:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – XXIX. — THE MAGIC SHOP. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – XXIX. — THE MAGIC SHOP. By H. G. Wells ::: I had seen the Magic Shop from afar several times; I had passed it once or twice, a […]
Durante años, Silicon Valley se esforzó en vendernos una narrativa muy concreta sobre la inteligencia artificial. Una narrativa casi mesiánica. La idea de que un pequeño grupo de visionarios estaba construyendo una tecnología revolucionaria pensando, ante todo, en el bien común. La promesa de que, esta vez sí, el progreso tecnológico sería diferente: más abierto, más colaborativo, más responsable. Y ahora, en una sala de tribunales de Oakland, California, esa narrativa se está desmoronando pieza a pieza. El […]
In this tutorial, we explore skfolio, a scikit-learn compatible portfolio optimization library that helps us build, compare, and evaluate different investment strategies in a structured Python workflow. We start by loading S&P 500 price data, converting it into returns, and creating a time-based train-test split suitable for financial analysis. From there, we build simple baseline portfolios, test mean-variance optimization, compare alternative risk measures, apply risk-parity methods, and use hierarchical clustering techniques such as HRP and Nested Clusters Optimization. […]
OpenAI on just launched Daybreak, a cybersecurity initiative that combines the company’s frontier AI models with Codex Security, its coding-focused agentic system, and a broad network of security partners. The program is aimed at developers, enterprise security teams, researchers, and government-linked defenders who need to find, validate, and patch software vulnerabilities earlier in the development cycle — not after exploits have already been identified in the wild. The core premise of Daybreak is a shift in how software […]
Where Agents Meet Data Foundations In the early days of analytics and AI projects, especially proofs of concept, data rarely lived where it should. We passed around CSV files, Excel sheets, and one-off extracts. Models were trained offline and insights were generated in isolation. It worked for demos, but it was never how production systems were meant to operate. Those days are largely behind us. In modern enterprises, data is cleansed, governed, and continuously available inside databases and warehouses. Pipelines […]
arXiv:2605.08552v1 Announce Type: new Abstract: Independent Component Analysis (ICA) is a foundational tool for unsupervised representation learning, yet its high-dimensional theory remains largely limited to single-component recovery. We develop an asymptotically exact mean-field theory for multi-component online ICA, capturing the coupling induced by simultaneous learning and orthogonalization. In the high-dimensional limit, the joint empirical distribution of learned estimates and ground-truth components converges to a deterministic process, yielding a closed ODE system for the overlap matrix between learned directions […]
arXiv:2605.08546v1 Announce Type: new Abstract: The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical and computational scaling remains an issue for high-dimensional problems. Slicing techniques offer an appealing route to scalability, but, unlike Wasserstein distances, GW problems do not generally admit closed-form solutions in one-dimension. We resolve this problem for the GW problem with inner product cost (IGW), propose a sliced IGW distance that enjoys a natural rotational […]
arXiv:2605.08485v1 Announce Type: new Abstract: We introduce the Sinkhorn treatment effect, an entropic optimal transport measure of divergence between counterfactual distributions. Unlike classical quantities such as the average treatment effect, this measure captures differences across entire distributions. We analyze this divergence as a statistical functional and show it can be written as a smooth transformation of counterfactual mean embeddings with an appropriate kernel. This characterization allows us to establish first-order pathwise differentiability in general, and second-order pathwise differentiability […]
arXiv:2605.08429v1 Announce Type: new Abstract: Post-deployment monitoring of healthcare AI requires statistically valid, label-efficient methods, but gold-standard labels from clinician chart review are expensive. Prediction-powered inference (PPI) and active statistical inference (ASI) reduce label cost by combining a small labeled sample with abundant model predictions, but both are restricted to a single predictor, a poor fit for modern clinical pipelines that have multiple predictors of differing cost and accuracy available at inference time. We propose Active Multiple-Prediction-Powered Inference […]
arXiv:2605.08263v1 Announce Type: new Abstract: This work studies decentralized novelty detection with global false discovery rate (FDR) control across heterogeneous composite null distributions, without sharing the raw data due to privacy and bandwidth considerations. We propose a framework based on the exchange of quantized surrogate models, allowing independent agents to share low-precision representations of locally learned non-conformity score functions. We prove that evaluating data against these quantized composite scores preserves conditional exchangeability, providing rigorous finite-sample guarantees for global […]