In a move to unify smart home devices, Apple, Google and Amazon have teamed up with other tech companies to make the home networks more universal and user-friendly. The tech majors have collaborated to sponsor Matter -- an alliance that certifies smart home devices by placing its logo on them as an indication, CNET reported. The Matter logo will look like three round-tipped arrows all pointing towards the center. Any smart home device such as Amazon Alexa-responding smart light bulb or Google Home-monitored video doorbell with the logo means they are Matter-certified. Through certifying smart home devices, the alliance aims to make the technology widely accepted because it is easier to develop.

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area. Finally, we share some new research directions in this vibrant area.

Instagram users will now be able to more easily specify their gender identity, after the platform launched a dedicated section on account profiles to share pronouns. The new feature, announced on Tuesday, allows users to share up to four pronouns selected from a pre-approved list of common pronouns including she, he, they, ze and others. The company said users can edit or remove pronouns at any time, and can fill out a form to have a pronoun added if it is not included. Users will also be able to determine if they want pronouns shared with followers only or publicly on Instagram. For users under the age of 18, pronouns will automatically be rendered private.

AI (artificial intelligence) has gone through a lot of changes in recent years, and it will surely dominate the future of technology for a long time. ML (machine learning) is only a small branch of AI, but it has also improved our lives tremendously and will continue to do so. Machine learning is simply the technology used for providing systems with the possibility to automatically learn from experience and without the need to be explicitly programmed. The development of computer programs plays a huge role in machine learning, as it accesses data and further uses it to learn. Online dating is something common nowadays, and it becomes more and more efficient thanks to machine learning.

UNESCO 2019 publication I d Blush if I Could revealed how much gender bias and stereotypes were engineered into Artificial Intelligence-powered voice-assistant applications. Beyond highlighting the overall gender imbalance of teams creating these new tools, it also showed evidence of alarming gender gaps in technology industries, even in countries that are close to achieving gender equality. In this Q&A, Mark West, Project Officer at UNESCO and lead-author of the publication, shares insights on where we stand regarding gender prejudice in AI since the report came out. Two years since the publication came out, where do we stand on the fight against gender prejudice in AI assistants? On the positive side, awareness is much higher than it was when we researched and wrote our report.

By the time Sony's first 360 Reality Audio speakers were available to the masses, it had been over two years since the company first introduced the platform. Although the immersive audio format has been part of streaming libraries since fall 2019, Sony initially relied on third-party speakers to evangelize the public. The company eventually added 360RA supports to its headphones, but until this spring, it didn't have any compatible speakers. At CES 2021, the company officially announced the SRS-RA5000 and SRS-RA3000 (after two years of teasing). The SRS-RA5000 is the larger and more powerful of the duo -- and it's the more expensive.

Amazon is upgrading two of its smart displays and adding another one to the lineup. The new Echo Show 8 has a 13-megapixel, wide-angle camera that can follow you as you move around the room. The device uses virtual panning and tilting, like Facebook's Portal display, and it's a useful feature if you like to move around a little while you're on video calls. The Echo Show 8 now has an adaptive color display that parallels the ambient light in the room. The device also boasts an octa-core processor and dual stereo speakers.

Classification problems with a large number of labels arise in language modelling [Mikolov et al., 2013, Levy and Goldberg, 2014], recommender systems [Covington et al., 2016, Xu et al., 2016], and information retrieval [Agrawal et al., 2013, Prabhu and Varma, 2014]. Such large-output problems pose a core challenge: losses such as the softmax cross-entropy can be prohibitive to optimise, as they depend on the entire set of labels. Several works have thus devised negative sampling schemes for efficiently and effectively approximating such losses [Bengio and Senecal, 2008, Blanc and Rendle, 2018, Ruiz et al., 2018, Bamler and Mandt, 2020]. Broadly, negative sampling techniques sample a subset of "negative" labels, which are used to contrast against the observed "positive" labels. One further applies a suitable weighting on these "negatives", which ostensibly corrects the sampling bias introduced by the dependence on a random subset of labels. Intuitively, such bias assesses how closely a scheme approximates the unsampled loss on the full label set. This bias is well understood for sampled softmax schemes (see, e.g., Bengio and Senecal [2008]); surprisingly, however, far less is understood about other popular schemes, e.g., within-batch and uniform sampling (cf.

Virtual assistants such as Amazon's Alexa, Apple's Siri, Google Home, and Microsoft's Cortana, are becoming ubiquitous in our daily lives and successfully help users in various daily tasks, such as making phone calls or playing music. Yet, they still struggle with playful utterances, which are not meant to be interpreted literally. Examples include jokes or absurd requests or questions such as, "Are you afraid of the dark?", "Who let the dogs out?", or "Order a zillion gummy bears". Today, virtual assistants often return irrelevant answers to such utterances, except for hard-coded ones addressed by canned replies. To address the challenge of automatically detecting playful utterances, we first characterize the different types of playful human-virtual assistant interaction. We introduce a taxonomy of playful requests rooted in theories of humor and refined by analyzing real-world traffic from Alexa. We then focus on one node, personification, where users refer to the virtual assistant as a person ("What do you do for fun?"). Our conjecture is that understanding such utterances will improve user experience with virtual assistants. We conducted a Wizard-of-Oz user study and showed that endowing virtual assistant s with the ability to identify humorous opportunities indeed has the potential to increase user satisfaction. We hope this work will contribute to the understanding of the landscape of the problem and inspire novel ideas and techniques towards the vision of giving virtual assistants a sense of humor.

Click-through rate (CTR) prediction is a critical problem in web search, recommendation systems and online advertisement displaying. Learning good feature interactions is essential to reflect user's preferences to items. Many CTR prediction models based on deep learning have been proposed, but researchers usually only pay attention to whether state-of-the-art performance is achieved, and ignore whether the entire framework is reasonable. In this work, we use the discrete choice model in economics to redefine the CTR prediction problem, and propose a general neural network framework built on self-attention mechanism. It is found that most existing CTR prediction models align with our proposed general framework. We also examine the expressive power and model complexity of our proposed framework, along with potential extensions to some existing models. And finally we demonstrate and verify our insights through some experimental results on public datasets.