The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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OpenAI is launching the OpenAI Academy for News Organizations, a new learning hub built with the American Journalism Project and The Lenfest Institute to help newsrooms use AI effectively. The Academy offers training, practical use cases, and responsible-use guidance to support journalists, editors, and publishers as they adopt AI in their reporting and operations.

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arXiv:2601.02441v1 Announce Type: new Abstract: Textual reasoning has recently been widely adopted in Blind Image Quality Assessment (BIQA). However, it remains unclear how textual information contributes to quality prediction and to what extent text can represent the score-related image contents. This work addresses these questions from an information-flow perspective by comparing existing BIQA models with three paradigms designed to learn the image-text-score relationship: Chain-of-Thought, Self-Consistency, and Autoencoder. Our experiments show that the score prediction performance of the existing […]

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Frequent OTA updates in large commercial fleets must be planned around limited ground windows and strict safety requirements. Our study develops a phased OTA strategy that adapts update decisions to aircraft operational states. Real-time ACARS/ADS-B data and maintenance logs were combined to train an LSTM-Attention model for predicting remaining ground time, achieving an RMSE of 4.7 minutes. An integer-programming scheme then assigned update granularity and rollback plans across short stops, overnight stays, and scheduled A/B/C checks. Experiments using […]

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arXiv:2601.12224v1 Announce Type: new Abstract: Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the task of referring segmentation, localizing surgical instruments based on natural language descriptions, remains underexplored in surgical videos, with existing approaches struggling to generalize due to reliance on static visual cues and predefined instrument names. In this work, we introduce SurgRef, a novel motion-guided framework that grounds free-form language expressions in instrument […]

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arXiv:2405.18499v4 Announce Type: replace Abstract: Robustness of deep neural networks to input noise remains a critical challenge, as naive noise injection often degrades accuracy on clean (uncorrupted) data. We propose a novel training framework that addresses this trade-off through two complementary objectives. First, we introduce a loss function applied at the penultimate layer that explicitly enforces intra-class compactness and increases the margin to analytically defined decision boundaries. This enhances feature discriminativeness and class separability for clean data. Second, […]

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