For a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. The algorithms in machine learning and data mining advance to a higher level of accuracy and flexibility and a data scientist should be prepared to implement the best algorithms and methods. The investigation of most common topics in machine learning and data mining provides an insight about the most relevant areas of research. To achieve this goal, I used the database of ScienceDirect.com. ScienceDirect has access to about 2,500 academic journals, more than 26,000 e-books and more than 13 million articles.

I had the pleasure of participating in Milan's FI-WARE VIP Bootcamp (4–6th May, 2016), under invitation by EBAN. There, along with several other investors and entrepreneurs, we assisted 15 startups from all across Europe to improve the structure and the presentation of their VC pitches. One of the people who actively participated, giving their full support to the event (and as a volunteer, nonetheless) was Federico Travella, founder and CEO of NoviCap, an extremely interesting venture in the "fintech" sector that came up with an innovative way of providing working capital for every kind of business. In discussion with Federico and other participants, the topic of AI was brought up, seen as a contributing factor to increasing unemployment, and through that, to social discontent and political instability. It's surely an interesting subject and is connected to the broader issue of technological progress and its social impact.

Screening for language disorders is best done early and often, but it's not always easy to get the equipment and staff to every kid in a timely fashion. At least a basic level of screening, however, may soon be able to be automated or done at home, if research out of MIT proves reliable. Computer scientists from the school discussed a new technique at the Interspeech conference in San Francisco; it's still very early in development, but it's more than a little promising. The system created by grad student Jen Gong and professor John Guttag uses recordings of many such performances as data for a machine learning system. By closely analyzing this dataset, it learns what patterns are associated with typical development and which suggest a nascent speech or language disorder -- patterns corroborated by previous research, it bears mentioning. It's not a replacement for a trained professional, but then again, a trained professional can't be packed into an app.

In this year of Rudolf Kalman's demise, this article is dedicated to his memory. We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same method can be used for classification and regression (including forecasting), offline and real-time applications and simple and hard ML problems. We have achieved our objective in the form of State-space Recurrent Kernel-projection Time-varying Kalman or "RKT-Kalman" method.

In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem, probabilistic programming, and RStan. The field of machine learning has seen the development of thousands of learning algorithms. Typically, scientists choose from these algorithms to solve specific problems. Their choices often being limited by their familiarity with these algorithms. In this classical/traditional framework of machine learning, scientists are constrained to making some assumptions so as to use an existing algorithm.

Approximately 8 of the 319 million people in the United States read the Wall Street Journal, about 2 percent of the population. If you look at the language -- standardized English -- being fed into many natural language processing units, it's based on the language of that 2 percent. And many machines literally use the venerable, business-focused newspaper to better understand the English language. It might seem like an obvious choice. Standardized English is taught in schools, it's used in legal documents, and it sets the basis for formal society.

The word on the streets of India's start-up world is that any mention of artificial intelligence (AI) makes venture capitalists drool. From Sequoia India to Tata Sons Ltd's chairman emeritus Ratan Tata, reputed investors are adding companies working on AI to their portfolio. This is only to be expected, given that the entire tech world can't stop talking about how AI, machine learning and neural networks will completely change the world over the next few decades. But it's also a daunting task for Indian companies, given that all the big global players are pouring massive resources into the space. There are several Indian companies that have taken AI out of the lab and found real-world applications for it.

We tend to think of bots as clever auto-responders we can query from inside social apps such as Facebook Messenger, but automation is no mere trinket: there's good reason to think it could limit or eliminate at least one important job. Dice data shows that, across the United States, 'Technical Support' remains one of the most in-demand jobs. Often trading with'Project Manager' for the top spot on a state-by-state basis, the lowest it ranks in any of the 50 states is fourth, in Hawaii. Technical Support isn't limited to one task or performant job description, but much of its work involves solving simpler problems that can derail a customer's workflow. Sometimes, technical support specialists are tasked with solving a company's internal issues, as well.

The Beatles may have disbanded decades ago, but thanks to artificial intelligence, the group is being reanimated -- sort of. Researchers at Sony are at work on an algorithm capable of generating new songs based on iconic musical styles, and their first crack at putting it in action starts with the iconic rock band. The song is called "Daddy's Car," which bears an eerie resemblance to the many songs that John, Paul, George, and Ringo used to play. It was made using a system called FlowMachines developed by the team at Sony CSL Research Laboratory, and was trained on a huge database of 13,000 songs. Once it understands a desired style, it can, with the help of a producer/arranger, churn out new songs in a variety of musical styles.

Google's image captioning software is part of its wider TensorFlow machine learning kit. Today, it announced a new release of the algorithm that comes with substantially improved performance. It is able to make more accurate descriptions that include more detail, enabling it to caption images with a standard as high as humans. In a blog post, Google provided some examples of images captioned by the new algorithm. They include "A person on a beach flying a kite" and "A man riding a wave on top of a surfboard."