Aligning intended learning outcomes, assessments, and teaching practices with the realities of AI-assisted programming.
Simson Garfinkel on differential privacy vs. statistical accuracy, considering policy issues from a policy-neutral point of view, and the challenge of the way that words are used.
Silent data corruptions (SDCs) originating from CPUs are increasingly recognized as a reliability concern in modern datacenters. Unlike crashes and exceptions, SDCs silently compromise data integrity, often remaining undetected until their impact propagates through large-scale datacenters. Despite growing awareness, existing strategies against CPU SDCs remain inefficient, in part because they are grounded in assumptions that do not fully reflect operational realities. In this article, we revisit CPU SDCs from a systematic, production-oriented perspective. Drawing on practical observations, we examine their presence in the wild, analyze patterns in corrupted data and SDC occurrence, and reassess existing strategies against CPU SDCs. Our findings reveal discrepancies between traditional assumptions and practical observations, helping to explain why many existing strategies and models remain inefficient or misaligned with practice. Based on these insights, we outline future directions for promoting the reliability of modern datacenter services.
Addressing the intertwined goals of sustainability and computational progress requires coordinated multidisciplinary action that spans research, technology development, and governance.
Alex Vakulov on the process of reconstructing a cyber breach to understand how the attacker got in, what was affected, and what must be fixed before recovery is safe.
