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An algorithm developed at MIT assists in predicting the occurrence rate of severe weather.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 22, 202434Views0Likes0Comments
Scientists from MIT and the Pacific Northwest National Laboratory have developed a way to increase the accuracy of large-scale climate models, allowing for more precise predictions of extreme weather incidents in specific locations. Their process involves using machine learning in tandem with existing climate models to make the models' predictions closer to real-world observations. This…

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Designing domestic robots to possess a bit of general knowledge.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Assistive technology, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Human-computer interaction, Machine learning, Research, Robotics, Robots, School of Engineering, UncategorizedJune 21, 202428Views0Likes0Comments
As robots are increasingly being deployed for complex household tasks, engineers at MIT are trying to equip them with common-sense knowledge allowing them to swiftly adapt when faced with disruptions. A newly developed method by the researchers merges robot motion data and common-sense knowledge from extensive language models (LLMs). The new approach allows a robot to…

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Scientists employ generative artificial intelligence to tackle intricate queries in the field of physics.

Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Machine learning, Mathematics, MIT Schwarzman College of Computing, Physics, Research, School of Engineering, School of Science, UncategorizedMay 16, 202438Views0Likes0Comments
Researchers from MIT and the University of Basel in Switzerland have developed a new machine-learning framework that can map phase diagrams for novel physical systems automatically. By applying generative artificial intelligence models, the team has developed a more efficient method for tracking and understanding phase transitions in water and other complex physical systems, which offers…

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Start developing a more efficient AI assistant by first understanding and replicating the unpredictable actions of humans.

Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedApril 19, 202431Views0Likes0Comments
Artificial Intelligence (AI) researchers at MIT and the University of Washington have created a model that can predict a human's decision-making behaviour by learning from their past actions. The model incorporates the understanding that humans can behave sub-optimally due to computational constraints — essentially the idea that humans can't spend indefinitely long periods considering the…

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Employing deep learning for imaging the Earth’s atmospheric boundary layer.

Algorithms, Artificial Intelligence, Climate change, Computer modeling, Data, Lincoln Laboratory, Machine learning, nasa, Research, Satellites, Uncategorized, Weather, Weather modelingApril 19, 202440Views0Likes0Comments
The planetary boundary layer (PBL), the lowest layer of the troposphere, significantly influences weather near the Earth's surface and holds the potential to enhance storm forecasting and improve climate projections. A research team from Lincoln Laboratory's Applied Space Systems Group has been studying the PBL with a focus on deploying machine learning for creating 3-D…

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An algorithm developed at MIT aids in predicting the occurrence of severe weather events.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherMarch 27, 202434Views0Likes0Comments
Assessing a community’s risk of extreme weather events – such as catastrophic storms and catastrophic flooding – is a key task for policymakers who are trying to prepare for the potential impacts of global climate change. Despite major advancements in technology and modeling techniques, however, these forecasts leave a lot to be desired. Now, a…

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Reprogramming domestic robots to possess a certain degree of common sense.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Assistive technology, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Human-computer interaction, Machine learning, Research, Robotics, Robots, School of Engineering, UncategorizedMarch 25, 202433Views0Likes0Comments
Robots are becoming increasingly adept at handling complex household tasks, from cleaning messes to serving meals. However, their ability to handle unexpected disturbances or difficulties during these tasks has been a challenge. Common scenarios like a nudge or a slight mistake that deviates the robot from its expected path can cause the robot to restart…

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AI Generation for Intelligent Grid Simulation

Artificial Intelligence, Climate, Computer modeling, Electrical Engineering & Computer Science (eecs), Energy, Funding, Grants, Laboratory for Information and Decision Systems (LIDS), Lincoln Laboratory, Machine learning, MIT Schwarzman College of Computing, School of Engineering, UncategorizedMarch 7, 202447Views0Likes0Comments
The Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT) has received a grant of $1,365,000 from the Appalachian Regional Commission (ARC). The funding supports LIDS's role in the "Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform" project. Made available through ARC's Appalachian Regional Initiative for Stronger…

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