Meta's LLAMA3 language model can be used for content moderation by leveraging carefully crafted system prompts, providing a cost-effective and flexible solution that reduces the need for additional compute resources and operational overhead. This approach can be generalized to other models, offering a streamlined method for integrating content moderation into AI applications and increasing resource efficiency.
The rapid advancement of artificial intelligence (AI) has raised new questions about how to define open-source principles within the context of complex machine learning models. Meta's licensing for its large language model, Llama, exemplifies a notable deviation from traditional open-source licenses like Apache, MIT, and GPL. While these traditional licenses emphasize unrestricted use, transparency, and community-driven development, Meta's approach introduces controlled access and usage limitations aimed at balancing open access with ethical safeguards. Recent legislative developments, such as California's proposed SB 1047 bill introducing an AI "Kill Switch," further complicate the discourse by highlighting regulatory efforts to mitigate AI risks.
As cyber threats evolve, the need for robust endpoint security solutions becomes paramount. This paper introduces a novel generative AI-based architecture for endpoint security agents, named "AI4Falcon," designed to enhance their predictive, detection, and response capabilities. We propose a comprehensive framework that integrates generative adversarial networks (GANs) and transformer models to create dynamic threat models capable of anticipating and mitigating zero-day vulnerabilities.
A collection of Research Papers enough to understand current state of the Art GenAI landscape