AI offers exceptional opportunities particularly in digital marketing while irrefutably revolutionizing and propelling the industry. AI is the ability of a computer or computer-enabled robotic systems to process massive amounts of in-depth data and produce outcomes similar to the thought processes of humans in learning, analysing, decision making, and problem-solving. Hence, AI has enabled marketers to comprehend vast data to gain valuable consumer insights, and in turn, improve digital marketing strategies. The applications of AI are essentially limitless, and the field of computer science is on a stark ascendance. The global AI market was worth $7.35 billion in 2018, where the largest portion of revenue was stirred from enterprise applications.

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.

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When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries. However, to build such a travel agent is non-trivial. First of all, travel naturally involves several sub-tasks such as hotel reservation, restaurant recommendation and taxi booking etc, which invokes the need for global topic control. Secondly, the agent should consider various constraints like price or distance given by the user to recommend an appropriate venue. In this paper, we present a Deep Conversational Recommender (DCR) and apply to travel. It augments the sequence-to-sequence (seq2seq) models with a neural latent topic component to better guide response generation and make the training easier. To consider the various constraints for venue recommendation, we leverage a graph convolutional network (GCN) based approach to capture the relationships between different venues and the match between venue and dialog context. For response generation, we combine the topic-based component with the idea of pointer networks, which allows us to effectively incorporate recommendation results. We perform extensive evaluation on a multi-turn task-oriented dialog dataset in travel domain and the results show that our method achieves superior performance as compared to a wide range of baselines.

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.