StoryFile offers a somewhat unique service that seems poised to look like something out of Black Mirror. The company – a relative newcomer – helps people to record themselves telling their stories, then presents those stories in an interactive, voice-activated interface. Users can ask the digital storytellers questions, which are interpreted by the StoryFile app and answered with snippets from their recorded stories. Now, StoryFile is announcing that it has acquired a new portfolio of patents covering the use of AI in creating "artificially intelligent interactive memories." StoryFile began with a greenscreen studio and select guests, including Nobel laureates, astronauts, Holocaust survivors, and many others.

Alexa has been smartening up homes since 2014, but for those who don't want to bother with the setup process, a new Amazon program will let apartment building owners offer units with Alexa built in. With Alexa for Residential, property owners set up Alexa devices across units, making the move to voice-assistant life easier for tenants moving in. Renters control their apartment's smart features--set alarms and reminders, get the news, and more via voice commands--without linking their Amazon accounts or buying an Echo device themselves. If residents want to link their Amazon accounts to their apartment's smart devices for full control of their privacy settings, they can. They can log out at any time, and smart devices are reset at move-out.

As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit. There has been considerable research on recommendation fairness. However, we argue that the previous literature has been based on simple, uniform and often uni-dimensional notions of fairness assumptions that do not recognize the real-world complexities of fairness-aware applications. In this paper, we explicitly represent the design decisions that enter into the trade-off between accuracy and fairness across multiply-defined and intersecting protected groups, supporting multiple fairness metrics. The framework also allows the recommender to adjust its performance based on the historical view of recommendations that have been delivered over a time horizon, dynamically rebalancing between fairness concerns. Within this framework, we formulate lottery-based mechanisms for choosing between fairness concerns, and demonstrate their performance in two recommendation domains.

Recommender systems are being employed across an increasingly diverse set of domains that can potentially make a significant social and individual impact. For this reason, considering fairness is a critical step in the design and evaluation of such systems. In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system. HyperFair models integrate variations of fairness metrics as a regularization of a joint inference objective function. We implement our approach using probabilistic soft logic and show that it is particularly well-suited for this task as it is expressive and structural constraints can be added to the system in a concise and interpretable manner. We propose two ways to employ the methods we introduce: first as an extension of a probabilistic soft logic recommender system template; second as a fair retrofitting technique that can be used to improve the fairness of predictions from a black-box model. We empirically validate our approach by implementing multiple HyperFair hybrid recommenders and compare them to a state-of-the-art fair recommender. We also run experiments showing the effectiveness of our methods for the task of retrofitting a black-box model and the trade-off between the amount of fairness enforced and the prediction performance.

Recommender systems are among the most fun and profitable applications of data science in the big data world. Training data (corresponding to the historical search, browse, purchase, and customer feedback patterns of your customers) can be converted into golden opportunities for ROI (i.e., Return On Innovation and Investment). The predictive analytics tools of data science yield a bonanza of mechanisms to engage your customers and enrich their customer experience. What better loyalty program can there be if not the one that offers the customer what they want before they ask (and sometimes, even before they think of it for themselves). Yes, we know of some cases that have gone bad (such as the secretly pregnant teen and the targeted coupons that Target sent to her father), and we recognize that there is a fine line between being intimate with your customers versus being intimidating, but usually people do like to receive offers for great products that they love.

Contemporary recommender systems act as intermediaries on multi-sided platforms serving high utility recommendations from sellers to buyers. Such systems attempt to balance the objectives of multiple stakeholders including sellers, buyers, and the platform itself. The difficulty in providing recommendations that maximize the utility for a buyer, while simultaneously representing all the sellers on the platform has lead to many interesting research problems.Traditionally, they have been formulated as integer linear programs which compute recommendations for all the buyers together in an \emph{offline} fashion, by incorporating coverage constraints so that the individual sellers are proportionally represented across all the recommended items. Such approaches can lead to unforeseen biases wherein certain buyers consistently receive low utility recommendations in order to meet the global seller coverage constraints. To remedy this situation, we propose a general formulation that incorporates seller coverage objectives alongside individual buyer objectives in a real-time personalized recommender system. In addition, we leverage highly scalable submodular optimization algorithms to provide recommendations to each buyer with provable theoretical quality bounds. Furthermore, we empirically evaluate the efficacy of our approach using data from an online real-estate marketplace.

A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommender system. Firstly, a fast adaptive PCA frameworkis presented which combines the fixed-precision randomized matrix factorization algorithm [1] and accelerating skills for handling large sparse data. Then, a novel termination mechanism for the adaptive PCA is proposed to automatically determine a number of latent factors for achieving the near optimal prediction accuracy during the subsequent model-based CF. The resulted CF approach has good accuracy while inheriting high runtime efficiency. Experiments on real data show that, the proposed adaptive PCA is up to 2.7X and 6.7X faster than the original fixed-precision SVD approach [1] and svds in Matlab repsectively, while preserving accuracy. The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy. It also owns the advantage of parameter free. Compared with the deep-learning-based CF approach, the proposed approach is much more computationally efficient, with just marginal performance loss.

So you're interested in AI? Then join our online event, TNW2020, where you'll hear how artificial intelligence is transforming industries and businesses. The coronavirus pandemic has clearly accelerated our dependency on technology, online activities, and artificial intelligence. AI is particularly important for businesses as it enables personalized services on a massive scale, and customers are increasingly demanding it. However, not every company has the knowledge or the tools to implement AI, nor do they know what is required from them to become AI-driven. In this post, I will discuss what options these companies have.

Amazon wants to make it easier to have a smart home without actually owning a home. Today, the company is unveiling its "Alexa for Residential" program that lets property managers add Alexa-enabled devices and experiences to rental properties. Landlords and property managers can install Alexa devices, pre-fill info like device name, address and Wi-Fi network, pre-enable Alexa skills and set interactions with other smart home devices, like lights and appliances. Residents will be able to connect their personal accounts and additional devices. Amazon says 84 percent of renters want an apartment with smart home amenities, and 61 percent said they would pay a monthly fee for a voice assistant.

TCL's upcoming true wireless earbuds will cost less than €100 (about $120), the company said today. The MOVEAUDIO S200 offer features you'd hope for in true wireless earbuds, like electronic noise reduction (ENC) to reduce background noise, touch controls, wear detection and Google Assistant and Siri compatibility. Battery life is nothing to brag about here. The MOVEAUDIO S200 only last 3.5 hours on a single charge, but you can stretch that to 23 hours if you use the charging case strategically. As a consolation, the earbuds are IP54 rated, so they'll survive water, dust and sweat.