Imagine you're hiking through the woods near a border. Suddenly, you hear a mechanical buzzing, like a gigantic bee. Two quadcopters have spotted you and swoop in for a closer look. They send the signals to a central server, which triangulates your exact location and feeds it back to the drones. Cameras and other sensors on the machines recognize you as human and try to ascertain your intentions.

Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? This, in turn, mirrors the results of the 2017 poll, which found that the top 10 methods remained unchanged from the 2016 poll (although, again, they were in a different order). The average respondent used 7.4 methods/algorithms, which is in-line with both the 2017 and 2016 results. Below is a comparison of the top methods and algorithms in this year's poll with their 2017 shares. The most notable increases this year were found in the usage of various neural network technologies, including GANs, RNNs, CNNs, reinforcement learning, and vanilla deep neural networks.

Artificial Intelligence no doubt is the cutting-edge innovation everybody is anticipating. China who wants to be the world head in Artificial Intelligence has included AI in the school educational programs of secondary school students. Currently, you can envision the significance of AI in the coming future. AI and Machine Learning (ML) joined with consistently expanding measures of data are changing our business and social landscapes. Artificial intelligence has put his legs on different verticals of the business including automobile, healthcare, finance, assembling and retail to give some examples.

Meet Team Caeli–the runners-up of Imagine Cup 2019 World Championship! Nine out of 10 people in the world breathe polluted air, causing as many as seven million deaths every year, the World Health Organization estimates. The World Bank pegs these premature deaths cost the global economy USD225 billion in lost labor income in 2013. Statistics like these and their own experience living in the vicinity of New Delhi, which has one of the world's worst air quality, led five students from Manav Rachna Institute of Research & Studies in Faridabad to embark on a mission to use technology to tackle the problem. The team, comprising Aakash Bhadana, Ishlok Vashishta, Vasu Kaushik, Dipesh Narwat, and Bharat Sundal, came up with Caeli, a smart anti-pollution face mask and portable nebulizer to help those with breathing ailments like asthma and other chronic respiratory diseases.

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If there's one thing we've learned in recent years, it's that humans aren't great at predicting the consequences of technology. After all, social media platforms, which began as a way for friends to connect online, are today being used to radicalize terrorists and potentially swing presidential elections. Imagine, then, the chaos that could ensue with new technologies that don't even pretend to be friendly. The advent of lethal autonomous weapons--"killer robots" to detractors--has many analysts alarmed. Equipped with artificial intelligence, some of these weapons could, without proximate human control, select and eliminate targets with a speed and efficiency soldiers can't possibly match.

What shoes go with that dress? Artificial intelligence is now answering those questions automatically for online shoppers in China, thanks to an algorithm developed by web giant Alibaba. The system recommends entire personalised outfits to users as they browse, mixing ensembles from recently viewed items and other items judged to coordinate well with them. A live trial of the tool has already recommended outfits to more than 5 million users.

The computational effort for the evaluation of numerical simulations based on e.g. the finite-element method is high. Metamodels can be utilized to create a low-cost alternative. However the number of required samples for the creation of a sufficient metamodel should be kept low, which can be achieved by using adaptive sampling techniques. In this Master thesis adaptive sampling techniques are investigated for their use in creating metamodels with the Kriging technique, which interpolates values by a Gaussian process governed by prior covariances. The Kriging framework with extension to multifidelity problems is presented and utilized to compare adaptive sampling techniques found in the literature for benchmark problems as well as applications for contact mechanics. This thesis offers the first comprehensive comparison of a large spectrum of adaptive techniques for the Kriging framework. Furthermore a multitude of adaptive techniques is introduced to multifidelity Kriging as well as well as to a Kriging model with reduced hyperparameter dimension called partial least squares Kriging. In addition, an innovative adaptive scheme for binary classification is presented and tested for identifying chaotic motion of a Duffing's type oscillator.

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

There was a time when if anyone needed any news, he or she would wait for the next-day newspaper. However, with the growth of online newspapers who update news almost instantly, people have found a better and faster way to be informed of the matter of his/her interest. Nowadays social-networking systems, online news portals, and other online media have become the main sources of news through which interesting and breaking news are shared at a rapid pace. However, many news portals serve special interest by feeding with distorted, partially correct, and sometimes imaginary news that is likely to attract the attention of a target group of people. Fake news has become a major concern for being destructive sometimes spreading confusion and deliberate disinformation among the people.