Google has updated Gemini for Home so that it no longer acts like a strict parent when you ask it for cocktail recipes. In the past, you may have encountered a message that says I cannot provide recipes for alcoholic beverages when you ask the AI assistant for a margarita recipe on Google smart home devices, such as the Nest Hub . Now, Google has updated its safeguards to prevent adult users from encountering filters meant for younger ones. Adults will now experience improved availability for general queries, including recipes for age-gated beverages, the company said in the Google Home support page . If Gemini still isn't responding when you ask it for instructions on how to make a cocktail, you may have to check you Parental Control settings and your Gemini for Home response filter settings in the Google Home app.

In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility of DP sampling with density ratio-based measures such as KL divergence. However, such formulations suffer from two key limitations: 1) they fail to capture the geometric structure of the support, and 2) they are not applicable when the supports of the distributions differ. To deal with these issues, we develop a novel framework for DP sampling with Wasserstein distance as the utility measure. In this formulation, we propose Wasserstein Projection Mechanism (WPM), a minimax optimal mechanism based on Wasserstein projection. Furthermore, we develop efficient algorithms for computing the proposed mechanisms approximately and provide convergence guarantees.

Well, it seems like those-rumored AirPods with cameras are close to being real, according to the latest report from Mark Gurman . The new earbuds are said to use low-resolution cameras on their stalks to capture low-resolution imagery, which will ultimately be fed to Apple's long-delayed AI Siri assistant. And the more I hear about them, the more they sound like Meta's Ray-Ban smart glasses, just without the ability to take clear photos and videos. The camera-equipped Airpods are reportedly in Apple's design validation testing (DVT) stage, where workers are using prototypes to test their capabilities. There's no word on when we may actually see them, but according to Gurman they were initially slated to debut as early as the first half of 2026, only to be pushed back by AI Siri delays.

Last month, Perplexity sought to better compete with the likes of Claude Cowork and get out ahead of Apple's delayed, generative AI-powered version of Siri by bringing Personal Computer to macOS . The AI assistant was previously only available to those on Perplexity's $200 per month Max plan, but now the company has opened it up to all Mac users. The company says everyone can download the new Perplexity macOS app and use Personal Computer for everyday queries, attachments and dictation. Usage is tied to Pro and Max plans' credit limits, Perplexity noted. Personal Computer can run tasks across local files, other apps, the web and Perplexity's own servers, according to the company.

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The goal of this thesis is to investigate the structural properties of certain sequential problems in order to bring the solutions closer to a practical use. In the first part, we put a special emphasis on structures that can be represented as graphs on actions. In the second part, we study the large action spaces that can be of exponential size in the number of base actions or even infinite. For graph bandits, we consider the settings of smoothness of rewards (spectral bandits), side observations, and influence maximization. For large structured domains, we cover kernel bandits, polymatroid bandits, bandits for function optimization (including unknown smoothness), and infinitely many-arms bandits. The thesis aspires to be a survey of the author's contributions on graph and structured bandits.

When you purchase through links in our articles, we may earn a small commission. Microsoft 365 is down to $69.99 for a full year, giving you premium Office apps, 1TB storage, and built-in AI tools across your devices. If you're already using Word, Excel, or PowerPoint --even occasionally--this is one of those upgrades that just makes your setup smoother across the board. Microsoft 365 is currently $69.99 for a 1-year subscription (MSRP $99.99), and it bundles together the apps you actually use with features that go beyond basic document editing. That means your files are accessible across devices, backed up, and easy to share without emailing attachments back and forth. One of the bigger upgrades here is the built-in AI assistant, Copilot .

Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing works assume that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g., rating) and user-user social data are usually generated by different platforms, both of which contain sensitive information. Therefore, How to perform secure and efficient social recommendation across different platforms, where the data are highly-sparse in nature remains an important challenge. In this work, we bring secure computation techniques into social recommendation, and propose S3Rec, a sparsity-aware secure cross-platform social recommendation framework. As a result, S3Rec can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms. Moreover, to further improve model training efficiency, we propose two secure sparse matrix multiplication protocols based on homomorphic encryption and private information retrieval. Our experiments on two benchmark datasets demonstrate that S3Rec improves the computation time and communication size of the state-of-the-art model by about 40 and 423 in average, respectively.

This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each product and user to overall sales revenue. The proposed framework encodes revenue contributions in the user-item matrix and computes customer similarity directly on this basis using suitable distance measures. This enables the segmentation of users according to the revenue-based similarity of their purchase baskets and supports recommendations aligned with profitability objectives. We compare conventional similarity metrics with a novel alternative tailored to high-dimensional contexts and propose three recommendation strategies based on revenue share, product popularity, and expected profit generation. The effectiveness of the proposed method is validated through simulation experiments and a real-world application using the UCI Online Retail dataset.