The New York Times writes about the new MIT Stephen A. Schwarzman College of Computing, calling MIT’s move “a particularly ambitious step.” President Reif says the College will “educate the bilinguals of the future,” people in fields like biology, chemistry, politics, history, and linguistics who are also skilled in the techniques of modern computing that can be applied to them.
CNBC reporter Andrew Zaleski writes that MIT researchers have developed a neuromorphic chip design that could help advance the development of computers that operate like humans. The design could “lead to processors capable of carrying out machine learning tasks with dramatically lower energy demands,” Zaleski explains.
Verge reporter James Vincent writes that MIT researchers have developed a challenge, the Minimal Turing Test, which prompts participants to select a word that can prove that they are human. “It tells you something about the gap between humans and smart robots,” explains graduate student John McCoy, “that people who have never had to think about this situation before came up with a lot of smart and funny results.”
In this Reuters video, Jim Drury highlights how MIT researchers have developed an activity simulator that could one day help teach robots how to complete household chores. The simulator, VirtualHome, could train robots to “help the elderly or disabled in their homes,” Drury explains.
MIT researchers have developed neural networks that can recognize speech patterns that are indicative of depression, writes Anna Powers for Forbes. “Because the model is generalized and does not rely on specific questions to be asked,” explains Powers, “the hope is that this model can be implemented into mobile apps that will allow people to detect depression through natural conversation.”
Forbes reporter Maribel Lopez writes about how researchers at the MIT-IBM Watson AI Lab are tackling a variety of AI challenges with real-world applications. Lopez notes that it’s great to see organizations like MIT and IBM coming together to “bridge the gap between science and practical AI solutions that can be used for both commercial and social good.”
Prof. John Leonard speaks with Bloomberg News about his work with the Toyota Research Institute on developing a system that combines machine learning technologies and sensors to make vehicles safer. “Imagine if you had the most vigilant and capably trained driver in the world that could take over in a situation where a teenager took a curve too fast,” says Leonard of the inspiration for the system.
Fortune reporters Aaron Pressman and Adam Lashinsky highlight graduate student Joy Buolamwini’s work aimed at eliminating bias in AI and machine learning systems. Pressman and Lashinsky note that Buolamwini believes that “who codes matters,” as more diverse teams of programmers could help prevent algorithmic bias.
MIT researchers have developed a model that can help detect depression by analyzing an individual’s speech patterns, reports Kaveh Waddell for Axios. Waddell explains that the researchers, “trained an AI system using 142 recorded conversations to assess whether a person is depressed and, if so, how severely.”
MIT researchers have developed a new system that can detect depression by examining a patient’s speech and writing, reports John Biggs for TechCrunch. Biggs writes that the system could “help real therapists find and isolate issues automatically versus the long process of analysis. It’s a fascinating step forward in mental health.”
Engadget reporter Jon Fingas writes that MIT researchers have developed an encryption method that can secure sensitive data in neural networks without slowing machine learning systems. The method, notes Fingas, could “lead to more uses of internet-based neural networks for handling vital info, rather than forcing companies and institutions to either build expensive local equivalents or forget AI-based systems altogether.”
A study co-authored by Prof. Erik Brynjolfsson and graduate student Daniel Rock finds that specific tasks, not jobs, are likely to become automated, writes Joe McKendrick for Forbes. The researchers explain that, “machine learning technology can transform many jobs in the economy, but full automation will be less significant than the re-engineering of processes and the reorganization of tasks."
MIT researchers have developed a machine learning system that could reduce the number of chemotherapy and radiotherapy treatments that glioblastoma patients receive, reports the Xinhua News Agency. The system “finds an optimal treatment plan, with the lowest possible potency and frequency of doses that should still reduce tumor sizes,” Xinhua explains.
In an article for Forbes about how AI could improve healthcare, Bernard Marr highlights an algorithm developed by MIT researchers that can analyze 3-D scans up to 1,000 times faster than is currently possible. “When saving minutes can mean saving lives, AI and machine learning can be transformative,” writes Marr.
In an article for the Financial Times, Prof. Erik Brynjolfsson writes about his new research showing that advances in machine learning could help fuel a surge in productivity and economic growth around the world. Brynjolfsson writes that as machine learning systems develop, “we can have not only higher productivity growth, but also more widely shared prosperity.”