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Figure 1: Schematic overview of the framework for on-road evaluation of explanations in automated vehicles (Credit: MIT CSAIL and GIST).
CSAIL article

The Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT) Editorial Board has awarded MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Gwangju Institute of Science and Technology (GIST) researchers with a Distinguished Paper Award for their evaluation of visual explanations in autonomous vehicles’ decision-making.

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alt="MIT CSAIL researchers helped design a new technique that can guarantee the stability of robots controlled by neural networks. This development could eventually lead to safer autonomous vehicles and industrial robots (Credits: Alex Shipps/MIT CSAIL)."
CSAIL article

Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task.

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Researchers from MIT and elsewhere designed a communication framework that enables academics to ask for research help on social media using meronymous communication, in which the asker only reveals certain verified aspects of their identity. They found that meronymous communication encouraged people to ask questions they otherwise might not have for fear of judgment from more senior scientists (Credits: MIT News; iStock).
CSAIL article

Have you ever felt reluctant to share ideas during a meeting because you feared judgment from senior colleagues? You’re not alone. Research has shown this pervasive issue can lead to a lack of diversity in public discourse, especially when junior members of a community don’t speak up because they feel intimidated.

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alt="FeatUp is an algorithm that upgrades the resolution of deep networks for improved performance in computer vision tasks such as object recognition, scene parsing, and depth measurement (Credits: Mark Hamilton and Alex Shipps/MIT CSAIL, top image via Unsplash)."
CSAIL article

Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people, and crosswalks, but almost no one can draw every detail with pixel-perfect accuracy. The same is true for most modern computer vision algorithms: They are fantastic at capturing high-level details of a scene, but they lose fine-grained details as they process information.

Category
Transportation
Language
Julia
Project Lead
Albert R. Gnadt

MagNav.jl contains a full suite of tools for airborne Magnetic anomaly Navigation (MagNav), including flight path & INS data import or simulation, mapping, aeromagnetic compensation, and navigation.

Department of the Air Force
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