An article in The Economist states that new research by MIT grad student Joy Buolamwini supports the suspicion that facial recognition software is better at processing white faces than those of other people. The bias probably arises “from the sets of data the firms concerned used to train their software,” the article suggests.
Research by Physics PhD candidate Sergio Cantu has led to the discovery of a new form of light, which happens when photos stick together, as opposed to passing through one another. “’We send the light into the medium, it gets effectively dressed up as if it were atoms, and then when it turns back into photons they remember interactions that happened in the medium,” Cantu explains to Leah Crane at New Scientist.
Originally created by the Zhang Lab in 2017, CRISPR tool SHERLOCK has been improved upon to be three times more sensitive for detecting viruses and infections using an inexpensive test strip. Sharon Begley writes for STAT News, “A paper strip, like in a pregnancy test, is dipped into a sample, and if a line appears, the target molecule was detected — no instruments required.”
Dave Gershgorn writes for Quartz, highlighting congress’ concerns around the dangers of inaccurate facial recognition programs. He cites Joy Buolamwini’s Media Lab research on facial recognition, which he says “maintains that facial recognition is still significantly worse for people of color.”
MIT physicists have created a new form of light that allows up to three photons to bind together, writes Daniel Oberhaus for Motherboard. While the research is experimental, Oberhaus writes that the trio of photons “are much more strongly bound together and are, as a result, better carriers of information” than other photonic qubits.
A gene-editing tool called SHERLOCK, developed in Prof. Feng Zhang’s lab, allows for faster detection of infections and viruses, such as Zika and Dengue fever. “It does this by combining different types of CRISPR enzymes, which are unleashed together to target distinct bits of DNA and RNA, another of the major biological molecules found in all forms of life,” writes Alessandra Potenza for The Verge.
Researchers in CSAIL are developing a steering program for drones that allows them to process uncertainty and avoid hitting objects while flying autonomously. Called Nanomap, the drone uses depth measurements to determine the safest path. “This technique creates an on the fly map that lets the drone handle uncertainty as opposed to being ready in every situation,” writes John Biggs for TechCrunch.
A new paper from graduate students in EECS details a newly-developed chip that allows neural networks to function offline, while drastically reducing power usage. “That means smartphones and even appliances and smaller Internet of Things devices could run neural networks locally” writes Eric Mack for Forbes.
Brian Heater for TechCrunch covers how researchers are creating a system that will allow robots to develop motor skills and process abstract concepts. “With this system, the robots can perform complex tasks without getting bogged down in the minutia required to complete them,” Heater writes.
MIT researchers have designed a new chip to enhance the functionality of neural networks while simultaneously reducing the consumption of power, writes Darrell Etherington of TechCrunch. “The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation,” he explains.
A study by MIT scientists has identified the neurons that fire at the beginning and end of activities, which is important for initiating a routine. “This task-bracketing appears to be important for initiating a routine and then notifying the brain once it is complete,” Prof. Ann Graybiel told Xinhua.
Graduate student Joy Buolamwini tested three different face-recognition systems and found that the accuracy is best when the subject is a lighter skinned man, reports Timothy Revell for New Scientist. With facial recognition software being used by police to identify suspects, “this means inaccuracies could have consequences, such as systematically ingraining biases in police stop and searches,” writes Revell.
Molly Wood at Marketplace speaks with Media Lab graduate student Joy Buolamwini about the findings of her recent research, which examined widespread bias in AI-supported facial recognition programs. “At the end of the day, data reflects our history, and our history has been very biased to date,” Buolamwini said.
Recent research from graduate student Joy Buolamwini shows that facial recognition programs, which are increasingly being used by law enforcement, are failing to identify non-white faces. “When these systems can’t recognize darker faces with as much accuracy as lighter faces, there’s a higher likelihood that innocent people will be targeted by law enforcement,” writes Katharine Schwab for Co. Design.
Writing for Gizmodo, Sidney Fussell explains that a new Media Lab study finds facial-recognition software is most accurate when identifying men with lighter skin and least accurate for women with darker skin. The software analyzed by graduate student Joy Buolamwini “misidentified the gender of dark-skinned females 35 percent of the time,” explains Fussell.