Microsoft is launching a whitepaper that outlines a taxonomy of failure modes in AI agents, aimed at helping security experts and machine learning engineers better understand potential failures in AI systems to enhance safety and security. Building on earlier efforts, the taxonomy addresses failures specific to agentic AI and provides insights into risks such as ...
Recent advances in Large Language Models (LLMs) highlight the critical need to align their behavior with human intentions. This alignment challenge arises when an LLM’s goals differ from those of its developers, leading to potential risks, especially as these models gain more independence. While current methods help make LLMs safer, they may not adequately address ...
A Stanford report reveals that Chinese AI is rapidly advancing, with their models performing comparably to US ones on benchmarks. China leads in publishing AI research and patents, while the US has produced more top AI models. Additionally, powerful AI technology is emerging globally, including from the Middle East and Latin America. Many AI models ...
In this insightful article, Bijit Ghosh emphasizes the crucial role of memory in AI agents. He discusses how effective memory management can enhance the relevance and reliability of AI systems, while also highlighting potential risks like data leaks and outdated information. Ghosh introduces a comprehensive Memory Risk Framework and Mitigation Playbook designed to help developers ...
Ilya Sutskever, co-founder of OpenAI, highlights that as AI systems become more capable of reasoning, their behavior may become less predictable. While he emphasizes the future development of superintelligent agents, the immediate focus is on understanding the risks posed by AI agents in everyday tasks, like booking flights. These agents can be vulnerable to external ...
As AI agents increasingly become essential in various industries, they automate complex tasks, make decisions, and interact with important systems. Their ability to work independently raises significant concerns, as errors can lead to serious consequences. A small design flaw or missed security detail can turn a helpful tool into a risk. It’s crucial not only ...
Building safe AI agents is essential to ensure they perform as intended. Key to this is creating a robust system prompt that details how AI will interact with users and complete tasks. Microsoft offers a free course called “AI Agents for Beginners” that highlights the use of a meta prompting system to streamline the creation ...
At SXSW, industry leaders discussed the importance of AI safety amidst rapid technology adoption. While concerns about AI’s potential for errors and biases remain, experts like those from IBM, Microsoft, and Meta emphasized that AI should enhance, not replace, human jobs. They cautioned against overusing AI for tasks where it may falter, advocating for thoughtful ...
At SXSW, industry leaders discussed the essential topic of AI safety amidst the rapid adoption of artificial intelligence. While AI has notable flaws, such as producing biased responses and hallucinations, experts agree that the technology won’t replace human jobs but will transform how we work. They emphasized the importance of selecting appropriate use cases for ...
In this article, we explore the innovative concept of “Constitutional Classifiers,” developed by Anthropic to strengthen the safety of large language models (LLMs) against jailbreaks. The approach involves creating a natural language constitution that defines what content is safe or harmful. This framework generates synthetic data to train classifiers that effectively monitor and filter unsafe ...
