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.
Boston Globe reporter Andy Rosen writes that the Broad Institute of MIT and Harvard has launched “a new, $300 million initiative that applies advanced computer science to some of the hardest problems in medicine — an endeavor it said could uncover new ways to fight cancer, infectious disease, and other illnesses.”
Graduate student Joy Buolamwini speaks with Molly Wood of Marketplace about her work uncovering bias in AI systems and her calls for greater oversight of facial recognition systems. “We need the laws, we need the regulations, we need an external pressure, and that’s when companies respond,” says Buolamwini. “But the change will not come from within alone because the incentives are not aligned.”
Boston.com reporter Mark Gartsbeyn spotlights “Coded Bias,” a new documentary that chronicles graduate student Joy Buolamwini’s work uncovering bias in AI systems. Gartsbeyn writes that in 2018, Buolamwini “co-authored an influential study showing that commercially available facial recognition programs had serious algorithmic bias against women and people of color.”
Writing for Forbes, research affiliate Tom Davenport spotlights how Stitch Fix “uses AI algorithms and human stylists working in combination to make recommendations to clients of items of clothing, shoes, or accessories.”
Research scientist Andreas Mershin speaks with Noam Hassenfeld of Vox about his work developing a new AI system that could be used to detect disease using smell.
A new AI-powered system developed by researchers from MIT and other institutions can detect prostate cancer in urine samples as accurately as dogs can, reports Tanya Lewis and Prachi Patel for Scientific American. “We found we could repeat the training you use for dogs on the machines until we can’t tell the difference between the two,” says research scientist Andreas Mershin.
Elisabeth Reynolds, executive director of the MIT Task Force on the Work of the Future, speaks with Soledad O’Brien about how to ensure workers aren’t left behind in the transition to a more digital workforce. “If we can find pathways to the middle where we do see growth and demand for workers - construction, healthcare, the trades, manufacturing, places where we are seeing opportunities - that move can really be a new lifeline for people,” says Reynolds.
ITV reporter Liz Summers spotlights how researchers from MIT and other institutions have developed a new system that could eventually be used to help detect diseases via smell. The researchers hope the results could “eventually result in the production of a ‘robotic nose’ perhaps in the form of a smartphone app.”
UPI reporter Brian P. Dunleavy writes that MIT researchers have developed a new system, modeled on a dog’s keen sense of smell, that could be used to help detect disease using smell. “We see the dogs and their training research as teaching our machine learning [sense of smell] and artificial intelligence algorithms how to operate,” says research scientist Andreas Mershin.
A team of researchers from MIT and other institutions have created a new sensor that could be used to sniff out disease, reports Charlie Jones for the BBC. Research scientist Andreas Mershin says "Imagine a day when smartphones can send an alert for potentially being at risk for highly aggressive prostate cancer, years before a doctor notices a rise in PSA levels.”
Fast Company reporter Ruth Reader writes that researchers from MIT and other institutions have developed a new miniaturized detector that could be used to detect diseases by smell. “This paper was about integrating all the techniques that we know can work independently and finding out what of all this can go and become [part of] an integrated smartphone-based diagnostic,” says research scientist Andreas Mershin.
UPI reporter Brooks Hays writes that MIT researchers have developed a new machine learning algorithm that can anticipate and recognize a protein’s varied structures. “The new AI-system,” writes Hays, “does more than image a diversity of conformations, it can also predict the varied motions of different protein structures.”
TechCrunch reporter Darrell Etherington writes that MIT researchers have developed a new “liquid” machine learning system that can learn on the job. Etherington notes that the system has “the potential to greatly expand the flexibility of AI technology after the training phase, when they’re engaged in the actual practical inference work done in the field.”