After years of being forced to use Apple services on the iPhone by default, those restrictions are finally easing up a bit. As noticed by MacRumors earlier today, the iOS 14.5 beta appears to let you set third-party music services as default with Siri. This means you can ask Siri to play a particular song or album and it'll go straight to Spotify or YouTube Music. Currently, Siri only searches and plays things from whatever music you have in the Apple Music app, be it your own collection of songs or the Apple Music subscription catalog. It sounds like after iOS 14.5 is installed, Siri will ask you what music service you want to use when you ask it to play a song.

If you're looking for an affordable color smart bulb that works with both Amazon Alexa and Google Assistant, the Sylvania A19 Smart Full Color bulb is a fine candidate, although you can't buy just one. This standard A19 bulb connects directly to Wi-Fi networks, meaning there's no need for a bridge or smart-home hub. Sylvania's mobile app offers a variety of grouping options, lighting scenes, and powerful automation tools. That said, we still prefer the more polished apps that come bundled with our favorite A19 smart bulbs from LIFX and Philips Hue. The Sylvania bulb also lacks the HomeKit support that LIFX and Hue bulbs deliver, but that will matter only to Apple users who use that ecosystem.

Clustering is a fundamental unsupervised learning task used for various applications, such as anomaly detection (Leung and Leckie, 2005), recommender systems (Shepitsen et al., 2008) and cancer diagnosis (Zheng et al., 2014). In recent years, research on sequential clustering has been actively studied, motivated by applications in which data arrives sequentially, such as online recommender systems (Nasraoui et al., 2007) and online community detection (Aggarwal, 2003). In this work, we study k-median clustering in the sequential no-substitution setting, a term first introduced in Hess and Sabato (2020). In this setting, a stream of data points is sequentially observed, and some of these points are selected by the algorithm as cluster centers. However, a point can be selected as a center only immediately after it is observed, before observing the next point. In addition, a selected center cannot be substituted later. This setting is motivated by applications in which center selection is mapped to a real-world irreversible action, such as providing users with promotional gifts or recruiting participants to a clinical trial. The goal in the no-substitution k-median setting is to obtain a near-optimal k-median risk value, while selecting a number of centers that is as close as possible to k.

The development of Artificial Intelligence technology was thought to be rapid, of course. But in the last few years, there has been an exponential increase in the number of platforms, applications, and tools based on machine learning and artificial intelligence technologies. Scientists and developers continue to design and develop intelligent machines that can mimic reasoning, develop and learn knowledge, and attempt to mimic how humans think. Of course, it is really difficult to follow technologies that are developing so fast. For this reason, in order to keep up with these technological developments, we tried to gather Top 10 Artificial Intelligence Technology trends for you.

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance.

Across the board, Philips Hue LED lights topped all of our tests thanks to an easy setup, a robust suite of features, and an intuitive app. Additional support with smart assistants like Alexa, Google Assistant, and Siri adds tremendous benefit to an already impressive portfolio. Once you throw in the additional rainbow of possible light colors, these are without a doubt the best smart bulbs you can buy. The only downside is that unlike some other options, Philips Hue lights do require a hub (the Philips Hue Bridge) in order to work remotely. While they can be controlled over Bluetooth, that will only work when you're physically near the bulb.

The Nooie Cam 360 has a rotating, high-def camera that automatically tracks you as you move about the room. Here are the Nooie Cam 360's specs: The Nooie Cam 360 is a budget-friendly indoor home security camera that features motion tracking and, as the name implies, 360-degree rotation. The camera is equipped with a 1080p high-def lens and two 940nm infrared LEDs. It has other smart camera features like two-way audio functionality, night vision, and a status light indicator that can be toggled on or off. Nooie smart alerts are sent when the camera detects sound or motion.

Amazon Echo speakers and smart displays, like the Echo Show 5 (pictured), come with a sliding mic mute button that prevents Alexa from listening. Privacy might seem like a thing of the past, as everywhere you go, there's some computer watching or listening. But in your own home, you can do a few things to protect your privacy, even from naturally invasive Alexa-friendly smart home devices. Amazon reviews voice recordings, including the wake word and voice command that follows, in an effort to improve the overall user experience. However, you can opt-out of having your voice recordings included in the review process by going to Settings Alexa Privacy Manage Your Alexa Data, then toggling off the setting that says Use Voice Recordings to Improve Amazon Services.

Chapter 5 of this free 15 chapter AI handbook provides an overview of natural language processing.

While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.