Smart LED light bulbs are one of the easiest ways to get into the IoT space. These smart lighting solutions let you control your home's illumination from your phone and other connected devices, and in addition to that practicality, they also inject some fun into your space. Color-changing bulbs have a plethora of RGB options for you to customize the lighting mood for your next movie night, date night or game day, or you can opt for cozy warm white light when you need to unwind at the end of a long day. It goes without saying that many of these smart LED light bulbs work with Amazon's Alexa and the Google Assistant, so if you already have a smart home setup in the works, you can find one that fits into your chosen ecosystem. And arguably the best thing about these devices is that they can fit into any budget; affordable and advanced options have flooded the space over the past few years. We've tested out a bunch of smart lights over the years, and these are our current favorites. If you've done any research into smart lights, you've probably come across Philips Hue bulbs.

Recent advancements in AI-driven conversational agents have exhibited immense potential of AI applications. Effective response generation is crucial to the success of these agents. While extensive research has focused on leveraging multiple auxiliary data sources (e.g., knowledge bases and personas) to enhance response generation, existing methods often struggle to efficiently extract relevant information from these sources. There are still clear limitations in the ability to combine versatile conversational capabilities with adherence to known facts and adaptation to large variations in user preferences and belief systems, which continues to hinder the wide adoption of conversational AI tools. This paper introduces a novel method, Conversational Agent for Multi-Source Auxiliary Context with Sparse and Symmetric Latent Interactions ( CoMAC), for conversation generation, which employs specialized encoding streams and post-fusion grounding networks for multiple data sources to identify relevant persona and knowledge information for the conversation. CoMAC also leverages a novel text similarity metric that allows bi-directional information sharing among multiple sources and focuses on a selective subset of meaningful words. Our experiments show that CoMAC improves the relevant persona and knowledge prediction accuracies and response generation quality significantly over two state-of-the-art methods.

The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecasts which item may better match user interest. Prior research typically develops two models to predict the CTR and search relevance separately, then ranking candidate items based on the fusion of the two outputs. However, such a divide-and-conquer paradigm creates the inconsistency between different models. Meanwhile, the search relevance model mainly concentrates on the degree of objective text matching while neglecting personalized differences among different users, leading to restricted model performance. To tackle these issues, we propose a unified \textbf{P}ersonalized Search RElevance Matching and CTR Prediction Fusion Model(PRECTR). Specifically, based on the conditional probability fusion mechanism, PRECTR integrates the CTR prediction and search relevance matching into one framework to enhance the interaction and consistency of the two modules. However, directly optimizing CTR binary classification loss may bring challenges to the fusion model's convergence and indefinitely promote the exposure of items with high CTR, regardless of their search relevance. Hence, we further introduce two-stage training and semantic consistency regularization to accelerate the model's convergence and restrain the recommendation of irrelevant items. Finally, acknowledging that different users may have varied relevance preferences, we assessed current users' relevance preferences by analyzing past users' preferences for similar queries and tailored incentives for different candidate items accordingly. Extensive experimental results on our production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed PRECTR method.

Efficiently searching for relevant case studies is critical in architectural design, as designers rely on precedent examples to guide or inspire their ongoing projects. However, traditional text-based search tools struggle to capture the inherently visual and complex nature of architectural knowledge, often leading to time-consuming and imprecise exploration. This paper introduces ArchSeek, an innovative case study search system with recommendation capability, tailored for architecture design professionals. Powered by the visual understanding capabilities from vision-language models and cross-modal embeddings, it enables text and image queries with fine-grained control, and interaction-based design case recommendations. It offers architects a more efficient, personalized way to discover design inspirations, with potential applications across other visually driven design fields. The source code is available at https://github.com/danruili/ArchSeek.

