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.
Speaking to Bloomberg’s Emily Chang and Selina Wang, Lecturer Luis Perez-Breva suggests that fear of AI stems from confusing it with automation. Perez-Breva explains that in his view, “we need to make better businesses that actually use this technology and AI to take advantage of the automation and create new jobs.”
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.”
Chris Bourg, director of the MIT Libraries, speaks with Lindsay McKenzie of Inside Higher Ed about how libraries can help foster interdisciplinary discussions about artificial intelligence. McKenzie writes that Bourg notes MIT’s, “long history of interdisciplinary research at its AI labs, the earliest of which was founded in 1959.”
With virtual personal assistants becoming more commonplace, Research Affiliate Jimena Canales suggests in an NPR article that it may be time to reconsider our views of them. Despite knowing that AI is not real, “the boundary between the simulated and the real is as contested as it ever was,” she writes.
Neural networks developed by CSAIL researchers that can identify the contents of images, videos, and audio are the basis for a new system that has added background sound to Google Street View, writes Mark Wilson of Co.Design.
Fortune reporter David Morris writes that MIT researchers have tricked an artificial intelligence system into thinking that a photo of a machine gun was a helicopter. Morris explains that, “the research points towards potential vulnerabilities in the systems behind technology like self-driving cars, automated security screening systems, or facial-recognition tools.”
Abigail Beall of New Scientist writes that MIT researchers have developed an algorithm that can trick an AI system, highlighting potential weaknesses in new image-recognition technologies used in everything from self-driving cars to facial recognition systems. “If a driverless car failed to spot a pedestrian or a security camera misidentified a gun the consequences could be incredibly serious.”