An algorithm developed by Prof. Iyad Rahwan and graduate student Bjarke Felbo has been trained to detect sarcasm in tweets that use emojis, writes Josh Lowe for Newsweek. After reading over 1 billion tweets with emojis, the algorithm predicted, “which emoji would be associated with a given tweet based on its emotional tone,” explains Lowe.
CSAIL researchers have developed an artificial neural network that generates recipes from pictures of food, reports Laurel Dalrymple for NPR. The researchers input recipes into an AI system, which learned patterns “connections between the ingredients in the recipes and the photos of food,” explains Dalrymple.
In this video for USA Today, Sean Dowling highlights Pic2Recipe, the artificial intelligence system developed by CSAIL researchers that can predict recipes based off images of food. The researchers hope the app could one day be used to help, “people track daily nutrition by seeing what’s in their food.”
Researchers at MIT have developed an algorithm that can identify recipes based on a photo, writes BBC News reporter Zoe Kleinman. The algorithm, which was trained using a database of over one million photos, could be developed to show “how a food is prepared and could also be adapted to provide nutritional information,” writes Kleinman.
MIT researchers have developed a new machine learning algorithm that can look at photos of food and suggest a recipe to create the pictured dish, reports Matt Reynolds for New Scientist. Reynolds explains that, “eventually people could use an improved version of the algorithm to help them track their diet throughout the day.”
CSAIL researchers have trained an AI system to look at images of food, predict the ingredients used, and even suggest recipes, writes Matt Burgess for Wired. The system could also analyze meals to determine their nutritional value or “manipulate an existing recipe to be healthier or to conform to certain dietary restrictions," explains graduate student Nick Hynes.
CSAIL researchers have developed drones that can drive and fly through a city-like setting, reports Gareth Mitchell for BBC News. The goal for this research is to have the vehicles “coordinate with each other and make intelligent decisions when they fly and drive,” says graduate student Brandon Araki.
FOX News reporter Grace Williams writes that MIT researchers have developed a new system to assist people with visual impairments in navigating their surroundings. “We wanted to primarily complement the white cane to allow users with visual impairments to quickly assess their environment in a contactless manner,” explains graduate student Robert Katzschmann.
Wired reporter Matt Simon writes that CSAIL researchers have developed a system of drones that can successfully fly and drive through a city-like setting. Simon explains that the framework is a good step, “toward imagining a transportation infrastructure that works in three dimensions, not just two.”
Bloomberg Businessweek reporter Arianne Cohen profiles graduate student Joy Buolamwini, who founded the algorithmic Justice League in an effort to make people more aware of the biases embedded in AI systems. “We’re using facial analysis as an exemplar to show how we can include more inclusive training data in the first place,” says Buolamwini of her work.
Prof. Daniela Rus and graduate student Robert Katzschmann speak with BBC reporter Gareth Mitchell about the device they developed to help the visually impaired navigate. Rus explains that they applied the technologies used for autonomous driving to develop a system that can, “guide a visually impaired person in the same way a suite of sensors can guide a self-driving car.”
TechCrunch reporter Brian Heater writes that MIT researchers have developed a vibrating wearable device to help people with visual impairments navigate. “In a world where computers help us with everything from navigating space travel to counting the steps we take in a day, I think we can do better to support visually impaired people,” explains Prof. Daniela Rus.
Fortune reporter Aaron Pressman highlights how MIT researchers have developed a new wearable device to help visually impaired people navigate and avoid obstacles. Pressman writes that CSAIL researchers are, “combining cutting edge techniques from 3D cameras and image recognition software to build an automated navigation system for the visually impaired.”
Boston Herald reporter Jordan Graham writes that MIT researchers have developed a wearable device aimed at helping visually impaired users navigate their environments. The system is equipped with, “a 3-D camera, a vibration pack and an electronic braille screen that will tell users not just where things are — but what they are.”
Graduate student Joy Buolamwini speaks with Guardian reporter Ian Tucker about her work fighting algorithmic biases. Buolamwini explains that she is, “trying to identify bias, to point out cases where bias can occur so people can know what to look out for, but also develop tools where the creators of systems can check for a bias in their design.”