Recent advances in general-purpose artificial intelligence systems have sparked interest in where the frontier of the field might move next—and what policymakers can do to manage emerging risks. This blog post summarizes key takeaways from… Read More
Recent discussions of AI have focused on safety, reliability, and other risks. Lost in this debate is the real need to secure AI against malicious actors. This blog post applies lessons from traditional cybersecurity to… Read More
“AI red-teaming” is currently a hot topic, but what does it actually mean? This blog post explains the term’s cybersecurity origins, why AI red-teaming should incorporate cybersecurity practices, and how its evolving definition and sometimes… Read More
Like traditional software, vulnerabilities in machine learning software can lead to sabotage or information leakages. Also like traditional software, sharing information about vulnerabilities helps defenders protect their systems and helps attackers exploit them. This brief… Read More
This paper is the first installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure… Read More
This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure… Read More
This paper is the second installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure… Read More
This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure… Read More