Introduction Even today, browsers still can’t provide the same smooth calling experience as native mobile apps. Because of that, many popular services simply block calls from mobile browsers and force users to install a native app instead. But sometimes a native app isn’t an option: the user didn’t install it, has no storage left, or just prefers using the web. So the question is: how close can we get to a native call experience using only the browser? […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowledge restructuring, we introduce Dream2Learn (D2L), a framework in which a model autonomously generates structured synthetic experiences from its own internal representations and uses them for self-improvement. Rather than reconstructing past data as in generative replay, D2L enables a classifier to create novel, semantically distinct dreamed classes that are coherent with its learned knowledge yet do […]

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Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating system environments with actual graphical user interfaces. Each one needs to run real software. Each one needs to handle unpredictable crashes. And you […]

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arXiv:2602.21478v1 Announce Type: new Abstract: We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, directional stability, which is strictly weaker than previously imposed target-agnostic stability conditions. Under directional stability, we show that estimators that would have been efficient under i.i.d. data remain asymptotically normal and semiparametrically efficient when computed from adaptively collected trajectories. The canonical gradient has a martingale form, and directional stability guarantees […]

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AI Security Exposure Detector is a web-based tool that helps organizations identify publicly exposed cloud files and sensitive data leaks using AI-powered classification and risk scoring. It enables users to quickly scan links, assess exposure risks, and generate actionable security reports to prevent data breaches.

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arXiv:2601.14609v1 Announce Type: new Abstract: Privacy-preserving model co-training in medical research is often hindered by server-dependent architectures incompatible with protected hospital data systems and by the predominant focus on relative effect measures (hazard ratios) which lack clinical interpretability for absolute survival risk assessment. We propose FedRD, a communication-efficient framework for federated risk difference estimation in distributed survival data. Unlike typical federated learning frameworks (e.g., FedAvg) that require persistent server connections and extensive iterative communication, FedRD is server-independent with […]

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arXiv:2601.12289v1 Announce Type: new Abstract: Learning representative embeddings for different types of speaking styles, such as emotion, age, and gender, is critical for both recognition tasks (e.g., cognitive computing and human-computer interaction) and generative tasks (e.g., style-controllable speech generation). In this work, we introduce ParaMETA, a unified and flexible framework for learning and controlling speaking styles directly from speech. Unlike existing methods that rely on single-task models or cross-modal alignment, ParaMETA learns disentangled, task-specific embeddings by projecting speech […]

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The role of a Solution Architect has always been to balance vision and reality. The question is, is there a change in how that process begins with the use of generative AI? The answer is, indeed, there is a change, but not in replacing the architect, but in using a powerful co-architect in the beginning of a design process. For many decades, Solution Architects have been using their experience, documentation, whiteboards, and lengthy discussions with stakeholders for designing […]

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