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To Protect and Preserve

Artificial intelligence in securing critical infrastructure: A double-edged sword [Outlook of AI Evolution]

August 31, 2026 by Evangelia Konstantopoulou, Nicolas Sklavos

Critical infrastructure (CI), including healthcare, transportation, energy management, and water supply, becomes increasingly digitized to improve efficiency. At the same time, this digital transformation increases CI’s vulnerability to cyberthreats. On one hand, integrating CI with artificial intelligence (AI) and machine learning (ML) algorithms can enable real-time response … [Read more...] about Artificial intelligence in securing critical infrastructure: A double-edged sword [Outlook of AI Evolution]

Artificial intelligence in climate modeling: The new frontier [Outlook of AI Evolution]

August 31, 2026 by Adam Muzaffar Abd Razak

Artificial intelligence (AI) is rapidly transforming atmospheric prediction by offering faster, data-driven approaches that complement traditional physics-based methods. This article examines the rise of modern AI climate models, with a focus on foundation systems such as Microsoft’s Aurora, alongside GraphCast and Pangu-Weather. It highlights how these architectures leverage … [Read more...] about Artificial intelligence in climate modeling: The new frontier [Outlook of AI Evolution]

The future of computing: Classical hardware or neuromorphic hardware [Outlook of AI Evolution]

August 31, 2026 by Tariq Jamil

The unprecedented expansion in machine learning (ML) and artificial intelligence (AI) has put unwonted pressures on conventional computing architectures, demanding particularly brain-like computations for tasks. While conventional von Neumann-based computing architectures have demonstrated immense flexibility and computational power across a wide array of algorithmic workloads, … [Read more...] about The future of computing: Classical hardware or neuromorphic hardware [Outlook of AI Evolution]

From the first flight to the stage at Harvard: What IEEE gave me before I even knew what artificial intelligence was [Techtravel]

August 31, 2026 by Péricles Oliveira

In 2015, I boarded a plane for the first time. I was born and raised in Salvador, Bahia, and had never left the state; the first time I saw the world from above was on my way to an IEEE Brazil Council National Student Branch Meeting in Brasília. I remember looking out the window and thinking that this was the farthest I had ever been from home. I didn’t know it yet, but that … [Read more...] about From the first flight to the stage at Harvard: What IEEE gave me before I even knew what artificial intelligence was [Techtravel]

Building smart factories in the university lab

August 5, 2026 by Ashish Jagani, Nikunj Rachchh

Smart factories are no longer the exclusive domain of multinational manufacturing corporations with extensive infrastructure and costly automation systems. Engineering students can now design, test, validate, and optimize entire smart factory ecosystems within their laboratories using advanced simulation platforms such as FlexSim and AnyLogic. Free trial versions, academic … [Read more...] about Building smart factories in the university lab

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About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

POPULAR ARTICLE

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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