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Serving Society

The pursuit of happiness [The Way Ahead]

August 31, 2026 by Thomas Coughlin

In February 2018, I wrote a blog on LinkedIn as the IEEE-USA president-elect called “Don’t Panic, You Know Things.” This blog reflected on the then recently launched SpaceX Heavy space vehicle and included the following comments: We are creatures who, by our nature, are curious about the world around us and have developed methods to determine what is real using the … [Read more...] about The pursuit of happiness [The Way Ahead]

IEEE Computer Society AI Caravan: Bridging the knowledge gap from Region 8 to the global stage [IEEE Student Activities]

August 31, 2026 by Islam Tharwat Abdel Halim

The rapid growth of artificial intelligence (AI) has created a gap between those who can develop AI solutions and those who only use technology. This gap is more than a challenge of access; it can prevent communities and regions from benefiting from the opportunities created by AI. In 2025, the IEEE Computer Society (CS) Member and Geographic Activities Board launched the IEEE … [Read more...] about IEEE Computer Society AI Caravan: Bridging the knowledge gap from Region 8 to the global stage [IEEE Student Activities]

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 … [Read more...] about Authenticity, creativity, and art in the age of artificial intelligence [Student Editorial]

Outlook of artificial intelligence evolution [Editorial]

August 31, 2026 by Supavadee Aramvith, Prashant R. Nair

Today, no aspect of our lives or jobs has not been impacted by artificial intelligence (AI). The year 2023 will perhaps go down as the year of ChatGPT. Generative AI models, led by ChatGPT and including Microsoft Copilot, Google Gemini, Llama, and Claude, which use large language models (LLMs), have become ubiquitous across all aspects of human endeavor. Disruptions due to AI … [Read more...] about Outlook of artificial intelligence evolution [Editorial]

Deepfakes and large language models: Risks, defenses, and the future of generative artificial intelligence [Outlook of AI Evolution]

August 31, 2026 by Alakananda Mitra, Saraju P. Mohanty, Elias Kougianos

Generative artificial intelligence (GenAI) is rapidly changing how digital content is created and consumed. Two widely used GenAI technologies are deepfakes and large language models (LLMs). Deepfakes generate or modify images, videos, audio, and text that imitate real people, while LLMs provide robust language understanding, reasoning, and multimodal coordination. When … [Read more...] about Deepfakes and large language models: Risks, defenses, and the future of generative artificial intelligence [Outlook of AI Evolution]

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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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