Apple Intelligence is fast becoming a disaster. Announced in June 2024 at Apple's World Wide Developer Conference, the artificial intelligence system arrived on the whole iPhone 16 family in October (and iPhone 15 Pro handsets, too), bringing things like generative tools for folks who can't be bothered to write emails, and summaries for those who can't be bothered to read, well, just about anything. December's addition, Genmoji--an AI emoji generator--didn't exactly bring much by way of excitement either. At the heart of the Apple Intelligence we were actually promised is a new Siri, an upgraded version of Apple's voice assistant, enhanced with some of the same smarts that made ChatGPT so beguiling at its launch in 2022. Amazon made similar moves recently with its upgrade to Alexa, but a more intelligent Siri is still MIA. It was meant to be here already.

Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes remains largely unexplored due to the challenge of assessing data importance without test labels. To address this gap, we propose Shapley-Guided Utility Learning (SGUL), a novel framework for graph inference data valuation. SGUL innovatively combines transferable data-specific and modelspecific features to approximate test accuracy without relying on ground truth labels. By incorporating Shapley values as a preprocessing step and using feature Shapley values as input, our method enables direct optimization of Shapley value prediction while reducing computational demands. SGUL overcomes key limitations of existing methods, including poor generalization to unseen test-time structures and indirect optimization. Experiments on diverse graph datasets demonstrate that SGUL consistently outperforms existing baselines in both inductive and transductive settings. SGUL offers an effective, efficient, and interpretable approach for quantifying the value of test-time neighbors.

As compliance with privacy regulations becomes increasingly critical, the growing demand for data privacy has highlighted the significance of machine unlearning in many real world applications, such as social network and recommender systems, many of which can be represented as graph-structured data. However, existing graph unlearning algorithms indiscriminately modify edges or nodes from well-trained models without considering the potential impact of such structural modifications on fairness. For example, forgetting links between nodes with different genders in a social network may exacerbate group disparities, leading to significant fairness concerns. To address these challenges, we propose a novel approach that jointly optimizes the graph structure and the corresponding model for fair unlearning tasks. Specifically,our approach rewires the graph to enhance unlearning efficiency by removing redundant edges that hinder forgetting while preserving fairness through targeted edge augmentation. Additionally, we introduce a worst-case evaluation mechanism to assess the reliability of fair unlearning performance. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed approach in achieving superior unlearning outcomes.

After ChatGPT broke cover in late 2022 and the tech industry embarked on its contemporary rendering of tulip mania, people started to wonder why the biggest tech giant of all – Apple – was keeping its distance from the madness. Eventually, the tech commentariat decided that there could be only two possible interpretations of this corporate standoffishness: either Apple was way behind the game being played by OpenAI et al; or it had cunning plans to unleash upon the world its own world-beating take on the technology. Finally, at its annual World Wide Developers' Conference (WWDC) on 10 June last year Apple came clean. For Apple, "AI" would not mean what those vulgar louts at OpenAI, Google, Microsoft and Meta raved about, but something altogether more refined and sophisticated – something called "Apple Intelligence". It was not, as the veteran Apple-watcher John Gruber put it, a single thing or product but "a marketing term for a collection of features, apps, and services". Putting it all under a single, memorable label made it easier for users to understand that Apple was launching something really novel.

The feedback that AI systems (e.g., recommender systems, chatbots) collect from user interactions is a crucial source of training data. While short-term feedback (e.g., clicks, engagement) is widely used for training, there is ample evidence that optimizing short-term feedback does not necessarily achieve the desired long-term objectives. Unfortunately, directly optimizing for long-term objectives is challenging, and we identify the disconnect in the timescales of short-term interventions (e.g., rankings) and the long-term feedback (e.g., user retention) as one of the key obstacles. To overcome this disconnect, we introduce the framework of MultiScale Policy Learning to contextually reconcile that AI systems need to act and optimize feedback at multiple interdependent timescales. For any two levels, our formulation selects the shorter-term objective at the next lower scale to optimize the longer-term objective at the next higher scale. As a result, the policies at all levels effectively optimize for the long-term. We instantiate the framework with MultiScale Off-Policy Bandit Learning (MSBL) and demonstrate its effectiveness on three tasks relating to recommender systems and text generation.

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