Slowly but surely, AI is quietly impacting the world through numerous and varied applications. AI technology is already powering many everyday activities, from driving us to work to automatically adjusting the thermostat, and often without our knowledge. According to Gartner, 40 percent of major businesses will implement AI solutions in 2020, and more than half will double existing implementations in 2020. This forecast was made before the Covid-19 pandemic hit, but even with this taken into account the rise of AI will be exponential. In some industries AI, machine learning (ML) and deep neural networking (DNN) have a greater number of applications.

At this point, digital privacy is long gone. There's always another device, feature or service tracking what we say, what we look at online and the places we go. Some devices are more intrusive than others, and you may be feeding digital assistants more information than you realize. Tap or click here to stop all the smart tech in your home from listening. Social media is another big offender. Tap or click for my answers to your most-asked social media privacy questions.

A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. However, current techniques for explanation generation tend to strongly uphold the recommended products instead of presenting both reasons for and reasons against them. We argue that an RS can better enhance overall trust and transparency by frankly displaying both kinds of reasons to users.We have developed such an RS by exploiting knowledge graphs and by applying Snedegar's theory of practical reasoning. We show that our implemented RS has excellent performance and we report on an experiment with human subjects that shows the value of presenting both reasons for and against, with significant improvements in trust, engagement, and persuasion.

Abstract--In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviors. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering (NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested using Bayesian Optimization (BO) for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors. With today's ever-increasing ease of access to the internet more advertisements, attract new clients, and retain existing and information, we have reached a point of information clients [6].

As revolutionary technology enters the market every day, how do marketers stay current? Today, consumers are absorbing information at a previously impossible rate. In a world where everyone walks around with a cell phone in their pockets, instant gratification has become the standard people expect. Digital upgrades are important for a company to stay on top and can make the biggest difference in increasing a company's consumer base. Artificial Intelligence (AI) is one technology that companies are using to stay ahead of their customers.

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce. Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life. This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies – not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie. When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background.

Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' and industry experts' attention is the cold start problem for new items. In this work, we present a graph neural network recommender system using item hierarchy graphs and a bespoke architecture to handle the cold start case for items. The experimental study on multiple datasets and millions of users and interactions indicates that our method achieves better forecasting quality than the state-of-the-art with a comparable computational time.

My kids, ages 8 and 5, are showing me the future. When I want to watch a movie or turn out the lights, I instinctively reach for the remote or flick a switch. My children find it far more natural to just ask Siri for Peppa Pig, or tell Alexa to darken the room. Why not just talk to the machines around us like we talk to each other? Of course, right now talking -- rather than touching -- also has serious safety upsides. Voice tech adoption has accelerated as the coronavirus pandemic makes everyone touchy about how sanitary it is to poke buttons and screens.

AI and ML are some of the smart techs used in creating intelligent systems. Both are commonly used as synonyms of each other and there's a major correlation between both. However, each can be distinguished from the other as described below. Artificial Intelligence can be described as intelligence which attempts to mimic human intelligence. As the name implies, "artificial"-Human-made, "Intelligence"- thinking power.

A popular model of preference in the context of recommendation systems is the so-called \emph{ideal point} model. In this model, a user is represented as a vector $\mathbf{u}$ together with a collection of items $\mathbf{x_1}, \ldots, \mathbf{x_N}$ in a common low-dimensional space. The vector $\mathbf{u}$ represents the user's "ideal point," or the ideal combination of features that represents a hypothesized most preferred item. The underlying assumption in this model is that a smaller distance between $\mathbf{u}$ and an item $\mathbf{x_j}$ indicates a stronger preference for $\mathbf{x_j}$. In the vast majority of the existing work on learning ideal point models, the underlying distance has been assumed to be Euclidean. However, this eliminates any possibility of interactions between features and a user's underlying preferences. In this paper, we consider the problem of learning an ideal point representation of a user's preferences when the distance metric is an unknown Mahalanobis metric. Specifically, we present a novel approach to estimate the user's ideal point $\mathbf{u}$ and the Mahalanobis metric from paired comparisons of the form "item $\mathbf{x_i}$ is preferred to item $\mathbf{x_j}$." This can be viewed as a special case of a more general metric learning problem where the location of some points are unknown a priori. We conduct extensive experiments on synthetic and real-world datasets to exhibit the effectiveness of our algorithm.