The Tepper School of Business developed the curriculum for the online Master of Science in Business Analytics (MSBA) program from the ground up with this question in mind. In consultation with global business leaders, they determined that the greatest need is for professionals who not only have advanced analytical skills, such as machine learning and optimization, but also the appropriate business knowledge and communication skills to solve complex problems and bring value to industry. Our students develop proficiency in the full range of state-of-the-art business analytics techniques; they also learn how to tell stories through and extract insights from data. Given the Tepper School's view of a curriculum as an organic entity, our faculty continually work in concert to ensure that courses harmonize, even as they are individually updated and modified to ensure learning outcomes for students are always in step with an ever-evolving industry. The flexible online format enables students to continue working while earning their degree and apply what they learn in the classroom to their work environment.

For many years, the American public thought of artificial intelligence, or AI, as some big, incredibly complex computer program that would one day achieve sentience and turn on humanity like a James Cameron conceived summer blockbuster. While movie franchises starring angry AI creations turning on their creators continue to do well at the box office, artificial intelligence has made its way into the modern American household. From algorithms on Amazon that suggest what we might want to read next to Siri and Alexa answering our questions to cars that understand traffic patterns and very soon might be driving us to work, artificial intelligence is rapidly disrupting industries. While there has been a great fear for a number of years that robots will replace workers across industries, this has largely not been born out. While robots powered by AI can often replace workers at some of the most menial, automated tasks in a factory, workers are often needed in more specialized capacities to repair, maintain, and respond to alarms that these robotic workers generate.

University of Maryland and Capital One on Tuesday announced a partnership to develop a workforce pipeline in data analytics, machine learning and cybersecurity. Capital One is investing $3 million through an endowment gift to advance machine learning and the two plan to create an innovation lab where students will apply classroom lessons to real-world problems. "This partnership will not only help attract and retain top faculty and students, but will also propel UMD to national prominence and excellence in these critically important fields," said Mary Ann Rankin, senior vice president and provost at University of Maryland. Just over $2 million of Capital One's gift will be used to endow a faculty chair in the department of computer science. The remaining $900,000 will support research and educational initiatives in machine learning, data analytics and cyber security.

A security robot is shooing away homeless people from outside smart office buildings as it patrols the streets of San Francisco. The non-profit organisation that occupies the office block has been warned by officials they will be fined $1,000 (£750) a day if they continue to use the robot without a permit. The Society for the Prevention of Cruelty to Animals (SPCA) say the security robot - dubbed K9 - was hired to deal with the growing amounts of crime related to homeless people on the sidewalks. The crime-fighting robots rely on cameras, Lidar, thermal-imaging to navigate the streets. Laser scanning can detect changes in an environment, while odour detectors can also detect other changes in the area and monitor air pollution.

IBM researchers have developed a program that can predict the products of organic chemistry reactions.1 Modelled on the latest language translation systems – like Google's artificial neural network – the AI picked the right product 80% of the time despite not having been taught any organic chemistry rules. 'What this tool is trying to do is imitate a top pro chemist in more or less the entire domain of organic chemistry,' says Teodoro Laino, one of the researchers involved in the study at IBM in Zurich, Switzerland. His ambitious goal is shared by other chemists who have been attempting to create a functioning AI chemist since the 1970s, when organic chemist E J Corey kick-started the field by creating a chemical knowledge database. However, making a tool based on chemistry knowledge can be time-consuming; Bartosz Grzybowski's team took 10 years to encode their Chematica retrosynthesis program with 20,000 chemical rules. Moreover, a knowledge-based AI has difficulty tackling reactions that lie outside of its rule set. 'There's a way to learn organic chemistry that's not memorising chemical rules, by just trying to find out the underlying patterns in reactions and trying to rationalise them,' Laino says, explaining the approach that his team took.

Google has massively cut prices for its managed cloud machine learning service just two weeks after AWS released a competing offering at its re:Invent user conference. The company has introduced massive price reductions for its Cloud Machine Learning Engine managed services. For example, customers using basic-tier compute for training a machine learning system will pay 43 percent less than they did earlier this year. Google also offered customers more clarity on what they'll be paying for those jobs. Information of the price reductions was first included in a blog post that appeared briefly yesterday on Google's website, then vanished.

By the time IBM introduced its personal computer in 1981, the company's "Job Training Program" was 13 years old. Secretaries and other administrative professionals across the country--workers whose jobs were affected by IBM's new computer and software--could go to one of the company's 74 job centers and gain skills in areas including computer programming, data entry, and word processing. Jobs in industries such as food services, transportation, and retail trade are at high risk of being automated, forcing workers to gain new skills to compete for well-paying jobs. From Google's self-driving cars to Apple's communication technology to Amazon's retail model, automation is becoming more and more pervasive. As communities across the U.S. witness growing gaps between the skills that workers have and the ones that employers need, workers will need training.

Hoy traemos a este espacio al FOURTH GLOBAL MACHINE INTELLIGENCE SUMMIT, que tedrá lugar el 28 - 29 JUNE 2017 en Amsterdam Informar de un error de Maps Postillion Convention Centre Amsterdam Paul van Vlissingenstraat 8 The Postillion Convention Centre Amsterdam is very conveniently located between the city and the arterial roads and 20 minutes from Amsterdam Airport Schiphol. TOPICS WE COVER NATURAL LANGUAGE PROCESSING INDUSTRIAL AUTOMATION Where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data. The Machine Intelligence Summit: where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data in the real world.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Artificial intelligence is already reshaping the labor market. Its impact will likely become even more disruptive. But experts have historically been bad at predicting which jobs and tasks will be lost to automation, and public officials have historically been slow to respond to technological advances with smart, effective regulations. That's the nutshell of a RAND Corporation report on "The Risks of Artificial Intelligence to Security and the Future of Work," released earlier this week. What can K-12 educators and policymakers take away from the work?