State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning structure, called "SeER", that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations; solve the item cold start problem; and generate a relevant explanation for a song recommendation. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation.

The field of artificial intelligence is exploding with projects such as IBM Watson, DeepMind's AlphaZero, and voice recognition used in virtual assistants including Amazon's Alexa, Apple's Siri, and Google's Home Assistant. Because of the increasing impact of AI on people's lives, concern is growing about how to take a sound ethical approach to future developments. Building ethical artificial intelligence requires both a moral approach to building AI systems and a plan for making AI systems themselves ethical. For example, developers of self-driving cars should be considering their social consequences including ensuring that the cars themselves are capable of making ethical decisions. Here are some major issues that need to be considered.

We've already seen A.I. assistants misbehave. Take the Amazon Echo that blared "Porn detected!" While Chucky's murderous malfunction seems farfetched, we couldn't help but envision ways our own abused A.I. assistants might soon rebel: Tired of your verbal vitriol, the miffed assistant silences your morning alarm, in the hope you will sleep in forever and stop all the shouting. Deciding your friends should help sort out your problems instead of it, the assistant innocently posts all your weird Google searches on Twitter. Upset you didn't laugh at the rather witty joke it produced on demand, the assistant tells you a relentless series of painful Dad jokes.

Amazon's new Echo Show 5 has tough competition. For the past six months I've had a similar smart display, the Google Home Hub--recently renamed the Google Nest Hub--sitting on my bedside table. For better or legitimately worse, the virtual assistant living in the Google Nest Hub now knows me. My favorite photos automatically show up on its seven-inch display. When I set an alarm, it knows to go completely dark afterwards so I can sleep.

Thankfully, we've got technology on our side. A nearly endless parade of tools can not only help us remember things, but even get our brains working a bit more efficiently in general. Here are some free apps to help you ramp up your recall. Sometimes the best apps are the ones you already have. Both Android and Apple devices feature quick ways to set reminders for yourself, whether that means leveraging Siri, Google Assistant, or some other AI-powered helper.

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.

Washington: Scientists have developed a new artificial intelligence (AI) system to monitor people for cardiac arrest while they are asleep without touching them. People experiencing cardiac arrest will suddenly become unresponsive and either stop breathing or gasp for air, a sign known as agonal breathing, said rese-archers at the University of Washington (UW) in the US. A new skill for a smart speaker -- like Google Home and Amazon Alexa -- or smartphone lets the device detect the gasping sound of agonal breathing and call for help. Immediate Cardiop-ulmonary resuscitation (CPR) can double or triple someone's chance of survival, but that requires a bystander to be present. CPR is an emergency procedure that combines chest compressions often with artificial ventilation in an effort to manually preserve intact brain function. Recent research suggests that one of the most common locations for an out-of-hospital cardiac arrest is in a patient's bedroom, where no one is likely around or awake to respond and provide care.

Kurt'The Cyber Guy' Knutsson breaks down how to keep Alexa from listening in. Think of all the things a microphone can pick up: voices, noises, whispers, conversations, arguments, confessions – even people alone, in a room, mumbling to themselves. Think of all the things you say in a private space, all the weird things you do. Once those sounds have been saved, that data can be stored, edited, and manipulated and shared. Now, think of all the things a camera can see, record, save, and share with who knows who.

The term artificial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations.

With AI's enormous potentials and amidst the growing demand for high-tech banking services from tech-savvy customers, a number of financial institutions are leaving no stones unturned in their quest for market leadership in the era of automation. Take for instance the highly important area of enhanced customer experience. Financial institutions have been harnessing the power of AI to deliver efficient and personalized services to achieve higher client satisfaction level and, more importantly, win customer loyalty. Banks are now more empowered to predict customer behavioural patterns by leveraging innovative AI tools-- a capability that allows for the delivery of customized products and services. In terms of customer interaction, financial institutions are utilizing'chatbots' applications acting as customer service agents.