Vice reporter Radhamely De Leon spotlights how researchers from MIT and Carnegie Mellon University have created “a search engine tool that shows what Google search results appear in different countries or languages, highlighting key differences in the algorithm between regions.”
Graduate student Shashank Srikant speaks with The Economist about his work developing a new model that can detect computer bugs and vulnerabilities that have been maliciously inserted into computer code.
Koch Institute fellow Dr. Rameen Shakur and his colleagues have developed a new computer tool that could allow doctors to personalize treatments for patients with inherited heart disease. “In areas such as cardiology and oncology, where large amounts of clinical and genetic data need to be analyzed, adopting a computer-based approach…can make diagnosis, outcome prediction and treatment more effective and efficient,” writes Helen Albert for Clinical OMICs.
Mashable spotlights Strolling Cities, a video project from the MIT-IBM Watson AI Lab, which uses AI to allow users to imagine what different words would like as a location. “Unlike other image-generating AI systems, Strolling Cities creates fictional cities every time,” Mashable notes.
A recent review by MIT researchers finds that “only about 23% of machine learning studies in health care used multiple datasets to establish their results, compared to 80% in the adjacent field of computer vision, and 58% in natural language processing,” writes Casey Ross for STAT. “If the performance results are not reproduced in clinical care to the standard that was used during [a study], then we risk approving algorithms that we can’t trust,” says graduate student Matthew McDermott. “They may actually end up worsening patient care.”
Times Higher Education reporter Simon Baker writes that Media Lab researchers have developed a new machine learning model that can predict research studies that will have the highest impact. The tool has the potential to “aid funders and research evaluators in making better decisions and avoiding the kind of biases and gaming that occurred with simpler metric assessments.”
Principal research scientist Leo Anthony Celi speaks with STAT reporter Katie Palmer about the importance of open data sharing in medical research, his new role as editor of PLOS Digital Health, and the challenges facing machine learning in medicine. “With digitization, we’re hoping each country will have an opportunity to create their own medical knowledge system,” says Celi.
In a forthcoming book, photographer Jessica Wynne spotlights the chalkboards of mathematicians, including Professor Alexei Borodin’s and Associate Professor Ankur Moitra’s, reports Clara Moskowitz for Scientific American.
Wired reporter Will Knight spotlights how MIT researchers have showed that “an AI program trained to verify that code will run safely can be deceived by making a few careful changes, like substituting certain variables, to create a harmful program.”
Graduate student Joy Buolamwini joins Kara Swisher on The New York Times' “Sway” podcast to discuss her crusade against bias in facial recognition technologies. “If you have a face, you have a place in this conversation,” says Buolamwini.
MIT researchers are applying machine learning algorithms typically used for natural language processing to identify coronavirus variants, reports Brian Browdie for Quartz. “Besides being able to quantify the potential for mutations to escape, the research may pave the way for vaccines that broaden the body’s defenses against variants or that protect recipients against more than one virus, such as flu and the novel coronavirus, in a single shot,” writes Browdie.
CSAIL researchers have developed a new material with embedded sensors that can track a person’s movement, reports Mashable. The clothing could “track things like posture or give feedback on how you’re walking.”
Fast Company reporter Elizabeth Segran spotlights how CSAIL researchers have crafted a new smart fabric embedded with sensors that can sense pressure from the person wearing it. “Sensors in this new material can be used to gather data about people’s posture and body movements,” writes Segran. “This could be useful in a variety of settings, including athletic training, monitoring the health of elderly patients, and identifying whether someone has fallen over.”
Wired reporter Will Knight writes that MIT researchers have found that many of the key AI data sets used to train algorithms could contain many errors. “What this work is telling the world is that you need to clean the errors out,” says graduate student Curtis Northcutt. “Otherwise the models that you think are the best for your real-world business problem could actually be wrong.”
TechCrunch reporter Brian Heater spotlights how MIT researchers have devised a neural network to help optimize sensor placement on soft robots to help give them a better picture of their environment.