The importance and uses of artificial intelligence in business are top of mind for leading corporations today. Artificial Intelligence technology benefits big and small businesses as it helps them grow, ensures smarter decision-making, and transforms the field of management. But how exactly AI affects the business world? How AI can help your business growth? Let's start with the definition. Artificial Intelligence is the ability of computers to perform tasks that usually people perform.

--South Indian classical music ( Carnatic music) is best consumed through live concerts. A carnatic recital requires meticulous planning accounting for several parameters like the performers' repertoire, composition variety, musical versatility, thematic structure, the recital's arrangement, etc. to ensure that the audience have a comprehensive listening experience. In this work, we present ragamAI a novel machine learning framework that utilizes the tonic nuances and musical structures in the carnatic music to generate a concert recital that melodically captures the entire range in an octave. Utilizing the underlying idea of playlist and session-based recommender models, the proposed model studies the mathematical structure present in past concerts and recommends relevant items for the playlist/concert. Our experiments on a vast collection of concert show that our model performs 25%-50% better than baseline models. The analysis of classical music has become a mainstay in the field of music information retrieval (MIR) over the last decade. Recent research has investigated several nuances of classical music, be it in rhythm cycles, tempo estimation, beat tracking, instrument classification, and melodic analysis, using data-driven techniques.

Today, the words'Artificial Intelligence' and'Machine Learning' are such types of buzzwords that we listen in our daily basis. Needless to say, they are not only our present but also they are the future of our technology-driven world. In other words, we can say that these two are the most prominent factors which bring our science in a new level and make ourselves busy from real life to virtual life. Almost all innovative AI and ML companies are using machine learning algorithms to make our experience better and comfortable. Though most of the experts use them interchangeably, there is a slight distinction between artificial intelligence (AI) vs machine learning (ML). Artificial intelligence is a board concept which helps a machine to work without expert guidance.

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Industries are adopting artificial intelligence and machine learning on a grand scale. Tech companies both big and small are working on artificial intelligence projects that will shape the future of industries such as healthcare, banking, business, education and more. We're not quite at the point where everything is automated and machine-run, but we're getting there. These technologies are all around us, quietly running in the background and keeping operations chugging along. AI is silently reshaping our society by affecting how we get things done, how we vote, how we purchase goods, and the choices we make.

Artificial Intelligence (AI) is by far one of the most praised trends in Silicon Valley. Yet, in the "real world", opinions tend to be split into two camps: those who desire AI-driven personal assistants on the one hand, and those who fear that such AI solutions will steal their jobs in the future, on the other. Nevermind that Hollywood-like scenario though – the truth is that AI is already here, reading our emails, listening to our conversations, recognizing our faces, and even smelling our breath! AI is not a shiny addition. It is a must-have for any business innovations, and e-commerce seems to be in the avant-garde.

A mere four years ago AI was not even able to pass a Grade eight science test. Seven hundred computer scientists competed in a contest with a significant amount of money as prize. They had to build artificial intelligence that could pass a Grade eight science test. The computer scientists did their best, but not even the most advanced AI system could score better than 60percent in the test. It seems that the AI was just not advanced enough to fully reach the language and logic skills expected of students in the eighth grade.

The Electronic Frontier Foundation (EFF) has published an extensive study into the hidden techniques and methods used by online service providers to collect and track our personal information and activities. On Monday, as shoppers plundered e-commerce websites for Cyber Monday bargains, the civil and privacy rights outfit released "Behind the One-Way Mirror," outlining corporate surveillance methods with a focus on behind-the-scenes tracking. The paper covers a variety of different tracking methods including browser fingerprinting, invisible pixel images, social widgets, mobile tracking, and facial recognition employed by tech giants including Amazon, Facebook, Google, Twitter, as well as countless data brokers, to "collect information about who we are, what we like, where we go, and who our friends are." Third-party tracking is usually invisible to the naked eye. Code, images, and plugins can all contain functions that track browsing, activities, purchases, the duration of visits, ad engagement, and clicks, and may link up different data sources to create a comprehensive shadow profile of your digital self. According to the EFF, for example, Facebook uses invisible "conversion pixels" to collect data on third-party websites and to track ad engagement; Google uses location information to track user visits to physical stores and makes use of transparent pixel images for tracking, and smart home devices -- including Amazon Echo and Google Home -- can harvest audio data and may be used by human employees to improve voice recognition technologies.

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

Most of us want digital privacy, and most of us also want autocorrection that works, speech to text that is accurate, and smart systems that find all our selfies with Serena, or surface the most important emails we need right now. But are those two imperatives in direct opposition? According to some tech analysts and AI experts, they are. Especially those who are experiencing huge issues with iPhone's autocorrection capability in Apple's latest mobile operating system upgrade, iOS 13. "It's way worse on my iPhone," says veteran industry observer Robert Scoble, chief strategy officer at Infinite Retina. "And I've tried several things to fix it, including deleting all the settings and deleting all the history and trying to reboot everything ... I'm seeing a lot of bugs in the spellchecker where it's putting capitalization where it doesn't need to go, where it's switching words a lot more often than it used to. Apple's iOS 13's spellcheck was so bad Scoble ran a Twitter poll, asking his 400,000 followers whether they had similar issues. Twitter polls are hardly scientific, of course. But there's a broad range of people who are claiming that Apple's recent software release has been a big backward step in terms of autocorrection. "iOS 13 got significantly worse for me," says mobile entrepreneur Albert Renshaw, CEO at Apps4Life. "I've been learning French with Duolingo and have been typing in French every day for a little over a year in that app, but have never had an issue with it affecting my autocorrect.