CyberGuy shows you how to create and customize events in the calendar app. When the pandemic hit and so many aspects of our lives went digital, older adults had to get accustomed to using more technology like Facetime, Zoom and more. Now, older adults have become a lot more tech-savvy and even have their favorite devices that they enjoy using. Here are five tech obsessions that older adults have adopted over the last few years. Perhaps the most popular devices among older adults are ones like Apple Watches, FitBits and other products that help people keep track of their health.

Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are split into multiple stages - candidate generation and heavy ranking - to balance computational cost against recommendation quality. We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC. We show that a system that combines a real-time light ranker with sourcing strategies capable of capturing additional information provides validated gains. We present two strategies. The first strategy uses a notion of similarity in the interaction graph, while the second strategy caches previous scores from the ranking stage. The graph based strategy achieves a 4.08% revenue gain and the rankscore based strategy achieves a 1.38% gain. These two strategies have biases that complement both the light ranker and one another. Finally, we describe a set of metrics that we believe are valuable as a means of understanding the complex product trade offs inherent in industrial candidate generation systems.

Recommender systems are increasingly successful in recommending personalized content to users. However, these systems often capitalize on popular content. There is also a continuous evolution of user interests that need to be captured, but there is no direct way to systematically explore users' interests. This also tends to affect the overall quality of the recommendation pipeline as training data is generated from the candidates presented to the user. In this paper, we present a framework for exploration in large-scale recommender systems to address these challenges. It consists of three parts, first the user-creator exploration which focuses on identifying the best creators that users are interested in, second the online exploration framework and third a feed composition mechanism that balances explore and exploit to ensure optimal prevalence of exploratory videos. Our methodology can be easily integrated into an existing large-scale recommender system with minimal modifications. We also analyze the value of exploration by defining relevant metrics around user-creator connections and understanding how this helps the overall recommendation pipeline with strong online gains in creator and ecosystem value. In contrast to the regression on user engagement metrics generally seen while exploring, our method is able to achieve significant improvements of 3.50% in strong creator connections and 0.85% increase in novel creator connections. Moreover, our work has been deployed in production on Facebook Watch, a popular video discovery and sharing platform serving billions of users.

Artificial intelligence: Danish startup Be My Eyes is developing an app based on the Open AI model that acts as a virtual assistant for people with blindness. Be My Eyes has a mission: to help blind people explore the world around them using smartphone technology. Created in 2015, the app developed by the Danish startup of the same name connects blind users with a network of sighted volunteers who, through a video call, 'lend' their eyes to observe and describe what they see through the device's camera. In the not-too-distant future, however, volunteers may no longer be needed. Be My Eyes is developing a beta version of the application that relies on a virtual assistant based on GPT-4, the latest version of Open AI's artificial intelligence model.

Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store historical and interactive features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) bridges the gap between the edge and cloud, enabling bi-directional knowledge transfer between both, sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate ECCT's effectiveness and potential for use in academia and industry.

Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their musical characteristics and matching user preferences remains a challenging task, commonly referred to as Automatic Playlist Continuation (APC). Besides, while these services often need to select the best songs to recommend in real-time and among large catalogs with millions of candidates, recent research on APC mainly focused on models with few scalability guarantees and evaluated on relatively small datasets. In this paper, we introduce a general framework to build scalable yet effective APC models for large-scale applications. Based on a represent-then-aggregate strategy, it ensures scalability by design while remaining flexible enough to incorporate a wide range of representation learning and sequence modeling techniques, e.g., based on Transformers. We demonstrate the relevance of this framework through in-depth experimental validation on Spotify's Million Playlist Dataset (MPD), the largest public dataset for APC. We also describe how, in 2022, we successfully leveraged this framework to improve APC in production on Deezer. We report results from a large-scale online A/B test on this service, emphasizing the practical impact of our approach in such a real-world application.

Numerous car companies are trying their hands at digital assistants, but Mini is planning something more... characterful. The automaker has unveiled Spike, an English Bulldog-inspired helper coming to future Mini models. While his exact functionality is still unknown, he'll walk you through the "operating concept" of a given car and is meant to foster an "emotional connection." We suspect this pup won't seem so loveable when you're in a hurry, but it might beat the personality-free assistants from other makes. Spike will make his debut in the cabin of the Mini Concept Aceman at the Shanghai auto show beginning April 18th.

Digital workers are intelligent software bots that automate everyday business processes like data entry, invoicing, or system queries. They will take over many repetitive and mundane tasks, creating new opportunities for businesses and workers. People aren’t just more productive when they work with digital workers; they are also happier. Automation is the future of work. Already, 74% of businesses use automation to drive efficiency and navigate the convergence of problems we face at the moment: the ongoing aftershocks of a global pandemic, raging inflation, and increasingly complex regulations. But the leaders among them will embrace and deploy digital workers to partner with…

Abstract--Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we discuss current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances. Graph-structured data is pervasive in diverse real-world Compared with achieving fairness in the context of independent applications, e.g., E-commerce [102], [121], health care [37], and identically distributed (i.i.d.) data, fulfilling [53], traffic forecasting [72], [100], and drug discovery [15], fairness in graph mining can be non-trivial due to two [172]. The first challenge is to formulate proper have been proposed to gain a deeper understanding of such fairness notions as the criteria to determine the existence of data. These algorithms have shown promising performance unfairness (i.e., bias). Although a vast amount of traditional on graph analytical tasks such as node classification [59], algorithmic fairness notions have been proposed centered [86], [161] and link prediction [4], [103], [109], contributing on i.i.d. For example, the same population can be most of them lack fairness considerations. Consequently, connected with different topologies as in Figure 1a and 1b, they could yield discriminatory results towards certain populations where each node represents an individual, and the color when such algorithms are exploited in humancentered of nodes denotes their demographic subgroup membership, applications [80]. Compared with the graph topology job recommender system may unfavorably recommend in Figure 1a, the topology in Figure 1b has more intra-group fewer job opportunities to individuals of a certain edges than inter-group edges. The dominance of intra-group gender [97] or individuals in an underrepresented ethnic edges in the graph topology is a common type of bias group [150].

Building a recommendation system involves analyzing user data, which can potentially leak sensitive information about users. Anonymizing user data is often not sufficient for preserving user privacy. Motivated by this, we propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization, which is one of the most popular algorithms for recommendation systems. As differential privacy is a powerful and robust mathematical framework for designing privacy-preserving machine learning algorithms, it is possible to prevent adversaries from extracting sensitive user information even if the adversary possesses their publicly available (auxiliary) information. We implement differential privacy via the Gaussian mechanism in the form of output perturbation and release user profiles that satisfy privacy definitions. We employ R\'enyi Differential Privacy for a tight characterization of the overall privacy loss. We perform extensive experiments on real data to demonstrate that our proposed algorithm can offer excellent utility for some parameter choices, while guaranteeing strict privacy.