Generative AI

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]

Mitigating manipulation in generative AI

December 27, 2024 by Yanyi Wu

In an era where generative artificial intelligence (GAI) can compose symphonies and generate news articles with equal facility, the boundary between creation and manipulation becomes increasingly nebulous. This article discusses the fascinating and risky field of GAI and explores its potential to manipulate through deepfake videos and AI-generated text. Rather than presenting a … [Read more...] about Mitigating manipulation in generative AI

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.

Generative AI

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