It goes without saying that Artificial Intelligence (AI) and Machine Learning are having a considerable influence on the world around us. The extent of its impact is said to have moved us from a world that is mobile-first, to one that is AI first (according to Google's CEO Sundar Pichai). Ten to fifteen years ago, very few people were even familiar with the idea of Machine Learning or AI; today, however, the marketplace is a very different place. A recent global study from Pega found that 72 per cent of people now understand what AI is and that only 28 per cent are uncomfortable with the thought of it. It's no surprise so many industries, companies – and those in the media – are so focused on it.

Today I'm writing this post to explain how it's possible to make geographic analysis and answer questions like: which is the richest area in my city? How many people do live in one neighborhood? You can do it combining shape files with an excel spreadsheet, let's understand it together... Then, we're gonna need one shape file and one excel spreadsheet. I'm from Brazil, and we do have a lot of open data from States and Cities.

Spacewalking astronauts stepped out Friday to wrap up months of repair work on the International Space Station's big robot arm. NASA astronaut Mark Vande Hei and Japan's Norishige Kanai emerged from the orbiting complex as the sun rose over Peru's western coast, 250 miles below. The 58-foot robot arm had both of its aging mechanical hands replaced on spacewalks conducted in October and January. Friday's work involved bringing one of those old hands inside so it can be returned to Earth for a tuneup and then flown back up, and moving the other gripper to a long-term storage location outside. This last spacewalk in the series should have been completed by now, but was postponed because of complications with the robotic hand that was installed last month. Further delays were caused by this week's late arrival of a Russian supply ship.

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Meaningful artificial intelligence (AI) deployments are just beginning to take place, according to Gartner, Inc. Gartner's 2018 CIO Agenda Survey shows that four percent of CIOs have implemented AI, while a further 46 percent have developed plans to do so. "Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," said Whit Andrews, research vice president and distinguished analyst at Gartner. "However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts." As with most emerging or unfamiliar technologies, early adopters are facing many obstacles to the progress of AI in their organizations. Gartner analysts have identified the following four lessons that have emerged from these early AI projects.

A number of this week's milestones in the history of technology link the rise of IBM, the introduction of the ENIAC, and the renewed fascination with so-called artificial intelligence. "IBM" was first used for CTR's subsidiaries in Canada and South America, but after "several years of persuading a slow-moving board of directors," Thomas and Marva Belden note in The Lengthening Shadow, Thomas J. Watson Sr. succeeded in applying it to the entire company: "International to represent its big aspirations and Business Machines to evade the confines of the office appliance industry." As Kevin Maney observes in The Maverick and His Machine, IBM "was still an upstart little company" in 1924, when "revenues climbed to $11 million – not quite back to 1920 levels. The upstart, according to Watson, was going to live forever. From a talk he gave at the first meeting of IBM's Quarter Century Club (employees who have served the company for 25 years), on June 21, 1924: The opportunities of the future are bound to be greater than those of the past, and to the possibilities of this business there is no limit so long as it holds the loyal cooperation of men and women like yourselves.

Predicting unobserved entries of a partially observed matrix has found wide applicability in several areas, such as recommender systems, computational biology, and computer vision. Many scalable methods with rigorous theoretical guarantees have been developed for algorithms where the matrix is factored into low-rank components, and embeddings are learned for the row and column entities. While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities. Such implicit information is especially useful in cases where the data is very sparse, as is often the case in real-world datasets. In this paper, we design a method to learn embeddings in the context of recommendation systems, using the observation that higher powers of a graph transition probability matrix encode the probability that a random walker will hit that node in a given number of steps. We develop a coordinate descent algorithm to solve the resulting optimization, that makes explicit computation of the higher order powers of the matrix redundant, preserving sparsity and making computations efficient. Experiments on several datasets show that our method, that can use higher order information, outperforms methods that only use explicitly available side information, those that use only second-order implicit information and in some cases, methods based on deep neural networks as well.

Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species. The Sehuencas water frog, who is the last known individual of his species, has not had a partner for more than 10 years. Conservation groups have teamed up with Match.com to give Romeo a platform and raise awareness for his story as well as funds for an expedition to find him a mate. If all else fails, one of the researchers on the project wont rule out cloning as a means of preserving this amphibian species which, like many others, is threatened by climate change, habitat loss and other environmental and ecological issues. Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species.

Networks observed in the real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real-world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches. To the best of our knowledge, this is the first work that integrates statistical modeling of dynamic networks with deep learning for community detection and link prediction.

In this post I'll share how I've been studying Deep Learning and using it to solve data science problems. It's an informal post but with interesting content (I hope). I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist.