Amazon's first in-house TVs may be showcases for Alexa, but that isn't precluding the company from supporting someone else's ecosystem. According to The Verge, Amazon has unveiled plans to add support for Apple's AirPlay 2 and HomeKit to both higher-end Omni and budget 4-series Fire TV sets now that they're available. You can use AirPlay 2 to cast content from your iPhone, iPad or Mac, but the HomeKit integration may be the most notable -- yes, you can use Siri to control an Amazon TV as part of your wider smart home network. Amazon would only say the support was coming "soon." The TVs themselves start at $370 for the 4-series, which provide the usual Fire TV integrations along with 4K and HDR support in sizes ranging from 43 inches to 55 inches.

Hinge is the latest dating app that's making a push into audio. Starting today, users can attach voice clips to profiles, in what's said to be a first for a major dating app. You can also send audio notes to your matches. You can add an audio clip to your profile by going to the Edit Profile section of the settings and selecting Voice Prompt. You can choose a prompt (a question or comment suggested by the app to highlight something about you) and respond with a 30-second recording.

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to simulate the way humans learn, gradually increasing its accuracy. IBM has a rich history of machine learning. One of them, Arthur Samuel, is famous for coining the term "machine learning" in his research on the game of checkers. Robert Neely, a self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962 and lost to the computer. This feat appears almost trivial in comparison to what can be done today, but it is regarded as a significant milestone in the field of artificial intelligence. Over the next two decades, data storage and processing technology will create some of the innovative products we know and love today, like the Netflix recommendation engine or self-driving cars.

When Amazon's Echo Frames smart glasses first became widely available, we found they were capable if somewhat boring. There simply weren't many styles, colors and sizes to choose from at launch. Thankfully, Amazon is working to address that issue. The company is introducing two new colors called "Quartz Grey" and "Pacific Blue." Amazon will offer both with a variety of lens options, including ones that filter out blue light.

Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models' input drive the final ranking decision, thus, they cannot often be understood by human stakeholders. In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable recommender systems without sacrificing their performance. In particular, we propose three types of explainable recommendation strategies with gradual change of model transparency: whitebox, graybox, and blackbox. Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. We apply these explainable models on five real-world data sets under the contextualized setting where users and items have explicit interactions. The empirical results show that our model achieves highly competitive ranking performance, and generates accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.

This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

Our smart devices are listening. Whether it's personally identifiable information, location data, voice recordings, or shopping habits, our smart assistants know far more than we realize. A survey on smart assistant usage conducted by Reviews.org After analyzing the terms and conditions of Alexa, Google Assistant, Siri, Bixby, and Cortana, though, it was clear that some degree of data collection is ultimately inescapable. All five services collect your name, phone number, device location, and IP address; the names and numbers of your contacts; your interaction history; and the apps you use.

Amazon has two new programs that integrate Alexa into hospitals and senior living communities, the company announced today. They're run through Alexa Smart Properties, which allows organizations to control a centralized Alexa system. "Early on in the pandemic, hospitals and senior living communities reached out to us and asked us to help them set up Alexa and voice in their communities," Liron Torres, global leader for Alexa Smart Properties, said in an interview with The Verge. Hospitals wanted ways to interact with patients without using protective equipment, and senior living communities wanted to connect residents with family members and staff, she says. The program lets senior living facilities use Amazon Echo devices to send announcements or other messages to residents' rooms.

This week, Infor is the latest to have announced the general availability of its digital assistant within Microsoft Teams. The integration will enable customers to access information from within their ERP systems. To do so, customers will interact with the Infor Coleman AI Digital Assistant app for Teams. The digital assistant bot was previously available via a web browser, the Infor Go mobile app, and Amazon Alexa for Business. Oddly, unlike many others, Infor has not yet added an integration.