Elisabeth Reynolds, executive director of the MIT Task Force on the Work of the Future, speaks with Annie Nova of CNBC about the Task Force’s new report, which lays out recommendations for ensuring Americans are able to secure good jobs in an era of automation. “We’re suggesting that people have access to affordable education and training,” says Reynolds. “I think there’s a real opportunity to help transition people and educate workers without four-year degrees.”
Axios reporter Bryan Walsh writes that a new report by MIT’s Task Force on the Work of the Future makes policy recommendations for ensuring American workers are able to secure good jobs. “If we deploy automation in the same labor market system we have now," says Prof. David Mindell, "we're going to end up with the same results.”
Three years after President L. Rafael Reif delivered an “intellectual call to arms” to examine the impact of technology on jobs, the MIT Task Force on the Work of the Future has published its final set of recommendations. “In an extraordinarily comprehensive effort, they included labor market analysis, field studies and policy suggestions for changes in skills-training programs, the tax code, labor laws and minimum-wage rates,” writes Steve Lohr for The New York Times.
Prof. James Collins speaks with Boston 25 reporter Julianne Lima about the growing issue of antibiotic resistant bacteria and his work using AI to identify new antibiotics. Collins explains that a new platform he developed with Prof. Regina Barzilay uncovered “a host of new antibiotics including one that we call halicin that has remarkable activity against multi drug-resistant pathogens.”
Fast Company reporter KC Ifeanyi writes about “Coded Bias,” which explores how graduate student Joy Buolamwini’s “groundbreaking discovery and subsequent studies on the biases in facial recognition software against darker-skinned individuals and women led to some of the biggest companies including Amazon and IBM rethinking their practices.”
A new algorithm developed by MIT researchers could be used to help detect people with Covid-19 by listening to the sound of their coughs, reports Zoe Kleinman for BBC News. “In tests, it achieved a 98.5% success rate among people who had received an official positive coronavirus test result, rising to 100% in those who had no other symptoms,” writes Kleinman.
Mashable reporter Rachel Kraus writes that a new system developed by MIT researchers could be used to help identify patients with Covid-19. Kraus writes that the algorithm can “differentiate the forced coughs of asymptomatic people who have Covid from those of healthy people.”
A new took developed by MIT researchers uses neural networks to help identify Covid-19, reports Alyse Stanley for Gizmodo. The model “can detect the subtle changes in a person’s cough that indicate whether they’re infected, even if they don’t have any other symptoms,” Stanley explains.
TechCrunch reporter Devin Coldewey writes that MIT researchers have built a new AI model that can help detect Covid-19 by listening to the sound of a person’s cough. “The tool is detecting features that allow it to discriminate the subjects that have COVID from the ones that don’t,” explains Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.
MIT researchers have developed a new AI model that could help identify people with asymptomatic Covid-19 based on the sound of their cough, reports CBS Boston. The researchers hope that in the future the model could be used to help create an app that serves as a “noninvasive prescreening tool to figure out who is likely to have the coronavirus.”
Forbes contributor Adi Gaskell writes that CSAIL researchers have developed a machine learning system that can determine whether a task is best performed by a human or AI. The researchers developed the system to be “capable of learning and adapting as it goes, such that it can identify,” Gaskell explains, “when the expert isn't available or whether they have a certain level of experience, before choosing whether to defer to them.”
Prof. Tonio Buonassisi writes for Indvstrvs about how businesses can use machine learning systems to help address some of the world’s most pressing challenges. “What has emerged through our research,” writes Buonassisi, “is a combination of automation, data science, and computation — building blocks that promise to accelerate the rate of new materials development tenfold, and eventually millions of times, faster.”
Researchers from the Singapore-MIT Alliance for Research and Technology (SMART) Critical Analytics for Manufacturing Personalized Medicine (CAMP) research group have been awarded new research grants aimed at supporting work exploring personalized medicine and cell therapy, reports Health Europa. “In addition to our existing research on our three flagship projects, we hope to develop breakthroughs in manufacturing other cell therapy platforms that will enable better medical treatments and outcomes for society,” says Associate Provost Krystyn Van Vliet.
NBC News reporters Lindsay Hoffman and Caroline Kim spotlight graduate student Joy Buolamwini’s work uncovering racial and gender bias in AI systems in a piece highlighting women who are “shattering ceilings, making groundbreaking discoveries, and spreading public awareness during the global pandemic.” Hoffman and Kim note that Buolamwini’s research "helped persuade these companies to put a hold on facial recognition technology until federal regulations were passed.”
Third-year student Casey Johnson speaks with Washington Post reporter Luz Lazo about his work exploring the feasibility of using GPS technology to determine when a scooter is on the sidewalk. Lazo explains that Johnson wrote a “surface categorization algorithm to detect the periodic cracks in a sidewalk. He then added an accelerator sensor — which costs less than $1 — to detect when the scooter is being used on an asphalt road versus a concrete sidewalk.”