Aside from Black Friday, Amazon Prime Day is the best time of year to pick up an Echo device since most of them are deeply discounted. Amazon didn't disappoint this year -- all of its Echo Show smart displays are on sale for Prime Day, key among them being the Echo Show 8 for $75. Also on sale are the Echo Show 5 for only $35, the swiveling Echo Show 10 for $180 and the Echo Show 15 for $180 as well. It's the second-generation Show 8 that you can get for $75, and we gave it a score of 87 when it came out last year. We like its 8-inch, 1,280 x 800-resolution touchscreen, minimalist design and solid sound quality.

The'intelligence' of AI is growing all the time. And AI has many forms, from Spotify's recommendation system to self-drive cars. AI utilises natural language processing (NLP) to deliver natural and human-like language. It mimics humans and generates human-like messages by analysing commands. That said, it is still challenging to create an AI tool that understands the nuances of natural human languages is hard.

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions), an implementation of a trusted execution environment, and TensorFlow framework, to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation, and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.

Today, we have more information readily available at our fingertips (or by simple voice command) than any other time in history. Whenever you pose a question to Amazon's Alexa or the Google Assistant, you're effectively asking an artificial intelligence (AI) search algorithm to cull the Internet for a brief sentence or two that will answer your question. Increasingly, architects are using AI-leaning software tools in a similar way, calling on algorithms to cull the world of architectural possibilities quickly and efficiently for design approaches that help to meet the growing demand for sustainable architecture and green technology. "In architecture, AI is generally synonymous with generative design -- or, as I like to call it, 'optioneering,'" explains Dan Stine, director of design technology at Lake Flato Architects, San Antonio, Texas. "Our software tools use algorithms that generate a large number of design options based on parameters we define, then rank those options according to how well they meet our criteria. Ultimately, we select the option that works best for a given project."

It's not just that one uncle who's not allowed at Thanksgiving anymore who's been spreading misinformation online. The practice began long before the rise of social media -- governments around the world have been doing it for centuries. But it wasn't until the modern era, one fueled by algorithmic recommendation engines built to infinitely increase engagement, that nation-states have managed to weaponize disinformation to such a high degree. In his new book Tyrants on Twitter: Protecting Democracies from Information Warfare, David Sloss, Professor of Law at Santa Clara University, explores how social media sites like Facebook, Instagram, and TikTok have become platforms for political operations that have very real, and very dire, consequences for democracy while arguing for governments to unite in creating a global framework to regulate and protect these networks from information warfare. Excerpted from Tyrants on Twitter: Protecting Democracies from Information Warfare, by David L. Sloss, published by Stanford University Press, 2022 by the Board of Trustees of the Leland Stanford Junior University.

In an online wild west populated by scammers and hackers, dating apps pose challenges beyond just finding a partner. It's getting harder to tell if your date is who they say they are, and that's before you consider the data security and privacy implications of using the apps on your smartphone. It's difficult to maintain privacy when apps such as Hinge, Tinder and Bumble need to collect data to match you with potential dates. Then there's the data you share with other users – including your sexual orientation, age and social media information – that could put you at risk if it gets into the wrong hands. Here's what you need to know about using dating apps safely and privately, while still getting the most out of them.

Online dating company Match Group wants the court proceedings in an investigation being carried out by the U.S. Federal Trade Commission (FTC) for allegedly sharing users' photos with a facial recognition company to proceed in secret. The news comes from a Reuters investigation, after it spotted an FTC petition filed on 26 May forcing Match to provide documents related to an alleged 2014 data-sharing deal between Match subsidiary OkCupid and biometric solutions provider Clarifai. The background is that in 2019 a New York Times article claimed that Clarifai built its database of faces for biometric algorithm training using OkCupid user photos provided by an OkCupid founder and Clarifai investor. At the time, both OkCupid and Match denied any commercial agreement with Clarifai, so in 2020 the FTC followed up by demanding documentation around that alleged deal. A lawsuit filed under Illinois' BIPA was dismissed for lack of jurisdiction, and the companies hid behind attorney-client and work-product privilege to avoid providing the requested 136 documents, which in turn led to May's petition by the FTC.

Amazon is one of the largest e-commerce industries in the market. It has to ensure that existing customers continue to buy products from them. If you are an Amazon customer, you've probably noticed the different automated tools Amazon uses to entice you to increase your order size, from product recommendations at checkout to "frequently bought together" widgets on the product listing. These are great tools for Amazon to cross-sell. Cross-selling is when you promote a different product or service to a customer to increase their order value. Unlike upsell, cross-sell allows the merchant to sell more items.The tactic is usually used to show shoppers that they must own the recommended product since it complements the product that they intended to buy.

This past week the headlines have been blaring, "are we entering a recession?". We'll leave it to the talking heads to decide whether we are already in a recession or if one is simply imminent. However, what we can do is offer you some clear cut ways organizations can leverage business process automation to more effectively prepare for and navigate these uncertain times. All too often and especially in times of crisis, organizations implement digital technology for one sole purpose: to cut costs. However, adopting such a miopic view of technology can hinder innovation and long-term growth.