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.”
CSAIL researchers have tricked a machine-learning algorithm into misidentifying an object, reports Louise Matsakis for Wired. The research, “demonstrates that attackers could potentially create adversarial examples that can trip up commercial AI systems,” explains Matsakis.
Media Lab researchers have teamed up with UNICEF on a new website that uses AI to show what cities would look like if they had gone through the war in Syria. As Timothy Revell notes in New Scientist, “such destruction is hard to imagine and can lead to fewer people contributing to fundraising campaigns,” which is something the researchers hope this project will change.
Prof. Erik Brynjolfsson speaks with the Financial Times about a new report that aims to assess how quickly intelligent machines are progressing. This effort “was prompted by growing concerns about their impact on things such as employment,” writes Richard Waters.
CBC News’ Anna Maria Tremonti explores a new study by MIT researchers that examines how children interact with AI toys. The study shows, “how children can develop emotional ties with the robots, which was cause for concern for the MIT researcher,” Tremonti explains.
Writing for The Boston Globe, President L. Rafael Reif issues a call for allies to help address the changing nature of work in the age of automation. “Automation will transform our work, our lives, our society," writes Reif. "Whether the outcome is inclusive or exclusive, fair or laissez-faire, is up to us.”
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.”