Writing for The New York Times, research scientists Chelsea Barabas and Karthik Dinakar argue that risk assessment algorithms designed to help predict people’s future criminal behavior are “fundamentally flawed. They give judges recommendations that make future violence seem more predictable and more certain than it actually is. In the process, risk assessments may perpetuate the misconceptions and fears that drive mass incarceration.”
STAT reporter Rebecca Robbins spotlights how the MIMIC database of de-identified medical records has helped advance AI research in medicine. “If you are developing an algorithm, let’s say for decision support or prediction, and you’re using machine learning, then you need a huge number of examples — and there are virtually no open-source databases like this,” explains Prof. Roger Mark. It’s the only one in town, pretty much.”
New York Times reporter Katie Hafner memorializes the life and work of Professor Emeritus Fernando Corbató, known for his work on computer time-sharing systems. Hafner notes that Corbató’s work on “computer time-sharing in the 1960s helped pave the way for the personal computer, as well as the computer password.”
CSAIL’s RoboRaise robot can successfully execute the Bottle Cap Challenge, removing a bottle cap with a well-placed kick, reports Darrell Etherington for TechCrunch. Etherington explains that the robot, “can mirror the actions of a human just by watching their bicep. This has a number of practical applications, including potentially assisting a person to lift large or awkward objects.”
Writing for The Atlantic, Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing, examines the potential impacts of AI on human culture, history and civilization. “We should accept that AI is bound to become increasingly sophisticated and ubiquitous, and ask ourselves: How will its evolution affect human perception, cognition, and interaction? What will be its impact on our culture and, in the end, our history?”
Writing for The Atlantic, Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing, examines the potential impacts of AI on human culture, history and civilization. “We should accept that AI is bound to become increasingly sophisticated and ubiquitous, and ask ourselves: How will its evolution affect human perception, cognition, and interaction? What will be its impact on our culture and, in the end, our history?”
Rachel Metz reports for CNN that researchers at MIT and the Qatar Computing Research Institute have developed an AI system that can look at a picture of a pizza and determine which toppings should go on which layer. Postdoc Dimitrios Papadopoulos “believes this research could lead to non-food applications as well, such as a digital shopping assistant that uses AI to figure out how to put together a fashionable outfit,” writes Metz.
Prof. Markus Buehler speaks with Ira Flatow of Science Friday about his research, which attempts to better understand and create new proteins by translating them into music. Buehler explains that they were able to listen to proteins after discovering that “amino acids have a unique frequency spectrum which we could then make audible using a concept of transposition.”
A study co-authored by Prof. Cynthia Breazeal found that a “social robot” teddy bear “boosted spirits, eased anxiety and even lowered perceived pain levels” among Boston Children’s Hospital patients aged 3 to 10 years old, reports Robert Preidt for US News & World Report. “We want technology to support everyone who's invested in the quality care of a child," says Breazeal.
Researchers at MIT and Brown University created an interactive data system that “could give everyone AI superpowers,” writes Darrell Etherington for TechCrunch. Known as ‘Northstar,’ the system can instantly generate machine-learning models to use with existing data sets in order to generate useful predictions, explains Etherington.
In a new study, Prof. Markus Buehler converted 20 types of amino acids into a 20-tone scale to create musical compositions. “Those altered compositions were converted back into a conceptual amino acid chain, which enabled the team to generate variations of proteins that have never been seen in nature,” writes Becky Ferreira for Motherboard.
Paul Carter of BBC’s Click highlights CSAIL research to teach a robot how to feel an object just by looking at it. This will ultimately help the robot “grip better when lifting things like the handle of a mug,” says Carter.
Gizmodo reporter Victoria Song writes that MIT researchers have developed a new system that can teach a machine how to make pizza by examining a photograph. “The researchers set out to teach machines how to recognize different steps in cooking by dissecting images of pizza for individual ingredients,” Song explains.
Using a tactile sensor and web camera, MIT researchers developed an AI system that allows robots to predict what something feels like just by looking at it, reports David Williams for CNN. “This technology could be used to help robots figure out the best way to hold an object just by looking at it,” explains Williams.
Forbes contributor Charles Towers-Clark explores how CSAIL researchers have developed a database of tactile and visual information that could be used to allow robots to infer how different objects look and feel. “This breakthrough could lead to far more sensitive and practical robotic arms that could improve any number of delicate or mission-critical operations,” Towers-Clark writes.