MLCF

Mohit Agarwal's work provides valuable insights into the transformative capabilities of MARL, highlighting its potential to drive innovation and efficiency in complex, real-time environments.

In the rapidly evolving world of artificial intelligence, few advancements have had as profound an impact as Large Language Models (LLMs). Rajnish Jain, a disti

Mandy Andress - CISO at Elastic - joins Xiou Ann Lim for this CSO Executive Sessions interview. They talk about how large language models are offering a countermeasure against AI risks, how banks can integrate them with existing SIEM systems, and more.

In this rapidly growing digital era, privacy-preserving machine learning (PPML) is revolutionizing data-driven applications by enabling organizations to harness vast datasets while ensuring user privacy.

The integration of ML-driven cybersecurity frameworks into financial institutions is an ongoing process, requiring continuous refinement to adapt to emerging cyber threats. Future research should explore the use of federated learning to enhance cybersecurity collaboration across financial networks while preserving data privacy.

In the modern era, artificial intelligence (AI) has rapidly evolved, giving rise to highly efficient and scalable architectures. Vasudev Daruvuri, an expert in AI systems, examines one such innovation in his research on Mixture of Experts (MoE) architecture.

This Research Topic explores the integration of large language models (LLMs) with edge computing to optimize generative AI for resource-constrained Internet

Landscape Management Network (LMN) today announced a strategic partnership with Attentive (Attentive.ai), a pioneer in AI-powered mapping and measurement for field service industries. Through the partnership,