Twenty-three years after his death, former Formula 1 world champion Ayrton Senna's name is almost as valuable as when he was alive - and it is making a difference in his home country of Brazil. It is Friday afternoon and children around the age of 12 are gathered in the computer lab of a public school in Itatiba, a small town an hour away from Sao Paulo. Class time is already over for the week, but these students have chosen to stay in school for extracurricular activities. They are learning Scratch, a piece of software developed by MIT experts that aims to teach kids how to code. Most public schools in Brazil don't have computer coding in their curriculum.

There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics. In astronomy, for example, one can dramatically improve estimates of the parameters that dictate the evolution of the Universe by working with full conditional densities instead of regression (i.e., conditional mean) estimates. More generally, standard regression falls short in any prediction problem where the distribution of the response is more complex with multi-modality, asymmetry or heteroscedastic noise. Nevertheless, much of the work on high-dimensional inference concerns regression and classification only, whereas research on density estimation has lagged behind. Here we propose FlexCode, a fully nonparametric approach to conditional density estimation that reformulates CDE as a non-parametric orthogonal series problem where the expansion coefficients are estimated by regression. By taking such an approach, one can efficiently estimate conditional densities and not just expectations in high dimensions by drawing upon the success in high-dimensional regression. Depending on the choice of regression procedure, our method can adapt to a variety of challenging high-dimensional settings with different structures in the data (e.g., a large number of irrelevant components and nonlinear manifold structure) as well as different data types (e.g., functional data, mixed data types and sample sets). We study the theoretical and empirical performance of our proposed method, and we compare our approach with traditional conditional density estimators on simulated as well as real-world data, such as photometric galaxy data, Twitter data, and line-of-sight velocities in a galaxy cluster.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

ContentWise, the personalization, discovery, analytics and metadata expert, today announced that Cablevisión Argentina (CVA) has successfully deployed the ContentWise personalization system in Cablevisión Flow, its new suite of multiscreen television services as part of its drive to provide new, next-generation services to its customers. ContentWise has been selected as part of a new, best-of-breed video platform, which includes the Minerva 10 multiscreen TV platform by Minerva Networks. The Contentwise Personalization system anticipates user s actions and facilitates discovery by sorting content based on user s taste as well as showing trending titles popular with other subscribers with similar viewing habits. ContentWise next-generation solution enables CVA to utilize: Personalized content discovery with context-aware algorithmic and social content recommendations; Automatic micro-genres, dynamically adapted to CVA Spanish offering; Assisted content curation tools, including business rules providing total control to editorial and marketing teams. ContentWise is the TV personalization software that gives broadcast, Pay TV and OTT operators total control over the curation and automation of the Personalized TV experience, providing a UX engine API that controls each user interface element across screens and apps.

Almost three-quarters (72 percent) of Brazilians have never been inside a museum, according to a 2010 study from the Brazilian Institute of Economic Research. There are probably many reasons for this, but among them is the feeling that art can seem inaccessible unless you've studied it. So, how do you get art to speak to you specifically? By getting it to speak, period. For the launch of IBM Watson in Brazil, Ogilvy Brazil created an interactive guide that lets people have conversations with work housed at the Pinacoteca de São Paulo Museum.

Artificial intelligence (AI) will "significantly transform" the insurance industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to a new report from Accenture. The Technology Vision for Insurance 2017 report, called Technology for People, released on Wednesday by the global professional services company, found that while the technology will be empowering, insurers face challenges integrating AI into their existing technology. Insurers cite issues such as data quality, privacy and infrastructure compatibility. The report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries in North America, Europe, Asia-Pacific, Africa and South America, Accenture noted in a press release. The goal of the survey was to identify the key issues and priorities for technology adoption and investment.

Eighty-four percent of large companies around the world say they are using artificial intelligence, and 62% say AI is important to remaining competitive in the year 2020. Tata Consultancy Services polled 835 executives and IT managers in North America, Europe, Asia Pacific and South America at companies that averaged $20 billion in revenues. It found AI to be almost universally important, but the average investment in it was one-third of one percent of revenues, or $67 million. Only 7% said they spent $250 million or more in 2016. The average was $67 million; the median for the whole group, only $3 million.

Rio de Janeiro's police force, like the rest of the city's public services, is broke. In the headquarters of the bomb-disposal unit, supplies of everything from soap to explosives are running out as the city struggles to pay its debts amid Brazil's deep recession. But, poor as it is, Rio's bomb squad is one of the most technologically advanced in South America. In the cramped storeroom of its base in northern Rio, a state-of-the-art robot takes pride of place. "The robot is a fundamental piece of equipment--it's vital to our day-to-day work," says the bomb squad's boss, Marcelo Corrêa.

The task of determining item similarity is a crucial one in a recommender system. This constitutes the base upon which the recommender system will work to determine which items are more likely to be enjoyed by a user, resulting in more user engagement. In this paper we tackle the problem of determining song similarity based solely on song metadata (such as the performer, and song title) and on tags contributed by users. We evaluate our approach under a series of different machine learning algorithms. We conclude that tf-idf achieves better results than Word2Vec to model the dataset to feature vectors. We also conclude that k-NN models have better performance than SVMs and Linear Regression for this problem.

Former 500 Startups accelerator company Pluto AI is announcing $2.1 million in fundraising today from Fall Line Capital, Refactor Capital, Unshackled Ventures, Comet Labs and additional angels. Pluto is taking advantage of the sensorification of modern water treatment plants to extrapolate insights that can save operators precious time, money and water. The Pluto analytics platform presents managers with a dashboard that quantifies the status of all assets at a given water treatment plant. These ratings, ranging from 0 to 100, take into account temperature and pressure readings in addition to other data from pumps and chlorinators to identify cause and effect relationships. Machine learning is the backbone behind Pluto's ability to ingest large quantities of unstructured data, but the end user isn't forced to get into the weeds of individual models to gain a better understanding of how plant assets are working in consonance. Utilizing historical data, Pluto can directly recommend steps to improve the functioning of plant infrastructure.