Hoaxes and trickery are almost as old as human history. When the Roman Republic first conquered the Italian peninsula between 500-200 BC, it was known to send fake refugees into enemy cities to "[subvert] the enemy from within." "Pope Joan" was believed to be a woman who allegedly tricked her way into become pope in the Middle Ages by pretending to be a man -- but the entire story is now viewed as fake, a fictional yarn spun centuries after her purported reign. "Vortigern and Rowena," a play that debuted in 1798, was initially touted as a lost work of William Shakespeare -- but was in fact a forgery created by William Henry Ireland.

Elon Musk says artificial intelligence poses more of a "risk" than a potential nuclear conflict between the US and North Korea. The CEO of Tesla issued the warning after an AI built by OpenAI, a company founded by Mr Musk, defeated the world's best Dota 2 players after just two weeks of training. "If you're not concerned about AI safety, you should be. Vastly more risk than North Korea," he tweeted shortly after the bot's victory, along with a picture of a poster bearing the slogan: "In the end, the machines will win". The poster, incidentally, is actually about gambling.

If you end up in a hospital in Europe, you have a one in 20 chance of acquiring an infection while there. One of the leading causes is a lack of hand hygiene. Even though hospitals are filled with alcohol-based gel dispensers and advisory posters, they're not working. The conclusion of a recent pilot study may just have found the solution. Using a combination of depth cameras and computer-vision algorithms, a research team has tracked people around two hospital wards and automatically identified when they used gel dispensers.

Take a look at the above image of a German street. At a glance it could be a blurry dashcam photo, or a snap that's gone through one of those apps that turns photos into paintings. But you won't find this street anywhere on Google Maps. That's because it was generated by an imaginative AI, stitching together its memories of real streets it was trained on. Nothing in the image actually exists, says Qifeng Chen at Stanford University, California, and Intel.

Elon Musk's SpaceX is poised to launch an unmanned cargo ship carrying a supercomputer to the International Space Station (ISS) today. The supercomputer is hoped to help direct astronauts on future deep-space missions. The goal is to test the computer for one year to see if it can operate in the harsh conditions of space - about the same amount of time as it would take for astronauts to arrive at Mars. The liftoff of the Falcon 9 rocket, carrying the Dragon cargo ship, is planned for 12:31pm ET (5:31pm BST) from Cape Canaveral, Florida. The goal is to test the Spaceborne Computer for one year to see if it can operate in the harsh conditions of space.

The biggest misconception about AI is that if we create intelligent systems, those intelligent systems will want to overthrow their human governors and to take over the world. You see this a lot in the movies - evil robots taking over the world. The question isn't whether robots would succeed in doing that if they wanted to. I think the more important question is whether they would want to in the first place. We have a tendency to anthropomorphize any kind of intelligence, because we live in a world in which humans are the only example of high-level intelligence.

Towards the end of the course, you will be working on a live project. We will emphasize on the concepts learned in the various modules through different case studies.

Bangalore: HSBC, the world's leading trade finance bank, is working with IBM to develop a cognitive intelligence solution combining optical character recognition with advanced robotics to make global trade safer and more efficient for thousands of businesses. HSBC's Global Trade and Receivables Finance (GTRF) team facilitates over $500 billion of documentary trade for customers every year, and in doing so must manually review and process up to 100 million pages of documents, ranging from invoices to packing lists and insurance certificates. The new solution uses IBM's advanced analytics technology, including intelligent segmentation and text analytics, to identify, digitise and extract key data within these documents before feeding it into the bank's transaction processing systems; boosting accuracy whilst freeing up staff for more value-adding activities. "The average trade transaction requires 65 data fields to be extracted from 15 different documents, with 40 pages to be reviewed," said Natalie Blyth, HSBC's Global Head of GTRF. "By digitising this process we will make transactions quicker and safer for both buyers and suppliers, leading our industry forwards, and we will reduce compliance risks through an enhanced ability to manage huge volumes of data."

Many scientific and engineering challenges -- ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations -- require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis test and the corresponding p-value for testing for the significance of the homogeneous structure in linear mixed models. A robust matching moment construction is used for creating a test that adapts to the size of the model sparsity. When unobserved heterogeneity at a cluster level is constant, we show that our test is both consistent and unbiased even when the dimension of the model is extremely high. Our theoretical results rely on a new family of adaptive sparse estimators of the fixed effects that do not require consistent estimation of the random effects. Moreover, our inference results do not require consistent model selection. We showcase that moment matching can be extended to nonlinear mixed effects models and to generalized linear mixed effects models. In numerical and real data experiments, we find that the developed method is extremely accurate, that it adapts to the size of the underlying model and is decidedly powerful in the presence of irrelevant covariates.

Intelligent Systems Group Univ. of the Basque Country (UPV/EHU) San Sebastian, Spain Missing data has a ubiquitous presence in real-life applications of machine learning techniques. Imputation methods are algorithms conceived for restoring missing values in the data, based on other entries in the database. The choice of the imputation method has an influence on the performance of the machine learning technique, e.g., it influences the accuracy of the classification algorithm applied to the data. Therefore, selecting and applying the right imputation method is important and usually requires a substantial amount of human intervention. In this paper we propose the use of genetic programming techniques to search for the right combination of imputation and classification algorithms. We build our work on the recently introduced Python-based TPOT library, and incorporate a heterogeneous set of imputation algorithms as part of the machine learning pipeline search. We show that genetic programming can automatically find increasingly better pipelines that include the most effective combinations of imputation methods, feature preprocessing, and classifiers for a variety of classification problems with missing data.