The Singapore-MIT Alliance for Research and Technology (SMART) plans to test a system that allows people to hail electric, autonomous cars, reports Kelly O’Brien for Boston.com. These “cars would be an efficient way to get people from their homes to a T stop or commuter rail station,” O'Brien explains.
Professors Erik Brynjolfsson and David Autor speak with Claire Cain Miller of The New York Times about how artificial intelligence is impacting the job market. “This is the biggest challenge of our society for the next decade,” says Brynjolfsson.
The Financial Times features an excerpt from Professor Erik Brynjolfsson and Dr. Andrew McAfee’s book “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies” on their shortlist of for the 2014 Financial Times and McKinsey Business Book of the Year Award.
Irving Wladawsky-Berger writes about research by Professor David Autor on the impact of technology on the workforce presented at this year’s Jackson Hole Federal Reserve Symposium. Autor argues that artificial intelligence still struggles to perform tasks that require flexibility, judgment and common sense.
Boston Magazine reporter Steve Annear writes about a new robot, designed by MIT undergraduate Patrick McCabe, that can play the game Connect Four. “It’s kind of a magical thing with computer science and technology, being able to leverage that to actually make something smarter than you are,” said McCabe of the device, which can beat its creator.
Writing for The Boston Globe, Jack Newsham interviews Professor John Leonard about the many challenges facing self-driving cars in a city like Boston. “Robots should just be able to navigate the way we navigate,” Leonard explains. “That’s been a challenge in robotics for 40 years.”
“Lexington-based MicroCHIPS, a developer of implantable drug delivery devices, has been quietly working on a birth control product that can be embedded in a woman’s body,” writes Steve Annear of Boston Magazine. The technology was originally developed in Professor Robert Langer’s lab.
In a piece for Wired, Robert McMillan examines new MIT research showing that computers “powered by the latest ‘deep learning’ algorithms,” are catching up in tests that compare their intelligence to those of monkeys.
In a piece for New Scientist about teaching robots to communicate like humans, Aviva Rutkin highlights how researchers from MIT developed a new approach to communicating with a robot called inverse semantics. Using this approach, “the robot tries to choose the right words by looking at its environment,” Rutkin writes.
Steve Lohr writes for The New York Times about Luminoso, a text analysis and artificial intelligence startup out of the MIT Media Lab. Luminoso analyzed social media communications before, during, and after the U.S.-Germany World Cup soccer match to create a minute-by-minute picture of peoples’ emotions.
Professor John Leonard speaks with Bloomberg Businessweek about Google’s new Auto Android and how it compares with the company’s driverless car project. Leonard sees the two projects as part of an overall effort to dramatically transform transportation in our everyday lives.
Writing for Wired, Olivia Solon describes a new algorithm that can identify human action in video. “The activity-recognising algorithm is faster than previous versions and is able to make good guesses at partially completed actions, meaning it can handle streaming video,” Solon writes.
Boston Globe reporter Hiawatha Bray reports on the 50th anniversary of MIT’s Project MAC. “When this started in 1963, the dream was to let multiple people use computers simultaneously,” Daniela Rus explains. “Fifty years later we’re now in a world where we find computing indispensable.”
In a piece for CNN, Professor Erik Brynjolfsson and Dr. Andrew McAfee write that the rapid rate of technological innovation is leaving a large number of people without the skills necessary to participate in the modern economy.
Jon Gold reports on how MIT researchers have developed an algorithm that can identify human activity from video input. “The researchers drew on natural language processing techniques,” Gold writes, “to create a 'grammar' for each action they wanted the system to recognize.”