In an article for Fortune, writer Larissa Zimberoff highlights Wise Systems, a startup launched out of a graduate entrepreneurship class. With machine learning, Wise creates algorithms for more efficient delivery routes, which “translates into fuel savings, lower wear and tear on trucks, and, for the driver, improved earnings based on higher productivity,” writes Zimberoff.
As Steve Rosenbush notes in the Wall Street Journal, MIT Intelligence Quest hopes to answer two questions: “How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines?” explains President Reif.
Dean Anantha Chandrakasan speaks with Kristin Toussaint of Metro about MIT Intelligence Quest. “When you bring together researchers from different disciplines, they end up collaborating and creating something very new that they individually couldn't have created,” said Chandrakasan.
Brian Heater writes for TechCrunch about the new Institute-wide initiative, MIT Intelligence Quest. “Many of the products are moonshoots. They involve teams of scientists and engineers working together. It’s essentially a new model and we need folks and resources behind that,” said Prof. James DiCarlo.
“Intriguing is that [MIT IQ] brings together researchers who study both human intelligence and artificial intelligence. The hope is that better understanding both of them will help develop new algorithms and techniques that can be used broadly,” writes Sean Captain for Fast Company.
MIT is launching a new initiative known as MIT Intelligence Quest, “in an effort to combine multiple disciplines to reverse engineer human intelligence, create new algorithms for machine learning and artificial intelligence and foster collaboration,” writes Larry Dignan for ZDNet.
MIT Intelligence Quest, a new Institute-wide initiative that will advance human and machine intelligence research, “will help connect researchers across disciplines and support projects in which they work together, as well as seek collaborations with industry,” writes Andy Rosen for The Boston Globe.
Prof. Josh Tenenbaum spoke with Bob Oakes on WBUR’s Morning Edition about MIT Intelligence Quest. “This is fundamentally about coupling the basic science of how intelligence works in the human mind and brain, with the quest to engineer new more powerful, more humanlike machines. And to do all of this in service of our mission to make a better world, with a longer-term vision that really only a university like MIT can have,” said Tenenbaum.
Jeff Engel writes for Xconomy about MIT’s ambitions for its newly announced Institute-wide initiative, MIT Intelligence Quest. “If we want A.I. breakthroughs, it’s going to take research in new science. That’s a central inspiration for MIT IQ,” said President Reif.
“The MIT Intelligence Quest or MIT IQ, based at an institution that has been at the forefront of artificial intelligence research since the 1950s, is a far-reaching academic effort to regain the initiative in AI,” writes Clive Cookson for The Financial Times.
Graduate student Joy Buolamwini is featured on NPR’s TED Radio Hour explaining the racial bias of facial recognition software and how these problems can be rectified. “The minimum thing we can do is actually check for the performance of these systems across groups that we already know have historically been disenfranchised,” says Buolanwini.
MIT researchers have designed a new chip that could advance the development of computers that operate like the human brain, reports James Vincent for The Verge. The development could, “lead to processors that run machine learning tasks with lower energy demands — up to 1,000 times less. This would enable us to give more devices AI abilities like voice and image recognition.”
MIT researchers are stress-testing AI systems by tricking them into misidentifying images, writes Dana Smith of Scientific American. Graduate student Anish Athalye notes that some neural nets are outperforming humans, “but they have this weird property that it seems that we can trick them pretty easily.”
Graduate student Anish Athalye speaks with the BBC about his work examining how image recognitions systems can be fooled. "More and more real-world systems are starting to incorporate neural networks, and it's a big concern that these systems may be possible to subvert or attack using adversarial examples,” Athalye explains.
New Scientist reporter Abigail Beale writes that MIT researchers have been able to trick an AI system into thinking an image of a turtle is a rifle. Beale writes that the results, “raise concerns about the accuracy of face recognition systems and the safety of driverless cars, for example.”