Laboratory for Information and Decision Systems (LIDS)

Researchers at MIT have developed “Clio,” a new technique that “enables robots to make intuitive, task-relevant decisions,” reports Jennifer Kite-Powell for Forbes. The team’s new approach allows “a robot to quickly map a scene and identify the items they need to complete a given set of tasks,” writes Kite-Powell. 

Researchers at MIT have found that commercially available AI models, “were more likely to recommend calling police when shown Ring videos captured in minority communities,” reports Kyle Wiggers for TechCrunch. “The study also found that, when analyzing footage from majority-white neighborhoods, the models were less likely to describe scenes using terms like ‘casing the property’ or ‘burglary tools,’” writes Wiggers. 

Researchers at MIT have developed a new method that “enables robots to intuitively identify relevant areas of a scene based on specific tasks,” reports Baba Tamim for Interesting Engineering. “The tech adopts a distinctive strategy to make robots effective and efficient at sorting a cluttered environment, such as finding a specific brand of mustard on a messy kitchen counter,” explains Tamim. 

TechCrunch reporter Kyle Wiggers writes that MIT researchers have developed a new tool, called SigLLM, that uses large language models to flag problems in complex systems. In the future, SigLLM could be used to “help technicians flag potential problems in equipment like heavy machinery before they occur.” 

Principal Research Scientist Kalyan Veeramachaneni speaks with Fast Company reporter Sam Becker about his work in developing the Synthetic Data Vault, which is helpful for creating synthetic data sets, reports Sam Becker for Fast Company. “Fake data is randomly generated,” says Veeramachaneni. “While synthetic data is trying to create data from a machine learning model that looks very realistic.”

Researchers at MIT have developed a new artificial intelligence system aimed at helping autopilot avoid obstacles while maintaining a desirable flight path, reports Kyle Wiggers for TechCrunch. “Any old algorithm can propose wild changes to direction in order to not crash, but doing so while maintaining stability and not pulping anything inside is harder,” writes Wiggers.

MIT researchers have developed Robust MADER, an updated version of a previous system developed in 2020 to help drones avoid in-air collisions, reports Brian Heater for TechCrunch. “The new version adds in a delay before setting out on a new trajectory,” explains Heater. “That added time will allow it to receive and process information from fellow drones and adjust as needed.”

Researchers at MIT have predicted that without improvements in hardware efficiency, energy consumption and emissions from autonomous vehicles could be “comparable to that of data centers today,” reports Sarah Wells for Popular Mechanics. “In order to reduce the future carbon footprint of AVs, scientists will need to make the computing systems of AVs, including smart sensors, far more efficient,” writes Wells. 

Writing for Fast Company, Visiting Scientist Priya Donti examines how “scientists are increasingly looking to AI to help us predict the weather, and some of the most promising approaches come from blending AI with existing scientific knowledge.” Donti notes that a “combination of innovative technology and human wisdom is the best way to harness AI to help us tackle the challenges of the future, especially climate change.”

An MIT study has found that the wide spread adoption of self-driving cars could lead to increased carbon emissions, reports Rima Sabina Aouf for Dezeen. “The study found that with a mass global take up of autonomous vehicles, the powerful onboard computers needed to run them could generate as many greenhouse gas emissions as all the data centers in operation today,” writes Aouf.

A new study by MIT researchers finds that “the energy required to run computers in a future global fleet of autonomous vehicles could produce as much greenhouse gas emissions as all the data centers in the world,” reports Sharon Udasin for The Hill. The researchers found that “1 billion such cars, each driving for an hour daily, would use enough energy to generate the same amount of emissions that data centers do today.”

Prof. Steven Barrett speaks with New York Times reporter Paige McClanahan about the pressing need to make air travel more sustainable and his research exploring the impact of contrails on the planet’s temperature. “Eliminating contrails is quite a big lever on mitigating the climate impact of aviation,” said Barrett.

Politico reporter Derek Robertson writes that a new study by MIT researchers finds the computing power required to replace the world’s auto fleet with AVs would produce about the same amount of greenhouse gas emissions as all the data centers currently operating. Robertson writes that the researchers view the experiment “as an important step in getting auto- and policymakers to pay closer attention to the unexpected ways in which the carbon footprint for new tech can increase.”

Graduate student Soumya Sudhakar speaks with BBC Digital Planet host Gareth Mitchell about her new study showing that hardware efficiency for self-driving cars will need to advance rapidly to avoid generating as many greenhouse gas emissions as all the data centers in the world.

Using statistical modeling, MIT researchers have found that the energy needed to power a fleet of fully autonomous EVs could generate as much carbon emissions as all the world’s data centers combined, reports Andrew Paul for Popular Science.