OpenAI is preparing a major structural shift: the company plans to reestablish itself as a for-profit business. A key step toward that goal came this week, as Microsoft agreed in principle to adjust the terms of their partnership. The article…
Man agreed to return more than 1,000 stolen DVDs to his former employer.
Authorities have named Tyler Robinson as a suspect in the murder of right-wing influencer Charlie Kirk, citing Discord messages as evidence of his alleged role.
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Ida Silfverskiöld on AI agents, RAG, evals, and what design choice ended up mattering more than expected The post Generalists Can Also Dig Deep appeared first on Towards Data Science.
Data is everywhere, but how do you draw insights from it? Often, structured data is stored in relational databases, meaning collections of related tables of data. For instance, a company might store customer purchases in one table, customer demographics in…
EU probe came after complaint by Slack in 2020.
Deepdub, an Israeli Voice AI startup, has introduced Lightning 2.5, a real-time foundational voice model designed to power scalable, production-grade voice applications. The new release delivers substantial improvements in performance and efficiency, positioning it for use in live interactive systems such as contact centers, AI agents, and real-time dubbing. Performance and Efficiency Lightning 2.5 achieves […] The post Deepdub Introduces Lightning 2.5: A Real-Time AI Voice Model With 2.8x Throughput Gains for Scalable AI Agents and Enterprise AI appeared first on MarkTechPost.
arXiv:2509.09078v1 Announce Type: new Abstract: Given-data methods for variance-based sensitivity analysis have significantly advanced the feasibility of Sobol' index computation for computationally expensive models and models with many inputs. However, the limitations of existing methods still preclude their application to models with an extremely large number of inputs. In this work, we present practical extensions to the existing given-data Sobol' index method, which allow variance-based sensitivity analysis to be efficiently performed on large models such as neural networks, which have $>10^4$ parameterizable inputs. For models of this size, holding all input-output evaluations simultaneously in memory -- as required by existing methods -- can quickly become impractical. These extensions also support nonstandard input distributions with many repeated values, which are not amenable to equiprobable partitions employed by existing given-data methods. Our extensions include a general definition of the given-data Sobol' index estimator with arbitrary partition, a streaming algorithm to process input-output samples in batches, and a heuristic to filter out small indices that are indistinguishable from zero indices due to statistical noise. We show that the equiprobable partition employed in existing given-data methods can introduce significant bias into Sobol' index estimates even at large sample sizes and provide numerical analyses that demonstrate why this can occur. We also show that our streaming algorithm can achieve comparable accuracy and runtimes with lower memory requirements, relative to current methods which process all samples at once. We demonstrate our novel developments on two application problems in neural network modeling.
arXiv:2508.11274v2 Announce Type: replace Abstract: This paper establishes the first polynomial convergence rates for Gaussian kernel ridge regression (KRR) with a fixed hyperparameter in both the uniform and the $L^{2}$-norm. The uniform convergence result closes a gap in the theoretical understanding of KRR with the Gaussian kernel, where no such rates were previously known. In addition, we prove a polynomial $L^{2}$-convergence rate in the case, where the Gaussian kernel's width parameter is fixed. This also contributes to the broader understanding of smooth kernels, for which previously only sub-polynomial $L^{2}$-rates were known in similar settings. Together, these results provide new theoretical justification for the use of Gaussian KRR with fixed hyperparameters in nonparametric regression.
