Samsung's AI assistant, Bixby, is getting an update on the shiny-new Galaxy S10 with Bixby Routines, a feature that learns your habits to preemptively launch apps or settings when you're most likely to need them. For instance, getting into your car could automatically trigger Spotify or turn on Do Not Disturb mode, while heading to bed could boot up battery-saving modes. Bixby Routines will work best once the software has a chance to learn your habits, so we weren't able to test it for our hands-on preview of the S10 and S10 . The phone that learns your habits to blend seamlessly into your day. Bixby has historically trailed behind competitors including Google, Amazon and Apple when it comes to virtual assistant technology.

More than a year on from a child safety content moderation scandal on YouTube and it takes just a few clicks for the platform's recommendation algorithms to redirect a search for "bikini haul" videos of adult women towards clips of scantily clad minors engaged in body contorting gymnastics or taking an ice bath or ice lolly sucking "challenge." A YouTube creator called Matt Watson flagged the issue in a critical Reddit post, saying he found scores of videos of kids where YouTube users are trading inappropriate comments and timestamps below the fold, denouncing the company for failing to prevent what he describes as a "soft-core pedophilia ring" from operating in plain sight on its platform. He has also posted a YouTube video demonstrating how the platform's recommendation algorithm pushes users into what he dubs a pedophilia "wormhole," accusing the company of facilitating and monetizing the sexual exploitation of children. We were easily able to replicate the YouTube algorithm's behavior that Watson describes in a history-cleared private browser session which, after clicking on two videos of adult women in bikinis, suggested we watch a video called "sweet sixteen pool party." Clicking on that led YouTube's side-bar to serve up multiple videos of prepubescent girls in its "up next" section where the algorithm tees-up related content to encourage users to keep clicking.

Google could face a backlash over privacy after admitting that Nest users were not told about the existence of a microphone on their devices. Early this month, Nest Secure was updated to allow users to enable Google Assistant, the tech giant's virtual-assistant technology, to be used by the system's hub and keypad. However, Nest users apparently did not know a microphone even existed on their security device. According to Business Insider, the existence of a microphone was not disclosed in any of the device's product materials. "The on-device microphone was never intended to be a secret and should have been listed in the tech specs. That was an error on our part. The microphone has never been on and is only activated when users specifically enable the option," a Google spokesperson told Fox News via email.

At the Mi 9 launch event in China, Xiaomi has revealed that it's working on a smart home hub -- one with looks that might conjure up images of the Lenovo Smart Clock and the Google Home Hub. It's called the Xiao Ai Touchscreen Speaker Box, and while details are scant at the moment, the electronics maker dropped some details about it. Unfortunately, it's still not clear if it has Google Assistant and if it uses the same software as Google's and Lenovo's devices. But it has the power to control Xiaomi's smart products, including ACs, air purifier, lights, cameras and door bell monitors, using touch or voice commands. Since the device is also a clock, it will have a customizable clock face and an alarm function that can wake you up using either music or video, as well. You'll also be able to use its four-inch display to play video and show content from iQiyi, Sogou, Tencent's QQ services and Sina.

This year the programme included a new stream, the Future of Work and that was the one in which I was invited to speak. Before summarising what I presented, I'd like to share some of the ideas and takeaways that I discovered about digital marketing and the impact of AI (artificial intelligence) and ML (machine learning). Most of us have grown up with text communication, but Gen Z, those born after 1996, are more comfortable with voice. They are less formal but far more impatient than previous generations. They expect Alexa, Siri, Cortana and similar voice-activated personal assistants to be available whenever they have a question.

For eaters, our system offers personalized restaurant recommendations, but ultimately eaters are looking for specific dishes to order. So, we are working on taking our recommendations to the dish level, creating more tailored eater experiences. This is analogous to the music industry's shift from selling albums to selling songs, and we believe it will be a huge leap forward in terms of the experience we can provide. In addition, for new eaters that are checking out the platform, we are working on methods to bootstrap our recommendations and solve the cold start problem often seen in recommender systems. For restaurant-partners, we are working to balance the surfacing of promotions and deals offered to eaters, as these short-term initiatives create interesting effects on the system by changing the behaviors of eaters who respond to them.

Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 864K items and 2 million users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.

In this paper, we propose a robust sequential learning strategy for training large-scale Recommender Systems (RS) over implicit feedback mainly in the form of clicks. Our approach relies on the minimization of a pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. Parameter updates are discarded if for a given user the number of sequential blocks is below or above some given thresholds estimated over the distribution of the number of blocks in the training set. This is to prevent from an abnormal number of clicks over some targeted items, mainly due to bots; or very few user interactions. Both scenarios affect the decision of RS and imply a shift over the distribution of items that are shown to the users. We provide a theoretical analysis showing that in the case where the ranking loss is convex, the deviation between the loss with respect to the sequence of weights found by the proposed algorithm and its minimum is bounded. Furthermore, experimental results on five large-scale collections demonstrate the efficiency of the proposed algorithm with respect to the state-of-the-art approaches, both regarding different ranking measures and computation time.

Whether through chatbots or voice assistants, bank customers are beginning to experience Banking AI and are responding favourably. Initially, the focus of these interfaces is similar to the task-oriented nature of channels that banks already provide. Though conversational interfaces have yet to attain the level of adoption expected in the banking space and has not yet matured as mobile has, there are already a variety of approaches for establishing a conversational banking capability. Going forward, I believe there will be at least four distinct banking models supported by apps and conversational interfaces. The majority of banks will begin their journey in the conversational banking space by implementing a conversational interface as an additional channel with a task-based focus.

Facebook wants to create its own chips to deliver the type of computing speeds necessary to take the next leap forward in artificial intelligence, according to a report in the Financial Times. The company also wants to make a digital assistant – similar to Apple's Siri or Amazon's Alexa – that can have conversations with users and that has "common sense." Yann LeCun, the company's chief AI scientist and a pivotal figure in modern AI, said the company also wants to use AI to help monitor video in real time and assist human moderators with content selection. Though the company is working with chip companies like Intel on the new chips, it is not ruling out the possibility of developing its own. "Facebook has been known to build its hardware when required -- build its own ASIC, for instance. If there's any stone unturned, we're going to work on it," LeCun said.