AI stirs up the recipe for concrete in MIT study
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
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Civil and environmental engineering
New tool evaluates progress in reinforcement learning
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
Read MoreDesigning a new way to optimize complex coordinated systems
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
Read MoreBy eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
Read MoreA new international collaboration unites MIT and maritime industry leaders to develop nuclear propulsion technologies, alternative fuels, data-powered strategies for operation, and more.
Read MoreU.S. Air Force engineer and PhD student Randall Pietersen is using AI and next-generation imaging technology to detect pavement damage and unexploded munitions.
Read MoreMaterials scientist is honored for his academic leadership and innovative research that bridge engineering and nature.
Read MorePuzzling out climate change
Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.
Read MoreNeed a research hypothesis? Ask AI.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
Read MoreMIT researchers develop an efficient way to train more reliable AI agents
The technique could make AI systems better at complex tasks that involve variability.
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