Kids love to use smart speakers, but it's all too easy for things to go horribly wrong -- including content that's decidedly not family-friendly. Google is trying to address those worries by both revising parental controls for Assistant and providing more child-oriented responses. To start, an update will let you disable some Assistant features, restrict the services children can use and establish downtime hours. You can bar kids from making calls, or limit them to watching YouTube Kids on a Nest Hub. The controls will be available in the "coming weeks" through the Google Assistant, Google Home and Family Link apps for Android and iOS.

You can set limits and block specific content on your child's smartphone, tablet, or computer, but the smart speakers and smart displays in your home are something of a loophole. If your kids are anything like mine, you've probably caught them asking weird questions to voice assistants or watching a video when it's homework time. Well, there's good news: Google is closing this loophole with a set of parental controls for Google Assistant. These new controls enable you to set a Downtime that works across shared family devices and will restrict content and functionality based on who is asking. You can restrict your kids to YouTube Kids for video and Spotify Kids for music, for example.

Mankind's technological advancements have been developing at such breakneck speeds that we often take for granted how they have made our lives easier. One of the most interesting results of human brainpower is the machine-like replication of itself: artificial intelligence. AI is already present in our daily tasks, from search engines, algorithms, virtual assistant technology, and the like. However, there seems to be budding movements in applying AI technologies to the cultural productions, with some already venturing into anime and art. Should this prove successful both commercially and critically, it is only inevitable that this AI movement would permeate the cultural zeitgeist of other media, and one that is of great interest is the medium of film.

We'll start with tried-and-true recommendation algorithms built on neighborhood-based collaborative filtering before moving on to more cutting-edge approaches like matrix factorization and deep learning using artificial neural networks. You'll learn about the problems you might run into when using these algorithms on a large scale and be able to use real-world data based on our vast experience in the field. You've probably seen automatic suggestions all over the place--on the Netflix home page, YouTube, and Amazon--as these machine learning algorithms discover your distinct tastes and provide you with the most relevant goods or entertainment. Understanding how these technologies function will make you very useful to the biggest and most prominent IT organizations out there. Beginning with tried-and-true algorithms for recommendations like neighborhood-based collaborative filtering, we'll next go on to more advanced strategies like matrix factorization and even deep learning using artificial neural networks.

Tinder's parent company, Match Group, beat revenue estimates for the last quarter as more users looking for matches took out paid subscriptions on the popular dating app. Their results were an outlier in what has been a quarter of poor performance for some of the biggest tech companies in the US. Match Group, who own a suite of dating apps including Hinge and OKCupid, saw their shares rise 16% on Tuesday. The results are welcome news for Tinder, which has been rocked this year by executive changes. In August, chief executive Renate Nyborg stepped down after less than a year in the job.

Various studies in recent years have pointed out large issues in Despite growing work that tests recommender systems online, offline the offline evaluation of recommender systems [11, 12], making it evaluation is still by far the most popular evaluation paradigm difficult to assess whether true progress has been made. However, used in recent research publications [38]. What has been troubling is there has been little research into what set of practices should that an increasing amount of research has pointed out important issues serve as a starting point during experimentation. In this paper, with common protocols for offline evaluation of recommender we examine four larger issues in recommender system research systems [5, 12, 14, 24] even leading some researchers to publicly regarding uncertainty estimation, generalization, hyperparameter call it a community-wide crisis [11]. The cumulative effect of these optimization and dataset pre-processing in more detail to arrive issues became widely visible when Dacrema et al. [12] performed a at a set of guidelines. We present a TrainRec, a lightweight and series of reproducibility experiments showing that reported gains flexible toolkit for offline training and evaluation of recommender vanished in most cases when baselines were tuned properly. In a systems that implements these guidelines. Different from other similar vein, Rendle et al. [32] showed that proper hyperparameter frameworks, TrainRec is a toolkit that focuses on experimentation selection makes traditional matrix factorization-based approaches alone, offering flexible modules that can be can be used together or competitive to more recent methods. Overall, these discoveries mirror in isolation.

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Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex SQL databases from different domains, data analysts do not necessarily have sufficient knowledge about different data tables and application domains. It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. We develop a NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.

Graph neural networks (GNNs), which have emerged as an effective method for handling machine learning tasks on graphs, bring a new approach to building recommender systems, where the task of recommendation can be formulated as the link prediction problem on user-item bipartite graphs. Training GNN-based recommender systems (GNNRecSys) on large graphs incurs a large memory footprint, easily exceeding the DRAM capacity on a typical server. Existing solutions resort to distributed subgraph training, which is inefficient due to the high cost of dynamically constructing subgraphs and significant redundancy across subgraphs. The emerging persistent memory technologies provide a significantly larger memory capacity than DRAMs at an affordable cost, making single-machine GNNRecSys training feasible, which eliminates the inefficiencies in distributed training. One major concern of using persistent memory devices for GNNRecSys is their relatively low bandwidth compared with DRAMs. This limitation can be particularly detrimental to achieving high performance for GNNRecSys workloads since their dominant compute kernels are sparse and memory access intensive. To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane. Based on the analysis, we provide guidance on how to configure Optane for GNNRecSys workloads. Furthermore, we present techniques for large-batch training to fully realize the advantages of single-machine GNNRecSys training. Our experiment results show that with the tuned batch size and optimal system configuration, Optane-based single-machine GNNRecSys training outperforms distributed training by a large margin, especially when handling deep GNN models.

The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."