Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks-- subtle, perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perception is constant exposure to low-fidelity visual stimuli in our peripheral vision. To investigate this hypothesis, we develop RBlur, an image transform that simulates the loss in fidelity of peripheral vision by blurring the image and reducing its color saturation based on the distance from a given fixation point. We show that compared to DNNs trained on the original images, DNNs trained on images transformed by RBlur are substantially more robust to adversarial attacks, as well as other, non-adversarial, corruptions, achieving up to 25% higher accuracy on perturbed data.

Alexander Eugene Smith faces multiple charges, including robbery and assault, after allegedly stealing a Washington State Patrol vehicle on Christmas Day.

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Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are complicated by the need to preserve the privacy of each individual's data, heterogeneity in their covariate distributions, and different data structures between sites. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and accommodate covariate mismatch between sites by developing a multiply-robust and privacy-preserving nuisance function estimation approach. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing state-of-the-art approaches. We apply our approach to study the treatment effect of percutaneous coronary intervention (PCI) on the duration of hospitalization for patients experiencing acute myocardial infarction (AMI) with data from the Centers for Medicare \& Medicaid Services (CMS).

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Deep neural networks (DNNs) are vulnerable to adversarial attacks, which lead to incorrect predictions. In black-box settings, transfer attacks can be conveniently used to generate adversarial examples. However, such examples tend to overfit the specific architecture and feature representations of the source model, resulting in poor attack performance against other target models. To overcome this drawback, we propose a novel model modification-based transfer attack: Blurred-Dilated method (BD) in this paper. In summary, BD works by reducing downsampling while introducing BlurPool and dilated convolutions in the source model.

A new op-ed explores how we can live and work with artificial intelligence without losing our humanity, which is featured atop the Fox News AI Newsletter.

Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult to measure through traditional means, machine learning offers a promising set of techniques for evaluating novelty, summarizing contributions, and embedding semantics. In this paper, we introduce the Harvard USPTO Patent Dataset (HUPD), a large-scale, well-structured, and multi-purpose corpus of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions of patent applications, not the final versions of granted patents, allowing us to study patentability at the time of filing using NLP methods for the first time.

Elon Musk sits ahead of Google's co-founder Larry Page and the Amazon founder, Jeff Bezos, in the overall rankings of the world's wealthiest billionaire. Elon Musk sits ahead of Google's co-founder Larry Page and the Amazon founder, Jeff Bezos, in the overall rankings of the world's wealthiest billionaire. Elon Musk's net worth increased by nearly 50% to $645bn with founders of Google and Amazon also seeing huge wealth gains Fri 26 Dec 2025 08.42 ESTLast modified on Fri 26 Dec 2025 21.30 EST A stock market boom in artificial intelligence companies has added more than half a trillion dollars to the wealth of America's tech barons in the past year, data shows. The top 10 US founders and bosses of some of the world's largest technology companies saw their finances swell to nearly $2.5tn, up from $1.9tn, in the year to Christmas Eve, according to figures from Bloomberg. Elon Musk, already the world's richest man, has again proved to be one of biggest winners as the AI gold-rush has pushed US stock markets to record highs.

OpenAI GPT-5.2 replaces GPT-5.1 with behind-the-scenes improvements for presentations, spreadsheets and image interpretation, though users may struggle to notice differences.