Researchers from MIT's Computer Science and Artificial Intelligence Laboratory, the Harvard-Smithsonian Center for Astrophysics, and the MIT Haystack Observatory have developed a new algorithm that could help astronomers produce the first image of a black hole. The algorithm would stitch together data collected from radio telescopes scattered around the globe, under the auspices of an international collaboration called the Event Horizon Telescope. The project seeks, essentially, to turn the entire planet into a large radio telescope dish. "Radio wavelengths come with a lot of advantages," says Katie Bouman, an MIT graduate student in electrical engineering and computer science, who led the development of the new algorithm. "Just like how radio frequencies will go through walls, they pierce through galactic dust. We would never be able to see into the center of our galaxy in visible wavelengths because there's too much stuff in between."

I'm hanging out with my 4-year-old daughter in the early evening, trying to keep her entertained and pull dinner together, when my phone buzzes. Normally I'd feel guilty for checking it immediately, and distracted even if I didn't. It's a timely suggestion from an app called Muse. Here's what it says: "Try playing'Simon Says' with L,. using directional words like: behind, around, between. 'Simon Says stand between the chairs.')" I can even call out the commands while chopping vegetables.

Nishogakusha University is getting a new professor, an android version of literary giant Natsume Soseki that will teach classes in commemoration of the opening of the 140-year-old institution next year. This year also marks the centennial of the death of the novelist who studied Chinese literature at the private university in Tokyo in 1881. In cooperation with Hiroshi Ishiguro, a robotics researcher at Osaka University who is famous for creating an android of himself, the university plans to have the Soseki robot recite the author's own works, as well as some Chinese poems, from next April. "It's often said that high school students today don't read books," said Kaori Echigoya, a spokeswoman for the university, which also runs junior and senior high schools. "We value Japanese language education. By recreating Soseki through the help of professor Ishiguro, we would like to nurture interest in reading and literature among students."

The position is within Informatics, with a specialization in data science. At the University of Skövde Informatics is defined as the science that addresses how information is represented, processed and communicated in artificial and natural systems, and how such systems are used and developed in order to achieve usable and effective applications and solutions for individuals, organizations or society. Data science can overall be defined as the collection of theories, methods and techniques that all strive to convert large volumes of complex and heterogeneous data into knowledge that supports various decision-makers. Data science, thus, overlaps with traditional scientific disciplines, such as applied mathematics, information science, computer technology, statistics and computer science, along with a rapidly increasing number of application areas, e.g., business intelligence, biomedicine, textual analysis, geo-temporal analysis and medical and healthcare informatics. As one of the oldest and most prominent research groups in artificial intelligence (AI) in Sweden, the Skövde Artificial Intelligence Lab (SAIL) at the University of Skövde consists of more than 15 researchers conducting research within applied AI in close collaboration with businesses and organizations.

Robots are becoming more commonplace in human environments, such as schools, homes, hospitals, and work settings, and are expected to accomplish a wide variety of tasks. Given the near infinite number of tasks robots might be expected to perform in these varied settings, it is not feasible for robot designers to completely pre-program machines before they are deployed. Many researchers have suggested this problem can be addressed via end-user robot programming, where users can modify and create new behaviors for their robot to best suit their needs and preferences [2], [1]. Learning from demonstration (LfD) is one such method that enables people to readily develop custom robot behavior [2]. In LfD, a learner automatically creates a mapping between states and actions by watching a teacher perform the task; the learner can then replicate the teacher's actions. The main benefit of LfD is that it is an intuitive way for people to teach robots and does not require the teacher to have highly specialized knowledge, such as the ability to directly program the robot [3]. There has been significant research in how to design and implement LfD systems, including how people want to teach robots. Work by Thomaz et al. [23] showed that LfD systems could be improved for both the teacher and learner if greater communicative channels could be employed during the learning process. We build upon this work, and specifically are interested in ways to enable human teachers to have more efficient and naturalistic interactions, by way of a common human-human interaction (HHI) phenomena: grounding sequences.

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.

We stand for science and technology, high tech, human touch, education and research that matter. The University of Twente is the only campus university in the Netherlands; divided over five faculties we provide more than fifty educational programs. The University of Twente has a strong focus on personal development and talented researchers are given scope for carrying out pioneering research. The Faculty of Behavioural, Management and Social Sciences strives to hold a leading position in their fields in relation to the science and technology research programs of the University of Twente. In all these fields, the faculty provides bachelor, master and professional development programs.

In the cosmic scale of things, black holes are a dime a dozen. Despite this, and despite what Christopher Nolan's sci-fi blockbuster "Interstellar" would have you believe, we humans have never actually seen one with our eyes. A team of researchers from the Massachusetts Institute of Technology's artificial intelligence laboratory and the Harvard University revealed Monday that they had developed an algorithm that may allow us to actually "see" black holes. "We would never be able to see into the center of our galaxy in visible wavelengths because there's too much stuff in between," Katie Bouman, an MIT graduate student in electrical engineering and computer science who led the development of the new algorithm, said in a statement. "A black hole is very, very far away and very compact. To image something this small means that we would need a telescope with a 10,000-kilometer diameter, which is not practical, because the diameter of the Earth is not even 13,000 kilometers."

The ALMA telescope array is one of many groups involved with the Event Horizon Telescope project which hopes to image black holes. Humans have never actually seen a black hole with our eyes. A new computer algorithm, created by MIT graduate student Katie Bouman and her team, will hopefully change that. Since black holes are surprisingly small, we need a gigantic telescope to look at them. The supermassive black hole at the center of our Milky Way galaxy is only about 17 times the diameter of the sun, according to an article published in BBC News, but it's 25,000 light years away... that's far.

The most common question asked by prospective data scientists is – "What is the best programming language for Machine Learning?" The answer to this question always results in a debate whether to choose R, Python or MATLAB for Machine Learning. Nobody can, in reality, answer the question as to whether Python or R is best language for Machine Learning. However, the programming language one should choose for machine learning directly depends on the requirements of a given data problem, the likes and preferences of the data scientist and the context of machine learning activities they want to perform. According to a survey on Kaggler's Favourite Tools, the open source R programming language turned out to be the favourite among 543 Kagglers of the 1714 Kaggler's listing their data science tools.