AI) is no longer the future; it is the present. The 2020s are definitely the era of AI. Although the concept has existed since the 1950s, recent years have seen a boom driven by various factors, including the evolution of technologies that enable the improvement of “intelligences.” Intelligence refers to the ability to understand, reason, and learn from experience. We already know for certain that it is possible to program AI based on experiences, basically data whether synthetic or real, but what about understanding? And what about creativity?

For more about this article see link below.

https://ieeexplore.ieee.org/document/11663548

For the open access PDF link of this article please click here.

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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