7 articles tagged with #best-practices. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
A new industry body called the Frontier Model Forum is being established to promote safe and responsible development of advanced AI systems. The organization will focus on advancing AI safety research, establishing best practices and standards, and facilitating communication between policymakers and industry stakeholders.
Cohere, OpenAI, and AI21 Labs have collaboratively developed a preliminary set of best practices for organizations developing or deploying large language models. This represents a significant industry effort to establish standards and guidelines for responsible AI development and deployment.
OpenAI has released 28 prompt engineering tips designed to improve ChatGPT's performance and decision-making quality. While better prompting techniques can enhance AI utility, the guidance implicitly acknowledges risks of over-relying on AI outputs for critical financial and business decisions.
Researchers challenge conventional LLM unlearning practices by demonstrating that single neighbor sets and standard 1:1 sampling methods are suboptimal for removing knowledge while preserving model utility. The study proposes Modular Entity-Level Unlearning (MELU) as a more effective alternative, establishing new best practices for reliable AI model unlearning.
This article provides a guide on 10 essential rules for beginner cryptocurrency investors, focusing on risk management, security practices, and portfolio construction. The guidance addresses foundational principles that help new investors navigate the volatile and complex crypto market while protecting their assets from common pitfalls.
Workshop participants from academia, industry, and government convened in November 2025 to establish best practices for designing reinforcement learning environments in autonomous cyber defence. The resulting framework and guidelines address a critical gap in documented knowledge about RL environment development for network security applications, including critical infrastructure protection.
AI developers share their latest insights on language model safety and misuse prevention to help the broader AI development community. The article focuses on lessons learned from deployed models and strategies for addressing potential safety concerns and harmful applications.
