If there's one thing that characterizes driving in any major city, it's the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. How much of a difference could that make' Would the impact of such systems in reducing emissions be worth the investment in the technology' Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing. 'We got interested a few years ago in the question: Is there something that automated vehicles could do here in terms of mitigating emissions'' says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in the Department of Civil and Environmental Engineering and the Institute for Data, Systems, and Society (IDSS) at MIT, and a principal investigator in the Laboratory for Information and Decision Systems. 'Is it a drop in the bucket, or is it something to think about',' she wondered....

What if data could help predict a patient's prognosis, streamline hospital operations, or optimize human resources in medicine' A book fresh off the shelves, 'The Analytics Edge in Healthcare,' shows that this is already happening, and demonstrates how to scale it. Authored by Dimitris Bertsimas, MIT's vice provost for open learning, along with two of Bertsimas' former students ' Agni Orfanoudaki PhD '21, associate professor of operations management at University of Oxford's Said Business School, and Holly Wiberg PhD '22, assistant professor of public policy and operations research at Carnegie Mellon University ' the book provides a practical introduction to the field of health care analytics. With an emphasis on real-world applications, the first part of the book establishes technical foundations ' spanning machine learning and optimization ' while the second part of the book presents integrated case studies that cover various clinical specialties and problem types using descriptive, predictive, and prescriptive analytics....

Americans, by and large, have become connoisseurs of preparation. Newlyweds scour online public-school ratings to decide on the neighborhood where they'll raise their notional children. Tutoring programs offer to help students with the SATS, MCATs, or just about any other standardized test. Leisure activities'pickleball, baking'tend to encourage rigging oneself up with just the right gear, and plenty of different product-review sites will recommend the best-fitting sports bra or superior pie dish. Even at rest, there is something to do: Rings and watches track heart rates and sleep states and inform wearers of their 'daily readiness' first thing in the morning. This phenomenon is rampant in the great American sport of childbirth and child-rearing. As Amanda Hess, a New York Times critic at large and a savvy analyst of the online world, lays out in her spot-on and brutally funny new book, Second Life: Having a Child in the Digital Age, approximately no amount of online prep actually readies you for the experience of having a baby. The only thing that can prepare you for parenthood is experiencing parenthood. But that experience is free (well, after accounting for the skyrocketing costs of caring for that child). So what parenting experts are selling'via the latest tech and all-seeing algorithms'is the illusion of control....

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data. AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model, called "state-space models," has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges ' they can become unstable or require a significant amount of computational resources when processing long data sequences. To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call 'linear oscillatory state-space models' (LinOSS), which leverage principles of forced harmonic oscillators ' a concept deeply rooted in physics and observed in biological neural networks. This approach provides stable, expressive, and computationally efficient predictions without overly restrictive conditions on the model parameters....