In response to physical distancing set into motion to address the Covid-19 pandemic, MIT Open Learning has created a number of new platforms for higher education students and faculty, as well as for teachers, parents, and K-12 students, while continuing to offer its existing online education resources and courseware. Leveraging over 20 years of online teaching and learning experience, MIT is providing online courses and other remote learning resources to instructors, parents, and students worldwide. For MIT Professor Sanjay Sarma, vice president of open learning, this work is imperative. “Learning and knowledge are more important than ever,” he remarks. “At MIT Open Learning, we have taught millions of learners, supported teachers at MIT and in middle and high schools worldwide, and developed new learning technologies in areas such as gamification and virtual reality. Developing agile responses to major issues of access to education is our stock-in-trade. It’s a privilege to share our experience during this challenging time to help teachers and students everywhere to continue effective education.”...

The fast and efficient generation of random numbers has long been an important challenge. For centuries, games of chance have relied on the roll of a die, the flip of a coin, or the shuffling of cards to bring some randomness into the proceedings. In the second half of the 20th century, computers started taking over that role, for applications in cryptography, statistics, and artificial intelligence, as well as for various simulations — climatic, epidemiological, financial, and so forth. MIT researchers have now developed a computer algorithm that might, at least for some tasks, churn out random numbers with the best combination of speed, accuracy, and low memory requirements available today. The algorithm, called the Fast Loaded Dice Roller (FLDR), was created by MIT graduate student Feras Saad, Research Scientist Cameron Freer, Professor Martin Rinard, and Principal Research Scientist Vikash Mansinghka, and it will be presented next week at the 23rd International Conference on Artificial Intelligence and Statistics....

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts. Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus. “Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology,” explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines)....

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