The MIT Statistics and Data Science Center (SDSC), a part of the Institute for Data, Systems, and Society (IDSS), announced two new academic programs today: the MicroMasters program in Statistics and Data Science, and the Interdisciplinary Doctoral Program in Statistics, both beginning in the fall. The MicroMasters program, currently under development by MIT faculty, will be offered online through edX. "Digital technologies are enabling us to bring MIT's data science curriculum to learners around the world regardless of their location or socioeconomic status," says Vice President for Open Learning Sanjay Sarma. The curriculum includes foundational knowledge of data science methods and tools, a deep dive into probability and statistics, and opportunities to learn, implement, and experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," says Dean for Digital Learning Krishna Rajagopal.
The experimental classroom is part of a wave of so-called maker spaces popping up at schools across the country. They're designed to facilitate complex play--open-ended, thought-provoking activities that involve tinkering and engineering with no "right" answer. According to Donna Ross, an associate professor of science education at San Diego State University, research suggests that these activities foster problem-solving, critical thinking and team-building skills--all attributes valued by today's employers. As screen time and highly structured activities have colonized children's off hours, "we're trying to build [complex play] into the formal school day," Ross said. The $200,000 space at Haine was funded by a state grant and opened in September; almost 1,500 students in kindergarten through sixth grade use it each week.
As a medical student, I used to enjoy the Fox show House M.D.--or at least, the first 20 minutes of the hourlong episodes. Each week, the cynical genius Dr. Gregory House would take on one new case, each seemingly more bizarre than the last. Early in the episode, House and his team would sit around a table kicking around the details of whatever mysterious ailment was afflicting their latest patient. They'd generated the so-called differential diagnosis, a list of possible conditions that should have included the real culprit. Their differential diagnosis was especially useful for a medical student because it was usually a reasonably accurate and inclusive list of the conditions that the patient ought to have had, were it not a fictional TV show.
The goal of this competition was to develop machine learning models for the prediction of two materials properties, namely the formation energy, which is an indication of the stability of a new material, and the electronic band gap, which determines a material's transparency over the visible range. The developed models can potentially facilitate the discovery of new transparent conductors and allow for advancements in (opto)electronic technologies. Inspired by Jacek Golebiowski, who made valuable contributions to the final solution, Lars used a smooth overlap of atomic positions (SOAP) based descriptor developed by Barto?k et al. [1, 2] to encode information about the crystal structure of the transparent conductive oxides that were studied in this competition. These SOAP features were then used to teach a Neural Network to predict the desired materials properties. Details about the competition and the top three submissions can be found here.
Which statement do you believe? Robots will wipe out our jobs. AI and robotics will make everything free. These extreme viewpoints are both vying for our attention. Singularity University, which aims to solve our global grand challenges through exponential technologies, widely reports that AI is the world's cure.
Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.
ASK 100 students what they want from an MBA programme and you're likely to get 100 different answers. However, ask them what they want more of, and trends are easier to discern. At the Kellogg School of Management at Northwestern University, a survey of the current class earlier this year asked what students wanted to learn more about. "It has rapidly consumed a lot of mental real estate with our MBA students," says Brian Uzzi, who teaches a course on AI to MBAs at Kellogg. AI has become a key tool for businesses in all industries.
As a software engineer at Microsoft, Elena Voyloshnikova's job is to make informed recommendations about how to improve the performance of software engineering tools. But too often, she spends her days manually analyzing the data she needs to make those decisions. Lately, her team has been discussing the potential of building machine learning models to automate that task – creating more time to focus on the decision-making. That's why she was intrigued when she received an email announcing an upcoming AI training session for Microsoft employees. "I asked my manager, 'Can I go to this?'" she said.
Speaking verbally and performing sign language require the same parts of the brain, according to a new study. Researchers at New York University found that the neural skills needed to perform sign language are the similar to those required for speaking out loud. Their report is the first of its kind to prove the association between the two communication forms. Sign language communicators and verbal English speakers rely on the same neural skills, a new report says. The new research was published in the journal Scientific Reports.
Researchers at Mt. Sinai's Icahn School of Medicine in New York at have a unique collaborator in the hospital: Their in-house artificial intelligence system, known as Deep Patient. The researchers taught Deep Patient to predict risk factors for 78 different diseases by feeding it electronic health records from 700,000 patients. Doctors now turn to the system to aid in diagnoses. While not a person, Deep Patient is more than just a program. Like other advanced AI systems, it learns, makes autonomous decisions, and has grown from a technological tool to a partner, coordinating and collaborating with humans.