Machine learning

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.

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.”

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. 

Bloomberg reporter Ashlee Vancee spotlights the work of alumnus Youyang Gu SB ’15, MEng’19, who developed a forecasting model for Covid-19 last spring that was widely considered to be one of the most accurate models of the pandemic’s trajectory. “The novel, sophisticated twist of Gu’s model came from his use of machine learning algorithms to hone his figures,” writes Vancee.

 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.”

TechCrunch reporter Darrell Etherington writes that MIT researchers have developed a new system that devises hardware architecture that can speed up a robot’s operations. Etherington notes that “this research could help unlock the sci-fi future of humans and robots living in integrated harmony.”

Forbes contributor Adi Gaskell spotlights how the MIT Task Force on the Work of the Future recently released a comprehensive report examining the future of work. Gaskell writes that the Task Force's report emphasizes the “pressing issues of our time as one of improving the quality of jobs to ensure that prosperity is shared across the economy.”