technocracy

arXiv:2602.24131v1 Announce Type: cross Abstract: In a typical two-phase design, a random sample is drawn from the target population in phase 1, during which only a subset of variables is collected. In phase 2, a subsample of the phase-1 cohort is selected, and additional variables are measured. This setting induces a coarsened data structure on the data from the second phase. We assume coarsening at random, that is, the phase-2 sampling mechanism depends only on variables fully observed. […]

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We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than ten thousand MLaaS service instances […]

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arXiv:2601.14302v1 Announce Type: new Abstract: Image transmission and processing systems in resource-critical applications face significant challenges from adversarial perturbations that compromise mission-specific object classification. Current robustness testing methods require excessive computational resources through exhaustive frame-by-frame processing and full-image perturbations, proving impractical for large-scale deployments where massive image streams demand immediate processing. This paper presents DDSA (Dual-Domain Strategic Attack), a resource-efficient adversarial robustness testing framework that optimizes testing through temporal selectivity and spatial precision. We introduce a scenario-aware trigger […]

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arXiv:2601.17013v1 Announce Type: new Abstract: In this Article, I explore the impending conflict between the protection of civil rights and artificial intelligence (AI). While both areas of law have amassed rich and well-developed areas of scholarly work and doctrinal support, a growing body of scholars are interrogating the intersection between them. This Article argues that the issues surrounding algorithmic accountability demonstrate a deeper, more structural tension within a new generation of disputes regarding law and technology. As I […]

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We’ve been running threat detection on production AI agent deployments and just published our second monthly report with some findings that might be interesting to the ML community. Dataset: 91,284 agent interactions across 47 unique deployments, month-to-date through Feb 23. Detection model is a Gemma-based 5-head multilabel classifier with a voting ensemble, covering threat family, technique, and harm classification. P95 inference latency is 189ms. Key findings from a methodology perspective DISTRIBUTION SHIFT IN ATTACK VECTORS The threat family […]

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Two years ago, I posted a list of Hello World examples for machine learning and AI on social. Here, the Hello World means beginner-friendly examples to showcase a method. I set a biennial calendar alert to revisit and append to it. I was thinking pretty hard about what a 2025 example could look like. So, here is a short post with the updated list and some explanations for more context.

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The Invisible Waste of the Centralized Cloud As we head into 2026, a staggering and largely ignored crisis is unfolding in the world of technology: the massive waste inherent in centralized cloud infrastructure. Enterprises are currently wasting upwards of $44 billion on cloud spending due to a legacy model of over-provisioning, where organizations pay for peak capacity that they rarely use. This waste isn’t just a financial burden; it’s a resource crisis. Centralized data centers consume vast amounts […]

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arXiv:2602.10018v1 Announce Type: cross Abstract: Selective conformal prediction aims to construct prediction sets with valid coverage for a test unit conditional on it being selected by a data-driven mechanism. While existing methods in the offline setting handle any selection mechanism that is permutation invariant to the labeled data, their extension to the online setting — where data arrives sequentially and later decisions depend on earlier ones — is challenged by the fact that the selection mechanism is naturally […]

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