From space robots to self-driving cars, Stanford’s Autonomous Systems Lab looks to push the boundaries of exploration and boost the safety and efficiency of everyday tasks.
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Novel drone navigation technology developed by Kenji Shimada was put to the test in an active Japanese tunnel construction site, enabling drones to approximate where a collision may occur and prevent it.
2 minutes read
One of the biggest transformations happening in wireless IoT connectivity right now is the introduction of artificial intelligence (AI) and machine learning (ML).
5 minutes read
Researchers at CMU combined two vision foundational models—models trained on large visual data sets—to help a robot arm recognize the shape and the type of fruit and vegetable slices.
3 minutes read
Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
5 minutes read
In this episode, we talk all about connectomics - the study of animal brains - and how researchers at MIT have started leveraging AI to break through the primary bottleneck: brain image acquisition.
18 minutes read
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
Accelerating Urban Efficiency through Technology. Explore How AI & IoT Drive the Future of Sustainable Urban Living
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Leopard Imaging wins global challenge.
Unlocking AI Potential Through Strategic Dataset Curation: A Comprehensive Guide for Enhancing Machine Learning Models
The butterfly effect—the idea that a butterfly flapping its wings in South America could change the course of a tornado in Texas—has been a popular way to illustrate the difficulty of making predictions about the behavior of chaotic systems like the weather. Because such systems are extremely sensitive to their initial conditions, even the tiniest of changes can send their behavior careening off into unexpected directions.
4 minutes read
