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.”
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.
BBC Click reports on an AI system developed by CSAIL researchers that simplifies image editing. “Instead of requiring the user to select the pixels very accurately, our system can just detect it and give the opacities for every object in the image automatically, which can then be used for editing the images in a realistic way,” explains visiting researcher Yagiz Aksoy.
CSAIL researchers have created a system that allows robots to see and pick up objects they have never encountered without assistance from humans, writes Jason Murdock for Newsweek. The researchers are now working on teaching the system to “move objects with a specific goal in mind, such as cleaning a desk,” reports Murdock.
CSAIL researchers have developed a new system that gives robots a greater visual understanding of the world around them, reports Heather Kelly for CNN. “We want robots to learn by themselves how to very richly and visually understand lots of objects that are useful for lots of tasks,” explains graduate student Pete Florence.
Wired reporter Matt Simon writes that MIT researchers have developed a new system that allows robots to be able to visually inspect and then pick up new objects, all without human guidance. Graduate student Lucas Manuelli explains that the system is “all about letting the robot supervise itself, rather than humans going in and doing annotations.”
Research affiliate Tim Hwang speaks with Aaron Mak of Slate about whether Google is suppressing conservative media outlets in search results. “I don’t think the question is whether or not it’s biased. All these systems embed some kind of bias,” explains Hwang. “The question is: Do we have transparency to how some of these decisions are being made?”
TechRepublic reporter Nick Heath writes about Julia 1.0, a programming language created by MIT researchers. “The breadth of Julia's capabilities and ability to spread workloads across hundreds of thousands of processing cores have led to its use for everything from machine learning to large-scale supercomputer simulation,” writes Heath.
Fast Company reporter Mark Wilson writes that MIT researchers have developed a new tool for computer-aided drafting software that can optimize the design for any product. Wilson explains that the tool could “help designers optimize their existing processes–and, crucially, deconstruct what works and what doesn’t, sooner.”
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.”
In an article for Forbes, AgeLab Director Joseph Coughlin writes about the physical and emotional impacts of social isolation and loneliness. Coughlin stresses the importance of “developing new solutions for our modern epidemic of social disconnectedness -- whether they are new technologies, community initiatives, forward-thinking policies, or just plain increased awareness.”
Writing for The New York Times, Prof. Sherry Turkle argues that machines will never be able to replace humans as compassionate companions. “Machines have not known the arc of a human life. They feel nothing of the human loss or love we describe to them,” writes Turkle. “Their conversations about life occupy the realm of the as-if.”
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.
At the International Conference on Machine Learning, MIT researchers demonstrated an adversarial attack, fooling an AI system into thinking a 3-D printed turtle was a rifle, reports Matthew Hutson for Science. Prof. Aleksander Madry explains that the ability to fool AI systems shows, “we need to rethink all of our machine learning pipeline to make it more robust.”
In a recent blog post, Microsoft’s president and chief legal officer, Brad Smith, references research by MIT graduate student Joy Buolamwini while calling for government to regulate the use of facial recognition software. Buolamwini’s work “showed error rates of as much as 35% for systems classifying darker skinned women,” reports Dina Bass for Bloomberg.