He can be contacted at m.bibhas@gmail.com) "Artificial intelligence is kind of the second coming of software". Instead of serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Prior to exploring the many ways how Artificial Intelligence (AI, hereafter) can be defined or recognise potential opportunities and challenges in machine or deep learning, common debates seem to first point out some of the ethical concerns that AI brings in the contemporary society. Policy makers and scientists thinks that AI: a) with increased automation technology would give rise to job losses, B) embodying the sophistication and complexity of AI would call for redeployment or retrain employees to keep them in jobs, C) will trigger the effect of continual machine interaction on human behaviour and attention; D) ignites the need to address algorithmic bias originating from human bias in the data; E) will develop the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently.

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.

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.

Decision-support systems are information systems that offer support to people's decisions in various applications such as judiciary, real-estate and banking sectors. Lately, these support systems have been found to be discriminatory in the context of many practical deployments. In an attempt to evaluate and mitigate these biases, algorithmic fairness literature has been nurtured using notions of comparative justice, which relies primarily on comparing two/more individuals or groups within the society that is supported by such systems. However, such a fairness notion is not very useful in the identification of fair auditors who are hired to evaluate latent biases within decision-support systems. As a solution, we introduce a paradigm shift in algorithmic fairness via proposing a new fairness notion based on the principle of non-comparative justice. Assuming that the auditor makes fairness evaluations based on some (potentially unknown) desired properties of the decision-support system, the proposed fairness notion compares the system's outcome with that of the auditor's desired outcome. We show that the proposed fairness notion also provides guarantees in terms of comparative fairness notions by proving that any system can be deemed fair from the perspective of comparative fairness (e.g. individual fairness and statistical parity) if it is non-comparatively fair with respect to an auditor who has been deemed fair with respect to the same fairness notions. We also show that the converse holds true in the context of individual fairness. A brief discussion is also presented regarding how our fairness notion can be used to identify fair and reliable auditors, and how we can use them to quantify biases in decision-support systems.

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.